faculty of pharmacy · 2019-11-21 · faculty of pharmacy gut microbiota and metabolomic changes...

FacultyofPharmacy

GUTMICROBIOTAANDMETABOLOMICCHANGESASSOCIATEDTOTHE

BENEFICIALEFFECTSOFPOLYPHENOLSONOBESITY

UsuneEtxeberriaAranburu

Pamplona,2015

FacultyofPharmacy

GUTMICROBIOTAANDMETABOLOMICCHANGESASSOCIATEDTOTHEBENEFICIAL

EFFECTSOFPOLYPHENOLSONOBESITY

MemoriapresentadaporDña.UsuneEtxeberriaAranburuparaaspiraralgradodeDoctorporla

UniversidaddeNavarra

UsuneEtxeberriaAranburu

ElpresentetrabajohasidorealizadobajonuestradirecciónenelDepartamentodeCienciasdela

AlimentaciónyFisiologíayautorizamossupresentaciónanteeltribunalquelohadejuzgar.

Pamplona,26deNoviembrede2015.

Dr.FerminI.MilagroYoldiProf.J.AlfredoMartínezHernández

If you always do what you always did, you will always get

what you always got

(Albert Einstein)

Acknowledgements/Agradecimientos

i


A lo largo de estas líneas quisiera mostrar mi más sincero agradecimiento a todas aquellas personas que de

alguna manera u otra, han contribuido y me han ayudado a llegar al final de esta etapa. Me siento una persona

privilegiada, he contado con vuestro apoyo, cariño y me habéis transmitido mucha fuerza y ánimo. Hoy, sólo

puedo y me queda daros las gracias, MUCHAS GRACIAS a todos.

En primer lugar, mis primeras palabras de agradecimiento están dedicadas a los directores de esta tesis el Prof.

Alfredo Martínez y el Dr. Fermín Milagro. Quisiera daros las gracias por haber sabido guiar y dirigir este trabajo.

Gracias porque en vosotros he encontrado el rigor y la diligencia necesarias, así como vuestro apoyo, tiempo,

paciencia e implicación en todo momento. Os quiero dar las gracias por la confianza que habéis depositado en mí

desde el primer momento, por haberme dado la oportunidad de llevar a cabo este trabajo. Junto a vosotros puedo

decir que he crecido no sólo profesionalmente, sino también personalmente. Os agradezco todo el tiempo que

habéis invertido en mí y sinceramente espero haber cumplido, aunque sea parcialmente, con vuestras

expectativas. Muchísimas gracias a los dos.

En segundo lugar, quiero agradecer a la Universidad de Navarra estos años de formación. A la Asociación de

Amigos de la Universidad de Navarra, así como al Gobierno Vasco la ayuda económica que me han otorgado sin

la cual todo esto no hubiese sido posible. En particular, quisiera agradecer al Departamento de Ciencias de la

Alimentación y Fisiología por haberme acogido, y haberme ofrecido la oportunidad de realizar esta etapa

investigadora junto a los mejores profesionales, a todos y cada uno de vosotros gracias. Gracias, a todos los

profesores, técnicos, PAS. En general, a todos los compañeros y amigos de CAF, aquellos con los que compartí mis

primeros años de tesis y aquellos con los que finalizo este camino. Así mismo, me gustaría a agradecer también a

los compañeros que han estado de paso por el departamento, gracias porque he tenido la oportunidad de conocer

a grandes personas. Gracias a mis amigos de máster E-MENU (Lucía, Blanca, Candela, Alfredo, Angels) por todos

los momentos disfrutados, nuestras conversaciones de café y comidas que echaré de menos. Gracias a Miguelin

por tantos y tantos momentos divertidos que hemos pasado en el laboratorio y fuera de él, has sido un gran apoyo

para mí, he ganado un gran amigo y espero poder compartir en el futuro muy buenos momentos contigo. Gracias

a mis chicas del “Granada team”, os echaré enormemente de menos, espero que tengamos oportunidad de

juntarnos y repetir algún que otro viaje juntas.

Este trabajo no hubiera sido posible sin la gran aportación y la colaboración del grupo Nutrición y Obesidad de la

Universidad del País Vasco. Muy especialmente, me gustaría trasladar mi más sincero agradecimiento a la Prof.


ii

Mª Puy Portillo, muchísimas gracias por todo, ha sido un gusto y una suerte tener la oportunidad de trabajar con

todos vosotros. Así mismo, me gustaría extender este agradecimiento a la Dra. Mª Teresa Macarulla, Dr. Alfredo

Fernández, Leixuri, Ana, Saioa, Iñaki y a todo el grupo en general. Por último, me gustaría agradecer a Noemí

Arias toda su disposición, ayuda y en definitiva su gran profesionalidad, muchas gracias, ha sido un lujo

conocerte y poder trabajar contigo.

I would like to express my gratitude to Prof. Nathalie Delzenne and Dr. Audrey Neyrinck for giving me the

opportunity to stay and work with your team in the Metabolism and Nutrition Research Group, Louvain Drug

Research Institute (Brussels). I would like to thank you for your warm welcome, for your trust on me, for you

generosity, hope we will have the chance to keep in touch in the future. It was a great experience working with

you. I would also like to thank all the people from the lab. Particularly, I wish to thank Remi for his help. I would

like to express also my sincere gratitude to Nuria and Marcelo.

Por la suerte que tengo de contar con vuestra amistad, quiero dar las gracias a todos mis amigos. A Anetxu, que

aunque no tengamos muchas oportunidades para juntarnos, siempre que nos vemos parece como si el tiempo no

hubiera pasado. A mis amigos de la Kuadrilla, quizás no sepáis qué es lo que he estado haciendo exactamente

durante todo este tiempo, pero vuestras preguntas especialmente vuestro continuo interés sobre “mis ratitas”, han

sido suficientes para arrancarme siempre una sonrisa. Por todos esos viernes de reencuentro, desconexión y

buenos momentos que me habéis dado, mila esker Kuadrilla. Quiero agradecer especialmente a Ainara su apoyo.

Askotan denbora luzez ikusi gabe egonda ere, asteroko mezutso hori falta izan ez degulako, zure animoengatik,

zure laguntasunagatik, mila esker Ainari. A mis pittinas Nai y Elitxu, qué deciros, habéis sido uno de mis mayores

apoyos, gracias por animarme, por creer tanto en mí, por alegraros con mis pequeños éxitos y avances

profesionales, gracias por estar a mi lado siempre, por vuestra amistad incondicional. Mila esker.

Hitz hauek ez dira nahikoak matti eskertu behar dizudan guztia eskertu eta nere pentsamendu guztiak papelean

isladatzeko, baina era labur batean bada ere saiatuko naiz. Maitia, esfortzu honen fruitu hau zurea ere delako

ESKERRIK ASKO, mila esker. Mila esker bide hau nerekin batera ibiltzeagatik, momento errazenetan bai, baina

momentu zailenetan ere, hor izan zaitudalako. Mila esker beti nigan sinistu izanagatik eta nik konfidantza falta

izan dudan momentu gogor horietan, indarrak eman eta aurrera eginarazi izanagatik. Eskerrak eman nahi

dizkizut, urte hauetako egunerokotasunean, eskeini eta eman dizkidazun maitasun, indar eta goxotasunarekin

etapa hau bizitzako epe polit eta ahaztezin bihurtu dituzulako. Asko maite zaitut.

Gracias, eskerrik asko, a mis “suegros” Maite y Xabier, y a toda la family, eskerrik asko zuei guztiei.


iii

Me gustaría dedicar las últimas palabras a mi familia. A mis tíos, tías, primos, primas que siempre han estado ahí,

mostrando interés por todo lo que hago y arrancándome una sonrisa cada vez que nos reuníamos. Me gustaría

agradecer muy especialmente a mi AMONA todo el cariño que me da, sus palabras de aliento, su fuerza y

vitalidad que hace que me sienta orgullosa de mí misma, sin saber que el orgullo es mío por poder tenerle a mi

lado. Eres única, una joya. Maite zaitut amona. Eskerrik asko Andertxo beti zugan aurkitu dudan babes eta

laguntzagatik, edozein momentutan entzuteko prest egon zarelako, aholkorik hoberenak zugan aurkitu izan

ditudalako. Izugarri haundia zarelako eta zorte handia dudalako zu anai bezela izateagatik, asko asko maite

zaitut eta faltan botatzen zaitut.

Mis últimas palabras de agradecimiento están dirigidas a mis padres. Aita, ama, no puedo agradeceros lo

suficiente todo lo que habéis hecho por mí. Sois lo más importante de mi vida. Sois el pilar de mi vida, sin vuestro

apoyo no hubiera llegado a estar hoy donde estoy y no me sentiría capaz de luchar por metas que se me hacen

inalcanzables. Gracias por todos vuestros consejos, vuestros ánimos y cariño, vuestra confianza y comprensión.

Os lo debo todo, absolutamente todo, a vosotros. Por ello, esta tesis os la quiero dedicar especialmente a vosotros

con todo mi cariño, os quiero mucho.

Dedicado a las dos personas que todo me lo han dado,

Mis aitas, Javier y Mertxe

Con especial cariño a,

Mi hermano, Ander

Mi amona, Mª Carmen

Maitia, beti zuri

Listofabbreviations/Listadodeabreviaturas

ix


‐ WHO WorldHealthOrganization

‐ BMI BodyMassIndex

‐ LEP Leptin

‐ LEPR Leptinreceptor

‐ MC4R Melanocortin4receptor

‐ POMC Pro‐opiomelanocortin

‐ PCSK1 Prohormoneconvertase1

‐ BDNF Brain‐derivedneurotrophicfactor

‐ SIM1 Single‐minded1

‐ NTRK2 Neurotrophictyrosinekinase,receptor,type2

‐ BW Bodyweight

‐ T2DM Type2DiabetesMellitus

‐ CVD Cardiovasculardisease

‐ AHA/ACC/TOS AmericanHeartAssociation/AmericanCollegeofCardiology/TheObesity

Society

‐ GI Gastrointestinal

‐ EPIC EuropeanProspectiveInvestigationintoCancer andNutrition

‐ UK UnitedKingdom

‐ GLUT Glucosetransporter

‐ PPARγ Peroxisomeproliferator‐activatedreceptorgamma

‐ HFD High‐fatdiet

‐ VASiNOS Visceraladiposetissueinduciblenitricoxide

‐ COX2 Cyclooxygenase‐2

‐ Nrf2 Nuclearfactor‐related factor‐2

‐ HO‐1 Hemeoxygenase‐1

‐ NF‐κβ NuclearfactorKappaβ

‐ ERK1/2 Extracellularsignal‐relatedkinase

‐ AMPK AMP‐activatedproteinkinase

‐ LPH Lactasephlorizinhydrolase

‐ UGT Uridine5’‐diphosphateglucuronosyltransferase

‐ COMP Catechol‐O‐methyltransferase

‐ PST Phenolsulfotransferase

‐ BCRP1 Breastcancerresistanceprotein1

‐ MRP2 Multidrugresistanceassociated‐protein2

‐ LDL Lowdensitylipoprotein

‐ CHD Coronaryheartdisease

‐ SIRT1 Sirtuin1

‐ HMP HumanMicrobiomeProject


x

‐ NIH NationalInstituteofHealth

‐ CFU Colony‐formingunits

‐ CNS Centralnervoussystem

‐ IBD Inflammatoryboweldisease

‐ CD Crohn’sdisease

‐ UC Ulcerativecolitis

‐ IBS Irritablebowelsyndrome

‐ GBA Gut‐brain‐axis

‐ ASD Autismspectrumdisorder

‐ GF Germ‐free

‐ SCFA Shortchainfattyacids

‐ RS Resistantstarch

‐ XOS Xylooligosaccharides

‐ IMOS Isomaltooligosaccharides

‐ SO Soybeanoligosaccharides

‐ POS Pectooligosaccharides

‐ PDX Polydextrose

‐ EPS Exopolysaccharides

‐ VLDL Verylowdensitylipoproteins

‐ SREBP‐1c Sterolresponseelementbindingprotein1c

‐ ChREBP Carbohydrateresponseelementbindingprotein

‐ GPCR Gprotein‐coupledreceptor

‐ FFR Freefattyacidreceptor

‐ GLP‐2 Glucagonlikepeptide‐1

‐ PYY Peptidetyrosinetyrosine

‐ TNF‐α Tumornecrosisalpha

‐ IL Interleukin

‐ HDAC Histonedeacetylases

‐ HAT Histoneacetyltransferases

‐ iNOS Induciblenitricoxidesynthase

‐ ICAM‐1 Intercellularadhesionmolecule‐1

‐ VCAM‐1 Vascularadhesionmolecule‐1

‐ TCR‐α Tcellreceptor‐α

‐ Fiaf Fasting‐inducedadiposefactor

‐ Angptl‐4 Angiopoietin‐like4protein

‐ FFA Freefattyacid

‐ LPL Lipoproteinlipase

‐ HFS High‐fatsucrose

‐ Pgc‐1 Proliferator‐activatedreceptorcoactivator

‐ PAMP Pathogen‐associatedmolecularpattern


xi

‐ PRR Patternrecognitionreceptor

‐ MAMP Microbe‐associatedmolecularpattern

‐ TLR Toll‐likereceptor

‐ NOD Nucleotideoligomerizationdomain

‐ NLR NOD‐likereceptor

‐ RIG‐I Retinoicacidinduciblegene‐I

‐ RLR RIG‐likereceptor

‐ LPS Lipopolysaccharide

‐ LPA Lipoteichoicacid

‐ REGIII Regeneratingislet‐derivedproteinIII

‐ DC Dendriticcell

‐ TJ Tightjunction

‐ ZO‐1 Zonulaocludens‐1

‐ DSS Dextransodiumsulphate

‐ SAT Saturatedfattyacid

‐ MUFA Monounsaturatedfattyacid

‐ PUFA Polyunsaturatedfattyacid

‐ FMT Faecalmicrobiotatransplantation

‐ rRNA Small‐subunitribosomalRNA

‐ NMR Nuclearmagneticresonance

‐ MS Mass‐spectrometry

‐ BCAA Branchedchainaminoacids

‐ SMCSO S‐methyl‐L‐cysteinesulphoxide

‐ UHPLC Ultra‐highperformanceliquidchromatography

‐ ESI Electrosprayionizationmode

‐ m/z Mass‐to‐chargeratio

‐ TOF Timeofflight

‐ IPGTT Intraperitonealglucosetolerancetest

‐ LBP Lipopolysaccharidebindingprotein

‐ DHA Docosahexaenoicacid

‐ NEFA Non‐esterifiedfattyacid

‐ ACSL3 Acyl‐CoAsynthetaselong‐chainfamilymember3

‐ LIPG Endotheliallipase

‐ KLF5 Krüppel‐likefactor5

‐ AREG Amphiregulin

Glossaryofterms/Glosariodetérminos

xiii


16SrRNA/rDNA: a componentof the30S small subunitofprokaryotic ribosomes. Sequencingof the16S

rRNA/rDNAhasbeenusedtoidentifyprokaryotictaxonomyincompleteenvironmentalsamplessuchasthe

microbiome(ChoandBlaser,2012).

Commensals: microorganisms which are neither harmful nor beneficial for the host

(Eloe‐FadroshandRasko,2013).

Co‐metabolites:metabolitesthataresynthesizedordegradedbythehostoroneormorebacterialspecies

(Leyetal.,2008).

Dysbiosis:alterations in the relativeabundanceofmicrobialgroupsor functions thatcausean imbalance

compared to a healthy state, generally leading to a detrimental change in health


Enterotype: to classify themicrobiota of an individual into discrete configurations. But recent data have

conflicted with this definition and suggest that enterotypes may be less discrete

(TremaroliandBackhed,2012).

Eubiosis:thenormalstateofthehumangutmicrobiota(Doréetal.,2010).

Gnotobiotic mice: from Greek gnosis (known/knowledge) and bios (life). Mice reared under sterile

(germ‐free) conditions are classified as gnotobiotic, as are animals that are reared under germ‐free

conditionsforaperiodoftheirlivesandthencolonizedwithvariouscollectionsofmicrobes(Gordon,2012).

Humanizedmice: germ‐freemice into which fresh or frozen human faecal microbial communities have

beentransplanted(Eloe‐FadroshandRasko,2013).

Metagenome: the total DNA that can be extracted from an environment. The humanmetagenome is the

aggregate of the DNA of the host and the microbiota. Metagenome and microbiome are often used

interchangeably(TremaroliandBackhed,2012).

Metagenomics: defined as culture‐independent analyses of the composition and dynamic operations of

microbialcommunities(Leyetal.,2008)

Mutualists:symbioticallybeneficialmicrobes(Aagaardetal.,2013).

Pathogens:microbeswhicharedetrimentalforthehost(Aagaardetal.,2013).

Phylotype: taxon‐neutral description of microorganisms based on phylogenetic relationship



xiv

Phylogeneticanalysis:ananalysisbasedonaphylogenetictreethattreatsomesequencesasmoreorless

similar to others based on the number of changes that they have accumulated over evolutionary time

(Leyetal.,2008).

Prebiotics:non‐digestiblefoodingredientsorsubstancesthatpassundigestedthroughtheupperpartofthe

gastrointestinaltractandstimulatethegrowthand/oractivityofhealth‐promotingbacteriathatcolonizethe

largebowel(Bindelsetal.,2015).

Probiotics:livingmicroorganismsthat,whenadministeredinadequateamountsconferahealthbenefitfor

the host. Many bacterial strains in the Lactobacillus and Bifidobacterium genera are considered to be

probiotic(TremaroliandBackhed,2012).

Listofpublications/Listadodepublicaciones

xv

Listofpublications/Listadodepublicaciones

ThesevenpublicationslistedbelowcompiletheworkcontainedinthepresentDoctoralThesis.

1. Chapter 1: Etxeberria U, de la Garza AL, Martínez JA and Milagro F.I. Diet‐induced

hyperinsulinemia differentially affects glucose and protein metabolism: a high‐throughput metabolomic

approachinrats.JournalofPhysiologyandBiochemistry,2013;69(3):613‐623.Doi:10.1007/s13105‐013‐

0232‐0.

2. Chapter2:EtxeberriaU,AriasN,BoquéN,MacarullaMT,PortilloMP,MilagroFI,MartínezJA.

Shiftsinmicrobiotaspeciesandfermentationproductsinadietarymodelenrichedinfatandsucrose.Beneficial

Microbes,2015;6(1):97‐111.Doi:10.3920/BM2013.0097.

3. Chapter3:EtxeberriaU,delaGarzaAL,MartínezJA,MilagroFI.Biocompoundsattenuatingthe

development of obesity and insulin resistance produced by a high‐fat sucrose diet. Natural Product

Communication,2015;10(8):1417‐1420.

4. Chapter4:EtxeberriaU,Fernández‐QuintelaA,MilagroFI,AguirreL,Martínez JA,Portillo

MP. Impact of polyphenols and polyphenol‐rich dietary sources on gutmicrobiota composition. Journal of

AgriculturalandFoodChemistry,2013;61(40):9517–9533.Doi:10.1021/jf402506c.

5. Chapter5:EtxeberriaU,AriasN,BoquéN,MacarullaMT,PortilloMP,MartínezJA,MilagroFI.

Reshaping faecal gut microbiota composition by the intake of trans‐resveratrol and quercetin in high‐fat

sucrose diet‐fed rats. Journal of Nutritional Biochemistry, 2015; 26 (6): 651‐660.

Doi:10.1016/j.jnutbio.2015.01.002.

6. Chapter6:EtxeberriaU,AriasN,BoquéN,Romo‐HualdeA,MacarullaMT,PortilloMP,Milagro

FI,MartínezJA.Metabolicfaecalfingerprintingoftrans‐resveratrolandquercetinfollowingahigh‐fatsucrose

dietarymodelusing liquidchromatographycoupled tohigh‐resolutionmass spectrometry. Food&Function,

2015;6(8):2758‐2767.Doi:10.1039/c5fo00473j.

7. Chapter 7: Etxeberria U, Castilla‐Madrigal R, Lostao MP, Martínez JA, Milagro FI. Trans‐

resveratrol inducesapotentialanti‐lipogeniceffect in lipopolysaccharide‐stimulatedenterocytes.Accepted in

CellularandMolecularBiology.

Abstract/Resumen

xvii

Abstract/Resumen

Thecontributionofthegutmicrobiotatothedevelopmentofalargenumberofdiseases,includingobesity,is

being explored. Although mechanisms are not fully understood, perturbations on gut microbiota

composition seem to be an important factor. Therefore, modulation of gut bacterial community with

approaches thatcouldenhance thegrowthof “friendly”bacteriaandreduceharmfulbacteriamightbean

effective therapeutic tool. In this context, polyphenols, widely present in fruits and vegetables, such as

flavonoids(i.e.quercetin)andnon‐flavonoids(i.e.trans‐resveratrol),exerthealthprotectiveeffectsandthe

bi‐directionalinteractionbetweenthesenaturalcompoundsandthegutmicrobiotatakingplaceatintestinal

level, has been proposed to be partly responsible for the health beneficial outcomes. Furthermore,

metabolism of polyphenols might lead to the formation of other metabolites or bacterial‐derived co‐

metabolites with higher biological activity. Consequently, knowledge about the impact of pure natural

compoundson gutmicrobiota and in turn, hostmetabolism is of relevance in order to be able to further

understandthehealthbenefitsderivedfromtheintakeofpolyphenols.

Therefore,thisthesisaimedtoprovideasnapshotofthiscomplexsystemconsistingofgutmicrobiota,diet

andpolyphenols,hostmetabolismandhealth.Forthispurpose,thisworkhastakenadvantageofadvanced

technologies namely next‐generation sequencing and untargeted metabolomics in order to address the

followingquestions:I)Howdoesdiet‐inducedobesityinfluenceonhostmetabolome?(Chapter1);II)How

doesdiet‐inducedobesityaffectthegutmicrobiotacomposition?(Chapter2);III)Dobioactivecompounds

exertanybeneficialeffectondiet‐inducedobesityandwhathasbeenstudiedabouttheirinterplaywithgut

microbiota up‐to‐date? (Chapter 3 and Chapter 4); IV) How does supplementation with polyphenols

modulategutmicrobiotadysbiosisindiet‐inducedobesity?(Chapter5);V)Doesfaecalmetabolomeanalysis

conducted in diet‐induced obese animals reflect the biological impact exerted by polyphenols’

supplementation? (Chapter 6); VI) What are the effects of polyphenols on gene expression level in

enterocytes?(Chapter7).

Insummary,theresultspresentedinthisthesishavereportedthatconsumptionofahigh‐fathigh‐sucrose

dietstronglyaffectshostmetabolomealteringserumlevelsofadifferentsetofmetabolites,whichmightbe

reflectiveofthephysiopathologyofdiet‐inducedobesity.Therefore,non‐targetedmetabolomicsperformed

in serum samples has been found to be an effective approach to distinguish disease from non‐disease

individuals.Moreover,theintakeofahigh‐fathigh‐sucrosedietstronglyimpactsgutmicrobiotacomposition

perturbingthebacterialbalancetowardsanobesity‐associatedgutmicrobialpattern.Remarkably,theuseof

purenaturalcompounds,particularlyquercetin,couldcounteractthedisturbanceofgutmicrobiotarelated

todiet‐inducedobesity,promotingthegrowthofsomebeneficialbacteriawhilereducingsomepathogenic

andobesity‐associatedmicrobes.Incontrast,trans‐resveratrolsignificantlyinfluencesgeneexpressionlevel

in the intestine in vivo, without notablymodifying gutmicrobial profile. In this context, trans‐resveratrol

seemstofavourintestinalepithelialhomeostasispromotingtherepairofintestinalepithelialbarrier,upon

damage.Furthermore,fromtheinvitrostudiesconductedinCaco‐2cells,theinhibitoryactionofthestilbene

on genes implicated in lipid metabolism has been observed. Finally, biological outcomes exerted by

Abstract/Resumen

xviii

polyphenolsonglobalhostmetabolomemightbedistinguishedthroughafaecalnon‐targetedmetabolomic

analysis, proposing the potential of this technique to elucidate interactions between gut bacteria and

polyphenols,andalsotodiscriminateindividualsindifferentgroupsbasedonthedietaryinterventionthey

havebeensubjectedto.

Lainfluenciadelamicrobiotaintestinaleneldesarrollodeenfermedadestalescomolaobesidadestásiendo

ampliamenteestudiada.Losmecanismosespecíficosquecontribuyenaesteprocesosonaúndesconocidos.

No obstante, ha sido demostrado que las alteraciones producidas en el perfil bacteriano intestinal

representanunfactorimportante.Enestesentido,lamanipulacióndelamicrobiotaatravésdeestrategias

que favorecen el crecimiento de las bacterias “beneficiosas” sobre las descritas como “patógenas”, se

consideraunaalternativaeficaza lahoradeprevenireldesarrollodediversaspatologías.Lospolifenoles,

compuestos naturales ampliamente distribuidos en frutas y verduras, como los flavonoides

(p.ej.quercetina)ylosno‐flavonoides(p.ej.elestilbenotrans‐resveratrol)ejercenefectosprotectoressobre

la salud. Cabe destacar la importancia del contacto bi‐direccional existente entre estos compuestos y las

bacteriasenelintestinoalahoradeexplicarlosefectosbeneficiososobservados.Asímismo,esbiensabido

que los co‐metabolitos generados como consecuencia de esta interacción, contribuyen a los efectos

biológicosfinales.Porello,parapodercomprendermejorelimpactodelconsumodelospolifenolessobrela

salud,esnecesarioanalizarlaaccióndelosmismossobrelamicrobiotaintestinalyelmetaboloma.

Elpropósitodeestatesisdoctoraleraofrecerunavisiónglobalsobreestesistemacomplejoqueenglobalas

bacterias intestinales, ladietaymásconcretamentelospolifenoles,elmetabolismodelhuéspedy lasalud.

Conestafinalidad,elpresentetrabajosehadesarrolladoenelmarcodelasciencias“Ómicas”,haciendouso

detécnicasavanzadascomolasecuenciacióndeúltimageneraciónylametabolómicanodirigida.Deacuerdo

conloexpresado,estetrabajodeinvestigaciónhatratadodeesclarecerlossiguientesinterrogantes:I)Cómo

afectalaobesidadinducidaporelconsumodeunadietaaltaengrasayazúcaralmetabolismodelhuésped?

(Capítulo 1); II) De qué manera afecta la obesidad inducida por la dieta sobre la composición de la

microbiotaintestinal?(Capítulo2);III)Puedenlospolifenolesejercerunpapelprotectoranteeldesarrollo

de la obesidadyqué se conocehasta la fecha sobre la relaciónentre estosbiocompuestos y lasbacterias

intestinales?(Capítulo3yCapítulo4);IV)Dequémaneraafectaríalasuplementaciónconpolifenolesala

“disbiosis”asociadaalaobesidad?(Capítulo5);V)Elanálisismetabolómiconodirigidorealizadoenheces

puede llegar a detectar el impactobiológicoderivadode la suplementaciónde los compuestosnaturales?

(Capítulo 6); VI) Cuál es el efecto de los polifenoles sobre la expresión de genes a nivel intestinal?

(Capítulo7).

Enresumen,losresultadosrecopiladosenestaTesisDoctoralconcluyenquelaingestadeunadietaricaen

grasayazúcarafectadeformasignificativaalperfilmetabolómicoensueroprovocandoalteracionesenlos

niveles de diversos metabolitos. Este perfil diferenciado puede reflejar la fisiopatología de la obesidad

inducidaporladieta,porlotanto,unanálisismetabolómiconodirigidoensueropuedeserconsideradouna

herramientaefectivaalahoradediferenciarpersonassanasfrenteapersonasconalgúntipodeenfermedad.

Porotraparte, el consumodeunadietaalta engrasayazúcar influyenotablemente la composiciónde la

Abstract/Resumen

xix

microbiotaintestinal,produciendomodificacionesenelequilibriobacterianodelintestinoeinduciendoun

perfil bacteriano asociado a la obesidad. Cabe señalar que la suplementación con polifenoles,

particularmentelaquercetina,puedellegaracontrarrestarloscambiosenlamicrobiotaintestinalasociados

a la obesidad inducida por la dieta. El trans‐resveratrol por su parte, pese a que no produce grandes

modificacionessobreelperfilbacteriano,muestra la capacidadpara regular laexpresióndegenesanivel

intestinal ymejora la integridad de la barrera intestinal. Además, el estudio in vitro realizado en células

intestinales Caco‐2, presenta la acción inhibitoria del trans‐resveratrol sobre genes implicados en el

metabolismolipídico.Finalmente,sehacomprobadoqueelanálisisdelashecesatravésdelametabolómica

no dirigida tiene la capacidad para detectar el impacto biológico de los compuestos naturales sobre el

metabolismo de los individuos, y parece ser una herramienta útil que permite explorar las interacciones

entre las bacterias intestinales y los polifenoles, así como para discriminar a las personas en diferentes

grupos,enfuncióndeltratamientoalquehansidosometidos.

Tableofcontents/Índice


Acknowledgements/Agradecimientos.....................................................................................................................................i

Listofabbreviations/Listadodeabreviaturas..................................................................................................................ix

Glossaryofterms/Glosariodetérminos............................................................................................................................xiii

Listofpublications/Listadodepublicaciones..................................................................................................................xv

Abstract/Resumen......................................................................................................................................................................xvii

I. INTRODUCTION..............................................................................................................................1

1. Nutritionandhealth................................................................................................................................................................3

2. Obesity...........................................................................................................................................................................................3

2.1. Definitionandprevalence..............................................................................................................................................3

2.2. Causesandphysiopathology........................................................................................................................................4

2.3. Therapeuticapproachesinthemanagementofobesity:useofpolyphenols.........................................5

2.3.1. Flavonoids...................................................................................................................................................................9

Flavonols:quercetin.....................................................................................................................................9 2.3.1.1.

2.3.2. Non‐flavonoids........................................................................................................................................................15

Stilbene:trans‐resveratrol......................................................................................................................15 2.3.2.1.

3. Gutmicrobiota:anewcontributoryfactorinobesitydevelopment...............................................................19

3.1. Conceptualapproach.....................................................................................................................................................19

3.2. Establishmentandevolution......................................................................................................................................20

3.2.1. Age‐relatedgutmicrobiotacomposition....................................................................................................21

3.2.2. Microbialcommunitythroughthebody:focusonthegastrointestinaltract.............................23

3.2.3. Gutmicrobiota:asignofmetabolicstatus.................................................................................................26

3.3. Crosstalkbetweengutmicrobiotaandthehost................................................................................................30

3.3.1. Metabolicfunctions...............................................................................................................................................30

3.3.2. Immunefunctionsandanti‐inflammatoryroles......................................................................................34

3.3.3. Structuralandprotectivefunctions...............................................................................................................35

3.4. Modulationofgutmicrobiotacompositionbydifferentapproaches.......................................................37

3.4.1. Dietarymodulation...............................................................................................................................................37

Effectofnon‐digestibledietarycarbohydrates,proteinsandfatintake............................39 3.4.1.1.

Effectofpolyphenolsintake...................................................................................................................40 3.4.1.2.

3.4.2. Useofprebiotics,probioticsandsynbiotics..............................................................................................40


3.4.3. Faecalmicrobiotatransplants..........................................................................................................................41

3.5. Metagenomicapproaches............................................................................................................................................42

4. Metabolomics:anew“Omics”scienceinnutrition.................................................................................................42

4.1. Conceptandstrategies..................................................................................................................................................44

4.2. Nutrimetabolomics.........................................................................................................................................................45

4.3. Dietandmicrobial‐derivedmetabolites................................................................................................................47

5. Integrationofmetagenomicsandmetabolomics.....................................................................................................48

II. HYPOTHESISANDAIMS..............................................................................................................49

1. Hypothesis.................................................................................................................................................................................51

2. Generalaim................................................................................................................................................................................51

3. Specificobjectives..................................................................................................................................................................51

III. MATERIALANDMETHODS........................................................................................................53

1. Metabolomicanalyses(Chapter1,Chapter6)...........................................................................................................56

1.1. ExperimentaldesignChapter1:ImpactofaHFSdietonserummetabolome.....................................56

1.2. Experimental design Chapter 6: Impact of trans‐resveratrol and quercetin on faecal

metabolome.....................................................................................................................................................................................58

2. Impactofdifferentphenoliccompoundsonobesity(Chapter3).....................................................................60

2.1. ExperimentaldesignChapter3:Impactofdifferentsetofphenoliccompoundsonobesity........60

3. Gutmicrobiotacompositionanalyses(Chapter2andChapter5)....................................................................61

3.1. ExperimentaldesignChapter2:ImpactofaHFSdietongutmicrobiota..............................................61

3.2. Experimental design Chapter 5: Impact of trans‐resveratrol and quercetin on faecal gut

microbiotacomposition..............................................................................................................................................................62

4. Invitroassayandmicroarrayanalysis(Chapter7)................................................................................................65

4.1. Experimental design Chapter 7: Impact of trans‐resveratrol on gene expression levels in

enterocytes.......................................................................................................................................................................................65

IV. RESULTS..........................................................................................................................................67

Chapter 1: Diet‐induced hyperinsulinemia differentially affects glucose and protein

metabolism:ahigh‐throughputmetabolomicapproachinrats...............................................................................69

Chapter2:Shiftsinmicrobiotaspeciesandfermentationproductsinadietarymodelenriched

infatandsucrose...........................................................................................................................................................................83

Chapter 3: Biocompounds Attenuating the Development of Obesity and Insulin Resistance

ProducedbyaHigh‐fatSucroseDiet.................................................................................................................................101


Chapter 4: Impact of Polyphenols and Polyphenol‐Rich Dietary Sources on Gut Microbiota

Composition..................................................................................................................................................................................107

Chapter5:Reshapingfaecalgutmicrobiotacompositionbytheintakeoftrans‐resveratroland

quercetininhigh‐fatsucrosediet‐fedrats......................................................................................................................127

SupplementarydataCHAPTER5.........................................................................................................................................139

Chapter6:Metabolicfaecalfingerprintingoftrans‐resveratrolandquercetinfollowingahigh‐

fat sucrose dietary model using liquid chromatography coupled to high‐resolution mass

spectrometry................................................................................................................................................................................147


Chapter 7: Trans‐resveratrol induces a potential anti‐lipogenic effect in lipopolysaccharide‐

stimulatedenterocytes.............................................................................................................................................................179


V. GENERALDISCUSSION.............................................................................................................203

1. Understandingthephysiopathologyofobesitythrough“Omics”approaches.............................................206

2. Influenceofnaturalbioactivecompoundsonobesegutmicrobiotaandmetabolome............................208

3. Strengthsandlimitations..................................................................................................................................................212

4. Corollary...................................................................................................................................................................................214

VI. CONCLUSIONS.............................................................................................................................215

VII. REFERENCES...............................................................................................................................219

VIII. APPENDICES................................................................................................................................241

Appendix1:Roleofdietarypolyphenols and inflammatoryprocessesondiseaseprogression

mediatedbythegutmicrobiota...........................................................................................................................................243

Appendix 2: Antidiabetic effects of natural plant extracts via inhibition of carbohydrate

hydrolysisenzymeswithemphasisonpancreaticalphaamylase.......................................................................247

Appendix 3: Helichrysum and grapefruit extracts inhibit carbohydrate digestion and

absorption,improvingpostprandialglucoselevelsandhyperinsulinemiainrats.......................................251

Appendix4:ModulationofhyperglycemiaandTNFα‐mediated inflammationbyhelichrysum

andgrapefruitextractsindiabeticdb/dbmice.............................................................................................................255

Appendix5:Helichrysumandgrapefruitextractsboostweightlossinoverweightratsreducing

inflammation................................................................................................................................................................................259

Appendix6:Congresscommunications...........................................................................................................................263

Appendix7:Othercongresscommunications...............................................................................................................271

I. INTRODUCTION

Introduction

3

1. Nutritionandhealth

Theroleofnutritionasthekeystonetohealthiswidelyrecognized(McNivenetal.,2011).Fromtheorigins

of the first civilizations the relationship among health and dietary habits has been suspected. In fact, the

GreekphysicianHippocrates,nearly2500yearsago,stated:“letfoodbethymedicineandmedicinebethy

food”,withoutbeingconsciousthathisallegationwouldconcurwithnowadaysconcept.However,untilthe

eraofHippocrates,statementsinancienttimeswerenotfoundedinscientificevidencesbutwereresultof

observations of the relation among diet and health that were probably passed down over many years

(Kleisiaris etal., 2014). In contrast,with time, our comprehensionof diet and its influence on health has

developed, and current scientists are able to go beyond associations to give conclusive information

(MitroiandMota,2008).

Modern nutrition has evolved from the perspective that food is mainly required for the survival of an

organism,totheconceptthatfoodisofrelevancetopreventillnessesandenhanceindividual’slifequality.

Thus, proper nutrition is essential to ensure individuals’ optimal growth, development and disease

prevention(Pangetal.,2012).Thechallengesofhumannutritionhavebeenfocusedonovercomingserious

problemsthatpopulationhassufferedthroughtheyears.Nutritionaldeficiencies,especiallythosegenerated

asaconsequenceofa lackofmicronutrientsandvitamins,havebeenamajorproblemforthepopulation,

indeed,theproblemofhungerandmalnutritionremainsup‐to‐date.However,thefocusofnutritionresearch

has changed to the problem of over‐nutrition (Morgan, 2012). Scientific research is demonstrating the

positivehealthoutcomesderivedfromtheadherencetospecificdietarypatternssuchastheMediterranean

diet (Gotsis et al., 2015). Besides, evidence‐based nutritional recommendations have been published

(Gargallo Fernandez et al., 2012) and although an increase in consumers’ interest on foods and food

components that have the capacity to improve health (namely functional foods) has been observed

(Pangetal., 2012), still,modernsocietiesare sufferingawaveof lifestyle‐relatedchronicdiseases,where

dietisastrongcontributor.Therefore,currentresearchneedstodirectitseffortstotheunderstandingofthe

mechanisms surrounding the interplay between diet and human bodymetabolism, in order to be able to

preventthedevelopmentofdiet‐relatedchronicdiseases.

2. Obesity

2.1. Definitionandprevalence

The concept of obesity has changed over the human history being partly considered as an evolutionary

processrelatedtofoodscarcity(Barja‐Fernandezetal.,2014).Prehistorically,humanbeingssufferedcycles

ofpestilenceandfaminethus,survivalofthehumanpopulationdependedontheabilityoftheindividualto

harvest energy from the least amount of food intake (Wells, 2012). This statementwas supportedby the

“thrifty gene hypothesis”, which proposes that individuals underwent a selection of genes that facilitate

efficient food gathering and fat deposition to survive in critical periods of famine

(vanderKlaauwandFarooqi,2015).

Introduction

4

The first’s statements pointing out the dangers of obesitywere given by the ancient Greeks. Hippocrates

mentionedtheassociationbetweenpoordietandillnesses,highlightingthehealthbenefitsderivedfromdiet

modifications(Haslam,2007).However,itwasnotuntiltheseventeenthcenturythattheterm“obesity”was

usedintheEnglishlanguagereferringtoexcessivefatnessorcorpulence(Eknoyan,2006),anditwasinthe

middleofthenineteenthcenturywhenobesitywasacknowledgedtoimpairhealth(Eknoyan,2006).

Nowadays, theWorldHealthOrganization (WHO) recognizesobesityas apreventable risk factor fornon‐

communicable diseases characterized by an abnormal or excessive fat accumulation (WHO, 2015).

Concerning obesity detection, currently, bodymass index (BMI) is commonly used to classify individual’s

nutritional status in clinical practice. This standard index is calculated as a person’sweight in kilograms

dividedbyheightinsquaremetres(BMI=kg/m2).WHOdefinedobesityasBMI≥30kg/m2andoverweightas

BMI≥25kg/m2(WHO,1995).

Theprevalenceofobesityandoverweighthasincreasedsubstantially,widespreadandinashortperiodof

time(Ngetal.,2014).In2014,over39%ofadults(≥18yearsold)wereoverweight,and13%wereobese

worldwide(WHO,2015).Thus,obesity isnowadaysconsideredaglobalpandemic(Swinburnetal.,2011)

thatrequiresaglobalactionplan(WHO,2013).

2.2. Causesandphysiopathology

Although theethiopathogenesis ofobesity is still unclear, itmightbe classifiedasamultifactorial disease

whichresultsfromachronicpositiveimbalancebetweenenergyintakeandenergyexpendituregivingrise

to the storage of energy as adipose tissue (Hall et al., 2012). Obesity is determined by genetic and

environmental factors (Speakman andO'Rahilly, 2012). In relation to genetic heritability,monogenic and

polygenicformsofobesityhavebeendescribed.Monogenicobesityisarareformofthediseasecausedby

mutations in a single gene (Levian et al., 2014). To date, eight genes encoding leptin (LEP), the leptin

receptor(LEPR),melanocortin4receptor(MC4R),pro‐opiomelanocortin(POMC),prohormoneconvertase1

(PCSK1),brain‐derivedneurotrophicfactor(BDNF),thesingle‐minded1(SIM1)andneurotrophictyrosine

kinase,receptor,type2(NTRK2)havebeenidentifiedformonogenicobesity(Waalen,2014;Apalasamyand

Mohamed, 2015). In contrast, polygenic or common obesity refers to the presence of DNA variations in

severalgenes(Hinneyetal.,2010),thus,itistheconsequenceofthecumulativeinfluenceofmanygenesthat

conferdifferentlevelsofsusceptibility(Albuquerqueetal.,2015).Nevertheless,thegeneticsusceptibilityto

obesity could not explain the contemporary obesity pandemic and inter‐individual differences in

susceptibility are ascribed to an important role of environmental factors interacting with the genome

(Martietal.,2008).Overall,evenifinheritedpropensityforthedevelopmentofobesityhasbeenagreedto

vary between 40% and 70% (Xia and Grant, 2013), this heritability might be influenced by diverse

environmentalfactors.

Dietary factors, especially consumption of highly processed foods rich in saturated fat and refined

carbohydrates, together with reduced physical activity patterns play a crucial role in BW gain

(Gortmaker et al., 2011). Nevertheless, other multiple features might also promote the development of

obesity(DhurandharandKeith,2014)(Figure1).

Introduction

5

A solid correlation between the enlargement of adiposity and the development of obesity‐associated co‐

morbiditieshasbeenascertained(Dixon,2010).Inthelastsyears,theroleofadiposetissueproducingpro‐

inflammatorycytokineshasbeenwell‐established(Leeetal.,2009).Importantly,obesitydrivessubstantial

directandindirectsocio‐economiccostsduetotheincrementinhealthcareutilizationderivedfromobesity

andobesity‐relatedhealthproblems(Lehnertetal.,2013).Allthesehealthco‐morbiditiesshowacommon

feature, the chronic low‐grade inflammatory status (Guh et al., 2009). In a comprehensivemeta‐analysis

conductedbyGuhetal.,2009(Guhetal.,2009),20co‐morbiditiesthatdetermineriskfactorsforoverweight

and obesity were reviewed, providing the incidence for 18 co‐morbidities ascribed to overweight and

obesitythatwereclassifiedwithintype2diabetesmellitus(T2DM),cancer,cardiovasculardiseases(CVDs)

andothers.Thesecomplicationsareconsideredtheleadingcauseofdeathinobesepeople(Pi‐Sunyer,2002).

2.3. Therapeuticapproachesinthemanagementofobesity:useofpolyphenols

Obesity requires a long‐term intervention involving primarily non‐pharmacologic treatment

(Orio et al., 2014). This approach consisting of lifestyle modifications is the keystone for obesity

management and involves controlled nutrition, physical activity and behavioural therapy (Aronne, 2014).

However,eveniflifestylechangesarethefirststepstobeperformed,patientswhohavenotmettheweight

or weight‐related treatment goals may be eligible for pharmacotherapy (Jensen et al., 2014). Moreover,

individuals with a BMI ≥30Kg/m2 or ≥27Kg/m2 that exhibit risk factors or illnesses that are considered

Figure1.Factorsthatmaypromotethedevelopmentofobesity.

Introduction

6

severe enough for pharmacologic treatment, such as hypertension, dyslipidaemia, CVD, T2DM, fatty liver

diseaseandobstructivesleepapneacouldmakeuseofthisadjuvanttherapy(Fitchetal.,2013).Inaddition,

based on the American Heart Association/American College of Cardiology/The Obesity Society

(AHA/ACC/TOS) panel, bariatric surgery might be an alternative in patients with BMI≥ 40Kg/m2 or

BMI≥35Kg/m2andobesityassociatedcomorbiditieswhoarewillingtoloseweightbuthavenotresponded

tobehaviouraltreatmentwithorwithoutpharmacotherapy(Fitchetal.,2013).

Although a large variety of anti‐obesity drugs have been developed acting on different mechanisms that

include appetite suppression, increased energy expenditure and decreased food absorption from the

gastrointestinal (GI) tract (Kushner, 2014), pharmacological therapy has been characterized by a large

number of failures and reiterated side effects (Di Dalmazi et al., 2013). Past and currently approved

anti‐obesitydrugsaresummarizedinTable1.

Introduction

7

Table1.Anti‐obesitydrugs

Mechanismofactionandadverseeffectsofanti‐obesitydrugsDrug Mechanismofaction MainadverseEffects

WithdrawnintheEuropeanmarket ThyroxinandTriiodotyronine Increasethebasalmetabolicrate Thyrotoxicosis2,4‐dinitrophenol Increasethebasalmetabolicrate HyperthermiaAmphetaminederivatives(desoxyephedrine,methamphetamine,phendietrazine,diethylpropion,benzphetamine,mazindol)

Stimulate release of norepinephrine anddopamine, act on satiety centers of thehypothalamicandlimbicregionsofthebrain

Intolerance, psychosis, cardiovascularproblems

Aminorex,fenfluramine,dexfenfluramine

Serotoninreleasers/reuptake inhibitors,suppressappetiteandreducefoodintake

Pulmonary hypertension and cardiacvalvulopathy

Sibutramine NorepinephrineandserotoninreuptakeinhibitorIncreased systolic and diastolic bloodpressure,increasedpulserate

Rimonabant Cannabinoidtype1receptorantagonistPsychiatric events (depressive mooddisorders, anxiety and suicidalideation)

Phentermine&Fenfluramine AppetitesuppressantHeart valve disease, pulmonaryhypertension

USFDAapproveddrugs(inthemarket)

Orlistat Pancreaticandgastriclipaseinhibitor

Gastrointestinal (GI) side effects(steatorrhea), increased defecation,soft stools, fatty or oily evacuation,abdominal pain, kidney and pancreasinjuries

Lorcaserin Selectiveserotonin2c(5‐HT2C)receptoragonistHeadaches, upper respiratory tractinfection,nauseaanddizziness

Liraglutide Glucagon‐likepeptide‐1(GLP‐1)analogueGastrointestinal effects (nausea andvomiting)

Naltrexone&BupropionAppetite suppressant via αMSH and pro‐opiomelanocortinneuralstimulation

Depression, nausea, headache,vomiting,dizziness

Phentermine&TopiramateER(extendedrelease)

Sympathomimeticaminewithanorecticeffect

Suicidal thoughts, heart palpitations,memory lapses, foetal toxicity, drymouth, constipation, paresthesia anddysgeusia

Otherongoingdrugs

MetforminDecreased hepatic synthesis of glucose anddecreasesperipheralinsulinresistance

Lacticacidosis,GIdisturbance

Zonisamide&Bupropion AppetitesuppressantHeadache,insomniaandnausea,whileurticarial(hives)

Exenatide,albiglutide,taspoglutide Glucagon‐likepeptide‐1(GLP‐1)analoguesGastrointestinal effects (nausea andvomiting)

TesofensineSerotonin/noradrenalin/dopamine reuptakeinhibitor

Increaseinbloodpressure,heartrate

Pramlintide AmylinanalogueAllergic reaction, GI discomfort,headache,nervousness,sweating

GT389‐255 Pancreaticlipase GIdiscomfortCetilistat Gastrointestinalandpancreaticlipaseinhibitor GIdiscomfortAOD9604 SyntheticallymodifiedhGH,stimulateslipolysis Nomajorsideeffects

Oleoyl‐estroneFatty acid ester of estrone, Selective B3 receptoragonist

Phase 2a studies for oral oleoyl‐estronefailed

PYY3‐36andOxyntomodulinY2RagonistandOxyntomodulin,aGLP‐1receptoragonist,areco‐secretedbyintestinalL‐cellsactingasappetitesuppressants

Nomajorsideeffects

TM30338 Y2‐Y4receptoragonist,appetitesuppressant NomajorsideeffectsAnti‐ghrelinvaccine Gastrointestinalpeptidehormoneantagonist NomajorsideeffectsTyrosinekinasereceptorTrkBagonists

Appetitesuppressant Nomajorsideeffects

Melaninconcentratinghormone(MCH)receptorantagonist

Appetitesuppressant ProlongQTcinterval

Melanocortinreceptoragonist(MC4)MK‐0493

Increaseenergyexpenditure Nausea,diarrhoeaandloosestools

FDA, Food and Drug Administration; GI, gastrointestinal; ER, extended release; GLP‐1, glucacon‐like peptide‐1; MCH, melanocortin

concentratinghormone; MC4,melanocortin receptor agonist.Adapted from (Khanetal., 2012;Adan, 2013;DiDalmazietal., 2013;

Patel,2015)

Introduction

8

Inthiscontext,theuseofnaturalproducts,asaneffectiveandasafetreatmenttofightagainstobesityand

related disorders might be an alternative therapy which is under intensive exploration

(Mohamedetal.,2014).

Alargerangeofmedicinalherbs(Vasudevaetal.,2012)andnaturalproducts(Mohamedetal.,2014)have

been used to induceweight loss and protect fromobesity through a variety ofmechanisms. Pure natural

compounds, namely dietary phytochemicals, are attracting much attention due to their effectiveness in

modulatingtheinflammatoryresponseindifferentchronicinflammatorydiseases(Panetal.,2010).Within

thewiderangeofnaturaldietaryphytochemicals,polyphenolsarethe leadingones.Someyearsagothese

compounds were considered anti‐nutritional components (Bravo, 1998). However, currently, there is

sufficientevidencederivedfrominvitroandinvivostudiestoassumenotonlytheirantioxidantproperties,

butalsotheircapacitytoinfluencedifferentbiologicalpathwaysthatareresponsibleforalteringdiseaserisk

(Josephetal.,2015).

Polyphenols are plant‐derived secondary metabolites composed of phenolic structures and one or more

hydroxyl moieties attached. Depending on the chemical structure, polyphenols have been traditionally

assigned into two main groups called flavonoids and non‐flavonoids (Andrés‐Lacueva et al., 2009)

(Figure2).Fromarecentcross‐sectionalanalysisconductedwithintheEuropeanProspectiveInvestigation

intoCancerandNutrition (EPIC)participants, itwas reported thatdailymean intakeof totalpolyphenols

was the highest in United Kingdom (UK) health‐conscious group (1521 mg/day), followed by non‐

Mediterranean countries (1284 mg/day) and the Mediterranean countries (1011 mg/day), which may

appearsurprisingaccordingtotheoriginoffoods(Zamora‐Rosetal.,2015).

Figure2.Classificationofphytochemicals (Andrés‐Lacueva etal., 2009; Gonzalez‐Castejon andRodriguez‐Casado,2011;DelRioetal.,2013).

Introduction

9

2.3.1. Flavonoids

Flavonoidsconsistofabasicstructure(C6‐C3‐C6)thatincludestwo

benzene rings (A and B) linked to a pyrane ring (C) as shown in

Figure3(TsaoandMcCallum,2009).Flavonoidsareconsideredone

of themost extensive groupsofphytochemicals that includemore

than4000compoundsandaredividedinseveralsub‐classestermed

flavones, flavonols, flavanols, flavanones, flavanonols,

anthocyanidins, isoflavones, chalchones and neoflavonoids

(TsaoandMcCallum,2009).Generally, inplants,flavonoidshappen

asglycosylatedformslinkedtoglucose,rhamnoseoreventoothersugars.Actually,inplantfoods,morethan

80differentsugarshavebeenfoundtobelinkedtoflavonoids(HollmanandArts,2000).Nevertheless,ata

lower extent, they might also occur as methylated, acylated and sulfated derivatives

(AndersenandMarkham,2005).Inacomprehensivestudy,ithasbeenestimatedthattotaldailyflavonoids

intake for Mediterranean countries is 449 mg/day and 522 mg/day for non‐Mediterranean countries

(Zamora‐Rosetal.,2015).

Flavonols:quercetin2.3.1.1.

Quercetin is an aglycone flavonoid with a phenyl benzo(c)pyrone‐

derived structure as shown in Figure4 (Nabavi et al., 2012). It is

usually found as the glycoside form linked to glucose or rutinose

(Lommen et al., 2000), being particularly abundant in fruits,

vegetablesandnuts(Harnlyetal.,2006).Theflavonoidcontentmay

greatly vary depending on a number of factors such as diseases,

insect/pest attack, climate stress, ultraviolet radiation, cultivar,

growing location, agricultural practices, harvesting and storage

conditions and processing and preparation methods (Haytowitz et al., 2013). Several efforts have been

conducted on elucidating the concentrations of different flavonoids in a range of food products

(Bhagwatetal.,2011).Thisinformationisrelevantinordertobeabletoassessthepreciseintakeofthese

compoundsfromthedietandhence,tounderstandtheoutcomesproducedbypolyphenolsonhumanhealth.

Quercetinisnearlyubiquitousinplantsanditsmostimportantdietarysourcesarethelettuce,chilipepper,

blackelderberry,onion,kale,broccoliandapplesasshowninTable2(Neveuetal.,2010).

Quercetin is considered a naturalmultifunctional compoundwith a broad number of biological activities

(Nabavi et al., 2015). In this context, the role of quercetin as anti‐inflammatory, anti‐proliferative, anti‐

atherogenic,hypolipidemic, anti‐diabetic, anti‐cancer,anti‐hypertensive, anti‐histamineandanti‐oxidant is

wellknown(Kleemannetal.,2011;Larsonetal.,2012;Siriwardhanaetal.,2013;Ariasetal.,2014).

Figure 3. Basic flavonoid backbone(TsaoandMcCallum,2009).

Figure 4. Chemical structure ofquercetin(Nabavietal.,2015).

Introduction

10

Table2.Amountofquercetininselectedfoods1

1DatabaseadaptedfromPhenol‐explorer3.0.Foodcontentofquercetinanalysedbychromatographyafterhydrolysis.

Anti‐obesityeffectsofquercetin:invitro,invivoandhumanstudies

As far as the amelioration of obesity is concerned, the use of quercetin has demonstrated to give rise to

obesity‐relatedprotectiveeffectsactingthroughdifferentmolecularpathways.

Invitrostudies

Fromstudiesindifferentcell lines(Motoyashikietal.,1996;Ohkoshietal.,2007;Parketal.,2008), itwas

concluded that quercetin exerts a delipidating effect reducing triacylglycerol content in pre‐adipocytes

(Ahnetal., 2008;Eseberrietal., 2015), increasing lipolysis (KuppusamyandDas,1992; Seoetal., 2015),

decreasingadipogenesis(Ahnetal.,2008)and inducingapoptosis(HsuandYen,2006;Yangetal.,2008),

throughspecificmolecularpathways(Aguirreetal.,2011).Studiesanalysinganti‐obesityeffectsofquercetin

invitroandinvivoaresummarizedinTable3.

FOODSOURCE QUERCETINMEANCONTENT(mg/100g)

Vegetables

Leafvegetables Redlettuce,raw 40.27

Fruitvegetables Yellowchillipepper 32.59

Onion‐family Redonion,raw 17.22

Cabbages Kale,raw 7.71

Podvegetables Greenvean,raw 3.12

Gourds Cucumber 0.04

Fruitsandfruitproducts

Fruits‐Berries Blackelderberry 42.00

Fruits‐Drupes Sourcherry 2.92

Fruits‐Pomes Apple,whole,raw 2.47

Fruits‐Citrus Lime 0.40

Seasonings

Herbs Lovage,fresh 170.0

Spices Horseradish,fresh 0.28

Seeds

Nuts Pistachio 1.33

Beans Whitebean,whole,raw 1.25

Alcoholicbeverages

Wine‐grapewines Redwine 0.87mg/100mL

Wine‐berrywines Blackurrantwine 0.84mg/100mL

Non‐alcoholicbeverages

Teainfusions GreenTea,infusion 1.53mg/100mL

Fruitjuices‐Citrusjuices Lemmonorpummelo,purejuice 0.49mg/100mL

Cocoabeverage Chocolate,milk,beverage 0.13mg/100mL

Introduction

11

Studiesconductedinanimalmodels

Quercetin has been demonstrated to exert a suppressive effect against obesity in numerous studies

(Table3). In some investigations, no effectonBWorbody fatwasobserved (Ariasetal., 2014; Leetal.,

2014),whileinothers,adecreaseinthesetwofeatureswasfound(Dongetal.,2014;Henaganetal.,2015).

Inarecentreview,thebeneficialroleofquercetintofightagainstobesityhasbeensummarized(Nabaviet

al., 2015). Molecularmechanisms reported are the following: inhibition of glucose transporters (GLUTs),

specifically GLUT4 and GLUT2, which mediate insulin‐stimulated glucose uptake; the suppression of the

expressionofimportantadipogenicgeneslikeperoxisomeproliferator‐activatedreceptorgamma(PPARγ),a

transcriptionfactorthatcontrolshigh‐fatdiet(HFD)‐inducedobesity;theimprovementoftheexpressionof

markersrelatedtooxidativestressandinflammation,suchasvisceraladiposetissueinduciblenitricoxide

synthase (VAT iNOS), cyclooxygenase‐2 (COX‐2), nuclear factor‐related factor‐2 (Nrf2), hemeoxygenase‐1

(HO‐1), nuclear factor kappa β (NF‐κβ); inhibition of extracellular signal‐related kinase (ERK1/2)

phosphorylation; enhanced phosphorylation of AMP‐activated protein kinase (AMPK) catalytic subunit;

inhibitionof thesecretionofpro‐inflammatorycytokines,amongothers(Nabavietal.,2015).Theauthors

concludedthat“indirect”mechanismsnamelyanti‐oxidantandanti‐inflammatoryactionsshouldalsoneed

tobetakenintoconsiderationwhennone“direct”targetsofquercetinareidentifiedasresponsibleforthe

amelioration of obesity, associating quercetin’s anti‐inflammatory and anti‐oxidant effects with the

attenuationofobesity(Nabavietal.,2015).

Introduction

12

Table3.Anti‐obesityeffectsofquercetininvitroandinvivo.

Animalmodel Treatmentconditions Majoroutcomes ReferencesInvitro anti‐obesityeffects

AdipocytesfromepididymaladiposetissueofmaleWistarrats

30‐50μM Stimulationoflipolysis (KuppusamyandDas,1992)

AdipocytesfromepididymaladiposetissueofmaleWistarrats

100‐500μMInhibitionoftheVanadate‐increasingeffectonLPLactivity

(Motoyashikietal.,1996)

3T3‐L1adipocytes 50‐250μM Inductionofapoptosis (HsuandYen,2006)AdipocytesfromSATandVAToffemaleC5BL/6J

100μM ‐ (Ohkoshietal.,2007)

3T3‐L1adipocytes 10‐100μMInductionofadipogenesisInhibitionofdenovolipogenesis

(Ahnetal.,2008)

3T3‐L1mouseembryofibroflasts

12.5‐100μM Increaseinapoptosis (Yangetal.,2008)

3T3‐L1mouseembryofibroflasts

25μMInhibitionoflipidaccumulation

(Parketal.,2008)

OP9cells 0,5,10,50and100μMInhibitdifferentiationDecreaseadipogenesisEnhancelipolyticactivity

(Seoetal.,2015)

3T3‐L1preadipocytes 0.1‐10μM Inhibitadipogenesis (Eseberrietal.,2015)Invivo anti‐obesityeffects

MaleC57BL/6Jmice 8g/kgdietfor3and8weeksNoeffectsonBWorbodyfat

(Stewartetal.,2008)

MaleZuckerrats10mg/kgBW/dayfor10weeks

DecreaseinBW (Riveraetal.,2008)

MaleandfemaleC57BL/6Jmice

66mg/kgBW/dayfor4weeksProtectionagainstBWgaininducedbyHFD

(Liangetal.,2009)

MaleC57/BL6Jmice 5g/kgdietfor20weeksDecreaseinBWDecreaseinvisceralandhepaticfat

(Koborietal.,2011)

MaleWistarrats 25mg/kgBW/dayfor4weeksDecreaseinplasmaticTGNoeffectonBW

(Weinetal.,2010)

Ovariectomizedrats 20‐625mg/kgfor8weeksDecreaseinBWandadiposity

(Laietal.,2011)

MaleSprague‐Dawleyrats100mg/kgBW/dayfor7weeks

NoeffectsonBW (Kimetal.,2011)

WistarratsfedaHFSdiet 8g/kgdietfor8weeks Decreaseinabdominalfat (Panchaletal.,2012)Malewistarratsmonosodium‐glutamate‐inducedobesity

75mg/kgi.p.for42days DecreaseBW (Seivaetal.,2012)

C57BL/6JmicefedaHFD 0.025%(w/w)for9weeks

DecreaseBWgainReduceepididymaladiposetissueandliverweightandliverfataccumulation

(Jungetal.,2013)

FemaleSprague‐DawleyratsfedaHFD

0,50,100or200mg/kgBWduringgestation(21days)andlactation(21days)

NoeffectsonmaternalBWDecreaseinBWofmaleandfemaleoffspring

(Wuetal.,2014)

MaleC7BL/6micefedaHFD0.05%(0.5gquercetin/100gdiet)for9weeks NoeffectsonBW (Leetal.,2014)

WistarratsfedaHFSdiet30mg/kgBW/day(0.045%diet)for6weeks

NoeffectonBWoradiposetissuesize

(Ariasetal.,2014)

MaleC57BL/6micefedaHFD

0.1%(w/w)for12weeks

DecreaseBWReduceadiposetissueweightandadipocytesizes

(Dongetal.,2014)

C57BL/6JmicefedaHFD 17mg/kgBWfor9weeks

AttenuationofBWincreaseDecreasefataccumulationandbodyfat

(Henaganetal.,2015)

LPL, lipoprotein lipase; SAT, subcutaneous adipose tissue; VAT, visceral adipose tissue; BW, body weight; HFD, high‐fat diet; TG,triglycerides;i.p.,intraperitoneal.Adaptedfrom(Aguirreetal.,2011)

Introduction

13

Humanstudies

Intervention studies using quercetin as a supplement are still relatively scarce. However, Egert and

collaborators (Egert et al., 2008) conducted a study in healthy human volunteers supplemented with

different doses of the flavonol (50, 100 and 150 mg/day for 2 weeks), in order to assess its effect on

antioxidant status, inflammation and metabolism. Plasma quercetin levels increased dose‐dependently

without affecting different metabolic markers (Egert et al., 2008). Dose‐dependent but highly variable

increaseswerealsoreportedinplasmaofparticipantsthatwerenotexplainedbydifferencesinage,gender,

BMI,fitnesslevelordietaryintake(Jinetal.,2010).Otherstudieshaveassessedtheeffectoforalquercetin

supplementation on the inhibition of platelet aggregation (Hubbard et al., 2004), on blood pressure in

hypertensivesubjects(Edwardsetal.,2007),oncardiometabolicrisk(Leeetal.,2011)andalsoonexercise

performance(Niemanetal.,2010).

Nevertheless, human intervention studies devoted to elucidate the protective or beneficial effects of this

flavonoidinoverweightandobesepatientsarelimited.Egertetal.(Egertetal.,2009)exploredtheeffectof

oralquercetinsupplementation(150mg/dayfor6weeks) inoverweightorobesesubjectswithmetabolic

syndrome traits and concluded that quercetin protected them against CVD by reducing systolic blood

pressureandplasmaoxidizedlow‐densitylipoprotein(LDL)concentrations.Interestingly,dependingonthe

ApolipoproteinEgenotypeofoverweight‐obesesubjects,adifferentialresponseinnotonlyblood‐pressure

lowering capacity (Egert et al., 2010), but also in BMI, BW and waist circumference was observed

(Pfeufferetal., 2013).Toourknowledge there isonlyone clinical trialongoing (ClinicalTrials.gov,2013),

whichaimsto investigatewhetherquercetin influences thewayglucose isabsorbedby thebody inobese

subjects or type 2 diabetic obese subjects. Further intervention studies are required so that the health

beneficialeffectsdemonstratedinvivocouldbeextrapolatedtohumans.

Intake,absorption,metabolismandexcretion

Quercetinintakehasbeenestimatedtobe17.4mg/day,anditisconsideredthehighestcontributor(70.2%)

tothemeantotalflavonoldietaryintake(24.8mg/day)(Zamora‐Rosetal.,2014).Quercetinshowspoorlow

watersolubilityandpoorbioavailability(Kaushiketal.,2012).As inthecaseofotherpolyphenols, its low

bioavailabilitymightbe theresultofa lowabsorption,anextensivemetabolism,and/orrapidelimination

(Manachetal.,2004).

Absorption of quercetin primarily depends on its chemical structure.While quercetin aglyconemight be

absorbed instomachandthesmall intestine,both throughapassivediffusionor throughapH‐dependent

mechanism (Nait Chabane et al., 2009), glycosidic forms of quercetin (glucoside, galactoside and

arabinoside) will firstly require a deglycosilation process in order to be absorbed in small intestine by

passivediffusion(Dayetal.,2003).

Ingeneral,polyphenolssufferaxenobioticmetabolism(DelRioetal.,2013)consistingof3phases:phaseI

conversion,whichcomprisesamodificationofafunctionalgroupthroughoxidation,reductionorhydrolysis;

phase IImodification, that includes conjugation processeswhere a highly hydrophilicmoiety is attached,

Introduction

14

whichmakethecompoundsmorewater‐solublesothattheycouldbeexcretedthroughurineorbile;andthe

phase III process, related to the elimination of the compounds (Melo‐Filho et al., 2014). Overall, these

processeswillbedevotedtolimitpolyphenolabsorptionandaccumulation(GuoandBruno,2015).PhaseI

metabolismofquercetinhasnotbeenreported,butitsstructureissimilartotheflavoneapigeninthattakes

partincytochromeP450metabolism,consideredamajorclassofenzymesinvolvedinPhaseImetabolism

(GuoandBruno, 2015). Concerningquercetin glycosides, as a first step, a deglycosylationor ahydrolysis

processoccurinordertoremovethesugarmoleculeattached.Thehydrolysisofsomeflavonoidglycosides

might be given already in the oral cavity by the action of both saliva and oral microbiota β‐glucosidase

activity. However, the main mechanism for flavonoid deglycosylation involves hydrolysis by the lactase

phlorizinhydrolase(LPH)inthebrushborderofthesmallintestineepithelialcells(Duenasetal.,2015).The

aglyconederivedasa resultof thisprocessenters theenterocytesbypassivediffusion. Subsequently, the

aglyconemoleculewill be transformed through glucuronidation,methylation and sulfation processes that

occur by Phase II enzymes, namely uridine 5’‐diphosphate glucuronosyltransferase (UGT), catechol‐O‐

methyltransferase(COMPT)andphenolsulfotransferase(PST)(ScalbertandWilliamson,2000).Asaresult,

alargevarietyofconjugatedmetabolitesareproducedthatwillbesecretedtoportalandlymphcirculation

(DelRioetal.,2013).Inthiscontext,thevastmajorityofquercetinintheportalveinplasmaorlymphhas

beenreportedtobeconjugatedquercetinmetabolites,whiletheaglyconeformseemstobebelowdetection

limits. Once reaching the liver through the portal vein, quercetin Phase II metabolites will be further

metabolizedbyPhaseIIconjugatingenzymes,andwillfinallyenterthecirculationorwillbeexcretedinthe

bile(GuoandBruno,2015).ThePhaseIIIeffluxofquercetinmetaboliteshavebeenalsodetected insmall

intestinal lumen(Williamsonetal.,2007).Ithasbeenevidencedthatthisprocessisprobablymediatedby

thePhaseIIItransporterspresentintheapicalsideofenterocytes,suchasbreastcancerresistanceprotein1

(BCRP1) and multidrug resistance associated‐protein 2 (MRP2). Moreover, quercetin that has not been

absorbedinthesmallintestine,togetherwiththeconjugatedmetabolitesthatreturntotheintestinallumen

throughenterohepaticcirculation,willreachthecolonicregionwheretheywillbesubjectedtotheactionof

intestinalmicrobiota(Duenasetal.,2015).

Quercetinisremovedinurineandfaecesanditsmetabolites3‐hydroxyphenylaceticacid,benzoicacidand

hippuric acid have been detected (Mullen et al., 2008). Despite the fact that deconjugation of quercetin

metabolitesinvivohasnotbeenwelldefined,aninvolvementofcolonicgutmicrobiotaisplausiblepriorto

thedegradationprocessofquercetinaglyconetophenolicacids(GuoandBruno,2015).

Consequently, knowledgeabout themetabolitesproduced fromquercetinbiotransformation (Figure5) is

essentialinordertobeabletocompletelyunderstandthehealthbenefitsattributedtothismolecule.

Introduction

15

Figure 5. Schematic representation of quercetin biotransformation (Guo and Bruno, 2015). COMT, catechol‐O‐methyl transferase; LPH, lactase phlorizin hydrolase; SULT, sulfotransferase; UGT, uridine 5’‐diphospho‐glucuronosyltransferase.

2.3.2. Non‐flavonoids

Thereareadifferentsetofnon‐flavonoidcompoundsthathavealsoshowndietarysignificancesuchas,the

stilbenes(trans‐resveratrol).

Stilbene:trans‐resveratrol2.3.2.1.

Resveratrol(3,5,4’trihydroxystilbene)isapolyphenolfromthestilbenoid

class (Figure 6) naturally present in and produced by many plants in

response to injury and fungal attack (Leiherer et al., 2013). The main

source of resveratrol in nature is a plant from traditional Chinese and

Japanese medicine called Polygonum cuspidatum, but it is also found in

smallamounts inpeanuts,mulberries,grapesandend‐productsofgrapes,

as for instance in red wine, present on average in 1.9 mg/L

(Stervboetal.,2007).Thecontentofresveratrolindifferentfoodsources

is shown in Table 4. Resveratrol might happen in two isoforms,

cis‐resveratrol and trans‐resveratrol, the latter being quite stable and the main isoform in nature

(Borriello etal., 2010). In 1990’s great attentionwas paid to resveratrol since itwas associatedwith the

Figure 6. Structure of resveratrol(McCormackandMcFadden,2013).

Introduction

16

French paradox, an epidemiological observation that French peoplewho consume great amount of fat as

saturated fats in their diet showed lower incidence of coronary heart disease (CHD)

(Renaud and de Lorgeril, 1992). The extensive research conducted on resveratrol comprehends studies

carriedouttoanalysetheroleofthismoleculetopreventortreataginganddiseasessuchascancer,CVD,

aging, metabolic syndrome, bone health, eye health and Alzheimer’s disease

(Britton et al., 2015; Rabassa et al., 2015). Indeed, it has been described as an anti‐oxidant, anti‐

inflammatoryandestrogenicmolecule(Gambinietal.,2015).

Table4.Amountofresveratrolinselectedfoods1

FOODSOURCE RESVERATROLMEANCONTENT(mg/100g)

Fruitsandfruitproducts

Fruits‐Berries Lingonberry 3.00

Seasonings

Otherseasonings Vinegar 0.01

Seeds

Nuts Pistachio,dehulled 0.07

Oils

Oils‐NutsOils Peanut,butter 0.04

Alcoholicbeverages

Wines‐berrywines Muscadinegrape,redwine 3.02mg/100mL

Wines‐grapewines Redwine 0.27mg/100mL

Wines‐sparklingwines Champagne 0.01mg/100mL

Non‐alcoholicbeverages

Cocoabeverage Chocolate,milk,beverage 0.04mg/100mL

Fruitjuices‐Berryjuices Greengrape,purejuice 0.01mg/100mL

1DatabaseadaptedfromPhenol‐explorer3.0.Foodcontentofresveratrolanalysedbychromatography.

Anti‐obesityeffectsofresveratrol:invitro,invivoandhumanstudies

Invitrostudies

AsrecentlyreviewedbyAguirreetal.(Aguirreetal.,2014),theinvitroanti‐adipogeniceffectsofresveratrol

havebeenthoroughlystudiedusingdifferent typesofadipocytesbutespecially,3T3‐L1pre‐adipocytes. In

this context, main outcomes described in the literature are the role of this compound in reducing lipid

accumulation,probablyasaconsequenceofadecreasedcellviabilityand inhibitionofadipogenesiswhen

concentrationshigherthan10μΜ(20‐100μΜ)areused(Aguirreetal.,2014).Theapoptoticpotentialofthis

moleculehasbeenalsoreportedathighconcentrations.Ontheotherhand,invitroresveratroldelipidating

andlipolyticeffectshavebeenalsoreported.In2003,resveratrolwasdesignatedasanactivatorofsirtuin1

(SIRT1)(Howitzetal.,2003)which is inducedbycalorierestrictionandexercise(Rudermanetal.,2010),

and is known to modulate aspects of glucose and lipid metabolism (Yu and Auwerx, 2009). Taking into

account that resveratrol induces theactivityof thismolecule, its roleasacalorierestriction‐likemolecule

hasbeensuggested(Timmersetal.,2011).Infact,manyofthemechanismsinvolvedseemtoberegulatedby

theresveratrol‐inducedactivationofSIRT1.

Introduction

17

Studiesconductedinanimalmodels

Previously mentioned activities have been also demonstrated in vivo. Moreover, increased

mitochondriogenesis that leads to an enhanced fatty acid oxidation in liver and skeletal muscle by

resveratrolhasbeenshowninseveralanimalstudies(Aguirreetal.,2014).Thismechanismisofrelevance,

asobesityisknowntopresentimpairedfattyacidoxidationprocess(Iskenetal.,2010).Plethoraofresearch

workhasbeenconductedtoelucidatetheeffectsofresveratrolonbodyfatinanimalmodels.Ingeneral,the

potentialofresveratroltoreducebodyfataccumulationhasbeenobservedatdosesthatvarybetween10

and200mg/kgBW/day(Arias,2015).Healthbeneficialeffectsofresveratrolagainstdiet‐inducedobesity

andassociatedcomorbidities(i.e. insulinresistance)arebasedonitsrole inreducingwhiteadiposetissue

andliverlipogenicactivity,enhancingfattyacidoxidationintheliverandimprovingbrownadiposetissue

andskeletalmusclethermogenesis(Alberdietal.,2011;Alberdietal.,2013a;Alberdietal.,2013b).

Humanstudies

Clinicalstudiesdevotedtoshedlightontheeffectsofresveratrolonoverweightorobesepatientsarescarce.

Up to date, five human studies have addressed the metabolic effects of resveratrol in the context of

overweightorobesity.Timmersetal. (Timmersetal., 2011)conducteda randomizeddouble‐blindcross‐

overstudywhere150mg/dayofresveratrolorplacebowasadministeredto11obeseparticipants for30

daysandimprovementsininsulinsensitivity, inflammation,hepaticlipidcontentandbloodpressurewere

found.Theauthorsconcludedthatresveratrolexertedcalorierestriction‐likeeffects.Furthermore,inamore

recentstudyfromthesamegroup,theimpactofresveratrolsupplementationinadiposetissuefunctionof

the previously described obese population was analysed (Konings et al., 2014). Microarray analysis and

adiposetissuemorphologywereexaminedinsubcutaneousadiposetissuebiopsies.Theauthorsconcluded

thatresveratrolsupplementationledtoanincreasedamountofsmalladipocytesandincreasedimmuneand

inflammatoryresponse.Incontrast,inanotherstudyconductedinnon‐obesepostmenopausalwomenwith

normal glucose tolerance, 75 mg/day of resveratrol supplementation for 12 weeks did not produce any

metabolic modifications (Yoshino et al., 2012). Besides, from a study conducted by Poulsen et al.

(Poulsen et al., 2013) where 24 male obese volunteers were given tablets containing 500 mg of trans‐

resveratrolperdayorplacebofor4weeks,nometaboliceffectswereobtained.Incontrast,overweightand

obese participants who presented hypertriglyceridemia and were treated with even higher doses of the

moleculeforashortperiodoftime(1000mg/dayfor1weekfollowedby2000mg/dayforthesubsequent

week), showed improvements in intestinal and liver lipoprotein formation (Dash et al., 2013). Anyway,

although in vitro and animal data might be promising, still more clinical studies are required since the

availableresultsarenotenoughtorecommendchronicadministrationtohumans(Vangetal.,2011).

Intake,absorption,metabolismandexcretion

Daily intake of the stilbenes has been estimated to be 3.1 mg/day in Mediterranean countries, and

2.1mg/day, inNon‐Mediterraneancountriesbeingwine themain foodsource (>92%) in thesecountries

(Zamora‐Rosetal.,2015).TheestimatedaverageofresveratrolintakeinSpanishpopulationis933μg/day

(Zamora‐Ros et al., 2008). Red wine contains high amounts of resveratrol, with a mean quantity of

Introduction

18

1.5to3.0mgperliter.Resveratrolisfoundintheskinofgrapes,thustheamountofthispolyphenolinwhite

wineislowerthaninredwine.Infact,theconcentrationofresveratrolindifferenttypesofwinemightvary

byafactorof20(Zamora‐Rosetal.,2008).

Largeamountofdataexistontheabsorption,transportandmetabolismofresveratrol(Kaldasetal.,2003).

Resveratrol metabolism is carried out at intestinal level within enterocytes, but also it is subjected to

hepatocytemetabolism. Ithasbeenconcludedthat trans‐resveratrol ismetabolized inenterocytesuptoa

limit.Accumulationoftrans‐resveratrol inCaco‐2cellshasbeenalsoreported,suggestingtherelevanceof

enterocytes as a target effect site (Kaldasetal., 2003).Besides, other target tissueshavebeenpostulated

suchaskidneys, liverand lungs(Bertelli,1998).NoPhase Imetabolitesofresveratrolhavebeendetected

(Yuetal.,2002).Incontrast,thiscompoundsuffersconjugationprocesseswithinPhaseIImetabolismgiving

rise to glucuronides and sulfates mainly, trans‐resveratrol‐3‐O‐glucuronide and the trans‐resveratrol‐3‐

sulfate(Yuetal.,2012b).Inthissense,whileglucuronidationhasbeenconsideredasthemajorconjugation

pathwayinhumans,otherstudieshaveproposedsulfationprocesstobemorerelevant(Walle,2011).Some

ofthemetabolitesformedintheliverwillbesecretedtothebile,inordertobedeconjugatedandreabsorbed

intheentero‐hepaticcirculation;otherwise,thesemetaboliteswillreachthecolon.Inthecolonictissue,the

roleofgutmicrobiotainthemetabolismofresveratrolhasbeendemonstratedandseveralmetaboliteshave

been identified, namely dihydroresveratrol, 3,4’‐dihydroxy‐trans‐stilbene and 3,4’‐dihydroxybibenzyl or

lunularin (Bode et al., 2013). In summary, it has been demonstrated that non‐methylated polyphenols

(i.e. resveratrol) are quickly metabolized and discarded. In contrast, methylated stilbenes such as

pterostilbene have been demonstrated to be protected from rapid hepatic metabolism and excretion,

conferringthemanincreasedintestinalabsorptionandhighermetabolicstability(WenandWalle,2006).

The output of stilbeneswillmainly happen through urine (53‐85%).However, the remaining amounts of

thesenaturalcompounds,whichhavenotbeenmetabolized,willbealsoexcretedinfaeces(Walle,2011).An

outlineofresveratrolmetabolisminvivoisreportedinFigure7.

Figure7.Outlineof resveratrolmetabolism invivo.Adapted from (Scalbert etal., 2002; vanDuynhoven etal.,2011).

Introduction

19

3. Gutmicrobiota:anewcontributoryfactorinobesitydevelopment

3.1. Conceptualapproach

Humanbeingshaveagutreservoirof1023‐1024microbialcells(Leyetal.,2006).Gutmicrobiotaisdefinedas

thecollectionofmicroorganismsthatincludebacteria,archaea,virusesandsomeunicellulareukaryotesthat

represents an incredibly complex, diverse and vast microbial community residing in the intestine

(Harrisetal.,2012;MarRodriguezetal.,2015;OgilvieandJones,2015).Theguthousesupto100trillion

(1014)bacterialcells(Backhedetal.,2005).Furthermore,anumberof1000to1500bacterialspecieshave

been reported to be widespread in the gut, and indicated that each person has around 160 species

(Qinetal.,2010).Moreover,microbialcellsexceed10timesthenumberofsomaticcellsinthehumanbody

and stand for a biomass of ~ 1.5 Kg in our organism (Backhed et al., 2005). The termmicrobiomewas

suggested by the Nobel laureate Joshua Lederberg to designate the collective genome of the microbiota

(HooperandGordon,2001).Accordingly,themicrobiomehasbeenfoundtocontain≥100‐foldmoregenes

thanthehumangenome,contributingextensivelytoourphysiologyandmetabolism(Qinetal.,2010).This

humanmicrobialgenome isknownas the “metagenome”and isusuallyrecognizedasoursecondgenome

(Aagaardetal.,2013).Inviewofthat,gutmicrobiotacouldbecontemplatedasasuperorganismwithinour

organism constituted of diverse cell lineages that have the capacity to communicatewith each other and

encodeplentyofbiochemicalandphysiologicfunctionsthatmaybenefitthehost(Petrosinoetal.,2009).

Inthelastdecadesscientificattentionongutmicrobiotahasraisedconsiderably.Thereisincreasinginterest

fromresearchcommunitytounderstandtheroleofthemicrobialcommunityinmaintaininghumanhealth.

Accordingly, several projects have been launched all over the world (Huse et al., 2012). One of the first

studies,namelyHumanMicrobiomeProject(HMP),wasconductedbytheNationalInstituteofHealth(NIH)

and began in 2008. TheHMP lasted 5 years,was funded by $150million and sought to characterize the

humanmicrobiome of healthy individualswith the aimof analysingmodifications of this ecosystemwith

population, genotype, disease, age, nutrition, medication and environment (Peterson et al., 2009).

Subsequently, more studies have appeared (Jeffery and O'Toole, 2013). In fact, there also exists an

InternationalHumanMicrobiome Consortium launched in 2008 (Table5), that coordinates the activities

and policies of the international groups studying the humanmicrobiome (Lepage etal., 2013). Outcomes

from these studies are enabling to demonstrate theplethora of biological functions inwhich the resident

microbiotaisinvolved(JefferyandO'Toole,2013).

Introduction

20

Table5.Mainmicrobiomeconsortiumsandmicrobiome‐relatedprojects.

Program DurationFunding

OrganizationConditionsofinterest

NationalInstituteofHealth(NIH)Jumpstart

program

2007‐2008

NIH,USAGenerate 200 complete bacterial genome sequences and performcompositionalanalysisofvariousbodyregions

NIHHumanMicrobiomeProject

2007NIHRoadmapProgram,USA

Characterize the microbes of the human body and correlate thechangesinthesemicrobialpopulationswithhumanhealth

DataAnalysisandCoordinationCenter

(DACC)

2008‐2013

NIHHumanMicrobiomeProject

(HMP),USA

Assist in standardization of data pipelines (storage, analysis anddisplayofdata)andprovideaccesstodata.

MetaHIT,MetagenomicsoftheHumanIntestinal

tract

2008‐2011

EuropeanCommission(FP7)

DescribetheroleofthemicrobiotainInflammatoryBowelDisease(IBD)andobesity,andgenerateareferencecatalogueofintestinalmicrobialgenes.

CanadianHumanMicrobiomeInitiative

2009CanadianInstituteofHealthResearch

(CIHR)

Anumberofproject relating tohumanmicrobial interactionsandtheireffectonhealth

TheAustralianJumpstartHumanMicrobiomeProject

2009

CommonwealthScientificand

IndustrialResearchOrganisation(CSIRO)

Sequencing of specific bacterial strains and the application ofmetagenomics techniques to investigate the interaction betweenintestinalmicrobesandtheirhost.

MicroObes,HumanIntestinalMicrobiome

inObesityandNutritionalTransition

2008‐2010

FrenchNationalAgencyforResearch

(ANR)

Identifymetagenomicsignaturesthatcharacterisetherelationshipbetween the intestinal microbiota and the nutritional andmetabolicstatusofthehost.

KoreanMicrobiomeDiversityUsingKoreanTwinCohortProject

2010‐2015

NationalResearchFoundationofKorea

Determinethemicrobiomesinvariousepithelialsitesofthehumanbody using Korean Twin Cohort and investigate the relationshipbetweenhumanmicrobiomesanddisease.ToestablishadedicatedcentreforKoreanmicrobiomeinformationandanalysis.

HumanMetaGenomeConsortiumJapan

(HMGJ)2005

MEXT,MinistryoflabourandWelfare,JSTandJSPS,Japan

Develop and establishmethods formetagenomics analysis of thehumangutmicrobiome

ELDERMETProject2007–2013

NationalDevelopmentFoodResearchHealthInitiativeandScienceFoundationIreland

Characterize the faecal microbiota associated with ageing andcorrelate diversity, composition, and metabolic potential of thefaecalmicrobialmetagenomewithhealth,dietandlifestyle

InternationalHumanMicrobiomeStandards

(IHMS)

2011‐2015


Optimizationofmethodsfortheassessmentoftheeffectsofthegutmicrobiome on human health through standardization ofproceduresandprotocols.

MetaGenoPolis2012‐2019

Frenchinitiative“Investissements

d’Avenir”

Demonstratetheimpactofthehumangutmicrobiotaonhealthanddisease, making available cutting‐edge metagenomics technology,quantitativeandfunctional,tothemedical,academicandindustrialcommunities.

MyNewGut2013‐2018


Analyse how the human gut microbiota and the microbiome (itsgenome) influence obesity, behavioural‐ and lifestyle‐relateddisorders and vice versa. Besides, it seeks to identify specificdietarystrategiestoimprovelong‐termhealthofpopulation.

HumanFoodProject 2012Crowdfundedandpartnerships

Understand modern diseases within the context of human’sancestralandmicrobialpast,includingtheco‐evolutionofhumansandtheirmicrobeswithintheirbodies.

Americangut 2013 Crowdfunding

Anopenaccessprojectthatallowsparticipantstobeinvolvedinfindingoutthegutmicrobiotacompositionoftheirgutsandcontributetoobtainingknowledgeonhowdifferentdietandlifestylehabitsaffecthumanhealththroughgutmicrobes‐

Britishgut 2013 CrowdfundingAn open access project that allows participants to be involved infindingoutthegutmicrobiotacompositionoftheirgutsinordertounderstandthebacterialdiversityoftheBritishGut.

3.2. Establishmentandevolution

Establishmentandevolutionoftheintestinalmicrobiotaisacomplexprocessdeterminedbytheinteraction

ofmany factorsas for instancedeliverymode, typeof feeding, exposure toantibiotics andenvironmental

factorssuchasdietandlifestylefactors(Figure8).Furthermore,theplasticityofthegutmicrobiotathrough

NIH, National Institute of Health; DACC, Data Analysis and Coordination Center; HMP, Human Microbiome Project; CIHR, CanadianInstitute of Health Research; CSIRO, Common wealth Scientific and Industrial Research Organisation; HMGJ, Human MetaGenomeConsortiumJapan;IHMS,InternationalHumanMicrobiomeStandards;ANR,FrenchNationalAgencyforResearch;MEXT,theMinistryofEducation,Culture,Sports,ScienceandTechnology‐Japan;JST,theJapanScienceandTechnologyAgency;JSPS,theJapanSocietyforthePromotionofScience.Adaptedfrom(JefferyandO'Toole,2013;Lepageetal.,2013;HattoriandTaylor,2014).

Introduction

21

lifespanispivotalforitsimplicationsinhealthanddisease(Douglas‐Escobaretal.,2013).Inthissense,the

developmentofgutmicrobiotaduringinfancyandchildhoodhasattractedmuchinterestduetoitsrelevance

for the correct development of immune system (Francino, 2014). However, phylogenetic and functional

changesoccurringwithage,andtheimpactofthesemodificationsinhealthandlongevityarealsomatterof

study(Biagietal.,2013).

Figure8.FactorsinfluencinggutmicrobiotadevelopmentAdaptedfrom(Matamorosetal.,2013;Arrietaetal.,2014;Munyakaetal.,2014).

3.2.1. Age‐relatedgutmicrobiotacomposition

Foetalgutmicrobiota

Untilnowitwasthoughtthatthefoetusgrowssterileinuteroandmicrobiotacolonizationwasbelievedto

beginatdeliverywhilepassingthroughmaternalbirthcanal(Tissier,1900).Conversely, if recent findings

are confirmed, the development of foetus in a sterile environment does not seem to be correct

(Romano‐Keeler and Weitkamp, 2015), since several studies have already corroborated the presence of

bacteria in placenta of normal pregnancy (Stoutetal., 2013;Aagaardetal., 2014). Interestingly, placenta

microbiomeprofilewasfoundtoresemblethenon‐pregnanthumanoralmicrobiome(Aagaardetal.,2014)

hypothesizingthatmother’soralbacteriamightfindawaytothefoetus.Despitethemechanismofinternal

maternaltransmissionofbacteriaisnotwellunderstood,apossiblepassageofthemicrobestotheplacenta

viathebloodstreamhasbeensuggested.

Gutmicrobiotainneonates

Thefirstyearafterbirth,themotherexertsarobustinfluenceontheinfant’smicrobiota.Indeed,thechild’s

intestinalmicrobiota isboth functionallyandphylogeneticallyclose tothemothersparticularlywithin the

firstmonthof life (Matamorosetal., 2013).Nonetheless, at 1 yearof age, although similarities remain at

functionallevel,phylogeneticmodificationsbegan(Vaishampayanetal.,2010).Inthisway,thereareseveral

Introduction

22

intrinsicandextrinsicfactorsthatmightinducehighlyvariablemicrobialpatternsontheneonatewhichwill

ultimatelyaffectinfant’shealth(FunkhouserandBordenstein,2013).

Mode of delivery has been found to be a factor strongly influencing the colonization process

(Matamorosetal.,2013).Theintestinalmicrobiotaofvaginallydeliveredinfantsresemblesmother’svaginal

microbiota(Dominguez‐Belloetal.,2010).Incontrast,theintestinalmicrobiotaofchildrenbornbyC‐section

ischaracterizedbythepresenceofmicrobesassociatedtohospitalenvironment(Biasuccietal.,2010)and

also, to be distinctive of skin surfaces (Biasucci et al., 2008). Notably, these alterations due to delivery

process have been reported to be permanent up for weeks (Kozyrskyj et al., 2011), months

(Gronlund etal., 1999), and even years (Jakobsson et al., 2014). Although the clinical relevance of these

modifications is unknown, there is awide range of evidence supporting the significance of the initial gut

microbiota colonization process on the postnatal evolving of immune system (Neu and Rushing, 2011).

Another factorsignificantlymodulating thegutmicrobiotacompositionof the infant is the typeof feeding

(LeHuerou‐Luronetal.,2010).Breast‐feedingisrecommendedoverformula‐feedingasthebestnutritional

option associated to short‐ and long‐term health protective effects (LeHuerou‐Luron etal., 2010). It has

beensuggestedthatthechemicalcompositionofthebreastmilkmightdirectly(promotingoravoidingthe

growthofspecificbacteria),orindirectly(inducingprotectivephysiologicaleffectsatintestinallevel)impact

ongutmicrobiota(LeHuerou‐Luronetal.,2010).Moreover,theexistenceofanentero‐mammarypathway

has been proposed as a possible explanation for the transfer of mothers’ gut bacteria to the child

(Jostetal.,2014).Apartfromthemothers’influence,familymembers,aswellasbiodiversityinthehomes

and the surrounding environment, are known to directly impact the microbiota of the infants

(Pendersetal., 2006). Inaddition, recent studieshave revealedpronouncedgeographicdifferences ingut

microbiota profiles among children and adults of Westernized and non‐Westernized countries

(Yatsunenko et al., 2012). Finally, the exposure to antibiotics during early life has been associated with

healthdetrimentaloutcomes(Husseyetal.,2011;Risnesetal.,2011).Importantly,therestorationcapacity

of the gutmicrobiota following antibiotic treatment is low, therebymodificationsmight remain during a

long‐termperiodoftimeproducingnegativeeffectsonhosthealth(DethlefsenandRelman,2011).

Gutmicrobiotainadults

Microbiome of infants experiences a profoundmodification towards the acquisition of a stable adult‐like

microbiota,especiallybetween9and18monthsafterbirth,parallel to thecessationof thebreast feeding

and the introduction of complementary foods (Bergstrom etal., 2014). Thus,muchmore important than

otherenvironmentalandphysiologicalfactors,dietaryhabitsareconsideredadecisivefactorforthelong‐

term gut microbiota composition, and in turn, for the individual’s predisposition to develop diseases

(Voreades et al., 2014). In any case, gut microbiome stability is not gained before 3 years of age

(Bergstrometal.,2014).

Almost 70% of the bacterial strains in an adult‐like microbiota are maintained unaltered over the time

(Faithetal.,2013).Inthissense,effortshavebeenmadeinordertodefinea“core”microbiota,whichreflects

a“normal”healthystate(Huseetal.,2012).Thisconceptwouldbeahelpfultoolsincevariationsonitmight

Introduction

23

be indicative of a health disturbance resulting from or contributing to disease development

(Voreadesetal.,2014). Indeed,classificationof individualsdependingonthe faecalgutmicrobiotaprofile

has been contemplated as a possibility to predict long‐term health risks. In this context, Arumugan and

collegues (Arumugam et al., 2011) identified three distinct “enterotypes” or variants, irrespective to

nationality,sex,ageorBMIandeachclusterwascharacterizedbytheabundanceofaspecificbacterialgenus

namely Bacteroides, Prevotella and Ruminococcus. Wu et al. (Wu et al., 2011) confirmed that faecal

microbiota from98healthyadults thatwere subjected toeitherahigh‐fat/low‐fibreor low‐fat/high‐fibre

diet, was partitioned into two main enterotypes depending on the dietary pattern. Indeed, Bacteroides

clusterwas associatedwith the consumption of aWesternized diet, high in animal protein and fat,while

Prevotellaclusterwasrelatedtolowcarbohydrateintake(Wuetal.,2011).Theauthorsalsoobservedthat

gutmicrobiotaprofilewasmarkedlymodifiedwithin24hours.Incontrast,enterotypeswerefoundtoonly

explainlong‐termdietarypatterns(Wuetal.,2011).Nevertheless,disparitieshavebeenfoundindifferent

studiesparticularlyasaresultofalackofconsistencyinthemethodologies(Korenetal.,2013).Therefore,

theexistenceofan“enterotype”ora“faecotype”isnotsoclearyet(Knightsetal.,2014).

Thehumangutmicrobiotawillbesusceptibletovariationsowingtoillnesses,stress,antibiotics,andalso,as

aconsequenceofdietandlifestyle(Zhangetal.,2015).Anyway,intheabsenceofrelevantstressorstheloss

ofbalanceinthegutmicrobiotawillappearwiththeoldage(Salazaretal.,2014).

Gutmicrobiotainelderly

Incontrast to childhoodand infancyperiods,notmanystudieshavebeenpublishedneither in relation to

phylogeneticandfunctionalchangesofgutmicrobiotainageingprocess,norregardingtheimpactofthese

rearrangements on health and lifespan (Biagi et al., 2013). Nonetheless, it is known that elderdy people

present innumerable clinical changes undergoing many comorbid diseases that lead to the continuous

administrationofmedicationsandantibiotics (ZapataandQuagliarello,2015).Alterations thatoccuratGI

level(Biagietal.,2012),impactontheabsorptionand/ormetabolismofnutrients(calcium,ironandvitamin

B12)andareassociatedwithnumerousdietaryhabitchanges(Biagietal.,2012).Togetherallthesefactors

could affect gut microbiota composition. In this sense, elderdy people have been reported to present a

drastic variability in gut microbiota profile between individuals compared to younger adults

(Claessonetal., 2011).Moreover, ithasbeenobserved thatgutmicrobiota remodellingdoesnot followa

linearrelationwithage(Biagietal.,2010).Interestingly,whereas30yearsoldadultsand70yearsoldyoung

elderlypeopleseemtosharesimilargutmicrobiotacompositionanddiversity,gutmicrobiotaecosystemof

centenarians show marked differences. These alterations are mainly characterized by a proliferation of

pathobiontsattheexpenseofsymbionts,asforinstanceFaecalibacteriumprausnutziiandClostridiumcluster

XIVabacteriawhichhaveascribedanti‐inflammatoryproperties(Biagietal.,2010).

3.2.2. Microbialcommunitythroughthebody:focusonthegastrointestinaltract

Colonizationofmicrobiotaoccurs inall surfacesof thebody thatareexposed toanexternalenvironment

(Sekirov et al., 2010). Composition of themicrobiota has been reported to be distinct depending on the

anatomical area of the body. It seems that the microbiota is a large ecosystem with different habitats

Introduction

24

(Sporetal.,2011). Indeed, locationthroughthebody isdescribedas themajor factor thatdeterminesgut

microbiota (Costello et al., 2009). Furthermore, it has been postulated that microbial profile is not

homogenous and might vary not only longitudinally along the GI tract, but also, radially from lumen to

mucosa(FrankandPace,2008).VariationsinmicrobialnumberandcompositionacrossthelengthoftheGI

tractareshowninFigure9.

TheGItractcontainsthelargestnumberofmicroorganismsinhumans.Everylocationhasitsownchemical

environment and thus, themicrobes inhabiting each site are extremely different as shown inFigure10

(Harrisetal.,2012).TheGItractextendsupto200m2andisdefinedasadynamicandcomplexorganwhere

interplay between several components (intestinal mucosal cells, defence molecules, immune system,

nutrientsandmicrobiota)takeplace(Lopetusoetal.,2014).Populationofmicrobialecosystemvariesalong

the digestive tract. At the stomach 103 colony‐forming units (CFU)/mL have been estimated, in the small

intestine between 102 and 109 CFU/mL and in the large intestine from 104 to 1012 CFU/mL

(Lopetuso et al., 2014). Indeed, the colon is the site exhibiting the highest density found in humans

(1012 microorganisms/mL). The major part of the intestinal microbiota is composed of strict anaerobes,

whereasfacultativeanaerobesandaerobeshavebeenconcludedtocorrespondlessinnumber(Sankaretal.,

2015).Thegutmicrobiotaiscomposedofmorethan30bacterialphylaand7phylahavebeenidentifiedas

the groups containing the largest amount of detected bacterial species: Firmicutes, Bacteroidetes,

Actinobacteria, Cyanobacteria, Fusobacteria, Proteobacteria and Verrucomicrobia (Sankar et al., 2015).

Moreover,amongthem,Firmicutes,Bacteroidetes,ProteobacteriaandActinobacteriaarelargelypresentin

human mucosal biopsies, luminal contents and faeces. Firmicutes and Bacteroidetes represent 98%, the

dominating phyla of the gut microbiota, and are classified within three groups of strict extremophile

anaerobes namely Bacteroides, Clostridium cluster XIVa (also called Clostridium coccoides group), and

Clostridium cluster IV(or theClostridium leptumgroup) (Holdetal.,2002;Eckburgetal.,2005;Leyetal.,

2005;Leyetal.,2006).FirmicutesphylumprimarilyincludesGram‐positivebacterialikeLactobacillusand

Figure9.Variations inmicrobialnumberandcompositionacross the lengthof theGI tract.Adaptedfrom(Sekirovetal.,2010).

Introduction

25

ClostridiumspeciesandhasthegreatestdiversityintheGItractwithlowGCcontent.Proteobacteriaphylum

is not so representative and this group includes the well‐known pathogen Escherichia coli and

Actinobacteria. Within Actinobacteria phylum, the most characteristic group is Bifidobacterium

(Linetal.,2013).

Importantly,thetypeofsamplecollectedforgutmicrobiotacompositionanalyseswillmakeresultstodiffer.

Till now,particularly,mucosaadheredmicrobiotaobtained frombiopsy samplesand faecal sampleshave

beenmostlyusedtoanalysegutmicrobiotacomposition(Gerritsenetal.,2011).Inthissense,therehasbeen

continuousdiscussion since, despite faecal samples are considered convenient as they are easy to isolate,

theyarenotconsideredaverygoodreflectofthewholemicrobialdiversityoftheproximallargeintestine.It

has been described that during the movement of the bolus through the colon, there are changes in

substrates, pH and water content that will make the composition of the microbiota to be modified

(Mai et al., 2010). Biopsies (colon biopsies) are not either representative of the real gut microbiota

compositionintheintestine(Maietal.,2010).Whenpreparingandcleaningcolonsforthesampling,partof

theoutercolonicmucosaisremovedandwiththis,themicrobiotaattachedtothissite.Somestudieshave

alreadyreportedtheimpactofbowelpreparationsinbiopsiesoncolonphysiology(Croucheretal.,2008).

Consequently, the discrepancies observed between bacterial community composition of different sample

typesandthemicrobiotacompositionfromdiversebodynichesshouldbeovercome.Notwithstandingasfar

as technologies are developed to bridge this gap, faecal samples and biopsies will still be utilized as

representativesofthecolonicmicrobiota(Rodriguezetal.,2015).

Figure 10. The relative abundance of the six dominantbacterial phyla in each of the different body sites(Sporetal.,2011).

Introduction

26

3.2.3. Gutmicrobiota:asignofmetabolicstatus

Ingeneral,gutbacteriaandthehostliveinamutualisticrelationship(SpasovaandSurh,2014).Commensal

bacteriaareappreciatedforitscontributionstohostdevelopmentandwell‐beingduetothemetabolicand

immunological functions that microbes fulfil ensuring homeostasis (Spasova and Surh, 2014).

Culture‐independent molecular techniques have currently allowed defining the normal state of the gut

microbiota, known as “eubiosis” or “normobiosis. This concept, paralelly, supports the identification of

deviationsofthisnormalstate,usuallydetected inmetabolic, immuneordegenerativediseases.Thesegut

ecosystemmodificationsarecalled“dysbiosis”(DoréandCorthier,2010;Doréetal.,2010),disruptionofthe

symbiosis that lead todisturbedhost‐microbehomeostasis and thus,mightbe the cause, or at least, be a

factorcontributingtoawiderangeofdiseases.

A number of diseases have been associated with gut microbiota dysbiosis, including GI tract‐related

disorders,systemicdiseasesorcentralnervoussystem‐(CNS)‐relateddisorders(Cardingetal.,2015).

GI tract‐related disorders: Inflammatory bowel disease (IBD) and its prevalent forms Crohn’s

disease (CD) and ulcerative colitis (UC), are characterized by recurrent intestinal inflammation.

Aberrant changes in gut microbiota composition have been identified in the pathophysiology of

these disorders, with reported differences having found between CD and UC

(Cammarota etal., 2015). Additionally, intestinalmicrobiota has been also implicated in irritable

bowel syndrome (IBS), coeliac disease and colorectal cancer, but without the identification of

consistent patterns ofmicrobiotamodifications (Carding etal., 2015). Furthermore, at present, it

couldnotbe concludedwhether themicrobial dysbiosis is adirect causeof thesedisordersor in

contrast,itisaconsequenceofanalteredecosystemintheGItract(Cammarotaetal.,2015).

CNS‐relateddisorders:Thereexistabidirectional communicationbetween thedigestive tractand

the brain (Dinan et al., 2015). This crosstalk occurs due to the “gut‐brain‐axis” (GBA)

(Rhee et al., 2009) and associations between gut microbiota and anxiety‐related, depressive‐like

behaviours (Diaz Heijtz et al., 2011), autism spectrum disorder (ASD) (Song et al., 2004),

schizophreniaandbipolardisordershavebeendemonstrated(Fondetal.,2015).

Systemicdiseases:InadditiontoT2DM(TilgandMoschen,2014),hypertension(Yangetal.,2015)

and CVD (Serino et al., 2014), one of the most important metabolic disease associated to gut

microbiotadysbiosisisobesity(Escobedoetal.,2014).Pioneeringdataproposingtherelationship

betweengutmicrobiotaandobesity,specificallywithenergyhomeostasisandfatstorage,rosefrom

studies conducted with germ‐free (GF) animals (Conterno et al., 2011), as briefly described in

Table6.Bäckhedetal.(Backhedetal.,2004)werethefirstauthorsdemonstratingthatGFmicethat

acquiredgutmicrobiotafromconventionallyraisedanimalshadincreasedfatdeposition,hencethey

werethefirstauthorsconnectinggutmicrobiotafunctionalitywithmammalianenergybalanceand

obesity (Backhedetal., 2004).Fewyears later,Turnbaughetal. (Turnbaughetal., 2006)showed

thatthistraitwastransmissiblesinceGFanimalsthatreceivedgutmicrobiotafromobeseanimals

exhibitedgreaterbodyweightandadipositythanthoseanimalscolonizedwithgutmicrobiotafrom

leananimals.Inthiscontext,obesity‐associatedgutmicrobiotawasfeaturedbyincreasedlevelsof

Firmicutesphylumcomparedtothatoftheleanones.Moreover,metagenomicsequencinganalysis

Introduction

27

confirmed that these bacterial profile modifications impacted on the metabolic potential of gut

microbiota. Furthermore, subsequent studies conducted using GF animals proposed that these

animalswereresistanttodiet‐inducedobesityandthatobesephenotypewasrapidlytransmissible

(Rabotetal.,2010;Ridauraetal.,2013;Ducaetal.,2014),althoughcontradictorydatahavebeen

alsoreportedbyFleissneretal.(Fleissneretal.,2010)whichmightbeattributedtodifferencesin

strains ofmiceused. Interestingly, the role of gutmicrobiota in reducing adiposity has been also

recently concluded from experiments where faecal slurry from bariatric surgery patients was

transplantedintoGFmice(Tremarolietal.,2015).

Greaterevidencefromimbalanceintheintestinalbacterialcommunityinobesitycamefromobese

(ob/ob) mice, deficient in the gene encoding leptin, a hormone that promotes satiety

(Leyetal.,2005).Leyetal.(Leyetal.,2005)demonstratedthatthegutmicrobiotafromob/obmice

differed from that of their lean ob/+ and +/+ siblings, particularly in relation to Firmicutes and

Bacteroidetesphyla.GeneticobesitywasassociatedtoanenlargementinFirmicutestogetherwitha

parallel decrease in Bacteroidetes. Interestingly, this was the first study showing that a single

geneticmutationcouldaffectgutmicrobiotacomposition.Theseresultswerealsoconfirmedinarat

modelofgeneticobesity resistant to leptin (Zucker fa/fa), thatbetter reflectdiet‐inducedobesity

typical from themodern society (Waldram etal., 2009). Importantly, data obtained from studies

conductedwith diet‐inducedmodels of obesity have uncovered the complex interaction between

dietaryhabits,thegutmicrobiota,andhostenergymetabolismpredisposingtothedevelopmentof

differentmetabolicdiseasesas, for instanceobesity (XuandKnight,2015). In this regard, several

studies have evidenced the impact of diet on gut microbiota composition, even independent of

obesity (Hildebrandtetal., 2009). Turnbaughetal. (Turnbaughetal., 2008) observed that adiet

high in sugar and fat led to an increase in Firmicutes phylum (especiallyMollicutes class) and a

decreaseinBacteroidetes.Growingdatafromhumanstudieshavealsodemonstratedadysbioticgut

microbiotainobesewhencomparedtoleanindividuals(Turnbaughetal.,2009a;Turnbaughetal.,

2009b). However, not all studies have obtained the same results (Schwiertz et al., 2010). For

instance,somehumanstudiescontradictthestatementthatobese‐typemicrobiotaischaracterized

byhigherFirmicutes/Bacteroidetes ratio, anyway, elevated faecal SCFAconcentrationshavebeen

found in obese subjects, agreeing with data obtained from ob/ob mice (Schwiertz et al., 2010).

Therefore,despitethefactthatcurrentfindingsarenotabletodetermineaspecificgutmicrobiota

signatureofobesesubjectsversusleansubjects,adistinctbacterialcommunitybetweenobeseand

leanpeoplehasbeenconsistentlydemonstrated.Theremainingquestioniswhetherdysbiosismight

beconsideredadirectcauseofmetabolicdiseasesorwhetherthisaberrantmicrobiotacomposition

isanadaptationtochangesinenvironmentalconditionssuchasthediet.Inthiscontext,twopoints

shouldbetaken intoaccount(Cardingetal.,2015).Ononehand,the fact thatmicrobiotatransfer

from lean donors into individuals with metabolic syndrome has been shown to be effective in

improvinginsulinsensitivityandgeneralsymptomsofthemetabolicdisorder(Vriezeetal.,2012),

and,ontheotherhand,thefactthatdietaryinterventionshavebeenshowntoinducerapidlyandin

a reversible way, changes in predominant bacterial groups (Walker et al., 2011). Anyway,

characterizationof“dysbiosis”indifferentdiseasesisanessentialstepinordertobeabletodesign

Introduction

28

strategies that will unable to restore the eubiosis and the homeostatic state of the host

(DoréandCorthier,2010).Nevertheless,thefunctionalityofthegutmicrobiota,whichwill impact

onhostphysiology,shouldbeborninmind.Inthissense,despitemicrobialtaxonomiccomposition

among individualsmightbehighlyvariantat leastat thedeeperphylogenetic levelorstrain level

(Greenblumetal.,2015), ithasbeendemonstratedthatfunctionalgenecompositionisubiquitous

across individuals (Turnbaugh et al., 2009a; 2012), proposing that we have a core microbiome.

Importantly, the identification of functional gene composition variations that may be limited to

specificbacterial speciesor strainsmighthelp todetect clinically relevant components thatcould

serveastherapeuticstrategies(Lozuponeetal.,2012).

Introduction

29

Table6.Mainstudiesconductedingerm‐freeanimalmodelssupportingthecausalroleofgutmicrobiotaonobesityandobesephenotypetransmissibility.

AnimalmodelExperimentalprocedure

Conclusions Reference

GF,CONV‐RmiceandCONV‐Dmice‐C57BL/6J

GF mice colonized withcecal content of CONV‐Rdonors(conventionalization)

A 60% increase in body fat andinsulin resistance was found uponconventionalization.

Microbiota promotedmonosaccharides absorption andFiafsuppression.

(Backhedetal.,2004)

GFC57BL/6Jmiceandobese(ob/ob)andlean(+/+)mice

GF mice are colonizedwith cecal content ofobeseandleanmice

An increased capacity for energyharvest is found in obesemicrobiome.

Colonization of GF animals withobese donors’ microbiota led to alargerincreaseintotalbodyfatthancolonization with lean donor’smicrobiota.

(Turnbaughetal.,2006)

GFandCONV‐RC57BL/6Jmice

Animalswere fed aHFDfor11weeks

GF mice were resistant to diet‐induced obesity and insulinresistance.

GF mice showed lower food intakeandincreasedfaecallipidexcretion.

(Rabotetal.,2010)

GFC57BL/6Jmiceandmonozygoticanddizygotictwinsdiscordantforobesity

Faecal samples fromeach twin (lean andobese) weretransplanted into GFmiceand“humanized”orgnotobiotic animalswereco‐housed.

A reliable replication of humandonormicrobiotaingnotobioticmicewasfound.

A reproducible transmission ofdonor’s body composition intognotobioticmicewasobserved.

Co‐housingmicethatcontaintheObmicrobiota with animals containingLnmicrobiota,showedthatbacterialmembers from Ln were transferredto Ob but not vice versa andpreventedthedevelopmentofobesemetabolictraits.

(Ridauraetal.,2013)

GFC57BL/6Jmice

GF mice wereconventionalized withfaecal microbiota of OPandORratsfedaHFD

OP and OR rats differed in gutmicrobiota composition profile andphenotypiccharacteristics.

Phenotype and behaviouralcharacteristics of OP and OR weretransferredtoGFmice.

GF mice inoculated with OPmicrobiotashowedobesephenotypeof the donor and associateddifferences in chemosensory,metabolicandneuraldysregulations.

(Ducaetal.,2014)

GFSwissWebsterfemalemice

GF mice were colonizedwith faecal slurry ofRYGM, VBG and OBSpatients.

GF mice colonized with RYGB andVBG patients’ faecal microbiota,accumulatedreducedfatmass.

MicereceivingRYGBmicrobiotahadreduced RQ (reduced use of CHO,increaseduseoffat).

(Tremarolietal.,2015)

GF,germ‐free;CONV‐R,conventionally raised;CONV‐D,conventionalized;Fiaf, fasting‐inducedadipocyte factor;HFD,high‐fatdiet;Ob, obese twin’s; Ln, lean twin’s; OP, obese‐prone; OR, obese‐resistant; RYGB, Roux‐en‐Y gastric bypass; VBG, vertical bandedgastroplasty;OBS,obese;RQ,respiratoryquotient;CHO,carbohydrates.

Introduction

30

3.3. Crosstalkbetweengutmicrobiotaandthehost

Until the last decade, intestinal bacteria have been regarded as organismswith pathogenicity thereby, as

microbes that could cause diseases. Fortunately, in the last decade, an increasing number of research

initiatives have surged studying the role of commensal bacteria onmammalian gut (Sekirovetal., 2010),

thus,theviewofgutmicrobiotaasathreathascompletelychanged.Nowadays,strongevidencesupportsthe

important and specific functions (structural, protective,metabolic and immune) of gutmicrobiota in host

physiologyandhealth(Villanueva‐Millanetal.,2015).

3.3.1. Metabolicfunctions

Gutmicrobial community plays a pivotal role on four important host’smetabolic processes: synthesis of

essential vitamins, fermentation of non‐digestible polysaccharides, production of short chain fatty acids

(SCFAs),andcontrolofhostenergybalanceandstorage.

Vitaminsynthesis

Apivotalserviceofthegutmicrobiotaisthesynthesisofvitaminsforthehost(Degnanetal.,2014).Vitamins

are essentialmicronutrients that usually act as precursors of enzymes involved in biochemical processes.

Humanbeingsarenotabletoproducethemajorityofvitaminssotheyneedtobetakenexogenouslyfrom

food(LeBlancetal.,2013).Gutmicrobiota,yet,suppliesalargevarietyofvitamins,asforinstance,mostof

hydrosolubleB vitamins (biotin, cobalamin, folates, nicotinic acid, pantothenic acid, pyridoxine, riboflavin

andthiamine),aswellasvitaminK(Hill,1997).AsrecentlyreviewedbyLeBlancetal.(LeBlancetal.,2013),

particularspeciesofBifidobacteriumandLactobacillusgenerahavebeenidentifiedassuppliersofspecificB‐

family vitamins. Interestingly, microbial genome sequencing is allowing gaining insights into the genetic

characteristicsofentericbacteria,whichwillhelptounderstandthecontributionofthismicrobiometothe

humanphysiology(Gilletal.,2006).

Fermentationofnon‐digestiblepolysaccharides

Anotherimportantfunctionwhichisnotevolvedbyhumanhostandrequirestheactionofgutmicrobiotais

processingoffoodconstituents(TremaroliandBackhed,2012).Thereisamutualdependencebetweenthe

host and symbiotic microorganisms since mammalian genome do not encode the enzymes required to

degradenon‐digestiblepolysaccharidesofplantorigin(Flintetal.,2012),butgutbacteriaprovidethehost

with glycoside hydrolase and polysaccharide lyases that will allow breaking down glycosidic linkages in

plantglycans(Backhedetal.,2005).Ithasbeenestimatedthat20to60gofdietarycarbohydrates(plantcell

wallpolysaccharidesandnon‐digestiblepolysaccharides,mainly)scapeenzymatichydrolysisandreachthe

colon every day (Cummings and Macfarlane, 1991; Silvester et al., 1995). Overall, major non digestible

carbohydratesinclude:

Resistant starch: Resistant starch (RS) has been defined as the “total amount of starch, and the

products of starch degradation that resist digestion in the small intestine of healthy people”

(Asp,1997).Thenon‐fermentableportionofstarchwillvarydependingonthedietcompositionand

Introduction

31

intake,cookingmethodsandindividualdifferences.Anyway,compellingevidenceisdemonstrating

the health beneficial effects derived from the fermentation of RS, partly mediated by its growth

stimulatingeffectsonintestinalmicrobiota(Keenanetal.,2015).

Plantcellwallpolysaccharides:Plantcellwallpolysaccharides,principallycellulose,hemicellulose

andpectin,are foundincereals,vegetablesandfruitsandareoneof themost importantfractions

within dietary fibres (Chassard et al., 2012). Europeans daily intake of plant cell wall

polysaccharidesvariesfrom10to25gorrepresentsabout30%ofthetotaldietaryfibreconsumed

(Lairon etal., 2003). Themicrobial digestionof these compounds is known to give rise to health

beneficial effects. However, there are limited studies analysing the digestive process of these

compounds. Nevertheless, cellulose‐degrading microbial organisms have been identified

(McDonaldetal.,2012).Ruminococcusgenus,belongingtoFirmicutesphylum,represents5‐15%of

the total colonic bacterial population that has been identified from human faeces and has been

demonstratedtocontaincellulolyticstrains(Chassardetal.,2012).Otherbacterialspecieswiththis

capacityareEnterococcussp.,Roseburiasp.andBacteroidessp.(Chassardetal.,2010).Surprisingly,

despite cellulose‐degrading bacteriamight be present in all individuals, Chassard and colleagues

(Chassard et al., 2010) discovered that their structure and activity was depended on the

methanogenic statusof individuals.Theseauthors foundout that inpeoplewhoexcretemethane,

Ruminococcus sp.was predominant,while in those individualswhowere non‐methane excreting,

Bacteroides sp. was identified, being hypothesized that there might be an interaction between

methanogenicarchaea(H2usersandmethaneproducers)andRuminococcussp.(H2producers).

Inulin, oligosaccharides and prebiotics: Prebiotics are currently defined as non‐digestible food

ingredients or compounds (oligosaccharides) that are not digested in the upper part of the

gastrointestinal tract and pass to the large intestine, where they stimulate the growth and/or

activity of health‐promoting bacteria (Bindels et al., 2015). Some of them include xylo‐

oligosaccharides (XOS), galacto‐oligosaccharides (GOS) and fructans, that comprise inulin and

fructo‐oligosaccharides (FOS), lactulose, lactosucrose, isomaltooligosaccharides (IMOS),

glucooligosaccharides, or soybeanoligosaccharides (SO) andnewones that are inphaseof study,

such as pectooligosaccharides (POS), polydextrose (PDX), bacterial exopolysaccharides (EPS) or

polysaccharidesfromseaweeds(Corzoetal.,2015).Severalstudiessuggestthatprebioticsmightbe

inducingbeneficialphysiologicaleffectsduetotheircapacitytomodulatetheintestinalmicrobiota.

Inthissense,thepositiveoutcomeshavebeenreportedtobesystemicandnotonlylocal(atcolonic

level), contributing to reduce the risk of developing intestinal and/or systemic diseases

(Corzoetal.,2015).

ProductionofSCFAs

The major end products of carbohydrate fermentation are SCFA, heat and gases (CO2, H2 and CH4)

(Macfarlane andMacfarlane, 2003). It should bementioned that specific amino acids (glycine, threonine,

glutamate, lysine, ornithine and aspartate) obtained from anaerobicmetabolism of peptides and proteins

might be an additional source of SCFA. In this process, other substances are formed (ammonia, amines,

phenols,thiolsandindoles)whichmightexertadditionalphysiologicaleffects(Neisetal.,2015).SCFAsare

Introduction

32

organic fatty acids of 1 to 6 carbon atoms length which might exist in straight‐ and brached‐chain

conformations (Miller and Wolin, 1979; Cummings and Macfarlane, 1991). These compounds reduce

intestinalpHandpresentantimicrobialpropertiesagainstspecificbacterialgroupsthatmightcauseharmful

effects(i.e.Escherichiacoli),andcouldalsoaffectthegrowthofcertaincommensalbacteriashowingvarying

sensibility toacidicpHsuchasBacteroides spp. (Duncanetal., 2009). SCFAsaredailyproduced fromthe

utilizationofdietary fibre inarangeof400‐600mmolSCFAs/day(Bergman,1990).95%of theSCFAsare

rapidly absorbed in the colon and it has been estimated that these microbial fermentation‐derived

compounds, considered together, contribute to approximately 5‐10% of the human energy requirements

(McNeil, 1984).Themost commonSCFAsare formicacid,propionic,butyric, isobutyric, valeric, isovaleric

andcaproicacid(Bergman,1990).However,from90‐95%oftheSCFAsinthecolonicregionareacetate(C2),

propionate(C3)andbutyrate(C5)(MortensenandClausen,1996).

ControlofenergyhomeostasisthroughSCFAs

Butyrate is particularly important and has been extensively studied since it is themajor energy fuel for

cellularmetabolisminthecolonicepithelium.Nevertheless,theimportanceofbutyrateisnotonlyduetoits

roleasenergysupply.Butyratehasalsobeenreportedtopreventcoloniccarcinogenesis,tohaveprotective

effects against inflammation, and oxidative stress, and to contribute to improve the intestinal barrier

(reducingintestinalpermeabilityandregulatingmucusbarrier)(Cananietal.,2011).Incontrast,propionate

is takenupby the liverand isused forgluconeogenesis,whileacetateenters theperipheralcirculationto

reach peripheral tissues andmight be used for lipogenesis (Puddu et al., 2014). Interestingly, the use of

propionateasagluconeogenicsubstrateinhibitstheutilizationofacetateforlipidsandcholesterolsynthesis,

whichmightberelatedtoanumberofstudiesthathaveevidencedreducedhepatic lipogenesisand lower

serum cholesterol and triglyceride concentrations after the consumption of fermentable carbohydrates

(Byrneetal.,2015).

SCFAsactasenergysourcefordifferentorganssuchasthecolonicmucosa,theliver,andatleastinpartfor

skeletalmuscleandadipose tissue(Woleveretal.,1989). Increased levelsof theseproductsareknownto

inducelipogenesisintheliverandtoincreasethesynthesisofvery‐low‐densitylipoproteins(VLDL),which

are responsible for transporting triglycerides from the liver to other tissues

(Backhedetal.,2004;Velagapudietal.,2010).Infact,ithasbeendemonstratedthatgutmicrobial‐derived

metabolites could regulate host gene expression, as evidenced by the observed activation of two

transcription factors, sterol response element binding protein‐1c (SREBP‐1c) and carbohydrate response

elementbindingprotein(ChREBP)ingerm‐freemice14daysafterreceivingmicrobiotafromconventional

mice (Backhed et al., 2004). Moreover, enrichment of gene functions associated to the fermentation of

dietary polysaccharides was found in ob/ob mice, which also present larger quantities of SCFAs in their

caecum, but lower energy content in their faeces when compared to their lean littermates

(Turnbaugh et al., 2006). Human studies, although indirectly, have also shown that obese people exhibit

higher levels of ethanol in their breath, as wells as higher amounts of faecal SCFAs, indicating that

fermentationmightbealteredintheseindividuals,possiblyasaconsequenceofenlargedenergyharvestby

thegutmicrobiota(Nairetal.,2001;Schwiertzetal.,2010).However,whentransplantingfaecalmicrobiota

oftwinsdiscordantforobesitytogerm‐freemice,leanmicewerefoundtopresentgreatercaecalpropionate

Introduction

33

and butyrate levels, suggesting that the increasedweight gain in obese animalswas not resultant of the

increased energy harvest by the gut microbiota, but instead, SCFAs impeded fat accumulation

(Ridauraetal.,2013).Inaddition,evidencesuggeststhatSCFAsincreaseenergyexpenditureas,forinstance,

rising oxygen consumption, enhancing both the adaptive thermogenesis and fat oxidation, as well as

promotingmitochondrialfunctioninrodents(Byrneetal.,2015).

SCFAscould,besides,actassignallingmoleculesandregulateenergyhomeostasis.Thiseffecthasbeenfound

toberelatedtotheactivationofGprotein‐coupledreceptors(GPCRs),GPR41(alsoknownasfreefattyacid

receptor or FFR3) and GPR43 (also known as FFAR2), which are SCFAs receptors. These receptors are

activated by physiological concentrations of SCFA, but affinity preferences have been reported

(FFAR2 for acetate and FFAR3 for propionate and butyrate). Furthermore, they have been found to be

expressed not only at intestinal epithelium, but also in adipose tissue, immune cells, skeletalmuscle and

withinperipheralnervoussystem(Chambersetal.,2014).

RegulationofguthormonesthroughSCFAs

SCFAscouldalsoactbyregulatingenergyintake(Conternoetal.,2011).IncreasedlevelsofSCFAshavebeen

associated with enhanced satiety and reduced food intake, partly as a consequence of the role of these

molecules on gut hormone secretion by the activation of FFAR2. FFAR2 and FFAR3 are expressed in

enteroendocrine(L)cells(Tolhurstetal.,2012)andthesecellsareknowntosecreteglucagon‐likepeptide1

(GLP‐1)andtheanorexigenicpeptidetyrosinetyrosine(PYY)whichareimportantguthormonesthatreduce

appetite and energy intake (Delzenne et al., 2010). The finding that FFAR2 and FFAR3were localized at

enteroendocrinecellsledtothehypothesisthattheactivationofthesereceptorsbySCFAsmightinducethe

release of GLP‐1 and PYY. Several investigations have been performed to confirm this statement

(Chambersetal.,2014)and,althoughhumanstudiesarestilllimited,evidencesuggeststhatconsumptionof

fermentablefibreandinturn,SCFAsproducedmightstimulatethesecretionofsuchanorectichormonesvia

activationofFFAR2(Chambersetal.,2014).

RegulationofinflammationandimmunitybySCFAs

SCFAs have been evidenced to inhibit the inflammatory process by acting on leucocyte, endothelial and

intestinalcellfunction(Vinoloetal.,2011).EvidencehasshownthatadministrationofbutyratetoHFD‐fed

rats leadtosignificantreductions inhepaticexpressionof tumornecrosis factor‐α(TNF‐α), interleukin‐1β

(IL‐1β)andIL‐6improvingsteatosisandinflammation(MattaceRasoetal.,2013).InStaphylococcusaureus‐

stimulated human monocytes, butyrate strongly inhibited the production of IL‐12 and enhanced IL‐10

secretion(Saemannetal.,2000).Down‐regulationofpro‐inflammatorycytokinesinadiposetissuehavealso

been reportedwithpropionate andbutyrate (Roelofsenetal., 2010). Themolecularmechanisms through

which thesemoleculesmightexert theanti‐inflammatoryoutcomeshavebeen related to theactivationof

FFAR2andFFAR3,butinterestingly,theirfunctionasinhibitorsofhistonedeacetylases(HDAC)activityhas

been demonstrated as well (Vinolo et al., 2011). HDAC, together with histone acetyltransferases (HAT),

regulatethedegreeofproteinacetylationandmodulategeneexpression.InhibitionoftheactivityofHDAC

has been shown by SCFAs, especially butyrate. This mechanism is of crucial relevance for the ability of

butyrate to regulate the expression of genes involved in colonic tissue homeostasis

Introduction

34

(DalyandShirazi‐Beechey,2006).Infact,butyrate‐inducedinhibitionofNF‐κβhasbeendemonstratedtoact

through this mechanism. Importantly, NF‐κβ is known to be a regulator of immunity‐related genes

(IL‐1β, TNF‐α, IL‐2, IL‐8, IL‐12, inducible nitric oxide synthase (iNOS), COX‐2, intercellular adhesion

molecule‐1 (ICAM‐1), vascular adhesionmolecule‐1 (VCAM‐1) and T cell receptor‐α (TCR‐α). Thus, their

inhibitionmightbethebasisfortheanti‐inflammatoryeffectsofbutyrate(Cananietal.,2011).Inaddition,

the HDAC‐dependent mechanism has been shown to be responsible for the formation of peripheral

regulatoryT‐cells(Furusawaetal.,2013)favouringhost‐microbeimmunologicaltolerance.

Controlofhostenergybalanceandstoragebygutmicrobiota

Importantly, gut microbiota has been demonstrated to control both sites of the energy equation

(Backhedetal.,2007).Ononehand,gutmicrobiotamight influenceenergystoragethroughitscapacityto

extractenergyfromfood.Ontheotherhand,gutmicrobiotaaffectshostgenesthatareinvolvedinregulating

thewayenergyisusedandstored(Backhedetal.,2007).

In this sense, gut microbiota has been demonstrated to reduce the intestinal expression of the protein

fasting‐induced adipose factor (Fiaf) or angiopoietin‐like 4 protein (Angptl‐4). Fiaf protein is critical

inhibitorof theenzymeresponsible for thehydrolysisofcirculating triglycerides to free fattyacids(FFA),

knownaslipoproteinlipase(LPL)(Sukoninaetal.,2006).Furthermore,Bäckhedetal.(Backhedetal.,2004)

demonstratedthatgerm‐freemice,followingconventionalization,werecharacterizedby122%enlargement

ofLPLactivityand,thus,enhancedbodyadiposity,whichwasassociatedtoareducedAngptl‐4expressionin

the ileum. Importantly, there are several interlaced pathways by which gut microbiota controls energy

balance(Backhedetal.,2007).DatafromGFmiceandGFFiaf‐deficientmiceshowedthatresistancetodiet‐

inducedobesityinGFmicemightbeassociatedwiththefactthattheanimalspresentlowerLPLactivityor,

atleastinpart,withthefactthatGFmicehadincreasedAMPKactivityandenlargedfattyacidoxidationin

peripheraltissues.Theseresultssuggestthatgutmicrobiotamayalsoactviaametabolicpathwayinvolving

phosphorylationofAMPK.Incontrast,theauthorsdiscoveredthatGFFiaf‐deficientmicelosttheirresistance

tohigh‐fatsucrose(HFS)diet‐inducedobesity,andexhibitedsignificantlyreducedexpressionofperoxisomal

proliferator‐activated receptor coactivator (Pgc‐1α)andenzymes involved in fattyacidoxidation,without

presentingdifferencesinphosphorylatedAMPKlevels(Backhedetal.,2007).

3.3.2. Immunefunctionsandanti‐inflammatoryroles

Thegutmicrobiotaplaysacentralrole inbothimmunesystemdevelopmentandimmunologicaltolerance

(Masonetal.,2008).Asubstantialpartoftheimmunesystemispresentedinintestinalmucosa.Indeed,itis

considered that intestinalmucosa is the largest surface area comprising almost 70%of the immune cells

which will be continuously subjected to the exposure of commensal microbiota and pathobionts

(deKivitetal.,2014).Thus,gutmicrobiotarepresentsthemajorityoftheantigensthatarepresentedtothe

host immunecells.The importanceof residentmicrobiota foracorrect immunesystemdevelopmentwas

observed in studies conducted in GF mice. These animals showed an impeded immune system

(Chung et al., 2012) and reconstitution of intestinal microbiota was demonstrated to restore mucosal

immunedefects (Chung etal., 2012). In this sense, intestinalmucosal immune systemneeds to have two

Introduction

35

mainfunctionsundercontrol:theleveloftolerancetotheoverlyingmicrobiotawiththeaimofavoidingan

excessiveanddeleterioussystemicimmuneresponse,andthecontroloftheovergrowthandtranslocationof

bacteria to systemic sites (Sekirovetal., 2010). Commensalmicrobiotahas theability to stimulate innate

immunesystem,whichisresponsiblefordetectingthepresenceandthenatureofinfectionandtoprovide

thefirstlineofdefence.Theinnateimmunesystemregulatestheadaptiveimmunesystem,whichtypicallyis

delayed to 4‐7 days (Medzhitov, 2001). Targets of the innate immune system are pathogen‐associated

molecularpatterns(PAMPs)andthereceptorsoftheinnateimmunesystemthatrecognizethesePAMPsare

knownaspatternrecognitionreceptors(PRRs)(Janeway,1989).Theseconservedmolecularstructuresalso

exist in non‐pathogenic microbes, thus the use of microbe‐associated molecular patterns (MAMP) is

expandingwhenreferringtohost‐commensalinteractions(Wellsetal.,2011).PPRsaresignallingreceptors

that induce the production of innate effector molecules (Rakoff‐Nahoum and Medzhitov, 2008) and are

classified in threemain families:Toll‐likereceptors (TLRs),nucleotideoligomerizationdomain(NOD)‐like

receptors(NLRs)andretinoicacid induciblegene I (RIG‐I)‐likereceptor(RLR).The interactionwithPRRs

does not uniquely involve live bacteria since they might be stimulated by diffusible compounds such as

bacterial lipopolysaccharides (LPS), lipoproteins, peptidoglycans and lipoteichoic acid (LTA) (Wells etal.,

2011).However,theirstimulationleadstotheactivationofsignallingcascadesthatpromoteepithelialcells

to produce a wide range of antimicrobial factors including defensins, cathelecidins, calprotectin, and

antimicrobial polipeptides, such as regenerating islet‐derived protein III (REGIII) proteins

(Wells et al., 2010). Themain function of secreted antimicrobial peptides will be to defend host against

entericpathogens(BevinsandSalzman,2011).

3.3.3. Structuralandprotectivefunctions

An important role of the gutmicrobiota is to promote epithelial barrier integrity (Yu et al., 2012a). The

intestinalbarrierisorganizedasamulti‐layersystemwheretwomajorpartsaredifferentiated:anexternal

physicalbarrierthataimstorestrictbacterialadhesionandregulatesparacellulardiffusiontotheunderlying

hosttissues,andaninternalfunctionalimmunologicalbarrier,thathastheabilitytodistinguishcommensal

bacteriafrompathogenicmicrobes(Scaldaferrietal.,2012).Foodingredientsthroughtheoralrouteallow

exogenousorganismstoentryintheGItract,thus,nutrientsandmicroorganismsareinclosecontactwith

intestinalmucosa(Yuetal.,2012a).The luminalsurfaceof theGI tract fromthestomachtotherectumis

coveredbyamonolayerofepithelialcells(Yuetal.,2012a). Initially, intestinalepitheliumwasconsidered

onlyasabarrierthatphysicallyprotectedandseparatedthegutlumenfromtheintestinalmucosa.However,

the underlying intestinal mucosa contain different cell types belonging to innate and adaptive immune

system including macrophages, dendritic cells (DC), T cells, and B cells. Kagnoff and collaborators

(Kagnoff, 2014) afterwards discovered that intestinal epithelial cells might modify their phenotype and

produceproinflammatorymediators(Jungetal.,1995).Moreover,itwasalsomentionedthattheyexpress

receptorsforawiderangeofcytokinesandchemokinesandproduceantimicrobialpeptides(Kagnoff,2014).

Intestinal crypts are recognized as developmental structures that suffer turnover rates consisting of cell

proliferation and shedding which, in turn, favour renewal of the intestinal epithelium during adulthood

(Sancho et al., 2003). The intestinal epithelium is composed of four distinguished cell types that are

responsibleforthefunctionsperformedbytheintestinalepithelium.Columnarcells,alsocalledenterocytes

Introduction

36

inthesmallintestineorcolonocytesinthelargeintestine,arethemostabundant.Theyarepolarized,witha

basalnucleusandabrushborder.Thesecondtypesofcellsaremucin‐secretingcellsorgobletcells,which

secretemucusthatwillprotectandlubricatethemucosa.Endocrinecells,alsoknownasneuroendocrineor

enteroendocrine cells, are present thorough the epithelium. These cells contain neuroendocrine

granulocytes and secret peptidehormones. Finally, Paneth cells,mainly located at the crypt base, contain

secretorygranulesandexpressdifferentproteinsandlowmolecularweightpeptidesthathaveantibacterial

actions. Paneth cells have also been reported to present phagocytic features (Yen and Wright, 2006).

Figure11showsanoutlineoftheproliferationanddifferentiationofstemcellsintoenterocytes.

Importantly, intestinal epithelial cells are sealedwith tight‐junctions (TJ). A healthy intestinal gut should

have an intact epithelial barrier that will avoid the paracellular entry of molecules and bacteria. In this

context, TJs regulate the influx between epithelial cells. Briefly, the main proteins contributing to the

developmentoftightjunctionsarethefollowing(Camillerietal.,2012):

Integral membrane proteins: Proteins from claudin family which are associated to other

transmembraneproteinscalledoccludins.

Junctionalcomplexproteins:Zonulaoccludens(ZO)‐1andothercytoplasmaticproteins.

Cellcytoskeletonstructures:Microtubules,intermediate‐andmicrofilaments.

Commensalbacteriahavebeendemonstratedtobeimplicatedinthecontrolofturnoverratesofenterocytes,

important for the regulation of epithelial permeability, a feature that has been associated with a large

numberofhumandiseases, suchas IBD, celiacdiseaseand IBS (Camillerietal., 2012).Evidence fromthe

capabilityofmicrobestomodulateintestinalpermeabilitycomesfrombothinvitroandinvivomodels.

It is importanttobearinmindthatabalanceexistsbetweencellproliferationandcelldeath.Reducedcell

death and thus, hyperproliferationmay lead to tumor formation,while increased apoptosismay result in

Figure11.Proliferationanddifferentiationofstemcells intoenterocytes in thecryptregions(Yuetal.,2012a).

Introduction

37

barrier damage (Yu et al., 2012a). In this sense, from studies conducted in GF, gnotobiotic and

conventionally‐raised animals, an important role of luminal bacteria in cell proliferation, apopotosis and

differentiationwaselucidated(Pulletal.,2005;Shirkeyetal.,2006).GFanimalspresentabnormalintestinal

morphology, with longer villi and shorter crypts, when compared to related conventionally raised

counterparts.Moreover, thesemodels lacking gutbacteria showed reducedepithelial apoptosis and crypt

cellproliferation(WillingandVanKessel,2007).Ontheotherhand,inconditionsofintestinalinflammation

(colitis)inducedbytheadministrationofdextransodiumsulphate(DSS),atoxiccompoundforcolonocytes

(Kitajima et al., 1999), conventionally‐raised animals were protected from epithelial injury. In addition,

intestinalepithelialrestitutionandwoundclosurehavebeenreportedtobefavouredbycommensalbacteria

in a redox dependent manner, suggesting that redox signalling might be a key mechanism in intestinal

homeostasis and restitution processes (Alam et al., 2014). Nevertheless, contradictory results have been

reported inGFmicemodels thatdidnotdevelopcolitis,proposingthatcommensalbacteriamay, insome

cases,inducechronicintestinalinflammation(Stroberetal.,2002).

3.4. Modulationofgutmicrobiotacompositionbydifferentapproaches

A century ago the idea ofmodifying gutmicrobiota composition in order to improve healthwas already

proposed (Metchnikoff, 2004). Currently, there is awareness that a dysbiotic gutmicrobiota composition

representsanimportantmechanismofdisease,thereby,thereisaneedtofindtherapeutictoolsthatrestore

gut microbial composition and its metabolic activity (Bindels et al., 2015). Approaches to modulate gut

microbiotaincludedietaryinterventions,useofprebiotics,probioticsandsynbiotic,transplantationoffaecal

microbiotaandtheuseofbioactivefoods(ZatorskiandFichna,2014).

3.4.1. Dietarymodulation

Gut microbiota is metabolically adaptable and diet is the primary factor that influences its composition.

Basedonthefindingsobservedinidenticaltwins,wheremarkedvariationsweredetectedongutmicrobial

ecology,therelevanceofdietongutmicrobiotahasbeenrecognizedtobeconsiderable,evengreaterthan

geneticfactors(Turnbaughetal.,2009a).Infact,foodmayaffecthealthnotonlydirectly,butalsoindirectly,

activatingcell‐surfaceornuclearreceptorsintargettissues(RyanandSeeley,2013).Moreover,foodisable

tointeractwithgutmicrobialcommunitytoinduceindirectsignals(Falluccaetal.,2015).Therefore,dietary

recommendationsrepresentanattractiveoptionincaseswerealinkbetweengutmicrobiotaandtheriskfor

the development of an illness is established, or even as a treatment when a disease condition has been

confirmed(DoreandBlottiere,2015).

Several studies have been conducted to examine the impact of different dietary patterns on intestinal

bacterial community profiles. As reviewed by Janssen et al. (Janssen and Kersten, 2015), diet strongly

influencesgutmicrobiotacompositionat thephylumlevel,butparticularlyatthelowesttaxonomic levels.

Recent studies have demonstrated clear differences in gut microbial profiles between populations with

distinct dietarypatterns. For instance,African children fromBurkinaFaso, characterizedby the intake of

diets rich in plant‐derived fibre, exhibited significantly increased levels of Prevotella and Xylanobacter,

higher amounts of SCFAs and reduced levels of Enterobacteriaceae, when compared to Italian children

Introduction

38

(DeFilippoetal.,2010).Asmentionedthoroughsection2ofthepresentthesis,dietsrichinanimalproteins

and fat, similar toawesterndietarypattern,will favour theBacteroides enterotype,whiledietswithhigh

contentof fruitsandvegetables,andthusrich in fibre,wouldbeassociatedwiththePrevotellaenterotype

(Wu etal., 2011). Analogously, as a consequence of different dietary patterns, Ou etal. (Ou etal., 2013)

linked the gut microbiota of native Africans to Prevotella ecologies. In contrast, African Americans were

associatedwiththeBacteroidesenterotypeandhigherlevelsofsecondarybileacids,whichhaveconfirmed

carcinogenicproperties (Bernsteinetal., 2011).Furthermore,drasticdifferenceswere identified inHadza

hunter‐gatherers fromTanzania,whofollowa foraging lifestyle,withdietaryhabitsclosetoourancestors

(Schnorr et al., 2014). In this community, higher microbial richness and diversity (i.e. large presence of

unrecognized taxa) were detected. Besides, sex differences were found in gut microbiota composition

probably attributed to sexual division of labour. Furthermore, apart from a clear influence of long‐term

dietary patterns on the structure and activity of intestinal bacteria, short‐term dietary alterations, as for

instancedietsthatcontainonlyanimalorplantproducts,havebeendemonstratedtostronglymodulategut

microbiotacomposition,beingthesemodificationslargerthaninterpersonaldifferences(Davidetal.,2014).

Inobesity,apart fromthealterations ingutmicrobialprofiles,a reducedbacterialgenerichnesshasbeen

described(Cotillardetal.,2013),anddifferencesinspeciesrichnessbetweenoverweightorobesesubjects

withadisparatedietarypatternhavebeenreported(Kongetal.,2014).Interestingly,inastudyconducted

byCotillardandcolleagues,anassociationbetweenreducedmicrobialrichness(40%)andhigherlow‐grade

inflammatory parameters was detected (Cotillard et al., 2013). Indeed, low gene richness and

hypercholesterolemia, inflammation and insulin resistance were shown to be improved with dietary

interventions.Moreover,sincetheresponsetodietaryinterventionintermsofweightlossandamelioration

ofmetabolicand inflammatorymarkerswasfoundtobebetter inobeseandoverweightsubjectswiththe

greatest gene richness, this feature was postulated as a possible predictor of the intervention efficacy

(Cotillardetal.,2013).Likewise,Salonenandcollaboratorsreportedthatmicrobialdiversitywasinversely

associatedwith the subject’s dietary responsiveness, concluding that individual’smight be stratified into

respondersandnon‐respondersbasedontheirbasalgutmicrobialtrait(Salonenetal.,2014).Interestingly,

thepotential of gutmicrobiota forpersonalizednutritionhasbeen stated. In a recent study conducted in

threedifferentcohortsofobeseindividuals(Belgium,FinlandandBritain)thepredictabilityoftheresponse

of gutmicrobiota to dietary interventions based on the composition of initialmicrobiota has been stated

(Korpelaetal.,2014). Inthiscontext, theprognosticvalueoftheintestinalmicrobiotainrelationtohost’s

metabolic response to a dietary interventionwasdemonstrated for the first time. For thispurpose, three

typesofdietary interventionswere tested: additionof aprebiotic compound,modificationsof the typeof

grains in the diet, and alteration of macronutrient composition of the diet. Noteworthy, the authors

evidencedthatparticularspecieswithinClostridialgroupmightbebioindicatorsof theadaptabilityof the

gut microbiota to dietary interventions and might be considered predictive organisms of the host lipid

metabolism(Korpelaetal.,2014).Inspiteoftheseproof‐of‐principlestudies,still,thereislackofawareness

regarding the ideal compositionofdiet and typeofmacronutrients to support a “healthy”microbiotaand

avoid dysbiosis (Nagpal et al., 2014). However, it is plausible that the knowledge on gutmicrobiotawill

revolutionize the concept of “we are what we eat” by “we are what our gut microbiome is”

(Nagpal et al., 2014). Therefore, we could adventure to say that information about individual’s gut

Introduction

39

microbiotaforpersonalizednutritionaltherapywillhavegreatchanceconcerningtreatmentofobesityand

obesity‐relatedmetaboliccomorbidities(Korpelaetal.,2014).

Sinceitisnotknownwhichdietarycomponentsornutrientsaffectmoretothegutmicrobiotacomposition,

theeffectsofthemajornutritionalcomponentswillbeaddressedbelow:

Effectofnon‐digestibledietarycarbohydrates,proteinsandfatintake3.4.1.1.

Gut bacteria are usually competing formost types of non‐digestible dietary carbohydrates that reach the

large intestine. Thus, non‐fermentable (for the host) carbohydrates have been extensively reviewed as

importantregulatorsofgutmicrobiotacomposition(Lopez‐Legarreaetal.,2014).Inthiscontext,thestudy

ofcommensalbacteriagenomesequenceshasshownthatbacterialspeciesaredependentonthesedietary

components for their growth (Louis et al., 2007). For instance, Bacteroides thetaiotaomicron and

Bifidobacterium longum species devoted at least 8% of their genomes to carbohydrate transport and

metabolism(Louisetal.,2007).Therefore,itisobviousthatthecontentofnon‐digestiblecarbohydratesin

thedietaffectthecountofspecificbacterialgroups(MaukonenandSaarela,2015).Accordingly,incontrast

to the role of cellulose and lignin which is still not well known, it has been reported that arabinoxylan

stimulatesBacteroidesspp.andRoseburiaspp.;thatresistantstarchenhancesthegrowthofbifidobacteria,

Bacteroides spp., Ruminococcus bromii, Eubacterium rectale and Roseburia spp.; that β‐glucans increase

bifidobacteria counts,while fructans induce thegrowthofbifidobacteria,Bacteroides spp, lactobacilli, and

butyrate‐producerspecies(MaukonenandSaarela,2015).Importantly,healthbeneficialpropertiessuchas

anti‐obesitycapacityofthesedietaryconstituentshavealsobeenlargelyexplored(Hobdenetal.,2015).

High‐proteindietsgiverisetofermentationofdiet‐derivedproteinsinthecolon,resultinginaplethoraof

compounds, namely amino acid‐derived products, branched‐chain fatty acids, phenylacetic acid, phenols,

indoles,p‐cresol, nitrogenousproducts (N‐nitroso compounds), andmanyof themhavebeen found to be

deleteriousforthehost,contributingtocarcinogeniceffectsandthus,topromotecancer(Louisetal.,2014).

Furthermore, changes in gut bacterial profiles have been observed after consuming a high‐beef diet as

comparedtoameatlessdiet.Ithasbeenalsodemonstratedthathighsulphidelevelsoriginatedfromahigh‐

beefdietpromotedtheincreaseofaspecificgroupofbacteriaknownassulphate‐reducingbacteria,which

havebeenshowntoexertharmfuleffectsincolonicepithelium(MaukonenandSaarela,2015).Moreover,in

an intervention study conducted in obese men following a high‐protein diet low in carbohydrates, a

reduction inRoseburia/Eubacteriumrectale groupwas found,whichwasrelated toadecrease inbutyrate

production. Besides, these weight‐loss diets high in protein were found to reduce cancer‐protective

metabolites,whileincreasedhazardousmetabolitesforcolonicepithelium(Russelletal.,2011).

Few intervention studies have been conducted to investigate the outcomes of consuming a HFD on gut

microbiota,andstill,fewerstudieshavebeenconductedtodeterminetheeffectsofdifferenttypesoffatson

microbialpopulation.Forinstance,bifidobacterialgroupwasinhibitedasconcludedinastudyconductedin

overweight obese participants that consumed a HFD with low‐carbohydrate content

(Brinkworthetal.,2009).Concerningthetypeoffat,distinctconsequenceshavebeenreportedinintestinal

bacterial profiles concluding that diets rich in saturated fat (SFA), monounsaturated fat (MUFA) and

Introduction

40

polyunsaturatedfat(PUFA)havevarianteffects,butmostlyinfluencebacterialrichness,bifidobacteriaand

lactobacillusgroups(MaukonenandSaarela,2015).Whatitisobviousisthattheintakeoffat‐richdietsleads

to increases in bile acids and fat that reach the colon, thereby they will be subject of gut microbiota

metabolismandresultantcompounds,suchassecondarybileacids,willbeabsorbedinthesmall intestine

(Favaetal.,2013).Asaconsequence,itmightbestatedthatconsumingdietswhicharebalancedintheirfat

contentwillbecriticalforthegutmicrobiotaandhenceforthehosthealth(MaukonenandSaarela,2015).

Effectofpolyphenolsintake3.4.1.2.

Thecompositionofbacteriainhabitingthegutisalsoinfluencedbytheintakeofphytochemicalsandtheir

derivedmetabolites.Numerousresearchhasbeen focusedonanalysingtheantimicrobialorbacteriostatic

activitiesof dietarypolyphenolsorphenolic substances and theirmetabolites againstpathogenicbacteria

such as Salmonella spp., Clostridium perfringens, Clostridium difficile, Escherichia coli and Staphylococcus

aureus(MaukonenandSaarela,2015).Otherpolyphenolsand/orpolyphenol‐richdietarysourceshavebeen

also observed to exert prebiotic‐like effects (Duenas et al., 2015). As reviewed by Maukonen et al.,

(Maukonen and Saarela, 2015) human intervention studies have reported the capacity of red wine

polyphenols consumed for 4 weeks to promote the growth of Enterococcus, Prevotella, Bacteroides,

Bifidobacterium,Egerthella and Lachnospiraceae familymembers,whereas drinking a high‐cocoa flavanol

beverage increased lactobacilliandbifidobacterianumbersandreducedclostridialcounts. Inaddition, the

intakeofellagitannins,commonlyfoundinstrawberries,raspberriesandcloudberries,hasbeenassociated

withchangesinLachnospiraceaeandRuminococcaceaemembers(MaukonenandSaarela,2015).Incontrast,

as reviewed by Cardona et al. (Cardona et al., 2013), a proanthocyanidin‐rich extract from grape seeds

administered to healthy adults for 2 weeks was reported to significantly enhance the growth of

bifidobacteria.Thisgroupofbacteriawasalsoincreasedasaconsequenceof6weekwildblueberrydrink

consumption.Summingup,themodulatoryeffectofpolyphenolsonintestinalecologyhasbeenascertained.

In fact, this influencehasbeenproposed tobe theresultof severalmechanisms.Overall,bacterialgrowth

andmetabolismwill be affected by polyphenols’ structure, the dosage administered and the presence of

microbialstrains.Indeed,thehigherresistanceofGram‐negativebacteriatopolyphenols,comparedtothat

oftheGram‐positivebacteria,probablyowingtodifferencesintheirwallcomposition(Cardonaetal.,2013),

hasbeenshown.

3.4.2. Useofprebiotics,probioticsandsynbiotics

Prebioticswerefirstlydefinedin1990sasapioneeringconceptsinceitbroughtgutmicrobiotaasafactor

influencinghumanandanimalnutrition(GibsonandRoberfroid,1995).Although,theconceptofprebiotics

hasbeenknown for thepast20years, it is still inevolution.Currentdefinitionofprebioticswasgivenby

Gibson etal. (Gibson etal., 2010) in 2010, defining it as “selectively fermented ingredient that results in

specific changes in the composition and/or activity of the gastrointestinal microbiota, thus conferring

benefit(s) upon host health”. However, this concept has been recently revisited by Bindels et al.

(Bindelsetal.,2015)proposingtheprebioticas“anondigestiblecompoundthat,throughitsmetabolization

bymicroorganismsinthegut,modulatescompositionand/oractivityofthegutmicrobiota,thusconferringa

beneficialphysiologicaleffectonthehost.”

Introduction

41

Definition of probiotics was approved in a recent consensus report as “live microorganisms that, when

administeredinadequateamounts,conferahealthbenefitonthehost”(Hilletal.,2014).Probioticscould

consistonasingleormultiple livebacterialspeciesthatdirectlymodulatethegutmicrobiotacomposition

andmostofthembelongtotwomaingenera,BifidobacteriaandLactobacilli(Gerritsenetal.,2011),butthe

probiotic effect of other species, such as Lactococcus, Streptococcus, and Enterococcus, even of non‐

pathogenic strainsofEscherichiacoli (E.coliNissle1917)andyeast strains (Saccharomycesboulardii)has

been also demonstrated (Miniello et al., 2015). Increasing evidence demonstrates the health‐benefits

attributedtoprobioticmicroorganisms(Wangetal.,2015). In thissense,probioticsmechanismsofaction

have been classified in three major levels. The first one would consist on the effect of probiotic

microorganismsonGItract,asforinstanceimprovinggutmicrobiotacomposition,maintainingitsstability,

impedingthegrowthofpathogensor influencing theenzymaticactivityof thegutmicrobiota.Thesecond

one,wouldbetheirabilitytointeractwiththeintestinalepitheliumandthemucuslayersincethecapacityof

probiotics to strength mucosal barrier by increasing mucus production, repairing and maintaining tight

juntionsinordertoreducegutpermeabilityhasbeenreported.Finally,thethirdmechanismthroughwhich

probioticswouldexertabeneficialeffectwouldbeasaresultoftheiranti‐inflammatoryeffectandimmune

systemmodulatoryeffectoutsidethegut(Gerritsenetal.,2011;Bermudez‐Britoetal.,2012). Itshouldbe

takenintoconsiderationthateachprobioticspeciesmightexertdifferenthealthbenefits,thus,physiological

effectsattributedtooneprobioticmaynotbeextrapolatedtoanotherstrain(Lopezetal.,2010).There is

also lack of sufficient evidence to ascertain that probioticmixtures aremore effective than single strains

(Chapmanetal.,2011).Inaddition,ithasbeendemonstratedthatnotallgutbacterialspeciesaffectedbythe

effect of probiotics will be functionally important phylotypes in relation to positive metabolic outcomes

(Wangetal.,2015).Althoughthestudyoftheeffectivenessofprobioticsadministrationindifferentdisease

states have showndistinct clinical outcomes such as IBD, IBS, constipation, diarrhoea, colon cancer, CVD,

allergicdisease,obesityandmetabolicsyndrome,itisstillcomplicatedtoconcludeaboutgeneralchangesin

microbiota composition derived from the intake of probiotics, principally due to the lack of standardized

methodsinthestudyofgutmicrobiota,andalsobecauseofitscomplexity(Gerritsenetal.,2011).

Synbiotics are products combining probiotics and prebiotics aiming to favour the survival and activity of

probiotics in vivo and also seek to promote the growth of anaerobic bacteria (Miniello etal., 2015). It is

considered that synergistic effects among both ingredients promote health benefits

(PatelandDuPont,2015).Forexample,theintakeofasynbiotic(Lactobacillusacidophilus,Bifidobacterium

bifidum and fructooligosaccharides) inelderdypeoplehasbeenshownto increaseHDLcholesterolandto

ameliorate glycemia levels (Moroti et al., 2012). Moreover, positive effects of synbiotics have been also

demonstrated when combining Bifidobacterium and FOS in the treatment of hepatic encephalopathy

(Malaguarneraetal.,2010).

3.4.3. Faecalmicrobiotatransplants

Faecal microbiota transplantation (FMT), also known as faecal bacteriotherapy, is considered another

approachtorestorehumangutmicrobialdiversitybytransferringgutmicrobiotafromhealthydonorstothe

patients(VandenAbbeeleetal.,2013).ThistechniquehasbeenalreadyappliedtotreatClostridiumdifficile‐

relatedinfectionreportingcureratesoverthe90%(Borgiaetal.,2015),andmicrobiotafromdonorfaeces

Introduction

42

hasbeenregardedasaspecialorgantobetransplantedregardlessofthedangerforimmunologicalrejection

(BorodyandKhoruts,2012).Interestingly,takingintoconsiderationthatmetabolicdiseasessuchasobesity,

maypartlyresultfrommicrobiota‐associateddisturbance,thistypeoftherapyhasintensifiedtheinterestto

investigateitsimpactonseveralextra‐intestinaldisorders(Xuetal.,2015).Accordingly,transferofintestinal

microbiota from lean donors to subjectswithmetabolic syndromewas demonstrated to improve insulin

sensitivity (Vrieze et al., 2012) and a similar study is currently ongoing (ClinicalTrials.gov, 2014).

Nevertheless,thisisanovelinterventionthatrequiresfurtherresearch(Singhetal.,2014).

3.5. Metagenomicapproaches

Metagenomic refers to the analysis of a complete genome from an environmental sample

(Thomasetal.,2012).Thedevelopmentofculture‐independenttechniqueshasexpandedtheknowledgeon

the diversity of GI tractmicrobial communities. Currently, there exist twomainmethods that enable the

characterization of the microbiome that are not based on pure culture (Hamady and Knight, 2009).

Sequencing technologies that are based on the small‐subunit ribosomal RNA (rRNA) sequence analysis,

where16SrRNAgenesequences(forarchaeaandbacteria)or18SrRNA(foreukaryotes)areusedasstable

phylogeneticmarkerstodefinewhichlineagesexistinasample,andmetagenomicsstudies,whicharebased

on thewholemetagenomeshotgun sequencing (Rondonetal., 2000).The latter approach is allowingnot

onlycharacterizingthediversemicrobialcommunitiesresidingatthedifferentbodycompartments,butalso

characterizing the complete functional genetic repertoire of the microbiota or the microbiome

(PreidisandHotez,2015).Insomecases,theformerarealsotermedas“metagenomic”studiesinthatthey

analyseaheterogeneoussampleofcommunityDNA(HamadyandKnight,2009).

4. Metabolomics:anew“Omics”scienceinnutrition

Currentnutritionalresearchisbeyondtheobjectiveofprovidingnutrientstonourishpopulationseekingto

improvehealthwiththediet.Healthpromotion,protectionanddiseasepreventionaremajorcornerstonesin

modern nutrition (Kussmann et al., 2006). Moreover, the development of “functional foods”, which are

known to give health benefits over the basic nutritional value (Koletzko et al., 1998), also requires the

understanding of the underlying molecular mechanisms, and the unravelling of the biologically active

moleculesaswellastheireffectivenessonwell‐being(Kussmannetal.,2006).Inthiscontext,thecomplex

physiologicalinteractionshappeningbetweennutrientsandbioactiveconstituentsandhumanorganism,in

thepast twentyyears, arebeing comprehensively investigated atmolecular level.Nutrition scienceshave

enteredinapost‐genomicera(ZulyniakandMutch,2011).Sincetheconceptsof“Nutrigenomics”(nutrients

affectthegeneticmakeupofanindividual)and“Nutrigenetics”(thegeneticmakeupofanindividualaffects

nutrientmetabolim)(OrdovasandMooser,2004;Abeteetal.,2012)other“Omics”disciplineshaveemerged

(Table7).

Introduction

43

Table7.“Omics”technologiesappliedtofoodandnutritionresearch.Omicssciences(Maincategories)

Omicssciences(Subcategories)

Target

Genomics Genomics Genes(DNAsequence)

Epigenomics ModificationsofDNAandDNA‐bindingproteins

Transcriptomics Transcriptomics mRNA

ncRNAomics Non‐codingRNA(includingmicroRNA)

Proteomics Proteomics Proteins

Phosphoproteomics Proteinphosphorylation

Localizomics Proteinlocalization

Fluxomics Proteinflux

Interactomics Protein‐proteininteraction

Structuralproteomics Proteinstructure

Metabolomics Metabolomics Metabolites

Lipidomics Lipids

Aminomics Aminoacids

Metagenomics Metagenomics Microbesspeciesandfunctions

Others Glycomics Sugarchains

Cytomics Cells

Populomics Humanpopulation

Exposomics Environmentalexposure

Source:(Katoetal.,2011;OrdovasMunoz,2013)

Indeed, it has been stated that there are three major characterization levels of biological systems

(Figure 12), from genomics‐transcriptomics that raised in 80s, proteomics that began in 90s, to

metabolomics,whichwasintroducedaround15yearsago(Courantetal.,2014).

Figure12.The“Omics”cascade.Modifiedfrom(Dettmeretal.,2007;Pattietal.,2012).

Introduction

44

4.1. Conceptandstrategies

Metabolomics is defined as a high‐throughput approach that analyses the small‐molecules (<1500Da) or

metabolitesthatexistinasampleofbiologicalorigin(Brennan,2013).Incontrasttogenesandproteins,the

complete set of metabolites, known as the metabolome, is considered a direct signature of biochemical

activity and thus, predictive of the phenotype (Patti et al., 2012). Therefore, the profiling of all the

metabolitesorthemetabolome,offersasnapshotofthemetabolismwhichcouldbeconsideredanindexof

thebiologicalstateofanorganism(AstaritaandLangridge,2013).

Metabolomicanalysismightbedirectedtoinvestigatepredefinedmetabolites(targetedapproach)ormight

aimtoconductaglobalapproach(non‐targetedapproach),whichmeansthatnospecifictypeofmetabolites

are pursuing; instead, the largest possible amount of metabolites aim to be detected (Patti et al., 2012).

Thereby, metabolomics studies are usually classified in hypothesis‐driven or exploratory studies

(Figure13AandB).

Figure 13. Typical workflow for a metabolomics approach. A) Non‐targeted metabolomics, B) Targetedmetabolomics.Adaptedfrom(Pattietal.,2012;AstaritaandLangridge,2013).

Two main analytical techniques are currently employed for metabolomics studies; nuclear magnetic

resonance (NMR) spectroscopy and mass spectrometry (MS)‐based techniques combined with

chromatographicseparation.Eachofthempresentssomeadvantagesanddisadvantages.Inaddition,owing

Introduction

45

to thecomplexnatureof thematrixandthe largeamountofmetabolites thatmightbepresent invarying

concentrations (frommillimolar topicomolar)within thebiofluids, theuse of a single analytical tool that

allows identifying and measuring all metabolites at the same time is unavailable

(O'GormanandBrennan,2015).Inthiscontext,NMRtechniquesarecharacterizedbyexaminingmolecules

bytheirsimilarproperties,theexistenceofNMRactivehydrogenorcarbonand,despiteityieldsrelatively

low‐sensitivity, this isaquantitative,reproducibleandnon‐destructivetechnique(Germanetal.,2005). In

contrast, MS‐based techniques analyse large set of small molecular weight metabolites based on their

molecularweights. It presents high sensitivity, but produces qualitative data instead of quantitative data

(German etal., 2005).Moreover, this technique allows to conduct themetabolic profiling of a number of

biofluidsasforinstance,wholeblood,serum,urine,faeces,saliva,cerebrospinalfluid,synovialfluid,semen

andtissuehomogenates (Zhangetal.,2012).Amongthem,urinesamplesarewasteproducts thatcontain

metabolicendproductsofbiochemicalprocessesthathaveoccurredintheorganism.Urinecomprisesboth

endogenousandexogenouscompoundsderivedfromdietandgutmicrobiota,hence,itisknowntoprovide

the overall picture of themetabolic phenotype. Blood samples are obtained in amore invasiveway; but,

plasmaand serum samples show thephysiological alterations occurring in the individual at the sampling

time.Faecalextractsorfaecalwatercontainmetabolicendproductsresultingfromtheinteractionsbetween

thegutmicrobiotaandtheorganism.Infact,metabolitesfromfaecalsamplesareconsideredtoberesultant

fromdietary intake andbiochemicalprocessesoccurringduring theirdigestion (Claus andSwann,2013).

However, for a more complete understanding of the system, the relevance of analysing different

compartmentshasbeendemonstrated(Joveetal.,2011).

Nowadays,metabolomics is applied to awide range of fields including toxicology and drug development

(Keun,2006),medicine(McNivenetal.,2011),aswellasinfoodscienceandnutrition(Ibanezetal.,2015).

Throughthisapproach,keybiomarkerscouldbeidentifiedthatmakepossiblenotonlytheearlydetectionof

theonsetofadisease,butalsotheidentificationofapre‐diseasestate(Ramautaretal.,2013).

4.2. Nutrimetabolomics

Nutrimetabolomics or nutritionalmetabolomics has appeared as the science that investigates humans or

animals’ metabolic response to dietary interventions and enables to understand variations in response

amongindividualstocertainnutrientsordiets,aswellasanalysingtheroleofgutmicrobiotainmammalian

metabolism(Zhangetal.,2008).Applicationsofmetabolomicsinnutritionmightbeclassifiedaccordingto

twomajorobjectives.Ononehand,dietaryinterventionsareconductedinordertounderstandtheeffectsof

foodandfoodconstituentsonmetabolicpathways.Ontheotherhand,metabolomicsisappliedasatoolfor

biomarkerdiscovery,notonlytoassessbiomarkersassociatedtodiseases,butalsotosearchforbiomarkers

of dietary intake, dietary surveillance and compliance to the treatment (Brennan, 2013). Moreover,

metabolomicsprofilehasbeenfoundtobeeffectiveinclassifyingsubjectsbasedonthedietarypatternthey

follow(O'Sullivanetal.,2011),whichhasbeendesignatedasthe“nutritype”.

Several studies have tried to identify obesity‐associated biomarkers. In a review conducted by

Rauschertetal. (Rauschertetal., 2014), itwas concluded that inobesehumans themetabolites typically

Introduction

46

indicative of obesitywere branched‐chain amino acids (BCAAs), several acylcarnitines, amino acids other

thanBCAAs(glutamine,methionine,andphenylalanine),lysophosphatidylcholineandphosphatidylcholines,

stating that, in general, alterations in fatty acid oxidation and increases in levels of BCAAs were given

(Rauschertetal.,2014).

Concerning the accurate estimation of nutritional intake, the identification of objective biomarkers

representsafeasiblealternativetotheinformationobtainedbyquestionnaires.Currently,thereexistsome

general biomarkers, such as urinary nitrogen, to estimate protein intake (Bingham, 2003). However,

biomarkersthatarespecificforcertainfoodsarelacking.Theidentificationofsuchindicatorswouldallowto

accuratelymonitordietaryintakeand,hence,wouldmakeeasiertoestablishcausallinksbetweendietand

health(ClausandSwann,2013).Biomarkersareclassifiedintoshort‐termmarkers(estimatetheintakeover

pasthours/days),medium‐termmarkers(estimatetheintakeoverweeks/months)andlong‐termmarkers

(estimatetheintakeovermonths/years),dependingonthetypeofsampleused,asforinstanceblood,hair

oradiposetissue(Potischman,2003).Goodbiomarkersofhabitualfoodconsumptionareconsideredthose

thatareabsorbedintheupperpartoftheGItractfrom1.0to1.5haftereating,aremetabolicallyinertand

areusuallyexcreted1.5‐2.5hlater(ClausandSwann,2013).Itshouldbebeardinmindthattherearesome

factors thatmayalso bias estimationsof intakeobtainedbybiomarkers.Among them, genetic variability,

lifestyle/physiologic factors (i.e. smoking), dietary factors (i.e. nutrient interactions), sample type and

analytical methodology will need to be considered (Hedrick et al., 2012). In conclusion, validity,

reproducibility and robustness across populations is required (Hedrick et al., 2012). Apart from food

macronutrient biomarkers, markers of different foods and dietary components have been identified

(Hedrick etal., 2012). For instance, proline betaine has been demonstrated to be a specific and sensitive

biomarkerofcitrusfruitconsumption(Heinzmannetal.,2010),theintakeofcruciferousvegetables,suchas

broccoli and Brussels sprout, has been associated to the appearance of S‐methyl‐L‐cysteine sulphoxide

(SMCSO) and its three derivatives (Edmands et al., 2011) and also to the ascorbate excretion in urine,

attributed to the high vitamin C content of broccoli (Lloyd et al., 2011). Meat consumption has been

associated to several putative biomarkers (carnitine, anserine and l‐methylhistidine) (Dragsted, 2010).

Biomarkers of consumption have been also described for caffeine, wine, cocoa and garlic intake among

others (Hedrick et al., 2012). However, more research is required so that these indicators might be

consideredsolidbiomarkersofintake.

Metabolomics approaches give rise to some challenges. Indeed, alterations produced by the intake of

relatively low doses of food compounds or bioactive molecules might be unmasked by inter‐individual

metabolic variations. On the other hand, the dynamism and complexity of metabolismmeans that many

factorsmightbe influencingtheperturbationsobservedand, thus, itwillbecomplextoassociatedetected

metabolitesbacktogenesandproteinsand itwillbealsocomplicatedtodifferbetween individualeffects

exertedbyeachcompound(Zhangetal.,2008).

Importantly, the fact that plant phytochemicals, particularly polyphenols, reduce disease risk has been

assumed from data obtained from epidemiological studies that associate a high fruit and vegetables

consumption with health sustaining (Cencic and Chingwaru, 2010). Accordingly, the identification of

Introduction

47

bioactive compounds responsible for these beneficial effects attracts large interest (Carpene etal., 2015).

Consequently,metabolomicsdisciplineisagoodtooltounderstandthecomplexbiologicaleffectsexertedby

these specific constituents (McGhie and Rowan, 2012). In this context, polyphenols are known to suffer

biotransformationbythebodyandthegutmicrobiota.Unlikemostcompounds,theirabsorptioninthesmall

intestine is small, and the majority reach the colon where they are subject of microbial activity

(Mocoetal.,2012).

4.3. Dietandmicrobial‐derivedmetabolites

Polyphenols’ absorption and bioactivity are facilitated by hydrolysis of polymeric, glycosylated and/or

esterifiedoriginalcompoundsbybrushborderand/ormicrobialenzymes(Bolcaetal.,2013).Subsequently,

aglyconesandphenolicacidssufferPhaseI(oxidation,reductionandhydrolysis)andPhaseII(conjugation)

transformation in ileal epithelium (enterocytes) and the liver (hepatocytes). Metabolites (methylated,

sulphatedorglucuronidatedconjugates)returntothelumenviaentero‐hepaticcirculation(Aura,2008)and

microbial glucuronidases and sulfatases deconjugate Phase II metabolites allowing their uptake. Luminal

phenoliccompoundsenterthecolonundergoingmicrobialtransformationsofthepolyphenoliccorethatare

based on ring‐fission and several cleavages of functional groups (dihydroxylation, demethylation and

decarboxylation)(Aura,2008).Comparedtothediversegroupofpolyphenols,metabolitesproduced from

theirdigestionarelimited(Rechneretal.,2004),butsomeofthemhavebeendemonstratedtoshowhigher

biologicalactivitythantheiroriginalcompounds,asithasbeendemonstratedforequolgeneratedfromsoy

isoflavonedaidzeinorenterodiolandenterolactoneobtainedfromdegradationofplantlignansbyintestinal

bacteria(Wotingetal.,2010;Matthiesetal.,2012).Duetotherapidandextensivetransformationsuffered,

theparentcompoundofcertainpolyphenols,suchasresveratrol,hasbeenestimatedtobeinconcentrations

lower than 1% in plasma, while circulating conjugates may reach concentrations up to 20‐fold higher

(Walle et al., 2004). In this sense, in vitro studies conducted in maturing pre‐adipocytes and in mature

adipocytes have evidenced that resveratrol metabolites (trans‐resveratrol‐3‐O‐sulfate,

trans‐resveratrol‐3‐O‐glucuronide, trans‐resveratrol‐4’‐O‐glucuronide) contribute to the observed

delipidatingeffects.Thus,ithasbeenproposedthattheseconjugatesmightcontributetosomeextenttothe

anti‐obesity outcomes exertedby thepolyphenol (Lasaetal., 2012; Eseberrietal., 2013). In this context,

resveratrolmetaboliteshavebeendetected in liver,adiposetissueandskeletalmuscle, tissuesinvolved in

the body fat lowering effect of the stilbene, while in contrast, the parent compound was not detected

(Andres‐Lacuevaetal.,2012).Moreover,Bodeetal.(Bodeetal.,2013)identifiedinfaecalsamplestwonovel

resveratrol co‐metabolites (lunularin and 3, 4’‐dihydroxy‐trans‐stilbene) produced by intestinal bacteria,

whose physiological effects need to be elucidated. The authors concluded that there might be relevant

interindividual differences in resveratrol bioconversion depending on the intestinal microbial diversity

(Bodeetal., 2013). Inaccordance,metabolismofpolyphenolshasbeen stated tobequitevariabledue to

three main reasons. First of all, because of inter‐individual differences attributed to differential gut

microbiota composition that makes individuals to be distinguished in low or high flavonoid converters.

Second, due to the small structural differences in polyphenols. And third, due to diet‐dependent factors

(vanDuynhovenetal.,2011).

Introduction

48

5. Integrationofmetagenomicsandmetabolomics

Thehomeostasisandmetabolismofanorganismisinfluencedbythehostandbytheinteractionamonghost

and its intestinal microbial population. Therefore, knowledge on this interaction is highly relevant to

understand the impact of nutrition on health (Xie et al., 2013). Gut bacterial ecosystem is considered a

metabolicallyactiveorganthatprovides thehostwithmetabolic traits thatarenotencoded in thehuman

genome.Metabolitesproducedbythegutmicrobiotaareindirectcontactwithhost’scellsandinfluenceits

physiology, locally and systemically. Dysbiotic states regarding gut microbiota composition have been

associated to numerous diseases (i.e. obesity). Interestingly, manipulation of gut microbial profile by

differentdietaryconstituents,suchaspolyphenols,isconsideredapromisingtherapeutictargettoprevent

diseasesorimprovehealth(Verbekeetal.,2015).Theinter‐relationshipbetweengutmicrobiota,diet,host

metabolismandhealthimpliestherequirementofanintegratedviewofdatafrom“Omics”technologiesat

different molecular levels. In this context, newest “Omics” technologies, namely metagenomics and

metabolomics,arerapidlydevelopingandtogetherwithother“Omics”approaches,willenableprogressfor

themorein‐depthunderstandingofthisarea(Figure14).

Itiscurrentlystatedthatitisnotsufficienttocharacterizewhichmicrobesareintheintestinalecosystem,

butitisnecessarytouncoverwhatisthegeneticpotentialorthefunctionalityofthesemicrobes,inorderto

comprehendtheinteractionbetweengutmicrobiotaandhostphysiologyorpathology(Lepageetal.,2013).

Inthiscontext,achallenge ingutmicrobiotastudies is todecipherthe linkbetweenmicrobialcommunity

structures with themetabolic capacity that could impact human cells. Metabolomics allows defining this

functional statusofhost‐microbial integration inbiological fluidsand tissues (i.e. urine,bloodand faeces)

(Marcobaletal.,2013).Therefore,despitethecomplexitytoconfirmthedistinctivesmallmolecules,when

established, these data will provide insights into the chemical intermediates that intercede in microbe‐

microbeandhost‐microbeinteractions(Marcobaletal.,2013).

Figure 14. Schematic overview of the integration of new “Omics” sciences(metagenomicsandmetabolomics)togaininsightaboutthelinkbetweendiet,gutmicrobiota,hostmetabolomeandhealth.

II. HYPOTHESIS AND AIMS

Hypothesisandaims

51

1. Hypothesis

Theintakeoffruitsandvegetablesrichinpolyphenolsisbeingpromotedowingtotheprotectiveeffectsthat

phenolic compounds exert on health.Moreover, the straight interaction that occur among food and food

constituents and microbes residing in the intestine, makes the use of natural compounds a possible

therapeutic approach to improve health via manipulation of gut bacterial community. Accordingly, the

integrationofnew“Omics”approaches,namelynextgenerationsequencingandmetabolomics,arebringing

promising insights on this complex system. These techniques, favour the identification of co‐metabolites

formedasaconsequenceoftheinterplaybetweendiet,gutmicrobiotaandthehostorganism,andenableto

findoutpotentialmarkersthatwillhelpforthepreventionandtreatmentofnutrition‐relateddiseases.

2. Generalaim

Themaingoalofthecurrentresearchwastoexplorethemodificationsproducedonthehostmetabolomeand

gutmicrobiotacompositioninadiet‐inducedobesitymodelandtoinvestigatetheinvivoanti‐obesityeffectsof

differentbioactivecompoundsbyanalysingtheirabilitytomodulategutbacterialcompositionandtheimpact

onmetabolomicsprofile.

3. Specificobjectives

1. Toexploreserummetabolomechangesreflectiveofadiet‐inducedobeseanimalmodel fedahigh‐fat

high‐sucrosediet(Chapter1).

2. Toanalysegutmicrobiotacompositiondysbiosisoccurringinanimalsfedahigh‐fathigh‐sucrosediet

(Chapter2).

3. Toevaluatethepossibleanti‐obesityeffectsofasetofdifferentphenoliccompoundsinadiet‐induced

animalmodelfedahigh‐fathigh‐sucrosediet(Chapter3).

4. Tooverview currentknowledgeon the impactofpolyphenolsorpolyphenol‐richdietaryextractson

intestinalbacterialpopulation(Chapter4).

5. Toassess thepotentialof twowell‐knownpolyphenols, trans‐resveratroland quercetin, to re‐shape

diet‐inducedobeseanimalmodelgutmicrobiotacomposition(Chapter5).

6. Tostudytheeffectsoftrans‐resveratrolandquercetinsupplementationinthefaecalmetabolomeofa

diet‐inducedobeseanimalmodelandtoexaminepossibleassociationswithanimal’sgutmicrobialcommunity

(Chapter6).

7. To investigatethe impactoftrans‐resveratrolongeneexpressionprofileat intestinal level,analysing

thedifferentialexpressionofgenesrelatedtometabolicpathwaysinhuman(Caco‐2)enterocytes(Chapter7).

III. MATERIAL AND METHODS

Materialandmethods

55

Figure15.OverviewoftheexperimentaldesignsconductedfromChapter 1toChapter7,abriefdescriptionoftheobjectivepursuedandtherelatedpublication.

Materialandmethods

56

The objectives proposed for the present work have been addressed in different chapters (Chapter 1 to

Chapter 7). Figure 15 shows an overview of the experimental design conducted in each chapter, the

objectivepursuedandtherelatedpublication.

Todescribematerialandmethodssection,theorderofthechaptershasbeenfollowed.However,inorderto

avoid repetitive information, anarrangementof the chaptershasbeendone taking intoconsideration the

twomajorapproachesconducted:metabolomicsandnext‐generationsequencing.Accordingly,firstofall,the

experimentaldesignsofChapter1andChapter6(belongingtonon‐targetedmetabolomicanalyses)have

beendescribed.Secondly,theexperimentaldesignofChapter3,referringtotheuseofpolyphenolsasanti‐

obesity compounds has been reported. Thirdly, Chapter 2 and Chapter 5 (belonging to gut microbiota

analyses)havebeenreported.Finally, theexperimentaldesignofChapter7, theunique invitrostudyhas

beenexplained.

Inthissectionabriefexplanationofeachexperimentaldesignandthemostrelevantlaboratorytechniques

andproceduresusedwillbegiven.Besides,ashortdescriptionaboutthegeneralbasisofthemainanalyses

conducted (metabolomics and next‐generation sequencing) and the respective equipment used will be

reported.

1. Metabolomicanalyses(Chapter1,Chapter6)

1.1. ExperimentaldesignChapter1:ImpactofaHFSdietonserummetabolome.

Theexperimentwasconductedon16maleWistarrats(n=16)thatwereassignedintotwodifferentdietary

intervention groups, a control group (n=8) fed a standard diet and a HFS diet‐fed group (n=8). The

techniquesusedinthisstudyandthemeasurementsthatwerecarriedoutarethefollowing:

‐ Body‐weightandfoodintakerecord:weightscale.

‐ Plasma biochemical parameters: Glucose HK‐CP kit (ABX, Diagnostic, Montpellier, France) for

glucose,TotalProteinCPkit(ABX,France)fortotalprotein,albuminwasmeasuredwithAlbuminCP

kit(ABX,France)andurealevels,withtheUreaCPkit(ABX,France)usingPentraC200equipment

(HoribaMedical,Kyoto,Japan).LeptinandinsulinweremeasuredusingspecificELISAkits.

‐ Non‐targetedmetabolomicanalysis:1D1H‐NMRspectrausinga600.2‐MHzfrequencyAvanceIII

600Bruckerspectrometer(Brucker,Germany).

Materialandmethods

57

Figure16.OverviewofthestudydesignofChapter1.Abbreviations:HFS,high‐fatsucrosediet;BW,bodyweight;H1‐NMR,one‐dimensionalnuclearmagneticresonance.

Materialandmethods

58

1.2. ExperimentaldesignChapter6:Impactoftrans‐resveratrolandquercetinonfaecal

metabolome.

Inthisstudy,Wistarrats(n=24)wererandomlyassignedintofourexperimentalgroups:thecontrolgroup

(HFS, n=6) fed a HFS obesogenic diet; trans‐resveratrol group (RSV, n=6), supplementedwith 15mg/kg

BW/dayoftrans‐resveratrol;quercetingroup(Q,n=6),supplementedwith30mg/kgBW/dayofquercetin;

and trans‐resveratrol+quercetin group (RSV+Q, n=6), treated with a combination of trans‐resveratrol 15

mg/kg BW/day and quercetin 30 mg/kg BW/day. The following reagents and equipments were used in

order to analyse body weight and food intake, serum biochemical parameters and faecal metabolomic

profile.


‐ Serum biochemical parameters: Glucose oxidase/peroxidase kit (Ref. 110504; BioSystems,

Barcelona, Spain) for glucose; Mercodia Rat Insulin ELISA kit (Ref. 10‐1250‐01; Mercodia AB,

Uppsala,Sweden)forinsulin.

‐ Non‐targetedmetabolomicanalysis:1290infinityultra‐highperformanceliquidchromatography

(UHPLC) system (Agilent Technologies) coupled to a 6550 ESI‐QTOF (Agilent Technologies)

operatedinpositive(ESI+)ornegative(ESI‐)electrosprayionizationmode.

Figure17.OverviewofthestudydesignofChapter6.Abbreviations:HFS,high‐fatsucrose;RSV, trans‐resveratrol; Q, quercetin; RSV+Q, trans‐resveratrol and quercetin; BW, bodyweight;UHPLC,ultra‐highperformance liquidchromatography,ESI, electrospray ionization;HRMS,high‐resolutionmassspectrometry.

Materialandmethods

59

Overviewoftheuntargetedmetabolomicprofiling

LC coupled toMSusing electrospray ionization (LC/ESI‐MS) is an especially popular platform that brings

highsensitivity,combiningaseparationandadetectionmethod(Smithetal.,2006).Wewillbrieflydescribe

thebasisoftheUHPLC‐(ESI)‐HRMStechnologywhenperforminganuntargetedanalysis.

DuringLCanalysis,thecomponentsofthesamplearedistributedintoastationaryandamobilephase.The

pressurizedliquidsolventcontainingthesampleispumpedthroughacolumnfilledwithasolidadsorbent

material. Separationof thecomponentsof thesamplewilldependon thesmalldifferences in interactions

withthestationaryphasewhichgeneratesdifferentflowratesthatleadtotheseparationofthecomponents

astheycomeoutofthecolumn.Moreover,dependingonthechemicalcompositionofbothphases,LCmight

work in normal or reversed‐phase. While in normal phase chromatography the stationary phase is

hydrophilic, reversed‐phase chromatography relies on the separation of the molecules based on their

hydrophobicity, mainly using gradients of increasing concentrations of organic solvents (Pitt, 2009). MS

detectors convert the analytes in charged (ionised) molecules and, then, ions are analysed taking into

account their mass‐to‐charge ratio (m/z). The ionisation technique used in the present study is the ESI,

which has been shown to be appropriate for moderately polar molecules including many metabolites,

xenobiotics and peptides. In fact, it has been mainly used for biological molecules (Pitt, 2009). For the

qualitativeanalysisofthemetabolomicprofileofasample,oneofthemostappropriatemassanalysersisthe

time of flight (TOF), which allows acquiring mass spectra quickly and with high sensitivity. It works

acceleratingionsthroughahighvoltage.Therefore,thetimethattakestheionstotraveldownaflighttube

andreachthedetectorreliesonthem/zvalueofmetabolites(Pitt,2009).AschematicoverviewofUHPLC‐

(ESI)‐HRMSmetabolomicanalysisisfoundinFigure18.

Figure 18. Schematic overview ofUHPLC‐ESI‐HRMSmetabolomic analysis.Modifiedfrom Agilent Technologies “Basics of LC/MS” (http://www.agilent.com/home).Abbreviations: HPLC, high‐performance liquid chromatography; ESI, electrosprayionization;TOF,timeofflight.

Materialandmethods

60

2. Impactofdifferentphenoliccompoundsonobesity(Chapter3)

2.1. ExperimentaldesignChapter3:Impactofdifferentsetofphenoliccompoundsonobesity

In this study,maleWistar rats (n=70)were randomly distributed into seven experimental groups: a

standard‐dietfedcontrolgroup(control,n=10);aHFSdiet‐fedgroup(HFS,n=10)andfivegroupsthat

were fed the same obesogenic diet but supplemented with either curcumin (Curcumin, n=10),

chlorogenicacid(Chl.A.,n=10),p‐coumaricacid(Cou.A.,n=10),naringin(Naringin,n=10)eachofthem

at a dose of 100mg/kg BW/day and leucine (Leucine, n=10) at a dose of 1% of diet. The following

measurementsandassessmentswereconducted.


‐ Glucose levels invivothroughan intraperitonealglucosetolerancetest(IPGTT):glucometer

(Roche Diagnostics, Mannheim, Germany) and blood glucose test trips (Optium Plus, Abbott

DiabetesCare,WitneyOxon,UK).

‐ Serum parameters: Mercodia Rat Insulin ELISA Kit (Ref. 10‐1250‐01; Mercodia AB, Uppsala,

Sweden)usinganautomatizedTriturus®equipment(GrifolsInternationalS.A.,Barcelona,Spain).

Figure19.OverviewofthestudydesignofChapter3.Abbreviations;HFS,high‐fatsucrose;Chl. A, chlorogenic acid; Cou. A, p‐coumaric acid; BW, body weight; IPGTT, intraperitonealglucosetolerancetest.

Materialandmethods

61

3. Gutmicrobiotacompositionanalyses(Chapter2andChapter5)

3.1. ExperimentaldesignChapter2:ImpactofaHFSdietongutmicrobiota.

Male Wistar rats (n=5) were fed a standard diet during the adaptation period and then changed to an

obesogenic HFS diet for 6 weeks. Samples were taken before and after HFS dietary intervention. The

following approaches and techniques were used in order to analyse body weight, food intake, serum

biochemicalprofile,bacterialcommunitystructure,andSCFAprofileinfaecalsamples.


‐ Serumparameters: Glucose oxidase/peroxidase kit (Ref. 110504; BioSystems, Barcelona, Spain)

forglucoseandMercodiaRatInsulinELISAkit(Ref.10‐1250‐01;MercodiaAB,Uppsala,Sweden)for

insulin.

‐ DNA extraction from faeces: QIAamp DNA Stool Mini kit (Qiagen, Hilden, Germany),

NanodropND‐1000spectrophotometer(ThermoScientific).

‐ Polymerasechainreaction(PCR):T100ThermalCycler(Bio‐radlaboratoriesInc.,Spain),PfuDNA

polymerase(ref.M774;Promega,Madison,WI,USA),CustomDNAoligos(Sigma‐Aldrich),QIAquick

PCRpurificationKit(Qiagen).

‐ Bacterial communityanalysis: 454 pyrosequencing on a Roche Genome Sequencer FLX system

(Roche,Penzberg,Germany).

‐ Gaschromatography‐massspectrometry(GC‐MS):7890Aseriesgaschromatographcoupledtoa

7000seriesTripleQuadGC/MS(AgilentTechnologies,SantaClara,CA,USA).

Figure20.OverviewofthestudydesignofChapter2.Abbreviations:HFS,high‐fatsucrose;BW,bodyweight;GC‐MS,gaschromatography‐massspectrometry.

Materialandmethods

62

3.2. ExperimentaldesignChapter5:Impactoftrans‐resveratrolandquercetinonfaecalgut

microbiotacomposition.

Dietaryinterventionconsistedoffourgroups:thecontrolgroupfedaHFSdiet(HFS,n=5);trans‐resveratrol

group (RSV, n=6) supplemented with 15 mg/kg BW/day of trans‐resveratrol; quercetin group (Q, n=6),

supplementedwith30mg/kgBW/dayof quercetin; and trans‐resveratrol+quercetin group (RSV+Q,n=6),

supplementedwithacombinationoftrans‐resveratrol15mg/kgBW/dayandquercetin30mg/kgBW/day.

Thefollowingtechniquesandmethodswereusedinordertoanalyse:bodyweightandfoodintake,serum

biochemicalparameters,geneexpressionanalysis,gutmicrobiotacompositionandSCFAsprofile.

‐ Body‐weightandfoodintake:weightscale.

‐ Serum biochemical parameters: Glucose oxidase/peroxidase kit

(Ref. 110504;BioSystems,Barcelona, Spain) for glucose;MerkodiaRat InsulinELISAkit (Ref. 10‐

1250‐01;MercodiaAB,Uppsala, Sweden) for insulin;TNF‐alphaPlatinumELISAkit (EBioScience,

Vienna, Austria) for TNFα levels; LPS levels, with the Limulus Amebocyte Lysate QCL‐1000

chromogenicassay(Lonza,Walkersville,MD,USA);andLPS‐bindingprotein(LBP)levels,withthe

MouseLBPELISAKit(CellSciences,Canton,MA,USA).

‐ DNA extraction from faeces: QIAamp DNA Stool Mini kit (Qiagen, Hilden, Germany).

NanodropND‐1000spectrophotometer(ThermoScientific).

‐ RNAextraction fromcolonicmucosa:AllPrepDNA/RNAMinikit (Qiagen).NanodropND‐1000

spectrophotometer(ThermoScientific).

‐ RT‐PCR: ABI PRISM 7000 HT Sequence Detection System and predesigned TaqMan Assays

(IL‐18, TNF‐α, TLR‐2, TLR‐4, LBP, MyD88, NFκ‐β, NKGd2 TJP‐1, TJP‐2, Ocln) from (Applied

Biosystems,Texas,USA).

‐ PCR: T100 Thermal Cycler (Bio‐rad laboratories Inc., Spain), Pfu DNA polymerase

(ref. M774; Promega, Madison, WI, USA), Custom DNA oligos (Sigma‐Aldrich), QIAquick PCR

purificationKit(Qiagen).

‐ Bacterial communityanalysis: 454 pyrosequencing on a Roche Genome Sequencer FLX system

(Roche,Penzberg,Germany).

‐ GC‐MS: 7890 A series gas chromatograph coupled to a 7000 series Triple Quad GC/MS

(AgilentTechnologies,SantaClara,CA,USA).

Materialandmethods

63

Figure21.OverviewofthestudydesignofChapter5.Abbreviations:HFS,high‐fatsucrose;RSV,trans‐resveratrol;Q,quercetin;RSV+Q,trans‐resveratrolandquercetin;BW,bodyweight;TNF‐α, tumor necrosis factor α; LPS, lipopolysaccharide; LBP, lipopolysaccharide bindingprotein; IL‐18, interleukin 18; TLR‐2, toll‐like receptor 2; TLR‐4, toll‐like receptor 4;MyD88,myeloiddifferentiationprimaryresponse88;NFκβ,nuclear factorkappaβ;NKGd2,killer celllectin‐likereceptorsubfamilyK,member1;TJP‐1,tight‐junctionprotein1;TJP‐2,tightjunctionprotein2;Ocln,occluding;GC‐MS,gaschromatography‐massspectrometry.

Materialandmethods

64

Basisofthehigh‐throughputDNAsequencing

The16SrRNAgeneishighlyconservedamongallmicroorganisms,itslength(around1500bp)isadequate

for bioinformatics analysis and it is considered an excellent molecule to differentiate evolutionary

relationshipsamongprokaryoticorganisms(VandePeeretal.,1996).Briefly,inordertostudythemicrobial

populationofanecosystem,inthisapproachweexaminedthesequenceofagenethatallmicrobeshaveand

used variations in this gene to identify differences in species. In this thesis, 16S rDNA gene has been

sequenced taking the V4 and V6 hypervariable regions as a target.Figure22 shows an overview of the

processcarriedouttoconductgutmicrobiotacompositionanalysis.

Figure22.Schematicoverviewofthehigh‐throughputsequencinganalysisperformedtodetectandquantifybacterialcommunityinanimal’sfaecalsamplesinChapter2andChapter5ofthepresentthesis.Abbreviations:16SrDNA,ribosomalDNA;RT‐PCR,real‐timePCR;bp,basepair.

Materialandmethods

65

4. Invitroassayandmicroarrayanalysis(Chapter7)

4.1. ExperimentaldesignChapter7:Impactoftrans‐resveratrolongeneexpressionlevelsin

enterocytes.

For the in vitro study a Caco‐2 cell line, a human intestinal epithelial cell model was used. For the

experiments cells were treated with standard LPS from E. coli K12 strain‐ TLR4 ligand

(InvivoGen, San Diego, CA, USA). Concentrations of LPS (1 μg/mL) and time of exposure (48h) were

establishedbasedonstudiespublishedintheliterature(Cianciullietal.,2012).BeforeLPSstimulationsome

wellswerepre‐treatedwithtrans‐resveratrol,dissolvedinethanol,inaconcentrationof50μM.Upon1hof

incubationat37°C, cell cultureswerestimulatedwithendotoxinaspreviouslymentioned.Untreatedcells

wereusedascontrols.

‐ RNAextractionfromcells:TRIzol®reagent(Invitrogen,CA,USA),RNeasyMini‐kit(Qiagen,Hilden,

Germany), nanodropND‐1000 spectrophotometer (ThermoScientific),AgilentRNANanoLabchip

(AgilentTechnologies).

‐ Microarrayanalysis:AffymetrixhumanGene2.0STmicroarray.

Figure23.OverviewoftheinvitroexperimentaldesignofChapter7.Abbreviations:LPS,lipopolysaccharide;RSV,trans‐resveratrol.

IV. RESULTS

69

CHAPTER1

Diet‐inducedhyperinsulinemiadifferentiallyaffectsglucoseandprotein

metabolism:ahigh‐throughputmetabolomicapproachinrats

EtxeberriaU.1,delaGarzaA.L.1,MartínezJ.A.1,MilagroF.I.1

1Dept.ofNutrition,FoodScienceandPhysiology,UniversityofNavarra,C/Irunlarrea,1,31008,Pamplona,Spain

JPhysiolBiochem,2013

doi:10.1007/s13105‐013‐0232‐0

ImpactFactor(2013):2.496

35/81inPhysiology(Q2)

Etxeberria U, de la Garza AL, Martínez JA, Milagro FI. Diet‐induced hyperinsulinemia

differentially affects glucose and protein metabolism: a high‐throughput metabolomic

approach in rats. Journal of Physiology and Biochemistry, 2013, 69(3): 613-623.

http://doi.org/10.1007/s13105-013-0232-0

http://doi.org/10.1007/s13105-013-0232-0

83

CHAPTER2

Shifts in microbiota species and fermentation products in a dietary

modelenrichedinfatandsucrose

EtxeberriaU.1,2,AriasN.3,BoquéN.4,MacarullaM.T.3,5,PortilloM.P.3,5,MilagroF.I.1,2,5,MartinezJ.A.1,2,4*

1DepartmentofNutrition,FoodScienceandPhysiology,UniversityofNavarra,C/Irunlarrea,s/n,31008,Pamplona,Spain

2CentreforNutritionResearch,UniversityofNavarra.IrunlarreadSt.E‐31008Pamplona,Spain

3NutritionandObesitygroup,DepartmentofNutritionandFoodSciences,FacultyofPharmacy,UniversityoftheBasque

Country(UPV/EHU),PaseodelaUniversidad7,01006Vitoria,Spain

4NutritionandHealthResearchGroup.TechnologicalCenterofNutritionandHealth(CTNS),TECNIO,CEICS.Avinguda

Universitat1,43204Reus,Spain;5CIBERobnFisiopatologíadelaObesidadyNutrición

5(CIBERobn),InstitutodeSaludCarlosIII,28029Madrid,Spain

BenefMicrobes,2015

doi:10.3920/BM2013.0097


35/77inNutrition&Dietetics(Q2)

57/119inMicrobiology(Q2)

Etxeberria U, Arias N, Boqué N, Macarulla MT, Portillo MP, Milagro FI, Martínez JA.

Shifts in microbiota species and fermentation products in a dietary model enriched in

fat and sucrose. Beneficial Microbes, 2015, 6(1): 97-111.

http://doi.org/10.3920/BM2013.0097

http://doi.org/10.3920/BM2013.0097

101

CHAPTER3

Biocompounds Attenuating the Development of Obesity and Insulin

ResistanceProducedbyaHigh‐fatSucroseDiet

EtxeberriaU.1,§,delaGarzaA.L.1,§,MartínezJ.A.1,2,*andMilagroF.I.1,2

1DepartmentofNutrition,FoodScienceandPhysiology,CentreforNutritionResearch,UniversityofNavarra,

C/Irunlarrea1,31008Pamplona,Spain

2CIBERobnPhysiopathologyofObesityandNutrition,CarlosIIIHealthResearchInstitute,Madrid,Spain

§Theseco‐authorscontributedequallytothiswork

NatProdCommun,2015

Publishedonline(www.naturalproduct.us)


76/123inFoodScience&Technology(Q3)

Biocompounds Attenuating the Development of Obesity and Insulin Resistance Produced by a High-fat Sucrose Diet Usune Etxeberriaa,§, Ana Laura de la Garzaa,§, J. Alfredo Martíneza,b,* and Fermín I. Milagroa,b aDepartment of Nutrition, Food Science and Physiology, Centre for Nutrition Research, University of Navarra, C/Irunlarrea 1,31008 Pamplona, Spain

bCIBERobn Physiopathology of Obesity and Nutrition, Carlos III Health Research Institute, Madrid, Spain §These co-authors contributed equally to this work [email protected] Received: April 15th, 2015; Accepted: May 13th, 2015

The use of biocompounds as agents with potential anti-obesity effects might be a feasible alternative to the prescription of traditional drugs in the near future. The goal of the present study was to screen five different compounds in relation to their ability to prevent body weight gain and ameliorate obesity-associated metabolic impairments, namely insulin resistance. For this purpose, seventy Wistar rats were randomly assigned into seven experimental groups. A standard diet-fed control group (control, n=10); a high-fat, high-sucrose diet-fed group (HFS, n=10) and five experimental groups which were fed the HFS diet supplemented with one of the following biocompounds; curcumin (100 mg/kg bw, n=10), chlorogenic acid (50 mg/kg bw, n=10), coumaric acid (100 mg/kg bw, n=10), naringin (100 mg/kg bw, n=10) and leucine (1 % of diet, n=10). These results confirm the effectiveness of all the compounds to reduce significantly food efficiency, despite the significant higher food intake. Moreover, visceral fat mass percentage was significantly decreased after naringin and coumaric acid supplementation. In fact, this finding might be related to the considerable amelioration of HOMA-IR index detected in naringin-treated animals. A significant reduction in serum insulin levels and an improvement in the intraperitoneal glucose tolerance test and AUC were found in leucine- and coumaric acid-treated rats, respectively. In summary, the tested biocompounds, particularly naringin, coumaric acid and leucine, showed potential benefits in the prevention of obesity-related complications in rats, at least at the proved doses. Keywords: Curcumin, Chlorogenic acid, Coumaric acid, Naringin, Leucine, Anti-obesity. Obesity has been defined as the pandemic of the 21st century by the World Health Organization [1]. Obesity is a health problem itself, but it is also associated with numerous metabolic complications [2], resulting in a public health burden that implies high socio-economic costs [3]. Even if obesity is considered a preventable disease, greatly influenced by diet and exercise [4], still, limited effective conventional medical treatments have been developed [5]. In this context, a growing interest has been focused on the development of natural products with clinical effectiveness, but lower side effects than the current anti-obesity drugs [6]. In the recent decades, the use of phenolic phytochemicals or natural extracts for the management of obesity and other related diseases

has been a scientific focus [7, 8]. In this sense, polyphenols such as flavanones (naringin), simple phenolic acids (coumaric acid and chlorogenic acid), curcuminoids (curcumin) [9], and other biocompounds, as for instance leucine, a branched-chain amino acid [10], have been suggested to exert potential health beneficial effects in the context of obesity. Indeed, knowledge of the impact of these types of compounds on the organism is being further unravelled through novel approaches such as the omics techniques [11]. The aim of this study was to evaluate the anti-obesity effects exerted by five biocompounds (naringin, curcumin, chlorogenic acid, coumaric acid and leucine) in a high-fat sucrose (HFS) diet-fed animal model generally used to mimic overweight and insulin resistance status of Westernized societies [12].

Figure 1: Body-weight gain development over the 37 days treatment period. Results are expressed as the mean ± SEM. Statistical analysis was performed using Student’s T-test to analyse differences in the mean of each group with HFS group. $(Curcumin), ~(Leucine), §(Coumaric acid), #(Naringin), p< 0.05; ***(Control), p< 0.001.

NPC Natural Product Communications 2015 Vol. 10 No. 8

1417 - 1420

1418 Natural Product Communications Vol. 10 (8) 2015 Etxeberria et al.

HFS + biocompounds Measurements Control

(n = 10) HFS

(n = 10) Curcumin

(n = 10) Chl.A.

(n = 10) Cou.A.(n = 10)

Naringin (n = 10)

Leucine(n = 10)

Initial body weight (g) 337 + 9 336 + 3 337 + 6 338 + 6 336 + 7 337 + 5 336 + 7 Food intake (g) 24.87 + 0.07 24.02 + 0.07 27.06 + 0.40 *** 26.72 + 0.54 *** 27.54 + 0.88 *** 27.01 + 0.32 *** 26.65 + 0.40 *** Liver weight (g/100 kg bw) 2.18 + 0.07 * 1.95 + 0.08 1.92 + 0.13 1.97 + 0.06 1.96 + 0.09 1.94 + 0.07 1.91 + 0.08 Spleen weight (g/100 kg bw) 0.17 + 0.01 * 0.15 + 0.00 0.15 + 0.01 0.14 + 0.01 0.16 + 0.01 0.16 + 0.01 0.15 + 0.01 Gastrocnemius weight (g/100 kg bw) 0.49 + 0.01 0.45 + 0.02 0.40 + 0.02 0.43 + 0.02 0.46 + 0.02 0.44 + 0.02 0.45 + 0.02 Fat mass/ gastrocnemius ratio 10.08 + 1.01*** 18.50 + 1.48 20.74 + 1.39 17.91 + 1.37 15.79 + 1.45 16.07 + 1.11 17.28 + 1.35

Table 1: Body-weight related parameters of rats at the end of the experimental period. Results are expressed as the mean ± SEM. Statistical analysis was performed using Student T-test to analyse differences in the mean of each group with HFS group. *p< 0.05; ***p< 0.001.

Figure 2: Food efficiency (g/ 100 Kcal) of rats at the end of the experimental period. Results are expressed as the mean ± SEM. Statistical analysis was performed using Student’s T-test to analyse differences in the mean of each group with HFS group. $(Curcumin), ~ (Leucine), §(Coumaric acid), p< 0.05; ###(Naringin), p< 0.001. Rats supplemented with the biocompounds (curcumin, coumaric acid, naringin and leucine) showed lower food efficiency than the non-treated HFS rats: Final body-weight gain, which was significantly increased (165%, p< 0.001) after the HFS diet treatment (Figure 1), was slightly reduced at the end of the study in the animals supplemented with naringin and leucine (Figure 1). Moreover, naringin and leucine significantly reduced body-weight gain at day 28 of the intervention period when compared with the HFS diet-fed control rats (Figure 1). Despite the fact that significantly greater food intake was observed in all supplemented groups (Table 1) when compared with the HFS control rats, food efficiency was significantly lowered in all the groups supplemented with biocompounds, except in rats consuming chlorogenic acid (Figure 2). These data suggest that the bioactive compounds impacted on the utilization of the food provided, as the animals gained less body-weight per ingested calories. Our data might agree with results obtained by Zhang et al. [10], who stated that high-fat diet (HFD) fed obese mice were highly fuel efficient, which implied a greater body-weight gain per unit of calories consumed compared with the chow diet-fed mice. In contrast, HFD fed animals supplemented with leucine showed higher resting energy expenditure with lower fuel efficiency [10]. In relation to the action of the biocompounds tested in the present study, although no data regarding animals’ energy expenditure is available, it might be suggested that the treatment period with either curcumin, chlorogenic acid, coumaric acid, naringin and leucine, might have impacted on the animals’ metabolism, inducing higher resting energy expenditure, higher capacity to burn food via diet-induced thermogenesis and/or inducing a greater capacity for fat oxidation [13]. Naringin and coumaric acid reduced the adiposity levels induced by a HFS diet: HFS diet-fed animals evidenced a significant enlargement in total body fat and visceral adipose tissue compared with the control rats (Figure 3). In contrast, coumaric acid produced a statistically significant reduction in visceral adipose tissue, while rats supplemented with naringin showed a statistically significant decrease in both visceral and total adipose tissue depots after a 37 days treatment period (Figure 3). However, no significant differences were achieved in other organ weights (liver, spleen and

gastrocnemius), neither in the mentioned groups nor in the rest of the supplemented groups (Table 1). Protective effects of naringin on fat deposition might confirm the already proposed mechanism of action dependent on the activation of AMP kinase, leading to a suppression of lipid synthesis and inhibition of insulin resistance [14]. The prevention of adipose tissue accumulation exerted by the intake of coumaric acid was also reported in a model of HFD-fed Wistar rats. In this study, the anti-obesity effects of this compound were accompanied by a decrease in dyslipidaemia, hepatic steatosis, and oxidative stress [15]. Indeed, this research previously demonstrated in vitro the potential of coumaric acid to inhibit intracellular triglycerides and glycerol- 3- phosphate dehydrogenase (GPDH) in 3T3-L1 adipocytes [16]. Furthermore, the role of coumaric acid was described to inhibit the expression of peroxisome proliferator-activated receptor gamma (PPAR ), involved in the regulation of insulin sensitivity and glucose homeostasis [17] and CCAT enhancer binding protein alpha (C/EBP ) protein levels, concluding the adipogenesis suppressive effect of these compounds. Naringin and leucine showed insulin sensitizing effects, while coumaric acid tended to improve impaired glucose tolerance: Impaired glucose tolerance is one of the major characteristics of insulin resistance [18] and the homeostasis model assessment of insulin resistance (HOMA-IR) index is usually used to assess the insulin resistance status. In our study, a significant reduction of HOMA-IR was found following naringin supplementation (Figure 4A) when compared with the HFS diet-fed obese rats, restating the beneficial effects of this flavonoid preventing the development of insulin resistance. Concomitantly, our data demonstrated that serum insulin levels were significantly lower in rats supplemented with leucine (Figure 4B). Leucine has been demonstrated to ameliorate glucose metabolism, in part due to a better insulin sensitivity resulting from a reduction in adiposity, while the induction of insulin secretion [19] has also been indicated as a possible mechanism responsible for the glycaemic control. Interestingly, in our study, even if leucine supplementation did not significantly reduce fat accumulation, this branched-chain amino acid was found to be effective in decreasing insulin levels, a feature that might be secondary to the improved insulin sensitivity, as suggested by Zhang et al. [10]. Furthermore, an intraperitoneal glucose tolerance test (IPGTT) was conducted to analyse the effects of biocompounds that showed the most promising effects concerning glucose metabolism. In this context, coumaric acid marginally decreased (p= 0.052) the area under the curve (AUC) and significantly reduced glucose levels 90 minutes after glucose administration (Figures 5A and 5B). The present work highlights certain health beneficial effects derived from the intake of biocompounds in relation to obesity and obesity-associated metabolic disorders. Although individual outcomes produced by each bioactive compound might not be particularly remarkable, emphasis should be made in the fact that foods such as fruits and vegetables are complex mixtures of polyphenols, thus, consuming a dietary pattern rich in this sort of food may induce beneficial outcomes due to possible synergistic effects among the different compounds.

Biocompounds with potential anti-obesity effects Natural Product Communications Vol. 10 (8) 2015 1419

Figure 3: Total adipose tissue and visceral adipose tissue at the end of the 37 days treatment period. Results are expressed as the mean ± SEM. Statistical analysis was performed using Student’s T-test to analyse differences in the mean of each group with HFS group. §(Coumaric acid), #(Naringin), p< 0.05; ***(Control), p< 0.001.

Figure 4: A) HOMA-IR index and B) Serum insulin levels of rats supplemented or not with naringin, leucine and coumaric acid. Results are expressed as the mean ± SEM. Statistical analysis was performed using Student’s T-test to analyse differences in the mean of each group with HFS group. ~ (Leucine), # (Naringin), p< 0.05.

Figure 5: A) AUC and B) Serum glucose levels during the IPGTT of rats fed a HFS supplemented or not with naringin, leucine and coumaric acid. Results are expressed as the mean ± SEM. Statistical analysis was performed using Student’s T-test to analyse differences in the mean of each group with HFS group. §(Coumaric acid), p< 0.05; t< 0.1. Experimental

Biocompounds and diets: A standard pelleted chow diet containing 20% of energy as protein, 67% as carbohydrates (5% sucrose and 62% starch) and 13% as fat by dry weight (290 kcal/100 g diet), was purchased from Harlan Ibérica (Teklad Global, Barcelona, Spain; ref. 2014). HFS diet contained 20% of energy as protein, 35% as carbohydrates (18% sucrose, 10% maltodextrin and 7% starch) and 45% as lipids by dry weight (473 kcal/100 g diet) as detailed by the supplier (Research Diets, New Brunswick, NJ, USA; ref. D12451). Naringin, curcumin, L-leucine, chlorogenic acid and p-coumaric acid were purchased from Sigma-Aldrich Co. (St. Louis, MO 63103 USA). Experimental animals: Seventy male Wistar rats from the Center for Applied Pharmacobiology Research (CIFA) of the University of Navarra (Pamplona, Spain), with an initial average weight of 337 + 20 g, were kept in an isolated room with a constantly regulated temperature between 21 and 23°C, controlled humidity (50 + 10%), under a 12 h:12 h artificial light/dark cycle and with water and food ad libitum. The experimental protocol was approved by the Animal Research Ethics Committee of the University of Navarra (04/2011). The animals were randomly divided into 7 groups (n=10) and were fed either a standard diet (control group) or a HFS diet with or

without supplementation of different biocompounds: curcumin (100 mg/kg bw), chlorogenic acid (50 mg/kg bw), coumaric acid (100 mg/kg bw), naringin (100 mg/kg bw) and leucine (1%). In order to select the mentioned doses, concentrations used in previous studies reporting obesity-associated metabolic improvements were taken into consideration [10, 15, 20-24]. Body-weight and energy intake were recorded 3 times per week. After 37 days of supplementation, rats were sacrificed by decapitation without anaesthesia, and blood was collected from the trunk. Liver, spleen, gastrocnemius muscle and different adipose depots (subcutaneous, retroperitoneal, epididymal and mesenteric) were carefully dissected and weighed. Tissue and serum samples were immediately stored and frozen (-80°C) for further analyses. Intraperitoneal glucose tolerance test: HFS, naringin, leucine and coumaric acid groups were selected to conduct an IPGTT during the 26th and 27th days of the total experimental period. After a 15-h fast, rats were injected intraperitoneally with glucose (2 g/kg bw in 30%, w/v, solution). Blood glucose levels were determined from the tail vein before (0’) and after glucose injection (30’, 60’, 90’, 150’, 210’) with a glucometer (Roche Diagnostics, Mannheim, Germany) and blood glucose test trips (Optium Plus, Abbott Diabetes Care, Witney Oxon, UK). The glucose content was expressed as mg/dL, and the AUC were determined by the trapezoidal rule approach [25], as measured elsewhere [8]. Serum measurements: Serum insulin (Mercodia AB, Uppsala, Sweden) was determined by enzyme-linked-immunosorbent assay (ELISA) using an automatized TRITURUS equipment (Grifols International S.A., Barcelona, Spain). Fasting glucose levels were measured using a glucometer (Roche Diagnostics) and blood glucose test trips (Optimum Plus, Abbott Diabetes Care). Insulin resistance was assessed by the HOMA-IR index formula [26]: [serum glucose levels (mmol/L) x insulin levels (mU/L)/ 22.5]. Statistical analysis: All the results are expressed as mean + standard error of the mean (SEM). Statistical significance of differences among the groups was evaluated using Student’s T-test. A level of probability of p< 0.05 was set as statistically significant.

1420 Natural Product Communications Vol. 10 (8) 2015 Etxeberria et al.

All analyses were performed using SPSS 15.0 packages of Windows (Chicago, IL). Acknowledgments – The authors wish to acknowledge Línea Especial about Nutrition, Obesity and Health (University of Navarra LE/97, Spain) and CIBERobn Physiopathology of Obesity and

Nutrition for the financial support. The authors also wish to thank Asociación de Amigos from University of Navarra for the predoctoral grant given to A.L. de la Garza and the Department of Education, Language policy and Culture from the Government of the Basque Country for the predoctoral grant given to U. Etxeberria.

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107

CHAPTER4

Impact of Polyphenols and Polyphenol‐Rich Dietary Sources on Gut

MicrobiotaComposition

EtxeberriaU.1,Fernández‐QuintelaA.2,3,MilagroF.I.1,3,AguirreL.2,3,MartínezJ.A.1,3,PortilloM.P.2,3

1DepartmentofNutritionandFoodSciences,PhysiologyandToxicology,UniversityofNavarra,Pamplona,Spain

2NutritionandObesityGroup.Dpt.NutritionandFoodSciences.FacultyofPharmacy.UniversityoftheBasqueCountry.

PaseodelaUniversidad,7.01006Vitoria(Spain)

3CIBERdeFisiopatologíadelaObesidadyNutrición(CIBERObn),InstitutodeSaludCarlosIII,Spain

JAgrFoodChem,2013

doi:10.1021/jf402506c.



11/71inChemistry,Applied(Q1)

2/56inAgriculture,Multidisciplinary(Q1)

Etxeberria U, Fernández‐Quintela A, Milagro FI, Aguirre L, Martínez JA, Portillo MP.

Impact of Polyphenols and Polyphenol‐Rich Dietary Sources on Gut. Journal of

Agricultural and Food Chemistry, 2013, 61(40):9517-9533.

http://doi.org/10.1021/jf402506c

http://doi.org/10.1021/jf402506c

127

CHAPTER5

Reshaping faecal gutmicrobiota composition by the intake of trans‐

resveratrolandquercetininhigh‐fatsucrosediet‐fedrats

EtxeberriaU.1,2,AriasN.3,5,BoquéN.4,MacarullaM.T.3,5,PortilloM.P.3,5,MartínezJ.A.1,2,5,*,MilagroF.I.1,2,5

1DepartmentofNutrition,FoodScienceandPhysiology,UniversityofNavarra.C/Irunlarreas/n,31008Pamplona,Spain

2CentreforNutritionResearch,UniversityofNavarra.IrunlarreaSt.E‐31008Pamplona,Spain



4NutritionandHealthResearchGroup.TechnologicalCenterofNutritionandHealth(CTNS),TECNIO,CEICS.Avinguda

Universitat1,43204Reus,Spain

5PhysiopathologyofObesityandNutrition,CIBERobn,CarlosIIIHealthResearchInstitute,28029Madrid,Spain

JNutrBiochem,2015

doi:10.1016/j.jnutbio.2015.01.002


14/77inNutrition&Dietetics(Q1)

Reshaping faecal gut microbiota composition by the intake of trans-resveratrol andquercetin in high-fat sucrose diet-fed rats

U. Etxeberriaa,b, N. Ariasc,e, N. Boquéd, M.T. Macarullac,e, M.P. Portilloc,e, J.A. Martíneza,b,e,⁎, F.I. Milagroa,b,e

aDepartment of Nutrition, Food Science and Physiology, University of Navarra, C/Irunlarrea s/n, 31008 Pamplona, SpainbCentre for Nutrition Research, University of Navarra, Irunlarrea St. E-31008 Pamplona, Spain

cNutrition and Obesity group, Department of Nutrition and Food Sciences, Faculty of Pharmacy, University of the Basque Country (UPV/EHU), Paseo de la Universidad 7, 01006 Vitoria, SpaindNutrition and Health Research Group, Technological Center of Nutrition and Health (CTNS), TECNIO, CEIC S. Avinguda Universitat 1, 43204 Reus, Spain

ePhysiopathology of Obesity and Nutrition, CIBERobn, Carlos III Health Research Institute, 28029 Madrid, Spain

Received 24 June 2014; received in revised form 25 November 2014; accepted 7 January 2015

Abstract

Diet-induced obesity is associated to an imbalance in the normal gut microbiota composition. Resveratrol and quercetin, widely known for their health beneficialproperties, have low bioavailability, andwhen they reach the colon, they are targets of the gutmicrobial ecosystem. Hence, the use of thesemolecules in obesitymightbe considered as a potential strategy tomodulate intestinal bacterial composition. The purpose of this studywas to determinewhether trans-resveratrol and quercetinadministration could counteract gut microbiota dysbiosis produced by high-fat sucrose diet (HFS) and, in turn, improve gut health. Wistar rats were randomised intofour groups fed an HFS diet supplemented or not with trans-resveratrol [15 mg/kg body weight (BW)/day], quercetin (30 mg/kg BW/day) or a combination of bothpolyphenols at those doses. Administration of both polyphenols together prevented body weight gain and reduced serum insulin levels. Moreover, individualsupplementation of trans-resveratrol and quercetin effectively reduced serum insulin levels and insulin resistance. Quercetin supplementation generated agreat impact on gut microbiota composition at different taxonomic levels, attenuating Firmicutes/Bacteroidetes ratio and inhibiting the growth of bacterialspecies previously associated to diet-induced obesity (Erysipelotrichaceae, Bacillus, Eubacterium cylindroides). Overall, the administration of quercetin wasfound to be effective in lessening HFS-diet-induced gut microbiota dysbiosis. In contrast, trans-resveratrol supplementation alone or in combination withquercetin scarcely modified the profile of gut bacteria but acted at the intestinal level, altering the mRNA expression of tight-junction proteins andinflammation-associated genes.© 2015 Elsevier Inc. All rights reserved.

Keywords: Flavonol; Stilbene; Gut bacteria; Inflammation; Obesity; Intestinal permeability

1. Introduction

Obesity is regarded as a major public health issue with anincreasing worldwide prevalence [1]. Strong evidence supports adirect link between intestinal dysbiosis, namely, alterations of the gutmicrobial composition, and metabolic diseases encompassing obesity[2,3], diabetes [4], liver disease [5,6], cardiometabolic complications[7] and even cancer [8]. Thesefindings have driven research interest to

converge on making clear the intimate relationships among the gutmicrobiota, diet, host metabolism and the immune system [9]. In thiscontext, the current use of genomic-based molecular techniques suchas transcriptomics, metabolomics and metagenomics, together withthe involvement of in vivo host models (i.e., germ-free animals), hasallowed to boost the studies aiming to identify themost representativeand significant gut bacterial groups or species in different diseaseconditions [10–12]. This specific characterisation is helping toelucidate the role of gut microorganisms in the development ofmetabolic diseases [13]. Obesity has been characterised by a specific“bacterial trademark” represented by enlarged Firmicutes and reducedBacteroidetes phyla [14], even if the association with the latter stillremains a matter of debate [15,16]. Furthermore, dysbiosis producedby high-caloric diets, i.e., high-fat diets [17,18], has been alsodemonstrated to be susceptible to modulation by a variety ofcomponents such as probiotics and prebiotics [19]. Apparently,nondigestible polysaccharides could come to the aid of proliferating“friendly bacteria” and enhance microbial diversity in the gut [20],which might contribute to the amelioration of obesity and obesity-associated metabolic disorders [21].

Available online at www.sciencedirect.com

ScienceDirect

Journal of Nutritional Biochemistry 26 (2015) 651–660

Abbreviations:HFS, high-fat sucrose; TJPs, tight-junction proteins; HOMA-IR, homeostasis model assessment of insulin resistance; LPS, lipopolysaccha-ride; LBP, LPS-binding protein; IL-18, interleukin- 18; NFκβ, nuclear factorkappa β; TNF-α, tumour necrosis factor α; TLR-2, toll-like receptor 2; TLR-4,toll-like receptor 4; TJP-1, tight-junction protein 1; TJP-2, tight-junctionprotein 2; Ocln, occludin; SCFA, short-chain fatty acid; GC/MS, gas-chromatography-mass spectrometry; SEM, standard error of the mean.⁎ Corresponding author. Department of Nutrition, Food Science and

Physiology, University of Navarra, C/Irunlarrea s/n, 31 008 Pamplona, Spain.Tel.: +34 948425600 (80 6424).

E-mail address: [email protected] (J.A. Martínez).

http://dx.doi.org/10.1016/j.jnutbio.2015.01.0020955-2863/© 2015 Elsevier Inc. All rights reserved.

Natural bioactive compounds are widely recognised to havepotential biological properties [22,23], such as antiadiposity [24] andantidiabetic effects [25,26]. Within the existing plant secondarymetabolites, trans-resveratrol (stilbene) and quercetin (flavonoid)have been described to exert anti-inflammatory and antiobesityeffects [27–29] andmight be potential candidates for the ameliorationof metabolic impairments (i.e., insulin resistance) associated tocomorbidities of overweight or obesity [30,31]. Nevertheless, thehealth outcomes are intimately dependent on the dose and the formthey are provided and also on their bioavailabilitywithin the organism[32]. Overall, it is estimated that around 5%–10% of the total dietarypolyphenols are absorbed in the small intestine, while 90%–95% of thetotal polyphenol intake reaches the colonic region unabsorbed, beingthe subject of enzymatic activities of gut microbial ecosystemproducing diverse metabolites with a variety of physiological roles[33]. Although themetabolism of these bioactive compounds has beenwell reported [34–36], few studies have described its bioconversion bygut bacteria [37]. Nevertheless, in view of the direct contact and thedual interaction existing between these natural bioactive compoundsand the colonic gut microbial ecosystem [38], possible compositionmodifications in intestinal bacteriamight be expected as a result of theconsumption of these compounds, which might ultimately contributeto functional modifications in the host.

Therefore, the present study was conducted to assess the potentialof trans-resveratrol, quercetin and a mixture of trans-resveratrol andquercetin supplementation to reverse HFS-diet-induced gut microbi-ota dysbiosis in rats. Furthermore, this research work sought toanalyse whether intestinal barrier integrity and inflammation mightbe affected as a consequence of polyphenol intake.

2. Material and methods

2.1. Animals, diets and serum biochemical parameters

The experiment was performed with a substudy of 23 Wistar rats obtained fromHarlan Ibérica (Barcelona, Spain). Animals were single housed in polypropylene cagesand kept in an isolated room with a constantly regulated temperature (22°C±2°C)under a 12-h:12-h artificial light/dark cycle. Concisely, the artificial light/dark cycle inthis experiment was inverted, so that light was switched off at 9:00 a.m. and switchedon at 9:00 p.m. so that animals were expected to start eating as soon as the dark cyclebegan. Ratswere fed a standard-chowdiet (C; 2.9 kcal/g) fromHarlan Iberica (ref. 2014,Barcelona, Spain) during an adaptation period that lasted 6 days. Afterwards, animalswere randomly distributed into four groups and changed to an HFS commercialobesogenic diet (ref. D12451M, OpenSource) (HFS; 4.7 kcal/g) for 6weeks. The HFS dietcontained 20% of energy as proteins, 35% of energy as carbohydrates (17% sucrose, 10%maltodextrin and 7% of corn starch) and 45% of energy as fat (31.4% as saturated fats,35.5% as monounsaturated fats, 33.1% as polyunsaturated fats). All animals had freeaccess to food and water. The experimental groups were distributed as follows: thecontrol group (HFS; n=5), rats were fed the HFS diet; trans-resveratrol group (RSV; n=6), rats were supplemented with trans-resveratrol 15 mg/kg body weight (BW)/day;quercetin group (Q; n=6), rats were supplemented with quercetin 30 mg/kg BW/day;and trans-resveratrol+quercetin group (RSV+Q; n= 6), rats were treated with amixture of trans-resveratrol 15 mg/kg BW/day and quercetin 30 mg/kg BW/day.Polyphenols were daily incorporated to the powdered diet in quantities that ensuredthat each animal consumed the prescribed levels. Briefly, in a previous study, it wasreported that resveratrol was degraded over the feeding period when it was mixed inthe diet. Moreover, it was observed that rats started eating immediately upon daily dietreplacement at the beginning of the dark period [39]. Thus, based on these assumptionsand taking into account that resveratrol and quercetin have limited solubility in water,the compounds were added to the surface of the diet dissolved in ethanol. Variations inthe amount of ethanol received by each animal were avoided as ethanol was added tothe diet to reach 1 ml/kg BW/day. Although the volume of ethanol provided wasnegligible, the control group was also given the same amount without naturalcompounds. Bodyweight and food intakewere daily recorded. Thedoses of polyphenolswere chosen based on previous studies [39–41]. Trans-resveratrol (N98% purity) wassupplied byMonteloeder (Elche, Spain); and quercetin (≥98% purity), by Sigma-Aldrich(St. Louis, MO, USA). All the experiments were performed in agreement with the EthicalCommittee of the University of the Basque Country (document reference CUEID CEBA/30/2010), following the European regulations (European Convention, Strasburg 1986,Directive 2003/65/EC and Recommendation 2007/526/EC). Fasting serum glucose wasmeasured using a glucose oxidase/peroxidase kit (ref. 110504; BioSystems, Barcelona,Spain), and insulin levels were determined by using a specific enzyme-linkedimmunosorbent assay (ELISA) kit according to the protocol described by the

manufacturer (ref. 10-1250-01, Mercodia AB, Spain). Insulin resistance was assessedby the homeostasis model assessment of insulin resistance (HOMA-IR) formula [42]:[serum glucose levels (mmol/L)×insulin levels (mU/L)]/22.5. Inflammatory-relatedmarkers were determined in serumwith commercial ELISA kits. Tumour necrosis factor(TNF)-α levels were determined with the TNF-alpha Platinum ELISA Kit (EBioScience,Vienna, Austria); lipopolysaccharide (LPS) levels, with the Limulus Amebocyte LysateQCL-1000 chromogenic assay (Lonza, Walkersville, MD, USA); and LPS-binding protein(LBP) levels, with the Mouse LBP ELISA Kit (Cell Sciences, Canton, MA, USA).

2.2. Faeces collection

Fresh faecal sampleswere collected at the end of the intervention period early in themorning, prior to the overnight fasting. Each animalwas taken one by one and located ina clean, single cage with the aim of obtaining faeces directly after defecation. A softabdominal massage was also exerted to the animals in order to facilitate bowelmovement and, in turn, fresh faeces collection. Samples were gathered in 15-ml Falcontubes and immediately frozen at −80°C for future analyses.

2.3. Tissue collection

At the end of the experimental period (6 weeks), animals were fasted overnight, onone hand, to ensure that all the animals were sacrificed in similar “feeding” conditionswithout the bias of the dietary treatment and, on the other hand, to guarantee thesuitability of biochemical values. Sacrifice was conducted under anaesthesia (chloralhydrate) by cardiac exsanguination between 9:00 a.m. to 12:00 p.m. Liver was collected,weighted and then stored frozen at −80°C. In all animals, colonic mucosa was excisedcarefully by scratching and frozen immediately in liquid nitrogen for future RNA isolation.

2.4. DNA extraction, bar-coded polymerase chain reaction (PCR) and bacterial 16SrDNA pyrosequencing

Faecal DNA was extracted using QIAamp DNA Stool Mini Kit (Qiagen, Hilden,Germany), following supplier’s instructions. DNA was measured by NanoDrop ND-1000spectrophotometer (Thermo Scientific, Wilmington, DE, USA). Samples for 454 pyrose-quencing were amplified for the 16S rDNA hypervariable regions (V4 to V6) using 16S-0515F (5'-TGYCAGCMGCCGCGGTA-3') and 16S-1061R (5'-TCACGRCACGAGCTGACG-3')universal primers. With the Roche 454 FLX chemistry, read lengths up to 550 nt areachieved on PCR products which are in the range of 600–800 nt when using the off-instrument in amplicon mode. This mode favourises the sequence quality of sequencingwells producinghighsignals. Since the 16Smolecules are veryhomogeneous andbalancedin nucleotide composition, this mode produces better results than the shotgun mode,which is more tolerant to low signal beads, typically carrying GC- or extreme AT-richcontents. With the V4–V6 amplicon being 560 nt in length for most bacteria, the fullamplicon sequence can be recovered using a library construction protocol involving bluntligation of sequencing adapters, the so-called Y-adapters, carrying DNA barcodes referredto as "multiplex identifiers" by the manufacturer. Sequencing on the 454 instrument issingle-ended by nature, yet through the blunt ligation approach, approximately as manymolecules are sequenced from the PCR 5' end as there are molecules sequenced from thePCR 3' end.

Given the unit read length, the overlap between forward and reverse sequences is inthe order of 90% and spanning the three variable regions. Thus, a sequence clusteringapproach using a de novo EST assembly process becomes possible without running therisk of generating chimeric sequence consensus. The Mira assembler [43] in EST modecan handle the kind of copy number variation corresponding with relative abundancedifferences observed among bacterial species in a metagenome sample. At the sametime, this assembler can be made tolerant to single base changes often occurringbetween individual copies of the 16S gene on a bacterial chromosome, which can be inthe order of zero to two nucleotide changes over 100 nucleotides. The Mira assemblerhas a 454 specific error correction model and thus is capable of making the distinctionbetween instrument basecall errors and intrinsic base-pair changes. Furthermore,combining forward and reverse sequences into a single consensus sequence not onlyachieves full-length coverage but also increases sequence reliability since the reversesequences have high quality where the forward sequences exhibit quality drop-off andvice versa. Sampleswith high species diversitywill generate a lower proportion of readsclustering than samples with more limited diversity.

2.4.1. Sequence postprocessingThe off-instrument 454 downstream processing software was used in amplicon

mode to optimise read length as outlined above and to demultiplex the libraries andtrim off 3' end adapter sequences, if any. The sequencing of the 23 amplicon librariesproduced 16S rDNA reads ranging from 9780 to 71690, generating read lengths from479 to 520 nt, with a modal of 510 nt.

The Mira assembler was used at 98% similarity threshold, meaning sequencescarrying up to 2 high-quality differences out of 100 in any interval were allowed toassemble alongside with similar sequences. Singletons were carried forward intoidentification provided their sequence length equalled at least 400 nt. The samecriterion was applied to contig consensus sequences produced. The proportion of readsproducing clusters ranged from 36.1% to 58.5% on a per-sample basis.

652 U. Etxeberria et al. / Journal of Nutritional Biochemistry 26 (2015) 651–660

The singleton and consensus sequences were blasted using NCBI blastall version2.2.22 in blastNmode, using an expectation value cutoff of 10^-5 and requesting the 25best hits produced against the RDP database release 10 update 29, which was curatedfor redundant entries representing identical sequence and entries with taxonomicclassification of less than four levels down, counting from the "bacteria" taxon. Thesehits were duplicated according to the per contig sequence count and kept as is forsingletons before being provided to the MEGAN classifier version 4 [44]. MEGANincorporates alpha rarefaction and assigns operational taxonomic units by comparingtaxonomic information associated with equivalent best hits for each input sequence.Construction of libraries and sequencing were all provided as a custom service ofBeckman Coulter Genomics.

2.5. Gene expression in colonic mucosa

Colonicmucosa RNAwas extracted using All-PrepDNA/RNAMini kit as described inthe manufacturer’s instructions (Qiagen, Germantown, MD, USA). RNA concentrationand quality were measured with a Nanodrop ND-1000 spectrophotometer (ThermoScientific). Subsequently RNA (2 μg) was reverse-transcribed to cDNA using MMLVMoloney murine leukemia virus reverse transcriptase (Invitrogen) as described bythe suppliers. Reverse transcriptase PCR assays were carried out following themanufacturer’s instructions using an ABI PRISM 7000 HT Sequence Detection System,Taqman Universal Master Mix and predesigned TaqMan Assays-on-Demand (AppliedBiosystems, Austin, TX, USA). Interleukin 18 (IL-18), Rn01422083_m1; nuclear factorkappa β (NFκβ), Rn00595794_m1; TNF-α, Rn01525859_g1; Toll-like receptor 2 (TLR2),Rn02133647_s1; Toll-like receptor 4 (TLR4), Rn00569848_m1; lipopolysaccharidebinding protein (LBP), Rn00567985_m1; myeloid differentiation primary response 88,Rn01640049_m1; killer cell lectin-like receptor subfamily K, member 1,Rn00591670_m1; tight-junction protein 1 (TJP-1), Rn 02116071_s1; tight-junctionprotein 2 (TJP-2), Rn01501483_m1; occludin (Ocln), Rn 00580064_m1. mRNA levelswere normalised with glyceraldehyde-3-phosphate dehydrogenase, Rn 99999916_s1,as a housekeeping gene (Applied Biosystems). All samples were analysed in triplicate.The relative expression level of each gene was calculated by the 2−ΔΔCt method.

2.6. Short-chain fatty acid (SCFA) analysis

Short-chain fatty acidswereanalysed in faeces. Briefly, 500 μl of acidwater, containinginternal standard (propionic acid-d6, 98 atom % D), was added to 25 mg of eachlyophilised sample. Overnight lyophilisation was carried out protecting samples fromlight. Mixture was shaken for 1 min. Afterwards, extractionwas carried out with 500 μl ofdiethyl ether followed by 1 min of shaking and centrifugation at 4°C at 14,000 rpm for10min. Supernatantswere analysedbygas chromatography–mass spectrometry (GC/MS)in a 7890A Series gas chromatograph coupled to a 7000 GC/MS Triple Quad (AgilentTechnologies, Santa Clara, CA, USA). The chromatographic column was a J&W ScientificHP-FFAP (30 m×0.25 mm i.d., 0.25-μm film) (Agilent Technologies). A volume of 1 μl ofsamplewas automatically injected into a split/splitless inlet (in splitlessmode),whichwaskept at 175°C. Helium (99.999% purity)was used as a carrier gas at a flow rate of 1ml/minin constant flow mode. The oven program was set at an initial temperature of 90°C,increased to 150°C at a rate of 12°C/min, then increased to 240°C at a rate of 20°C/min andheld at 240°C for 5min. Ionisationwas donebypositive chemical ionisationwithmethanegas. SIM mode and GC-QqQ MassHunter software (Agilent Technologies) were used fordata acquisition.

2.7. Statistical analysis

Results are expressed as the mean±standard error of the mean (S.E.M.). Normalitywas tested by Shapiro–Wilk test. For parametric variables, one-way analysis of variance(ANOVA) test followed by Dunnett post hoc test was carried out. For nonparametricvariables, statistical significance of differences among the groups was tested by

nonparametric Kruskal–Wallis test followed by Mann–Whitney U test when Pb.05.Bonferroni correction test was applied as a correction for multiple comparisons. SPSS15.0 software (Chicago, IL, USA) was used to perform statistical analysis.

3. Results

3.1. Animal characteristics

Body-weight-related measurements and serum biochemical vari-ables in the four experimental groups are reported in Table 1. Animalssupplemented with a combination of trans-resveratrol and quercetinpresented a trend towards prevention of body weight gain induced byan HFS diet intake and showed a slight reduction in liver proportions(% body weight) (Table 1). Moreover, supplementation with trans-resveratrol and quercetin alone or in combination significantly reducedserum insulin levels. HOMA-IR was also decreased in the groupssupplementedwith trans-resveratrol or quercetin (Table 1). In addition,serum inflammation- and endotoxin-related markers (LBP, LPS andTNF-α levels)weredetermined. Although slightly higher amountswereobserved for LPS and TNF-α in trans-resveratrol supplemented rats,these results were not statistically significant (data not shown).

3.2. Gut microbiota analysis

At the phylum level, quercetin supplementation was found toconsiderably modify relative percentages of detected phyla whencompared to the HFS-diet-fed control rats (Table 2). Quercetinsupplementation was able to marginally (P=.053) reduce Firmicutes(−34.2%). This value reflects the percentage of change measuredtaking into account the mean relative abundance of Firmicutes

Table 1Body-weight-related measurements and serum biochemical parameters of animals fedwith an HFS diet supplemented or not with trans-resveratrol and quercetin for 6weeks.

HFS(n=5)

RSV(n=6)

Q(n=6)

RSV+Q(n=6)

ANOVA

Body weight measurementsInitial body weight (g) 194±2 193±1 191±2 201±5 NSBody weight gain (g) 176±7 169±6 162±7 144±11 P=.070Food intake (g/day) 17.0±0.2 16.6±0.4 17.0±0.8 15.8±0.4 NSFood efficiency (g/kcal) 5.3±0.2 5.2±0.1 5.1±0.1 4.7±0.2 P=.076Liver weight (% BW) 2.7±0.1 2.7±0.0 2.6±0.1 2.4±0.1 P=.056

Serum biochemical variablesGlucose (mg/dl) 119±4 105±4 107±3 110±6 NSInsulin (mU/L) 51.5±8.7a 24.3±4.8b 24.6±7.0b 27.4±2.6b Pb.05HOMA-IR 15.2±2.6a 6.0±1.6b 6.5±1.9b 8.3±0.8ab Pb.05

All results are expressed as the mean±S.E.M. Statistical analyses were performed usingone-way ANOVA followed by Dunnett post hoc test. Data with different superscriptletters are significantly different, Pb.05.

Table 2Relative abundance (% of total 16S rDNA) of detected bacterial phyla in faeces of HFS-diet-fed rats supplemented or not with trans-resveratrol and quercetin for 6 weeks.

Phyla HFS (n=5) RSV (n=6) Q (n=6) RSV+Q (n=6) P value

Firmicutes 85.0±4.5 87.6±2.7 56.0±8.1 72.2±7.8 .053Bacteroidetes 10.8±3.8 9.3±2.3 27.3±9.3 14.3±3.8 NSProteobacteria 2.7±0.5 2.7±0.5 1.9±0.5 2.8±0.7 NSVerrucomicrobia 1.0±0.7 0.3±0.1 14.6±7.7 7.5±4.5 NSTenericutes 0.4±0.2 0.4±0.1 0.2±0.1 2.8±2.1 NSActinobacteria 0.1±0.0 0.2±0.0 0.3±0.1 0.2±0.0 NSCyanobacteria 0.5±0.0 0.5±0.0 0.4±0.1 0.4±0.1 NSDeferribacteres 0.5±0.0 0.4±0.1 0.4±0.1 0.5±0.0 NSUnclassified bacteria 0.3±0.1 0.4±0.1 0.3±0.1 0.2±0.1 NS

All results are expressed as the mean±S.E.M. Statistical analyses were performed usingKruskal–Wallis followed by Mann–Whitney U test, and P values were corrected withthe Bonferroni test. tb0.1 after correction with the Bonferroni multiple comparison testwas set out as marginal trend. NS, nonsignificant values after Kruskal–Walliscomparison test.

Fig. 1. Firmicutes/Bacteroidetes ratio of HFS-diet-fed rats supplemented or not withtrans-resveratrol and quercetin for 6 weeks. Results are expressed as mean±S.E.M. ofthe relative abundance (% of total 16 S rDNA) of each phylum.

653U. Etxeberria et al. / Journal of Nutritional Biochemistry 26 (2015) 651–660

phlylum in the quercetin-supplemented group and the mean relativeabundance of this bacterial group in the HFS-diet-fed group, which isconsidered the reference group in the present study. Thus, these datasuggest that, after quercetin supplementation, this bacterial phylum,usually up-regulated in rats fed an HF diet [45], was reduced in by34.2% whenmeasured against the levels found in the reference group,although this diminution was not statistically significant aftercorrection with the Bonferroni multiple comparison test. Moreover,a considerable decrease (−80.5%) was detected in Firmicutes/Bacteroidetes ratio of animals supplemented with quercetin (Fig. 1).In contrast, bacterial composition of faeces from the groups supple-mented with trans-resveratrol or the combination of trans-resveratroland quercetin was not significantly affected at the phylum level.

At the class level, Erysipelotrichi (classified within the Firmicutesphylum) was the most affected bacterial group after quercetinsupplementation, as the mean relative abundance of this bacterialgroup was significantly (P=.018) reduced (−83.9%) compared to theHFS-diet-fed control rats (Appendix Table A.1).

At the family level, groups supplemented with quercetin and thecombination of trans-resveratrol and quercetin presented a bacterialprofile which substantially differed from the relative abundance ofbacterial families detected in the faeces of theHFS-diet-fed control andtrans-resveratrol-supplemented rats, with this variation being espe-cially notable for the Erysipelotrichaceae family (Fig. 2). Nevertheless,trans-resveratrol supplementation produced a statistically significantinhibition in the Graciibacteraceae family (−57.7%) compared to theHFS-diet-fed control rats. Relative abundance (% of total 16S rDNA) ofthe most representative families is also reported (Table 3).

Regarding the genus level, quercetin supplementation for 6 weekssignificantly reduced the levels of Bacillus genus (−74.3%) whencompared to the HFS-diet-fed rats (Appendix Table B.1). In contrast,trans-resveratrol supplementation alone significantly lessened theabundance of Parabacteroides genus (−76.3%) compared to the meanrelative abundance found in the reference group (Appendix Table B.2).The combination of both polyphenols did not produce any changes atthis taxonomic level.

RSV+Q

HFS

Q

RSV

Fig. 2. Relative bacterial composition at the family level (% of total 16S rDNA) in faeces of HFS-diet-fed rats supplemented or notwith trans-resveratrol and quercetin for 6weeks. All dataare displayed on a relative scale based on the 16S rDNA frequency taxa.

Table 3Relative abundance (% of total 16S rDNA) of the most representative families in faeces ofHFS-diet-fed rats supplemented or not with trans-resveratrol and quercetin for 6 weeks.

Family HFS (n=5) RSV (n=6) Q (n=6) RSV+Q (n=6) P value

Erysipelotrichaceae 42.7±6.5a 51.9±5.0a 6.9±2.2b 23.2±4.3a .018Ruminococcaceae 17.5±2.4 14.7±2.0 18.0±2.5 23.4±5.1 NSClostridiaceae 14.2±1.7 10.7±1.3 16.1±2.3 12.8±1.5 NSBacteroidaceae 8.0±2.6 7.2±1.6 20.5±7.0 11.2±2.9 NSLachnospiraceae 3.7±0.6 4.5±0.9 3.8±1.1 5.3±1.3 NSAcidaminococcaceae 2.1±1.1 1.0±0.1 2.5±0.7 1.7±0.6 NSEubacteriaceae 1.9±0.1 1.8±0.2 2.5±0.6 2.1±0.3 NSPrevotellaceae 0.6±0.1 0.3±0.1 2.2±1.1 0.7±0.3 NSAcholeplasmataceae 0.4±0.2 0.4±0.1 0.2±0.1 2.7±2.1 NSLactobacillaceae 0.2±0.1 0.5±0.3 1.9±1.7 0.8±0.5 NSGraciibacteraceae 0.2±0.0a 0.1±0.0b 0.2±0.1a 0.4±0.1a .033

Results are expressed as the mean±S.E.M. of the relative abundance (% of total 16SrDNA) of different families detected in faecal samples by 16S rDNA pyrosequencing.Statistical analyses were performed using Kruskal–Wallis followed by Mann–WhitneyU test, and P values were corrected by Bonferroni test. Pb.05was considered statisticallysignificant after correction with the Bonferroni multiple comparison test. Data withdifferent superscript letters are significantly different.


At the species level, the group supplemented with quercetinshowed a statistically significant inhibition in four bacterial speciesafter multiple adjustments with Bonferroni test (Fig. 3). In this regard,the statistically significant down-regulation detected in the meanrelative abundance of Eubacterium cylindroides (−80.4%) compared tothe relative percentage found in the control group is noteworthy(Fig. 4). Moreover, supplementation with trans-resveratrol signifi-cantly reduced the mean relative abundance of different Clostridiaspecies such as Clostridium aldenense (−93.1%), Clostridium hathewayi(−73.2%), Clostridium sp. C9 (−76.3%) and Clostridium sp. MLG661(−53.7%) in opposition to themean relative abundance of Clostridiumsp. XB90 (266.6%) that was notably enhanced when compared to theHFS-diet-fed control rats (Fig. 3). Furthermore, the percentage ofchange in themean relative abundance of Gracilibacter thermotolerans(−57.7%) and Parabacteroides distasonis (−77.4%) was negativelyaffected by trans-resveratrol in comparison to that detected in theHFS-diet-fed control group (Fig. 3). As far as the effect of the combined

consumption of both polyphenols is concerned, the relative abun-dance of two Clostridia species (Clostridium clariflavum, 327.0% andClostridium methylpentosum, 245.6%), as well as the relative abun-dance of a member of Lachnospiraceae family (Blautia stercoris,321.7%), was significantly enhanced compared to the percentagedetermined in the reference group (Fig. 3). Moreover, althoughnonstatistically significant, the relative percentage of diverse bacterialspecies was also found to be differentially affected by the intake ofpolyphenols (Appendix Tables C.1, C.2 and C.3).

In addition, Akkermansia muciniphila ATCC BAA-835, the uniquebacteria belonging to Verrucomicrobia phylum, showed a notableincrease (1384.0%) after the 6-week period of quercetin intake whencompared to the relative abundance detected in diet-inducedoverweight control rats (Fig. 5). However, these differences werenot statistically significant. Furthermore, the relative abundance (% oftotal 16S rDNA) of bacterial species represented in Fig. 6, especiallybelonging to Bacteroidia class, such as Bacteroides sp. S-18 (105.2%),Bacteroides sp. dnLKV (87.8%), Barnesiella intestinihominis (127.6%),

Fig. 3. Relative abundance (% of total 16S rDNA) of bacterial species significantly different from the HFS-diet-fed control rats. Results are expressed as the mean±S.E.M. Statisticalanalyses were performed using Kruskal–Wallis followed by Mann–Whitney U test, and P values were corrected with the Bonferroni test. *Pb.05 after correction with the Bonferronimultiple comparison test. NS, nonsignificant values after Kruskal–Wallis comparison test.*Pb.05, HFS vs. RSV; $Pb.05 HFS vs. Q; #Pb.05, HFS vs. RSV+Q.

Fig. 4. Relative abundance (% of total 16S rDNA) of Eubacterium cylindroides in faeces ofHFS-diet-fed rats supplemented or not with trans-resveratrol and quercetin for6weeks. Results are expressed as themean±S.E.M. Statistical analyses were performedusing Kruskal–Wallis followed by Mann–Whitney U test, and P values were correctedwith the Bonferroni test. $Pb.05, HFS vs. Q after correction with the Bonferroni multiplecomparison test.

Fig. 5. Relative abundance (% of total 16S rDNA) of Akkermansia muciniphila ATCC BAA-835 in faeces of HFS-diet-fed control rats supplemented or not with trans-resveratroland quercetin for 6 weeks. Results are expressed as the mean±S.E.M. Statisticalanalyses were performed using Kruskal–Wallis followed by Mann–Whitney U test, andP values were corrected with the Bonferroni test.


Bacteroides dorei (295.0%), Bacteroides chinchillae (334.8%) andCandidatus Prevotella conceptionensis (200.5%), was boosted byquercetin supplementation, whereas one species belonging toDeltaproteobacteria class (Bilophila wadsworthia, −36.9%) was down-regulated in comparison to theHFS-diet-fed control group. These species,even if the differenceswere not statistically significant, were found to bethe most affected after a 6-week dietary treatment with quercetin.

3.3. Gene expression in colonic mucosa

After 6 weeks of supplementation, the rats treated with quercetinshowed a significantly higher colonic expression of IL-18. In contrast, theintakeof trans-resveratrol produceda considerable increase inmost of themeasured inflammation-related parameters, reaching statistical signifi-

cance with TLR4, LBP and IL-18 gene expression (Fig. 7). No statisticaldifferences were found in colonic inflammation-related gene expressionin the rats supplemented with the combination of both polyphenols.

Regarding variables related to intestinal permeability, those exper-imental groups supplemented with trans-resveratrol alone or with themixture of trans-resveratrol and quercetin showed a statisticallysignificant up-regulation in the expression levels of TJP-2 and Oclngene (Pb.05). In contrast, quercetin supplementation did not seem tomodify the intestinal barrier permeability associated markers (Fig. 8).

3.4. SCFA analysis

SCFA profile detected by GC/MS analysis in faecal contents of thefour experimental groups revealed no statistically significant

Fig. 6. Relative abundance (% of total 16S rDNA) of bacterial species in faeces of HFS-diet-fed control rats supplemented or not with trans-resveratrol and quercetin for 6 weeks. Resultsare expressed as the mean±S.E.M.

Fig. 7. Relative mRNA expression of inflammation-related genes in colonic mucosa. Results are expressed as themean±S.E.M. Statistical analyses were performed using Kruskal–Wallisfollowed by Mann–Whitney U test.*Pb.05, HFS vs. RSV; $Pb.05, HFS vs. Q. MyD88, myeloid differentiation primary response 88; NKGd2, killer cell lectin-like receptor subfamily K,member 1.


differences. Supplementationwith quercetin, although not significant,was found to slightly increase concentrations of acetate (2.00±0.24 mg/g dry weight), propionate (0.51±0.07 mg/g dry weight) andbutyrate (0.58±0.11 mg/g dry weight) when compared to the HFS-diet-fed control rats (1.68±0.39, 0.41±0.11 and 0.38±0.11 mg/g dryweight, respectively). In contrast, dietary supplementation with trans-resveratrol alone and with the combination of trans-resveratrol andquercetin produced no changes in SCFA levels (Appendix Table D.1).

4. Discussion

Therapeutic effects have been attributed to resveratrol in meta-bolic derangements [46], and also to quercetin,mainly due to allegedlyantioxidant and anti-inflammatory properties [47,48]. Mechanismssurrounding their beneficial effects have attracted research interest[49]. Nevertheless, studies investigating the interaction withinbioactive compounds and gut microbes are rather limited [50–52].The goal of the present research was to determine whethersupplementation with trans-resveratrol and/or quercetin was able tomodify gut microbiota composition, differing from microbial profileobserved in HFS-diet-fed control rats. Moreover, we sought to analysewhether these modifications in microbial ecology concur not onlywith bodyweight and biochemical changes but alsowith alterations atthe intestinal epithelial level.

In the current research, dietary treatment with pure polyphenoliccompounds, especially when jointly administered, decreased bodyweight gain. The lack of antiobesity effect of quercetin is in agreementwith data in the literature, which suggest the need of treatmentperiods longer than 9weeks for this polyphenol to achieve an effectivebody-fat-lowering effect [53]. As far as resveratrol is concerned, thedose used in the present experimentwas effective in reducing body fatafter 6 weeks of treatment, but in a model of genetic obesity (Zuckerfa/fa rats) orally administered resveratrol for 6 weeks reduced liverweight, visceral adiposity and plasma cytokine levels [40,54]. In amodel of obesity induced by using the samehigh-fat, high-sucrose dietused in the present study, our group previously showed that a dose of6 mg/kg BW/day of resveratrol did not induce changes in adiposetissue weight [39]. By contrast, a dose of 30 mg/kg BW/day was veryeffective [41]. It seems that, in this obesity model, a dose higher than15mg/kg BW/day is needed to effectively reduce body fat. The dosageused in the current experiment for resveratrol and quercetin waseffective to decrease insulin levels and also to improve insulinsensitivity. These findings support results from previous studiesdescribing resveratrol and quercetin as potential candidates for the

treatment of metabolic diseases such as insulin resistance [30,31]. Toour knowledge, few studies have explored the effects of pureresveratrol and quercetin consumption alone or combined on residentmicrobiota composition in order tofind a possible associationbetweentheir beneficial properties in the host and specific gut microbialmodifications [55]. In contrast, there is strong evidence supporting therole of gut microbiota in several metabolic functions [56]. Further-more, modifications carried out in microbial ecology have beendemonstrated to derive in host metabolic phenotype modulation,influencing host biochemistry and increasing host susceptibility todiseases [57,58]. The obese metabolic phenotype transmissibility ingerm-free mice that were transplanted the gut microbiota from obese(ob/ob) mice has been also proven [3]. Nevertheless, gut microbiotamodifications have been attributed both to genetic obesity [2] and todiet-induced alterations independent of obesity [45].

In general, obese gut microbiota has been repeatedly associatedwith a reduced Bacteroidetes/Firmicutes ratio [2,59]. Our findings agreewith this feature, as HFS-diet-fed control rats exhibited the highestFirmicutes/Bacteroidetes ratio. Interestingly, quercetin administrationto the HFS diet prominently decreased this ratio. Likewise, animalssupplemented with this polyphenol showed a considerable reductionin Firmicutes phylum commonly dominating in obesity and over-weight status [14,60]. The enrichment of Erysipelotrichaceae family indiet-induced obesity has been similarly reported in several studies[61,62]. Indeed, in a metagenomic study carried out by Turnbaughet al.[61] inmice that developed obesity by the intake of high-fat, high-sucrose diet, it was concluded that the microbiome of HFS-diet-fedmicewas rich in genes encoding components of amajor system,whichis essential for the sugar uptake for most intestinal bacteria [61]. Thus,it was suggested that Erysipelotrichaceae contributed to the function ofthese genes, leading to amore efficient energy extraction from the diet[61,63]. In the current investigation, the potential of quercetin todeeply modify general bacterial profile at the family level is under-lined, indicating an important reduction in the relative abundance ofErysipelotrichaceae family, in contrast to what it was found withtrans-resveratrol administration alone or in combination. Likewise, itwas previously demonstrated by our group [17] that Erysipelotrichitaxon was profoundly enlarged (1025.4%) after an HFS diet treatmentfor 6weeks [17]. Interestingly, concerning the enrichment found in theBacteroidetes phylum, quercetin supplementation also deeply in-creased the abundance of Bacteroidaceae and Prevotellaceae families,which were stated to be negatively influenced in high-fat-fed mice[45]. Besides, quercetin supplementation resulted in the reduction ofthe genus Bacillus, another predominant bacterium inWestern-diet-fed

Fig. 8. Relative mRNA expression of TJPs and Ocln in colon. Results are expressed as the mean±S.E.M. Statistical analyses were performed using Kruskal–Wallis followed by Mann–Whitney U test. P values were corrected by Bonferroni test.*Pb.05, HFS vs. RSV; #Pb.05, HFS vs. RSV+Q.


obese mice [64]. Surprisingly, trans-resveratrol solely did not producesignificantmodifications at the highest division levels but was detectedto follow the opposite pattern of the previously mentioned results.Furthermore, when combining both polyphenols together, gut micro-biota modifications emulated quercetin’s individual consequences,although these alterations were found to be weaker. Notwithstanding,at the species level, trans-resveratrol supplementation significantlyinhibited several bacterial species, such as Gracilibacter thermotolerans,Parabacteroides distasonis and species from Clostridia class (Clostridiumaldenense, Clostridium hathewayi, Clostridium sp. MLG661), whereas itsignificantly enhanced Clostridium sp. XB90. Of especial mention,Parabacteroides distasonis has been found to be reduced in obeseindividuals [65], and suppressions in Clostridium aldenense andClostridium hathewayi species, members of Clostridium cluster XIVa,have been reported to be major butyrate producers [66]. On the otherhand, the differential sensitivity to polyphenols has been postulated,indicating a higher sensitivity to phenolic compounds of gram-positivebacteria rather than the gram-negative bacteria, possibly attributable tochanges in their wall composition [67]. The intake of quercetinpositively affected the growth of three species (Bacteroides vulgatus,Clostridium clariflavum and Clostridium sp. MLG661) and stronglyinhibited the abundance of Eubacterium cylindroides. Bacteroidesvulgatus and Clostridium clariflavum were previously found to bediminished in obese subjects [65,68] and HFS-diet-fed overweight rats[17]. Indeed, Eubacterium cylindroides has been detected in faecalmicrobiota of obese humans [14]. It was up-regulated in high-fat-diet-induced obesity [14,61] and reported to be enlarged (4072.1%) in ratsfed anHFS diet for 6weeks [17]. Blautia stercoris, Clostridium clariflavumand Clostridium methylpentosum were also up-regulated by thecombination of polyphenols. In agreement with these results,Clostridium and Eubacterium have been considered as the maingenera involved in the metabolism of a large variety of phenoliccompounds, including isoflavones (daidzein), flavones (naringeninand ixoxanthumol), flavan-3-ols (catechin and epicatechin) andflavonols (quercetin and kaempferol) [69]. According to our data,given the poor absorption of quercetin into the bloodstream from thegut, the mechanisms contributing to the amelioration of insulinresistance might imply a direct gut microbiota modulatory effect. Incontrast, resveratrol might act as an antimicrobial compound thatcounteracts the individual effects of quercetin, suggesting that theimprovements in insulin resistance could be due to indirect mecha-nismswhich do not involve big changes in gutmicrobiota composition.

Given the connection between gut microbiota, metabolism andintestinal epithelium [70], the expression of genes implicated in thepermeability of the gut epitheliumwas analysed. It iswidely known thatintact mucosal barrier is essential to avoid the development ofinflammation [71]. Indeed, impaired intestinal barrier function isregarded as a relevant contributory factor to obesity and associatedmetabolic disturbances [4,72]. In the present study, although beingaware that the fasting period to which animals were subjected beforesacrifice might have an effect on intestinal integrity [73], an up-regulation in the expression of TJPs andOclnwas found tobe inducedbythe administration of trans-resveratrol alone and when combined withquercetin, which was not perceived with the intake of quercetin alone.Furthermore, the expression levels of inflammation-associated geneswere induced by trans-resveratrol supplementation, such as theexpression of TLR2 and, specially, TLR4, LBP and IL-18. TLRs, morespecifically TLR2 and TLR4, have been largely implicated in inflamma-tory states in the gastrointestinal tract [74–76]. TLRs comprise a familyof pattern-recognition receptors that identify conserved molecularproducts of microorganisms, such as lipopolysaccharides and lipotei-choic acid [77]. Thus, TLR2 recognises gram-positive-bacteria-derivedproducts, whereas TLR4 is stimulated by LPS derived from gram-negative bacteria [77]. Their expression is dependent on dysbiosisassociated to alteredhost–bacterial interactions [78]. Indeed, TLRs act as

sensors of microbial infection, initiating inflammatory and immunedefence responses [79]. However, the bacterial ligands are shared by allclasses of bacteria, being also produced by commensal microorganisms[80]. In line with this statement, Rakoff-Nahoum et al. demonstrated anonimmune function of TLRs in the maintenance of epithelialhomeostasis [80]. Based on the recognition of commensal bacteria ashealth- promoting “symbionts”, emerging evidence has confirmed therole of luminal microbes to signal the interfacing epithelial layer, tocontrol the turnover rate of enterocytes and to fortify the epithelialturnover regenerative and barrier functions [81]. Rakoff-Nahoum et al.suggested a beneficial role generated from the recognition of commen-sal bacteria by TLRs [80]. The recognition of commensal bacteria andposterior activation of TLRs was hypothesised to induce generation ofprotective factors (i.e., cytokines) upon epithelial damage that havebeen previously reported in vivo and in vitro to play a beneficial role byinitiating repair responses (i.e., process known as restitution) [82] andhave been defined as markers of protective responses in colonic tissue[80]. In fact, a growing body of evidence indicates the relevance ofcertain mediators (IL-α, IL-1β, IL-18) to preserve mucosal homeostasis,as well as to protect and restore epithelial barrier [83]. In particular, theup-regulation of IL-18 (a family member of IL-1) found in our studymight be related to its dichotomous role in thegut, as IL-18derived fromintestinal epithelial cells has been described to facilitate tissue repairand promote mechanisms to induce homeostasis in the early phase ofinflammation [84]. Experiments conducted by other researchers haveconcluded the essential role of IL-18 tomaintain epithelial integrity andprevent the translocation of bacteria, associating this feature to theactivation of TLRs in inducing the signal to repair [85,86]. Fromour data,a presumable promotion of intestinal epithelial repair by protectivefactors (i.e., IL-18) induced by commensal-bacteria-derived activationof TLR signalling might be hypothesised, particularly after trans-resveratrol administration, as a possible mechanism leading tointestinal epithelial homeostasis. It might be speculated that, althoughnonstatistically significant, inflammation- and endotoxin-relatedmarkers (LBP, TLR4 and TLR2 expression levels; LPS serum levels)showed a trend to a subtle increase after trans-resveratrol supplemen-tation. This outcomemight underlie a slight inflammation and immuneresponse taking place in the intestine [87]. TLRs could be acting upondamage as a defence mechanism to prevent bacteria and bacterialproducts translocation through rearrangement of TJPs to enhanceintestinal epithelial integrity [88–90]. Nevertheless, these resultsdeserve further research in order to confirm the mechanisms throughwhich polyphenols interact with gut microbiota to cause host geneexpression modifications and also to be able to conclude the overallhealth consequences of this interactions in health.

5. Conclusions

From this experiment, it was concluded that the intake of trans-resveratrol and quercetin at the same time acted synergistically toreduce body weight gain. Moreover, administration of both polyphe-nols alone or in combination improved basal insulin levels andHOMA-IR index. On the other hand, one of the major findings was related tothe impact of quercetin supplementation on gut microbiota compo-sition. In fact, treatment with quercetin attenuated the increase inFirmicutes/Bacteroidetes ratio in HFS-diet-fed rats and notably mod-ified gut microbial pattern at the family level, significantly reducingthe abundance of Erysipelotrichaceae and the genus Bacillus, closelyrelated to the consumption of Western diets and body weight gain.Furthermore, supplementation with quercetin produced a significantalteration in specific bacterial species, increasing some that have beeninversely related to obesity (Bacteroides vulgatus, Akkermansiamuciniphila) and reducing the relative abundance of others associatedtodiet-inducedobesity (Eubacteriumcylindroides,Bilophilawadsworthia).In contrast, after trans-resveratrol supplementation, no profound effects


on gut microbiota composition were identified, especially at the highestdivision levels. Indeed, from results combining trans-resveratrol withquercetin, it can be speculated that individual effects attributed toquercetin were lessened. On the other hand, trans-resveratrol supple-mentation altered the mRNA expression levels of TJPs as well as someinflammation-associated markers.

In summary, this study demonstrates the gut microbiota modula-tory role of quercetin when administered to HFS-diet-fed overweightanimals, and left some doubts for future research about themechanisms of trans-resveratrol to exert health benefits due to itsuncertain potential to modulate gut microbial population and itsimpact on host intestinal gene expression. Limitations of this study arethat there is no a group fed with a standard diet to identify themodifications induced by the type of diet to conclude whetherpolyphenols, especially quercetin, reversed diet-induced gut micro-biota dysbiosis. On the other hand, this study could not differentiatewhether the outcomes discovered were derived from the originalcompounds provided or from the metabolites produced in the gut.Another issue to be considered is the fact that SCFA measurementswere conducted ex vivo in faeces, which could not be an estimation ofthe real production of SCFA, as rats are caecum fermenters and a greatamount of these compounds (95%–99%) is absorbed in the largeintestine [91]. In addition, themeasurement of themRNA levels of TJPsis an indirect method to assess the impact of these polyphenols on gutintegrity; gene expression levels are not always a reflection of theirfunctional active proteins. As a result, we should be cautious becausefurther analyses are required to be able to draw conclusions about theprotective functions of these polyphenols on intestinal epithelialbarrier. Furthermore, an important question addressed in this study isthat the positive effects derived from the combination of trans-resveratrol and quercetin in body weight were not explained by gutmicrobiota modifications. Quercetin produced the most profoundeffect on gut microbial ecology, and this outcomewas associated withsignificantmetabolic improvements. In contrast, the administration oftrans-resveratrol also improved some metabolic derangements, butthis effect was notmediated by amodulatory effect on gut microbiota.From the present data, it could be stated that quercetin supplemen-tation robustly reshapes gut microbiota dysbiosis associated to aWesternised dietary intake, which presumably might contribute tobeneficial metabolic effects. Besides, from the interaction betweenpolyphenols and bacteria, changes at the intestinal level might bepostulated in the host (local inflammatory tone and intestinalmucosalbarrier integrity), principally with trans-resveratrol administration.

Supplementary data to this article can be found online at http://dx.doi.org/10.1016/j.jnutbio.2015.01.002.

Acknowledgments

This study was supported by grants from the Ministerio deEconomía y Competitividad (AGL2011-27406-ALI), Instituto deSalud Carlos III (CIBERobn), Government of the Basque Country (IT-572-13) and University of the Basque Country (UPV/EHU) (ELDUNA-NOTEK UFI11/32). The authors wish to acknowledge Línea Especialabout Nutrition, Obesity and Health (University of Navarra LE/97,Spain) for the financial support and the Department of Education,Language Policy and Culture from the Government of the BasqueCountry for the predoctoral grant given to Usune Etxeberria.

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SupplementarydataCHAPTER5

Supplementary data Chapter 5

141

Table A.1. Frequencies and percentage of change (%) of the relative abundance of bacterial groups at class level in HFS diet-fed control rats and rats supplemented with quercetin for 6 weeks.

Ribosomal Database Project classification

Bacterial group HFS

(n=5) Q30

(n=6)

Unadjusted p

value

Adjusted p value

Change (%)

Firmicutes; Erysipelotrichi

Erysipelotrichi 42.69 ± 6.45 6.87 ± 2.20 0.006 0.018 -83.9*


Bacterial group

HFS (n=5)

Q30 (n=6)

Unadjusted p value

Adjusted p value

Change (%)

Firmicutes; Bacilli; Bacillales; Bacillaceae; Bacillus

Bacillus 0.50 ± 0.00 0.13 ± 0.08 0.013 0.038 -74.3*


Bacterial group HFS

(n=5) RSV15 (n=6)

Unadjusted p value

Adjusted p value

Change (%)

Bacteroidetes; Bacteroidia; Bacteroidales; Porphyromonadaceae; Parabacteroides

Parabacteroides 0.26 ± 0.06 0.06 ± 0.01 0.006 0.018 -76.3*

Taxonomic breakdown of intestinal bacterial V4- V6 regions obtained by pyrosequencing indicated as percentage of

the identified reads. All results are expressed as the mean ± SEM. Statistical analyses were performed using Kruskal-

Wallis followed by Mann Whitney U test and p values were corrected with the Bonferroni test. *p< 0.05 after

correcting for Bonferroni multiple comparison test.

Table B.1. Frequencies and percentage of change (%) of the relative abundance of bacterial groups at genus level in HFS diet-fed control rats and rats supplemented with quercetin for 6 weeks



Wallis followed by Mann Whitney U test and p values were corrected with the Bonferroni test. *p< 0.05 after

correcting for Bonferroni multiple comparison test.



Wallis followed by Mann Whitney U test and p values were corrected by Bonferroni test. *p< 0.05 after correcting for

Bonferroni multiple comparison test.

Table B.2. Frequencies and percentage of change (%) of the relative abundance of bacterial groups at genus level in HFS-diet fed control rats and rats supplemented with trans-resveratrol for 6 weeks.


142

Phyla Family Genus Bacterial group HFS

(n=5) Q30

(n=6) Unadjusted p

value Adjusted p value

Change (%)

Firmicutes Oscillospiraceae Oscillibacter Oscillibacter valericigenes Sjm18-20

0.20 ± 0.05 0.43 ± 0.07 0.045 0.128 99.2

Firmicutes Ruminococcaceae Ruminococcus Ruminococcus sp. YE281 0.19 ± 0.05 0.52 ± 0.09 0.018 0.052 177.4

Firmicutes Clostridiaceae Clostridium Clostridium methylpentosum

0.05 ± 0.01 0.18 ± 0.07 0.018 0.052 250.3

Firmicutes Peptococcaceae Dehalobacter Dehalobacter sp. MS 0.25 ± 0.11 0.50 ± 0.00 0.037 0.106 104.5

Firmicutes Clostridiaceae Clostridium Clostridium sp. FG4 0.24 ± 0.11 0.50 ± 0.00 0.037 0.106 106.4

Tenericutes Acholeplasmataceae Acholeplasma Acholeplasma modicum 0.22 ± 0.12 0.50 ± 0.00 0.037 0.106 128.6

Bacteroidetes Bacteroidaceae Bacteroides Bacteroides dorei 0.26 ± 0.08 1.02 ± 0.27 0.018 0.052 295.0

Bacteroidetes Bacteroidaceae Bacteroides Bacteroides sartorii 0.50 ± 0.00 0.19 ± 0.10 0.034 0.100 -62.6

Bacteroidetes Bacteroidaceae Bacteroides Bacteroides sp. dnLKV3 0.50 ± 0.00 0.19 ± 0.10 0.034 0.100 -62.4

Taxonomic breakdown of intestinal bacterial V4- V6 regions obtained by pyrosequencing indicated as percentage of the identified reads. All results are expressed as the mean ± SEM.

Statistical analyses were performed using Kruskal-Wallis followed by Mann Whitney U test and p values were corrected by Bonferroni test.

Table C.1. Frequencies and percentage of change (%) of the relative abundance of bacterial species in HFS diet-fed control rats and rats supplemented with quercetin for 6 weeks.


143


(n=5) RSV15 (n=6)

Unadjusted p value Adjusted p value

Change (%)

Firmicutes Erysipelotrichaceae Eubacterium cylindroides

19.20 ± 4.10 32.45 ± 4.20 0.045 0.128 69.0

Firmicutes Ruminococcaceae Ruminococcus Ruminococcus sp. MLG080-3

0.51 ± 0.18 0.13 ± 0.05 0.028 0.083 -74.6

Firmicutes Ruminococcaceae Ruminococcus Ruminococcus sp. YE281

0.19 ± 0.05 0.43 ± 0.09 0.018 0.052 130.8

Firmicutes Clostridiaceae Clostridium Clostridium polysaccharolyticum

0.41 ± 0.10 0.28 ± 0.10 0.040 0.115 -89.8

Firmicutes Clostridiaceae Clostridium Clostridium sp. FG4 0.24 ± 0.11 0.50 ± 0.00 0.037 0.106 106.0

Tenericutes Acholeplasmataceae Acholeplasma Acholeplasma modicum

0.22 ± 0.12 0.50 ± 0.00 0.037 0.106 128.2

Firmicutes Clostridiaceae Clostridium Clostridium sp. ID5 0.50 ± 0.00 0.20 ± 0.10 0.034 0.100 -60.0

Bacteroidetes Bacteroidaceae Bacteroides Bacteroides vulgatus 0.29 ± 0.09 1.16 ± 0.39 0.018 0.052 296.7



Table C.2. Taxa frequencies and percentage of change (%) of the relative abundance of bacterial species in HFS diet-fed control rats and rats supplemented with trans-resveratrol

for 6 weeks


144


(n=5) RSV15+ Q30

(n=6) Unadjusted p value Adjusted p value Change (%)

Firmicutes Ruminococcaceae Ruminococcus Ruminococcus sp. YE281

0.19 ± 0.05 0.55 ± 0.18 0.045 0.128 195.5

Firmicutes Lachnospiraceae Blautia Blautia sp. canine oral taxon 143

0.23 ± 0.05 0.50 ± 0.08 0.018 0.052 118.1

Firmicutes Clostridiaceae Clostridium Clostridium sp. ID4

0.50 ± 0.00 0.22 ± 0.09 0.034 0.100 -56.9



Table C.3. Taxa frequencies and percentage of change (%) of the relative abundance of bacterial species in HFS diet-fed control rats and rats supplemented with a combination of

trans-resveratrol and quercetin for 6 weeks.


145

SCFA levels of faeces (mg/g) HFS

(n=5) RSV

(n=6) Q

(n=6) RSV+Q (n=6)

Acetate 1.68 ± 0.39 1.60 ± 0.44 2.00 ± 0.24 1.13 ± 0.12 Propionate 0.41 ± 0.11 0.28 ± 0.09 0.51 ± 0.07 0.21 ± 0.04 Butyrate 0.38 ± 0.11 0.28 ± 0.10 0.58 ± 0.11 0.33 ± 0.11

All results are expressed as the mean ± SEM. Statistical analyses were performed using One-

Way ANOVA followed by Dunnett post-hoc test. HFS, high-fat sucrose diet fed control rats; RSV,

supplemented with 15 mg/kg BW/day of trans-resveratrol; Q, supplemented with 30 mg/kg

BW/day of quercetin; RSV + Q, supplemented with a combination of trans-resveratrol and

quercetin at the same doses.

Table D.1. Taxa frequencies and percentage of change (%) of the relative abundance of

bacterial species in HFS diet-fed control rats and rats supplemented with a combination of

trans-resveratrol and quercetin for 6 weeks.

147

CHAPTER6

Metabolic faecal fingerprinting of trans‐resveratrol and quercetin

followingahigh‐fatsucrosedietarymodelusingliquidchromatography

coupledtohigh‐resolutionmassspectrometry

EtxeberriaU.1,2,AriasN.3,4,BoquéN.5,Romo‐HualdeA.2,4,MacarullaM.T.3,4,PortilloM.P.3,4,

MilagroF.I.1,2,4andMartínezJ.A.*,1,2,4

1DepartmentofNutrition,FoodScienceandPhysiology,UniversityofNavarra.C/Irunlarrea1,31008Pamplona,Spain

2CentreforNutritionResearch,UniversityofNavarra.C/Irunlarrea1,31008Pamplona,Spain



4CIBERFisiopatologíadelaObesidadyNutrición(CIBERobn),InstitutodeSaludCarlosIII,Madrid,Spain

eNutritionandHealthResearchGroup.TechnologicalCenterofNutritionandHealth(CTNS),TECNIO,CEICS.Avinguda

Universitat1,43204Reus,Spain

FoodFunct,2015

doi:10.1039/c5fo00473j



Etxeberria U, Arias N, Boqué N, Romo‐Hualde A, Macarulla MT, Portillo MP, Milagro

FI, Martínez JA. Metabolic faecal fingerprinting of trans‐resveratrol and quercetin

following a high‐fat sucrose dietary model using liquid chromatography coupled to high‐

resolution mass spectrometry. Food & Function, 2015, (8):2409-2864.

http://doi.org/10.1039/c5fo00473j

http://doi.org/10.1039/c5fo00473j

159


179

CHAPTER7

Trans‐resveratrol induces a potential anti‐lipogenic effect in

lipopolysaccharide‐stimulatedenterocytes

EtxeberriaU.1,Castilla‐MadrigalR.1,LostaoP.1,3,MartínezJ.A.1,2,3,*,MilagroF.I.1,2

1DepartmentofNutrition,FoodScienceandPhysiology,CentreforNutritionResearch,UniversityofNavarra.

C/Irunlarrea1,31008Pamplona,Spain

2CIBERFisiopatologíadelaObesidadyNutrición(CIBERobn),InstitutodeSaludCarlosIII,Madrid,Spain

3IdiSNA,Navarra’sHealthResearchInstitute.Pamplona,Spain

AcceptedinCellMolBiol

Accepted in Cell Mol Biol

181

Trans-resveratrol induces a potential anti-lipogenic effect in lipopolysaccharide-stimulated

enterocytes

Etxeberria U1, Castilla-Madrigal R1, Lostao MP1,3, Martínez JA1,2,3,*, Milagro FI1,2

1Department of Nutrition, Food Science and Physiology, Centre for Nutrition Research, University of Navarra.

C/Irunlarrea 1, 31008 Pamplona, Spain

2CIBER Fisiopatología de la Obesidad y Nutrición (CIBERobn), Instituto de Salud Carlos III, Madrid, Spain

3IdiSNA, Navarra’s Health Research Institute. Pamplona, Spain

*Corresponding author: Professor J. Alfredo Martínez

Department of Nutrition, Food Science and Physiology, University of Navarra. C/Irunlarrea 1, 31 008

Pamplona, Spain. Tel.: +34 948425600 (Ext. 80 6424); Fax: 00 34 948 425 649

Email address: [email protected]

mailto:[email protected]


182

Abstract

A DNA microarray analysis was conducted in Caco-2 cells to analyse the protective effects of trans-

resveratrol on enterocyte physiology and metabolism in pro-inflammatory conditions. Cells were pre-treated

with 50 μΜ of trans-resveratrol and, subsequently, lipopolysaccharide (LPS) was added for 48 h. The

microarray analysis revealed 121 genes differentially expressed between resveratrol-treated and non-

treated cells (B> 0, is the odd that the gene is differentially expressed). Inhibitor of DNA binding 1 (ID1),

histidine-rich glycoprotein (HRG), NADPH oxidase (NOX1) and sprouty homolog 1 (SPRY), were upregulated

by LPS treatment, but significantly blocked by trans-resveratrol pre-treatment (padj< 0.05, after adjusting for

Benjamini-Hocheberg procedure). Moreover, genes implicated in synthesis of lipids (z-score= -1.195) and

concentration of cholesterol (z-score= -0.109), were markedly downregulated by trans-resveratrol. Other

genes involved in fat turnover, but also in cell death and survival function, such as transcription factors

Krüppel-like factor 5 (KLF5) and amphiregulin (AREG), were also significantly inhibited by trans-resveratrol

pre-treatment. RT-qPCR-data confirmed the microarray results. Special mention deserves acyl-CoA

synthetase long-chain family member 3 (ACSL3) and endothelial lipase (LIPG), which were downregulated by

this stilbene and have been previously associated with fatty acid synthesis and obesity in other tissues. This

study envisages that trans-resveratrol might exert an important anti-lipogenic effect at intestinal level under

pro-inflammatory conditions, which has not been previously described.

Keywords: gut, resveratrol, in vitro, lipid metabolism, inflammation, enterocytes,

Running title: Gene expression modulatory capacity of trans-resveratrol in enterocytes


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Background

Our intestine is the first defence barrier located between the host and the luminal environment (Peterson

and Artis, 2014). The intestinal epithelium controls the passage of nutrients and fluids, but also protects the

organism from the permeation of external antigens into the intestinal mucosa and circulatory system

(Romier et al., 2009). Scientific evidence has demonstrated the implication of intestinal barrier integrity

impairment in the development of abnormal inflammatory response (Azuma et al., 2013). Intestinal

inflammation is a continuous and protective process that aims to maintain gut integrity and normal

functionality (Martin and Wallace, 2006). For this purpose, a crosstalk between different cell types from the

gut is required, which results in the regulation of the secretion of a range of cytokines and growth factors.

However, when a dysregulation of one of these components happens, an inappropriate inflammatory

stimulus could lead to several diseases such as inflammatory bowel disease, including ulcerative colitis and

Crohn’s disease (Van De Walle et al., 2010), celiac disease, food allergies, inflammatory bowel syndrome and

metabolic diseases (Brown et al., 2012). Moreover, although controversial results have been reported about

the occurrence of alterations in gut barrier integrity in obese animals (Kless et al., 2015), emerging data

corroborate the presence of an intestinal inflammatory condition (de La Serre et al., 2010). Importantly, the

presence of a chronic low-grade inflammatory response that leads to metabolic dysfunctions is well

established in obesity (Bondia-Pons et al., 2012) and it has been reported that obesity-related comorbidities,

such as type 2 diabetes and atherosclerosis, are usually accompanied by higher circulating levels of pro-

inflammatory cytokines (Laakso, 2010).

The administration of natural compounds (i.e. polyphenols) to fight against inflammation-related metabolic

diseases is under research (Carpene et al., 2015). In this context, trans-resveratrol

(trans-3, 5, 4’-trihydroxystilbene), is a stilbene that has been extensively studied for its antioxidant, anti-

adipogenic and anti-lipogenic properties (Aguirre et al., 2014). In addition, beneficial properties of

resveratrol on cardiovascular system have been widely studied (Riccioni et al., 2015). Accordingly, within

the mechanisms involved in the anti-atherogenic effects of resveratrol, modulation of lipid metabolism has

been reported (Ramprasath and Jones, 2010). Besides, the anti-inflammatory role of the stilbene seems to be

implicated in the protection against the development of cardiovascular risk factors (Ramprasath and Jones,

2010) and the action of this molecule against acute inflammation at intestinal level has been demonstrated

(Bereswill et al., 2010).

Caco-2 cells are a human intestinal epithelial cell model that has been previously used to investigate the

intestinal absorption and metabolism of trans-resveratrol (Kaldas et al., 2003) and the impact of the stilbene

on intestinal barrier function (Carrasco-Pozo et al., 2013). Lipopolysaccharide (LPS), a Gram-negative

bacterial outer membrane constituent, is known to interact with intestinal epithelial cells and to induce

stimulation of transcription and translation of pro-inflammatory mediators (Lu et al., 2008). In this sense,

high LPS levels are considered to be an important factor in the pathophysiology of intestinal inflammatory

diseases, has been postulated as one of the leading causes of obesity (Boutagy et al., 2015) and has been

associated with increased risk of atherosclerosis in humans (Pussinen et al., 2007; Martinez et al., 2013).


184

Thus, induction of inflammation in enterocytes by LPS administration is considered an appropriate in vitro

model to mimic intestinal inflammation (Wang et al., 2015).

Accordingly, this investigation sought to analyse the molecular functions and pathways that might be

affected by trans-resveratrol in LPS-treated enterocytes, a model that mimics the low-grade inflammatory

condition usually present in metabolic diseases, such as obesity and atherosclerosis (Boutagy et al., 2015).

Material and methods

Cell culture and treatment

Caco-2 cells were maintained in an incubator set at 37°C and 5% carbon dioxide and 90% of relative

humidity. The cells were cultured in Dulbecco’s modified Eagle’s medium with GlutaMax

(DMEM, Gibco, Rockville, MD, USA) containing 10% fetal bovine serum (FBS, Gibco), 1% of non-essential

amino acids (NEAA, Lonza, Basel, Switzerland), 1% penicillin (10000 U/mL)-streptomicyn (10000 μg/mL)

(Gibco) and 1% amphoterizin B (250 μg/mL, Gibco). The culture medium was changed every 2 days. Once

cells reached 80% confluence, confirmed by microscopic observance, they were dissociated with 0.05%

trypsin-EDTA solution and subcultured on a 75 cm2 flasks at a density of 250000 cells per cm2. The cells were

seeded in 6-well cell culture plate at 30,000 cells per cm2. Culture medium was replaced every 2 days until

the day of the experiment. Experiments were conducted 15-19 days post-seeding.

For the experiments, cells were treated with standard LPS from E. coli K12 strain- TLR4 ligand

(InvivoGen, San Diego, CA, USA). Concentrations of LPS (1 μg/mL) and time of exposure (48h) were

established based on previous studies (Cianciulli et al., 2012). Before LPS stimulation, some samples were

pre-treated with 50 μM of trans-resveratrol, dissolved in ethanol, kindly provided by Prof. María Puy Portillo

(Nutrition and Obesity group, University of the Basque Country, Vitoria, Spain). This dose was reported to be

a realistic polyphenol concentration commonly found in the gut following the intake of 500 mg of

polyphenols (Scalbert and Williamson, 2000). Upon 1h of incubation at 37°C, cell cultures were stimulated

with endotoxin as previously mentioned. Untreated cells were used as controls.

RNA isolation and microarray analysis

Total RNA was extracted from Caco-2 cells using TRIzol® reagent according to the manufacturer’s

instructions (Invitrogen, Carlsbad, CA, USA). As a last step of the extraction procedure, the RNA was purified

with the RNeasy Mini-kit (Qiagen, Hilden, Germany). Before cDNA synthesis, RNA integrity from each sample

was confirmed by using Agilent RNA Nano LabChips (Agilent Technologies, Santa Clara, CA, USA).

The sense cDNA was prepared from 300 ng of total RNA using the Ambion® WT Expression Kit

(Thermo Fisher Scientific, Waltham, MA, USA). The sense strand cDNA was then fragmented and biotinylated

with the Affymetrix GeneChip® WT Terminal Labeling Kit (PN 900671). Labeled sense cDNA was hybridized

to the Affymetrix Human Gene 2.0 ST microarray according to the manufacturer protocols and using


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GeneChip® Hybridization, Wash and Stain Kit. Genechips were scanned with the Affymetrix GeneChip®

Scanner 3000.

Microarray analysis

Both background correction and normalization were done using RMA (Robust Multichip Average) algorithm

(Irizarry et al., 2003). After quality assessment, a filtering process was performed to eliminate low

expression probe sets. Applying the criterion of an expression value greater than 16 in 3 samples for each

experimental condition (CONTROL, LPS, LPS+RSV), 38959 probe sets were selected for statistical analysis. R

and Bioconductor (Gentleman et al., 2006) were used for preprocessing and statistical analysis. LIMMA

(Linear Models for Microarray Data) was used to find out the probe sets that showed significant differential

expression between experimental conditions (Smyth, 2004). Adjusted p value was calculated with

Benjamini-Hochberg procedure. Genes were selected as significant using criteria of B> 0. The Log Odds or B

value is the odds or probability that the gene is differentially expressed, meaning that a gene with B=0 has a

50% chance to be differentially expressed.

Functional enrichment analysis of Gene Ontology (GO) categories was carried out using standard

hypergeometric test (Draghici, 2003). The biological knowledge extraction was complemented through the

use of Ingenuity Pathway Analysis (Ingenuity Systems, www.ingenuity.com), whose database includes

manually curated and fully traceable data derived from literature sources.

Microarray data are accessible at the NCBI Gene Expression Omnibus website

(http://www.ncbi.nlm.nih.gov/geo/) with the accession number of GSE73650.

Confirmatory real-time quantitative PCR

Some genes whose expression was affected by trans-resveratrol in the microarray analysis were validated

using quantitative real-time polymerase chain reaction (RT-qPCR). For this purpose, RNA concentrations and

quality were assessed by Nanodrop Spectrophotometer 1000 (Thermo Scientific, Wilminton, DE, USA). RNA

(2 μg) were reverse-transcribed to cDNA using Moloney murine leukemia virus reverse transcriptase

(MMLV, Invitrogen). Taqman® Universal Master Mix and the following pre-designed Taqman® assays-on-

demand were used: KLF5 (Krüppel-like factor 5 (intestinal), Hs00156145_m1; ACSL3 (acyl-CoA synthetase

long-chain family member -3), Hs00244853_m1; LIPG (lipase, endothelial), Hs00195812_m1; AREG

(amphiregulin), Hs00950669_m1; NOX1 (NADPH oxidase 1), Hs01071088_m1; GAPDH (glyceraldehyde-3-

phosphate dehydrogenase), Hs02758991_g1 (Applied Biosystems, Foster City, CA, USA). Amplification and

detection of specific products were conducted using ABI PRISM 7000 HT Sequence Detection System

(Applied Biosystems). All samples were analysed in duplicate. The relative expression of each gene was

calculated by the 2-∆∆Ct method (Livak and Schmittgen, 2001).

Statistical analyses

Results are expressed as the average mean ± standard error of the mean (SEM). Statistical significance

between experimental groups was assessed by Student’s t test when results were validated. A probability of

http://www.ncbi.nlm.nih.gov/geo/


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p< 0.05 was set up for determining statistically significant differences. SPSS 15.0 for Windows (SPSS,

Chicago, IL, USA) was used for statistical analyses.

Results

Differential gene expression profile in cells treated with trans-resveratrol

When comparing the gene expression profile of Caco-2 cells that were treated with LPS and those that were

previously exposed to trans-resveratrol (50 μΜ), a total of 121 genes showed a B> 0 in the microarray

analysis: 63 genes were downregulated and 58 genes upregulated (Supplementary table 1). From these,

there were four that were upregulated by LPS, but were significantly reversed (padj< 0.05) by trans-

resveratrol pre-treatment (Figure 1): the inhibitor DNA binding 1, dominant helix-loop-helix protein

(ID1, log FC= -0.88 and padj< 0.01), histidine-rich glycoprotein (HRG, log FC= -1.24 and padj=0.01), NADPH

oxidase 1 (NOX1, log FC= -1.94 and padj=0.01) and sprouty homolog 1, antagonist of FGF signalling

(SPRY1, log FC= -0.57 and p=0.03).

Biologically relevant networks and pathways

Functional enrichment analysis with Ingenuity Pathway Analysis (IPA) software, detected altered (B> 0)

molecular and cellular functions in the imported data set associated to cell death and survival; infectious

diseases; lipid metabolism, small molecule biochemistry; lymphoid tissue structure and development, tissue

morphology; cellular development, cellular growth and proliferation; DNA replication, recombination and

repair. The genes involved in these pathways are listed in Table 1.

Networks analysed by the Ingenuity software describe the functional relationship between gene products

based on known interactions reported in the literature. The most significant network that was affected by

trans-resveratrol was cell death and survival, cellular assembly and organization, cellular function and

maintenance (44 score). A second network was related to hereditary disorder, neurological disease and organ

morphology (38 score). The third network was related to lipid metabolism, molecular transport, small

molecule biochemistry (31 score) and, finally, the forth and the fifth networks (28 score each) were

associated with cell cycle. Figure 2 represents the integrated network analysis and shows the relationship

between genes involved in the first network related to cell death and survival, cellular assembly and

organization, cellular function and maintenance and those related to the third network, lipid metabolism,

molecular transport, small molecule biochemistry -related network.

Validation of the expression of genes implicated in lipid metabolism

Genes that were selected for validation by RT-qPCR were implicated in both, lipid metabolism but also in cell

death and survival categories (Table 1). Genes of interest were ACLS3, LIPG, NOX1, KLF5 and AREG. All the

genes selected for validation showed a B> 1. Due to the action of resveratrol as an anti-oxidant compound,

NOX1 was selected based on its relevance in oxidative stress processes. ACSL3 and LIPG were chosen since

they were genes belonging to the lipid metabolism pathway and were not implicated in other pathways.


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Finally, KLF5 and AREG were selected since they were implicated in both lipid metabolism and in the first

important pathway altered by resveratrol (cell death and survival).

The RT-qPCR findings were consistent with data of the microarray analysis (Figure 3).

In summary, the expression of key genes involved in synthesis of lipids (ACSL3, AREG and KLF5), cholesterol

metabolism (LIPG) and control of reactive oxygen species (NOX1) was downregulated by trans-resveratrol in

LPS-stimulated Caco-2 cells.

Discussion

The burden of chronic diseases related to inflammation and characterized by metabolic dysregulations is

increasing (Rodriguez-Hernandez et al., 2013). In order to be able to prevent such disturbances or to find a

treatment for these problems, it is essential to understand the pivotal cellular components and the specific

tissues that participate in such metabolic impairments (Strable and Ntambi, 2010). In the present research,

Caco-2 cells were selected since enterocytes have been described to be a target site for trans-resveratrol

action (Kaldas et al., 2003). Gene expression analysis conducted in this in vitro study revealed some of the

previously reported data on the anti-proliferative role and apoptosis-promoting effect of resveratrol

(Vanamala et al., 2010). In this context, in the current study, molecular pathways involved in cell death and

survival (apoptosis) were enhanced (z-score= 1.174), while pathways related to cellular development, cellular

growth and proliferation (proliferation of tumor cell lines) were inhibited (z-score= -1.625). Moreover,

resveratrol has been described to affect all aspects of DNA metabolism (Gatz and Wiesmuller, 2008).

Accordingly, in our study, a suppressive action of trans-resveratrol in DNA replication, recombination and

repair (metabolism of DNA) was detected (z-score= -1.996). This outcome might be produced through direct

mechanisms, as for instance inhibition of genes related to tumorigenesis, but also through indirect

mechanisms (Gatz and Wiesmuller, 2008). Trans-resveratrol significantly repressed the expression levels of

LPS-induced ID1 and HRG genes, that particularly in the case of ID1, have been associated with tumorigenesis

(Ma et al., 2013; Johnson et al., 2014). However, indirect mechanisms that involve the abrogation of

endogenous reactive oxygen species (ROS) formation might also influence this molecular pathway, since the

redox state of a cell has been reported to control the stability of genomic DNA (Gatz and Wiesmuller, 2008).

In this context, ROS formation might be avoided when the amide adenine dinucleotide phosphate (NADPH)

oxidase or NOX1 activity (Gatz and Wiesmuller, 2008) is suppressed since it is considered one of the most

relevant enzymes of intracellular ROS generation (Fu et al., 2014). Accordingly, in the current study, NOX1

expression, significantly upregulated by LPS treatment was inhibited by trans-resveratrol in enterocytes. In

this context, it has been previously reported that resveratrol treatment inhibited LPS-induced NOX1

expression and ROS generation in macrophages, which has been related to a suppression of LPS-induced

foam cell formation (Park et al., 2009). Nevertheless, another mechanism previously reported for other

antioxidant molecules (i.e. ascorbic acid) might be plausible (Carcamo et al., 2004). This mechanism is

related to the capacity of resveratrol to inhibit the activation of nuclear factor kappa β (NF-κβ) (Cianciulli et

al., 2012). NF-κβ is a transcription factor important in the regulation of immunity, inflammation, cell

proliferation, cell transformation and tumour development. It has been demonstrated that ROS stress is a

relevant stimuli that activates NF-κβ through the activation of inhibitors of kappa β kinase (IKK)


188

(Trachootham et al., 2008). Since resveratrol has been reported to suppress nuclear translocation of p65

through the inhibition of IKK in LPS-stimulated Caco-2 cells, downregulation of NOX1 by the stilbene

observed in our study might be avoiding the NF-κβ signalling pathway, favouring an anti-inflammatory and

apoptosis promoting effect of the stilbene in addition to its antioxidant function.

The main finding of this research work is the potential action of trans-resveratrol on the lipid synthesis

process occurring at intestinal level. Indeed, the general molecular pathway associated with lipid synthesis

was found to be inhibited (z-score= - 1.195). As far as we know, this is the first study conducted in intestinal

cells showing an anti-lipogenic effect of resveratrol.

The small intestine synthesizes triglycerides (TG) through two main processes, the monoacylglycerol (MAG)

pathway, which occurs in enterocytes after feeding, and the glycerol-3-phosphate (G-3-P) pathway, which is

the de novo pathway for triglyceride synthesis (Shi and Cheng, 2009). Under normal conditions, these

processes have been reported to represent 20 to 80% of total TG levels in chylomicrons (Ho et al., 2002). In

addition, in the absence of dietary fat, the contribution of the intestine to the total TG levels in plasma has

been reported to be around 20% or more (Cenedella and Crouthamel, 1974), while, in vivo, it might supply

up to 40% in fasting conditions (Ockner et al., 1969). Long-chain acyl-CoA synthetases (ACSL) are enzymes

responsible for the activation process of fatty acids that precede their entrance to MAG or G-3-P pathway for

TG synthesis.

Five members of ACSL family have been described but, in small intestine, the expression of two isoforms

(ACSL3 and ACSL5), seems to predominate (Mansbach and Gorelick, 2007). ACSL3 is located in lipid droplets

or endoplasmic reticulum, and it is supposed to play a key role in fatty acid uptake and lipid synthesis

(Bu et al., 2009; Poppelreuther et al., 2012). In this trial, an inhibitory action of trans-resveratrol on the

expression of ACSL3 gene was detected. In a study conducted by Bu et al. (2009), it was observed that

knockdown of ACSL3 significantly suppressed the gene activity of certain lipogenic transcription factors such

as peroxisome proliferator activation receptor-γ (PPAR-γ), carbohydrate responsive element-binding

protein (ChRBP), sterol regulatory element-binding protein-1c (SREBP-1c), and liver X receptor-α (LXR), as

well as the expression of their target genes (Bu et al., 2009). In accordance, data from the current

investigation showed that treatment of Caco-2 cells with 50 μM of trans-resveratrol, repressed the

expression of ACSL3, but also inhibited the expression levels of certain transcription factors such as KLF5 and

AREG. Likewise, in relation to cholesterol metabolism, trans-resveratrol inhibited the expression of

endothelial lipase (LIPG). This lipase belongs to the TG lipase gene family (Lamarche and Paradis, 2007) and

the members of this group show different substrate specificity. It has been stated that high density

lipoprotein (HDL) is the main substrate for LIPG (McCoy et al., 2002). Interestingly, LIPG was implicated in

the pathway related to concentration of cholesterol showing a z-score of - 0.109. It should be taken into

consideration that the small intestine plays a role in cholesterol homeostasis (Lutton and Champarnaud,

1994). Remarkably, from human studies, a positive correlation between plasma levels of endothelial lipase

and obesity-associated parameters (i.e. body mass index and waist circumference) have been demonstrated

(Badellino et al., 2006). From our data, it might be proposed that, apart from the already described inhibitory


189

effect of trans-resveratrol on hepatic lipogenesis (Jin et al., 2013) and adipogenesis in 3T3-L1 adipocytes

(Baile et al., 2011), the stilbene may affect TG synthesis and cholesterol metabolism in enterocytes.

In our study, the expression of fatty acid synthase (FASN), which is considered one of the rate limiting

enzymes in de novo lipogenesis, was not differentially expressed between the experimental groups (log FC= -

0.34 when comparing the trans-resveratrol pre-treated Caco-2 cells and those exposed to LPS). However,

FASN has been shown to be target of resveratrol and either the expression of the gene (Gracia et al., 2014) or

the activity of the enzyme (Alberdi et al., 2011) have been found to be affected by the stilbene. Importantly,

regulation of FASN has been reported to take place mainly at transcriptional level, but also at post-

transcriptional level (Katsurada et al., 1990; Kim et al., 1992). In our study, trans-resveratrol inhibited the

expression of particular transcription factors that regulate the expression of FASN, such as KLF5 and AREG.

KLF5, also called basic transcription element-binding (BTEB) 2, is highly expressed in the gut (Conkright et

al., 1999) and has been reported to control proliferation of different cell types, including fibroblasts, smooth

muscle cells, white adipose tissue and intestinal epithelial cells (Brey et al., 2009). Importantly, KLF5 plays a

key role in the pathogenesis of cardiovascular diseases (Brey et al., 2009). KLF5 has been demonstrated to be

a pivotal regulator in the control of FASN expression (the key lipogenic gene) through an interaction with

SREBP-1 (Brey et al., 2009). AREG is a common ligand for the epidermal growth factor receptor (EGFR),

which contributes to the growth of various cell types including intestinal epithelial cells (Nam et al., 2015).

Ligands of EGFR, such as the EGF peptide, have been found to stimulate FASN expression mediated by

SREBPs in certain cancer cell types (Swinnen et al., 2000). Besides, increases in protein expression of EGF

(Carver et al., 2002) and EGFR (Scheving et al., 1989) in the liver appeared to be related to cholesterol

synthesis (Edwards et al., 1972) and fatty acid synthesis (Hems et al., 1975). Accordingly, in human studies, a

positive correlation between EGF ligands and serum cholesterol levels has been reported (Berrahmoune et

al., 2009). Therefore, our data suggest that downregulation of AREG and KLF5 expression by trans-

resveratrol might be linked somehow to alterations in lipid metabolism, including cholesterol levels, and a

reduction of lipogenesis.

Noteworthy, resveratrol is believed to modulate lipoprotein metabolism, decreasing circulating levels of LDL

cholesterol and reducing cardiovascular disease risk (Riccioni et al., 2015). Supplementation with high doses

of resveratrol to healthy humans with mild hypertriglyceridemia has been demonstrated to reduce intestinal

and hepatic triglyceride-rich lipoproteins (TRL) production, independent of its action on plasma TG

concentrations (Dash et al., 2013). In this context, it might be hypothesized that the potential action of

resveratrol at either intestinal level and also in the liver might be a mechanism to be taken into consideration

when exploring the protective role of this stilbene against metabolic diseases characterized by a low-grade

inflammatory status such as atherosclerosis. To the best of our knowledge, the inhibitory action of trans-

resveratrol on lipid metabolism has not been previously described in enterocytes. Hence, results from this

microarray and further validation analysis might be taken into consideration when evaluating the

mechanisms implicated in the hypolipidemic effects of trans-resveratrol, particularly based on the interest of

this compound owing to the potential health-promoting effects on cardioprotection.


190

Conclusions

In summary, the microarray analysis performed in the current study evidenced that trans-resveratrol acts

upon LPS-stimulated enterocytes activating genes related to cell death and repressing genes associated with

cellular development, growth and proliferation, as well as DNA replication, recombination and repair.

Indeed, the main finding of this experimental trial is that pre-treatment of Caco-2 cells with trans-resveratrol

before exposure to LPS significantly affects the expression of enzymes directly associated with lipid

metabolism, particularly those involved in fatty acid synthesis and cholesterol turnover. Moreover, a

significant influence of the stilbene was also demonstrated on the expression of several transcription factors,

such as KLF5 and AREG that, through their target genes, might impact on intestinal lipid metabolism and

blood cholesterol and TG levels, contributing to further explain the beneficial effects of resveratrol.

List of abbreviations

LPS, lipopolysaccharide; DMEM, Dulbecco’s modified Eagle’s medium; FBS, fetal bovine serum; NEAA, non-

essential amino acids; RT-Qpcr, quantitative real-time polymerase chain reaction; KLF5, krüppel-like factor

5; LIPG, endothelial lipase; AREG, amphiregulin; NOX1, NADPH oxidase-1; GAPDH, glyceraldehyde-3-

phosphate deshydrogenase; SEM, standard error of the mean; ID1, inhibitor DNA binding 1, dominant helix-

loop-helix protein; HRG, histidine-rich glycoprotein; SPRY1, sprouty homolog 1, antagonist of FGF signalling;

IPA, Ingenuity Pathway Analysis; ROS, reactive oxygen species; NADPH, amide adenine dinucleotide

phosphate; MAG, monoacylglycerol; G-3-P, glycerol -3-phosphate; ACSL, long-chain acyl-CoA synthases;

PPAR-γ, peroxisome proliferator activation receptor-γ; ChRBP, carbohydrate responsive element-binding

protein; SREBP1c, sterol regulatory element-binding protein-1c; LXR, liver X receptor-α; HDL, high-density

lipoprotein; FASN, fatty acid synthase; EGFR, epidermal growth factor receptor; TRL, triglyceride-rich

lipoprotein.

Acknowledgements

This study was supported by Instituto de Salud Carlos III (CIBERobn) and IdiSNA. The authors wish to

acknowledge Línea Especial about Nutrition, Obesity and Health (University of Navarra, LE/97, Spain) and

MINECO (Nutrigenio Project, AGL2013-45554R) for the financial support and the Department of Education,

Language Policy and Culture from the Government of the Basque Country for the predoctoral grant given to

Usune Etxeberria. The authors acknowledge Victor Segura for the help provided with the IPA.


191

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Figure 1. Average expression of genes that were downregulated by trans-resveratrol pre-treatment, according to Affymetrix Human Gene 2.0 ST DNA microarray analysis. Results are presented as mean ± SEM. Linear Models for Microarray Data was used to show probe sets with significant differential expression. *padj< 0.05, **padj< 0.01, ***padj< 0.001 vs CONTROL group; #padj< 0.05, ##padj< 0.01 vs LPS group. Adjusted p value was calculated with Benjamini-Hochberg procedure. SEM, standard error of the mean; LPS, lipopolysaccharide; RSV, trans-resveratrol; ID1, inhibitor of DNA binding 1, dominant negative helix-loop-helix protein; NOX1, NADPH oxidase 1; HRG, histidine-rich glycoprotein; SPRY1, sprouty homolog 1, antagonist of FGF signalling.

Figure 2. Integrated network analysis of upregulated and downregulated genes in Caco-2 cells pre-treated with trans-resveratrol and exposed to lipopolysaccharide. The node colour indicates the gene expression level.


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Figure 3. Average and relative expression of genes chosen for validation. A) Average expression of genes from microarray analysis in LSP-stimulated Caco-2 cells pre-treated with trans-resveratrol. B) Relative expression of genes following validation by RT-qPCR in LPS-stimulated Caco-2 cells pre-treated with trans-resveratrol. Statistical analyses were conducted with Student T-test, ***p< 0.001, **p< 0.01, *p< 0.05. LPS, lipopolysaccharide; RT-qPCR, quantitative real-time polymerase chain reaction.


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Table 1. Classification of metabolic pathways and genes targeted by trans-resveratrol in LPS-treated Caco-2 cells.

Category Diseases or

functions annotation

p value z-score Genes

Cell Death and Survival

Necrosis 2,97E-03 1,804 ABCB1,AHR,ANKRD1,AREG,BLNK,CLYBL,ENC1,EPHX2,HOXB9,HSPA8,ID1,IER3,IFI6,KLF5,KRT18,LUM,MT1X,MT2A,NEO1,NFE2L2,NOX1,PHB2,PKP2,RPS3,SLC20A1,SPTBN1,SSTR5-AS1,TOP1

Apoptosis 8,98E-03 1,174 ABCB1,AHR,ANKRD1,ANXA4,AREG,BBS2,BLNK,ENC1,GLS2,HOXB9,HRG,HSPA8,ID1,IER3,IFI6,KLF5,KRT18,LUM,MT2A,NFE2L2,NOX1,PHB2,PKP2,RPS3,SLC20A1,SPTBN1,TOP1

Infectious diseases

Infection of cells 2,06E-03 -1,140 AREG,BMP2K,CRIM1,ENC1,KRT18,MT1X,MT2A,NOP56,OSBPL3,RPL5,SLC20A1,SPTBN1,ZBTB2

Viral Infection 3,35E-02 -0,595 ABCB1,AHR,AREG,BMP2K,CRIM1,ENC1,FAM135A,IER3,IFI6,KRT18,MT1X,MT2A,NOP56,OSBPL3,RPL5,SPTBN1,ZBTB2

Lipid metabolism, Small molecule Biochemistry Synthesis of lipids 1,48E-02 -1,195 ABCB1,ACSL3,ACSM3,AHR,AREG,EPHX2,HSPA8,KLF5,ME1,NOX1

Fatty acid metabolism 6,43E-03 -1,060 ABCB1,ACSL3,ACSM3,AREG,EPHX2,HSPA8,KLF5,ME1,SC5D Concentration of cholesterol 2,62E-02 0,109 AHR,EPHX2,LIPG,LPGAT1,SC5D

Lymphoid tissue structure and development, tissue morphology Quantity of lymphatic system cells 1,99E-02 -1,408 ABCB1,AHR,BLNK,ID1,SLC20A1

Cellular Development, Cellular Growth and Proliferation

Proliferation of tumour cell lines 3,29E-02 -1,625 ABCB1,AHR,AREG,CLYBL,DDX21,ENC1,HOXB9,ID1,IER3,KLF5,MT2A,MTUS1,NEO1,NFE2L2,NFS1,TOP1

DNA replication, Recombination and Repair Metabolism of DNA 3,50E-02 1,996 ABCB1,AHR,AREG,FAM135A,ID1,TOP1

Functional enrichment analysis conducted by the Ingenuity Pathway Analysis.

197



199

Gene Name Gene Description Transcript ID Log FC B

Downregulated genes ID1 inhibitor of DNA binding 1, dominant negative helix-loop-helix protein NM_002165 -0.88 7.39 HRG histidine-rich glycoprotein ENST00000232003 -1.24 5.78 NOX1 NADPH oxidase 1 NM_001271815 -1.94 5.50 SPRY1 sprouty homolog 1, antagonist of FGF signaling (Drosophila) NM_001258038 -0.57 4.26 ETV4 ets variant 4 NM_001079675 -0.89 3.29 TOP1 topoisomerase (DNA) I NM_003286 -0.54 3.14 ABCB1 ATP-binding cassette, sub-family B (MDR/TAP), member 1 NM_000927 -1.42 2.71 SLC7A6 solute carrier family 7 (amino acid transporter light chain, y+L system), member 6 NM_001076785 -0.62 2.68 KRT18P15 keratin 18 pseudogene 15 ENST00000458108 -0.66 2.66 AREG amphiregulin NM_001657 -1.11 2.14 ANKRD1 ankyrin repeat domain 1 (cardiac muscle) NM_014391 -0.62 2.14 DDX21 DEAD (Asp-Glu-Ala-Asp) box helicase 21 NM_001256910 -0.67 2.11 MTUS1 microtubule associated tumor suppressor 1 NM_001001924 -0.70 1.83 LPGAT1 lysophosphatidylglycerol acyltransferase 1 NM_014873 -0.75 1.81 KRT18P10 keratin 18 pseudogene 10 ENST00000388836 -0.66 1.80 LIN54 lin-54 homolog (C. elegans) NM_001115007 -0.65 1.77 IER3 immediate early response 3 NM_003897 -0.46 1.66 C3orf52 chromosome 3 open reading frame 52 NM_024616 -0.63 1.64 RAPH1 Ras association (RalGDS/AF-6) and pleckstrin homology domains 1 NM_203365 -0.58 1.55 LUM lumican NM_002345 -0.98 1.50 MGAT4A mannosyl (alpha-1,3-)-glycoprotein beta-1,4-N-acetylglucosaminyltransferase, isozyme A ENST00000264968 -0.56 1.43 SPTBN1 spectrin, beta, non-erythrocytic 1 NM_003128 -0.63 1.34 LIPG lipase, endothelial NM_006033 -0.51 1.29 ACSL3 acyl-CoA synthetase long-chain family member 3 ENST00000357430 -0.41 1.22 KLF5 Kruppel-like factor 5 (intestinal) NM_001286818 -0.44 1.18 ZBTB2 zinc finger and BTB domain containing 2 NM_020861 -0.37 1.10 TNPO1 transportin 1 NM_002270 -0.47 1.01 MIR3916 microRNA 3916 NR_037480 -0.51 0.95 NFE2L2 nuclear factor, erythroid 2-like 2 NM_001145412 -0.58 0.94 ANXA4 annexin A4 NM_001153 -0.46 0.89 MIR622 microRNA 622 NR_030754 -0.68 0.86

Supplementary table 1. List of genes whose expression was significantly affected by trans-resveratrol in LPS-induced Caco-2 cells .


200


SC5D sterol-C5-desaturase NM_001024956 -0.47 0.80 MIR548V microRNA 548v NR_036103 -0.80 0.79 SAMD5 sterile alpha motif domain containing 5 NM_001030060 -0.71 0.73 MIR3911 microRNA 3911 NR_037473 -0.34 0.73 SLC20A1 solute carrier family 20 (phosphate transporter), member 1 NM_005415 -1.00 0.73 AHR aryl hydrocarbon receptor NM_001621 -0.49 0.70 OSBPL3 oxysterol binding protein-like 3 NM_015550 -0.42 0.68 SPAG1 sperm associated antigen 1 NM_003114 -0.45 0.63 IFI6 interferon, alpha-inducible protein 6 NM_002038 -0.62 0.62 KRT18P49 keratin 18 pseudogene 49 ENST00000427083 -0.56 0.62 LACTB2 lactamase, beta 2 NM_016027 -0.49 0.56 PGP phosphoglycolate phosphatase NM_001042371 -0.48 0.52 ENC1 ectodermal-neural cortex 1 (with BTB domain) NM_001256574 -0.64 0.51 PKP2 plakophilin 2 NM_001005242 -0.44 0.39 BZW1 basic leucine zipper and W2 domains 1 NM_001207067 -0.39 0.38 BMP2K BMP2 inducible kinase NM_198892 -0.79 0.31 GPR111 G protein-coupled receptor 111 NM_153839 -0.77 0.30 COL12A1 collagen, type XII, alpha 1 NM_004370 -0.64 0.29 FNDC3B fibronectin type III domain containing 3B NM_001135095 -0.62 0.25 BLNK B-cell linker NM_001114094 -0.51 0.22 MAK16 MAK16 homolog (S. cerevisiae) NM_032509 -0.37 0.16 DYNLT3 dynein, light chain, Tctex-type 3 NM_006520 -0.60 0.16 ME1 malic enzyme 1, NADP(+)-dependent, cytosolic NM_002395 -0.64 0.15 PSME4 proteasome (prosome, macropain) activator subunit 4 NM_014614 -0.39 0.15 CRIM1 cysteine rich transmembrane BMP regulator 1 (chordin-like) NM_016441 -0.46 0.15 ATP1B1 ATPase, Na+/K+ transporting, beta 1 polypeptide ENST00000494797 -0.46 0.15 MPZL2 myelin protein zero-like 2 NM_144765 -0.38 0.15 ZNRF2 zinc and ring finger 2 NM_147128 -0.53 0.14 HOXB9 homeobox B9 NM_024017 -0.67 0.13 KRT18P54 keratin 18 pseudogene 54 ENST00000513396 -0.62 0.11 LCOR ligand dependent nuclear receptor corepressor NM_001170765 -0.54 0.10 FAM135A family with sequence similarity 135, member A NM_001105531 -0.86 0.10

Suplementary table 1. Continued


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Upregulated genes CLYBL citrate lyase beta like NM_206808 0.58 3.17 SNORD100 small nucleolar RNA, C/D box 100 NR_002435 0.52 2.25 ZBED5-AS1 ZBED5 antisense RNA 1 ENST00000529014 0.47 1.95 SNORD82 small nucleolar RNA, C/D box 82 ENST00000365530 0.54 1.93 SNORA76A small nucleolar RNA, H/ACA box 76A NR_002980 0.47 1.82 IRF2BPL interferon regulatory factor 2 binding protein-like NM_024496 0.40 1.72 SNORD55 small nucleolar RNA, C/D box 55 NR_000015 0.60 1.60 SSTR5-AS1 SSTR5 antisense RNA 1 NR_027242 0.49 1.52 TSTD1 thiosulfate sulfurtransferase (rhodanese)-like domain containing 1 NM_001113206 0.42 1.48 MT1X metallothionein 1X NM_005952 0.84 1.38 SNORD36A small nucleolar RNA, C/D box 36A NR_002448 0.62 1.37 SNORD104 small nucleolar RNA, C/D box 104 NR_004380 0.67 1.28 SNORD92 small nucleolar RNA, C/D box 92 NR_003074 0.53 1.24 SNORA80E small nucleolar RNA, H/ACA box 80E NR_002974 0.50 1.16 MT2A metallothionein 2A ENST00000245185 0.89 1.07 SNORD21 small nucleolar RNA, C/D box 21 NR_000006 0.41 1.02 SNORA65 small nucleolar RNA, H/ACA box 65 NR_002449 0.78 0.98 SNORA72 small nucleolar RNA, H/ACA box 72 NR_002581 0.76 0.94 MFSD4 major facilitator superfamily domain containing 4 NM_181644 0.38 0.90 MGC16275 uncharacterized protein MGC16275 NR_026914 0.56 0.90 NEO1 neogenin 1 NM_001172623 0.36 0.90 RNU6ATAC RNA, U6atac small nuclear (U12-dependent splicing) NR_023344 0.49 0.73 RNU6ATAC2P RNA, U6atac small nuclear 2, pseudogene ENST00000387943 0.48 0.72 SNORA20 small nucleolar RNA, H/ACA box 20 NR_002960 0.77 0.70 EPHX2 epoxide hydrolase 2, cytoplasmic NM_001256484 0.60 0.61 RNA5SP242 RNA, 5S ribosomal pseudogene 242 ENST00000364171 0.54 0.57 SNORD56 small nucleolar RNA, C/D box 56 NR_002739 0.41 0.57 ALDH9A1 aldehyde dehydrogenase 9 family, member A1 NM_000696 0.38 0.56 SNORA64 small nucleolar RNA, H/ACA box 64 NR_002326 0.43 0.54 ACP6 acid phosphatase 6, lysophosphatidic NM_016361 0.41 0.53

Supplmentary table 1. Continued

Genes identified by Affymetrix Human Gene 2.0 ST DNA microarray. Downregulated and upregulated genes are ranked by B criteria.


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PARM1 prostate androgen-regulated mucin-like protein 1 NM_015393 0.34 0.51 SNORD46 small nucleolar RNA, C/D box 46 NR_000024 0.43 0.50 SNHG8 small nucleolar RNA host gene 8 (non-protein coding) NR_003584 0.45 0.50 HYKK hydroxylysine kinase NM_001013619 0.35 0.44 SNORA54 small nucleolar RNA, H/ACA box 54 NR_002982 0.53 0.41 OGDHL oxoglutarate dehydrogenase-like NM_001143996 0.42 0.39 SNORD15A small nucleolar RNA, C/D box 15A NR_000005 0.43 0.35 NYNRIN NYN domain and retroviral integrase containing NM_025081 0.35 0.34 GLS2 glutaminase 2 (liver, mitochondrial) NM_001280796 0.44 0.29 LINC00349 long intergenic non-protein coding RNA 349 ENST00000448748 0.44 0.27 ACSM3 acyl-CoA synthetase medium-chain family member 3 NM_005622 0.53 0.26 SCARNA12 small Cajal body-specific RNA 12 NR_003010 0.36 0.22 OSER1-AS1 OSER1 antisense RNA 1 (head to head) NR_038337 0.30 0.20 CES3 carboxylesterase 3 NM_001185177 0.53 0.19 SNORD87 small nucleolar RNA, C/D box 87 NR_002598 0.37 0.18 SNORD85 small nucleolar RNA, C/D box 85 NR_003066 0.89 0.12 SNORD14C small nucleolar RNA, C/D box 14C NR_001453 0.39 0.12 LOC154761 family with sequence similarity 115, member C pseudogene NR_015421 0.37 0.12 RNA5SP422 RNA, 5S ribosomal pseudogene 422 ENST00000516778 0.34 0.11 IFT46 intraflagellar transport 46 homolog (Chlamydomonas) ENST00000525060 0.41 0.09 UQCR10 ubiquinol-cytochrome c reductase, complex III subunit X NM_001003684 0.34 0.08 TRAPPC6A trafficking protein particle complex 6A NM_001270891 0.42 0.08 SNORD22 small nucleolar RNA, C/D box 22 NR_000008 0.52 0.08 CMBL carboxymethylenebutenolidase homolog (Pseudomonas) NM_138809 0.33 0.06 BBS2 Bardet-Biedl syndrome 2 NM_031885 0.29 0.05 NFS1 NFS1 cysteine desulfurase ENST00000374085 0.35 0.04 SNORD59A small nucleolar RNA, C/D box 59A NR_002737 0.70 0.03

Supplementary table 1. Continued

Genes identified by Affymetrix Human Gene 2.0 ST DNA microarray. Downregulated and upregulated genes are ranked by B criteria.

V. GENERAL DISCUSSION

Generaldiscussion

205

Obesityhasbecomeaworldwidehealthburdenthataffectsnotonlydevelopedcountriesbutalso,low‐and

middle‐incomecountries(WHO,2015).IncreasingBMIhasbeenrelatedtoasetofcomorbiditiesincluding

hypertension, glucose intolerance, T2DM, dyslipidaemia, kidney failure, osteoarthritis, as well as asthma,

heartfailureandmentaldisorders(Martin‐Rodriguezetal.,2015).Thereby,thismetabolichealthproblem

leads to high morbidity and mortality rates (Martin‐Rodriguez et al., 2015). In addition, obesity is a

multifactorialdisease,wheremanyfactorsareinvolved(Harrisetal.,2012).Amajorcontributoryfactoris

the consumption of high‐caloric diets rich in fat and sucrose, accompanied by inactive lifestyle habits

(Brantleyetal.,2005).Thisoutcomehasmadeessentialtheimplementationofactionsdirectedtopromote

healthydietarypatternsandincreasedphysicalactivity(WHO,2015).Moreover,sincelifestyleinterventions

mightnotbeeffectiveenoughinsomecases,pharmacologicaltreatmentisconsideredanimportantstrategy

(Hussain etal., 2015). Unfortunately, themain reasonwhy only few anti‐obesity agents are found in the

market,isattributedtothelargeamountofadverseeventsassociatedtothedrugs(Hussainetal.,2015).

Takingthisinmind,effortsinscientificresearchhavetwomajorchallenges.Ononehand,it isessentialto

understandthesurroundingmechanismsinthephysiopathologyofobesity.Thisawarenessisofrelevance

to find new therapeutic targets that will allow searching for effective treatments, helping tomanage the

disease and associated complications and to improve individual’s life quality. On the other hand, this

informationwillguideprofessionals tomakenutritionalrecommendationsandmedical treatments froma

personalizedpointofview(McNivenetal.,2011).

Accordingly, in nutritional sciences, the concept of “molecular nutrition research” has risen

(Norheimetal.,2012).Thistermimpliestheuseofmoleculartechniquesinnutritiontofurtherunderstand

the impact of nutrients and food or food components on whole body physiology and health status

(Norheim et al., 2012). This way, scientific world is gaining knowledge on the molecular mechanisms

underlyingobesity,whichhasfavouredtheidentificationofnewcontributoryfactorsinobesityandrelated

comorbidities, such as the gut microbiota. Besides, this notion is enabling researchers to explore new

therapeutic targets with potential beneficial biological activities, but without or minor side effects

(i.e.plantpolyphenols),thatmaycontributetopreventthedevelopmentofmetabolicdiseasesandevenhelp

totreatthem(Wangetal.,2014).

The present investigation, through the application of the newest “Omics” technologies, was conducted to

shed some light on the complex interaction existing among host metabolism, gut microbiota, food or

food‐derived constituents and health. More specifically, this research work sought, firstly, to understand

metabolomic alterations and gutmicrobiota compositionmodifications related to the intake of a high‐fat

high‐sucrosediet(resemblingdietarypatternsfromwesternizedsocieties);andsecondly,toinvestigatethe

effectivenessoftheuseofcertainpolyphenols(i.e.trans‐resveratrol,quercetin)tocounteractoratleastto

mitigatesomeof thediet‐inducedobesity‐associatedperturbations, toprovidenewdataandviewsonthe

biological effects of such compounds and, thus, the possibility to use them as a promising therapeutic

approachagainstobesity.

Generaldiscussion

206

1. Understandingthephysiopathologyofobesitythrough“Omics”approaches

Metabolic alterations are a key feature in disorders such as obesity. Therefore, processes directed to

measureglobalmetabolicchangeswillprovideanoverallpicturethatmightbeconsideredadescriptionof

thephenotypeofthedisease(Duetal.,2013)andcouldhelptofinddiseasebiomarkersthatwillallowearly

diagnosis and the possibility to stratify healthy and diseased patients. Besides,metabolomics approaches

identify disturbed biological pathways and the detection of these smallmolecules ormetabolites favours

monitoringofthediseaseprogressionorevaluatingtheeffectivenessofthetreatment(Zhangetal.,2013).

In our study (Chapter 1), animals that were subjected to two different dietary interventions

(standard diet vs HFS diet) for the same period of time showed a differential metabolomic pattern.

Biologicallysignificantmetabolitesidentifiedinourresearchworkprovidedinformationonthebiochemical

pathwaysthatmighthavebeenaffectedasaconsequenceofthetreatmentthatledtheanimalsfedtheHFS

diet gain more BW than the control rats, and besides, acquire obesity associated complications, such as

hyperglycaemia, hyperleptinemia, hyperinsulinemia and insulin resistance. In this context, serum non‐

targetedmetabolomicsapproachdetectedalteredlevelsofaminoacids,particularlythoseofBCAAs(namely

isoleucine, leucine and valine), but also others, such as phenylalanine and tyrosine. Impaired levels of

nitrogenous(urea,creatineandcreatinine)andlipidcompounds,namelytotalPUFA,docosahexaenoicfatty

acid (DHA), and phospholipids (i.e. phosphatidylcholines and sphingomyelin), were likewise detected.

Moreover, differences in the levels of certain aqueous compounds (choline, acetyl carnitine, formate and

allantoin)were also identified. Taking into consideration thesedata, itwasproposed that apart from the

well‐knowncarbohydrateandlipidmetabolicdisturbancesfoundinobesity(Singlaetal.,2010),HFSdiet‐fed

obese animals might present impaired protein metabolism. The data suggested that diet‐induced obese

animals might be more effective preserving body protein during an overnight fasting. Thus, it was

hypothesized that a protein‐sparing effect of fat might be given. Furthermore, mitochondrial function

processes related to purine biosynthesis and remethylation processes were proposed to be disturbed.

Importantly,thisexploratorymetabolomicsapproachconductedinserumsampleswasabletodiscriminate

metabolitesbetweendiseaseandnon‐diseaseconditions.Thesedataarepartlyinaccordancewithscientific

evidencereportingthemetabolicsignatureofhumanobesitytobecharacterizedbyalteredlevelsofBCAAs,

nonesterified fattyacids (NEFAs),organicacids, acylcarnitinesandphospholipids (Rauschertetal., 2014).

Nevertheless,results fromourstudyshowcontroversial levels insomeof thementionedmolecules,as for

instancetheextensivelystudiedBCAAswhichhavebeenrepeatedlyreportedtobesignificantlyincreasedin

bloodsamplesofobesepeople (Xieetal.,2014).This findingrequires the interpretationof thedata tobe

madewithcautionsincethereareconfoundingfactorsthatmightaffecttheobservedresults.Inourstudy,an

experimental animalmodel of obesity was used as reported elsewhere (Lomba et al., 2010). Thismodel

allowsastrictcontrolofagentsnamelydietandotherenvironmentalfactors(Xieetal.,2012).Nonetheless,

componentssuchasthenutritionalstatusoftheanimalsatthetimeofsamplingmightinfluencetheresults

(Mellert et al., 2011). Indeed, the overnight fasting period of animals might be a key issue to take into

consideration.Infact,ourresultsagreedwithotherstudiesconductedinanimalsthatwerefastedpriorto

serum sample collection (Shearer et al., 2008; Duggan et al., 2011). However, we have not been able to

compare results since, as far aswe are concerned, there are nometabolomics studies comparing control

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207

diet‐fedleanratsandHFSdiet‐fedobeserats,wheretheeffectsoffastingperiodonmetabolomicsprofilehas

beendetermined.

From the evidences emerging in the last decade, it has been confirmed that another factor that deeply

influences host metabolism is the gut microbiota (Marcobal et al., 2013). Indeed, recently, a urinary

metabolic signature of adiposity has been described in a metabolomic study conducted in two different

populations (Elliott et al., 2015). Interestingly, in this study, gut microbial‐derived co‐metabolites that

showedassociationswithBMIwereidentifiedreportingseveralhost‐guttransformationmicrobialpathways

(Elliottetal.,2015).Theresidentmicrobiotaandthehostinteractinamutualisticmanner.Thus,metabolites

thatareproducedbymicrobiota influence thehostwellness (Nicholsonetal., 2012). In this sense, it isof

relevance to acquire knowledge on the bacterial species and their functionalities, in order to be able to

establishthebacterialspeciesresponsiblefortheproductionofspecificmetabolites(AwandFukuda,2015).

Insummary,whentrying tounderstandmetabolicalterations indiseaseconditions,arelevantaspect that

mightnotbedisregardedisthegutmicrobiotaorthegutmicrobiome(Drososetal.,2015).

Consequently,apurposeofthepresentresearchwasdevotedtoassessthebacterialtrademarkassociatedto

a HFS diet‐induced overweight condition (Chapter 2). In this sense, faecal samples from animals fed a

control diet that subsequently were turned to a HFS diet were analysed by next generation sequencing

approach(Claessonetal.,2012).Furthermore,basedonthefactthatSCFAsareconsideredtheprimaryend‐

productsofgutmicrobialmetabolisminthelargeintestine(MacfarlaneandMacfarlane,2003),theimpactof

thedietontheexcretedSCFAlevelswasalsoevaluated.Themostrelevantfindingfromourworkwasthat

diet‐induced overweight led to an imbalance, also known as dysbiosis, in gut microbiota composition. A

robusteffectofdieton intestinalbacterialcommunitywasrevealed,evidencingnotonlyalterations inthe

two dominant phyla, the Gram‐positive Firmicutes and the Gram‐negative Bacteroidetes, which are

consistentwith previous reports (Fleissner etal., 2010; Parks etal., 2013), but also, perturbations in the

levels of other minor bacterial groups (Erysipelotri,Deltaproteobacteria,Desulfovibrionaceae, Bacteroidia,

Bacilli, Prevotellaceae, Clostridia) and bacterial species (Mitsuokella jalaludinii, Eubacterium ventriosum,

Eubacteriumcylindroides,Bilophilawadsworthia,Lactobacillusreuteri,Prevotella spp.,Bacteroides spp.and

Clostridiumspp.)wereidentified.Therefore,oneoftheconclusionsfromourstudyisthatanobesogenicdiet

richinfatandsucrosehasthepotentialtoinfluencethecompositionofthegutmicrobiotanegatively.The

bacterialfingerprintidentifiedinanimalsattheendoftheinterventionwascharacterizedbyenhancement

ofcertainpathogensatexpensedof symbioticcommensalmicrobes.Nonetheless, if gutmicrobiotaprofile

described in our study is compared with results published in another study using the mentioned diets

(Fleissneretal.,2010),someoftheshiftsfoundinbacterialgroupsinourexperimentareconsistent,whilein

contrast,therearesomemodificationsthatdonotagreewithresultsreportedfromstudieswhereonlythe

impactofaHFDhasbeenevaluated(Hildebrandtetal.,2009;deLaSerreetal.,2010)bringingtolightour

inabilitytodifferentiatebetweenmodificationsattributedtothefatcontentofthedietorotherwise,tothe

carbohydrate content of diet. Importantly, it was found that some bacterial species showed an opposite

pattern to the familyor genus theybelong to, for instance familyPrevotellaceae andEubacteriaceaewere

found to be decreased but Prevotella nanceiensis and Eubacterium ventriosum within these groups were

increased, genus Mitsuokella was reduced while Mitsuokella jalaludini was enlarged. Moreover, an

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208

unexpectedresultwastofindoutthatClostridiagroupwasreducedaftertheHFSdiet,butshowedapositive

correlationwithvisceraladiposetissue.Thisoutcomewasattributedtothevariationinthetypeandcontent

ofcarbohydrates,suggestingtherelevanceofspecificnutrientsofthedietincertainbacteria.Furthermore,a

Lactobacillusstrain,commonlycharacterizedasaprobioticspecies(Tingetal.,2015),wasenhancedthrough

theHFSdietarytreatment.DiscrepanciesexistinrelationtothelevelsofthisLactobacillusstraininobesity.

Somestudieshavefoundhigherlevelsofthisbacteriuminobesity,showingpositivecorrelationswithBMI

(Millionetal.,2012;Millionetal.,2013).Importantly,anotherstudydemonstratedthestrain‐specificeffects

ofLactobacillus reuteri on BW and adiposity (Fak and Backhed, 2012). Therefore, these facts re‐assure a

relevantconclusionfromourstudycorroboratingtherelevanceofanalysinggutmicrobiotamodificationsat

deeptaxonomicranks,sinceeffectsmightbespecies‐oreven,strain‐dependent.

Inaddition,resultsobtainedregardingSCFAslevelsinfaecesledusconfirmthestrongimpactofthedietas

reportedbypreviousresearchworks(ToppingandClifton,2001;Jakobsdottiretal.,2013).Inthissense,the

relevance of the type of substrate in the rate and ratio of SCFAs has been widely explored

(den Besten et al., 2013). In our experiment, at the end of the treatment period, levels of major SCFAs

(acetate and propionate), and almost those of butyrate, were significantly lower compared to the

concentrationsdetectedatthebeginningofthestudy.Theseresultswereexpectedasthequantityofdietary

fibre in the HFS diet was considerably lower compared to that of the standard diet. Nevertheless, in

genetically obese phenotypes a greater production of these molecules has been stated

(Leyetal.,2005;Turnbaughetal.,2006),whichcorroborates thehypothesis that theexperimentalmodel

used (diet‐induced overweight animals) may be the cause for the divergences found among the studies.

Importantly, in our trialwe could not discriminatewhether certain variations inmicrobiota composition

mightresultfromtheBWgainorotherfeaturesassociatedtoadiposity.Anyway,thisworkis,asfaraswe

know, the first study devoted to investigate the effect of a high‐fat high‐sucrose diet on gut microbiota

compositionofWistarrats.

Microbesexertametabolicactivitywhichmightbeplayinganimportantroleonthedevelopmentofdiseases

(Guinane andCotter, 2013).Hence, understandingwhether the involvement of these bacteria is direct or

indirect (throughmetabolites theyproduce), is a subject ofmuchdebate (Drososetal., 2015).Moreover,

knowledgeaboutthedegreeofbacteria’ssusceptibilitytodietaryconstituentsisofrelevance,sinceplasticity

ofthegutmicrobiotatodietarymodificationsmakesitmanageablewithseveralbioactivecompoundssuch

aspolyphenols.

2. Influenceofnaturalbioactivecompoundsonobesegutmicrobiotaandmetabolome

There ismuchworkbeingconducted inrelationtomodulationofthegutmicrobiotacompositionthrough

the use of probiotics (Bubnov et al., 2015), prebiotics (Druart et al., 2014) and in the last years faecal

transplants are also capturing research interest (Vrieze et al., 2012). Likewise, the links between

polyphenoliccompoundsandthegutmicrobiotaarebeingexploredtogain insightsonthehealthbenefits

derived frompolyphenols (Koutsos etal., 2015). In a studywere flavonoid targetswere analysed, itwas

demonstrated that around 1000 targets present homologues with human gut metagenome and

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209

demonstrated that the majority were related to bacterial metabolism. These results confirm that health

benefitsofflavonoids,atleasttosomeextent,comefromthegutmicrobiotamodulatorycapacitythataffects

metabolismofbacteriaand,inturn,hostmetabolism(Luetal.,2013).

Accordingly,partofourworkaimedtoevaluatecurrentproofofprincipalonthebi‐directionalinteraction

between polyphenols and the gutmicrobiota. In this sense, there are twomain concepts that need to be

consideredwhendetermininghealthbeneficialeffectsofpolyphenolsand/ortheirmetabolites.Ononehand,

weshouldbeawarethateachindividualcontainsauniquebacterialprofile,whichmightbereferredasour

fingerprintorsignature,andthis factmakesthecapacityof individualstotransformpolyphenolsvariable,

affectingtheformationoffinalmetabolites(Nakatsuetal.,2014).Briefly,thehostfunctionalitytometabolize

polyphenolswill be dependent on the inter‐individual variability related to gutmicrobiota. On the other

hand,purepolyphenolsor foods rich innatural compounds, aswell as theirmetabolites,mightaffect gut

microbiota composition, not only acting as antimicrobial compounds with bacteriostatic or bactericidal

propertiesagainstdiversebacteria,butalsopromotingthegrowthofcommensalbeneficialbacteria,leading

totheemergenceoftheideathatpolyphenolsmightexertprebiotic‐likeeffects(Cardonaetal.,2013).Inany

case, while most of the efforts have been directed to study the metabolism of polyphenols by the gut

microbiota,itisinthelastyearsthatresearchworkhasstartedtoaddressthisrelationshipintheopposite

direction.However,inmostcases,studieshavefocusedontheuseofpolyphenol‐richsupplementsordietary

extracts(Valdesetal.,2015).Accordingly,andbasedonthefactthatpolyphenolshavebeenproposedasthe

most important candidates to explain health protective effects of plant‐derived foods, it is of interest to

explorewhetherpotentialanti‐obesityeffectsandimprovementsonobesity‐relatedcomorbiditiesproduced

bycertainpurenaturalcompoundsmightbemediatedthroughchangesongutmicrobiotacomposition.

In this sense, ournextobjectivewas focusedon screening a setofbioactive compounds to evaluate their

anti‐obesitypotential(Chapter3).Fromthetestedcompounds,mainlynaringin,followedbycoumaricacid

andleucineshowedsomemildbeneficialeffects,namelyaslightreductioninfinalbodyweight,significant

decrease inadiposityand/or improved insulin resistance inHFSdiet‐fedobeseanimals.Nevertheless, the

individual effects were not very encouraging; hence, further experiments were conducted with natural

compoundsthatinscientificliteraturehaveshowngreaterpromiseasanti‐obesitymolecules,suchastrans‐

resveratrol and quercetin (Aguirre et al., 2014) (Chapter5). In this context, in our study, the combined

administration of trans‐resveratrol and quercetin, considerably decreased BW gain. However, neither

trans‐resveratrol nor quercetin alone exerted an anti‐obesity effect. To achieve this goal, for quercetin, a

supplementation period of 9 weeks has been demonstrated to be needed (Arias et al., 2014). As far as

trans‐resveratrolisconcerned,whenorallyadministeredfor6weeks,thedoseusedinourstudywasfound

tobeeffectiveinreducingliverweight,visceraladiposityandplasmacytokinelevels inamodelofgenetic

obesity (Zucker fa/fa rats) (Gomez‐Zorita et al., 2012; Gomez‐Zorita et al., 2013). However, based on

previousstudies(Macarullaetal.,2009;Alberdietal.,2011),inadiet‐inducedobesitymodel,higherdosesto

the ones administered in our study might be required to observe a BW gain preventive effect of

trans‐resveratrol.Inanycaseboth,trans‐resveratrolandquercetin,werefoundtosignificantlyreduceserum

insulinlevelsandtoimproveHOMAindexdemonstratingtheireffectivenessasnaturalcompoundstotreat

obesity‐associatedmetabolic complications such as insulin resistance. Active research is available around

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210

thecomplexinteractionamongredwineconsumption,gutmicrobiota,itsmetabolitesandfinalhealtheffects

(Queipo‐Ortunoetal.,2012;Cuevaetal.,2015).Incontrast,fewstudies(Qiaoetal.,2014)havebeendevoted

to analyse the influence of one of the most famous components of the red wine (trans‐resveratrol) and

another polyphenol (quercetin) on gut microbiota composition. In this sense, according to our work

performedinHFS‐dietfedrats,itcouldbeconcludedthatat leastattheproveddoses,gutmicrobiotawas

especiallysusceptibletotheactionofquercetin(Chapter5).Likewise,itmightbeproposedthatdirectgut

microbiota modulatory capacity of quercetin might be involved in the mechanisms leading to improved

insulin sensitivity. Interestingly, treatmentwith quercetin considerably reduced Firmicutes/Bacteroidetes

ratio and particularly decreased the levels of some bacterial groups (i.e. Firmicutes phyla and

Erysipelotrichaceaegroup)commonlyassociated todiet‐inducedobesity (Fleissneretal., 2010). In fact, in

ourpreviouswork(Chapter2),thesebacteriawerefoundtobemoreabundantintheratsthatfollowedHFS

dietary intervention. Moreover, this compound suppressed the growth of previously defined pathogenic

bacterial species such as Eubacterium cylindroides, while it markedly promoted the growth of a specific

strainofAkkermansiagenus,knownasAkkermansiamuciniphila,whichhasbeenassociatedwithimproved

metabolic health at the beginning and at the end of aweight loss dietary intervention (Dao etal., 2015).

Indeed,theabundanceofthismucin‐degradingbacteriumhasbeeninverselyassociatedtoBWanddiabetes

(Everardetal.,2013).Importantly,oraladministrationofAkkermansiaasaprobiotichasbeenreportedto

reversediet‐inducedobesity associatedmetabolic disorders (Everardetal., 2013). In addition, in a study

conductedwithapolyphenol‐richextractsuchasthecranberryextract,althoughtheauthorswerenotable

toestablisha causal relationship, thepositivemetabolic effectsobserved inHFD‐inducedobesemice that

were administered the cranberry extract, were associated with a significant increase in Akkermansia

population (Anhe etal., 2015). Therefore, in our study, itmight be theorised that themodulatory role of

quercetin on general gut microbiota composition and maybe, the specific modifications the flavonol

producedincertainbacterialgroups,mightbeimplicatedintheameliorationofinsulinsensitivityfoundin

our study. As far as the influence of trans‐resveratrol is concerned, no significant modifications were

identified at phylum level. Unexpectedly, gut microbiota modification patterns with the intake of this

compoundwere opposed to that of quercetin. However, trans‐resveratrol significantly acted on intestinal

gene expression, upregulating inflammation‐associated genesTLR4, LBP and IL‐18, and also inducing the

expressionofvariablesrelatedtointestinalbarrierintegrity,particularlyTJP‐1andOcln.Thisoutcomemight

beexplainedbythefactthatupondamage,commensalbacteria‐derivedactivationofTLRsignallingmightbe

given which acts as a defence mechanism, promoting intestinal repair processes

(Rakoff‐Nahoum et al., 2004). Nevertheless, the possibility of a slight inflammatory state in the intestine

couldnotbedisregarded.Inanycase,fromourdataitcouldbetheorisedthattheinsulinsensitizingeffect

producedbytrans‐resveratrolmightbeattributedtoindirectmechanismsthatdonotinvolvechangesongut

microbiotacomposition.Moreover,thecombinationofthestilbenewithquercetinwasfoundto lessenthe

impactoftheflavonolongutmicrobiotaandnosynergisticeffectwasfoundasexpectedinitiallybasedon

thereporteddata(Zhouetal.,2012).The inhibitoryeffectsofquercetinonthesulfationprocessof trans‐

resveratrol has been previously described in vitro and this action has been considered to increase the

bioavailability of trans‐resveratrol (De Santi et al., 2000). In contrast, this feature has not been found in

humans (la Porte et al., 2010). Anyway, in our study, this explanationwould be acceptable to argue if a

synergistic impact of polyphenols on gut microbiota composition was observed in the combined group.

Generaldiscussion

211

Accordingly,wecouldnotspeculateandproposeahypothesisinthisregardandfurtherstudiesarerequired

tofindout if theseeffectsarereproducedandtoelucidatethepathwaythroughwhichthecombinationof

bothpolyphenolsdoesnotshowsynergisticeffectsongutmicrobialecology.

Notably,faecescontainagreatamountofinformationthatmightbeavaluablesourceofdataonthehealth

andnutrition statusof the tested individual (Dedaetal., 2015).Moreover, the straight interplaybetween

polyphenols andgutmicrobial speciesmaybe reflected inmetabolites that areproducedanddetected in

faecalsamples(Duda‐Chodaketal.,2015).Therefore,throughanon‐targetedmetabolomicsapproach,faecal

metaboliteswere analysed to identify candidate compounds thatmightbe generatedas a consequenceof

intestinalmicrobialmetabolicactiononpolyphenols,andviceversa(Chapter6).Inthissense,exploratory

metabolomics allowed the detection of specific microbial co‐metabolites of trans‐resveratrol, namely

dihydroresveratrol and lunularin. Interestingly, non‐targeted metabolomics offers the opportunity to

identifyindicatorsoftheeffectofpolyphenolsonmetabolismprovidinginformationaboutthecontribution

of thetestednaturalcompoundstohealthbeneficialordetrimentaloutcomes. In thiscontext,our interest

wasfocusedonmetabolitesthatweredifferentially identifiedinfaecesofeachexperimentalgroup.Inthis

sense, similarly to the impact of trans‐resveratrol and quercetin supplementation on gut microbiota

composition followed an opposite pattern, a different set of putative metabolites was also identified in

faeces.Trans‐resveratrol groupwas characterized by compounds related to nucleotidemetabolism,while

tentatively identified compounds in quercetin treated rats, were related to carbohydrate derivatives or

conjugates. Moreover, putative metabolites proposed in each intervention group showed a significant

correlationwithcertainbacterialspeciessuggestingapossiblerelationshipbetweengutmicrobialecologyof

theintestine,metabolitesproducedandhostphysiology.Despitethefactthatdetectedcompoundshavenot

been previously reported and no commercial standards have been used to confirm them, these results

revealed that each polyphenol exerted a marked metabolome alteration that was profound enough to

discriminate experimental groups one from each other. Therefore, faecal untargeted metabolomics was

effective indiscriminatingadistinctmetabolic fingerprintbasedon thedietary interventionanimalswere

subjected to and this metabolic signature was considered to be reflective of the overall impact of

trans‐resveratrolandquercetinonhostmetabolism.Thesekindofstudiesareofinterest,particularlyinthe

case of trans‐resveratrol, since its low bioavailability leads to obtain negligible presence of the original

compoundinthebloodstream,thus,itisconsideredthattheseconcentrationsarenotsufficienttojustifythe

beneficial effects of the compound (Tome‐Carneiroetal., 2013).Therefore, it is ofparticular relevance to

detectthemetaboliteormetabolitesthatmightberesponsiblefortheeffectsobserved,ifany.

Polyphenols reach the intestine and may produce alterations at this local site with potential systemic

consequences.Inthissense,theinfluenceofpolyphenolssupplementationatcoloniclevelwasstudiedinvivo

andtheexpressionofspecificgenes‐relatedtoinflammationandintestinalbarrierintegritywerefoundtobe

altered(Chapter5).Thehumanequivalentdosesfortrans‐resveratrolandquercetininapersonof70kgin

our study would be 170 mg/day and 340 mg/day, respectively (Reagan‐Shaw et al., 2008). Concerning

trans‐resveratrol, thedoseprovided issimilartotheonethathasbeenreportedinahumanstudycarried

out with obese healthy people (150 mg/day (99%); resVida™ for 30 days) where beneficial effects on

metabolicprofilehavebeendemonstratedattributedtothecalorie‐restriction‐likeeffectsofthecompound

Generaldiscussion

212

(Timmersetal., 2011). In relation toquercetin, thedoseused inour studydoubled theonesprovided in

human intervention studies conductedwith overweight or obese people withmetabolic syndrome traits

(150mg/day for 6weeks), however in this study although improvements in systolic blood pressure and

plasma concentrations of LDL were shown, no effects on body composition were observed

(Egertetal.,2009).Sincetrans‐resveratrolwasfoundtoproducemodificationsatintestinallevelinvivo,to

furtheranalysetheroleofthestilbeneingeneexpressionprofileinaninflamedintestinalcondition,astudy

wasconductedinCaco‐2cells,ahumanintestinalepithelialcellmodel,stimulatedwithLPSandpretreated

with50μΜoftrans‐resveratrol(Chapter7).Thisdosewasbasedonpreviouslypublishedstudiesthatwere

able to produce effective anti‐inflammatory effects (Cianciulli et al., 2012). Nevertheless, while this dose

seems to be proximate to the levels of total polyphenols present in plasma following ~ 500mg of wine

consumption, in the intestine higher proportions are estimated to reach (Scalbert etal., 2002). From the

microarrayanalysisitwasconcludedthattrans‐resveratrolinhibitedtheexpressionlevelsofgenesinvolved

incellulardevelopment,cellulargrowthandproliferation,aswellastheexpressionofgenesinvolvedinDNA

replication,recombinationandrepair.Incontrast,trans‐resveratrolwasfoundtopromotecellapoptosisin

LPS‐stimulatedCaco‐2 cells.Nevertheless, themajor finding from this studywas the actionof thenatural

compoundontheexpressionofgenesrelatedto lipidmetabolism. Indeed, trans‐resveratrolwasshownto

suppress lipid metabolism, repressing the expression of genes implicated in lipid synthesis i.e. acyl‐CoA

synthetase long‐chain family member 3 (ACSL3) and cholesterol metabolism, such as endothelial lipase

(LIPG),aswellasdownregulatingtheexpressionlevelsoftranscriptionfactorsknownasKrüppel‐likefactor

5(KLF5)andamphiregulin(AREG),whichhavebeentheorisedtobeimplicatedinthismetabolicpathway.

Theactionoftrans‐resveratrolinenterocytesandmorespecifically,itsroleinlipidmetabolismatintestinal

level might be taken into consideration when exploring the mechanisms of the stilbene explaining its

protectiveoutcomesagainstmetabolicdiseasessuchasatherosclerosis.

3. Strengthsandlimitations

This research work provides a descriptive picture of the global impact that polyphenols exert on gut

microbiotacompositionandmetabolomeofdiet‐inducedobeseanimals.Theuseof innovative techniques,

based on the 16S rDNA sequencing and exploratory metabolomic analyses, are a key component to be

highlighted (Odriozola and Corrales, 2015). Indeed, through the experiments carried out, it has been

demonstratedthatthe“Omics”approachesareusefultoidentifymodificationsatdeeptaxonomiclevels,to

identify biomarkers of the effect produced by polyphenols on host metabolome and to discriminate

individuals into different clusters depending on the dietary treatment they have been subjected to.

Nevertheless,nomechanisticstudieshavebeenconductedthatmayhelptoexplainsomedata.Accordingly,

themajorlimitationofthisstudyisthatnoclearassociationcouldbegivenamonghealthbeneficialeffects

foundwiththeadministrationoftrans‐resveratrolandquercetinwithchangesobservedongutmicrobiota

orhostmetabolome.Inconclusion,thisworkopensnewquestionsthatneedtobeaddressedinthefuture

andalsopresentsseverallimitationsthatshouldbedeclared.

Firstly,amajorconcernisrelatedtotheexperimentaldesign.Inordertobeabletostatethatperturbations

observedongutmicrobialecologyarediet‐drivenalterations, inallcases,acontrolgroupofanimalsfeda

Generaldiscussion

213

standarddietforthesameperiodoftimeasthedurationoftheHFSdietaryinterventionwouldberequired.

Thus,at least, the impactofdietonmicrobeswouldbedifferentiatedfromthe impactofadiposity,weight

gain and other obesity‐associated parameters. Still, other confounding factors would be present, as for

instancetheinfluenceofaging.WhenstudyingmetabolomicchangesattheendofaHFSdietarytreatment,

althoughresultswerecompared todataobtained fromacontroldiet‐fedgroup,a similar limitationarose

since there could not be certainty that changes inmetabolite levelswere derived as a result of the diets

consumedortothedevelopedobesity.Inthiscase,anothergroupfedthesameobesogenicdiet,butwhich

didnotdevelopobesity,wouldovercometheproblem.Importantly,alterationsproducedinmetabolomeand

intestinalmicrobes could not be attributed to a specific component of the diet. Thus, itwouldhave been

interesting to be able to differentiate between a high‐fat diet and a high‐sucrose diet to elucidatewhich

macronutrient is mostly affecting. Ideally, if metabolomics and gut microbiota modifications would have

beenconductedinthesameexperiment,abetteroverviewandunderstandingoftheinfluenceofaHFSdiet

ongutmicrobiotacompositionandhostmetabolomewouldbeachieved.

Secondly,samplesizeparticularly in the first twostudies(Chapter1andChapter2)wassmall (n=8and

n=5), which could be stated as a weakness. Moreover, inmost of the studies, inter‐individual variability

between animals is amatter that should be taken into consideration, since variability among individuals

mightbeunmaskingresults that in fact,areofbiological relevance. Indeed,concerning intestinalbacterial

population,therelevanceofinter‐individualvariabilitycomparedtothatofintra‐subjecthasbeenreported

to be strong (Eckburg et al., 2005). Importantly, inter‐individual variability on gut microbes has been

demonstratedtoleadtoadifferentresponsetothediet(ConlonandBird,2015),whichsupportstheneed

forapersonalizedrecommendationconsideringgutmicrobiotaprofile.

Thirdly, another important shortcomingofour research is thatmetabolomicanalyses arenothypothesis‐

driven, but exploratory analysis. As far as the last study is concerned, no confirmationof themetabolites

identified has been conducted by the use of commercial standards and the use of a targeted approach.

Therefore, we could only propose different hypothesis based on candidate compounds that could not be

verified.Likewise,ourstudyhasbeenfocusedongutmicrobiotacomposition“dysbiosis”andthepotentialof

polyphenols to restore it.However, nowadays, it iswidely accepted that the collective genomeof the gut

microbiota, namely the gut microbiome, should be analysed owing to the relevance of these bacterial

functionalities. It seems that there exists a functional core which does not fully correspond to the

phylogenetic core (Gerritsen et al., 2011). Therefore, knowledge about gut microbiota compositional

diversity (“who is present”) does not necessarily lead to the understanding of its function

(“whataretheydoing”)(Lozuponeetal.,2012).

Fourthly,amajorlimitationinallgutmicrobiotaexperimentsisthelackofstandardization.Theproportion

ofbacteriadetectedwillbedependentondifferentfactorsincludingsamplingandstorage,DNAextraction

method,PCRerrors,hypervariableregionsanalysed,primersemployed,butalsoonthesequencingmethod

used.16SsequencingisthemostlargelyappliedtechniqueandtheIlluminasequencing(IlluminaInc.,San

Diego, CA) or 454 pyrosequencing (Roche, Brandfort, CT) the most popular sequencers, but they may

providedifferentinformation(vanBestetal.,2015).

Generaldiscussion

214

Fifthly,gutmicrobiotacompositionanalyseswereperformedinfaecalsamplessincetheyareeasilyobtained

inanon‐invasivemanner.Thissituationrepresentsanotherpotentialshortcomingasthesesamplesmaynot

beagoodpredictorofthewholebacterialcommunityintheGItract(Durbanetal.,2011),whichincontrast,

hasbeenfoundtobesimilarinmucosalbiopsiesalongthecolon(Lepageetal.,2005).

Lastly, in this experimental investigation, a dietary murine model has been chosen to conduct the

experiments.Cautionshouldbepaidwhentryingtomakeparallelsbetweenratsandhumangutmicrobiota,

sincedespitesimilarphysiologyandanatomicalstructures,thereareseveralmacroscopic(i.e.intestinalvilli

longitude, cecum size, presence of appendix) and microscopic (i.e. mucosal wall, distribution of mucin‐

producing goblet cells and Paneth cells) differences that might affect gut microbiota composition, as a

consequenceoftheinfluencesinhost‐microbiotainteractions(Nguyenetal.,2015).

4. Corollary

Thework carried out in the present thesis shows that the intake of a high‐fat high‐sucrose diet and the

developedoverweight/obesityexertsastrongimpactonhostserummetabolomeandgutmicrobialprofile.

Moreover,ourresearchaddsnovelfindingstotheexistingliteratureontheplasticityofgutmicrobiotatobe

manipulatedbydietaryfactorssuchaspolyphenols,emphasizingtherelevanceofintestinalbacterialecology

asatherapeutictargettobeconsideredinmetabolicdiseases,asforinstanceobesity.

Ourworkproposesthattrans‐resveratrolandquercetin,atleastatthedosesadministered,improveinsulin

sensitivityandprotectfrombodyweightgainindiet‐inducedobeseanimalswhencombined.Furthermore,

thiswork shows thatparticularly the aglyconequercetin, is able tomodulate gutmicrobiota composition

dysbiosis, favouring the growth of “friendly” bacteria at the expense of some pathogenicmicrobeswhich

were enhanced as a result of a dietary treatment with an obesogenic diet. In contrast, trans‐resveratrol

induced fewerchanges inmicrobiotacomposition,suggestingthatthebeneficialeffectsof thestilbeneare

not probably related to its effects onmicrobiota.Moreover, it couldbe emphasized that biological effects

exertedbytestedpolyphenols(trans‐resveratrolandquercetin),althoughnotperfectlyelucidated,causeda

significant impact on host faecal metabolome. Importantly, faecal non‐targeted metabolomics approach,

conductedby thecombinationofLC‐MS,hasbeenshown tobea robust technique thatcouldbeuseful in

nutritionscienceevidencingitspotentialtoclusterindividualsbasedonthedietaryinterventiontheyhave

beensubjectedto.Thistechniquehasallowedustodetectanddiscriminatethemetabolicfingerprintofthe

impactofquercetinandtrans‐resveratrolonmetabolism.Besides,thecapacityofthiskindofapproachesto

detect gut microbial co‐metabolites has been proven. Furthermore, based on the fact that beneficial

outcomesofpolyphenolssuchastrans‐resveratrolmightcomefromitsstraightcontactwiththeintestine,in

our study the strong influence produced by such polyphenol at the intestinal level has been revealed,

demonstratingthattrans‐resveratrolcouldaffecttheexpressionpatternsofdifferentgenesinenterocytes,

especiallythoseassociatedtolipidmetabolism.

VI. CONCLUSIONS

Conclusions

217

Conclusions

1. A non‐targeted metabolomics (H1‐NMR) approach identified a differential serum metabolic

fingerprintbetweencontroldiet‐fed leanandHFSdiet‐fedobeserats, reflectingalterations inbiochemical

pathways,specificallyinfluencingaminoacidandproteinmetabolism.

2. High‐throughput next generation sequencing evidenced that the intake of an obesogenic diet

producesstructuralmodificationsingutmicrobiotacompositionleadingtoanobesity‐associatedmicrobial

profile. Intestinalbacterialdysbiosiswas characterizedbymodificationsatdifferent taxonomic levels,not

onlyaffectingtheabundanceofmajorphyla,butalsothelevelsofseveralpathogenicspecies.

3. From all the tested compounds, naringin was the most promising one, producing a slight

ameliorationondiet‐inducedobesity‐associatedfeaturessuchasadiposityandinsulinsensitivity.

4. Insulin sensitizing effects of quercetin might be mediated by the re‐shaping of diet‐induced gut

microbiota,beingparticularlyremarkablethereversionofFirmicutes‐to‐Bacteroidetesratio,theinhibition

ofErysipelotrichaceae andEubacterium cylindroides,and the enhancement ofAkkermansiamuciniphila. In

contrast,trans‐resveratroladministrationscarcelymodifiedthegutmicrobialcommunityprofile.

5. Trans‐resveratrolmodulated the expression of inflammation‐related genes (TLR‐4, IL‐18, LPB) in

colonic mucosa inferring that upon damage, this stilbene induces a defence mechanism through the

activation of pattern recognition receptors that may promote intestinal repair mechanisms, such as the

increaseintightjunctionproteinsandoccludin.

6. Faecal non‐targeted metabolomics identified gut microbial trans‐resveratrol co‐metabolites

(i.e. dihydroresveratrol and lunularin) and compounds related to nucleotide metabolism. However,

candidatecompoundsinthequercetin‐treatedgroupwererelatedtocarbohydratederivativesorconjugates.

Adistinctive faecalmetabolic fingerprintwasdetected,whichmight be reflective of the overall impact of

polyphenolsonhostmetabolome.

7. Pre‐treatment with trans‐resveratrol exerted an inhibitory action on the expression of lipid

metabolism‐related genes in human intestinal cells. Enzymes directly involved in lipid synthesis and

cholesterol metabolism (ACSL3 and LIPG), but also transcription factors (KLF5 and AREG) were

downregulated,suggestingthattrans‐resveratrolmightexertanti‐lipogeniceffectsatintestinal levelunder

pro‐inflammatoryconditions.

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VIII. APPENDICES

243

Appendix1:Roleofdietarypolyphenolsandinflammatoryprocesseson

diseaseprogressionmediatedbythegutmicrobiota

RejuvenationRes,2013

doi:10.1089/rej.2013.1481

Correspondence

Role of Dietary Polyphenols and Inflammatory Processeson Disease Progression Mediated by the Gut Microbiota

J. Alfredo Martınez,1–3 Usune Etxeberrıa,1 Alicia Galar,4 and Fermın I. Milagro1,2

Abstract

Mendelsohn and Larrick have recently discussed in a recent article in Rejuvenation Research that dietary modi-fications of the gut microbiota affect the risk for cardiovascular disease. In this context, dietary patterns andsingle specific nutrients appear to produce singular consequences on the gut microbiota, subsequently impactingon maintenance of well-being and disease onset or evolution, whose intimate influences and mechanisms arenow starting to be disentangled. Thus, the consumption of dietary fiber and particular polysaccharides affectscolonic fermentation processes involving short-chain fatty acid production, accompanying changes in the en-vironmental pH, inhibiting Bacteroides spp., and rising levels of butyrate-producing Gram-positive bacteria. Thisscenario may contribute to the design of novel therapeutic approaches to manipulate gut microbiota to treatcardiovascular diseases and obesity. Indeed, cardiovascular risk may be indirectly dependent on pathwaysassociated with microbe-induced obesity or diabetes through inflammation. Diverse components of the diet,including bioactive molecules with bactericidal functions, such as polyphenols, may play a role on intestinalmucosa inflammation and permeability and contribute to explaining the mutual interactions between obesity,diabetes, and adverse cardiovascular events.

Introduction

Mendelsohn and Larrick have discussed in a recentarticle in Rejuvenation Research that dietary modifica-

tions of the gut microbes affect the risk for cardiovasculardisease.1 Indeed, the intestinal microbiota (gut microbiome)consists of a complex community of microorganisms, mainlybacteria, that have been considered as the forgotten organbecause it may contribute to perhaps 2 kg in mass to thebody weight.2 A broad-range sequencing screening of theenterotypes of the human gut microbiome has providedevidence that despite the fact that a high variability amongindividuals exists, some genes significantly correlate withage and three functional modules are associated with thebody mass index, hinting at a diagnoses potential of micro-bial markers.3

In this context, dietary patterns and single specific nutri-ents appear to produce singular consequences on the gutmicrobiota, subsequently impacting on maintenance of well-being and disease onset or evolution, whose intimate influ-ences and mechanisms are now starting to be disentangled.4

Thus, the consumption of dietary fiber and particularly

polysaccharides affects colonic fermentation processes in-volving short-chain fatty acids production and accompany-ing changes in the environmental pH that inhibit Bacteroidesspp. and increase butyrate-producing Gram-positive bacte-ria.5 On the other hand, subjects on a habitual diet rich inanimal protein could increase Bacteroides populations bysupplying specific amino acids and saturated fat.5

Within this framework, the commentary from Mendel-shon and Larrick1 emphasizes this concept because the in-take of some meat components, such as carnitine and lecithin(phosphatidylcholine), which are metabolized by intestinalmicrobes to trimethylamine (TMA), and in turn by liver en-zymes to form trimethylamine-N-oxide (TMAO), may havedeleterious effects on cardiovascular health mediated by thegut microbiota. These findings complement another recentresearch report from Hazen and collaborators6 that associ-ated the circulating levels of TMAO with an increased inci-dence of major adverse cardiovascular events. Globally,both studies suggest that the presence of specific as yet un-identified gut microorganisms linked to diet are required forinducing high TMAO levels and TMAO-mediated cardio-vascular disease progression.1,6 This scenario may contribute

1Department of Nutrition, Food Science and Physiology, University of Navarra, Pamplona, Spain.2CIBER de Fisiopatologıa de la Obesidad y Nutricion (CIBERObn), Instituto de Salud Carlos III, Madrid, Spain.3Department of Nutrition, Harvard School of Public Health, Boston, Massachusetts.4Division of Infectious Diseases, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts.

REJUVENATION RESEARCHVolume 16, Number 5, 2013ª Mary Ann Liebert, Inc.DOI: 10.1089/rej.2013.1481

435

Documento sujeto a copyright. Uso exclusivo para fines de investigación. Prohibida su distribución o copia.

247

Appendix2:Antidiabeticeffectsofnaturalplantextractsvia inhibition

ofcarbohydratehydrolysisenzymeswithemphasisonpancreaticalpha

amylase

ExpertOpinTherTargets,2012

doi:10.1517/14728222.2012.664134

1. Introduction

2. Antidiabetic plants used in

traditional medicine

3. Pancreatic alpha amylase

inhibitory activity in vitro

4. Phenolic phytochemicals


inhibitory activity of natural

plant extracts in vivo


inhibitory activity of natural

plant extracts in humans

7. Side effects of natural

alpha-amylase inhibitors

8. Conclusions

9. Expert opinion

Review

Antidiabetic effects of naturalplant extracts via inhibition ofcarbohydrate hydrolysis enzymeswith emphasis on pancreaticalpha amylaseUsune Etxeberria, Ana Laura de la Garza, Javier Campion,J Alfredo Martınez† & Fermın I MilagroUniversity of Navarra, Department of Nutrition, Food Science, Physiology and Toxicology,

Pamplona, Spain

Introduction: The increasing prevalence of type 2 diabetes mellitus and the

negative clinical outcomes observed with the commercially available anti-

diabetic drugs have led to the investigation of new therapeutic approaches

focused on controlling postprandrial glucose levels. The use of carbohydrate

digestive enzyme inhibitors from natural resources could be a possible strat-

egy to block dietary carbohydrate absorption with less adverse effects than

synthetic drugs.

Areas covered: This review covers the latest evidence regarding in vitro and

in vivo studies in relation to pancreatic alpha-amylase inhibitors of plant

origin, and presents bioactive compounds of phenolic nature that exhibit

anti-amylase activity.

Expert opinion: Pancreatic alpha-amylase inhibitors from traditional plant

extracts are a promising tool for diabetes treatment. Many studies have con-

firmed the alpha-amylase inhibitory activity of plants and their bioactive com-

pounds in vitro, but few studies corroborate these findings in rodents and

very few in humans. Thus, despite some encouraging results, more research

is required for developing a valuable anti-diabetic therapy using pancreatic

alpha-amylase inhibitors of plant origin.

Keywords: alpha-amylase, flavonoids, proanthocyanidins, tannins, type 2 diabetes mellitus

Expert Opin. Ther. Targets (2012) 16(3):1-29

1. Introduction

Diabetes mellitus is one of the world´s major diseases, with an estimated 347 millionadults affected in 2011 [1]. Type 2 diabetes mellitus, by far the most common type,is a metabolic disorder of multiple etiology characterized by carbohydrate, lipid andprotein metabolic disorders that includes defects in insulin secretion, almost alwayswith a major contribution of insulin resistance [2]. These abnormalities could lead tolesions such as retinopathy, nephropathy, neuropathy and angiopathy [3]. In thiscontext, the inhibition of carbohydrate digestive enzymes is considered a therapeutictool for the treatment of type 2 diabetes [4]. The most important digestive enzyme ispancreatic alpha-amylase (EC 3.2.1.1), a calcium metalloenzyme that catalyzes thehydrolysis of the alpha-1,4 glycosidic linkages of starch, amylose, amylopectin, gly-cogen and various maltodextrins and is responsible of most of starch digestion inhumans. A second enzyme named alpha-glucosidase or maltase (EC 3.2.1.20) cata-lyzes the final step of the digestive process of carbohydrates acting upon 1,4-alphabonds and giving as a result glucose [4].

10.1517/14728222.2012.664134 © 2012 Informa UK, Ltd. ISSN 1472-8222 1All rights reserved: reproduction in whole or in part not permitted

251

Appendix3:Helichrysumandgrapefruitextracts inhibit carbohydrate

digestion and absorption, improving postprandial glucose levels and

hyperinsulinemiainrats

JAgricFoodChem,2013

doi:10.1021/jf4021569

Helichrysum and Grapefruit Extracts Inhibit Carbohydrate Digestionand Absorption, Improving Postprandial Glucose Levels andHyperinsulinemia in RatsAna Laura de la Garza,† Usune Etxeberria,† Marıa Pilar Lostao,†,§ Belen San Roman,# Jaione Barrenetxe,†

J. Alfredo Martínez,*,†,§ and Fermın I. Milagro†,§

†Department of Nutrition, Food Science and Physiology, University of Navarra, Pamplona, Spain§Physiopathology of Obesity and Nutrition, CIBERobn, Carlos III Health Research Institute, Madrid, Spain#Discovery Laboratory, Research Department, Biosearch S.A., 18004 Granada, Spain

ABSTRACT: Several plant extracts rich in flavonoids have been reported to improve hyperglycemia by inhibiting digestiveenzyme activities and SGLT1-mediated glucose uptake. In this study, helichrysum (Helichrysum italicum) and grapefruit (Citrus ×paradisi) extracts inhibited in vitro enzyme activities. The helichrysum extract showed higher inhibitory activity of α-glucosidase(IC50 = 0.19 mg/mL) than α-amylase (IC50 = 0.83 mg/mL), whereas the grapefruit extract presented similar α-amylase and α-glucosidase inhibitory activities (IC50 = 0.42 mg/mL and IC50 = 0.41 mg/mL, respectively). Both extracts reduced maltosedigestion in noneverted intestinal sacs (57% with helichrysum and 46% with grapefruit). Likewise, both extracts inhibitedSGLT1-mediated methylglucoside uptake in Caco-2 cells in the presence of Na+ (56% of inhibition with helichrysum and 54%with grapefruit). In vivo studies demonstrated that helichrysum decreased blood glucose levels after an oral maltose tolerance test(OMTT), and both extracts reduced postprandial glucose levels after the oral starch tolerance test (OSTT). Finally, both extractsimproved hyperinsulinemia (31% with helichrysum and 50% with grapefruit) and HOMA index (47% with helichrysum and 54%with grapefruit) in a dietary model of insulin resistance in rats. In summary, helichrysum and grapefruit extracts improvepostprandial glycemic control in rats, possibly by inhibiting α-glucosidase and α-amylase enzyme activities and decreasingSGLT1-mediated glucose uptake.

KEYWORDS: α-amylase, α-glucosidase, Caco-2 cells, flavonoids, SGLT1

■ INTRODUCTION

Type 2 diabetes mellitus (T2DM) is a metabolic disorder thataffects different organs and tissues and is characterized bychronically elevated levels of blood glucose due to insulinresistance.1 The increasing incidence of T2DM, one of theworld’s most common chronic diseases, is primarily associatedwith lifestyle-related obesity and sedentarism and is modulatedby the genetic background.2,3 Considering that postprandialhyperglycemia is an early detected symptom in T2DM related tooverconsumption of carbohydrate-rich foods, consumption ofphytochemicals occurring in natural products could be a possiblestrategy to control T2DM.4

Phytochemicals are non-nutritive plant compounds that havehealth-protective properties. One of the most relevant families ofphytochemicals with proven health benefits is the polyphenols.They are found in many plants including vegetables, fruits, andgrains,5 being flavonoids the most prevalent phenolic com-pounds. Epidemiological data strongly suggest that diets rich inflavonoids are generally associated with a preventive role againstthe development of chronic diseases.6−8

This situation has led to an increased interest in finding specificnatural dietary sources that could be used for treating diabetes. Inthis sense, numerous studies have investigated flavonoid-richplant extracts that could reduce or suppress intestinal glucoseuptake through the inhibition of digestive enzyme activities.9−16

In mammals, dietary carbohydrates are hydrolyzed bypancreatic α-amylase and intestinal α-glucosidase enzymes.

Hence, the inhibition of these enzymes is an interesting strategyfor the control of postprandial hyperglycemia.8,17,18 Otherapproaches are directed to develop agents that inhibit intestinalglucose uptake.17 Thus, in the intestine, glucose is absorbedmainly by two transporters, depending on the luminal glucoseconcentration. At low concentrations, glucose is transportedacross the brush border membrane against a concentrationgradient by the sodium-dependent glucose transporter 1(SGLT1). At higher concentrations, glucose is transportedmainly by the low-affinity facilitated transporter, glucosetransporter 2 (GLUT2).19 Dietary flavonoids, given their relativesafety and low incidence of adverse gastrointestinal side effects,15

are candidate agents for managing postprandial hyperglycemiadue to their interactions with the intestinal α-glucosidase andpancreatic α-amylase and the inhibition of glucose uptake.17

In this context, there are more than 500 species of theHelichrysum genus distributed around the world.20 Plants of thisgenus have been found to possess antimicrobial, antiallergic,antioxidant, and anti-inflammatory properties.20,21 The bio-logical activities of Helichrysum plants have been attributed toseveral classes of flavonoids detected in different parts of theplant, such as kaempferol-3-O-glucoside and other flavanones.22

Received: May 17, 2013Revised: October 15, 2013Accepted: November 22, 2013Published: November 22, 2013

Article

pubs.acs.org/JAFC

© 2013 American Chemical Society 12012 dx.doi.org/10.1021/jf4021569 | J. Agric. Food Chem. 2013, 61, 12012−12019

255

Appendix 4: Modulation of hyperglycemia and TNFα‐ mediated

inflammationbyhelichrysumandgrapefruitextracts indiabeticdb/db

mice

FoodFuct,2014

doi:10.1039/c4fo00154k

Food &Function

PAPER

Modulation of hy

aDepartment of Nutrition, Food Science

C/Irunlarrea 1, 31008 Pamplona, Navarra

+34 948425740; Tel: +34 948425600bDepartment of Biochemistry and Genetics,cDepartment of Nutritional Sciences, UniverdPhysiopathology of Obesity and Nutrition

Institute, Madrid, Spain

Cite this: Food Funct., 2014, 5, 2120

Received 25th February 2014Accepted 7th June 2014

DOI: 10.1039/c4fo00154k

www.rsc.org/foodfunction

2120 | Food Funct., 2014, 5, 2120–2128

perglycemia and TNFa-mediatedinflammation by helichrysum and grapefruitextracts in diabetic db/db mice

Ana Laura de la Garza,a Usune Etxeberria,a Sara Palacios-Ortega,b

Alexander G. Haslberger,c Eva Aumueller,c Fermın I. Milagroad

and J. Alfredo Martınez*ad

Type-2 diabetes is associated with a chronic low-grade systemic inflammation accompanied by an

increased production of adipokines/cytokines by obese adipose tissue. The search for new antidiabetic

drugs with different mechanisms of action, such as insulin sensitizers, insulin secretagogues and a-

glucosidase inhibitors, has directed the focus on the potential use of flavonoids in the management of

type-2 diabetes. Thirty six diabetic male C57BL/6J db/db mice were fed a standard diet and randomly

assigned into four experimental groups: non-treated control, (n ¼ 8); acarbose (5 mg per kg bw, n ¼ 8);

helichrysum (1 g per kg bw, n ¼ 10) and grapefruit (0.5 g per kg bw, n ¼ 10) for 6 weeks. The mRNA

expression in pancreas, liver and epididymal adipose tissue was determined by RT-PCR. DNA methylation

was quantified in epididymal fat using pyrosequencing. Mice supplemented with helichrysum and

grapefruit extracts showed a significant decrease in fasting glucose levels (p < 0.05). A possible

mechanism of action could be the up-regulation of liver glucokinase (p < 0.05). The antihyperglycemic

effect of both extracts was accompanied by decreased mRNA expression of some proinflammatory

genes (monocyte chemotactic protein-1, tumor necrosis factor-a, cyclooxygenase-2, nuclear factor-

kappaB) in the liver and epididymal adipose tissue. The CpG3 site of TNFa, located 5 bp downstream of

the transcription start site, showed increased DNA methylation in the grapefruit group compared with

the non-treated group (p < 0.01). In conclusion, helichrysum and grapefruit extracts improved

hyperglycemia through the regulation of glucose metabolism in the liver and reduction of the expression

of proinflammatory genes in the liver and visceral fat. The hypermethylation of TNFa in adipose tissue

may contribute to reduce the inflammation associated with diabetes and obesity.

Introduction

Type 2 diabetes mellitus (T2DM) is a metabolic disorder char-acterized by chronic hyperglycemia as a result of impairmentsin insulin secretion and insulin action in target tissues.1 Insulinresistance (IR) is produced as soon as the pancreatic b-cellscannot compensate a reduced insulin function, leading toelevated circulating glucose levels.2 Insulin inhibits gluconeo-genesis in the liver and reduces lipolysis in adipose tissue.3

Likewise, adipose tissue in diabetes and obesity is characterizedby hypertrophy, relative hypoxia, low-grade chronic inamma-tion and endocrine dysfunctions.4 In this context, the pro-

and Physiology, University of Navarra,

, Spain. E-mail: [email protected]; Fax:

University of Navarra, Pamplona, Spain

sity of Vienna, Vienna, Austria

, CIBERobn, Carlos III Health Research

inammatory cytokines, many of them secreted by the hyper-trophied adipocytes, are controlled through transcriptionnuclear factor-kappaB (NF-kB), whereby the inammatoryresponse can be down-regulated.5 In addition, this transcriptionfactor represents a link between inammation and IR, as it isactivated by factors known to promote IR and T2DM.6 Oneimportant downstream target of NF-kB is cyclooxygenase 2(COX2), which catalyzes the production of prostaglandins, thekey molecules in inammation processes of the body.7 More-over, NF-kB is involved in the expression of many cytokines,including TNFa.5 On the other hand, epigenetic changes areheritable yet reversible modications that occur without alter-ations in the primary DNA sequence. These modications mayprovide a link between the environment (i.e. nutrition) andT2DM.8 Recently, epigenetic modications have also beenimplicated in disease-associated changes inuencing geneexpression.9

Targeting the reduction of chronic inammation is a bene-cial strategy to combat several metabolic diseases, includingT2DM.10 Thus, numerous studies have underlined the interest

This journal is © The Royal Society of Chemistry 2014

http://crossmark.crossref.org/dialog/?doi=10.1039/c4fo00154k&domain=pdf&date_stamp=2014-08-17

259

Appendix5:Helichrysum and grapefruit extractsboostweight loss in

overweightratsreducinginflammation

JMedFood,2015

doi:10.1039/c4fo00154k

Helichrysum and Grapefruit Extracts Boost Weight Lossin Overweight Rats Reducing Inflammation

Ana Laura de la Garza,1,2 Usune Etxeberria,1,2 Alexander Haslberger,3 Eva Aumueller,3

J. Alfredo Martınez,1,2,4 and Fermın I. Milagro1,2,4

1Department of Nutrition, Food Science and Physiology, University of Navarra, Pamplona, Spain.2Centre for Nutrition Research, Faculty of Pharmacy, University of Navarra, Pamplona, Spain.

3Department of Nutritional Sciences, University of Vienna, Vienna, Austria.4Physiopathology of Obesity and Nutrition, CIBERobn, Carlos III Health Research Institute, Madrid, Spain.

ABSTRACT Obesity is characterized by an increased production of inflammatory markers. High levels of circulating free

fatty acids and chronic inflammation lead to increased oxidative stress, contributing to the development of insulin resistance

(IR). Recent studies have focused on the potential use of flavonoids for obesity management due to their antioxidant and anti-

inflammatory properties. This study was designed to investigate the antioxidant and anti-inflammatory effects of helichrysum

and grapefruit extracts in overweight insulin-resistant rats. Thirty-eight male Wistar rats were randomly distributed in two

groups: control group (n= 8) and high-fat sucrose (HFS) group (n= 30). After 22 days of ad libitum water and food access, the

rats fed HFS diet changed to standard diet and were reassigned into three groups (n= 10 each group): nonsupplemented,

helichrysum extract (2 g/kg bw), and grapefruit extract (1 g/kg bw) administered for 5 weeks. Rats supplemented with both

extracts gained less body weight during the 5-week period of treatment, showed lower serum insulin levels and liver TBARS

levels. Leptin/adiponectin ratio, as an indicator of IR, was lower in both extract-administered groups. These results were

accompanied by a reduction in TNFa gene expression in epididymal adipose tissue and intestinal mucosa, and TLR2

expression in intestinal mucosa. Helichrysum and grapefruit extracts might be used as complement hypocaloric diets in weight

loss treatment. Both extracts helped to reduce weight gain, hyperinsulinemia, and IR, improved inflammation markers, and

decreased the HFS diet-induced oxidative stress in insulin-resistant rats.

KEY WORDS: � flavonoids � grapefruit � helichrysum � insulin resistance � TLR2 � TNFa

INTRODUCTION

H igh-fat diets may play an important role in the in-creased prevalence of obesity.1 Obesity is a multifac-

torial disease usually accompanied by insulin resistance (IR)and an increase in oxidative stress and inflammatory mark-ers, including tumor necrosis factor-a (TNFa) and toll-likereceptors (TLRs).2 TNFa is an important mediator of IRacting as a link between obesity and inflammation.3 Adiposetissue not only stores fat, but also acts as a major endocrineand secretor organ that produces different adipokines suchas leptin and adiponectin.4 Thus, leptin is a hormone se-creted by adipocytes that regulates energy homeostasis,5

while adiponectin is an anti-inflammatory adipokine thatimproves insulin sensitivity.6 TLRs play a crucial role in theinnate immune response, being their expression elevated

in obese adipose tissue.7 There is increasing evidence con-cerning the involvement of TLRs in obesity-induced in-flammation and IR.8 In this sense, TLR2 has been shown tobe stimulated by free fatty acids, and the activation of thesereceptors has been found to induce IR.9 Likewise, oxidativestress is a mediator mechanism implicated in these patho-logical conditions and contributes to the release of cytokinesand inflammation.10 Oxidative stress induces tissue damage,which is responsible for the lower insulin sensitivity indifferent tissues.11

Numerous strategies have been developed to treat chronicdiseases. Natural extracts have been studied as dietary sup-plements for body weight management. Helichrysum andgrapefruit extracts contain different kind of flavonoids,12,13

and they are appreciable candidates like antioxidants and anti-inflammatory agents because they are abundant in nature andmay have fewer side effects.14,15 Likewise, other investiga-tions demonstrated antihyperglycemic and anti-inflammatoryproperties of helichrysum and grapefruit extracts in diabeticmice,16 or the role of naringenin (a flavanone occurring in bothextracts) concerning the hypocholesterolemic properties in

Manuscript received 26 May 2014. Revision accepted 2 December 2014.

Address correspondence to: J. Alfredo Martınez, PhD, MD, RN, Department of Nutrition,Food Science and Physiology, University of Navarra, C/Irunlarrea 1, Pamplona 31008,Navarra, Spain, E-mail: [email protected]

JOURNAL OF MEDICINAL FOODJ Med Food 18 (8) 2015, 890–898# Mary Ann Liebert, Inc., and Korean Society of Food Science and NutritionDOI: 10.1089/jmf.2014.0088

890

263

Appendix6:Congresscommunications

265

Poster

20thInternationalCongressofNutrition(IUNS)

AnnalsofNutritionandMetabolism.2013;63(1):1264(P02085);Granada(Spain),15th‐20thSept2013

Effectoftheintakeofahigh‐fatsucrosedietongutmicrobiotacompositioninrats

EtxeberriaU1,AriasN2,MacarullaMT2,3,PortilloMP2,3,MilagroFI1,3,MartínezJA1,3

1DepartmentofNutrition,FoodScienceandPhysiology,UniversityofNavarra,Pamplona,Spain

2DepartmentofNutritionandFoodScience,FacultyofPharmacy,UniversityofPaísVasco,Vitoria,Spain

3CIBERFisiopatología,ObesidadyNutrición(CIBERObn),InstitutodeSaludCarlosIII,Spain

Background and objective: Intestinal microbiota has been recently considered as an important factor related to

obesity.Gutmicrobiotaisalteredasaconsequenceofalong‐termdietarymanipulation,whichcouldhaveametabolic

impact and affect host health. In this sense, the understanding of the diet‐responsive groups of bacteria could give

valuableinformationtotreatdiet‐inducedobesityandaccompanyingcomplications.Therefore,theaimofthisstudywas

toinvestigatetheeffectproducedbytheintakeofahigh‐fatsucrosedietongutmicrobiotacompositioninrats.

Methods:MaleWistarratswerefedonahigh‐fatsucrose(HFS)dietduring6weeks.Faeceswerefreshlycollectedat

baselineandattheendofthetreatment.DNAwasextractedandanalyzedbyRT‐qPCR.Specificprimersforthefollowing

groups of bacteria were used: Bacteroidetes, Bacteroides‐Prevotella, Clostridium coccoides‐ Eubacterium rectale,

Bifidobacteriumspp.,Lactobacillusgroup,EnterobacteriaceaeandActinobacteria.

Results:High‐fat sucrose diet treatment significantly (p< 0.05) reduced the levels of Bacteroidetes and Clostridium

coccoides‐Eubacteriumrectale.However,nostatisticaldifferenceswerefoundinBifidobacteriumspp.,Lactobacillusspp.,

Bacteroides‐ Prevotella, Actinobacteria and Enterobacteriaceae levels at the end of the study when compared to the

baseline.

Conclusion: This study showed that the intake of an obesogenic diet rich in fat and sucrose induces changes in the

populationsofgutmicroorganisms,confirmingthatdietisshapingthecompositionofgutmicrobiota.HFSdiet‐induced

obesityisaccompaniedbylowerlevelsofoneofthemajorbacterialphylainmammaliangutmicrobiota.

Keywords:Microbiota,diet‐inducedobesity,gastrointestinaltract,RT‐qPCR

Acknowledgemets:TheauthorsthankthefinancialsupportoftheDepartmentofEducation,UniversitiesandResearch

of Basque Government for the pre‐ doctoral grant given to Usune Etxeberria Aranburu. LE/97 of the University of

Navarraisalsogratefullycredited.

266

Poster

21stEuropeanCongressonObesity(ECO)

ObesityFacts.2014;7(1):139(PO.094);Sofia(Bulgaria),28th‐31stMay2014

Evidenceofgutmicrobialspeciesdifferentialcolonizationunderamodelofhigh‐fatsucrosedietaryintake:a

high‐throughputapproachby16SrDNApyrosequencing

EtxeberriaU1,AriasN2,MacarullaMT2,3,PortilloMP2,3,MartinezJA1,3,MilagroFI1,3

1DepartmentofNutrition,FoodScienceandPhysiology,UniversityofNavarra,Pamplona,Spain


Country(UPV/EHU),Vitoria,Spain

3CIBERobnFisiopatologíadeObesidadyNutrición.InstitutodeSaludCarlosIII,Madrid,Spain

Introduction:Gutmicrobiota (GM) is recognized as the bacterial community that colonizes the gastrointestinal tract

(GI). These commensal microbes perform essential metabolic functions for the host, while modifications on the

composition of microbial population are known to influence the susceptibility to several diseases such as obesity.

Westernizeddiets,richinfatandsucrosemacronutrients,couldproducegutmicrobiotadysbiosisresultingingrowthor

inhibition of both pathogenic or beneficial bacterial species. The aim of the present study was to characterize gut

microbialchangesproducedasaconsequenceoffeedingratswithadietrichinfatandsucrose(HFS).

Material and Methods: Comparison of the faecal microbiota composition at baseline and after a 6‐week HFS

interventionwasperformed.DNAextractedfromratfaeceswasanalysedbytheamplificationoftheV4‐V6regionofthe

16SrDNA(ribosomalDNA)genetailedoneachendwiththeRochemultiplexidentifiersby454pyrosequencing.

Results: HFS diet feeding not only produced an increase in animal weight and adiposity‐related markers, but also

displayed statistically significant differences in gutmicrobiota composition.At phylum level,Firmicutes,Bacteroidetes

andProteobacteria,weremarkedlyaffectedandanimportant“bloom”inErysipelotrichiclasswasfoundattheendofthe

dietary intervention. Species fromVeillonellaceae,PrevotellaceaeandClostridiaceae familieswere themost influenced

aftertheHFSdietconsumption.

Conclusion: This study illustrates the potential of culture‐independent methods to characterize and more precisely

identifymembersofthegutmicrobiotaasinfluencedbydietsenrichedinfatandsucroseinvolvingacquiredoverweight.

Funding:This studywas supported by grants fromMinisterio de Economía y Competitividad (AGL2011‐27406‐ALI),

Instituto de Salud Carlos III (CIBERobn), Government of the Basque Country (IT‐572‐13), University of the Basque

Country(UPV/EHU)(ELDUNANOTEKUFI11/32)andtheLíneaEspecialaboutNutrition,ObesityandHealth(University

ofNavarraLE/97).

267

Poster

BelgianNutritionSociety(IUNS)

ArchivesofPublicHealth2014,72(Suppl1):P7,2014;Brussels(Belgium),25thApril2015

Supplementationwithcruderhubarbextractlessensliverinflammationandhepaticlipidaccumulationina

modelofacutealcohol‐inducedsteato‐hepatitis

NeyrinckAM*,EtxeberriaU*,JouretA,DelzenneNM

LouvainDrugResearchInstitute,MetabolismandNutritionResearchGroup,UniversitécatholiquedeLouvain,Brussels,

Belgium

*Theseauthorscontributedequallytothiswork

Background: Binge consumption of alcohol is an alarming global health problem and is implicated in the

pathophysiologyofalcoholicliverdisease(ALD)[1].Initsearlystages,ALDitischaracterizedbyfattyliver[1].Moreover,

numerousstudiesaresupporting theconcept thatbeyondthishepaticsteatosisphysiologicalprocesses, inflammation

occurred in the progression of the disease [2]. Strategies directed to reduce fat accumulation and local hepatic

inflammationcausedbyalcoholconsumptionmightsucceedblockingtheevolutionofALD.Inthissense,naturalplants

richinbioactiveconstituentsareattractingagrowinginterestasnewtherapeuticagents.Severalstudieshavereported

healthbenefitscomingfromrhubarbextractrichinanthraquinones[3,4].Theaimofthepresentstudywastotestthe

potential hepatoprotective effects of rhubarb extract supplementation in a model of acute alcohol‐induced steato‐

hepatitis.

Materialandmethods:MaleC57Bl6Jmicewerefedwithacontroldietsupplementedornotwith0.3%rhubarbextract

(LaboratoiresOrtis, Belgium). After 17 days,mice received a huge dose of ethanol (6g/kg bw) andwere sacrificed 6

hoursafterthealcoholchallenge.LiveroilredOstainingandhepaticlipidcontentsweredeterminedtoassesshepatic

steatosiswhereastheexpressionofpro‐inflammatorymarkerswereanalysedinthelivertissuebyquantitativePCRto

assesshepaticinflammation.

Results: Ethanol administration caused a massive increase of hepatic triglycerides and, in a lesser extent, in total

cholesterol inside the liver tissue (table 1). Rhubarb extract decreased hepatic triglyceride content. This effect was

confirmedbyhistologicalanalysis(figure1).Interestingly,rhubarbextractsupplementationbluntedtheethanol‐induced

F4/80 expression, suggesting a lower recruitment of inflammatory cells inside the liver (figure 2). Moreover,

proinflammatorymarkers such as TNF‐α, IL‐6, MCP‐1 and COX‐2were down‐regulated in the liver ofmice fedwith

rhubarbextract(figure2).

Conclusions:Theseresultssuggestthatrhubarbextractrichinanthraquinonesmightdecreaselivertissueinjury,

namelyhepaticlipidaccumulationandinflammatorydisorderscausedbyacutealcoholconsumption.

Acknowledgements:FinancialsupporthasbeenprovidedbyagrantfromtheWalloonRegion(Nutrigutiorproject,

Convention6918)

268

Poster

XVICongressoftheSpanishNutritionSociety(SEÑ)

NutriciónHospitalaria.2014;30(1):34(PO067);Pamplona(Spain),3rd‐5thJul2014

Lasuplementaciónconquercetinarevierteloscambiosenlacomposicióndelamicrobiotaintestinalasociadasa

laingestadeunadietaricaengrasasyazúcares

EtxeberriaU1,AriasN2,MacarullaMT2,3,PortilloMP2,3,MilagroFI1,3,MartínezJA1,3

1DepartamentodeCienciasdelaAlimentación,FisiologíayNutrición,UniversidaddeNavarra,Pamplona,España

2Área de Nutrición y Bromatología, Facultad de Farmacia, Universidad del País Vasco (UPV/EHU), Paseo de la

Universidad7,01006,Vitoria,España

3CIBERobnFisiopatologíadelaObesidadyNutrición(CIBERobn),InstitutodesaludCarlosIII,28029Madrid,España

Introducción: Lamicrobiota intestinal, comoagentequemodulael riesgodepadecerobesidad,ha sidoampliamente

descrita loqueha contribuidoa la realizacióndenumerosos estudiosparadeterminar la relevanciade losdiferentes

gruposbacterianoseneldesarrollodelamisma.Ladietaesunfactorclavequemodificaelecosistemamicrobianodel

intestino, pudiendo generar un desequilibrio entre los diferentes grupos bacterianos. Debido a la estrecha relación

metabólicaexistenteentrelosnutrientesingeridosylasbacterias,lacomposicióndelamicrobiotaintestinalpuedeser

modificadaconelfindefavorecerelcrecimientodelasespecies“beneficiosas”frentealas“patógenas”.

Objetivo: El objetivo del presente trabajo fue esclarecer el potencial de los polifenoles resveratrol y quercetina para

revertirladisbiosisprovocadaporelconsumodeunadietaricaengrasayenazúcaresinvivo.

Materialymétodos:Elexperimentoserealizócon23ratasWistaralimentadasconunadietaaltaengrasasyazúcares

durante6semanas.Grupocontrol(HFS,n=5),gruporesveratrol(RSV;15mg/kgp.c/día,n=6),grupoquercetina(Q;30

mg/kgp.c/día,n=6)ygruposuplementadoconlacombinacióndeambospolifenolesalamismadosis(RSV+Q,n=6).El

DNAdelashecesfueextraídoyelanálisisfilogenéticorealizadoatravésdelapirosecuenciación.

Resultados:LasuplementaciónconquercetinaredujosignificativamentelosnivelesdelgrupoFirmicutes(‐34.2%)yel

ratio Firmicutes/Bacteroidetes disminuyó considerablemente (‐4.9 %). Además, la suplementación con quercetina

provocóunadisminuciónsignificativaenlafamiliaErysipelotrichaceae(‐83.9%).Alcontrario,losgrupossuplementados

con resveratrol o la combinación de ambos compuestos no mostraron cambios significativos tras el test de

comparacionesmúltiples.

Conclusión: La suplementación con quercetina mitigó los cambios en la composición de la microbiota intestinal

asociadosalaingestadeunadietadetipooccidental.

269

Poster

3rdCongressoftheSpanishFederationofSocietiesinNutrition,FoodandDietetics(FESNAD)

NutriciónClínicaenMedicina;IX(1):4(P099);Sevilla(Spain),5th‐7thMarch2015.

Perfilmetabolómicofecalenanimalessuplementadosconresveratrol,quercetinaylacombinacióndeambos

compuestos

EtxeberriaU1,2,AriasN3,5,BoquéN4,MacarullaMT3,5,PortilloMP3,5,MilagroFI1,2,5,MartínezJA1,2,5

1DepartamentodeCienciasdelaAlimentaciónyFisiología,UniversidaddeNavarra,Pamplona

2CentrodeInvestigaciónenNutrición,UniversidaddeNavarra,Pamplona

3ÁreadeNutriciónyBromatología,FacultaddeFarmacia,UniversidaddelPaísVasco(UPV/EHU),Vitoria

4GrupodeInvestigaciónenNutriciónySalud(GRNS).CentroTecnológicodeNutriciónySalud(CTNS),TECNIO,CEICS,

Reus

5CIBERFisiopatologíadelaObesidadyNutrición(CIBERobn),InstitutodeSaludCarlosIII,Madrid

Introducción: La metabolómica permite detectar cambios en metabolitos asociados a enfermedades, y resulta una

herramientaútilparacaracterizar lacontribuciónde ladietay/o loscomponentes finalesprocedentesde lamismaal

bienestargeneral.

Objetivo:Identificarloscambiosmetabolómicosobservadosenlashecesenrelaciónconlasuplementacióndietéticade

doscompuestosbioactivos,trans‐resveratrolyquercetina,enanimalesalimentadosconunadietaaltaengrasayazúcar.

Métodos:Lacromatografíadelíquidos(LC)acopladaalaespectrometríademasascomosistemadedetección(TOF)fue

utilizadaparallevaracabounanálisisnodirigidoenheces.LasmuestrasseobtuvieronderatasWistarpertenecientesa

lossiguientesgruposexperimentales:Control(HFS,n=5),Resveratrol(RSV;15mg/kgpc/día,n=6),Quercetina(Q;30

mg/kgpc/día,n=6)ylaMezcladeambos(RSV+Q,n=6).ElanálisisserealizómedianteANOVAdeunfactorseguidode

la prueba HSD de Tukey y los resultados estadísticamente significativos se corrigieron por elmétodo de Benjamini‐

Hochberg.Elanálisisdecomponentesprincipales(PCA)sellevóacaboconelsoftwareMassProfilerProfessional.

Resultados: El análisis de los datos evidenció diferencias estadísticamente significativas en 59 de los metabolitos

cuantificados(p<0,05).ElgrupoQuercetinasecaracterizóporlapresenciadiferencialde15metabolitos(p<0,05,Log

FC> 5 o Log FC< ‐5). Algunos de losmetabolitos candidatos identificados se relacionaron con elmetabolismo de los

hidratosdecarbono.Asuvez,elgrupoResveratrolsedistinguióporlapresenciadiferencialdeotros16metabolitos(p<

0,05,LogFC>5oLogFC<‐5),reflejandocambiosenelmetabolismode losaminoácidos, los lípidosy losnucleótidos,

preferentemente.

Conclusiones: Los metabolitos detectados reflejaron modificaciones en varias vías metabólicas y permitieron la

estratificaciónporPCAdelosgruposexperimentalesenfuncióndelostratamientos.

270

Poster

37thCongressofTheEuropeanSocietyforClinicalNutritionandMetabolism(ESPEN)

ClinicalNutrition;4(1):S142(PP040);Lisboa(Portugal),5th‐6thSeptermber2015.

Acruderhubarbextractprotectsfromacuteethanol‐inducedliverdamageinassociationwithincreased

Akkermansiamuciniphilapopulationinthegutmicrobiotaofmice

NeyrinckAM1*,EtxeberriaU1*,TaminiauB2,DaubeG2,CaniPD1,BindelsLB1,DelzenneNM1

1LouvainDrugResearchInstitute,MetabolismandNutritionResearchGroup,UniversitécatholiquedeLouvain,Brussels,

Belgium

2FundamentalandAppliedResearchforAnimalandHealth,UniversitédeLiège,Belgique

*Theseauthorscontributedequallytothiswork

Rationale:Bingeconsumptionofalcoholisanalarmingglobalhealthproblemandisinvolvedinthepathophysiologyof

alcoholic liver disease (ALD). Strategies directed to reduce fat accumulation and hepatic inflammationmight succeed

blocking the evolution of ALD in its early stage. Several studies have reported health benefits coming frombioactive

constituents of rhubarb extract, such as anthraquinones. The aim of the present study was to test the potential

hepatoprotectiveeffectsofrhubarbextractsupplementationinamodelofacutealcohol‐inducedsteato‐hepatitisandto

determinewhethertheseeffectscanberelatedtoamodulationofthegutmicrobiota.

Methods:MaleC57Bl6Jmicewerefedacontroldietsupplementedornotwith0.3%rhubarbextract(LaboratoiresOrtis,

Belgium).After17days,micereceivedanintragatricadministrationofethanol(6g/kgbw)andwerenecropsied6hours

afterthealcoholchallenge.

Results:Ethanoladministrationcausedamassivehepaticsteatosis,asshownbyoilredstaining.Rhubarbextractdidnot

significantlymodifiedhepaticlipidcontent.Importantly,asignificantdown‐regulationinkeyinflammatorygenes(TNF‐

α, IL‐6,MCP‐1)andmacrophage‐relatedmarkers(F4/80,CD68,CD11c) inthe livertissuewereevidenceduponcrude

extractsupplementation.CombinationofpyrosequencingandqPCRanalysesofthe16SrRNAgenerevealedthatrhubarb

extract decreased microbial diversity and richness of caecal microbiota which was associated with a huge increase

Akkermansiamuciniphilaproportion.Inaddition,theexpressionoftwoantimicrobialproteins(Reg3gandPla2g2a)was

upregulatedfollowingrhubarbtreatment.

Conclusion:Rhubarbextract rich inanthraquinonesdecreasedbingedrinking‐induced liver inflammation.Thiseffect

was associated with both a higher Akkermansia muciniphila abundance and a regulation of antimicrobial protein

secretion in the colon, suggesting an improved gut barrier function. Our study supports the importance of research

focusingongutmicrobes‐hostinteractionsformanagingsystemicdiseases,suchasALD

271

Appendix7:Othercongresscommunications

273

‐ “Metabolomicanalysisofserumfromobeseratsfedahigh‐fatsucrosedietwithanemphasisinaminoacids

profile”.EtxeberriaU,de laGarzaAL,Martínez JA,MilagroFI.9thComunidaddeTrabajode losPirineosMeeting.

Toulouse,France,2012(20July).Oralcommunication.

‐ “Búsquedadeextractosricosenpolifenolesconactividad inhibidorade laα‐amilasapancreática:efectos

sobre lapruebaoraldetoleranciaalalmidónenratas.”de laGarzaAL,EtxeberriaU,MilagroFI,OlivaresM,

BañuelosO,CampiónJ,MartínezJA.XIVCongressoftheSpanishNutritionSociety(SEÑ),Zaragoza,Spain,2012(27‐

29September).NutrHosp2012;27(5):1728(PO76).

‐ “Polyphenolicinhibitorsofpancreaticalpha‐amylaseretardprogressiontodiabetesindb/dbmice.”dela

Garza AL, Etxeberria U, Campión J, Olivares M, Bañuelos O, Martínez JA, Milagro FI. EMBO│EMBL Symposium

DiabetesandObesity.Heidelberg,Germany,2012(13‐16September).

‐ “Antiobesityandantihyperglicemiceffectofnaturalinhibitorsofpancreatic‐amylaseinwistarrats.”A.L.

de laGarza,U. Etxeberria, J. Campión,M.Olivares, Bañuelos,A.Martínez, F.Milagro.19thEuropeanCongresson

Obesity(ECO),Lyon,France,2012(6‐9May).ObesFacts2012;5(Suppl1):143(530).

‐ “Anti‐inflammatoryandantioxidantpropertiesof flavonoid‐richextracts fromgrapefruitandhelichysum

ininsulinresistantrats.”delaGarzaAL,U.EtxeberriaU,CampiónJ,OlivaresM,BañuelosO,MartínezJA,Milagro

FI. IUNS 20th International Congress of Nutrition (ICN), Granada, Spain, 2013 (15‐20 September). Oral

Communication.AnnNutrMetab2013;63(suppl.1):244(PO172).

‐ “La suplementación con extractos de helicriso y pomelo ricos en flavonoides disminuye la inflamación

asociadaaladiabetesenratonesdb/db.”delaGarzaAL,U.EtxeberriaU,HaslbergA,AümullerE,MartínezJA,

MilagroFI.XVICongressoftheSpanishNutritionSociety(SEÑ),Pamplona,Spain,2014(3‐5July).NutrHosp2014;

30:37(PO66).

‐ “Modulatoryeffectofresveratrolandquercetinonhigh‐fatsucrosediet‐inducedgutmicrobiotadysbiosis”.

EtxeberriaU,AriasN,MacarullaMT,PortilloMP,MilagroFI,Martínez JA.HerrenhausenConference:Beyond the

IntestinalMicrobiome.Hanover,Germany,2014(8‐10October).

‐ “Evaluacióndecincocompuestosbioactivosconpotencialimplicaciónenlaprevencióndelaobesidadyla

resistencia a la insulina inducida por una dieta alta en grasa y sacarosa”. de la Garza AL, Etxeberria U,

Martínez JA,MilagroFI.XXCongressofAMMFEN (AsociaciónMexicanadeMiembrosdeEscuelasyFacultadesde

Nutrición),Cancún,México,2015(5‐8May).

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