chemical impacts of the microbiome across scales reveal novel … · 1 1 chemical impacts of the...

1

Chemical Impacts of the Microbiome Across Scales Reveal Novel Conjugated Bile Acids 1

21,2Robert A. Quinn, 3Alison Vrbanac, 1Alexey V. Melnik, 3Kathryn A. Patras, 1Mitchell Christy, 31Andrew T. Nelson, 1Alexander Aksenov, 1,3Anupriya Tripathi, 3Greg Humphrey, 1Ricardo da 4

Silva, 4Robert Bussell, 5Taren Thron, 1Mingxun Wang, 1Fernando Vargas, 1Julia M. Gauglitz, 51Michael J. Meehan, 3Orit Poulsen, 6Brigid S. Boland, 6John T. Chang, 6William J. Sandborn, 63Meerana Lim, 7,8Neha Garg, 9Julie Lumeng, 10Barbara I. Kazmierczak, 10Ruchi Jain, 10Marie 7

Egan, 3Kyung E. Rhee, 3Gabriel G. Haddad, 1Dionicio Siegel, 5Sarkis Mazmanian, 1,2,3Victor 8

Nizet, 2,3,12Rob Knight and 1,2Pieter C. Dorrestein 9

101Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San 11

Diego, La Jolla, CA 122Center for Microbiome Innovation, University of California San Diego, La Jolla, CA 133Department of Pediatrics, University of California San Diego, La Jolla, CA 144Department of Radiology, University of California San Diego, La Jolla, CA 155Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, 16

CA 176Division of Gastroenterology, Department of Medicine, University of California San Diego, La 18

Jolla, CA. 197School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, GA 208Emory-Children’s Cystic Fibrosis Center, Atlanta, GA 219Department of Pediatrics, University of Michigan, Ann Arbor, MI 2210Department of Internal Medicine, Yale School of Medicine, New Haven, CT 2311Department of Pediatrics, Yale School of Medicine, New Haven, CT 2412Department of Computer Science and Engineering, University of California San Diego, La 25

Jolla, CA 26

27

Contributions 28

PCD, RK and RQ designed the project 29

RQ, AA, AM, FV, JG, NG, AT, MC, ATN, MM, GH, RdS, and RB generated data 30

RQ, AV, AT and MC analyzed data 31

RQ, BB, ML, OP, JC, ML, JL, KP, BK, RJ, ME, KR, GH, KR, GC, WS and RB collected 32

samples 33

.CC-BY-NC-ND 4.0 International licenseis made available under aThe copyright holder for this preprint (which was not peer-reviewed) is the author/funder. It. https://doi.org/10.1101/654756doi: bioRxiv preprint

https://doi.org/10.1101/654756

http://creativecommons.org/licenses/by-nc-nd/4.0/

2

PCD, RK, SM, VN and DS guided experimental design and analysis. 34

MW converted the data in GNPS, developed spectral search and molecular explorer. 35

TT, VN and SM raised animals and guided experimental design. 36

RQ and PD wrote the manuscript 37

38

Abstract 39

A mosaic of cross-phyla chemical interactions occurs between all metazoans and their 40

microbiomes. In humans, the gut harbors the heaviest microbial load, but many organs, 41

particularly those with a mucosal surface, associate with highly adapted and evolved 42

microbial consortia1. The microbial residents within these organ systems are increasingly well 43

characterized, yielding a good understanding of human microbiome composition, but we have 44

yet to elucidate the full chemical impact the microbiome exerts on an animal and the breadth 45

of the chemical diversity it contributes2. A number of molecular families are known to be 46

shaped by the microbiome including short-chain fatty acids, indoles, aromatic amino acid 47

metabolites, complex polysaccharides, and host lipids; such as sphingolipids and bile acids3–4811. These metabolites profoundly affect host physiology and are being explored for their roles 49

in both health and disease. Considering the diversity of the human microbiome, numbering 50

over 40,000 operational taxonomic units12, a plethora of molecular diversity remains to be 51

discovered. Here, we use unique mass spectrometry informatics approaches and data 52

mapping onto a murine 3D-model13–15 to provide an untargeted assessment of the chemical 53

diversity between germ-free (GF) and colonized mice (specific-pathogen free, SPF), and 54

report the finding of novel bile acids produced by the microbiome in both mice and humans 55

that have evaded characterization despite 170 years of research on bile acid chemistry16. 56

57

Main 58

In total, 96 sample sites, covering 29 organs, producing 768 samples (excluding 59

controls, Fig. S1) were analyzed from four GF and four colonized mice by LC-MS/MS mass 60

spectrometry and 16S rRNA gene sequencing. The metabolome data was most strongly 61

influenced by organ source, but as expected, the microbiome was dictated by colonization 62

status (Fig. 1a,b). GF mice and sterile organs in SPF mice clustered tightly with background 63

sequence reads from blanks (reflecting their sterility), whereas colonized organs within the 64

SPF mice clustered apart from these samples (Fig. 1a,b). Mapping the principle coordinate 65

values of the two data types onto the murine 3-D model showed how the gut samples were 66


https://doi.org/10.1101/654756


3

similar, but important differences were observed, including separation of the stool sample 67

from the upper GI tract in the metabolome but not in the microbiome, and similarity between 68

the esophageal and gut microbiomes. The strongest separation in the metabolome between 69

colonization states was present in the stool, cecum, other regions of the GI tract, and 70

samples from the surface of the animals including ears and feet (Fig. 1c). The liver also had 71

signatures suggestive of metabolomic differences between the GF and SPF mice, but these 72

were not significant compared to the within individual variation (Fig. 1, Fig. S2). 73

Molecular networking is a novel spectral alignment algorithm that enables identification 74

of unique molecules in mass spectrometry data and the relationships between related 75

spectra14. Applying molecular networking to this comprehensive murine dataset identified 76

7,913 unique spectra (representing putative molecules) of which 14.7% were exclusively 77

observed in colonized mice and 10.0% were exclusive to GF (Fig. 2). Although the overall 78

profiles exhibited the strongest difference in the GI tract, molecular networking showed that 79

all organs had some unique molecular signatures from the microbiome, ranging from 2% in 80

the bladder to 44% in stool (Fig. 2). As expected, the metabolome of the cecum, site of 81

microbial fermentation of food products, was profoundly affected by the microbiota, but other 82

GI sites had weaker signatures. Spectral library searching enabled annotation of 8.86% of 83

nodes in the molecular network (n=700 annotated nodes13,17); which included members of the 84

molecular families of plant products, such as soyasaponins and isoflavonoids (sourced from 85

the soybean (Glycine max, f. Fabaceae) component of mouse chow), host lipids and 86

microbial metabolic products (Fig. 2a). Many of the unique signatures attributed to the 87

microbiome were the result of metabolism of plant triterpenoids and flavonoids from food 88

(Supplemental Data, Fig. S3, S4). These effects were location specific, indicating that the 89

microbiome inhabits spatially distinct and varied niche space throughout an organism, 90

exerting location-dependent effects on host physiology through the metabolism of xenobiotics 91

and modification of host molecules. 92

The strong impacts from the microbiome in the gastrointestinal (GI) tract led to deeper 93

analysis of the molecular changes in this organ system. A random forests classification was 94

used to identify the most differentially abundant molecules between the GF and SPF GI 95

tracts. The metabolome of both the GF and SPF mice changed through the different sections 96

of the digestive system (Fig. 3a). While changes through the upper GI tract were subtle in GF 97

mice, SPF animals had progressive transitions in this region (Fig. 3a). A major transition 98

occurred between the ileum and cecum in both groups, but the specific molecules that were 99


https://doi.org/10.1101/654756


4

changing were different between them (Fig. 3a). Many unique metabolites in SPF mice were 100

unknown compounds, but known molecules were also identified including bile acids and 101

soyasaponins (Fig. 3a, Supplementary Data, Fig. S3,S5). The Shannon diversity index of the 102

GF and SPF mouse metabolome was mirrored in the upper GI tract, both being low in the 103

esophagus and higher in the stomach and duodenum, however, upon transition to the cecum, 104

the diversity of the two groups of mice began to separate (Fig. 3c,d). The molecular diversity 105

in the cecum and colon of colonized mice was significantly higher than GF mice (Mann-106

Whitney U-test), but not in the stool samples (Fig. 3c). 107

We also compared the changing microbial community through the GI tract in the 108

context of the changes observed in the molecular data. Similar to the metabolites, 109

microbiome transitions were observed traversing the GI tract (Fig. 3b). The corresponding 110

microbial diversity of the colonized animals showed a similar profile to the metabolome, 111

mostly stable through the upper parts of the system and then abruptly increasing at the 112

cecum, followed by a decrease in the colon and stool (Fig. 3d). However, an interesting 113

contrast was observed where a high diversity of the metabolome in the duodenum 114

corresponded to a lower microbial diversity. We hypothesize that this contrasting result was 115

due to the secretion of bile acids from the gallbladder at this location. Because these 116

molecules possess antimicrobial properties, their high abundance may explain the lower 117

microbial diversity in the upper GI tract18, while simultaneously, microbial modification of the 118

molecules increases their molecular diversity. After the duodenum, changes in the diversity of 119

microbiome and metabolome were closely aligned, but colonized mice had greater molecular 120

diversity in the cecum and colon. This shows that microbial activity in these organs was 121

altering the molecules present, particularly bile acids, soyasaponins, flavonoids, and other 122

unknown compounds, which expanded the metabolomic diversity of the cecum 123

(supplementary results). 124

Molecular networking also enabled meta-mass shift chemical profiling19 of the GF and 125

SPF GI tract, which is an analysis of chemical transformations based on parent mass shifts 126

between related nodes without the requirement of knowing the molecular structure. For 127

example, a unique node found in colonized mice with an 18.015 Da difference represents 128

H2O and 2.016 Da is H2. In colonized animals, there was a strong signature for the loss of 129

water in the duodenum and jejunum and the loss of H2, acetyl and methyl groups in latter 130

parts of the GI tract (Fig. 3e,f). GF mice had notable mass gains corresponding to 131

monosaccharides in all regions of the GI tract, which were absent in SPF animals. Instead, a 132


https://doi.org/10.1101/654756


5

mass gain of C4H8 was seen in the jejunum and ileum of SPF mice, which was associated 133

with the conjugated bile acid glycocholic acid (Fig. 3e,f). A significant portion of the 134

dehydrogenation and dehydroxylation mass shifts from the microbiome were associated with 135

bile acids, indicating that microbial enzymes acted on C-C double bonds of the cholic acid 136

backbone and removed hydroxyl groups, which is a known microbial transformation3. 137

Deacetylations were also prevalent in SPF animals, though the metabolites upon which these 138

losses were occurring remain mostly unidentified. Overall, both GF and SPF mice had many 139

cases of mass loss between related molecules, but there were comparably fewer molecules 140

in the colonized mice that showed gain of a molecular group (Fig. 3f). This indicates that the 141

microbiome contributes more to the catabolic breakdown of molecules and less to anabolism; 142

however, one interesting anabolic reaction that was detected was the addition of C4H8 on 143

glycocholic acid, which we subsequently investigated further. 144

Glycine and taurine conjugated bile acids were detected in both GF and SPF mice. As 145

they moved through the GI tract, the conjugated amino acid was removed in SPF mice only, 146

representing a known microbial transformation (Fig. S5,20). In the bile acid molecular network 147

that contained taurocholic acid and glycocholic acid there were modified forms of these 148

compounds that were only present in colonized animals. These nodes were related to the 149

glycocholic acid through spectral similarity and to the sulfated form (Fig. 4a) and one of them 150

corresponded to the addition of C4H8 described above. Analysis of the MS/MS spectra of the 151

three nodes m/z 556.363, m/z 572.358 and m/z 522.379 showed maintenance of the core 152

cholic acid, but with a fragmentation pattern characteristic of the presence of the amino acids 153

phenylalanine, tyrosine and leucine through an amide bond at the conjugation site in place of 154

glycine or taurine (Fig. S6). In the extensive bile acid literature, representing 170 years of bile 155

acid structural analysis and greater than 42,000 publication records in PubMed, the only 156

known conjugations of murine (and human) bile acids were those of glycine and taurine16. 157

Here, we have found a set of unique amino acid conjugations to cholic acid mediated by the 158

microbiome creating the novel bile acids phenylalanocholic acid, tyrosocholic acid and 159

leucocholic acid. These structures were validated with synthesized standards using NMR and 160

mass spectrometry methods (Supplemental methods and Fig. S7, S8, S9, S10, S11). These 161

uniquely conjugated bile acids were detected in the duodenum, jejunum and ileum of SPF 162

mice, with phenylalanocholic acid being the most abundant (Fig. 4). In comparison, 163

glycocholic acid was present in the latter parts of the GI tract (cecum and colon), whereas 164

taurocholic acid was most abundant in the upper parts of the GI tract (reduced through the 165


https://doi.org/10.1101/654756


6

lower GI tract in SPF mice). The concentration of phenylalanocholic acid in mouse ileal 166

content from the four mice was 0.59 μM (s.d. 0.21) in the duodenum, 3.0 μM (s.d. 4.43) in the 167

jejunum and 5.25 μM (s.d. 2.42) in the ileum, with its highest concentration reaching 13.24 168

μM in a single jejunum sample (Fig. S12). These findings demonstrate that these novel amino 169

acid conjugates are abundant in the upper GI tract of mice on a normal soy-based diet and 170

require the microbiota for their production, but were subsequently absorbed, further modified, 171

or deconjugated again upon travel to the cecum. 172

Because GNPS is a public repository of mass spectrometry data from a wide variety of 173

biological systems, we used an analysis feature called “single spectrum search” to search all 174

739 publically available data sets for the presence of MS/MS spectra matching these 175

conjugated bile acids (April 27, 2018,13). Spectral matches corresponding to 176

phenylalanocholic acid, tyrosocholic acid and leucocholic acid were found in 19 other studies 177

comprising samples from the GI tract of both mice (with at least one conjugate found in 3.2 to 178

59.4% of all samples, Fig. S13) and humans (in 1.6 to 25.3% of all samples, Fig. S13). In a 179

crowd-sourced fecal microbiome and metabolome study at least one of these unique bile 180

acids was found in 1.6% of human fecal samples with tyrosocholic acid being the most 181

prevalent (n=490, the American Gut Project 21, Fig. 4b). They were found in higher frequency 182

in fecal samples collected without swabs, including studies of patients with inflammatory 183

bowel syndrome, cystic fibrosis (CF) and infants (Fig. 4b). Re-analysis of data from a 184

previously published study of the murine microbiome and liver cancer enabled a comparison 185

of the abundance of these molecules in mice fed a high-fat-diet (HFD) and treated with 186

antibiotics22, Fig. 4b). Supporting the role of the microbiome in their production, the 187

Phe/Tyr/Leu amino acid conjugates were decreased with antibiotic exposure, whereas 188

glycocholic acid, which is synthesized by host liver enzymes, was not. In contrast, these 189

microbial bile acids were more abundant in mice fed HFD, with no change observed in the 190

host conjugated glycocholic acid22. In a separate data set where atherosclerosis-prone mice 191

were similarly fed a HFD the novel conjugates were also increased over time, but not on 192

normal chow and the host-conjugated taurocholic acid did not change significantly (Fig. S14). 193

Finally, exploration into the metadata associated with a public study of a pediatric CF patient 194

cohort showed that there was a higher prevalence of these compounds in CF patients 195

compared to healthy controls, particularly those with pancreatic insufficiency (Fig. 4b). 196

Insufficient production of pancreatic lipase in the CF gut results in the buildup of fat and a 197

microbial dysbiosis23, which parallels the gut microbial ecosystem in mice fed HFD. 198


https://doi.org/10.1101/654756


7

The first chemical characterization of a bile acid was in 184824, the first correct 199

structure of a bile acid related molecule was elucidated in 193225 and bile acid metabolism by 200

the microbiome has been known since the 1960s26. Since then, microbial alteration of bile 201

acids has been known to occur through four principal mechanisms: dehydroxylation, 202

dehydration and epimerization of the cholesterol backbone, and deconjugation of the amino 203

acids taurine or glycine3,27,28. Here, using a simple experiment with colonized and sterile 204

mice, we have identified a fifth mechanism of bile acid transformation by the microbiome 205

mediated by a completely novel mechanism: conjugation of the cholesterol backbone with the 206

amino acids phenylalanine, leucine and tyrosine. Further research is required to determine 207

the microbial producers of these compounds and their role in gut microbial ecology, 208

especially considering the important findings that microbiome based bile acid metabolism can 209

affect C. difficile infections29 or regulate liver cancer30. The findings reported here show that 210

all bile acid research to date have overlooked a significant component of the human bile acid 211

pool produced by the microbiome. 212

In conclusion, the chemistry of all major organs and organ systems are affected by the 213

presence of a microbiome. The strongest signatures come from the gut through the 214

modification of host bile acids and xenobiotics, particularly the breakdown of plant natural 215

products from food. Addition of chemical groups to host molecules were more rare, but those 216

that were detected were sourced from a unique alteration of host bile acids by the 217

microbiome that changes our understanding of human bile after 170 years of research16. As 218

the connections between us and our microbial symbionts becomes more and more obvious, a 219

combination of globally untargeted approaches and the development of tools that interlink 220

these data sets will enable us to identify novel molecules, leading to a better understanding of 221

the deep connection between our microbiota and our health. 222

223

Acknowledgements: The authors would like to acknowledge funding from the National 224

Institutes of Health under project 5U01AI124316-03, 1R03CA211211-01, 1R01HL116235 225

and R01HD084163. Additionally, B.S.B. was supported by UCSD KL2 (1KL2TR001444), J.L. 226

by grant 13EIA14660045 from the American Heart Association. We would like to 227

acknowledge Gail Ackerman for her contributions to curate the metadata. We further thank 228

Alan Hofmann and Lee Hagey for their insightful discussions on structural characterization of 229

bile acids. 230

231


https://doi.org/10.1101/654756


8

Data Availability: All metabolomics data is available at GNPS (gnps.ucsd.edu) under the 232

MassIVE id numbers: MSV000079949 (GF and SPF mouse data). Additional sample data: 233

MSV000082480, MSV000082467, MSV000079134, MSV000082406. The sequencing data 234

for the GF and SPF mouse study is available on the Qiita microbiome data analysis platform 235

at Qiita.ucsd.edu under study ID 10801 and through the European Bioinformatics Institute 236

accession number ERP109688. 237

238

References 239

1. Consortium, H. Structure, function and diversity of the healthy human microbiome. 240

Nature 486, 207–14 (2012). 241

2. Dorrestein, P. C., Mazmanian, S. K. & Knight, R. Finding the Missing Links among 242

Metabolites, Microbes, and the Host. Immunity 40, 824–832 (2014). 243

3. Ridlon, J. M., Kang, D. J., Hylemon, P. B. & Bajaj, J. S. Bile acids and the gut 244

microbiome. Curr. Opin. Gastroenterol. 30, 332–8 (2014). 245

4. Gilbert, J. A. et al. Microbiome-wide association studies link dynamic microbial 246

consortia to disease. Nature 535, (2016). 247

5. Wikoff, W. R. et al. Metabolomics analysis reveals large effects of gut microflora on 248

mammalian blood metabolites. Proc. Natl. Acad. Sci. U. S. A. 106, 3698–703 (2009). 249

6. Marcobal, A. et al. Metabolome progression during early gut microbial colonization of 250

gnotobiotic mice. Sci. Rep. 5, 11589 (2015). 251

7. Miller, T. L. & Wolin, M. J. Pathways of acetate, propionate, and butyrate formation by 252

the human fecal microbial flora. Appl. Environ. Microbiol. 62, 1589–92 (1996). 253

8. Gillner, M., Bergman, J., Cambillau, C., Fernström, B. & Gustafsson, J. A. Interactions 254

of indoles with specific binding sites for 2,3,7,8-tetrachlorodibenzo-p-dioxin in rat liver. 255

Mol. Pharmacol. 28, 357–63 (1985). 256

9. Martin, F.-P. J. et al. A top-down systems biology view of microbiome-mammalian 257

metabolic interactions in a mouse model. Mol. Syst. Biol. 3, 112 (2007). 258

10. Moriya, T., Satomi, Y., Murata, S., Sawada, H. & Kobayashi, H. Effect of gut microbiota 259

on host whole metabolome. Metabolomics 13, 101 (2017). 260

11. Swann, J. R. et al. Systemic gut microbial modulation of bile acid metabolism in host 261

tissue compartments. Proc. Natl. Acad. Sci. U. S. A. 108 Suppl 1, 4523–30 (2011). 262

12. Structure, function and diversity of the healthy human microbiome. Nature 486, 207–14 263

(2012). 264


https://doi.org/10.1101/654756


9

13. Wang, M. et al. Sharing and community curation of mass spectrometry data with Global 265

Natural Products Social Molecular Networking. Nat. Biotechnol. 34, 828–837 (2016). 266

14. Watrous, J. et al. Mass spectral molecular networking of living microbial colonies. Proc. 267

Natl. Acad. Sci. U. S. A. 109, 1743–52 (2012). 268

15. Protsyuk, I. et al. 3D molecular cartography using LC–MS facilitated by Optimus and ’ili 269

software. Nat. Protoc. 13, 134–154 (2017). 270

16. Hofmann, A. F. & Hagey, L. R. Key discoveries in bile acid chemistry and biology and 271

their clinical applications: history of the last eight decades. J. Lipid Res. 55, 1553–95 272

(2014). 273

17. Yang, J. Y. et al. Molecular networking as a dereplication strategy. J Nat Prod 76, 274

1686–1699 (2013). 275

18. Hofmann, A. F. & Eckmann, L. How bile acids confer gut mucosal protection against 276

bacteria. Proc. Natl. Acad. Sci. U. S. A. 103, 4333–4 (2006). 277

19. Hartmann, A. C. et al. Meta-mass shift chemical profiling of metabolomes from coral 278

reefs. Proc. Natl. Acad. Sci. U. S. A. 114, (2017). 279

20. Wahlström, A., Sayin, S. I., Marschall, H.-U. & Bäckhed, F. Intestinal Crosstalk 280

between Bile Acids and Microbiota and Its Impact on Host Metabolism. Cell Metab. 24, 281

41–50 (2016). 282

21. McDonald, D. et al. American Gut: an Open Platform for Citizen Science Microbiome 283

Research. mSystems 3, e00031-18 (2018). 284

22. Shalapour, S. et al. Inflammation-induced IgA+ cells dismantle anti-liver cancer 285

immunity. Nature 551, 340–345 (2017). 286

23. Manor, O. et al. Metagenomic evidence for taxonomic dysbiosis and functional 287

imbalance in the gastrointestinal tracts of children with cystic fibrosis. Sci. Rep. 6, 288

22493 (2016). 289

24. Strecker, A. Untersuchung der Ochsengalle. Ann. der Chemie und Pharm. 65, 1–37 290

(1848). 291

25. BERNAL, J. D. Crystal Structures of Vitamin D and Related Compounds. Nature 129, 292

277–278 (1932). 293

26. Gustafsson, B. E., Gustafsson, J. A. & Sjövall, J. Intestinal and fecal sterols in germfree 294

and conventional rats. Bile acids and steroids 172. Acta Chem. Scand. 20, 1827–35 295

(1966). 296

27. Midtvedt, T. Microbial bile acid transformation. Am. J. Clin. Nutr. 27, 1341–1347 (1974). 297


https://doi.org/10.1101/654756


10

28. Gérard, P. & Philippe. Metabolism of Cholesterol and Bile Acids by the Gut Microbiota. 298

Pathogens 3, 14–24 (2013). 299

29. Buffie, C. G. et al. Precision microbiome reconstitution restores bile acid mediated 300

resistance to Clostridium difficile. Nature 517, 205–208 (2015). 301

30. Ma, C. et al. Gut microbiome-mediated bile acid metabolism regulates liver cancer via 302

NKT cells. Science 360, eaan5931 (2018). 303

31. Sumner, L. W. et al. Proposed minimum reporting standards for chemical analysis 304

Chemical Analysis Working Group (CAWG) Metabolomics Standards Initiative (MSI). 305

Metabolomics 3, 211–221 (2007). 306

307

Figures and Figure Legends 308

309Figure 1. a) Principal coordinate analysis (PCoA) of microbiome and mass spectrometry data 310

highlighted by sample source as GF or SPF. b) Same data highlighted by organ source. c) 311

PC1 13.74% PC1 13.74%

Adrenal Gland

Bladder

Blood

Brain

Cecum

CervixColon

Duodenum

EarEsophagus

Foot

Gall BladderHeart

IleumJejunumKidneyLiver

LungMouth

OvaryPancreas

SkinSpleenStomach

StoolThymus

TracheaUterus

Vagina

PC2 9.3%PC2 9.3%

PC3 7.42% PC3 7.42%

SPF

GF

PC1 52.57%

PC2 11.73%

PC3 5.03%

PC1 40.47%

Met

abol

ome

Mic

robi

ome

a) b)

PC2 11.73%

PC1 52.57%

PC3 5.03%

0.8

0.6

0.4

0.8

0.6

0.4

0.8

0.6

0.4

0.2

0.8

0.6

0.4

0.8

0.6

0.4

0.2

0.8

0.6

0.4

0.8

0.6

0.4

1.0

0.8

0.6

0.4

0.2

1.0

c)

GF-SPF Within

Bra

y-C

urtis

Dis

sim

ilarit

y

Stool

CecumIleum

Colon

DuodenumStomach

Ear Foot

GF-SPF WithinGF-SPF Within

GF-SPF Within

GF-SPF WithinGF-SPF Within

GF-SPF Within GF-SPF Within

Bra

y-C

urtis

Dis

sim

ilarit

y

Metabolome

p<0.00001p=0.032

p<0.00001

p<0.00001

p<0.00001

p<0.00001

p=0.02 p=0.019

0.2

0.4

0.6

0.8

Between WithinJejunum

Jejunump<0.00001

GF-SPF Within

0.2

0.4

0.6

0.8

Between WithinJejunum

GF-SPF Within

Liverp=0.077

Ce

StlCoI

J

D

StmBr

Er

Es Tr

LgLv

Ft

Mo

Hd

Kd

Ad

Bl

Vg

Cx

UtOv

Ce

StlCoI

J

D

StmBr

Er

Es Tr

LgLv

Ft

Mo

Hd

Kd

Ad

Bl

Vg

Cx

UtOv

Stl

Co

I

J

Br

Er

EsTr

LgLvFt

Mo

Hd

Kd

BlVg

CxUt Ov

Stl

Co

I

J

Br

Er

EsTr

LgLvFt

Mo

Hd

Kd

BlVg

CxUt Ov

Met

abol

ome

Mic

robi

ome

CRANIAL CAUDAL

CRANIAL CAUDAL CRANIALCAUDAL

CRANIALCAUDAL

1st Principle Coordinate 1st Principle Coordinate

1st Principle Coordinate1st Principle Coordinate

d)


https://doi.org/10.1101/654756


11

Bray-Curtis dissimilarities of the metabolome data collected from murine organs. The 312

dissimilarities are calculated within individual mice of the same group (GF or SPF, “Within”) or 313

across the GF and SPF groups (GF-SPF). Organs with multiple samples are pooled, but only 314

samples collected from exact same location are compared. d) 3-D model of murine organs 315

mapped with the mean 1st principle coordinate value from the four GF and four SPF mice. 316

High values across the 1st PC are shown in red and lower values are shown in blue. The PC1 317

values are from the data in panels a) and b). (Er=ear, Br=brain, Ad=adrenal gland, 318

Es=esophagus, Tr=trachea, Stm=stomach, Kd=kidney, Mo=mouth, D=duodenum, Ov=ovary, 319

Co=colon, Stl=stool, Hd=hand, Lg=lung, Lv=liver, J=jejunum, Ce=cecum, Bl-bladder, 320

Ut=uterus, Cx=cervix, Vg=vagina, Ft=feet) 321

322

323Figure 2. a) Molecular network of LC-MS/MS data with nodes colored by source as GF, SPF, 324

shared, or detected in blanks. Molecular families with metabolites annotated by spectral 325

matching in GNPS are listed by a number corresponding to the molecular family. These are 326

level 2 or 3 annotations according to the metabolomics standards consortium 31. b) 327

percentage of total nodes from each organ sourced from GF only, SPF only or shared and 328

the total number of unique nodes from each murine class per organ. 329

330

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Skin

Ea

r Fo

ot

Mou

th

Bloo

d Br

ain

Adre

nal

Thym

us

Hea

rt Tr

ache

a Lu

ng

Live

r G

all

Kidn

ey

Blad

der

Panc

reas

Sp

leen

Es

opha

gus

Stom

ach

Duo

denu

m

Jeju

num

Ile

um

Cec

um

Col

on

Stoo

l Va

gina

C

ervi

x O

vary

U

teru

s

Perc

ent o

f Uni

que

Spec

tra

Shared SPF GF Blanks

1

1. Soyasaponins

2

2. Taurocholic acid 3. Lysophospholipids

3

4. Eicosanoids

4 5

5. Tryptophan 6. Peptides

6

7. Ampicillin Standard

7 8

8. Phthalates

9

9. Phenylalanine

33

10

10. Phosphoethanolamines

11

11. Cholic acid

1213

12. Ceramides

2

13. Unknown microbial

3 6 14

14. Palmitoylcarnitine

11

15. Flavanoids

15 6 16

16. Soyaspongenols

6 17

17. Epoxy-keto-decanoic acids

18

18. Docosenamide

8 10 19

19. Urobilin

620 21`

21. 12-OAHSA 22. Riboflavin

22`1523`

23. Ketodeoxycholic acid

24`

24. Indoles25. Deoxyguanosine

25`

a) b)

0

500

1000

1500

2000

2500

Skin

Ea

r Fo

ot

Mou

th

Bloo

d Br

ain

Adre

nal

Thym

us

Hea

rt Tr

ache

a Lu

ng

Live

r G

all

Kidn

ey

Blad

der

Panc

reas

Sp

leen

Es

opha

gus

Stom

ach

Duo

denu

m

Jeju

num

Ile

um

Cec

um

Col

on

Stoo

l Va

gina

C

ervi

x O

vary

U

teru

s Num

ber o

f Uni

que

Spec

tra

SharedSPFGF

20. Deoxycholic acid


https://doi.org/10.1101/654756


12

331Figure 3. a) Mean normalized abundance of the top 30 most differentially abundant 332

metabolites between GF and SPF mice. The metabolites are colored according to molecular 333

family, where bile acids are green and blue, respectively, soyasaponins are pink and 334

unknown molecules are brown/yellow. Colors corresponding to taurocholic acid (green) and 335

deoxymuricholic acid (teal) are highlighted for reference. b) Microbiome of the murine GI tract 336

in SPF mice. Taxa of relevance are color coded according to the legend. c) Mean and 95% 337

confidence interval of the Shannon-Weiner diversity of the metabolomic data in each GI tract 338

sample for GF and SPF mice. Statistical significance between metabolome diversity in the 339

same sample location between GF and SPF mice was tested with the Mann-Whitney U-test 340

(*=p<0.05). d) Mean Faith’s phylogenetic diversity (with 95% confidence interval) of the 341

microbiome through the SPF GI tract. e) Results of meta-mass shift chemical profiling 19 342

showing the relative abundance of the parent mass differences between unique nodes in 343

either GF or SPF mice to the total. Each mass difference corresponds to the node-to-node 344

gain or loss of a particular chemical group. f) Counts of the number of mass shifts of the 345

parent mass differences between nodes showing where the most abundant molecular 346

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100% Uknown Unknown Unknown Unknown Unknown Unknown Unknown Unknown Uknown SoyasaponinSoyasaponin I Na+ Soyasaponin I Soyasaponin 1 MN-Taurocholic Acid MN-Taurocholic Acid MN-Taurocholic Acid MN-Taurocholic Acid MN-Taurocholic Acid MN-Taurocholic Acid MN-Taurocholic Acid MN-Taurocholic Acid

MN-Bile Acid MN-Bile Acid MN-Bile Acid MN-Bile Acid MN-Bile Acid MN-Bile Acid

Deoxymuricholic Acid

Muricholic Acid

4

5

6

3osEcFG 2osEbFG 1osEaFG 2hcamotSfFG 1hcamotSeFG 4osEdFG 2ouDiFG 1ouDhFG 3hcamotSgFG 5ouDlFG 4ouDkFG 3ouDjFG 2elIoFG 2elInFG 1elInFG 6ouDmFG 6elIsFG 5elIrFG 4elIqFG 3elIpFG 3ujeJvFG 2ujeJuFG 1ujeJtFG 6ujeJyFG 5ujeJxFG 4ujeJwFG 3muceCbzFG 2muceCazFG 1muceCzFG 4muceCczFG 6muceCezFG 5muceCdzFG 3noloChzFG 2noloCgzFG 1noloCfzFG 6noloCkzFG 5noloCjzFG 4noloCizFG 2osEbFPS 1osEaFPS lootSlzFG 1hcamotSeFPS 4osEdFPS 3osEcFPS 3hcamotSgFPS 2hcamotSfFPS 3ouDjFPS 2ouDiFPS 1ouDhFPS 6ouDmFPS 5ouDlFPS 4ouDkFPS 3ujeJoFPS 2ujeJoFPS 1ujeJnFPS 4ujeJqFPS 4ujeJpFPS 3ujeJpFPS 6ujeJrFPS 5ujeJrFPS 5ujeJqFPS 3elIvFPS 2elIuFPS 1elItFPS 6ujeJsFPS 6elIyFPS 5elIxFPS 4elIwFPS 3muceCbzFPS 2muceCazFPS 1muceCzFPS 4muceCczFPS 6muceCezFPS 5muceCdzFPS 2noloCgzFPS 1noloCfzFPS 4noloCizFPS 3noloChzFPS lootSlzFPS 6noloCkzFPS 5noloCjzFPSSample

Shannon

3

Eso Sto Duo Jej Ile Cec Col Fec

Shan

non

Inde

x

EsophagusStomachDuodenumJejunumIleumCecumColonStool

*

* *****

*

**

*

Eso Sto Duo Jej Ile Col FCec Eso Sto Duo Jej Ile Col FCec

Nor

mal

ized

Abu

ndan

ceGF SPFa)

c) d)

b)

Rel

ativ

e A

bund

ance

GF SPF

o_Bacteroidales; f_S24-7 k_Bacteria; p_Firmicutesc_Bacilli; o_Bacillalesf_Lactobacillaceae; g_Lactobacilluso_Clostridiales, f_Lachnospiraceaeo_Clostridiales; f_Ruminococcaceaeg_Akkermansia; s_muciniphilaOther

SPF

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Stom

ach

Duo

denu

m

Jeju

num

Ile

um

Cec

um

Col

on

Stoo

l

Stom

ach

Duo

denu

m

Jeju

num

Ile

um

Cec

um

Col

on

Stoo

l

Stom

ach

Duo

denu

m

Jeju

num

Ile

um

Cec

um

Col

on

Stoo

l

Stom

ach

Duo

denu

m

Jeju

num

Ile

um

Cec

um

Col

on

Stoo

l Rela

tive

Abun

danc

e of

Uni

que

Mas

s Lo

ss

2x Aglycone OH C5H8O4-Sugar C6H10O5-Glycone C6H10O4-Sugar CO2 CH2O2/NO2 C2H6 2 Unsaturations C4H8 SO3 CH4 C2 C C2H2 NH3 CH2O O C2H4 H2O Methyl Acetyl H2

0

100

200

300

400

500

600

700

800

900

Sto

mac

h D

uode

num

Je

junu

m

Ileum

C

ecum

C

olon

S

tool

Sto

mac

h D

uode

num

Je

junu

m

Ileum

ce

cum

C

olon

S

tool

Sto

mac

h D

uode

num

Je

junu

m

Ileum

ce

cum

C

olon

S

tool

Sto

mac

h D

uode

num

Je

junu

m

Ileum

ce

cum

C

olon

S

tool

Spec

tral

Cou

nt o

f Uni

que

Mas

s Lo

ss 2x Aglycone

OH C5H8O4-Sugar C6H10O5-Glycone C6H10O4-Sugar CO2 CH2O2/NO2 C2H6 2 Unsaturations C4H8 SO3 CH4 C2 C C2H2 NH3 CH2O O C2H4 H2O Methyl Acetyl H2

GF SPF GF SPF GF SPF GF SPFMass Loss Mass Gain

SPF

Faith

’s P

hylo

gene

tic D

iver

sity

EsophagusStomachDuodenumJejunumIleumCecumColonStool

10

12

14

8

6

5

Mass Loss Mass Gaine) f)2x Aglycone OH C5H8O4-Sugar

C6H10O4-Sugar CO2 CH2O2

C2H6 2 Desaturations C4H8 SO3 CH4 C2 C C2H2 NH3 CH2O O C2H4 H2O Methyl Acetyl H2

C6H10O5-Glycone

2x Aglycone OH C5H8O4-Sugar

C6H10O4-Sugar CO2 CH2O2

C2H6 2 Desaturations C4H8 SO3 CH4 C2 C C2H2 NH3 CH2O O C2H4 H2O Methyl Acetyl H2

C6H10O5-Glycone

MN-Bile Acid

MN-Bile Acid

Bile Acids

Soyasaponins

Unknowns

Taurocholicacid

Deoxymuricholicacid


https://doi.org/10.1101/654756


13

transitions are detected in the murine gut. 347

348

349Figure 4. a) Structures, molecular network, 3D-molecular cartography and abundance 350

through GI tract of novel microbiome associated bile acids in this murine study. Structures of 351

the previously known conjugates glycocholic acid, taroursocholic acid and taurocholic acid 352

are shown for comparison to structures of the newly discovered amino acid conjugates. The 353

molecular network of these bile acids is shown with mapping to the GF and SPF mice 354

according to the color legend. An inset highlighting the parent masses and mass differences 355

between the newly discovered molecules is shown for clarity. 3D-molecular cartography 356

maps the mean abundance and standard deviations of the mean of the newly discovered 357

conjugates onto a 3D-rendered model of the murine GI tract and the relative abundances of 358

the molecules through the GI tract samples compared to the host produced glycocholic acid 359

and taurocholic acid are also shown. b) Bar plots of the percent of samples positive for the 360

HFD NC No Ab Ab

*

33.9839

23.93

56.063

40.05

15.995

26.049

106.042

74.034

63.98

34.955

16.013

29.025

50.968

90.047

79.993

10.054

522.379

572.358

482.329

517.284

466.316

546.309

556.363 49.9789

1

1. Glycocholic Acid

2. Phenylalanocholic Acid

4. Leucocholic Acid

23

4

Duodenum Bile Acid Cluster

GFBothSPF

Stl

Co

I

J

D

Stm

CAUDAL CRANIAL

2. Phenylalanocholic Acid

Stl

Co

I

J

D

Stm

3. Tyrosocholic Acid

CAUDAL CRANIAL

Stl

CoI

JD

Stm

CAUDAL CRANIAL1

10

100

1000

10000

Stomach1

Stomach2

Stomach3

Gall

Duodenum1

Duodenum2

Duodenum3

Duodenum4

Duodenum5

Duodenum6

Jejunum1

Jejunum2

Jejunum3

Jejunum4

Jejunum5

Jejunum6

Ileum1

Ileum2

Ileum3

Ileum4

Ileum5

Ileum6

Cecum1

Cecum2

Cecum3

Cecum4

Cecum5

Cecum6

Colon1

Colon2

Colon3

Colon4

Colon5

Colon6

Stool Nor

mal

ized

Abu

ndan

ce x

1e5

Phenylalanocholic acid Tyrosocholic acid Leucocholic acid SPF Glycocholic Acid SPF Taurocholic Acid GF Taurocholic Acid GF Glycocholic Acid

4. Leucocholic Acid

Chemical Formula: C26H43NO6Exact Mass: 465.309

O

HO OH

NH

H

H

H

H

H

OHOH

O

Chemical Formula: C26H45NO7SExact Mass: 515.292

O

HO OH

NH

H

H

H

H

H

OHS OHO

O

Taurocholic acid

Chemical Formula: C26H46NO7SExact Mass: 498.297

O

HO OH

NH

H

H

H

H

HS OHO

O

Tauroursocholic acid

Acetyl-Tauroursocholicacids

Amino AcidConjugates

**Glycocholic acid Phenylalanocholic acid Tyrosocholic acid Leucocholic acid All

558.345

573.354

568.329570.345

570.345

530.313

480.278

496.273480.278 498.288 514.284

539.3

540.332

542.349

542.35

482.329

572.358

522.379

517.284

546.309

466.316

556.363

778.528

800.51826.526

614.276

647.392

647.392

561.356

596.256

527.288

532.294

531.309496.273

496.273514.283

549.319

462.267

480.278

464.282

464.282

501.306

500.304

500.304

462.266

598.281 482.293517.329

576.332

516.3

512.267

498.289

802.528

575.335

533.325

563.334

533.324O

HO OH

NH

H

H

H

H

H

OH

Chemical Formula: C33H49NO6Molecular Weight: 555.76

OH

O

Phenylalanocholic acid


3. Tyrosocholic AcidO

HO OH

NH

H

H

H

H

HOH OH

O

OH

Tyrosocholic acid

O

HO OH

NH

H

H

H

H

HOH


OH

O

Leucocholic acid

GB

GB

GB

GB

1. Glycocholic Acid

0

5

10

15

20

25

30

35

40

45 Glycocholic Acid

Phenylalanocholic

Tyrosocholic acid

Leucocholic acid

Any Microbial

Murine

**

NS

1e 2

1e 3

1e 4

1e 5

Stl

Co

IJ

D

Stm

Acetyl-Taurocholicacids

Ion

Inte

nsity

* **

NS

AGP IBD CF

***

0

10

20

30

40

50

60

70

80

CF-PI CF-PS non-CFInfants

Perc

ent o

f Sam

ples

Perc

ent o

f Sam

ples

a)

b)

n = 25 n = 8 n = 10n = 155n = 173n = 490 n = 310

CAUDAL CRANIAL


https://doi.org/10.1101/654756


14

novel bile acids from publically available datasets on GNPS. Percent of patients where novel 361

bile acids were detected from two human studies of cystic fibrosis patients compared to non-362

CF controls. Comparison of the abundance of novel conjugates in a controlled murine study 363

previously published where animals fed high fat diet (HFD) or normal chow (NC) were 364

compared and those treated with antibiotics 22. AGP = American Gut Project 21, IBD = 365

Inflammatory Bowel Disease, CF = cystic fibrosis, PI = pancreatic insufficient, PS = 366

pancreatic sufficient. 367


https://doi.org/10.1101/654756


chemical impacts of the microbiome across scales reveal novel … · 1 1 chemical impacts of the...

Documents