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Going deep into the acute phase protein response

Leuchsenring, Anna Barslund

Publication date:2020

Document VersionPublisher's PDF, also known as Version of record

Link back to DTU Orbit

Citation (APA):Leuchsenring, A. B. (2020). Going deep into the acute phase protein response. DTU Bioengineering.

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Division for Immunology and Vaccinology, DTU Vet

Department of Biotechnology and Biomedicine, DTU Bioengineering

Technical University of Denmark

Going deep into the acute phase protein response:

Use of targeted mass spectrometry for antibody independent serum amyloid A isoform profiling Ph.D. thesis Anna Barslund Leuchsenring April 2020

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Supervisor Professor Peter M. H. Heegaard Innate Immunology Group, Veterinary Institute National, DTU Vet Department of Biotechnology and Biomedicine, DTU Bioengineering Currently: Center for Diagnostics, DTU

Assesment committee Professor MSO Maher A. Hachem (chairperson) Institut for Bioteknologi og Biomedicin, DTU Bioengineering Technical University of Denmark

Professor Theo Niewold Senior Consultant

Associate professor Per M. Hägglund Department of Biomedical Sciences University of Copenhagen

Going deep into the acute phase protein response: Use of targeted mass spectrometry for antibody independent serum amyloid A isoform profiling Ph.D. thesis 2020 © Anna Barslund Leuchsenring. Cover: SRM data produced in this Ph.D. project Pig stock photo, obtained from colourbox.com under DTU license

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Contents

Contents ........................................................................................................................... 3

Preface .............................................................................................................................. 6

Acknowledgments ............................................................................................................. 7

Abbreviations .................................................................................................................... 9

Summary ........................................................................................................................ 11

Sammendrag (Danish summary) ..................................................................................... 13

Chapter 1 ........................................................................................................................ 15

Introduction .................................................................................................................... 15

1.1 Objectives .............................................................................................................. 16

1.2 Outline of Ph.D. thesis .......................................................................................... 17

Chapter 2 ........................................................................................................................ 18

Background ..................................................................................................................... 18

2.1 The acute phase response ...................................................................................... 18

2.2 Acute phase proteins ............................................................................................. 19

2.2.1 Serum Amyloid A............................................................................................ 23

Chapter 3 ........................................................................................................................ 33

Technical aspects ............................................................................................................ 33

3.1 Mass Spectrometry ................................................................................................ 34

3.2 Liquid Chromatography-Mass Spectrometry (LC-MS) .......................................... 36

3.3 Matrix-assisted laser desorption ionization (MALDI) ............................................ 37

3.4 Electrospray ionization (ESI) ................................................................................ 39

3.5 Mass analyzers ....................................................................................................... 40

4

3.6 Quadrupole Mass Analyzers .................................................................................. 41

3.7 Time of Flight (TOF) mass analyzers ................................................................... 42

3.8 Tandem MS (MS/MS)........................................................................................... 44

Chapter 4 ........................................................................................................................ 46

Discovery and Targeted proteomics ................................................................................ 46

4.1 Parallel Reaction Monitoring (PRM) .................................................................... 47

4.2 Selected Reaction Monitoring (SRM) .................................................................... 48

4.3 Selection of target peptides .................................................................................... 51

4.4 SRM transitions..................................................................................................... 53

4.5 Quantification by SRM ......................................................................................... 58

4.6 Tandem Mass Tag (TMT) Mass Spectrometry ..................................................... 59

4.7 Data-independent acquisition and SWATH-MS .................................................... 61

Chapter 5 ........................................................................................................................ 64

Materials and Methods ................................................................................................... 64

5.1 Biological samples .................................................................................................. 64

5.1.1 Staphylococcus aureus (Sa) pig infection models ............................................. 65

5.1.1.1 Sa intravenous infection model ................................................................. 65

5.1.1.2 Implant-Associated Osteomyelitis (IAO) Sa infection model .................... 66

5.1.2 Actinobacillus pleuropneumoniae (Ap) pig infection models ........................... 66

5.2 SAA ELISA ........................................................................................................... 67

5.3 Triton X-114 cloud point extraction (CPE) .......................................................... 67

5.4 TCA Precipitation ................................................................................................. 69

5.5 2D gel electrophoresis ............................................................................................ 69

5.6 MALDI-TOF ......................................................................................................... 70

5.7 SRM method development .................................................................................... 70

Chapter 6 ........................................................................................................................ 71

Paper I ............................................................................................................................ 71

6.1 Summary of Paper I .............................................................................................. 72

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6.2 Paper 1 as submitted to Journal of Proteomics ..................................................... 73

Chapter 7 ...................................................................................................................... 103

Results and discussion .................................................................................................. 103

7.1 Two-dimensional gel electrophoresis and MALDI-TOF ....................................... 104

7.2 Relative SAA concentrations in porcine serum samples ...................................... 108

7.3 Absolute concentrations of SAA isoforms ............................................................ 111

7.4 Conclusions .......................................................................................................... 117

7.5 Perspectives ......................................................................................................... 117

References ..................................................................................................................... 119

Supplementary .............................................................................................................. 138

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Preface

The work presented in this thesis was carried out from November 2014 to April 2020

(interspersed with two separate maternity leaves of around 12 months each). From

November 2014 until April 2019 the Ph.D. project was performed at the National

Veterinary Institute of DTU (DTU Vet) and from May 2019 until April 2020 the Ph.D.

project was performed at the Department of Biotechnology and Biomedicine, DTU

Bioengineering. The project was financed with internal funding and supervised by

Professor Peter M. H. Heegaard. This Ph.D. has been done in collaboration with Johan

Malmström, Department of Infection Medicine, Lund University, where all SRM

experiments took place.

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Acknowledgments

Through all the years working on this project, I have had the pleasure to work with many

wonderful people; all of whom have helped, supported, and guided me. The first and

biggest acknowledgment goes to my supervisor Peter Heegaard who gave me the

opportunity to work with this exciting project. We’ve had many obstacles thrown at us,

but Peter has always been supportive and found a way through. I am very grateful for

your patience, your guidance, and your great knowledge – I hope at least some of it has

rubbed off on me.

I also want to thank Christofer Karlsson who has taught me the practical proteomics skills

I needed and always answered every question with detailed and thoughtful answers no

matter how small or big. His calm presence and patience guidance helped me through

many a stressful situation and convinced me to keep going, even when it got tough. I am

also very grateful to Johan Malmström for the cooperation and for the opportunity to

work in his lab where I’ve learned so much and felt like part of the group from my very

first day. I want to thank Anahita Bakochi for being a wonderful friend and colleague and

for welcoming me so warmly into the Infection Medicine group along with Emma Åhrman

and Lotta Happonen.

Thanks should also be given to Flemming Jessen and Hanne Lilian Stampe-Villadsen at

DTU Food for helping me learn the many steps of 2D gel electrophoresis and more.

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Louise Brogaard and I have shared an office for most of my time at DTU, and I want to

thank her for making it joyous to come to work and for being like a wiser older Ph.D.

sister whom I could look up to, ask all the stupid (or clever) questions and tag along to

see how to get things done the right way – and for accepting my crazy Christmas

decorations. I am going to miss her very much.

Last, but definitely not least, I want to thank my entire family for all of their support

throughout the years. Especially my husband Mikkel has patiently listened to my joys and

sorrows and has been my rock when everything seemed impossible. He has taken care of

me and our kids while I have been working like crazy to finish this Ph.D. project. I love

you and I could not have done this without you.

A very special thanks goes to my mother, who has supported my dreams since I could first

express them, and who have always done her absolute best to help me realize them. While

writing this Ph.D. thesis she has helped taking care of my kids, and I can surely say that

it would not have been finished if it hadn’t been for her. Thank you from the bottom of

my heart.

Anna Barslund Leuchsenring, April 2020

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Abbreviations

2DE Two-dimensional gel electrophoresis

Ap Actinobacillus pleuropneumoniae

APPs Acute phase proteins

APR Acute phase response

BW Bodyweight

CFU Colony-forming unit

CID Collision-induced dissociation

CPE Cloud point extracted

CRP C-reactive protein

DDA Data-dependent acquisition

DIA Data-independent acquisition

DP Declustering potential

ELISA Enzyme-Linked Immunosorbent Assay

ESI Electrospray ionization

FPR Formyl peptide receptor

HDL High-density lipoprotein

HPLC High-Pressure Liquid Chromatography

IEF Isoelectric focusing

IFN Interferon

IL Interleukin

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IAO Implant-Associated Osteomyelitis

LC Liquid chromatography

LC-MS Liquid Chromatography MS

M/z Mass-to-charge ratio

MALDI Matrix-assisted laser desorption/ionization

MS/MS Tandem mass spectrometry

MS Mass spectrometry

pI Isoelectric point

PI Post infection

PRM Parallel reaction monitoring

RAGE Receptor for advanced glycation end-products

rt Retention time

RT Room temperature

Sa Staphylococcus aureus

SAA Serum amyloid A

SR-BI Scavenger receptor class B type I

SRM Selected reaction monitoring

SWATH-MS Sequential Windowed Acquisition of All Theoretical Fragment Ion

MS

TCS Trichloroacetic Acid

TGF Transforming growth factor

TLR Toll-like receptor

TMT Tandem mass tags

TNF Tumor necrosis factor

TOF Time-of-flight

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Summary

Tissue perturbation and infection are accompanied by a generalized set of immediate host

reactions at the site of disturbance as well as an array of systemic reactions known as the

acute phase response (APR). The APR leads to major changes in circulating

concentrations of a subset of blood proteins called acute phase proteins. Serum amyloid A

(SAA) is a well-described acute phase protein in vertebrates synthesized in the liver and

non-hepatic tissue. Most species have four genes each encoding a specific isoform of SAA,

which have found to be differentially regulated depending on infection and tissue. This is

a unique feature of SAA not found in other acute phase proteins and makes circulating

SAA and its isoforms very interesting biomarker candidates. However, SAA isoform

analysis is cumbersome, non-quantitative and no antibodies specific for SAA isoforms have

been developed, making SAA isoform distribution at the protein level less characterized.

Using mass spectrometry (MS)-based methods may overcome these problems as MS is

more specific and adequately sensitive. In this Ph.D. project, the native composition and

presence of SAA and its isoforms in pig serum was first investigated through 2D gel

electrophoresis (2DE) and the MS-based method Matrix-assisted laser

desorption/ionization (MALDI) with a time-of-flight (TOF) mass spectrometry.

Subsequently a new targeted quantitative MS method was developed based on selected

reaction monitoring (SRM) mass spectrometry. 2DE and MALDI-TOF confirmed the

presence of an APR in the investigated serum samples, but only SAA2 and SAA4 could

be identified. As this experimental set-up is both time demanding and not the most

suitable for biomarker development, the SRM method developed was used to discriminate

12

and quantify the porcine SAA isoforms with high specificity and sensitivity in porcine

serum samples from two separate and previously performed infection studies. For improved

quantitative performance, isotopically labeled SAA peptides were used to calibrate the

method. In Paper I, the SRM method was applied to sequential serum samples from a

previous experiment in which pigs were infected with Staphylococcus aureus (Sa) where

all circulating porcine SAA isoforms were detected. The SAA isoform profiles correlated

with the total SAA response previously established by ELISA, and a close correlation

between the relative hepatic SAA isoform mRNA abundance and SAA isoform profiles in

the circulation was also found. This proved that antibody independent MS-based

quantification is possible at high specificity and sensitivity allowing discrimination between

a set of very similar circulating SAA variants directly in serum samples. The results

unequivocally establish SAA2 as the main circulating porcine SAA isoform. Porcine serum

samples from pigs infected with Actinobacillus pleuropneumoniae (Ap) in another previous

experiment were also analyzed, and while all SAA isoforms could be detected, only relative

SAA concentrations could be measured as the selected proteotypic peptides did not

perform well enough to provide absolute quantification. An additional set of porcine serum

samples from a previous experiment on Sa osteomyelitis in pigs were analyzed in which all

SAA isoforms were detected and quantified. Both relative and absolute SAA

concentrations were measured and correlated well with each other in this sample set.

Relative and absolute SAA isoform concentrations were compared across the three sample

sets, and each set showed a different SAA isoform composition supporting the concept

that different types of infections - for example having different tropism as with Ap and Sa

- will result in infection-specific SAA isoform profiles.

The prospect of using SAA isoform profiling to achieve increased diagnostic power from

acute phase protein biomarkers, and to discriminate between different infections and/or

infections with different tissue tropism, is of great importance. It would be very interesting

to investigate further the potential of this approach for a detailed characterization of mass

variants of circulating proteins and the possible links to their tissue origin.

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Sammendrag (Danish summary)

Kroppens forsvar mod infektion, vævsskade og patogener varetages af en lang række

immunologiske mekanismer. Den første reaktion på en infektion el.lign. er det innate

immunforsvar, som igangsætter en kaskade af systemiske reaktioner og opregulering af

blandt andet proteiner kendt som akut fasereaktanter. Serum amyloid A protein A (SAA)

er en vigtigt og velbeskrevet akut fasereaktant som produceres både i leveren og i ikke-

hepatisk væv, hos hvirveldyr. Hos de fleste undersøgte arter findes der fire gener, der hver

koder for en bestemt isoform af SAA, som har vist sig at være afhængig af væv og

infektionstype. Dette er et unikt træk ved SAA, der ikke findes i andre akutte fase-

proteiner og gør cirkulerende SAA og dets isoformer meget interessante

biomarkørkandidater. Den gængse metode til at analysere SAA-isoform ekspression er

tidskrævende, ikke kvantitative og de antistoffer, der er tilgængelige for SAA, kan ikke

skelne mellem isoformerne, hvilket betyder at SAA isoform-fordeling på proteinniveau har

været svær at analysere og er derfor ikke afklaret. Massespektrometri (MS) -baserede

metoder kan overvinde disse problemer, da massepektrometri er meget mere specifik og

lige så sensitiv som de traditionelle metoder.

I dette Ph.D. projekt blev kompositionen af nativ SAA først undersøgt vha. 2D

gelelektroforese (2DE) og den MS-baserede metode ”Matrix-assisted laser

desorption/ionization” (MALDI) med et ”time-of-flight” (TOF) massespektrometer, og

derefter ved hjælp af en ”targeted” kvantitativ MS-metode, der er udviklet i dette ph.d.-

projekt, kaldet ”Selected reaction monitoring” (SRM). 2DE og MALDI-TOF bekræftede

tilstedeværelsen af et akut fase respons i serumprøverne, men kun SAA2 og SAA4 kunne

14

identificeres. Da denne eksperimentelle opsætning både er tidskrævende og ikke den bedst

egnede til biomarkørudvikling, blev SRM-metoden, der blev udviklet, brugt til at

diskriminere og kvantificere de porcine SAA-isoformer med høj specificitet og følsomhed i

porcine serumprøver fra to separate, tidligere udførte infektionsundersøgelser. Isotopisk

mærkede SAA-peptider blev også benyttet til at kalibrere metoden for at gøre den

kvantitativ.

I Paper I blev SRM-metoden anvendt på sekventielle serumprøver fra et tidligere

eksperiment hvor svin blev inficeret med Sa og det var muligt at påvise alle cirkulerende

porcine SAA-isoformer. SAA-isoform-profilerne korrelerede fint med det totale SAA-

respons, tidligere bestemt med ”enzyme-linked immunosorbent assay” og en tæt korrelation

mellem mængden af SAA-isoform mRNA og SAA-isoform-profilerne i cirkulation blev også

fundet. Dette beviste, at antistof-uafhængig MS-baseret kvantifikation med høj sensitivitet

og specificitet er mulig og kan bruges til at diskriminere mellem et sæt af næsten identiske

cirkulerende SAA-isoformer direkte i serumprøver. Resultaterne viste desuden også, at

SAA2 uden tvivl er den primære cirkulerede SAA-isoform hos grise.

I dette Ph.D.-projekt blev eksisterende porcine serumprøver fra svin inficeret med Ap også

analyseret, og mens alle SAA-isoformer kunne påvises, kunne kun relative SAA-

koncentrationer måles, da de valgte proteotypiske peptider ikke var gode nok til absolut

kvantificering. Et yderligere sæt eksisterende porcine serumprøver fra svine med

eksperimentel Sa osteomyelitis blev analyseret, hvor alle SAA-isoformer blev påvist og

kvantificeret. Både relative og absolutte SAA-koncentrationer blev målt og korrelerede

godt med hinanden i dette prøvesæt. Relative og absolutte SAA-isoformkoncentrationer

blev sammenlignet på tværs af de tre prøvesæt, og hvert sæt havde en separat SAA-

isoformsammensætning. Dette underbygger tidligere fund, der antyder infektionsafhængig

vævstropisme af SAA-isoformer. Det vil være meget interessant at videreundersøge

potentialet af denne fremgangsmåde for at kunne opnå detaljeret karakterisering af

forskellige masse-varianter af andre cirkulerende proteiner og afdække mulige forbindelser

til deres oprindelsesvæv.

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Chapter 1

Introduction

This Ph.D. project started with the idea that acute phase proteins, in particular, SAA (see below)

might have an untapped potential as biomarkers for neurodegenerative diseases like Alzheimer’s

disease and Parkinson’s disease [1-3], obesity and obesity-related disorders [4-6] and autoimmune

diseases [7-9], that all share the same inflammatory components [9-11], and are not caused by a

single identifiable extraneous component. These diseases are on a rise on a global scale and there

is an urgent need for an early and specific diagnosis, making the development of reliable biomarkers

extremely important.

The early stages of these diseases do not allow their use as biomarkers for differential diagnosis

and/or staging of disease, but the shared inflammatory component is important, as circulatory

serum acute phase proteins (APPs) are synthesized as part of the acute phase response (APR) in

response to inflammation [12-14]. The APPs constitute a very powerful set of inflammation

biomarkers [15-17], as these proteins by definition change their serum concentrations by >25% in

response to pro-inflammatory cytokines and are highly sensitive indicators of inflammation [16,

18, 19]. However, they are most commonly used as generalized markers of inflammation/infections.

Serum Amyloid A (SAA) is one of the most dramatically induced APPs, sometimes reaching a

more than 1000 fold increase in serum concentration [20-22]. It has an increased potential as a

disease-specific biomarker, as four SAA genes, exist in most species [23-25], and there seems to be

16

a preferential difference in hepatic expression as opposed to peripheral expression between isoforms

[26-28]. Interestingly, gene expression evidence shows that the specific isoforms of SAA seem to be

regulated differently depending on tissue and infection [29], and this tissue-specific differential

regulation of isoforms is a unique feature of SAA making it an exciting biomarker candidate and

thus the focus of this Ph.D. project. There is a growing amount of data on the differential increase

of various APPs, not only SAA, and by using MS-based methods with high specificity and

sensitivity, the biomarker potential of APPs such as SAA may be bigger than previously assumed

[15].

This Ph.D. project was based on porcine serum samples from two separate previously performed

infection studies; samples from a study performed by Olsen et al. (2013) [29] where pigs were either

intravenously infected with Staphylococcus aureus (Sa) or intranasally infected with Actinobacillus

pleuropneumoniae (Ap), and from a study by Jensen et al. (2016) [30] that used an Implant-

Associated Osteomyelitis (IAO) model where a steel implant was inserted into the tibia bone along

with a dose of Sa. Pigs were chosen as the porcine model permitting highly controlled challenges

to be performed, and disease development to be monitored closely in an organism that closely

mirrors human beings regarding immune response. Pigs were inoculated with porcine pathogenic

strains of either Sa or Ap, thereby creating an innate immune response from which the levels of

APPs have been detected [29]. The serum samples were analyzed with several MS-based methods

such as MALDI-TOF and SRM MS. The latter MS method was developed and validated in this

work specifically for the detection of porcine SAA isoforms and used to analyze SAA levels in the

porcine serum samples.

1.1 Objectives

The hypotheses for this Ph.D. project can be summarized as follows:

I. SAA has potential as a biomarker with increased diagnostic specificity by using targeted

MS to quantify specific SAA isoforms.

17

II. Quantification of tissue and infection specific SAA isoforms during infection will show that

SAA isoforms are differentially regulated depending on tissue origin and infection type.

III. Based on the resulting isoform profiles, targeted MS of SAA isoforms can be used to

diagnose specific infection types and specific types and locations of inflammation.

The aim was thus to develop a quantitative mass spectrometry-based method for accurate

quantification of SAA isoforms, since a method for accurate quantification of SAA isoforms on the

protein level is not currently available. Such a method will be an extremely important tool to

investigate the hypothesis that the accurate quantification of SAA isoforms in patients’ blood

samples can be used for the early, specific diagnosis of autoimmune, neurodegenerative and other

diseases having an inflammatory component. Furthermore, accurate mapping of SAA isoform

fluctuations will contribute significantly to the understanding of the role of this highly regulated

host response protein in health and disease. The project specifically relied on selected reaction

monitoring (SRM) mass spectrometry (MS) for antibody-free quantification of specific proteins in

complex biological samples.

1.2 Outline of Ph.D. thesis

This Ph.D. thesis will first present relevant background knowledge in chapters 2, 3, and

4, followed by a review of materials and methods in chapter 5.

After introduction, background, technical aspects, and materials and methods chapters (1-

5), results are presented in the form of a submitted paper in chapter 6, and in chapter 7,

additional results are presented along with a discussion of all results, a conclusion, and

perspectives on future work.

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Chapter 2

Background

2.1 The acute phase response

The innate immune system directs the initial response to immunological stress initiated

by tissue and cell destruction and by pattern recognition receptors on the surface of innate

immune cells, recognizing pathogen-associated molecular patterns [13, 18]. This initial

response is the acute phase response; a prominent systemic reaction comprised of a number

of responses [12, 13, 18]. Following an inflammatory stimulus, the local release of pro-

inflammatory cytokines leads to the acute phase response (APR), activation of the

vascular system and activation of further inflammatory cells, which then leads to the

production of more cytokines and other inflammatory mediators that diffuse to the

extracellular fluid compartment and circulate into the blood [12, 18]. The cytokines induce

the liver to produce acute phase proteins (APPs) which, together with cytokines and other

inflammatory mediators generate a systemic response (fig. 1) [12, 14].

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During an APR, the APPs are released, very quickly, into the circulation and their serum

concentrations increase substantially while the APP concentration in healthy animals and

humans vary from a baseline level to almost undetectable [31, 32]. The type and magnitude

of APP expression vary between the species, but in general, the APP concentration reaches

a peak within 24–48 hours and if no new stimulus appears and inflammation ceases, APPs

regulation will limit the response within 4 to 7 days after the challenge. In the case of

chronic inflammation, a longer and slightly increased serum concentration of APPs

compared to acute inflammation is observed [38-42].

Fig. 1 An inflammatory stimulus such as an infection or tissue injury, activates monocytes and macrophages that in turn release cytokines. These cytokines induce the production of acute phase proteins by the liver that collaborates with the cytokines to generate a systemic response. IL-1/6: Interleukin 1/6, TNF-α: tumor necrosis factor α, IFN-γ: Interferon γ, TGF-β: Transforming growth factor β, SAA: Serum Amyloid A, CRP: C-reactive protein.

2.2 Acute phase proteins

The APPs are broadly divided into two categories – the positive APPs whose circulating

levels increase in response to an inflammatory stimulus, and the negative APPs whose

circulating levels decrease in response to an inflammatory stimulus [31]. Profound

interspecies’ variations in the hepatic response have been observed, reflected by the serum

levels of individual APPs. In general terms it can be said that C-reactive protein (CRP),

Serum Amyloid P, Serum Amyloid A (SAA), α1-Acid Glycoprotein, Haptoglobin, α 1-

20

Proteinase Inhibitor, α2-Macroglobulin, Ceruloplasmin, Complement component C3, and

Fibrinogen behave as positive APR proteins, while Albumin and Transferrin respond as

negative APPs [31, 33-36] (fig 2).

Fig. 2 Shown are peak concentration change during a prototype APR of selected APPs in Swine, Cattle, Dog, Cat, Human being, Mouse, Rat, and Rabbit. NEG: Decrease in concentration (10–30%); 0: No change; I: 50% to 100% increase; II: Between 100% and 10 times increase; III: More than 10 times increase. * Normal level and acute phase concentration changes of Serum amyloid P differ strongly between different mouse strains. ?: Nothing reported about the reaction of the protein indicated in the literature [35].

Synthesis of positive APPs is induced mainly by cytokines such as Interleukins IL-6, IL-

8, IL-1β, IL-22, and tumor necrosis factor α (TNFα) [2, 13–16]. Simultaneously negative

APPs are downregulated. The most typical APPs and their general function are

summarized in table 1. The early and significant onset of certain APPs have made them

interesting candidates of inflammation biomarkers for diagnostic purposes in human and

veterinary medicine. APPs have poor specificity when it comes to detecting the cause for

a disease, but they have high sensitivity in broadly detecting the different conditions that

can cause subclinical infection or inflammation [37-39]. Neurodegenerative diseases like

Alzheimer’s disease and Parkinson’s disease [1-3], obesity-related disorders [4, 5], and

obesity itself [4, 6], and autoimmune diseases [7-9] have all been associated with increased

levels of APPs.

21

APPs are have several important applications as they can provide:

- Alternative means of monitoring animal and human health

- Objective information about the presence and extent of ongoing lesions in humans

and animals

- Information regarding the presence of a disease

Table 1. Classical positive APPs upregulated during an APR - exact classifications and kinetics are species-specific Acute phase protein Known mail role(s)

C-Reactive protein Binding pathogens and damaged cells, opsonin, complement

activation [13, 40, 41]

Serum amyloid P Binding pathogens, opsonin, complement activation,

regulation of innate defense and repair [42, 43]

Serum amyloid A

Opsonin, regulation of innate defense, induction of

extracellular matrix degrading enzymes, chemoattractant,

apolipoprotein with HDL, regulation of lipid metabolism [13,

44-47]

Lipopolysaccharide

binding protein

Pattern recognition, binding, and disaggregation of

lipopolysaccharides, opsonin [48, 49]

Complement factors Pathogen recognition, destruction, chemotaxis, opsonin,

vasoregulation [13]

α1 Acid glycoprotein Binding of plasma proteins and mediators, chaperone,

regulation of innate defense [13]

Fibrinogen Clot formation

Haptoglobin Scavenging of hemoglobin, antioxidants, angiogenesis,

chaperone [13, 50]

Additional references [13, 16, 29, 51, 52]

22

- Prognostic information, using the magnitude and duration of the APR as a

reflection of the severity of the disease.

- A practical means to monitor the effectiveness of antibiotic therapy in the

treatment of bacterial infections

In addition, the APPs can help monitor optimal animal growth by detection of small

changes in APPs concentrations, using an acute phase index [35, 39, 53-56].

In humans, CRP indicates low-grade inflammation associated with osteoarthritis and the

risk for cardiovascular events [57, 58]. In most healthy beings, plasma levels of CRP are

usually 1mg/L with normal being defined as <10 mg/L (0.06-8 mg/L), and during an APR

the CRP concentration exponentially increases (100-500 mg/L), doubling every 8-9 hours

[39]. CRP is used widely as diagnostic and prognostic markers for infectious diseases,

severe inflammation, sepsis, acute kidney injury, impaired lung function, cardiovascular

risk, inflammatory bowel diseases, Crohn’s disease, and more [16, 40, 59-61]. CRP

measurements are generally performed by immunoassays using species-specific CRP

antibodies [39].

SAA is normally found at 20-50 mg/L, and the levels rise up to 1000-fold within the first

24h of an APR, and studies indicate SAA involvement in chronic inflammatory conditions,

atherosclerosis, lung disorders [62-67]. SAA has been proposed as a possible serum

biomarker for many types of tumors such as ovarian, lung, renal, endometrial, uterine,

and melanoma [68-76]. For SAA, monoclonal antiserum against human SAA has been

used in a sandwich ELISA, and can be used successfully for other species as well [39].

APPS may also be measured by several methods using mass spectrometry.

Numerous studies indicate different patterns of APP increase, not solely explained by one

common factor as the levels of circulating APPs depend on several factors [13, 37, 77-79].

In humans, CRP is more elevated during bacterial pneumonia than in severe viral acute

respiratory syndrome. In the latter case, alpha 1-antitrypsin, SAA, and Haptoglobin is

instead highly increased [15, 79]. In different cancer types, a dramatic rise in SAA

23

concentration is seen while other acute inflammatory signs are still clinically silent. A

differential expression of SAA isoforms depending on tissue and infection type, have also

been observed [29, 80, 81]. These differences in APP levels might be due to different

costimulatory cytokines, that can be secreted depending on the underlying failure and in

that way cause the varying hepatic expression rates, kinetics, and nascent storage size [13,

15, 82], or it could be due to a differential metabolism and clearance of APPs, differentially

regulated by the underlying alteration [44, 83-85].

2.2.1 Serum Amyloid A

Serum amyloid A (SAA) is a small (9 to 14 kDa) highly regulated acute-phase protein [22,

44, 86]. It is present in the blood circulation at low levels at homeostasis and is, during

the APR accompanying inflammation in most vertebrates induced several 100-fold,

becoming the predominant apolipoprotein of high-density lipoprotein, replacing

apolipoprotein A1 [22, 44, 86]. SAA has been found involved in many processes such as

binding of, transporting and scavenging cholesterol to the liver during inflammation [87],

inhibition of phagocyte oxidative burst [54, 88], inhibition of platelet aggregation [35],

binding to Gram-negative bacteria and opsonization [89, 90], inhibition of lymphocytes

and endothelial cells proliferation [91] and detoxification of endotoxin by binding to

lipopolysaccharide [83, 91]. SAA binds to high-density lipoprotein (HDL) [92, 93] and

during inflammation, increased incorporation of SAA into HDL is observed, which appears

to accelerate the removal of these particles from the circulation [31, 34]. SAA is found to

function as a chemoattractant for human monocytes, neutrophils, and T lymphocytes

recruiting inflammatory cells into inflammatory lesions [31, 44, 94, 95]. SAA is therefore

widely accepted as important during the early phases of inflammation [96, 97]. The

predominant site for synthesis of SAA is the liver [25, 29, 98], but SAA has been detected

24

in many different tissue types; liver, lymph node, spleen, tonsils, and blood leukocytes [99,

100], lung, salivary gland and diaphragmatic muscle [101] and mammary tissue [102, 103].

Despite the small size of SAA, its 3-dimensional structure(s) has been difficult due to its

association with HDL and subsequent poor aqueous solubility despite hydrophobic residues

not prominent in the sequences. In 1986, Turnell et al. proposed a two α-helices and a β-

sheet region model based on algorithms, and a SAA monomer structure by

multiwavelength anomalous dispersion using a selenomethionine derivative to 2.2-Å

resolution was determined by Lu et al. (2014) and supported by Derebe et al. [104-107].

The monomer comprises four α helices in a cone-shaped array with the C-terminal residues

wrapped around the bundle (fig. 3 A). Consistent with earlier findings Lu et al. found a

hexamer formed by two identical trimers, and potential HDL binding sites were predicted

to overlap near the residues R-1, R-62, and H-71 in helix h4 [106, 108-111].

With this structural model, Frame and Gursky noted that there are both hydrophobic and

hydrophilic faces in helices 1 and 3, and that the hydrophilic faces interact with helices 2

and 4 leaving the hydrophobic domains able to bind the lipid surface of HDL. SAA can

thereby serve as a “hub”, binding HDL on one side and interacting with

glycosaminoglycans, cystatin C, retinoic acid, and cell receptors on the other side, with

domains specifically involving the C-terminus (fig. 3 B) [106, 112].

25

Fig. 3 (A) Atomic structure of hSAA1.1 (PDB ID 4IP8). Blue: h1, residues 1–27, teal: h2, 32–47, green: h3 50–69, orange: h4, 73–88, red: CT tail 89–104 including a 3/10 helix h0, 90–96. Asterisks mark predicted amyloidogenic segments. Indicated is also the highly conserved GPGG motif [112, 113]. (B) Structural model of SAA bound to an HDL particle. The SAA monomer binds to HDL on the concave apolar surface site of helices h1 and h3 (blue and green rectangles), thereby protecting the amyloidogenic segments from initiating the misfolding. The C-tail, cleaved in AA amyloidosis, binds cell receptors and ligands, bridging them together and mediating interactions. When inflammation is resolved and SAA plasma levels drop, SAA is released from HDL and this dynamic network dissociates. Figure modified from [112].

One precise function of SAA and its role during the acute phase response has not been

established, but its participation is clear. A recent comprehensive review by George H.

Sack Jr provides an excellent insight into what is known regarding the function of SAA,

and gather information from numerous SAA studies into a detailed and comprehensive

overview of SAA function [62]. Well-described characteristics include the ability of

circulating SAA protein to bind to high-density lipoprotein (HDL) [92, 93] and to directly

induce pro-inflammatory cytokines and chemokines in monocytes and granulocytes [114].

It has chemotactic effects in various cell types through binding to formyl-peptide receptor-

1 (FPR1), formyl-peptide receptor-2 (FPR2), and Toll-like receptor-2 (TLR2) [115-118].

In cultured human neutrophils, SAA was found to cause a dramatic increase in TNFα, IL-

1β, and IL-8 when incubated with the cells [119].

26

Fig. 4 A wide selection of SAA interactions through receptors has been determined. HDL: high-density lipoproteins, SR-BI: scavenger receptor class B type I, FPR2: formyl peptide receptor 2, P2X7: An ATP receptor, TLR2/4: Toll-like receptor 2/4, RAGE: receptor for advanced glycation end-products. SAA from blood and tissue is shown separately to illustrate their different functions [116].

Most recently cytokine-like properties have been ascribed to SAA, being intimately

implicated in the intestinal control of Th17, immunity itself being directly induced in

epithelial cells of mucosal tissue by IL22, which can be controlled by the microbiota in the

absence of inflammation [120-122]. In very brief summation:

- SAA participates in the APR

- SAA helps link the complex network of cells (macrophages, hepatocytes, and others)

and proteins mediating inflammation

- SAA functions as a cytokine-like protein in cell-cell communication as well as

feedback in pathways that are immunologic, neoplastic, and protective during

inflammation [62].

Most of these interactions are summarized in fig.4 [116].

27

Across species, the sequence of SAA and its isoforms has been very well conserved

throughout evolution (fig.5) and this, together with its rapid and huge acute phase

response, also implies that SAA proteins probably have important functions during the

response to tissue perturbations [23, 123]. The SAA superfamily consists of four very

similar SAA genes, each coding for a specific isoform of SAA (SAA1-4). They have been

demonstrated having the same four-exon three-intron structure in all species [38, 44] with

an 18 amino acid long signal sequence is present in the initial transcript, which is removed

in the resulting serum proteins. The human SAA gene cluster is comprised of four gene

loci (i.e. saa1, saa2, saa3, and saa4), clustered within a 160 kb region of chromosome

11p15.1 [124-126]. Within the species, SAA1 and SAA2 are highly homologous, while

SAA3 is less homologous to the other SAA genes and SAA4 is the most divergent isoform

[127-130]. The evolutionary relationships of the SAA superfamily members show that all

of them have undergone concerted evolution on the nucleotide level. This means that there

are no strict evolutionary homologs between the mammalian SAAs known, as they have

been homogenized within the species, e.g. human SAA1, SAA2 and SAA3p are more

related to each other than to their murine orthologous, as the murine genes are more

related to each other than to the human orthologous [23, 131]. Functions and disease

associations can therefore not be presumed to be the same for one type of SAA in one

species as in another [23].

28

Fig.5 Shown are available sequences of SAA isoform aligned across species, and categorized within SAA isoform type. Each sequence is shown next to its UniprotID1. Human (Homo sapiens), pig (Sus scrofa), mouse (Mus musculus), mink (Neovison vison), hamster (Mesocricetus auratus), and rabbit (Oryctolagus cuniculus). Colored squares and * indicate identical amino acids across sequences.

The detection of the individual SAA isoforms remains a difficult process, and isoforms

have in many cases been wrongly identified. Many of the early reports of SAA did not

distinguish among SAA isoforms at all, others have used outdated nomenclature and in

1 Due to updates, the sequences for porcine SAA2 and SAA4 are currently moved to the Uniparc archive, and can be found at uniprot.org/uniparc with the following ID: SAA2: UPI0001E86A58, SAA4: UPI0001E86A59

29

general, the field has been quite confusing and not always easy to navigate. Purification

and detection of SAA isoforms are difficult, as SAA tends to either form large protein

aggregates [15] or precipitate [13] when separated from HDL, and the fact that the SAA

isoforms have very similar sequences, make antibodies for SAA isoform distinctions very

difficult to develop. No solid reports of SAA isoform antibody have been produced, except

for one substantiated recent report demonstrating a selective antibody which does not

react with porcine SAA4, but do also not discriminate between porcine SAA2 and SAA3,

nonetheless (SAA4 being the most divergent of the SAA isoforms) [132]. Immuno-based

SAA assays are most often not based on purified species-specific SAA but instead based

on calibration and standards consisting of recombinant SAA, heterologous SAA, or pooled

acute phase serum [133-137]. Using species-specific calibration material from the native

purified protein is important to obtain precise measurements of SAA isoforms, but

currently mainly size-exclusive chromatography and isoelectric focusing (IEF) are used

[138-140], using delipidation steps [140-142] like ultracentrifugation or hydrophobic

interaction chromatography [143, 144] to obtain SAA, not in complex with HDL [137].

In IEF, molecules are separated by their isoelectric point (pI) in a gel with a pH gradient,

making proteins in a pH region below its pI positively charged and migrating toward the

cathode. As it migrates through the gradient of increasing pH, the overall charge will

decrease, and when the protein reaches a pH region corresponding to its pI, there is no net

charge and migration stops, thereby focusing the protein at a point in the pH gradient

corresponding to its pI. IEF is followed by Western Blotting where antibodies are used to

detect the protein of interest. SAA isoform detection at the protein level is possible with

IEF as the different isoforms of SAA have different pI due to their different amino acid

sequences, making isoelectric focusing the primary method used for detection of the SAA

isoforms on the protein level for many years. IEF is not a precise method, with the results

relying on manual inspection of more or less slurred blot as presented in fig. 6. The earliest

information regarding the SAA isoforms on the protein level was based on IEF [130, 140,

145-149]. The method, however, is not very precise and as mass spectrometry-based

30

methods in the field of proteomics have improved, they may provide the sensitivity and

specificity needed for SAA isoform detection.

Fig. 6 Human SAA isoforms extracted from acute phase sera were electro-focused between pI 5-8 and detected by Coomassie blue staining (track I) and by electroblotting to PVDF membranes and immunoenzymatic detection (tracks 2-4) [149].

IEF results have been the base of most knowledge regarding SAA isoforms, and a very

interesting finding was that some SAA isoforms are present locally in e.g. colostrum. SAA3

has been found in bovine, equine, and bovine colostrum, and Sack et al. found SAA1 in

human colostrum [102, 150]. In several species, SAA3 has been proposed as the main

isoform of local inflamed tissue, as it is the only SAA isoform detected locally in e.g. joints

in horses [27], dogs [151], bovine udders [28] and adipose tissue in mice [152]. SAA3 has

also been found upregulated in lung tissue of mice during an APR induced by inhalation

of zinc oxide nanoparticles [153] and in porcine bone in pigs with implant-associated

osteomyelitis [154].

During the acute phase response, the predominant site for synthesis of SAA is the liver.

Generally speaking, SAA1 and SAA2 are expressed preferentially in the liver while SAA3

is found in hepatic as well as non-hepatic tissues during an APR [25, 29, 98]. In the pig,

31

SAA gene expression is also induced both hepatically and extrahepatically during an APR

[99, 155] and elevated levels of circulating SAA proteins have been found [17, 156]. Murine

saa3 is induced in several different tissue types, whereas there have been no reports of in

vivo transcription of the human homolog SAA3p [98, 128, 157, 158]. Murine saa4

expression has only been found in liver tissue, but human SAA4 has been found expressed

in both liver and extrahepatic tissue [129, 130, 159]. The SAA isoforms (SAA1, SAA2 and

SAA3) have been recognized at the transcriptional, as well as protein level, in many other

species than human and mice [44, 160], where the main isoform of local inflamed tissue is

most often found to be SAA3 [27, 28, 80]. A porcine SAA3 gene transcript has been

characterized [100] and SAA3 is proposed to be the most prominent circulating SAA

isoform in pigs [101, 161], making the pig unique as SAA3 has not been detected as a

circulating isoform in other species examined [27, 28, 80, 102, 152, 162-164].

Olsen et al. [29] studied the relative expression of porcine genes encoding SAA1, SAA2,

SAA3, and SAA4 in tissue samples from lung, liver, and spleen, respectively, from pigs

experimentally infected with either the Gram-negative swine specific pathogen

Actinobacillus pleuropneumoniae (Ap), or a porcine strain of the Gram-positive pathogen

Staphylococcus aureus (Sa). They identified two previously uncharacterized porcine SAA

transcripts as SAA2 and SAA4, and found no transcript corresponding to porcine SAA1,

concluding that porcine SAA1 is most likely a non-expressed pseudogene. They found

that both infection types induced increased hepatic expression of SAA2, SAA3, and SAA4

and found elevated pulmonary and splenic expression of SAA3 [29]. An interesting detail

was that different types of infection models differentially induced the expression of the

SAA isoforms. In both experiments a marked increase in the expression of SAA2 was found

in liver tissue samples from infected pigs compared with control pigs. In lung tissue from

Ap-infected pigs an increased SAA2 expression was observed, and while the change was

negligible in the lung tissue from the Sa-infected pigs, in spleen the SAA2 regulation was

negligible in both infection experiments [29].

32

In both infections model, severe infectious states were observed with clear systemic

affection, but during the Ap infection, it was observed that the lowest increase in relative

SAA3 expression was found in spleen tissue when compared to liver and lung, while the

opposite was the case in the Sa infection experiment. This tissue-specific expression pattern

of SAA3 mRNA makes it a potential marker of tissue-specific involvement of acute

inflammation in pigs [29]. In the Ap-infected pigs, when compared to control pigs, an

average increase of 8.6-fold was observed in the SAA4 hepatic transcription levels, but in

the Sa-infected pigs only a slight increase was observed when comparing infected pigs and

control pigs. In samples from lung and spleen tissue, SAA4 transcription was below

detection [29]. The tissue-specific differential of isoforms during different diseases seems to

be a unique feature of SAA and makes it a very interesting biomarker candidate.

It is possible to imagine a form of SAA fingerprint where specific SAA isoform expression

profiles indicate the presence of a specific infection or disease, improving diagnosis and

discovery early during the disease or infection; it might also improve the possibilities of

early treatment.

As one of the major acute phase proteins, SAA is implicated in a wide range of diseases

and has been proposed as a biomarker for many of these diseases [15, 62-64, 165]. For

example, local SAA1 and SAA4 production have been found in the colon suggesting a role

in colonic tumorigenesis and may have both prognostic and therapeutic applications, as

no SAA was detected in normal-looking colonic epithelium [166]. In ovarian carcinomas,

the SAA1 and SAA4 genes are overexpressed when compared with normal ovaries, and

SAA levels increase when epithelial cells progress through the benign and borderline

adenomas onto the primary and metastatic adenocarcinomas. The high serum levels of

SAA strongly correlated with the ovarian tumor marker CA-125, suggesting an

involvement of SAA in ovarian tumorigenesis [167]. Overexpression of SAA has been

reported in renal-cell, uterine serous papillary, and endometrial carcinomas [68, 69, 73,

77]. Local SAA isoform levels may be used as a biomarker for cancer aiding early detection

and diagnosis.

33

Chapter 3

Technical aspects

As mentioned above, others and we have shown by transcriptional analysis, that SAA

isoforms are differently expressed depending on tissue location and type of trigger [29].

Until now, however there has been no way of quantifying circulating SAA protein isoforms,

as no rapid and reproducible method exists for this purpose. We therefore developed a

specific, quantitative and antibody-free mass spectrometry-based method for quantifying

circulating porcine SAA isoforms. Porcine serum samples from two separate previously

performed infection studies were analyzed; one with pig infected with either Sa or Ap, and

one with pigs infected with Sa in an IAO model, allowing us to correlate the tissue-specific

expression of SAA isoforms with SAA isoforms in the circulation. This is of interest not

only for elucidating possible specific biological functions of SAA isoforms, but also for

describing the possible role of SAA isoforms as differential biomarkers of infection. The

phases of experimental work performed in this Ph.D. project are depicted in fig.7 and the

technical aspects of the mass spectrometry methods used will briefly be described in the

following section.

34

Fig. 7 Experimental parts of the Ph.D. project simplified into four groups. The project started with SAA ELISA to measure total SAA levels in the serum samples, then 2DE, and MALDI-TOF was performed to investigate native SAA isoforms and subsequently a SRM method was then developed to further analyze the samples. Future work of interest would use methods such as Parallel reaction monitoring (PRM), SWATH-MS as a method for data-independent acquisition (DIA), and tandem mass tags (TMT).

3.1 Mass Spectrometry

Mass spectrometry (MS) is a valuable tool for the analysis of proteins with a wide range

of variations and applications and is frequently used within biomarker research and

veterinary research of APPs [168-170].

A mass spectrometer has six essential interlinked components: a system for sample

delivery, a vacuum system, an ionization chamber (ion source), a mass analyzer where

ions are separated according to mass, a detector, and a computer for data analysis (fig.8)

[171, 172]. The basis of mass spectrometry can be summarized as follows: First, the sample

is introduced into the ion source through a series of ion lenses and ion optics that focus

and guide the ion beam to a mass analyzer. A high vacuum is required to prevent the ions

from reacting with or being scattered by air molecules. Liquid chromatography (LC) is

often used here, to separate sample molecules by their interaction with absorbent material

in an LC column, which causes different flow rates as they pass from the column into the

35

ion source where they are ionized. Ionization can be done by e.g. electrospray ionization

(ESI) or Matrix-assisted laser desorption ionization (MALDI). Regardless of the ionization

method applied, a successful ionization requires an energy transfer to the analyte, which

is extremely important since the applied energy and the way it is applied to determine the

produced mass spectrum. The ions are subsequently directed into the mass analyzer where

they are sorted.

Fig. 8. Schematic overview of a mass spectrometer. Through a sample delivery system, the sample is introduced into the ion source through a series of ion lenses and ion optics that guide the ions to the mass analyzer, where they are sorted according to their mass-to-charge ratios and recorded by a detector. The recorded signals are converted into mass spectra by a computer. A series of vacuum and turbo pumps maintain the vacuum.

The sorting can be according to their mass-to-charge ratios (m/z), or based on the time

the ion take to fly from the ion source to the detector (time-of-flight) [171-173]. The ions

reach the detector at different times and this information is collected and gathered into a

MS spectrum. The spectrum is recorded as a bar graph where ion intensities are normalized

so that the largest peak in the mass spectrum, called the base peak, is set to an intensity

of 100 units, and all other ion abundances are expressed as a percentage of this. An

36

additional stage of ion fragmentation may be included before detection to obtain structural

information in a technique known as tandem MS [172, 173].

3.2 Liquid Chromatography-Mass Spectrometry (LC-MS)

Samples for MS analysis may be solid, gas phase, or in liquid. Each type of sample has a

designated sample delivery system. In this Ph.D., liquid samples have been used, and focus

is therefore chosen to be on this type of sample delivery system i.e. Liquid Chromatography

MS (LC-MS) [171, 172]. LC-MS combines the physical separation capabilities of LC with

the mass analysis capabilities of MS, and with its high molecular specificity and detection

sensitivity it can provide a structural identity of the individual components [174, 175].

Electrospray ionization (ESI) is commonly used to couple High-Pressure Liquid

Chromatography (HPLC) with mass spectrometry, by facilitating the transition from a

high-pressure environment to high-vacuum conditions needed at the MS analyzer [174,

175]. HPLC uses pumps to pass the sample mixture and solvent as a pressurized liquid

through a column filled with solid adsorbent material to separate, identify, and quantify

each component in a mixture. The components in the sample interacts with the adsorbent

material in a slightly different manner, which causes different flow rates for the different

components and thereby separates the components as they flow out of the column [171,

172].

The components of the system being analyzed are termed analytes. As mentioned earlier,

they elute at specific time points from the chromatographic column and enter the MS

ionization chamber. A time delay of elution of the different analytes is caused by

physicochemical interactions between the stationary phase on the chromatographic

column, the analytes, and the mobile phase. The intensity of the detectable m/z is

registered inside the MS detector, its value representing the approximate amount of ionized

molecules of each m/z detected at the exact retention time (rt) [176]. A typical dataset

37

from an LC-MS experiment is represented as a set of data points in a three-dimensional

space defined by the rt, mass-to-charge ratio (m/z), and intensity (counts), as shown in

fig. 9. At a specific rt in the mass spectrum, one “slice” of the 3d data set is selected, as

an accumulation of all the detections from the MS detector during a very short time-period

[176].

Fig. 9. 3D view of a LC-MS mass chromatogram with intensity, retention time and mass-to-charge ratio making up the mass spectrum from the recorded signals in an LC-MS run [177]

3.3 Matrix-assisted laser desorption ionization (MALDI)

MALDI is one of the most commonly used protein ionization techniques. It was first

described in 1985 by Michael Karas et al. and successfully used for protein ionization in

1988 by Tanaka et al. [178, 179]. The method is characterized by easy sample preparation

and large tolerance to contamination by salts, buffers, detergents, etc. It is a soft ionization

technique with minimal fragmentation yielding intact molecular ions.

38

Different types of samples such as cells, tissues, and biological fluids can be analyzed by

MALDI. The extracted and purified protein or peptide mixture is mixed with a matrix –

an organic compound with a strong optical absorption at the laser wavelength, usually in

the UV region, that will facilitate the ionization by absorbing the laser energy and

transferring it to the sample analytes. The sample/matrix mixture is placed on a target

plate, usually a stainless steel plate with multi-well support that allows for a spatial

separation of multiple samples. The sample mixture airdry whereby the sample co-

crystallizes with the matrix. The sample-matrix crystals are then irradiated by a laser

pulse, inducing desorption of the crystals surface i.e. the sample analytes and matrix

molecules desorb and sublimates resulting in the formation of gas-phase ions. In this vapor

phase, proton transfer from the matrix to the analyte molecule occurs. (fig. 10) [175, 180,

181]. The ions that are detected first are the smallest, as ions with different m/z are

accelerated to different velocity inversely proportional to their mass, at a fixed kinetic

energy. This is known as linear mode and the mass range achievable here is ranging from

tens to hundreds of thousands of Daltons [175, 180, 181]. In the resulting MALDI mass

spectrum, the dominant peaks will mostly correspond to the (singly) protonated molecule.

Multiply charged ions, doubly, and triply charged forms, are at low abundance [175, 181].

Fig. 10. The analyte is embedded in a large excess of a matrix compound deposited on the target plate. A brief laser pulse irradiates the spot thereby desorbing and ionizing the sample. The energy of the laser is absorbed by the matrix which carries the analyte molecules into the gas phase. The analyte molecules are ionized by being protonated with the matrix molecules [182].

39

3.4 Electrospray ionization (ESI)

Another soft ionization technique is ESI. Ions are generated by applying a strong electric

field, under atmospheric pressure, to a liquid passing through a capillary tube with a low

flow rate (normally 1– 10 µL/ min). The high ion spray voltage applied to the electrode

tip of the emitter results in the formation of a Taylor cone containing positively or

negatively charged ions depending on machine mode, depending on the HPLC buffer

system and molecules being analyzed. Charged parent droplets are released from the

Taylor cone and as the solvent molecules evaporate, the charges are brought closer

together until they reach the threshold for ionic repulsion and a columbic fission occurs.

The charged ions enter the MS through a heated capillary, which evaporates the remaining

solvent molecules. Both singly-charged and multiply-charged ions are generated (fig.11).

Depending on the flow rate of the electrosprayed liquid, ESI can be subdivided into

microelectrospray [183] and nanoelectrospray [184]. Microelectrospray ion sources maintain

flow rates over 500 nL/min, whereas nanoelectrospray is of approximately 30 nL/min.

Fig. 11. The sample is introduced from HPLC to ESI and positively charged ions are generated as the solvent-analyte mixture passes through the spraying nozzle. The solvent evaporates resulting in columbic fission of the charged droplets, forming charged ions that are detected by the mass analyzer as described above.

40

3.5 Mass analyzers

The mass analyzer of the mass spectrometer separates the ionized atoms and molecules

according to their mass-to-charge ratio (m/z) by the use of electrical or magnetic fields to

apply a force to charged particles to induce separation or differential focusing of ions into

the detector [171]. The resolution of a mass analyzer is an important instrumental

characteristic that measures the instrument’s ability to separate ions i.e. the ability to

distinguish two peaks of slightly different mass-to-charge ratios in a mass spectrum. A

larger resolution indicates a better separation of peaks, and higher resolution also leads to

higher absolute mass accuracy [173]. The resolution varies from instrument to instrument

as well as from instrument combinations such as MALDI-TOF, Q-TOFs, QQQs, Q-

Orbitrap etc. The specifications and abilities of the instruments are changing fast, as they

are continuously improved. It is therefore important to investigate the resolution of the

instrument one is considering to use for an experiment and determine whether the

resolution and specifications are suited for the experiment in question. There are several

different types of mass analyzers as presented in table 2, and as mentioned several

hyphenated mass analyzers where different mass analyzers are coupled together, have also

been developed.

Tabel 2. Overview of selected mass analyzers and their separation principle

Mass analyzer m/z separation principle

Quadrupole Stability of ion trajectory

TOF Velocity of ions

Orbitrap Oscillating along the axis

Ion trap Resonance frequency

41

3.6 Quadrupole Mass Analyzers

The quadrupole mass analyzers are ion beam analyzers, comprised of four parallel rods

with cylindrical geometry arranged in a specific three-dimensional fashion around the path

of the ion beam between the ion source and the detector (fig. 12).

A DC voltage is applied across the rods so that two rods have positive charges and two

have negative charges - opposing rods carry the same charge. An alternating current is

applied to both pairs of rods, and when the ions enter the electric field created within the

rods, they oscillate, and only ions with a stable oscillation will have sufficient kinetic

energy along the length of the filter to reach the detector. Depending on the voltage and

frequency of the quadruple, only certain ions will make it through, while other ions oscillate

with increasing amplitude until they collide with the rods and fail to reach the detector

(fig. 12) [171, 172].

Fig. 12. Schematic diagram of a quadrupole mass analyzer. Four parallel rods are arranged in a 3D fashion around the path of the ion beam between the ion source and the detector. Only ions with stable oscillation (“correct m/e”) will have sufficient kinetic energy along the length of the filter to reach the detector [171].

42

The quadrupole mass analyzer can operate in different modes depending on voltage and

frequency settings:

- It may allow ions with an m/z within a certain interval to be transmitted

simultaneously

- It may allow ions of a specific m/z ratio through, whereas ions with different m/z

ratios collide with the rods and are lost

- It may allow ions of increasing m/z to pass through in small intervals (e.g. 0.1 to

0.2 Da). In this operation mode, the quadrupole mass analyzer is said to be

scanning.

Because of these filtering options, a quadrupole mass analyzer is commonly referred to as

a mass filter [172]. The quadrupole mass analyzers are commonly known as Qs, and if

quadrupole mass analyzers are coupled together with other mass analyzers, they may be

called Q-TOFs, QQQs, or Q-MALDIs etc.

3.7 Time of Flight (TOF) mass analyzers

In TOF mass analyzers, ions are produced over a very short period using e.g. pulsed laser

desorption to expel the ions from the ion source. The generated charged ions are

accelerated by a constant electric field and acquire a similar kinetic energy. They

subsequently fly at constant, ion-specific speed towards the detector through a free-field

region called ‘flight tube’ being separated according to their m/z (fig.7). As the ions have

different masses, their velocity is different from each other and thus each ion traverses a

fixed distance to the detector in a different time [171, 172]. In theory, there is no upper

limit to the mass range of the TOF instrument, making them very useful for the analysis

of large molecules. TOF mass analyzers also have fast cycles as they technically transmit

43

all m/z ions (full scan mode) [172]. Using ion mirrors known as reflectrons may increase

the resolution of the TOF instrument as the reflector increases the ion drift path length

by bouncing ions off it (fig. 13) [180].

Fig. 13. Basic principles of TOF MS with a reflectron. The reflectron reflects the ion beam with a constant electrostatic field, towards the detector. Ions with a high kinetic energy (smaller dots) penetrate deeper into the reflector thereby taking a slightly longer path to the detector than ions with lower kinetic energy (bigger dots circles).

The reflector is made up of an electric field with a rising electrode potential, composed of

a circular electrode that directs the ions by changing the direction of the field. Ions pass

through the reflector until they lose their kinetic energy, their flight paths varying

according to the differences in their kinetic energy so ions with high kinetic energy stay

longer in the reflector than low energy ions. This leads to the correction of a flight time

by the reflector greatly improving the resolution of the TOF analyzer. The TOF mass

analyzers are very often coupled with a MALDI source.

44

3.8 Tandem MS (MS/MS)

While single-stage mass spectrometry does play a role in protein identification, many

protein identifications are performed by tandem mass spectrometry (MS/MS) of peptides

derived from protein digests [185, 186]. By coupling two or more mass analyzers together

with a reaction region between them, separate MS functions can be applied to analyze the

sample mixture using sequential processes [171, 186]. A variety of instrument setups can

be used for MS-MS, but the basic concept is that precursor ions are selected in the first

MS. The ions then move into a second MS that acts as a reaction collision cell, fragmenting

the precursor ions into product ions (also called daughter ions). The product ions are

focused by the third MS and detected as individual ions or as spectra [171, 186].

Fig. 14. Cleavage of the amide bonds in the peptide backbone generates fragment ions. a, b, and c ions are generated from the N-terminus while x, y, and z ions are generated from the C- terminus [187].

Each MS can be set to scan a mass range or select one or more individual ions [171]. Fig.

14 illustrates the ion pairs that can be generated from backbone bond fragmentation when

MS/MS is performed on a peptide. Fragmentation of the backbone from a singly-charged

45

peptide ion produces an ion and a molecule. N-terminal fragments give rise to a, b, or c

ions, and C-terminal ions are called x, y, or z ions. The most common cleavage at the

amide bond results in b or y ions i.e. tryptic peptides with a C-terminal basic residue

generally exhibit predominant y ions [187, 188]. The peptide fragment ions produced in

the collision cell provide sequence information that can be searched against protein

database, using algorithms to match the measured peptide MS/MS, and thereby enabling

protein identification [189].

Collision-induced dissociation (CID) is a slow activation method, and the most widely

used MS/MS technique. The precursor ions are accelerated to higher kinetic energy and

allowed to collide with neutral gas atoms or molecules such as helium, nitrogen, or argon.

This collision results in a conversion of some of the ions kinetic energy into internal

vibrational energy which subsequently results in breakage of covalent bonds in the peptide

[189, 190]. In addition to CID, there are other fragmentation techniques such as electron

transfer dissociation and high collision dissociation, and the fragmentation can be done in

a variety of ways [189].

46

Chapter 4

Discovery and Targeted proteomics

In the early years of proteomics, the focus was mostly on identifying individual proteins

or protein complexes, and while proteomic analysis is a perfect tool for discovery and

qualitative identification of high numbers (<1000) of proteins in cells or from other

biological samples, there is also a need to quantify these proteins.

Within the fields of proteomics, a distinction is made between discovery proteomics and

targeted proteomics. Discovery proteomics intends to identify as many proteins as possible

across a broad dynamic range, wherein targeted proteomics, the analysis focuses on a

subset of proteins of interest in a sample – one or more targets are chosen and focused on.

Discovery proteomics is often used as a way to inventory as many proteins as possible in

a sample or to detect the differences in the abundance of proteins between samples. In

contrast, targeted proteomics are used in pharmaceutical and diagnostic settings to

quantifying proteins in complex samples. Targeted proteomics may follow discovery

proteomics to quantify proteins of interest found during the discovery experiments with

high precision, sensitivity, specificity, and throughput. Especially within the field of

47

biomarker research targeted proteomics are valuable tools. Most biomarkers are found in

blood plasma, as it is accessible and assumed to contain a quantifiable amount of proteins

that reflect the physiological and pathological state of the human body [191, 192]. The

blood-based biomarkers, however, are complex mixtures with a wide dynamic range of

protein concentrations [193].

One of the most common approaches for verification and validation of biomarker

candidates is the sandwich Enzyme-Linked Immunosorbent Assay (ELISA), a method with

high specificity but restricted to the availability of antibodies for novel candidate proteins.

The targeted LC-MS methods Parallel Reaction Monitoring (PRM) and Selected Reaction

Monitoring (SRM) are excellent methods for protein quantification as they are capable of

detecting low abundance proteins in highly complex sample mixtures with high selectivity,

reproducibility, and sensitivity.

4.1 Parallel Reaction Monitoring (PRM)

Parallel Reaction Monitoring (PRM) is a targeted MS method that simultaneously

analyzes all fragment ions of a pre-selected peptide of interest. PRM uses a setup with two

quadrupoles in connection with an Orbitrap or TOF mass analyzer allowing for a targeted

proteomics strategy that can monitor all transitions of a given targeted precursor peptide

simultaneously with high resolution and high mass accuracy. The operation is as described

above regarding MS/MS; the precursor ion is selected in Q1, the precursor ion is

fragmented in the collision cell Q2 and then all product ions are scanned with high

resolution and high accuracy in the Orbitrap. All transitions are in this way co-detected

and distinguished from one another as well as from the background, in the final mass

analysis stage (fig.15) [194, 195].

48

Fig. 15. The principles of PRM. The precursor ion is selected in Q1, the precursor ion is fragmented in the collision cell Q2 and then the Orbitrap scans all product ions with high resolution and high accuracy. Figure modified from [196]

PRM has quantitative analysis capabilities as well as an increased qualitative ability. It is

highly specific since all potential product ions of a peptide upon fragmentation are

available to confirm the identity of the peptide instead of just 3–5 transitions as in SRM.

Since PRM monitors all transitions, the prior knowledge of target transitions before

analysis is eliminated, however, precursor peptide selection before MS analysis is still

required, and the demands for proteotypicity and quantotypicity are just as strict for PRM

as SRM. PRM can thereby provide a higher tolerance for co-isolated background

peptides/species with the numerous ions available for identification and quantitation,

which makes the presence of interfering ions in a full mass spectrum less disruptive than

in a narrow mass range as in SRM. A PRM method is also easier to develop as much of

the effort required to determine optimal transitions and further optimize a traditional

SRM assay is eliminated [194, 197, 198].

4.2 Selected Reaction Monitoring (SRM)

SRM is also a targeted MS/MS technique for the detection and quantification of specific,

predetermined analytes. The SRM method has an increased sensitivity as data collection

49

is limited to a low number of pre-selected protein-derived tryptic peptides each having a

set of characteristic fragment ions ideal for antibody-free quantification of specific proteins

in complex biological samples, including tissue samples and serum [199-201]. SRM is most

effectively used in LC-MS systems, where the capillary LC column is connected to the ESI

source of the mass spectrometer [200]. SRM has a high sensitivity, a large dynamic range,

high specificity, and reproducibility and it can be multiplexed to increase the throughput

[199, 202-204].

In SRM, a mass spectrometer with a triple quadrupole (QQQ) mass analyzer is used to

filter out unrelated ions both at the tryptic peptide stage and at the fragment ion stage

according to preselected mass to change rations [205, 206]. The first quadrupole (Q1) is

set to only admit a predetermined m/z value of ionized peptides. The admitted peptides

are then fragmented in the second quadrupole (Q2) by CID and then only specific ions

will reach the third quadrupole (Q3) that also has a fixed m/z filter allowing the passage

of a fixed (low) number of ions with defined m/z ratios only. Each Q1 and Q3 pair is

termed a transition. For a given peptide, several transitions will occur, as a result of the

fragmentation of the peptide. This is outlined in fig. 16 where the general SRM method is

depicted at the top, and a peptide with possible fragmentations marked is shown above a

table of what is called an SRM assay. The SRM assay is the data fed into the MS,

controlling which ions are allowed through the mass filter. As can be seen in fig. 16, the

mass filter in Q3 changes, and only the associated fragment ion is detected.

The two levels of m/z filtering in SRM result in high selectivity and low background

signals and thereby an increased signal-to-noise ratio, as fewer ions will reach the detector.

The workflow of SRM-based proteomics is comprehensive, but the time and effort needed

to establish the SRM method (SRM assay) is well spent considering the high sensitivity

and selectivity, which are the hallmarks of SRM. Once the SRM assay is established, it

can be used indefinitely in any study with the same protein of interest [199].

50

Fig.16. Representation of the triple quadrupole mass spectrometer used in SRM. Ions are introduced into the first quadrupole mass analyzer (Q1) and the precursor ions are filtered from predetermined m/z. In the second quadruple (Q2) the ions are fragmented and then filtered once more in the third quadrupole (Q3) that also has a fixed m/z filter. The top part of the figure (SRM principle) is modified from [201], the bottom part of the figure is made for this Ph.D. thesis.

51

4.3 Selection of target peptides

As mentioned, the workflow of developing an SRM method is comprehensive with several

time-consuming steps as shown in fig. 17.

Fig. 17. The protein of interest is used as a base for finding the best proteotypic peptides, which are then validated and used in SRM to optimize the SRM method. When proteotypic peptides have been determined and the optimal transitions have been found, the SRM method can be used for quantitative sample analysis [199].

The first step of the SRM experiment workflow is the selection of the protein of interest.

Once the target protein is determined, the target peptides unique to the target protein are

selected. The choice of peptides is crucial as it determines the sensitivity of the SRM assay.

The peptides should be proteotypic peptides easily detectable by mass spectrometry [207,

208]. A proteotypic peptide is always observed for a specific peptide, is unique for this

peptide, and should fragment well in a mass spectrometer such that they can be confidently

identified [208-210].

52

A first step is therefore to determine the peptides resulting from e.g. tryptic digestion of

the protein in question. These peptides should then be evaluated for their ability as

proteotypic peptides. In principle, all possible peptides can be tested, but using previous

information from e.g. discovery MS would reduce the time spent on testing. Results from

MS experiments performed for several organisms have been deposited in online repositories

such as PeptideAtlas [211] and is a good place to start. In these databases, information of

peptides, which have been reproducibly detected and thereby are likely to be associated

with the most intense signals, can be found. For peptides not found in these, there are

several computational tools developed for evaluating proteotypic peptides [208, 211-215]

Proteotypic peptides suitable for quantification are termed quantotypic peptides. Not all

proteotypic peptides meets the higher standards for quantotypic peptides, but all

quantotypic peptides are proteotypic. The abundance of a quantotypic peptide must

correlate with the abundance of the parent protein [208-210]. Where proteotypic peptides

need “only” to be unique, continuously observed, and fragment well in a mass spectrometer,

more demands are set for quantotypic peptides, as have been compiled in table 3. As

issues affecting the quantitative accuracy cannot be completely avoided in praxis, it is

important to monitor multiple well-behaving peptides for each targeted protein [199, 200].

Table 3. Properties of quantotypic peptides and corresponding rationale

Property Rationale

Unique amino acid sequence Ensures that peptide is diagnostic for a single protein

Avoid short hydrophilic and long

hydrophobic peptides

m/z value of the peptides should be within mass-range of

the instrument. Peptides of 8–25 amino acids are preferred

Amino acid modifications Can alter the cleavage pattern of peptides. Avoid peptides

susceptible to chemical modification

No methionine or tryptophan Side chains of these amino acids are prone to oxidation

No glutamine or asparagine Side chains of these amino acids are prone to deamidation

53

4.4 SRM transitions

In Q1 and Q3 a selection of specific m/z settings is made. The combination of these two

m/z settings is referred to as a transition (fig.15). The m/z value in Q1 is determined by

the mass and the predominant charge state of a peptide and the m/z of the fragment ion

of the peptide for Q3 is easily calculated. The intensities of the individual fragments may

differ substantially, so to obtain high sensitivity it is essential to select the transitions

characterized by the most intense fragment [199].

For sensitive detection, it is essential to target the predominant precursor charge state,

which can be deduced through experiments on other ESI instruments, but one should be

aware of possible differences in instruments, flow rate, solvent and background as these

No glutamate or N-terminal

glutamine

Glutamate and N-terminal glutamine residues are quickly

transformed to pyro-glutamate under acidic conditions.

No N- or C-terminal peptides Sequences in the native proteins are susceptible to

degradation

No sites of known or putative post-

translational modification

Post-translational modification may split the analyte signal

into modified and unmodified variants, which compromise

quantification.

No dibasic context Dibasic motifs tend to be cleaved at one or the other of the

basic residues. Signal from the analyte is then split between

the two variants, but the standard maps to only one of them

Not susceptible to missed cleavage Prevents stoichiometric release of the peptide from the

native protein and protein-level standard.

Detectable in a LC–MS experiment The shape of chromatographic peak of a peptide should be

symmetrical with narrow width. The peptide should ionize

efficiently and provide a stable and intense signal.

The information compiled in this table is found in the following references [199, 200, 208-210, 212-215].

54

can change the charge state distribution [199, 216]. Ion source parameters can also impact

ionization as the process of dissolvement and dissociation of ion clusters are supported by

the declustering potential (DP). The DP is a voltage applied to the orifice (i.e. the opening

where ions enter the mass spectrometer) that helps to prevent the ions from clustering

together, and this can slightly differ from instrument to instrument, depending on the

manufacturer of the ionization device. The individual transitions of a peptide will have

the same DP optimum when they are derived from the same precursor charge state,

making it important to pay attention to instrument-specific impacts when developing an

SRM method [199]. Regarding fragmentation conditions, in a linear collision cell singly

charged y-ions are the predominant type of fragments generated by CID. Only small b-

ions are usually observed here, and it is important to avoid fragments with m/z values

close to the precursor as these transitions usually are “noisy” i.e. they have low intensity

and add to the background noise of the instrument. Instead, fragments with m/z values

above the precursor should be chosen, as they have the highest selectivity since the singly-

charged chemical background does not result in fragments with higher m/z than the

precursor [199].

The collision energy is also important to take into account. As the collision energy

increases, more precursor ions are fragmented and the ion intensity will increase until it is

overcompensated by losses due to secondary fragmentation events. The optimal collision

energy can be predicted as it is an approximately linear correlation with the precursor

mass of a given charge state, but some peptides or fragments may deviate from this

predicted optimal collision energy, which may need to be taken into account. Ionization

and fragmentation conditions can be optimized by testing the possible transitions in direct

infusion mode and plot the parameters or through add-ons for acquisition software [199].

55

Fig. 18. Validation of transitions for peptide VFAQFSSFVDSVIAK. (A) Peak 1 and peak 2 with their co-eluting transitions are seen at 37.5 and 43.3 min, respectively. They have the same precursor mass thereby complicating correct peak identification. The five different transitions are traced in different colors. (B) MS/MS spectra from SRM of peak 1 (upper panel) and 2 (lower panel). Y ion matches are colored in red. At 43.3 min the SRM transition intensities are higher but the MS/MS spectra show that the targeted peptide is eluting at 37.5 min [199].

Even though SRM analysis has high specificity, a particular transition may not be specific

for a targeted peptide in a complex sample. As seen in fig.18 there may be unspecific

signals from other peptides with closely related sequences resulting in precursor/fragment

ion pairs of similar masses. Unspecific signals might be above the detection limit and more

intense than the signal for the targeted peptide, so these signals can be mistakenly

identified as derived from the targeted peptide and lead to mis-quantifications. Validation

is therefore important [199, 200, 214]. One way of validating the transitions is a so-called

SRM-triggered MS/MS scanning where the QQQ instrument used, obtain a full fragment

ion spectrum whenever a signal for a particular transition is detected, and them comparing

the MS/MS spectra with the predicted peptide fragments. In this way, the major MS/MS

peaks are matched and the detected SRM signals are confirmed as being derived from the

targeted peptide (Fig. 18).

An alternative to acquiring MS/MS spectra for validation is to use heavy isotope-labeled

peptides spiked into the sample prior to analysis. Using heavy labeled peptides with

incorporated 15N and 13C labeled amino acids that match the sequence of the targeted

56

peptide will co-elute with the non-labeled target peptides and allow for distinguishing

between the detection of low-abundant peptides from unspecific signals. The signal

intensity ratios of the transitions are identical for the labeled and unlabeled peptides, but

stable isotope-labeled peptides are expensive and may not be a possibility in every study

design [199, 200].

When running SRM, the instrument repeatedly cycles through the specified list of

transitions spending a defined time on each transition, called the dwell time. The number

of transitions that are measured per cycle and the dwell time are both mutually dependent

at a fixed cycle time since targeting ten peptides with five transitions, 20ms dwell time

will add up to a cycle time of 1s (10*5*20ms), meaning that every second an intensity

value is recorded for each of the transitions. To achieve high sensitivity, the dwell time

therefore has to be long enough to accumulate sufficient signal. Cycle time can also be

adjusted to analyze a higher number of transitions at a fixed dwell time, but if not enough

data points are being recorded over the chromatographic elution time of a peptide, the

accuracy will decrease leading to peak shape deterioration, where the peak might be too

broad or split (fig. 19) [199, 200]

Fig. 19. Shown are the resulting peak shape using varying dwell and cycle time. Using cycle times of 10 or 20s peak height cannot be estimated correctly despite the excellent accuracy of the individual data points at 500ms dwell time. Reducing the dwell time from 50 to 5ms decreases accuracy, but at 50ms dwell time and 2s cycle time, a near-perfect peak is produced. Changes in dwell time do not affect absolute signal intensity as it is normalized as ‘counts per second’ [199].

57

At least eight data points should be obtained across the elution peak to ensure precise LC-

MS quantification, and a compromise between dwell time and cycle time is therefore

necessary. This is achieved by mainly two strategies;

- Dwell times for each transition are specifically adjusted to the expected

concentration of targeted peptides. In this way, more time is spent on low

abundance peptides and shorter dwell times are chosen for high abundance peptides.

This necessitates that the concentration of a peptide is known beforehand.

- Scheduling transitions so that the acquisition of particular transitions is restricted

to a window around the elution (retention) time of the corresponding peptide and

thereby using the full cycle time to detect and quantify the expected eluting

peptides in a given time window.

The detection limit will however always depend on the instrument used. A detection limit

of around 10-50 amol should be expected for a well-ionizing peptide with state-of-the-art

QQQ instruments in combination with nano-LC [199, 200].

If many proteins are targeted, numerous transitions in a single experiment are required

which reduces the dwell time of the individual transition, the total number of targeted

peptides that can be quantified is therefore limited. As an example, if every protein should

be quantified by two peptides with two transitions each, 50 proteins can be quantified in

total. Fig. 20 show how the number of transitions pr. peptide correlates with dwell-time

settings. The best 2-4 transitions per peptide is therefore usually selected for the SRM

assay, based on data from previously performed experiments like discovery proteomics or

experimentally determined on a QQQ instrument. It may also be based on calculated

fragment ion masses that are then experimentally tested on a QQQ instrument which

yields the most reliable selection, but can be very time-consuming depending on the

number of peptides selected [199, 200]. Performing scheduled SRM would surpass this, as

transitions for a particular peptide are only acquired in the time window around the

expected rt. This increases the number of detected peptides (and associated proteins) that

58

can be quantified in a single LC-MS experiment. Using scheduled SRM <1000 transitions

can be quantified with high sensitivity and reproducibility.

Fig. 20. Depending on the cycle and dwell time, the number of transitions will dictate how many peptides can be quantified [199].

4.5 Quantification by SRM

SRM can be used to quantify targeted proteins using the transitions for specific peptides,

validated, and optimized as described above. In some cases it will be sufficient to determine

the relative changes of protein amounts based on the absolute signal intensity of the

individual samples. However, performing precise label-free quantification can be a

challenge as signal intensities vary from one LC-MS analysis to another and within the

LC-MS analysis itself. There may be fluctuations in ionization efficiency, as the intensities

recorded for a peptide will depend on the peptide sequence, the background, the sample,

and several experimental factors that cannot be precisely controlled. Normalization, with

the assumption that the majority of protein/peptide concentrations in the sample are not

changing, can only correct for global shifts and do not compensate for local suppression or

enhancement effects on individual peptides [199, 200, 214, 217].

Label-free quantification can be successfully performed under well-controlled settings, but

adding a stable isotope-labeled internal standard can efficiently overcome the problems of

fluctuations in signal intensity. In this case, the relative quantification is based on the

relative intensities of the sample analytes compared to the internally labeled standard.

Absolute quantification is in this way possible with SRM.

59

By using individual isotopically labeled reference peptides or proteins spiked into the

samples, it is possible to calculate an accurate ng/ml concentration of a protein in e.g.

blood from the signal intensity of the light/heavy transitions as the amount of these

peptides/proteins is precisely determined [199, 217-220]. Selection and validation of the

transitions is performed as described above, and the SRM assay is then supplemented with

the corresponding light or heavy transitions. In this way, a pair of light/heavy transitions

is produced for every precursor/fragment combination, and each peptide is then quantified

relatively to its matching heavy-labeled peptide. To minimize technical variability, the

isotopic label should be introduced as early in the sample preparation as possible [199,

221].

The sample amount is often based on total protein mass that is determined by an assay

e.g. Lowry, Bradford or BCA, but these assays are often not sufficiently accurate, so an

alternative can be to normalize the samples on basis of stable proteins, that are expected

to be constant throughout the experiment. This could be so-called “housekeeping” proteins

that are more abundant and constantly present at similar levels in all samples. The

proteins are included in the SRM assay and quantified together with the target protein(s),

and normalization is then performed after acquisition during the data analysis [199, 222-

224]. The stable isotope-labeled peptides can be ordered by several suppliers and are often

referred to as AQUA peptides, but they are pricey which may be a problem if a large

number of peptides are required [199].

4.6 Tandem Mass Tag (TMT) Mass Spectrometry

Using tandem mass tags (TMT) is a non-targeted MS/MS labeling strategy for relative

quantification. It uses isobaric tags typically composed of a mass reporter, a mass

normalizer, and an amine reactive group (fig.21) [225]. The mass reporter and mass

normalizer are used to incorporate stable isotopes in multiple configurations allowing the

60

mass reporter’s mass to be resolved in a MS/MS spectrum, while the intact mass of each

tag variant is the same [225]. These tags are designed so that on analysis by CID, the

TMT fragment is released to give rise to an ion with a specific m/z ratio and the intensities

recorded in the resulting MS/MS spectrum allow for relative peptide quantification [225,

226]. Using TMT up to ten samples can be compared simultaneously, as the proteins are

labeled with separate tags, pools, co-eluted, and then analyzed by LC-MS/MS. This

multiplexing ability allows for an experimental design very suitable for biomarker research

[227]. The isobaric and chemically identical tags allow identical peptides labeled with

different tags to have the same chromatographic elution profile, greatly simplifying the

interpretation of peaks. This leads to a more accurate quantification and improves the

signal-to-noise ratio of quantitative measurements by enabling quantification at the

MS/MS level [225]. The co-migration of TMT-labeled species results in an MS signal for

each peptide pair which is not split into two peaks, as in conventional isotope labeling,

thereby improving sensitivity in the MS mode [226].

Fig. 21 Structure of TMT reagents. A: A TMT reagent contains a mass reporter, a mass normalizer, and an amine reactive group. Between the mass reporter and mass normalizer, a HCD cleavable linker is placed. B: Variants of TMT tags. Heavy labeled 12C and 13N are indicated by asterisks. 10 total variants are possible. Figure modified from [225].

61

4.7 Data-independent acquisition and SWATH-MS

Data-dependent acquisition (DDA) methods, while having high sensitivity and being

powerful methods for detecting peptides representing proteins of interest with high

quantitative accuracy, these approaches are not suitable for discovery-based applications

[228]. Data-independent (DIA) acquisition methods that allow for accurate peptide

quantification without being limited to profiling predefined peptides of interest, have also

been developed. DIA does not require any kind of labeling, and all information from a

sample is recorded in contrast to DDA MS/MS analysis where only a limited number of

precursor ions are selected according to the intensities of precursor ions [228]. Data-

independent acquisition (DIA) performs predefined MS/MS fragmentation and collect data

regardless of the sample content, which results in a more sensitive and accurate protein

quantification compared to DDA. SRM and PRM are types of targeted DIA, and SWATH-

MS is an untargeted DIA method [229]. In SRM a single fragment is monitored with high

sensitivity and in PRM all fragments are recorded with high mass resolution. In both

cases, all intact precursor ions are measured with a wide precursor window (between 2 and

200 m/z) for fragmentation that usually contains several peptide precursors. All obtained

fragment ions are measured from this precursor isolation window, before moving to the

next precursor isolation window and measuring MS/MS fragmentation here, continuing

until the entire MS1 mass range is covered [229].

In SWATH-MS the derived data depends on the defined m/z window, and for each cycle

the instrument focuses on this narrow mass window and acquires MS/MS data from all

precursors detected here. The mass window is changed so it ultimately reaches across the

entire mass range, systematically collecting MS/MS data from every mass and all detected

precursors (fig.22) [228, 230, 231].

62

Fig. 22 SWATH-MS measurements are performed using a quadrupole as the first mass analyzer and a TOF or Orbitrap as the second mass analyzer. A single precursor ion (MS1) spectrum is recorded, followed by a series of fragment ion (MS2) spectra with wide precursor isolation windows. Data is recorded through repeated cycling of consecutive precursor isolation windows with a defined mass range and includes continuous information of all detectable fragment and precursor ions. Extracted ion chromatograms (XIC) can then be generated on both MS1 and MS2 level. Figure modified from [230].

In the excellent review by Ludwig et al. (2018) an overview of the pro and cons for

SWATH-MS, DDA and targeted DIA (SRM and PRM) have been composed, and is seen

here in fig.23 [230].

As can be seen, each method excels in several categories and lacks in others, underlining

the importance of understanding which type of experiment and what type of output is

wanted from sample analysis.

63

Fig. 23. Performance benefits and limitations of DDA, SWATH-MS, SRM, and PRM as presented in [230]. *Least optimal performance, **Medium performance, ***Best performance.

64

Chapter 5

Materials and Methods 5.1 Biological samples

Pigs were chosen as it is a very suitable animal model for studying the innate immune

response. The porcine model allows for highly controlled challenges to be performed, and

disease development to be monitored closely in an organism that closely mirrors humans

regarding many aspects including antiviral immune response. All animal procedures were

conducted according to protocols approved by the Danish Animal Experiments

Inspectorate. Samples from [29] have License Numbers 2001/561-350 and 2008/56121465

for the Actinobacillus Pleuropneumoniae (Ap) and Staphylococcus aureus (Sa) infection

studies, respectively. Samples from [30] have License No. 2013/15-2934-00946.

For the 2DE and MALDI-TOF experiments and first parts of SRM development, two

porcine serum pools were made with additional samples available from previous infection

studies performed at DTU Vet, both from pigs undergoing an acute phase response, were

used.

65

5.1.1 Staphylococcus aureus (Sa) pig infection models From two separate infection studies, samples were obtained. The study performed by

Olsen et al. (2013) [29] studies pigs intravenously infected with a porcine strain of the

Gram-positive pathogen Staphylococcus aureus (Sa) and the study by Jensen et al. (2016)

[30] used a model based on the insertion of a small steel implant into the right tibial bone

with either Sa or saline. Based on inoculum, pigs were allocated into three groups which

were high-inoculum dosed (104 colony-forming unit (CFU); Group A), low-inoculum dose

(102 or 103 CFU; Group B), and controls (Saline; Group C). Samples analyzed in this

Ph.D. were from Group A and Group C.

5.1.1.1 Sa intravenous infection model

Details of experimental design and procedures can be found in Jensen et al. [232] and

Leifsson et al. [233]. Briefly, three clinically healthy 8-week-old female SPF Danish

landrace/Yorkshire crossbred pigs with a bodyweight (BW) of 20–25 kg were inoculated

intravenously with a bolus of 108 CFU/kg BW of a porcine pathogenic strain of Sa. After

12h, the pigs received a second bolus equal to the first. The pigs were euthanized after

24h, and the postmortem examination was immediately done. From the same herd three

control pigs were sham-infected intravenously with sterile saline and managed in parallel

with the infected pigs. Blood samples were taken at 0h (before inoculation) and 24h post-

infection (PI) at euthanasia, wherein SAA protein concentration was measured.

66

5.1.1.2 Implant-Associated Osteomyelitis (IAO) Sa infection model

Details of experimental design and procedures are found in [30]. Briefly, 42 female Danish

Landrace pigs obtained from specific pathogen-free herds at the age of 3 months (30 kg

BW) or 8 months (67-77 kg BW) were included. Under anesthesia, an implant cavity was

drilled in the tibial bone followed by insertion of a small implant made of stainless steel,

2x15mm (pigs of 30 kg BW) or 2x20mm (pigs of 80 kg BW), and simultaneous inoculation

of Sa bacteria (n=32) or saline (n=10) into the implant cavity. The inoculum of Sa was

prepared as previously described [234] and diluted with sterile 0.9% isotonic saline to

obtain inoculation doses/volumes of 104 CFU in 10 mL. All animals were euthanized 5

days after the insertion of implants. Blood samples were taken on the days of surgery and

euthanasia, and evaluation of the systemic acute phase response by measurement CRP

and SAA respectively, by enzyme-linked immunosorbent assay [162, 235].

5.1.2 Actinobacillus pleuropneumoniae (Ap) pig infection models

Experimental design and procedures have been described in detail by Skovgaard et al. [99,

155]. Briefly, three clinically healthy 8–10 weeks old castrates of Danish

landrace/Yorkshire/Duroc crossbreeds from a specific pathogen-free herd were inoculated

intranasally with Ap pleuropneumoniae serotype 5b, isolate L20, a highly pathogenic

porcine bacteria causing severe acute necrotizing pleuropneumonia as described previously

by Skovgaard et al. [236]. Cultivation of bacteria was performed as previously described

[237]. Inoculation was performed by dripping bacterial solution in each nostril during

inhalation (1 ml McFarland suspension mixed 1:1 with Brain Heart infusion Broth + 0.5%

NAD, containing approximately 0.7 107 CFU/ml) [236]. Three non-inoculated clinically

healthy pigs from the same herd served as control pigs. The pigs were euthanized 14–18h

after inoculation and necropsied immediately. SAA protein concentration was measured

67

in serum sampled immediately prior to inoculation as well as at euthanasia for verification

of initiated acute phase response [99].

5.2 SAA ELISA

SAA protein concentration was measured in blood, sampled using a commercially available

sandwich ELISA (Phase SAA assay, Tridelta Development Ltd.). The assay is based on

antihuman monoclonal antibodies in a sandwich set-up, as originally described by

McDonald et al. [162]. Samples were tested according to the manufacturer’s instructions

and all samples including standards were determined in duplicate. Sample values were

calculated from the curve fitted to the standard.

5.3 Triton X-114 cloud point extraction (CPE)

Two separate pools of porcine serum samples were prepped for 2D gel electrophoresis and

MALDI TOF analyzed using Triton X-114 cloud point extraction [238]. Two separate

set-ups were used (summarized in table 4). Firstly, duplicates of the porcine serum sample

pools were either treated with the protocol as described below or treated with the same

protocol, but where all amounts had been cut in half. In total 8 2D gels were run in this

setup. The second setup was a dilution experiment where the porcine serum sample pools

were diluted prior to TX-114 CPE, in total 8 2D gels were run in this setup.

Table 4. Summation of the two separate 2DE setups

Setup 1

Gel 1 & 2 Duplicates of porcine serum pool 1 Full amounts during TX-114 CPE

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Gel 3 & 4 Duplicates of porcine serum pool 2 Full amounts during TX-114 CPE

Gel 5 & 6 Duplicates of porcine serum pool 1 Half amounts during TX-114 CPE

Gel 7 & 8 Duplicates of porcine serum pool 2 Half amounts during TX-114 CPE

Setup 2

Gel 1 Pool 1 Undiluted prior to TX-114 CPE

Gel 2 Pool 1 2 times diluted prior to TX-114 CPE

Gel 3 Pool 1 3 times diluted prior to TX-114 CPE

Gel 4 Pool 1 4 times diluted prior to TX-114 CPE

Gel 5 Pool 2 Undiluted prior to TX-114 CPE

Gel 6 Pool 2 2 times diluted prior to TX-114 CPE

Gel 7 Pool 2 3 times diluted prior to TX-114 CPE

Gel 8 Pool 2 4 times diluted prior to TX-114 CPE

400 µl sample was diluted with 1350 µl cold (0-4°C) Tris-buffer (50 mM Tris (pH 7.4), 150

mM NaCl) in a conical tube and 750 µl cold (0-4°C) 10 % (w/v) TX-114 was added. The

tube was turned several times for gentle mixing and put on ice for 30 min. The sample

was then carefully overlaid on 2 ml of Sucrose-TX-114 (6% (w/v) sucrose and 0.06% (w/v)

TX-114 in buffer A) in a conical tube, incubated 5 min in a water bath at 37°C for 5 min.

and then centrifuged at room temperature (RT) in a swing-out rotor; 5 min, 5000 g. After

centrifugation the supernatant constituting the aqueous phase of the sample and the

sucrose-containing layer were removed carefully, leaving the detergent phase at the bottom

of the tube. The tube was put on ice and the detergent phase (sample) was re-dissolved

in 2250 µl Tris-buffer. When the solution has cleared up, it was overlaid on 2 ml of Sucrose-

TX-114, incubated at 37°C for 5 min, and centrifuged (5000g) for 5 min at RT. The

supernatant was discarded, and the detergent phase was re-dissolved in 1000 µl cold (0-

4°C) Tris-buffer and stored at -20°C.

69

5.4 TCA Precipitation

The cloud point extracted (CPE) sample was subsequently treated with a Trichloroacetic

Acid (TCA) Precipitation by transferring it to an Eppendorf tube, and 100 µl 65% (w/v)

TCA was added to a final concentration of 15%. The sample was mixed, incubated on ice

for 30 min and centrifuged (15.000g) for 15 min at 4°C. Supernatant was carefully removed

and the precipitate was washed twice with acetone (one wash consisted of the addition of

1 ml acetone (90%), mixing, centrifugation (15.000g) for 5 min at 4°C, and removal of

supernatant). Washing was repeated and following supernatant removal, the pellet was

left to dry at room temperature for about 30 min.

5.5 2D gel electrophoresis

Immobiline Drystrips (GE Healthcare, 18cm, pI 3-10) were used for the first-dimensional

protein separation. The air-dried TCA precipitated sample was solubilized in 450µl 1D

buffer (8M urea, 2M thiourea, 50mM DTT, 1.5% Chaps, 2% Pharmalyt 3–10, and 10 mM

Tris-HCl (pH 8.3)) with Orange G as a dye. All 350 µl was loaded on an Immobiline

Drystrip by reswelling overnight and the gel was run approx. 18h, with 4 phases (0.01h at

100V 1mA, 5h at 100V 1mA, 5h at 3500V 1mA, and then staying at 3500V 1mA until

stopped (all steps were at 5W) and stored at -80°C until the second dimensional SDS-

PAGE.

Immediately after thawing, the Immobiline Drystrip was incubated for 20 min in 10ml

equilibration buffer (6M urea, 50mM Tris-HCl (pH 8.8), 30% (w/v) glycerol, and 2% SDS)

with 1% (w/v) DTT; followed by alkylation for 20min in 10ml equilibration buffer with

4.5% iodoacetamide. The Immobiline Drystrip was placed and sealed by 0.5% (w/v)

agarose, 25mM Trizma base, 192mM glycine, and 0.1% SDS on top of an acrylamide gel

70

(T = 12%), run on a Hoefer DALT system (Amersham Biosciences) with 90V, 400mA for

17,5h followed by 17h at 40V, 40mA, at 15°C. After fixation for 60min in 40% (v/v)

ethanol and 10% (v/v) acetic acid, the gel was stained by Colloidal Coomassie Brilliant

Blue [ref 22]. The gels were digitized using a charge-coupled device camera (Camilla,

Raytest, Straubenhardt, Germany). Progenesis SameSpots software (version 4.5,

Nonlinear Dynamics, Newcastle, UK) was used for image analysis with the built-in

algorithms of Progenesis SameSpots for alignment, matching, and quantification of the

spots, as well as for normalization of spot volumes based on a spot volume abundance

ratio between gels.

5.6 MALDI-TOF

From the 2DE gels, spots of interest were excised, reduced, and alkylated (using DTT and

iodoacetamide, respectively) and digested overnight with trypsin. Digested samples were

put with the matrix (4-HCCA) on a MALDI plate and analyzed using an Ultraflex II

TOF/TOF mass spectrometer (Bruker-Daltonics, Bremen, Germany). Ionization was

performed in positive mode and mass spectra were analyzed by FlexAnalysis. The obtained

mass spectra were then used as input to MASCOT MS/MS Ion searches of the porcine

subset of the NCBI database. These searches were performed assuming the formation of

single-charged peptides, carbamidomethylation of cysteine residues, possible oxidation of

methionine residues, and up to 1 missed cleavage.

5.7 SRM method development

The details regarding SRM development – sample preparation, SRM assay construction,

MS analysis, and data analysis can be found in Paper I.

71

Chapter 6

Paper I

Targeted mass spectrometry for Serum Amyloid A (SAA) isoform

profiling in sequential blood samples from experimentally

Staphylococcus aureus infected pigs

Anna Barslund Leuchsenring, Christofer Karlsson, Louise Bundgaard, Johan Malmström,

Peter M. H. Heegaard.

72

6.1 Summary of Paper I

In Paper I, an SRM method was developed and applied to sequential serum samples from

an infection experiment with Sa. It was possible to detect all three porcine SAA isoforms.

The porcine SAA isoform profiles correlated with the previously established total SAA

response [29] and a close correlation between the SAA isoform hepatic mRNA abundance,

and SAA isoform profiles in the circulation was also found. This proved that antibody

independent MS-based quantification is possible at high specificity and sensitivity,

allowing discrimination between a set of very similar circulating SAA variants directly in

serum samples. In addition to this, the results unequivocally establish that SAA2 is the

main circulating porcine SAA isoform, being induced around 10 times during the porcine

APR to Sa infection. A discrepancy was observed between the total SAA serum

concentrations detected in Paper I and those established previously by immunoassay [29],

and the peptides used for SRM did not correlate equally well for every SAA isoform. The

underlying biological reason for this is interesting to investigate. The SRM approach can

be generalized to other circulating biomarkers and MS methods have a great potential for

high-number multiplexing.

Paper I was submitted to Journal of Proteomics on April 24th 2020.

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6.2 Paper 1 as submitted to Journal of Proteomics

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Chapter 7

Results and discussion

As described in chapters 1 and 2, SAA is an APP highly upregulated during an APR, and

an interesting biomarker candidate for early diagnosis of infection and diseases with an

inflammatory component such as neurodegenerative diseases, autoimmune diseases,

obesity and obesity-related diseases [15, 62-64, 165].

SAA isoform genes have been found differentially regulated depending on infection and

tissue; a unique feature of SAA not found in other APPs to our knowledge, making SAA

a very interesting biomarker candidate [29, 44, 98, 99, 132, 154]. Investigating this

potential, however, has been difficult, due to methods for SAA isoform detection are

limited as SAA antibodies are incapable of discriminating between the highly similar SAA

isoforms. In addition to detecting the SAA isoforms, quantifying them would provide

important information regarding possible specific biological functions of SAA isoforms and

allowing us to investigate the possible role of SAA isoforms as differential biomarkers of

infection. This Ph.D. project therefore developed a specific, quantitative, and antibody-

free targeted mass spectrometry-based method for detection and quantification of

circulating porcine SAA isoforms in serum samples to provide the rapid and reproducible

method with high sensitivity and specificity, we are currently missing.

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The developed SRM MS method was used to analyze the porcine serum samples described

in chapter 5 and resulted in Paper I, presented in chapter 6. Additional experiments were

performed in this Ph.D. project in the process of analyzing the porcine samples and

developing the SRM method and the results not shown in Paper I will be presented in this

chapter.

7.1 Two-dimensional gel electrophoresis and MALDI-TOF

In order to examine the presence of native SAA isoforms in porcine serum samples proteins

were obtained by TX-114 cloud point extraction (CPE) of porcine serum sample pools and

analyzed by 2DE as described in chapters 5.3-5.5. In total 16 separate 14% 2D gels were

run within the pI range 3–10. From the first setup, it was concluded that full amounts of

TX-114 CPE provided best results, and from the second setup it was concluded that

samples could easily be 2-3 times diluted while still providing clear spots for identification

(data not shown). After completed 2DE the gels were scanned, and spots of interest were

marked. In fig. 24, scans of a gel with spots from pool 1 and pool 2 respectively, from the

second experimental setup is shown. From pool 1, spots 1-3 were of interest and from pool

2 spots 4-12 were of interest as they were within the Mw range expected for SAA isoforms

(9-14 kDa).

105

Fig. 24. Proteins obtained by CPE of 400µl porcine serum pools (left: pool1, right pool2). The two 2D gels are representative of 16 gels, pretreated with TX-114, and run on 14 % gels with a pI range of 3-10. The numbered spots were identified and identities are given in table 5.

The spots of interest were numbered, excised, and subsequently identified using MALDI-

TOF as described in chapter 5.6. In total 12 spots were analyzed, resulting identifications

are summarized in table 5, and as can be seen, there was a positive identification of SAA2

and SAA4 in several spots, but no identification of SAA3. MALDI-TOF MS and MS/MS

analysis resulted in mass spectra as shown in fig. 25 and a mass list. The software

FlexAnalysis and BioTools (Bruker) were used for peak picking and a database search was

then performed for the identification of the protein. Fig. 24 shows a representative MALDI-

TOF spectrum from analysis using FlexAnalysis, the remaining spectra can be found in

the supplementary chapter.

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Fig. 25 MALDI-TOF mass spectrum. Following 2DE, gel spots were excised and prepped for MALDI-TOF analysis. The analysis resulted in mass spectra such as this, and a mass list which was used to perform a database search for the identification of the protein.

Table 5. 2D gel spot identifications

Sample Spot number Identification

Pool1 1 Inconclusive

Pool1 2 SAA2

Pool1 3 SAA4

Pool2 4 SAA2

Pool2 5 SAA2

Pool2 6 SAA4

Pool2 7 Hemoglobin α

Pool2 8-12 Inconclusive

The porcine serum samples from pigs with ongoing APR were analyzed by Triton X-114

CPE followed by 2DE. The TX-114-based CPE on serum samples is used to enrich

lipoproteins such as SAA, before MALDI-TOF as the SAA isoforms can be hard to

107

visualize in unfractionated plasma when using 2DGE with no pretreatment [238]. These

procedures are tedious and comprehensive and ultimately resulted in the identification of

the presence of SAA2 and SAA4 in the samples, with no detection of SAA3. The presence

of an APR had been confirmed previously for the samples used so the absence of SAA3

may be due to misidentification due to absence or insufficient reference MS spectra in a

database, or it could propose that the amount of SAA3 present in the samples were simply

too low to be detected.

Serum samples are very complex in terms of peptide and protein composition, and the 10

most abundant proteins constitute almost 90% of the serum proteome by mass and may

mask the more low abundant proteins, making pretreatment of the sample a prerequisite

in order to reduce their complexity [239, 240]. Hemoglobin α was also identified, and with

a Mw of 15,258 kDa, it makes sense to find this subunit of Hemoglobin in serum samples.

SAA3 may be masked by background noise or by higher levels of SAA2, as their molecular

weights are very close to each other (and with 86% sequence similarity). From our results

using SRM, we found that the absolute concentration of SAA3 was much lower than that

of SAA2, supporting this theory.

MALDI-TOF offers a fast analysis with high resolution and a simple setup for sample

preparation, but many aspects impact the MALDI experiments which therefore require

highly standardized sample protocols and pretreatment protocols to ensure good

reproducibility. Furthermore, reproducibility is also influenced by the matrix and

variations in the crystallization process. The crystallization process depends on aspects

such as the contamination of the sample, ratio of matrix to analyze, and atmospheric

conditions such as humidity, temperature, etc. [240]. Matrix preparation also influences

background intensity, resolution, signal strength, and detectability, and the resulting

distribution of the analyte on the sample spot may be uneven and differ from experiment

to experiment [240].

In the collection of MALDI spectra generated from the same sample a substantial

variability in the noise level, baseline and peak intensities will therefore occur and the

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uneven sample distribution results in so-called ‘hot-spots’ on the target surface where the

ratio of analyte to matrix is optimal. In these ‘hot-spots’ analyte signal is high in relation

to other locations, thereby impacting reproducibility, making it important to find the

optimal sample preparation method and to perform standard calibrations in order to

obtain the highest possible accuracy [197, 240, 241]. This is very time-demanding.

Quantitative analysis can be performed with MALDI-TOF MS, but it is time-consuming

and challenging due to the variations in signal intensity. It requires understanding and

consideration of the sample and instrument conditions as most of the critical problems

stem from sample preparation (e.g. choice of matrix compound, concentration, solvents

and crystallization conditions), ionization chemistry, electric fields, and detector properties

– parameters that are interrelated and adjusting one parameter, changes the optimal

conditions of others. It is therefore also important to acquire and average many single-

shot spectra from several positions within a given sample spot to get representative sample

data [197].

7.2 Relative SAA concentrations in porcine serum samples

SRM was instead chosen as a suitable method, as it is a highly sensitive and selective

method for the targeted approach of peptide detection and the corresponding proteins in

complex biological samples. SRM also allows for absolute quantification when using spiked-

in stable isotope-labeled (‘heavy’) peptides. SRM method development is also somewhat

time-consuming, but the resulting method can be used on porcine samples from endless

experiments once it is developed, and it is easily adapted to e.g. human samples.

As described in Paper I, the SRM assay was first tested on pooled porcine serum samples

with synthetic peptides spiked in, and once it was confirmed that all spiked-in peptides

could be identified by the method, it was used on sequential porcine serum samples from

infections experiments previously performed [29, 30]. The relative SAA concentration in

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serum samples from pigs infected with either Sa (intravenous infection model and Implant-

Associated Osteomyelitis (IAO) model, respectively) or Ap (intranasal infection model),

was then determined using the SRM method (fig. 26).

In the study by Jensen et al. (2016) [30] a model IAO with low inoculum infection of Sa

was evaluated. The samples showed clear clinical signs of infections with significant results

on all tested parameters and a clear correlation between the number of injected bacteria

and development of IAO was observed [30]. SAA levels were measured by ELISA and used

to evaluate the presence of an APR, and a significant increase in the SAA levels in infected

pigs compared to control animals was found [30].

By the developed SRM method all SAA isoforms (SAA2, 3, and 4) were detected in the

pig IAO samples. Mean relative concentrations were similar when comparing infected pigs

and control pigs, and higher levels of SAA were measured in infected pigs than in control

pigs. While not significantly different, the relative SAA levels did correlate with those

measured by ELISA in Jensen et al. (2016) [30] as the SAA levels were higher in infected

pigs than control pigs.

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0.00

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Rela

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leve

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Relative SAA isoform levels in Sa infected and control pigs, IAO model

Infected Controls0.00

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Infected Controls0.00

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Relative SAA isoform levels in Sa infected and control pigs

Fig. 26. Mean relative serum levels of SAA isoforms 2, 3, and 4, respectively, in three sets of porcine serum samples before and 24 hours after infection / sterile saline injection (controls). Columns indicate mean values. In addition, individual concentrations are also shown for infected and control pigs for each SAA isoform. Top: Results from serum samples from the Sa infection experiment (IAO model), note that all results shown here are PI, as no 0h samples were made. Middle: Results from serum samples from the Ap infection experiment (Intranasal infection model). Bottom: Results from serum samples from the Sa infection experiment (Intravenous infection).

111

When analyzing Ap and Sa samples from Olsen et al. (2013) [3], the relative SAA levels

measured by SRM confirmed the presence of an APR, as the SAA isoform levels were

higher after 24h of infection. For the samples from Ap-infected pigs this was only the case

for SAA4, while SAA2 and SAA3 levels were not elevated. This does not correlate with

the results presented in [3].

In the Olsen et al. study, serum samples in the Ap experiment were taken just before

inoculation (0h) and 24h PI as described previously showing a prominent APR and clinical

signs of infections. Tissue samples obtained from the liver, lungs, and spleen were collected

during necropsy [3] as described previously [232, 233] showing a prominent APR and

clinical signs of infections [29]. No clear reason could be found in the experimental setup

for the discrepancy between results found in this Ph.D. and in [3], implying that the SAA

peptide profile selected may not be adequate to detect SAA peptides in this type of

infection model. While the results did not correlate completely with the results found in

[3], they clearly showed that the SRM method was successfully developed and could detect

all porcine SAA isoforms. The development of this method is important, as it will enable

us to obtain more information on the absolute concentration of SAA isoforms at the

protein level and of the correlation between tissue-specific SAA isoform expression and

circulating SAA isoform profiles.

7.3 Absolute concentrations of SAA isoforms

In addition to providing a picture of the relative SAA levels in the porcine serum samples

the data obtained during SRM without isotopically labeled peptides allowed for selecting

the final quantotypic peptides used for the quantification of SAA isoforms as presented in

Paper I. As mentioned, not all proteotypic peptides are quantotypic, and the abundance

of a quantotypic peptide must correlate with the abundance of the parent protein [205-

207]. Where proteotypic peptides need “only” to be unique, continuously observed and

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fragment well in a mass spectrometer, quantotypic peptides needs to comply with more

criteria as described in table 3. One criteria is, that the peptide should be detectable in a

LC-MS experiment, and the peptide should ionize efficiently and provide a stable and

intense signal, which were not the case for one peptide for each of the SAA isoforms, as

described in Paper I [205-207]. The remaining two quantotypic peptides were subsequently

used for the absolute quantification for each of the porcine SAA isoforms. Not shown in

Paper I is the concentrations of the SAA isoforms in serum samples from pigs infected

with Sa in the IAO model, which are presented in fig. 27.

0

1

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Seru

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LControlsIAOpigs

SAA concentrations at euthanasia in Sa infected pigs - IAO infection model

Fig. 27. Mean serum concentrations of SAA isoforms 2, 3, and 4, respectively, 5 days after implantation of a steel implant along with inoculation with Sa and before and 5 days after sterile saline injection (controls) measured by the SRM MS method.

As seen in fig. 27, serum concentrations of all three SAA isoforms were higher in the pigs

subjected to the IAO model when compared to control pigs at euthanasia; SAA2 reached

around 2 µg/mL, SAA3 and SAA4 reached around 0.3 µg/mL. This correlated well with

the relative SAA concentrations where the largest difference was observed for SAA2 for

IAO pigs after euthanasia when compared to control pigs, and minor differences were

observed for SAA3 and SAA4.

In addition to these results, presented in Paper I are also the absolute concentrations of

the SAA isoforms. The serum concentrations of both SAA2 and SAA3 isoforms were higher

in all infected pigs, 24h after Sa infection when compared to control pigs and 0h

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measurements in infected and control pigs. At 24h PI, the concentration of SAA2 was 2-

3 µg/mL, SAA3 around 0.1 µg/mL and 0.02-0.03 µg/mL for SAA4. The results were not

as consistent for SAA4 as for SAA2 and SAA3, but a slight elevation was also seen for

this isoform 24h after infection. The serum concentration of SAA3 was much lower than

that of both SAA2 and SAA4 confirming that the major circulating isoform of pig SAA is

SAA2 as originally suggested by Olsen et al. (2013) [29].

Comparing the SAA isoform concentrations in serum samples from pigs with the IAO

model to those of pigs with an intravenous Sa infection model, is interesting, as SAA seems

to be differently regulated depending on tissue and infection. The results presented in fig.

27 show that the SAA concentrations differ and in fig. 28 the mean fold changes for the

SAA isoforms in serum samples of the two Sa infection models (intravenous and IAO)

when compared to control pigs (sterile saline-injected) at 24 hours PI.

SAA2 SAA3 SAA40

5

10

15

Fold

cha

nge

(CTR

L=1)

Intravenous Sa infection model

SAA2 SAA3 SAA40

5

10

15

Fold

cha

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L=1)

IAO infection model

Fig. 28 Mean fold changes of SAA isoforms in serum samples of Sa-infected pigs compared to control pigs (sterile saline-injected) at 24 hours PI. For comparison, the corresponding fold change found for total SAA as determined by ELISA in samples analyzed in [29] is indicated by the horizontal line. Left: Intravenous SA infection model Right: IAO infection model.

In the IAO samples, serum concentration fold changes from normal controls to Sa–infected

pigs were found to be approximately 15 for SAA2, 6 for SAA3, and 1.5 for SAA4. In

114

comparison, the fold changes from the intravenous Sa-infected pigs were 10 for SAA2, 15

for SAA3, and 2.6 for SAA4. The SAA2 isoform serum concentration fold change found in

here (and presented in Paper I) corresponded well with the hepatic SAA2 mRNA fold

change reported previously in [29] for the same animals. This implies a hepatic origin of

the circulating porcine SAA2 isoform and indicates that the SRM MS method allows to

elucidate the tissue origin of circulating biomarkers, as also noted by Qin et al. (2015)

[242]. Absolute concentrations of SAA isoforms were also different when comparing the

two sample sets, with a higher SAA2 concentration was observed with the intravenous Sa

infection model (2-3 µg/mL compared to around 2 µg/mL), for SAA3 a higher

concentration was observed for the IAO model (around 0.3 µg/mL compared to around

0.1 µg/mL). For SAA4, higher concentrations were found in the IAO model (around 0.3

µg/mL compared to 0.02-0.03 µg/mL).

These differences in SAA concentration and fold changes in different infection models may

be an indication of the tissue-specific differential regulation of isoforms during the APR

observed by Olsen et al. and others at the gene expression level [29, 44, 98, 99, 132, 154].

When comparing the relative SAA levels for the two infection models, the levels are more

similar to the highest levels found in the intravenous Sa model.

In Olsen et al. (2013) [29] an ELISA was used to quantify the total SAA concentrations

in the same serum samples as investigated here. They found the total SAA concentrations

to be below 50 µg/mL for infected pigs before inoculation and in control pigs, while after

PI it reached a mean of approximately 460 µg/mL. This contrasts strongly with the results

found in this Ph.D. and Paper I. SAA concentrations measured here are much lower than

those measured by Olsen et al. [29], although fold changes were similar in both studies.

In the Jensen et al. (2016) study [30] total SAA concentrations were also quantified by

ELISA to around 30 µg/mL for pigs infected with Sa using the IAO model. This is again

much higher values than detected by the SRM method developed in the Ph.D.

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Several possible reasons exits, including:

- Inaccurate calibration of the commercial standard used for the SAA immunoassay.

According to information from the company (Gallagher, pers. comm. 2020), the purified

human SAA standard used to calibrate the Tridelta Ltd. multispecies SAA ELISA kit was

quantified by amino acid analyses and compared to a purified standard of porcine SAA,

likewise quantified by amino acid analysis. The comparison was done in the ELISA and

used to calibrate the human standard to represent the porcine standard. However, this

procedure may not be as precise as there is a risk of the porcine SAA standard not being

pure, as porcine SAA is notoriously difficult to purify [161]. This can also be suspected for

the primary human standard where any error in quantification would heavily influence

the accuracy of the quantification of the secondary standards, including porcine SAA.

There is therefore no guarantee that the kit standard is accurate for porcine SAA.

- Incomplete tryptic fragmentation.

The SAA isoform quantification of the SRM MS method in this Ph.D. project assumes full

tryptic fragmentation and a subsequent full fragment ionization of all SAA peptides used

for quantification. If this were not the reality, possibly due to SAA being shielded from

full trypsin digestion by its complexation with HDL, the tryptic peptide concentrations

measured would be lower and not correlate to the concentration of the cognate SAA

isoform protein. In addition to this, not all tryptic peptides will become successfully ionized

[243].

- Presence of other SAA isoforms.

The peptides used in this study were based on SAA sequences found in Uniprot. The

entries for SAA2 and SAA4 were un-reviewed and had not undergone manual curation by

a Uniprot biocurators. They were therefore updated automatically following the Ensembl

gene model which resulted in their removal due to an Ensembl update in December 2019.

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As the sequence for SAA3 found in Uniprot (Q2HXZ9) is based on the porcine SAA cDNA

sequence published by Chang et al. [100], and had been reviewed, it was not deleted. In

general, there still is some confusion regarding nomenclature and sequences for the SAA

isoforms, making it difficult to verify if the protein discovered is in fact SAA2 or SAA3.

In other organisms analyzed SAA2 and SAA3 share the highest degree of homology (86%

in pigs) while SAA4 is the most divergent; a pattern that is also seen with the porcine

SAA sequences. Results in this Ph.D. project and from the study done by Olsen et al.

(2013) [29] clearly show that the porcine SAA proteins with the corresponding peptides

analyzed are present in the porcine serum samples. From our data, Uniprot has now

curated the porcine SAA sequences, which will be publicly available at the UniProt release

on June 17th, 2020 (Release 2020_03) as SAA2 (P0DSO0) and SAA4 (P0DSN9). However,

as presented in Paper I, not all unique SAA peptides were equally good proteo and

quantotypic peptides, raising the question of whether all variants of SAA isoforms are

quantifiable or if the peptides may represent differently regulated specific SAA isoforms.

The results presented in Paper I may indicate the existence of porcine SAA variants that

do result in the anticipated tryptic fragments which, however, may exist in the circulating

porcine SAA population as sub-variants. These are not detected by the SRM method

developed here, but they are detected in immunoassays. Several human SAA subtypes

have been found in blood [75, 150, 165, 244] and a PRM method for quantitation in

depleted human serum samples from non-small cell lung cancer patients compared to

healthy controls have been described [245], and it would be very interesting to explore the

presence of such SAA protein isoforms in the pig. To what extent are they accounting for

the acute phase variations quantified by ELISA? For this a non-targeted, global

quantitative MS method will be needed, such as isobarically labeled samples analyzed by

tandem MS (TMT based MS), or for more accurate quantification methods such as

SWATH-MS [226, 230].

117

7.4 Conclusions

Conclusively, in this Ph.D. project a validated quantitative SRM MS method was

developed and for the first time SAA isoform abundance was directly quantified in crude

porcine serum samples, enabling the correlation between variations in isoform mRNA in

various tissues and isoform protein to be established. This is of great importance for the

further development of pigs as animal models for human inflammation-based diseases. A

direct comparison between separate pig infection models was possible, and the developed

method will be important in answering fundamental questions on the tissue distribution

of SAA isoforms, and their regulation during health and disease to be elucidated.

However, the unique SAA isoform peptides available are not all suitable as quantotypic

peptides and using other methods with higher specificity and the possibility of untargeted

sample analysis in correlation with SRM to analyze samples will be necessary

7.5 Perspectives

Future studies using the developed SRM method on porcine serum samples from additional

infection models such as influenza A, Osteomyelitis, Rheumatoid arthritis, and even

coronavirus, would be of great interest along with studies comparing bacterial infections

with viral infections and studies of diseases with inflammatory components to samples

taken at different phases as the disease progresses. Compiling data regarding SAA isoform

concentrations in the mentioned conditions will provide further support of the tissue-

specific differential regulation observed and allow for a greater understanding of not just

the role of SAA, but also the potential of SAA as a biomarker.

Additional studies of interest therefore include developing an SRM method for SAA

isoform detection and quantification in human serum samples as current methods using

118

acute phase proteins, as biomarkers are often not specific enough to distinguish between

diseases. If the same tissue-specific differential regulation is observed in human samples,

quantified SAA isoform levels could provide this distinction and allow for an early

differential diagnosis of such diseases.

Along with SRM, PRM would be a suitable method of choice for these further

developments, as it offers a higher specificity than SRM, as it obtains a full scan (all

transitions), this also results in an easier and faster method development phase, and a

future method for biomarker measurements should be fast, easy to set up and vary and

with high reproducibility. In addition to this, as all potential product ions of a peptide

upon fragmentation are available to confirm the identity of the peptide and since PRM

monitors product ions with high resolution interfering ions are less of a problem [194, 197,

198].

It would also be interesting to analyze samples using Tandem Mass Tags (TMT). Using

TMT allows for peptides from different samples to be identified by their relative

abundance with greater ease and accuracy than other labeling methods and can

simultaneously determine the identity and relative abundances of peptide pairs. Compared

to other MS type measurements, TMT has a very high signal-to-noise ratio and would

make for a very suitable method to measure the protein changes over time and obtain a

more detailed picture of the dynamics of SAA isoform levels and would be a perfect method

of choice for gathering information regarding the existence of possible unidentified SAA

variants as it is a non-targeted method [225, 226].

119

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138

Supplementary

MALDI-TOF mass spectra from all excised 2DE gel spots analyzed.

Spot1: Inconslusive

Spot2: SAA2

139

Spot3: SAA1 or SAA4 (notes not clear = inconclusive)

Spot4: SAA2

140

Spot5: SAA2

Spot 6: SAA4

141

Spot 7: Hemoglobin α

Spot 8: inconclusive

142

Spot 9: inconclusive

Spot 10: inconclusive

143

Spot 11: inconclusive

Spot 12: inconclusive