DIAGNOSIS OF ACUTE AND CHRONIC ENTERIC FEVER USING …1146502/... · 2017-10-03 · Metabolomik är studien av metaboliter (små kemiska molekyler), som aminosyror, socker och fettsyror,

DIAGNOSIS OF ACUTE AND

CHRONIC ENTERIC FEVER

USING METABOLOMICS

Elin Näsström

Department of Chemistry

Umeå 2017

This work is protected by the Swedish Copyright Legislation (Act 1960:729) Dissertation for PhD ISBN: 978-91-7601-773-9 Cover: Marcus Östman. Watercolour, 31x23 cm on Saunders Waterford, 300 g. Electronic version available at: http://umu.diva-portal.org/ Printed by: Lars Åberg, VMC-KBC Umeå Umeå, Sweden 2017

“I am among those who think that science has great beauty”

Marie Curie

i

Table of Contents

Abstract ii

Populärvetenskaplig sammanfattning iii

List of Publications v

Abbreviations and notations vii

1. Aims of this work 1

2. Background 2

2.1. General description of the problem 2 2.2. Enteric fever 3 2.3. Diagnosis 11 2.4. Metabolomics 16

3. Methods 20

3.1. Sample matrices 20 3.2. Sample preparation for metabolomics analysis 21 3.3. Metabolite analysis 23 3.4. Processing of metabolomics data 28 3.5. Multivariate data analysis 32 3.6. Validation and interpretation of metabolomics data 36 3.7. Comparison of metabolites between studies 40

4. Results 42

4.1. Paper I 42 4.2. Paper II 46 4.3. Paper III 52 4.4. Metabolites and metabolite patterns 58

5. Conclusions and Future perspectives 60

6. Acknowledgements 62

7. References 64

ii

Abstract

Enteric (or typhoid) fever is a systemic infection mainly caused by Salmonella

Typhi and Salmonella Paratyphi A. The disease is common in areas with poor

water quality and insufficient sanitation. Humans are the only reservoir for

transmission of the disease. The presence of asymptomatic chronic carriers is a

complicating factor for the transmission. There are major limitations regarding

the current diagnostic methods both for acute infection and chronic carriage.

Metabolomics is a methodology studying metabolites in biological systems

under influence of environmental or physiological perturbations. It has been

applied to study several infectious diseases, with the goal of detecting diagnostic

biomarkers. In this thesis, a mass spectrometry-based metabolomics approach,

including chemometric bioinformatics techniques for data analysis, has been

used to evaluate the potential of metabolite biomarker patterns for diagnosis of

enteric fever at different stages of the disease.

In Paper I, metabolite patterns related to acute enteric fever were investigated.

Human plasma samples from patients in Nepal with culture-confirmed S. Typhi

or S. Paratyphi A infection were compared to afebrile controls. A metabolite

pattern discriminating between acute enteric fever and afebrile controls, as well

as between the two causative agents of enteric fever was detected. The strength

of using a panel of metabolites instead of single metabolites as biomarkers was

also highlighted. In Paper II, metabolite patterns for acute enteric fever, this

time focusing only on S. Typhi infections, were investigated. Human plasma

from patients in Bangladesh with culture-positive or -negative but clinically

suspected S. Typhi infection were compared to febrile controls. Differences were

found in metabolite patterns between the culture-positive S. Typhi group and

the febrile controls with a heterogeneity among the suspected S. Typhi samples.

Consistencies in metabolite patterns were found to the results from Paper I. In

addition, a validation cohort with culture-positive S. Typhi samples and a

control group including patients with malaria and infections caused by other

pathogens was analysed. Differences in metabolite patterns were detected

between S. Typhi samples and all controls as well as between S. Typhi and

malaria. Consistencies in metabolite patterns were found to the primary

Bangladeshi cohort and the Nepali cohort from Paper I. Paper III focused on

chronic Salmonella carriers. Human plasma samples from patients in Nepal

undergoing cholecystectomy with confirmed S. Typhi or S. Paratyphi A

gallbladder carriage were compared to non-carriage controls. The Salmonella

carriage samples were distinguished from the non-carriage controls and

differential signatures were also found between the S. Typhi and S. Paratyphi A

carriage samples. Comparing metabolites found during chronic carriage and

acute enteric fever (in Paper I) resulted in a panel of metabolites significant

only during chronic carriage. This work has contributed to highlight the

potential of using metabolomics as a tool to find diagnostic biomarker patterns

associated with different stages of enteric fever.

iii

Populärvetenskaplig sammanfattning

Enterisk feber (eller tyfoidfeber) är en vanlig infektionssjukdom i länder med

dålig vattenkvalitet och undermåliga sanitära förhållanden. Sjukdomen orsakas

främst av bakterierna Salmonella Typhi och Salmonella Paratyphi A. Till

skillnad från många andra infektionssjukdomar, som malaria och

fågelinfluensa, som sprids via djur, kan enterisk feber endast överföras mellan

människor, vilket i sig skapar möjligheter för att eliminera sjukdomen. En

faktor som dock komplicerar spridningen av sjukdomen är förekomsten av

kroniska bärare, dvs. människor som kan sprida bakterierna utan att själva ha

några symptom av sjukdomen. Symptomen vid enterisk feber är ofta generella

och har likheter med många andra febersjukdomar. Vanliga symptom är feber,

allmänpåverkan, huvudvärk, hosta, muskelvärk, magsmärta och förstoppning

eller diarré. I dagsläget så diagnostiseras akut enterisk feber oftast genom

blododling, dvs. isolering av bakterien från ett blodprov. En stor nackdel med

blododling är att metoden inte alltid hittar bakterien hos personer som har

sjukdomen, på grund av låg och varierade känslighet. Det tar också lång tid att

få resultat och det krävs avancerad mikrobiologisk utrustning för att utföra

odlingen, något som inte alltid finns i områden där sjukdomen är som vanligast.

Det finns även andra diagnosmetoder för enterisk feber. Dock har alla sina

begränsningar. Därför behövs nya, snabba, känsliga och specifika diagnos-

metoder som kan användas i områden med begränsade resurser. Bättre

diagnosmetoder kan leda till snabbare och mer specifik behandling av patienter

vilket kan minska risken för komplikationer och fortsatt spridning av

sjukdomen. I ett större perspektiv kan förbättrade diagnosmetoder ge en

säkrare uppskattning av den totala sjukdomsbördan i specifika regioner.

Förbättrade diagnosmetoder behövs inte bara för akut enterisk feber utan även

för att detektera kroniska bärare för att på så sätt minska spridningen av

sjukdomen.

I den här avhandlingen har metabolomik i kombination med avancerade

dataanalysmetoder använts för att undersöka möjligheten att använda en panel

av metaboliter för att diagnosticera enterisk feber i olika sjukdomsstadier. Den

övergripande hypotesen i denna avhandling är att en kombination av

korrelerade (samverkande) biomarkörer, en så kallad latent biomarkör, har en

starkare diagnostisk potential jämfört med enskilda biomarkörer. Metabolomik

är studien av metaboliter (små kemiska molekyler), som aminosyror, socker och

fettsyror, i biologiska prover (t.ex. blod, urin eller vävnad). Specifikt undersöks

ofta förändringar av metabolitnivåer i ett biologiskt system under någon form av

störning, t.ex. en sjukdom. En sjukdom kan tänkas ge upphov till ett specifikt

metabolitmönster som sedan kan jämföras mot andra sjukdomar. För att mäta

dessa metaboliter har en kombination av kromatografi och masspektrometri

iv

använts för att separera molekyler (baserat på deras kemiska egenskaper) och

sedan detektera och identifiera dem (baserat på deras massa). Dessa mätningar

genererar stora mängder data. Vanligtvis mäts hundratals metaboliter och för

att kunna tolka dessa stora och ofta komplexa data används kemometriska

metoder eller mer specifikt multivariat dataanalys. Multivariat dataanalys tar

hänsyn till alla metaboliter i alla prover samtidigt och skapar en statistisk

modell där relationen mellan de analyserade proverna kan visualiseras och

tolkas i detalj. Baserat på dessa modeller kan sedan latenta biomarkörer hittas.

I den första studien analyserades metabolitmönster relaterade till akut enterisk

feber. Plasmaprover från patienter i Nepal med infektion orsakad av S. Typhi

eller S. Paratyphi A jämfördes mot icke-febrila kontroller. Ett metabolitmönster

hittades som kunde skilja prover från akut enterisk feber från kontroller samt

mellan infektion orsakad av S. Typhi och S. Paratyphi A. Dessutom

demonstrerades styrkan med att använda en kombination av metaboliter istället

för en metabolit som biomarkör.

I den andra studien användes ett liknande tillvägagångssätt för att undersöka

akut enterisk feber, den här gången med fokus på S. Typhi-infektion.

Plasmaprover från patienter i Bangladesh med en positiv blododling för S.

Typhi eller en negativ blododling men kliniskt misstänkt S. Typhi-infektion

jämfördes mot febrila kontroller. Metaboliter hittades som skiljde odlingspositiv

S. Typhi från febrila kontroller. I gruppen med misstänkt tyfoidfeber hade en

del metabolitmönster som liknade bekräftade tyfoidfallen medan andra hade

mönster liknade kontrollerna. Metabolitmönstret jämfördes mot resultaten i

den första studien och signifikanta metaboliter med samma riktning

(ökning/minskning i tyfoidgruppen jämfört med kontroll) hittades. En

valideringsstudie innehållande prover från patienter med bekräftad S. Typhi-

infektion samt kontroller med både malaria och infektion orsakade av andra

patogener analyserades också.

I den tredje studien låg fokus på kroniska bärare av Salmonellabakterier.

Plasmaprover från patienter i Nepal, som under en gallblåseoperation

bekräftades som bärare av S. Typhi eller S. Paratyphi A i gallblåsan, jämfördes

mot icke-bärare. Salmonella-bärare kunde skiljas från icke-bärare och tecken på

separation fanns även mellan bärare av S. Typhi och S. Paratyphi A. Vid en

jämförelse av metaboliter relaterade till kroniskt bärarskap och akut enterisk

feber (från den första studien) hittades metaboliter som endast var signifikanta

för kroniska bärare. Arbetet i denna avhandling har visat på potentialen i att

använda metabolomik som ett verktyg i sökandet efter diagnostiska markörer

för enterisk feber i olika sjukdomsstadier. Sammantaget kan dessa resultat ses

som ett steg på vägen mot förbättrade diagnosmetoder för enterisk feber.

v

List of Publications

This thesis is based on the following papers, referred to in the text by the Roman

numerals in bold font.

I. Näsström E, Nga TVT, Dongol S, Karkey A, Vinh PV, Thanh TH,

Johansson A, Aryal A, Dolecek C, Basnyat B, Baker S, Antti H.

Salmonella Typhi and Salmonella Paratyphi A elaborate distinct

systemic metabolite signatures during enteric fever. eLife, 3, e03100

(2014).

II. Näsström E, Parry CM, Nga TVT, Maude RR, de Jong HK, Fukushima

M, Rzhepishevska O, Marks F, Panzner U, Im J, Jeon H, Park S,

Chaudhury Z, Ghose A, Samad R, Van TT, Johansson A, Dondorp AM,

Thwaites GE, Faiz A, Antti H, Baker S. Reproducible diagnostic

metabolites in plasma from typhoid fever patients in Asia and Africa.

eLife 6, e15651 (2017).

III. Näsström E, Jonsson P, Johansson A, Dongol S, Karkey A, Basnyat B,

Nga TVT, Tan TV, Twaites GE, Antti H, Baker S. Metabolite biomarkers

of typhoid chronic carriage.

Submitted manuscript.

Papers I and II, published in eLife, are distributed under the terms of the

Creative Commons Attribution License.

vi

Author´s contributions

Summary of the contribution of Elin Näsström to the papers included in this

thesis:

I. Performed sample preparation for metabolomics analysis, analysis of

samples with GCxGC-TOFMS, data processing (including investigation

of methods to process GCxGC-TOFMS data), metabolite identification

and multivariate data analysis. Drafted parts of the initial manuscript

(including figures and tables) and revised the manuscript for

resubmission and publication.

II. Performed sample preparation for metabolomics analysis, analysis of

samples with GCxGC-TOFMS and UHPLC-Q-TOFMS, planning of

analysis of samples with GC-TOFMS, data processing (including

investigation of methods to process UHPLC-Q-TOFMS data),

metabolite identification and multivariate data analysis. Drafted parts

of the initial manuscript (including figures and tables) and revised the

manuscript for resubmission and publication.

III. Performed sample preparation for metabolomics analysis, analysis of

samples with GCxGC-TOFMS, data processing, metabolite

identification and multivariate data analysis. Drafted the manuscript

(including figures and tables) and revised the manuscript for

resubmission.

vii

Abbreviations and notations

Abbreviations

ANOVA Analysis of variance

AUC Area under the curve

CV Cross-validation

DA Discriminant analysis

EI Electron ionization

ESI Electrospray ionization

GC Gas chromatography

GC-TOFMS Gas chromatography time-of-flight mass spectrometry

GCxGC Comprehensive two-dimensional gas chromatography

GCxGC-TOFMS Comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry

HMCR Hierarchical multivariate curve resolution

IS Internal standard

LC Liquid chromatography

LPS Lipopolysaccharide

m/z Mass-to-charge ratio

MDR Multidrug-resistant

MIC Minimum inhibitory concentrations

MPP Mass Profiler Professional

MS Mass spectrometry

MSTFA N-methyl-N-trimethylsilyl-trifluoroacetamide

NIPALS Non-iterative partial least squares

NMR Nuclear magnetic resonance

OPLS Orthogonal partial least squares

PAMP Pathogen-associated molecular pattern

PCA Principal component analysis

PCR Polymerase chain reaction

PLS Partial least squares

QC Quality control

RDT Rapid diagnostic test

RI Retention index

ROC Receiver operating characteristic

RT1 First-dimension retention time

RT2 Second-dimension retention time

SMC Swedish Metabolomics Centre

SPI Salmonella pathogenicity island

TLR Toll-like receptor

TMCS Trimethylchlorosilane

TOF Time-of-flight

viii

UHPLC-Q-TOFMS Ultra-high performance liquid chromatography quadrupole-time-of-flight mass spectrometry

WHO World Health Organization

Notations

The following notations have been used in this thesis. Matrices are denoted by

bold capital letters (e.g. X) and vectors are denoted by bold lower case letters

(e.g. t). Vectors are column vectors unless stated otherwise. Transposition is

denoted by a superscript T.

Matrix dimensions

A Number of components in model

K Number of columns in X

M Number of columns in Y

N Number of rows in X and Y

Matrices

B Matrix of regression coefficients for X, [KxM]

E Residual matrix of predictor variables, [NxK]

F Residual matrix of response variables, [NxM]

P Matrix of loading vectors for X, [KxA]

T Matrix of score vectors for X, [NxA]

U Matrix of score vectors for Y, [NxA]

X Matrix of descriptor variables, [NxK]

Y Matrix of response variables, [NxM]

Vectors

c Weight vector for Y, [Mx1]

p Loading vector for X, [Kx1]

t Score vector for X, [Nx1]

u Score vector for Y, [Nx1]

w Weight or covariance loading vector for X, [Kx1]

Other notations

o Orthogonal

p Predictive

1

1. Aims of this work

The overall hypothesis of this work has been that a combination of correlated

biomarkers in a marker pattern, a so-called latent biomarker, has a stronger

diagnostic potential as compared to single biomarkers. Here this hypothesis was

applied to diagnosis of the infectious disease enteric fever in human plasma,

with metabolites as the individual building blocks of the potential latent

biomarkers.

The overall aims of this work were to:

Evaluate the potential of plasma metabolite marker patterns for

diagnosis of enteric fever at different disease stages using mass

spectrometry-based metabolomics combined with chemometric

bioinformatics.

Suggest diagnostic biomarker patterns suitable for the development of

point-of-care diagnostic tests.

Provide clues for further investigations into the disease mechanisms of

enteric fever.

To achieve this, the specific aims have been to:

Investigate metabolite patterns

o for acute stage enteric fever compared to afebrile and febrile

controls (Papers I and II)

o for culture-positive and culture-negative S. Typhi infection

(Paper II)

o for S. Typhi and S. Paratyphi A enteric fever (Paper I and III)

o for chronic stage enteric fever compared to non-carrier controls

(Paper III)

Compare detected metabolite patterns

o during acute stage enteric fever across cohorts (Paper II)

o between acute and chronic stage enteric fever (Paper III)

2

2. Background

2.1. General description of the problem

Enteric fever is an infectious disease receiving relatively little attention in global

media. The disease is no longer a common disease in high-income countries but

constitutes a major problem in many low- and middle-income countries. In

2010, enteric fever was ranked as the 13th most common cause of years of life

lost in Southeast Asia (21th in sub-Saharan Africa) compared to around 100th

place in Western Europe and North America1. In Western Europe and North

America, diabetes and breast cancer have the same rank as enteric fever in

Southeast Asia1.

There is no animal reservoir for enteric fever and therefore transmission occurs

only among humans. The transmission is complicated by the presence of

chronic asymptomatic carriers, which forms a reservoir for the infecting

bacteria. Enteric fever arises mainly in areas with poor water quality and

sanitation. These two factors along with the availability of vaccines suggest that

there should be possibilities for regional elimination of the disease. However,

large efforts are needed in many areas to reach such a goal2. One area of great

importance is the improvement of diagnostic methods since current methods

suffer from major limitations3. Inaccurate diagnostic methods have

consequences for infected individuals, with increased risks of complications due

to inaccurate treatment. It is also of importance for estimations of the global

disease burden, which can aid in the decision-making regarding vaccination

programs and water/sanitation infrastructure investments. In addition, the

ability to detect chronic carriers would be relevant as a public health tool.

In this thesis, we have applied a mass spectrometry-based metabolomics

approach, including chemometric bioinformatics techniques for data analysis,

to investigate the metabolite patterns in plasma and urine from patients with

acute and chronic stages of enteric fever. We aimed to search for metabolite

patterns with the potential for use in the development of new diagnostic tests.

These approaches may not offer a simple and applicable solution to the

problem. Instead, this work should be considered as a contribution to the field

of improved diagnostic methods for enteric fever. This section aims to provide a

background to the work presented in the thesis including a description of

several aspects of enteric fever, diagnostic tests especially in resource-limited

areas, and finally an introduction to the field of metabolomics with specific

focus on infectious diseases.

3

2.2. Enteric fever

2.2.1. Causative bacteria

Enteric fever is a systemic bacterial infection caused by the Gram-negative

Salmonella enterica serovar Typhi (S. Typhi) and the Paratyphi serovars A, B

and C (S. Paratyphi A, B and C) of which S. Paratyphi A is most common4.

Enteric fever is a generic term for infections caused by both S. Typhi and S.

Paratyphi. Typhoid and paratyphoid fever refers to the infections caused by the

individual serovars. Throughout this work enteric fever will be mostly used, but

in cases focusing on S. Typhi infections typhoid fever will also be used.

S. Typhi has historically been the most common cause of enteric fever but

recently there have been several reports on the emergence of enteric fever

caused by S. Paratyphi A especially in Asia5–7. However, there is an uncertainty

in the true disease burden caused by S. Paratyphi A in many regions in Asia and

Africa8. S. Paratyphi A has been known to cause a milder disease compared to S.

Typhi4. This was questioned in a Nepali study where both seovars were shown to

cause similar clinical syndromes9. The thought that S. Paratyphi A causes a

milder disease has resulted in a limited focus on S. Paratyphi A, especially in

regards to vaccine development. There are however known differences between

S. Typhi and S. Paratyphi A, including different epidemiology and routes of

transmission. S. Typhi is associated with poor water quality and within-

household risks while S. Paratyphi A is associated with street food consumption

and risks outside the household10,11. The main microbiological difference

between S. Typhi and S. Paratyphi A is that S. Paratyphi A lacks the Vi-

polysaccharide capsule that is characteristic for S. Typhi12 and forms the basis of

one of the licensed typhoid vaccines. There are no licenced vaccines against S.

Paratyphi A4. There are also indications in some geographical areas that S.

Paratyphi A is more prone to develop antimicrobial resistance than S. Typhi9.

2.2.2. Disease burden

Enteric fever is most commonly occurring in countries with insufficient clean

water supply and poor hygiene and sanitation standards13. It is a common

disease in many countries in South and Southeast Asia, while the disease

burden in Africa has not been well described but is considered to be high14–16. In

high-income countries the occurrence of enteric fever decreased dramatically in

the first parts of the 1900s as a result of improved water and sanitary conditions

and is now mainly associated with travellers returning from endemic areas17.

The disease is most common among children and young adults4. A number of

estimates of the global burden of enteric fever has been made but these

estimates are often variable and uncertain due to limitations in current

diagnostic methods2,13. Estimates suggests that there were around 21 million

4

cases caused by S. Typhi and 5 million cases caused by S. Paratyphi A during

200018 and roughly 27 million cases caused by S. Typhi during 201019. A map

showing the global distribution of enteric fever is shown in Figure 1. The

average fatality rate for treated cases is estimated to less than 1% but there can

be large regional differences (with fatality rates up to 50% in parts of Indonesia

and Papua New Guinea)13. Even with this fairly low average fatality rate, enteric

fever remains a major problem in many parts of the world considering the high

number of estimated cases each year.

Figure 1. Map showing the global distribution of enteric fever with endemic and highly endemic countries highlighted. Illustration based on data from WHO.

2.2.3. Transmission and the carrier state

S. Typhi and S. Paratyphi A are human-restricted pathogens, thus there is no

animal reservoir for enteric fever12. The disease is transmitted through the

faecal-oral route, mainly via water and food contaminated by faeces containing

the Salmonella bacteria4. The type of source that is contaminated and the

amount of bacteria will have a large impact on the number of people that will

become infected as well as on the duration of the incubation period12.

Enteric fever has a documented carrier state where the infection is not entirely

cleared and bacteria can be shed through the faeces. The carrier state can be

divided into different categories depending on the duration of the carriage post

infection; convalescent: three weeks to three months, temporary: three months

to one year and chronic: over one year13,20. The chronic carriage state is present

in around 2-5% of the acute enteric fever cases and carriers are generally

asymptomatic21,22. In addition, up to 25% of the chronic carriers have no

documented history of typhoid fever13. One famous chronic carrier was Typhoid

Mary, working as a cook in New York in the early 1900s spreading typhoid fever

5

without having symptoms of the disease23. Like other pathogens with an ability

to multiply within infected hosts cells, S. Typhi can subvert the immune system

to survive undetected or without causing much symptoms of infection. Such

“stealth” features of S. Typhi (and S. Paratyphi A)24 allows persistent

colonization of the bone marrow and the gallbladder21,25. One of the strategies

used by the bacteria to colonize the gallbladder is to resist the antimicrobial

properties of the bile26. Another mechanism associated with gallbladder

colonization is the formation of biofilms on gallstones27. General risk factors for

chronic carriage are age and sex, with higher risk among females over the age of

4021. There are also correlations with gallbladder disease, such as cholecystitis28.

In addition, an increased risk of gallbladder cancer has been associated with

chronic Salmonella carriage (an observed/expected ratio of 167 for gallbladder

cancer has been reported for chronic carriers compared to acute typhoid fever

cases)29. The exact contribution of chronic carriers to the transmission of enteric

fever remains unclear. Studies have reported that chronic carriers might have a

more important role in the transmission of enteric fever in high-income

countries (often caused by single outbreaks) compared to endemic areas

(transmission from multiple sources)12,30,31. Alternatively, convalescent carriers

could be increasingly responsible for disease transmission in endemic areas10,32.

However, it is apparent that chronic carriers harbour and shed the bacteria.

Thus, it would be of great importance to be able to detect them, especially if the

long-term goal is to reach regional elimination20.

2.2.4. Pathogenesis and host-pathogen interactions

The human-restricted nature of S. Typhi and S. Paratyphi A has limited the

research of pathogenesis and host-pathogen interactions. There have been

several attempts to establish suitable animal models in which the disease

mimics enteric fever in humans with the most common being different murine

models of S. Typhimurium33. However, due to differences in the genome

between S. Typhimurium and S. Typhi34,35, caution should be taken when

interpreting results from the S. Typhimurium models. A promising discovery is

that mice lacking TLR-11 can be infected with S. Typhi, which could make it a

suitable animal model, although it is unclear how similar this infection is to

enteric fever in humans36,37. Another strategy is to use so-called human

challenge models, where healthy individuals are infected with S. Typhi38,39.

Typically, enteric infections can be divided into three main groups40. The first

two groups are classified as gastroenteritis; either non-inflammatory diarrhoea,

caused by Vibrio cholerae and different E. coli pathovars, or inflammatory

diarrhoea, caused by Shigella spp., Campylobacter jejuni, and the non-

typhoidal Salmonella40,41. The third group is classified as enteric fever and is

characterized by fever and abdominal pain, usually without diarrhoea, caused

by typhoidal Salmonella41. In this classification, two types of Salmonella

6

bacteria are included that cause different diseases, typhoidal (S. Typhi and S.

Paratyphi serovars) and non-typhoidal Salmonella (S. Typhimurium and S.

Enteritidis). This fact together with the use of S. Typhimurium in animal models

of enteric fever makes it interesting to compare the diseases caused by typhoidal

and non-typhoidal Salmonella. Some differences are listed in Table 1.

Typhoidal Salmonella infection has a number of unique features41,42: i) it is of

gastrointestinal origin but often lacks the characteristic signs of mucosal

inflammation and diarrhoea and ii) it is systemic but has a low number of

bacteria in the blood and is not typically associated with septic shock. Non-

typhoidal Salmonella usually causes a localized gastrointestinal inflammation

with diarrhoea in otherwise healthy patients. In patients with a weakened

immune defence bacteraemia is also a common feature and it is associated with

septic shock41.

Table 1. Differences between typhoidal and non-typhoidal Salmonella.

Typhoidal Salmonella Non-typhoidal Salmonella

Salmonella serovars S. Typhi, S. Paratyphi A, B, C S. Typhimurium, S. Enteritidis and many other

Host Human-restricted Wide range of hosts

Type of disease Enteric fever Gastroenteritis with inflammatory diarrhoea

Location Intestine and mesenteric lymph nodes and systemic spread to liver, spleen and bone marrow

Localized to intestine and mesenteric lymph nodes

Incubation time 7-14 days13 12-72 h

Disease duration Up to 3 weeks <10 days

Stool examination Mononuclear cells Neutrophils

Acute phase response Weak Strong

Associated with septic shock

Not usually Yes

Unique SPIsa (S. Typhi vs S. Typhimurium)35

SPI-7, 15, 17, 18 SPI-14

Vi capsule In S. Typhi and S. Paratyphi C Not present

Typhoid toxin43 In S. Typhi and S. Paratyphi A Not expressed

Information from Tsolis et al.41 and Raffatellu et al.44 unless stated otherwise. aSPI – Salmonella pathogenicity island.

Invasion of bacteria normally cause innate immune system detection of so-

called pathogen-associated molecular patterns (PAMPs) by the hosts toll-like

receptors (TLRs) which produces neutrophil-attracting chemokines and pro-

inflammatory cytokines45. The pro-inflammatory cytokines causes fever and

stimulates an acute phase response and if the acute phase response is too strong

it can lead to systemic-inflammatory response syndrome and sometimes septic

shock46. Patients with enteric fever have higher levels of these cytokines in

serum compared to healthy persons but much lower compared to patients

during septic shock44,47. The presence of the Vi capsule in S. Typhi (mentioned

7

in chapter 2.2.1. Causative bacteria), has been associated with the prevention of

recognition of one of the PAMPs, bacterial outer membrane lipopolysaccharide

(LPS), by host TLR-448. The Vi capsule is not present in S. Typhimurium and

might be one contributing factor to why the classical response with neutrophilia

and inflammatory diarrhoea is not initiated during S. Typhi infection. This

could be one of the stealth mechanisms S. Typhi uses to undergo detection of

the immune defence and cause the unique typhoidal disease. This can also

explain the longer incubation period compared to non-typhoidal Salmonella. It

should be highlighted that there must be other mechanisms and processes

unique to typhoidal Salmonella since the S. Paratyphi A and B serovars lack the

Vi capsule42. The Vi capsule will be discussed more in chapter 4.3. Paper III.

Another important feature related to the host-pathogen interactions during

enteric fever is the presence of the typhoid toxin, that is expressed specifically

by both S. Typhi and S. Paratyphi A43. The toxin induces many of the typical

enteric fever symptoms when administrated to mice, but more research is

needed to investigate the mechanisms of the toxin49.

Many of the observations regarding the mode of infection during enteric fever

have been made in the mouse models using S. Typhimurium, and therefore the

exact mechanisms in human enteric fever are unclear. Initially, the bacteria

enter the gastrointestinal tract, after ingestion of contaminated food or water,

and survive the gastric acid13. In the small intestine they adhere to and invade

the mucosal epithelial cells, preferably the microfold (M) cells on Peyer´s

patches (layer of lymphoid nodules)13,50. The bacteria translocate to the

lymphoid follicles, the mesenteric lymph nodes and to reticuloendothelial cells

in the spleen and liver via the blood stream (transient primary bacteraemia)13.

During this phase the bacteria encounter macrophages and become

phagocytosed, however they can form Salmonella-containing vacuoles and

survive and replicate within the macrophages51. After the incubation period (7-

14 days) the bacteria then re-enter the bloodstream, but in a low concentration

(<1 colony-forming unit/ml blood)52. When this secondary bacteraemia is

reached the clinical symptoms with fever and general discomfort usually start to

appear13. Secondary infections can also appear in the liver, spleen, bone marrow

and gallbladder.

2.2.5. Clinical features

The clinical features of enteric fever are variable and range from a mild disease

with few symptoms to a severe illness with multiple complications that can be

fatal12,53. There are also differences in the clinical features between children and

adults, as well as between children of different age groups54. The most common

feature is fever that is slowly rising and then becomes sustained. Due to the fact

than many people in endemic areas self-treat with antimicrobials prior to

8

seeking care, this classic fever-pattern is not always present55,56. Other common

features are headache, general discomfort (malaise), loss of appetite (anorexia),

dry cough, muscle pain (myalgia), abdominal discomfort, coated tongue, and

hepatomegaly (enlarged liver) and/or splenomegaly (enlarged spleen)13,53,57,58.

The variety of clinical features is further highlighted by that fact that either

constipation or diarrhoea can occur (diarrhoea is more common among young

children)59,60. A relative bradycardia (slower heart rate) is also associated with

enteric fever (compared to tachycardia in sepsis) although it is not common

everywhere13. Rose spots, small lesions usually on the chest, are considered to

be specific for enteric fever however the frequency is varying between 5-30%13.

Complications can occur in 10-15% of the cases and the most common are

gastrointestinal bleeding (is usually self-limiting but can be fatal), intestinal

perforation (is considered the most severe complication) and typhoid

encephalopathy (has often high mortality)13.

2.2.6. Treatment and antimicrobial resistance

Before enteric fever could be treated with antimicrobials, fatality rates of up to

26% were reported56. Even today young children, elderly and immuno-

compromised patients are at high risk for severe disease56,61 with higher risks

during infections by antimicrobial-resistant strains. With appropriate

treatment, fatality rates decreases dramatically which indicates the importance

of correct and timely treatment. The successful treatment results in the

prevention of severe disease and preferably complete eradication of the bacteria

to decrease the risk of relapse and transmission4. The risk of relapse can vary

between 5-20% and it often occurs with milder symptoms12. Most cases of

uncomplicated enteric fever can be treated at home with antimicrobials, but for

more complicated cases hospital care is needed with close monitoring for

detection and management of complications12,13.

Chronic carriers of S. Typhi or S. Paratyphi A should be treated with

antimicrobials for up to 28 days and if gallstones are present a combination of

antimicrobials and surgery to remove the gallbladder (cholecystectomy) is

usually needed4. In some cases the carriage state can remain even after removal

of the gallbladder20. It can be discussed if treatment of all chronic carriers with

antimicrobials can be motivated, however the contribution to disease

transmission and the increased risk of gallbladder cancer highlights the

importance of monitoring this group.

The choice of antimicrobial is crucial for the treatment and it is of major

importance to have information about the distribution of antimicrobial

sensitivity for the S. Typhi and S. Paratyphi A strains in the current area62.

Other factors to take into account are the effectiveness, cost of and accessibility

to the antimicrobials under consideration12. As for many other bacterial

9

infections, antimicrobial resistance is a major problem for enteric fever.

Epidemics caused by S. Typhi strains resistant to the first antimicrobial used for

treating enteric fever, chloramphenicol, was reported in the early 1970s with

outbreaks in many parts of the world63–65. During the late 1980s and the 1990s

there was a large number of reports of strains exhibiting resistance to the

traditional first-line antimicrobials, chloramphenicol, ampicillin and

trimethoprim-sulfamethoxazole66–69. These strains were named multidrug-

resistant (MDR), and lead to great difficulties in the treatment of enteric fever70.

Due to the increase of MDR strains, fluoroquinolones were recommended and

widely used71–74. Advantages with fluoroquinolones include high efficiency even

at short courses of 3-5 days, rapid fever clearance and low rates of relapse and

faecal carriage, especially when compared to the first-line antimicrobials12,13.

The fluoroquinolones are often well tolerated. There were previous concerns

regarding the treatment of children but short courses were proven suitable75,76.

However, S. Typhi and S. Paratyphi A strains with decreased susceptibility and

also resistance to fluoroquinolones have emerged in multiple countries77–80.

Treatment recommendations for uncomplicated enteric fever from the World

Health Organization (WHO) states that fluoroquinolones should be the first

choice in areas where there is no quinolone resistance12. In areas with quinolone

resistance, azithromycin or the third-generation cephalosporin, ceftriaxone

should be used with cefixime as alternative12. Recommendations are similar for

severe enteric fever but with a general increase in treatment duration.

Azithromycin is considered safe and of high efficacy81. It has however been

noted a “back-to-the-wall-option” and even though azithromycin-resistant

strains are currently rare they may become more common if this drug becomes

empirical82. Ceftriaxone has limitations due to the route of administration

(parenteral) and the relative high cost83. The oral cefixime has been associated

with a high rates of treatment failure84.

Two recent studies from Nepal demonstrated increased minimum inhibitory

concentrations (MICs) for fluoroquinolones over time which resulted in longer

clearance times and worse outcome83 and problems in the efficacy of the fourth-

generation fluoroquinolone gatifloxacin due to fluoroquinolone-resistant

strains85. These studies stated that fluoroquinolones should no longer be

recommended as the first choice treatment. Together with a commentary82 they

highlight the major problems associated with the treatment of enteric fever due

to antimicrobial resistance, which in the worst of cases could lead to fatality

rates more similar to the pre-antimicrobial area. Again, this can be related to

the insufficiency of current diagnostic methods that can lead to both

inappropriate, ineffective and sometimes unnecessary antimicrobial treatment

which postpones the administration of correct treatment and subsequently

prolongs the illness and increases the risk of complications2.

10

2.2.7. Prevention and vaccination

There are five main areas important for the prevention and control of enteric

fever, according to the WHO: water safety, food safety, sanitation, health

education and vaccination12. For water safety both improved water quality and

quantity is of importance12. It has been suggested that efforts to improve the

water quality is of greater value compared to the improvement of sanitation

when it comes to enteric infections86. Health education is an area that cannot be

neglected since the spread of information about disease transmission and the

importance of hygiene can lead to changes in people’s behaviour12. The

successful major reduction of enteric fever in most high-income countries due to

improvements in water quality and sanitation has shown the value of these

strategies87. Most beneficial for resource-limited areas, where enteric fever is

common, would be a general improvement of the living standards. This would

dramatically reduce the occurrence of enteric fever and many similar diseases.

The work towards such improvements should be made in combination with

other preventative measures such as vaccination.

The first vaccine against S. Typhi was a killed whole-cell vaccine, but this type is

no longer in use due to too many severe reactions towards the vaccine88. There

are two licensed vaccines currently in use for S. Typhi infections, a single dose

parenteral Vi polysaccharide vaccine and a live attenuated oral vaccine called

Ty21a (usually three doses)88. Both vaccines are safe and usually well tolerated,

but they have relatively short duration and they are not licensed to use in

children <2 years88,89. An oral route of administration (as for Ty21a) instead of

parenteral (as for Vi) is easier to administrate and usually more accepted90.

There is a large number of novel vaccines under development and the most

promising are the typhoid conjugate vaccines that should be possible to

administrate to young children, last longer and have higher efficacy15,91. Another

important area is the development of a S. Paratyphi A vaccine since there is

currently no licensed vaccine available92. In a position paper, the WHO

recommends that “countries should consider the programmatic use of typhoid

vaccines for controlling endemic disease” and offers suggestions on how

vaccination should be considered for parts of endemic populations at increased

risk, children in specific areas, during outbreaks and for travellers to endemic

areas88.

Improved diagnostic methods are of great importance for enteric fever

prevention, since accurate assessments of disease burden in specific areas will

assist in decision-making regarding vaccination programs and water/sanitation

improvement measures2. In addition, the ability to detect carriers is an

important strategy to decrease the spread of enteric fever62.

11

2.3. Diagnosis

The diagnosis of enteric fever is still primarily clinical. The most reliable

laboratory test is repeated blood cultures or culture of bone marrow aspirates.

These culture tests are complicated, time consuming, and require access to a

high-quality microbiological laboratory, which is often lacking in locations

where the disease is most common.

2.3.1. Diagnostics in resource-limited areas

The highest disease burden for enteric fever and thus the greatest need for

accurate diagnosis is in areas with limited resources. This fact adds substantial

complexity to the area of diagnostics. For many of the infectious diseases

common in low- and middle-income countries there are possibilities for

treatment. However, the lack of adequate diagnostic tests leads to both under-

treatment and over-treatment93,94. Under-treatment can result in disease

complications and prolonged illness. Over-treatment can lead to missed

alternative diagnoses and to a shortage in the supply of common treatments.

Most importantly, over-treatment could increase the development of

antimicrobial-resistant pathogens.

The WHO has summarized the characteristics of what would be an ideal

diagnostic test in resource-limited areas using the acronym ASSURED. This test

should be affordable, sensitive, specific, user-friendly, rapid, equipment-free

and delivered to those who need it95. To have a sufficiently accurate test that is

also suitable for use in limited infrastructure settings is usually problematic96. A

few infrastructural resources that should be considered are electricity, clean

water supply, cold storage possibilities, laboratory space and the need for

trained personnel96. In addition to accuracy and equipment requirements, a

crucial characteristic of a diagnostic test is the speed (i.e. time to result). To

properly guide the treatment a rapid diagnostic result is crucial. In many

primary healthcare settings it is noted that patients do not always return for the

result of a test that takes long time and therefore risk not receiving appropriate

treatment95. The type of sample used and the volume needed to obtain accurate

diagnosis is also of importance since the sample collection method will affect the

suitability for use in resource-limited settings93. Blood, urine, faeces and saliva

are commonly used sample types, but it should be noted that there can be

negative cultural attitudes towards blood samples is certain areas93.

The development of new diagnostic tests can be divided into several steps and

include identification of suitable diagnostic targets/biomarkers, the conversion

into a suitable test format and high-quality clinical validation of the test.

Scientific and technological advances has led to an increased understanding of

12

host-pathogen interactions during infection and new techniques including the

omics approaches may be used to identify diagnostic targets/biomarkers

originating from both the pathogen and the host97. Advances in areas such as

microfluidics and nanotechnology are also promising when it comes to

converting the discovered target/biomarkers into simple and reliable diagnostic

tests98.

Apart from the scientific and technical issues, another complicating factor for

the development of diagnostic tests for use in resource-limited areas is the lack

of market-based funding but there is a rise in initiatives through public sector

investments95. Other factors to consider during the development of a diagnostic

test are cultural and social aspects in the specific areas and also safe disposal of

waste products93. Once a test has been developed and validated a final barrier is

to make sure it is used, especially in cases where treatment is easily available

and cheap94. One positive example of this is the distribution of rapid diagnostic

tests for malaria in retails such as drug shops99. This demonstrates that new

modern tests can be implemented in resource-limited areas of the world.

2.3.2. Enteric fever diagnosis

Parry et al. proposed the need for three types of diagnostic tests for enteric fever

with different purposes and characteristics2. First, there is a need for a rapid,

simple, sensitive and specific test for acute disease. This would provide more

appropriate care which would be beneficial for several reasons. The risk for

complications would decrease along with shortened disease duration and a

decreased risk of transmitting the bacteria. In addition, a more rational use of

antimicrobials might lead to a decreased development of antimicrobial

resistance. Second, a reliable test for the detection of chronic carriers is needed

to decrease transmission. Finally, a test for both acute and convalescent patients

is needed to make accurate estimations of the disease burden. Such information

can guide decisions regarding vaccination programs and other preventative

strategies. Focusing both on acute case detection and disease surveillance is in

line with the recommendations for diagnostic test development95. However,

diagnostic methods for related febrile illnesses also need to be improved in the

work towards improved diagnosis of enteric fever94.

In recent years there have been several reviews focusing on current diagnostic

methods for enteric fever and novel methods under development2,3,94,100. These

reviews all highlight the importance of improved diagnostic methods. Some

factors contributing to the difficulties of enteric fever diagnosis are: the rather

long incubation period, the varying and unspecific clinical features, the low

numbers of bacteria (especially in blood) and the cross-reactivity of antigens3. A

brief overview of current and novel methods for diagnosis of enteric fever will be

given in the text below and in Table 2.

13

Table 2. Summary of current diagnostic methods of enteric fever.

Method Description Advantages Disadvantages

Acute enteric fever Microbiological culture

Isolation of S. Typhi or S. Paratyphi A

Definite diagnosis, use for antimicrobial susceptibility testing

Long time to result, require microbio-locial laboratories

Blood culture Easier collection vs. bone marrow

Inaccurate sensitivity

Bone marrow culture

Improved sensitivity vs. blood

Invasive and techni-cally challenging

Serology Widal test Agglutinating

antibodies against LPS, flagella

Rapid, simple, low laboratory requirements

Inaccurate sensitivity and specificity, ideally need paired samples

Other RDTs Antibody detection against LPS, outer membrane protein etc.

Rapid, simple, low laboratory require-ments, improved accuracy vs. Widal

Still inaccurate sensitivity and specificity

Nucleic acid amplification

Detection of specific gene sequences through PCR

Amplification of low number of bacteria, detect unculturable bacteria, rapid

Variable sensitivity in culture neg., high laboratory requirements

Chronic enteric fever Bile detection Isolation of S. Typhi or

S. Paratyphi from bile or gallstones

Definite diagnosis Highly invasive

Serial stool samples Isolation in several samples due to intermittent shedding

Definite diagnosis Inaccurate sensitivity, logistic issues

Vi antigen Detect antibody response to Vi antigen

More practical vs. bile detection and serial stool samples

High background levels in endemic areas

2.3.2.1. Current diagnostic methods for acute enteric fever

The symptoms and other clinical features of enteric fever are unspecific and

therefore it is usually difficult to make a reliable clinical diagnosis. It has to be

differentiated from other febrile illnesses including dengue fever, malaria,

rickettsial infections, leptospirosis, tuberculosis, brucellosis and sepsis due to

other pathogens2,4,101. Attempts have been made to combine clinical signs and

laboratory measurements to make diagnostic predictions, but so far with limited

success94. For a definitive diagnosis of enteric fever the bacteria must be

isolated, usually from blood or bone marrow12. The isolated bacteria can then be

used for antimicrobial susceptibility testing. Blood culture is considered the gold

standard method but comes with several limitations. The sensitivity is widely

varying, detecting 40% to 80% of the presumptive cases25,52,102. Factors affecting

the sensitivity are prior antimicrobial treatment, blood collection volume and

time13. Bone marrow culture usually has a higher sensitivity compared to blood

culture, mainly due to a higher number of organisms in bone marrow but also

because of smaller negative impact of prior antimicrobial treatment25,52.

14

However, the main limitations with bone marrow culture are the invasiveness

and technical equipment needed which makes it rarely used. Overall, the

limitations of microbiological culture are the need for substantial laboratory

equipment, which is lacking in many endemic countries103, and the long time to

result (which can range from 1-5 days for blood culture)94. The limitations of

blood culture also affect the evaluation of new diagnostic methods since it is

widely used as the reference method.

Among the serological methods, the Widal test is the most widely known where

agglutinating antibodies, against the S. Typhi or S. Paratyphi A antigens LPS or

flagella, are measured in serum. Due to its simplicity, speed and minor

equipment needed it is still widely used in several areas104,105. However, there

are major problems associated with the Widal test, including inadequate

sensitivity (affected by prior antimicrobial treatment) and specificity (due to

antigenic cross-reactions), inaccurate interpretation, misuse and the need for

paired samples (to compare acute and convalescent phases)94,104. More recent

serological rapid diagnostic tests (RDTs) have been developed with the desired

properties of simplicity and speed for point-of-care use. Most of these tests

detect antibodies using different methods. See review by Andrews and Ryan for

detailed information94. A recent database review over studies evaluating RDTs

concludes that they have moderate accuracy but also that many of the studies

themselves are somewhat biased106. The RDTs may show increased sensitivity

and specificity compared to the Widal test but they are still not sensitive and

specific enough to be routinely used2. Nucleic acid amplification methods

through polymerase chain reactions (PCR) (both conventional and real-time

PCR) have been examined. They have the potential of amplifying small numbers

of bacteria and also detect bacteria that cannot be cultured2. Another advantage

is the increased speed compared to blood culture. The PCR-based methods

usually have high sensitivity for blood-culture-positive samples but lower for

blood-culture-negative samples107,108. Limitations to the use of PCR methods in

low-resource settings includes the need for specialized laboratory equipment,

relatively large blood volumes, and the relatively high cost2.

2.3.2.2. Current methods for carrier detection

The current gold standard method for diagnosing chronic Salmonella carriers is

detection of Salmonella bacteria in bile or on gallstones during surgery to

remove the gallbladder. However, this is a highly invasive method and can be

applied only to a restricted patient group. Thus there is a need for other less

invasive methods20. Another approach has been to collect serial stool samples

but this lacks in sensitivity and has logistic issues109. The detection of the Vi

antigen has been suggested as an option for carriage detection but this method

might have limited use in endemic areas due to high background levels of anti-

Vi antibodies110.

15

2.3.2.3. Novel diagnostic approaches for enteric fever

Several diagnostic methods for enteric fever are under development. They can

be divided into two categories; improvements and further developments of

current methods and novel approaches. A few examples in the first category are

improved culturing (improved growth conditions, electricity-free incubation,

improved amplification methods (enrichment steps to improve sensitivity,

isothermal procedures to simplify instrumentation, combined culture and PCR

for higher speed) and improved serological methods3,94,100. The second category

include amplification-free molecular methods (DNA capture probes),

immunoscreening to detect novel antigens and antibodies and detection of host

factors apart from antibodies (including the omics techniques transcriptomics,

proteomics and as further described and discusses below, metabolomics)3,94,100.

16

2.4. Metabolomics

In this section, the research field of metabolomics will be covered including

applications with special emphasis on biomarker detection and infectious

disease. Method details and technical aspects will be described and discussed in

chapter 3. Methods.

2.4.1. General metabolomics

Metabolomics is the complete and quantitative analysis of metabolites in a

biological system during environmental or physiological stimuli. Metabolites are

defined as low molecular weight molecules including a wide range of compound

classes, such as sugars, amino acids, fatty acids, nucleosides and organic acids.

Together constituting the metabolome. Metabolomics, as we know it today, has

since the early 2000s been described in accordance with the definitions of

metabolomics or metabonomics by Fiehn and Nicholson111,112. However,

approaches very similar to metabolomics were used already in the 1960s113.

Metabolomics is the most recent part of the so-called omics cascade also

including genomics, transcriptomics and proteomics. Since metabolites are the

end products of this omics cascade the metabolome is considered to be closest

to the function or phenotype114, being the result of the combination of genetic

and environmental factors115. The metabolome is extremely dynamic suggesting

that any changes occurring to the studied system would be reflected instantly at

the metabolite level providing a snapshot of the status of the system116.

Due to the wide variety of molecules classified as metabolites and the large

concentration differences between them it is impossible to obtain a complete

metabolite coverage using only one analytical technique. Therefore, a

compromise in terms of coverage must be made when selecting a single method.

Alternatively, the combination of several methods can be used to increase the

coverage117. Two main types of analytical techniques are commonly used in

metabolomics, mass spectrometry (MS)-based techniques and nuclear magnetic

resonance (NMR). The MS-based techniques are often used in combination with

a chromatographic step for separation of metabolites prior to MS detection.

Advantages with the MS-based techniques compared to NMR are the higher

sensitivity, the possibility of using smaller sample volumes and the lower

instrument costs116. Advantages of NMR includes the non-destructive nature of

the method and the simple sample preparation118. There are two main analytical

approaches within metabolomics: non-targeted and targeted analysis116. In non-

targeted or global metabolomics a wide range of metabolites are analysed to

gain information about general differences in the metabolome in relation to a

specific condition. In targeted metabolomics only specific pre-selected

metabolites are measured and quantified, for example a specific metabolite class

17

or a set of metabolites involved in a biochemical pathway of interest. There are

advantages with both approaches and they can be used complementary115,116.

First, a non-targeted approach can be applied resulting in a broad metabolite

coverage, although with a compromise in terms of metabolite detection and no

absolute quantification. Second, a targeted approach can be applied based on

the results of the non-targeted analysis to gain absolute quantification of

specific metabolites of interest. Metabolomics analysis results in very complex

data, especially for the non-targeted approach. Therefore, sophisticated data

processing and analysis methods are needed to get the data into a representative

format and to extract the relevant information from it. For this, methods based

on multivariate data analysis, able to simultaneously handle, model and

investigate large number of metabolites, are commonly used. For more

information see chapters 3.4. Processing of metabolomics data and 3.5.

Multivariate data analysis. A number of recent reviews summarizes several

aspects of the metabolomics field including recent advancements115,119,120.

2.4.2. Biomarker detection using metabolomics

A biomarker can be defined as “a characteristic that is objectively measured and

evaluated as an indicator of normal biological processes, pathogenic processes,

or pharmacologic responses to a therapeutic intervention”121. There are many

different applications of biomarkers in human disease. The characteristics of the

disease and the clinical setting determine what type of biomarker/information

that is most relevant. Biomarkers can for instance be used for disease diagnosis

and prognosis, treatment selection and treatment evaluation121,122. There is also

a growing interest in biomarkers for personalized or precision medicine123.

The likelihood of finding a single biomarker specific enough for a particular

disease is low. With the exception of diseases classified as inborn errors of

metabolism, more than one biomarker (metabolite) is commonly needed to

reach high specificity124. The concept of using a combination of biomarkers is

not new, instead it is commonly used clinically when combining different

physiological parameters and observations to obtain the correct diagnosis.

Metabolic biomarker panels have another advantage apart from just combining

several metabolites. It can also take advantage of the correlation between the

included metabolites to increase predictivity and specificity. Using multivariate

data analysis methods (described in chapter 3.5. Multivariate data analysis)

these correlation structures can be used to find a panel of biomarkers, a so-

called latent biomarker to facilitate prediction and interpretation.

In a review by Mamas et al. three questions related to the usefulness of

metabolomics in the search for biomarkers are addressed125. The first question:

Can the clinician measure the biomarkers? – Many metabolites are already

being commonly measured in clinical settings. Well-known examples are

18

glucose and cholesterol. However, these examples are often single-metabolite

measurements. With the goal of using a biomarker panel, the ideal situation

would be to get a simultaneous measurement of several metabolites. This could

be achieved by developing specific targeted analysis methods e.g. using mass

spectrometry. However, to design more clinically feasible “bench-side” devices

for biomarker panel measurement further developmental work will be required.

The second question: Do the biomarkers add new information? – New

information can be provided applying the hypothesis that metabolites specific

for a disease condition can be secreted into the blood or other biofluids and then

be detected122. Using blood as the sample matrix can be considered to give a

more general picture of the biochemical status in the system126. This would be

beneficial especially for diseases with a complex pathophysiology that affects

many different systems. A great advantage of metabolite biomarkers is the

possibility of gaining information about disease mechanisms during the

discovery process. Another benefit is that the metabolome changes rapidly over

time which makes it possible to study disease progression and treatment

response (due to this, variation unrelated to the question will also be detected).

To be useful the biomarkers need to be validated in several studies from

multiple locations. The third question: Do the biomarkers help the clinician to

manage patients? – As mentioned previously there are many clinical uses for

biomarkers including diagnosis, treatment selection and treatment response.

Since many biomarkers can be used for these purposes in the clinic today it

should be possible to use metabolomics-based biomarkers in the future.

However, rigorous validation of the detected markers is needed before they can

be introduced to the clinic which will result in an increased development time.

2.4.3. Metabolomics in infectious diseases

In infectious diseases, there are a number of problems that metabolomics might

help to address, with improved diagnostics being one of main importance. More

specific, it would be of major impact if specific biomarker patterns could be

detected for individual infections or groups of related infections with similar

disease management. Another area of high importance is the detection of

biomarker patterns related to antimicrobial resistance to guide treatment and

monitor treatment responses. Finally, metabolomics could be used as a tool for

increased understanding of disease mechanisms and host-pathogen interactions

during disease. When using metabolomics to study infectious diseases there

may be a risk of detecting metabolites changing as a response of the general

infection and thus being too unspecific for a diagnostic test. However, it should

be possible to detect changes associated with host responses more specific to a

certain infection. Apart from the host response to infection there is also a

possibility of detecting metabolites originating from the pathogens themselves.

A combination of host- and pathogen-related metabolites would be optimal for

specific diagnosis.

19

Metabolomics has been used to study various infectious diseases. A few

examples will be covered here with focus on the different applications

mentioned above and on infections somewhat related to enteric fever. Malaria

and dengue fever are infections presenting many similar clinical features to

enteric fever. In two separate studies metabolite patterns were analysed in

human serum samples from patients with and without dengue fever and during

different disease stages127,128. These studies aimed to find metabolites with

diagnostic and prognostic values and to get an increased understanding of the

disease mechanism. Metabolomics has been applied to study malaria infections

several studies. Examples include the investigation of i) metabolites in plasma

samples from young children comparing controls and patients with mild and

severe malaria129, ii) metabolites in urine samples comparing patients with

malaria infection and non-malarial fever to healthy controls130 and iii)

metabolites in urine samples from mice with malaria infection with and without

concurrent helminth infection131. Metabolomics has also been used to study

sepsis. Examples include metabolomics on human plasma or serum for

comparing i) children with septic shock or systemic inflammatory response to

healthy controls to obtain diagnostic and prognostic metabolites132, ii) patients

surviving sepsis infection to patients that did not with the aim to find a

combination of metabolites and clinical features that could be used for

predictions of sepsis outcome133 and iii) healthy volunteers either given an

endotoxin or placebo using multiple time points (often used as a model to

investigate systemic inflammatory response)134. Interestingly, the results of the

two studies in ii) and iii) above were compared and showed that similar

responses were seen during sepsis and in the endotoxin challenge135. The studies

mentioned here highlight the potential of using metabolomics for the study of

infectious diseases.

2.4.4. Omics studies related to enteric fever

There are other metabolomics studies related to enteric fever, apart from the

studies presented in this thesis. However, none of them focuses on diagnosis or

includes human samples. Two studies analyses metabolites during growth of

Salmonella Typhimurium. The first study investigates the mechanisms of

growth in bile while the second study the effects of metabolites on virulence

expression136,137. In another study the impact of S. Typhimurium infection on

host metabolism was investigated in a murine typhoid model138. For other omics

techniques, human samples of typhoid fever have been analysed with diagnostic

focus. In a transcriptomics study, peripheral blood of patients with S. Typhi

infection was analysed during the acute, convalescent and recovery phases139. In

addition a proteomics study investigated plasma proteins in chronic typhoid

carriers140. The lack of studies using metabolomics with focus on diagnosis of

enteric fever emphasizes the need for the studies included in this work.

20

3. Methods

Metabolomics involve many different techniques and procedures on the route

from sample collection to interpretable results. An overview of the

metabolomics workflow is shown in Figure 2 and will be described and

discussed below.

3.1. Sample matrices

The main sample matrix in this work has been human plasma. However, in

Paper II human urine samples were also analysed.

3.1.1. Plasma/serum

Blood sampling is widely used for diagnostic purposes in the health care system.

Plasma, serum or whole blood are commonly used in metabolomics studies

since blood is easy accessible and routinely collected. Blood can be divided into

a cellular part and a fluid part. The cellular part includes erythrocytes (red blood

cells), leukocytes (white blood cells) and platelets. The fluid part, called plasma,

contains a large proportion of water (up to 95%) but also include proteins,

Figure 2. Overview of the metabolomics workflow.

21

metabolites and electrolytes141. If the plasma is allowed to clot and the clotting

proteins are removed the remaining fluid is called serum. Apart from

metabolites related to blood clotting the metabolites of plasma and serum

should be comparable. However, several studies have reported differences in

metabolite patterns between plasma and serum142–144. In this work plasma has

been the biofluid of choice which facilitates comparison between the studies.

See Papers I-III for more information about the plasma collection procedures.

3.1.2. Urine

Urine contains mainly water-soluble waste products excreted from the kidneys

but its role as a diagnostic biofluid is still of major importance. Comparing the

human urine and serum metabolomes145,146 reveals that a large number of urine

metabolites were not reported to be found in blood, although theoretically all

urinary metabolites should be present in blood. This could be related to the

concentration differences of metabolites in the two biofluids but also due to

analytical differences145. Advantages with the use of urine in metabolomics are

the non-invasive nature of the sample collection, the lower protein content

facilitating metabolite extraction, the large diversity of metabolites and the lack

of volume-limitation145,147. However there are also disadvantages such as large

dynamic range (also a challenge for plasma) and highly varying pH, ionic

strength and osmolarity that complicates the analytical measurements148. See

Paper II for more information about the urine collection procedures.

3.2. Sample preparation for metabolomics analysis

Sample preparation, by extraction of metabolites from the sample

matrix/biofluid in use, is an important step in metabolomics analyses. The

sample matrix/biofluid and also the analytical technique to be used will affect

the choice of sample preparations. In this work, two combinations of

biofluid/analytical technique have been used, human plasma/GC(xGC)-TOFMS

and human urine/UHPLC-Q-TOFMS.

3.2.1. Metabolite extraction

To analyse metabolite levels in biofluids the cells have to be disrupted to release

their content. Molecules of high molecular weight (proteins) need to be removed

while keeping the low-weight metabolites. Since the goal of untargeted

metabolomics is to detect as many metabolites as possible (preferably using one

single method) and the fact that the metabolites are of high chemical diversity,

the extraction method must provide a good compromise to allow the extraction

of many metabolites.

22

3.2.1.1. Plasma extraction

The protocol for extraction and derivatization of human plasma used in this

work was developed by A et. al149 and forms the basis for the sample preparation

protocol for plasma/serum samples at the Swedish Metabolomics Centre (SMC)

(http://www.swedishmetabolomicscentre.se). Briefly, the metabolites were

extracted using a methanol/water extraction mix (8:1 v/v) including isotopically

labelled internal standards (IS) in a bead mill. After time on ice the samples

were centrifuged and the supernatants evaporated to dryness. Starting volumes

of 50 or 100 µl were used in the different studies depending on original sample

amounts available.

3.2.1.2. Urine extraction

For extraction of metabolites from urine (in Paper II) a modified version of the

protocol described by Want et al.148 was used. Compared to the extraction

procedure for plasma the extraction of metabolites from urine is simpler and

often faster with only two main steps including centrifugation and water

extraction. 100 µl urine was used as starting volume and the samples were

centrifuged followed by addition of 100 µl water (including two isotopically

labelled IS) to 50 µl supernatant.

3.2.2. Derivatization for GC-TOFMS and GCxGC-TOFMS analysis

To be able to run gas chromatography the compounds need to be volatile and

thermo-stable, which is not the case for many metabolites with more polar

properties. Therefore a chemical derivatization process is required. The most

common derivatization method is silylation where the hydrogens in -OH, -NH

and -SH groups of the metabolites are replaced by silyl groups150. There are

several reagents that can be used for silylation of metabolites and a common

reagent in metabolomics analysis is MSTFA (N-methyl-N-trimethylsilyl-

trifluoroacetamide) in combination with TMCS (Trimethylchlorosilane). In

addition to the silylation step there is usually a need for an oximation step to

stabilize carbonyl groups in aldehydes and ketones to prevent the formation of

enols151. The oximation step also prevents cyclization of sugars which decreases

the number of derivatization products for each sugar152. However, even with a

suitable choice of derivatization reagents there is still risk of bias due to artefact

formation, incomplete derivatization and formation of several derivatization

products. The same derivatization process (described in detail in Paper I) has

been used in all papers and includes methoxymation with O-

methylhydroxylamine hydrochloride in pyridine (16 h reaction time) and

silylation with MSTFA + 1% TMCS (1 h reaction time).

23

3.3. Metabolite analysis

Three different analytical techniques have been used for metabolite analysis in

this work, all with a chromatographic method for metabolite separation coupled

to mass spectrometry for detection. The following techniques were used: one-

dimensional gas chromatography time-of-flight mass spectrometry (GC-

TOFMS), comprehensive two-dimensional gas chromatography time-of-flight

mass spectrometry (GCxGC-TOFMS) and ultra-performance liquid

chromatography quadrupole-time-of-flight mass spectrometry (UHPLC-Q-

TOFMS). An overview of the analytical methods for all papers/cohorts is shown

in Table 3.

Table 3. Overview of analytical techniques and processing methods used in the papers/cohorts.

Analytical method Data processing method Paper/cohort

GCxGC-TOFMS ChromaTOF/Guineu Paper I – Nepali cohort

2D HMCR Paper II – Bangladeshi primary

cohort, plasma

2D HMCR Paper III – Nepali cohort

GC-TOFMS Target GC-MS Paper II – Bangladeshi/Senegalese

validation cohort

UHPLC-Q-TOFMS Mass Hunter Qual/MPP Paper II – Bangladeshi primary

cohort, urine

3.3.1. Chromatography

The main principle of chromatography is separation of compounds in a mixture

through different degree of interaction between a mobile phase (containing the

sample) and a stationary phase. As the compounds are separated due to

differences in chemical properties they are registered in a detector at the time of

elution from the chromatographic system.

3.3.1.1. One-dimensional gas chromatography

In one-dimensional gas chromatography (GC), the sample, containing

derivatized compounds (metabolites), is made volatile through heated injection.

Once the sample is injected it is transported via a carrier gas (He, H2 or N2) as

the mobile phase to a column, usually a fused-silica capillary column, coated

with a stationary phase. Due to the different boiling points and polarities of the

metabolites, the interaction between the compounds in the mobile phase and

the stationary phase will be of different strengths and therefore provide a

separation of the metabolites. A temperature program for the column oven, with

stepwise temperature increase, is suitable for efficient separation. The

compounds eluting from the column are transferred to a detector. Examples of

detectors used in combination with GC are mass spectrometers, flame ionization

24

detectors and thermal conductivity detectors, but only the MS technique will be

described here.153

3.3.1.2. Comprehensive two-dimensional gas chromatography

The general principle of comprehensive two-dimensional gas chromatography

(GCxGC) is similar to one-dimensional GC. The main difference is the addition

of a second column. The use of two separate columns with different properties

will increase the separation and resolution compared to GC and will therefore

decrease the problem with co-eluting peaks154. The most common column setup

is to use a nonpolar first-dimension column and a shorter mid-polarity second-

dimension column155. However, it has been suggested that a polar first-

dimension column and a nonpolar second-dimension column would improve

the use of the second-dimension space156. Another important feature of GCxGC

is the use of a thermal modulator between the two columns that traps the

eluting metabolites from the first dimension and releases them to the second

column in segments regulated by hot pulses with cold times in-between157. The

result of the modulator is that narrow peaks for each metabolite will be created

in the second dimension which increases the sensitivity of the analysis158.

3.3.1.3. Liquid chromatography

In liquid chromatography (LC), a liquid mobile phase transports the sample

through the column, usually a packed column with a bound stationary phase

attached to a silica surface. There are different methods for LC separation, with

the most common method being reversed-phase chromatography, where a

nonpolar stationary phase is used together with a more polar solvent159. Similar

to the temperature programming in GC, gradient elution is used in LC. In

reversed-phase chromatography the solvent gradient is normally started with

mainly water followed by a gradual increase in the amount of organic solvent

(such as acetonitrile). The most common LC detectors are ultra violet detectors

(UV-vis) and mass spectrometers.

3.3.2. Mass spectrometry

The main principle of mass spectrometry is ionization of compounds at an ion

source, separation of the ions based on mass-to-charge ratio (m/z) in a mass

analyser and finally registration of the ions by a detector153. A common

ionization technique when GC is coupled to the MS is electron ionization (EI),

where high-energy electrons interact with the gaseous molecules. EI is a hard

ionization technique producing multiple fragments which makes it possible to

identify metabolites by comparing their fragmentation patterns. There are

extensive publicly available mass spectral libraries used for identification of EI

mass spectra. In contrast, when LC is coupled to the MS soft ionization

techniques are more commonly used. In soft ionization there is limited

fragmentation and the molecular ion is mainly observed. A high-resolution

25

instrument is needed for sufficient mass accuracy to be able to identify the

metabolites based on the molecular ion. Another strategy to facilitate the

identification is to use tandem MS (MS/MS) to obtain a higher degree of

fragmentation in the second MS step. An example of a soft ionization technique

is electrospray ionization (ESI), where a strong electric field is used to create an

aerosol of the liquid sample160. Electrospray ionization can be performed in

positive and negative mode and usually both modes are run for a wider

coverage.

After the ionization, the ions are separated in a mass analyser. One type of mass

analyser is the time-of-flight (TOF). In the TOF technique, the ions are

accelerated by an electrical field creating ions with the same kinetic energy and

then the ions enter a field-free region where the velocity is determined by

m/z153. Therefore, ions with a smaller m/z will travel through the field-free

region and reach the detector in a shorter time than ions with larger m/z.

Another type of mass analyser is the quadrupole, which contains four rods

between which a radio frequency field is applied and depending on the voltage

ions with specific m/z values can reach the detector160. Scanning is performed to

allow ions with all different m/z values to be detected. As the ions reach the

detector a signal is created with an intensity related to the abundance of the

specific ion.

3.3.3. GC-TOFMS analysis

Gas chromatography coupled to mass spectrometry (GC-MS) is a common

analytical method in metabolomics152,161,162. The main advantages of GC-MS

techniques compared to LC-MS techniques are i) the large number of

commercially available mass spectral libraries for identification, ii) less

problems with matrix effects, iii) the ability to use comparable retention indexes

(RI) during the identification and iv) the higher chromatographic

resolution116,160. The main drawbacks compared to LC-MS are i) the limited

range of detectable metabolites and ii) the need for derivatization160.

The GC-TOFMS analysis in this work was performed according to the protocol

developed by A et al.149. In short, the samples were automatically injected in

splittless mode. Using helium carrier gas, the samples were transported to a

fused-silica capillary column coated with a nonpolar stationary phase. A

temperature program with a rapid temperature increase was used. Following

the chromatographic separation, the eluted compounds were ionized with EI

and the ions separated using the TOF mass analyser followed by detection of the

derivatized compounds. For more details regarding the analysis see

supplementary material for Paper II. Examples of a chromatogram and an EI

mass spectrum from a GC-TOFMS analysis are shown in Figure 3 and Figure

4.

26

Figure 3. Example of a chromatogram from GC-TOFMS analysis of one plasma sample from Paper II. The retention times are shown along the x-axis and the peak intensities are shown along the y-axis.

Figure 4. Example of an EI mass spectrum from GC-TOFMS analysis from Paper II. The mass-to-charge ratios, m/z, of the ions are shown along the x-axis and the ion intensities are shown along the y-axis.

3.3.4. GCxGC-TOFMS analysis

Comprehensive two-dimensional gas chromatography coupled to mass

spectrometry (GCxGC-MS) has become more commonly used in different

applications in the metabolomics field due to its sensitivity and resolution

advantages over GC-MS163–166. Another advantage is the fact that the mass

spectral libraries available for GC-MS can be utilized in the identification

process. The drawbacks with GCxGC-MS are the more complex data which

makes the data processing more challenging155,167 and the often relatively long

analysis times.

The procedure for GCxGC-TOFMS analysis used in this work has been similar in

Papers I-III with some minor modifications. Briefly, the samples were

automatically injected in splittless mode with helium as carrier gas. The column

setup was a first-dimension polar column followed by a second-dimension

shorter, nonpolar column. A quad-jet thermal modulator was used between the

two columns with 5 s modulation time. The same, slow temperature program

was used in both columns with a temperature offset for the second column of

+15◦C. After the chromatographic separation the eluted metabolites were

ionized with EI and the ions were separated using the TOF mass analyser

27

followed by detection. An example of a surface plot chromatogram from

GCxGC-TOFMS analysis is shown in Figure 5. For more details regarding the

GCxGC-TOFMS analysis see Paper I.

Figure 5. Example of a surface plot chromatogram from GCxGC-TOFMS analysis of one plasma sample from Paper I. First- and second-dimension retention times (RT1 and RT2) on the x- and y-axes respectively and the peak intensities on the z-axis (visualized by a colour scale.

3.3.5. UHPLC-Q-TOFMS analysis

As mentioned above there are advantages of using LC-MS compared to GC-MS,

with the wider range of metabolites that can be detected being one of the most

central advantages. Due to these advantages LC-MS is a widely used technique

in metabolomics168–170. However, as discussed there are also drawbacks with LC-

MS, mainly associated with more problematic metabolite identification.

In the UHPLC-Q-TOFMS analysis in this work a reverse-phase C18 column was

used and gradient elution was performed with a mix of acetonitrile and

isopropanol as the organic solvent. ESI was used as ionization technique and the

samples were analysed in both positive and negative ionization mode. All

samples were analysed in MS mode and in addition one quality control (QC)

sample was run in auto MS/MS mode to aid in the identification process. For

more details regarding the UHPLC-Q-TOFMS analysis see Paper II.

3.3.6. Additional analytical aspects

An important aspect of metabolite analysis is the construction of the analytical

run order. For example, in an analytical run where all cases are analysed in the

first half of the run and all controls in the second half any true differences in

metabolite concentrations cannot be distinguished from differences due to

28

analytical drift. Therefore, attempts should be made prior to the analysis to

avoid these confounding effects. In this work, since all statistical analysis has

been on cohort level, the main strategy has been to randomize the samples

based on the sample groups of main interest. Furthermore, after the analysis all

putative metabolites showing a strong correlation to the analytical run order has

been excluded as a filtering step prior to the multivariate data analysis.

In the GC-MS and GCxGC-MS analyses an alkane series (from C8-C40) was

included in the run to be able to calculate retention indexes for use in the

identification processes. One of the advantages of GC-MS techniques with EI is

the ability to use comparable retention indexes instead of the more varying

retention times to compare metabolites analysed on different instruments.

During the sample extraction procedure a number of QC samples were created

by pooling aliquots from all or a selection of the samples. For the LC-MS

analysis the QC samples were prepared as a dilution series with five different

concentrations. The QC samples were then analysed in the beginning and end of

each run and also throughout the run (for example after every 10th sample). The

QC samples are important for evaluating the analytical procedure and they

should preferably display only minor variation compared to the biological

samples. In addition to the QC samples, blank samples (usually extraction mix

and water instead of sample) were included as method control all the way from

the metabolite extraction.

3.4. Processing of metabolomics data

The main purpose of the processing of metabolomics data is to convert the data

obtained from the metabolite analysis into a format suitable for data analysis

and evaluation (in this case multivariate modelling). In hyphenated

chromatography/mass spectrometry methods, the chromatogram and mass

spectra for each sample need to be converted into a table containing relative

metabolite concentrations with corresponding pure mass spectral profiles of the

metabolites for all samples. One of the difficulties in processing metabolomics

data is the presence of overlapping or co-eluting peaks in the chromatograms

which can lead to mass spectra with mixed signals. Therefore, a curve resolution

or deconvolution step is needed to resolve the chromatographic profiles for each

individual metabolite. In this work, several data processing methods have been

used, mainly depending on the analytical technique (see Table 3 for overview).

The processing methods will be described and discussed below with main focus

on the processing of GCxGC-TOFMS data since the majority of the data in this

work are acquired using that technique.

29

3.4.1. Processing of GCxGC-TOFMS data

For GCxGC-TOFMS data the addition of the second chromatographic dimension

makes the data more complex and thus the data processing more challenging. In

Paper I, a combination of two software was used for the data processing:

ChromaTOF (from LECO accompanying the GCxGC-TOFMS instrument) and

Guineu167 (VTT, Espoo, Finland, publicly available). Simply put, peaks need to

be detected and deconvoluted to obtain pure chromatographic and mass

spectral profiles. The idea behind deconvolution/curve resolution is to

mathematically separate overlapping chromatographic peaks using differences

in mass spectral profiles155. Even though the resolution is increased in two-

dimensional data compared to one-dimensional data there is still a need for

deconvolution of GCxGC-TOFMS data. For two-dimensional data, several peaks

from the same compound can elute in different second-dimension modulations

and therefore they need to be merged by spectral matching155. The steps

performed in the ChromaTOF software were baseline correction, peak

detection, mass spectra deconvolution, merging of second-dimension peaks,

automated mass spectral library searches for initial identification and

calculation of peak areas. In ChromaTOF all these steps were performed for one

sample at the time and resulted in one file per sample containing peak and mass

spectral information. To be able to use the data for multivariate data analysis

several additional steps were required such as alignment of peaks and matching

of metabolites between samples to allow between-sample comparisons. For this

the Guineu software was used, performing the following steps: alignment

including spectral similarity search, normalization with IS, removal of artefacts

and filtering of peaks detected in few samples. These steps resulted in one file

containing relative metabolite concentrations for all metabolites over all

samples along with mass spectral information. Even though an automated mass

spectra library search was performed in ChromaTOF, the average mass spectra

obtained from Guineu was used for manual investigation for confirmation of

putative metabolite identities (see chapter 3.4.4. Metabolite identification).

In Papers II and III, an in-house developed extension of the hierarchical

multivariate curve resolution (HMCR) methodology was used. The HMCR

methodology was originally developed for one-dimensional GC-MS data171 but

was here applied to two-dimensional data (2D HMCR). Compared to the regular

deconvolution/curve resolution procedure described above the HMCR approach

contains two important additional features, as reflected in the name, the

hierarchical and multivariate features171,172. The multivariate feature allows the

curve resolution to be performed simultaneously for all samples; this gives the

advantage that the same variables (putative metabolites) are found for all

samples. For one putative metabolite there will be a resolved chromatographic

profile for all samples, reflecting the relative metabolite concentration in each

sample, but only one common pure mass spectral profile, that can be used in the

30

identification. Therefore, no matching of metabolites between different samples

is needed. The hierarchical feature allows for curve resolution of complex GC-

MS data by dividing the chromatogram into time windows that are then

resolved separately. In 2D HMCR this was done taking both dimensions into

consideration (setting time windows using boxes). Steps included in the HMCR

processing are background reduction, smoothing of the mass channels,

alignment of samples (by finding the maximum covariance between the

chromatograms), division of the chromatogram into time windows and

multivariate curve resolution of each time window to obtain pure

chromatographic and mass spectral profiles171.

3.4.2. Processing of GC-TOFMS data

The GC-TOFMS data was processed using a targeted approach with a stand-

alone (Matlab) program developed in-house. In this processing an in-house

mass spectral library is used as the target. The fact that only the compounds

searched for can be detected can be considered both an advantage and a

disadvantage, since all compounds will have a putative identity but interesting

compounds, not included in the target library, will be missed. The steps

performed included: alignment (using multiple IS), calculation of RI, peak

detection, mass spectra deconvolution and mass spectra library targeting. After

the processing the detected peaks were investigated and a manual mass spectra

library search was performed for further confirmation of the putative

metabolites.

3.4.3. Processing of UHPLC-Q-TOFMS data

Processing of the UHPLC-Q-TOFMS data was performed using a combination

of the MassHunter software from Agilent. Qualitative Analysis was initially used

for Molecular Feature Extraction using chromatographic deconvolution. For

automatic feature extraction in all samples the DA Reprocessor software was

used. This resulted in one file for each sample containing all detected

compounds including molecular weight, retention time, m/z and abundance.

These files were imported to Mass Profiler Professional (MPP) for compound

alignment and filtering (to remove compounds present in few samples). A so-

called recursion step was then performed by importing the filtered compound

list into Qualitative Analysis and use the Find by Formula feature. The reduced

compound list works as a target list to search for these compounds in the

samples. This two-step process of molecular feature extraction and recursion

gives an improved reliability in the detected peaks. In addition, Quantitative

Analysis was used for quantification of IS. The QC samples were used to exclude

compounds with insufficient correlation to the QC dilution series.

31

3.4.4. Metabolite identification

Identification of metabolites is a crucial but often cumbersome process. To be

able to develop diagnostic tests and to make biological interpretations

metabolite identities are required. Another reason for identifying the

metabolites is to be able to highlight and remove unwanted peaks, such as

analytical artefacts, prior to the multivariate data analysis. As mentioned

previously, metabolites need to be derivatized prior to GC(xGC)-MS analysis.

Thus, there is an important distinction between identification in GC(xGC)-MS

and LC-MS data. In GC(xGC)-MS the derivatized metabolites are detected and

identified and therefore the identity must be converted to the original

metabolite. In LC-MS on the other hand no derivatization is needed and the

original metabolites are therefore detected and identified.

Metabolite identification in both GC-TOFMS and GCxGC-TOFMS usually

includes comparison of mass spectra to available mass spectral libraries, here

in-house and publicly available libraries (from the National Institute of Science

and Technology (NIST) and the Max Planck Institute in Golm) have been used.

For the mass spectral library search NIST MS Search 2.0 software can be used,

where the normalized dot product between the spectra is being searched and the

library spectra hit is calculated to give a match value where 999 corresponds to

an identical spectra. A match value above 800 is generally considered a good

identity. However, it can be difficult to set a general threshold for the match

value. It is important to note that all of the “identified” metabolites in this work

are putatively annotated compounds only, having used retention indexes and

mass spectra library matches in the identification173. Confirmatory identification

requires the analysis of compound standards117. In addition to the putatively

annotated metabolites there are also metabolites that have a putatively

annotated class and metabolites with unknown identity included in this work.

Even though identified metabolites are required to make biological

interpretations it can still be of interest to include unidentified metabolites.

Potential metabolites without proper library matches can be of special interest

in studies of bacterial infections since some metabolites might be of bacterial

origin and can be missing in the libraries. In this work, unidentified metabolites

have been compared between papers/cohorts to be able to find common

unidentified metabolites of interest (see chapter 3.7. Comparison of metabolites

between studies).

Metabolite identification in UHPLC-Q-TOFMS is more challenging than for GC-

MS-based techniques. The soft ionization techniques often used in LC-MS does

not produce any substantial fragmentation; instead the molecular mass can be

obtained. If the mass accuracy is high enough a limited number of hits can be

found in database searches (common databases are METLIN and MassBank)174.

32

Another, or often complementary, strategy is to perform MS/MS analysis to

increase the fragmentation, however these fragmentation patterns are not as

stable as the EI GC-MS spectra and will therefore differ between instruments175.

This adds another complication to the identification process.

3.5. Multivariate data analysis

In metabolomics, the number of measured metabolites is often much larger

than the number of samples. This creates a data table which is complex and

difficult to analyse based on classical statistical assumptions. Therefore, data

analysis methods are needed to be able to extract relevant information from this

type of data tables. Multivariate projection-based methods are commonly used

in metabolomics due to their ability to handle a large number of often correlated

variables that may contain missing values176. The main principle of these

projection methods is to reduce the multi-dimensional data (one dimension for

each variable) to low-dimensional models to facilitate visualization and

interpretation177. These models can aid in the extraction of latent

variables/biomarkers, a combination of several variables (metabolites) that

provides a stronger predictive pattern as compared to single variables. The

multivariate projection methods used in this work will be described in detail

below.

3.5.1. PCA

Principal component analysis (PCA)178 is a multivariate projection method used

to explain the systematic variation among a large number of variables (x

variables) in a lower number of new, independent variables, called principal

components or latent variables. These principal components contain

information about all the measured variables and also take into account the

correlation between the variables. It is therefore possible to find patterns that

would not be detected using classical statistical methods. PCA is an

unsupervised method, which means that no information about class belonging

or similar is given to the model a priori. In metabolomics analysis, a matrix X is

formed based on N rows, where each row represents one sample (for example a

human plasma sample) and K columns, where each column represents a

variable i.e. a metabolite (with the relative metabolite concentrations for each

sample). Principal components are calculated based on this matrix and in this

work the non-iterative partial least squares (NIPALS)179 method has been

applied. The first principal component will describe the largest variation in the

data and the second component, orthogonal to the first, will describe the second

largest variation and so on. For each principal component a score vector, t, is

calculated containing score values for all samples summarizing all original

variables. A corresponding loading vector, p, is also calculated for each principal

33

component containing weight values for each variable highlighting their

individual importance for the principal component.

The PCA model of X can be summarized according to the following equation:

X = TPT + E

where T is the score matrix (combination of the score vectors t), P is the loading

matrix (combination of the loading vectors p) and E is the residual matrix

containing the variation not explained by the model. Preferably, the residual

matrix should contain only unstructured noise.

Interpretation of a PCA model is usually done by plotting the scores and

loadings side by side. The score plot shows the distribution of the samples based

on the original variables where similarities and differences between the samples

can be found. In the loading plot the variables (metabolites) giving rise to the

pattern seen in the score plot can be found. Examples of score and loading plots

can be seen in Figure 6.

Figure 6. Example of a PCA model with data from Paper I comparing S. Typhi infection, S. Paratyphi A infection and afebrile controls. A) Score plot of the first two principal components (t[1] vs t[2]) where each point represents a patient sample and samples close together represents samples with similar metabolite patterns. A separation can be seen between the acute enteric fever samples and the afebrile controls, although not clearly along the first or second component separately. B) Corresponding loading plot of the two first principal components (p[1] and p[2]), contributing to the distribution of the samples seen in A, where each point represents a metabolite and the higher loading value a metabolite has the more it is contributing to the distribution in A. Top 5 enteric fever and afebrile control metabolites highlighted in filled and open blue circles respectively (i.e. metabolites having higher relative concentration in one group compared to the other).

34

3.5.2. OPLS

Multivariate regression methods are suitable when the measured variables

(metabolites) in matrix X can be related to other information in one or more y

variables, so-called response variables. A response variable can be qualitative,

such as information about sample groupings related to disease or patient sex,

but also qualitative, such as patient age or analytical run order. One well-known

multivariate regression method is partial least squares (PLS)180. However, in

this work the extension of PLS, orthogonal PLS (OPLS)181 was the method of

choice. OPLS and PLS are supervised methods since a priori information about

the samples (the response data) is used in the modelling. The OPLS components

are calculated in a somewhat similar way to PCA but instead of maximizing the

variation described in X the components in OPLS should maximize the co-

variation between X and Y at the same time as they should describe the

variation in X and Y individually in the best possible way. In OPLS, compared

to PLS, the X information is divided into two parts, one predictive part with

systemic variation in X that is related to Y and one orthogonal part with

systemic variation in X that is not related (orthogonal) to Y. This division

makes the visualization and interpretation of the models easier, since the

information of interest (the information related to the response) can be

distinguished from other types of systematic variation and thus investigated

separately.

The OPLS model can be summarized according to the following equations for X

and Y respectively:

X = TpPpT + ToP0

T + E

Y = TpCpT + F

where T is the score matrix, P is the loading matrix for X, C is the weight matrix

for Y and the predictive components are denoted with p and the orthogonal

components with o.

3.5.2.1. OPLS-DA

When the response data is qualitative, e.g. case/control information or similar,

classification of the samples can be performed using multivariate discriminant

analysis (DA). In the case of OPLS discriminant analysis (OPLS-DA)182 a so-

called dummy matrix with a binary description of sample class is used as the

response Y. In the OPLS-DA model the measured variables in the X matrix are

correlated to the response variables in the Y dummy matrix to obtain the

maximum separation between the a priori defined sample classes. In a similar

way as for PCA the OPLS-DA models can be interpreted in terms of related

score and loading plots. In this case the covariance loadings w*. Two ways of

35

visualizing scores and covariance loadings are shown in Figure 7. Due to

primary interest in the first predictive components of the OPLS-DA models the

representation in Figure 7C and D has been used in the description of Papers

I-III in chapter 4. Results.

Figure 7. Example of a 2-class OPLS-DA model with data from Paper I comparing S. Typhi infection and afebrile controls with two differing ways of visualizing scores and covariance loadings. A) Cross-validated scores for the first predictive and first orthogonal components (tcv[1] vs tocv[1]) showing the relationships between the samples similar to the PCA scores plot in Figure 6A. B) Corresponding covariance loadings of the first predictive and first orthogonal components (w*[1] and wo[1]) showing the distribution of metabolites similar to the PCA loadings in Figure 6B, here highlighting the top 5 S. Typhi and afebrile control metabolites in filled and open blue circles respectively (i.e. metabolites having higher relative concentration in one group compared to the other). C) Cross-validated scores for the first predictive component (tcv[1]) as an alternative way of visualizing scores when mainly one component is of interest. D) Covariance loadings for the first predictive component (w*[1]) corresponding to the scores in C, also highlighting the top 5 S. Typhi and afebrile control metabolites in filled and open blue circles respectively.

3.5.3. Use of multivariate techniques in the papers

In all papers, PCA has been used for peak investigation during the identification

process, overview of the data, detecting general trends and highlighting possible

outliers. In Paper I, PCA was also used to investigate the distribution of

analytical replicates. OPLS has been used to correlate the metabolites to the

36

analytical run order, to the age of the included patients but also to quantitative

clinical parameters available. The greater part of the multivariate modelling was

however done using OPLS-DA. In Papers I and III, 3-class OPLS-DA models

were used to get an additional overview of the data when having three classes (S.

Typhi, S. Paratyphi A and controls). However, the more detailed modelling has

been done using 2-class OPLS-DA models in all papers. These models have also

been used to highlight significant metabolites as potential biomarker patterns.

All data has been mean centred and scaled to unit variance prior to modelling.

3.6. Validation and interpretation of metabolomics data

Validation is a crucial part of metabolomics research and includes validation of

multivariate models and resulting biomarkers or biomarker patterns. In this

section, a few aspects of validation and interpretation in metabolomics analysis

will be described.

3.6.1. External and internal validation

For validation of multivariate models the different approaches can be divided

into two types, external and internal validation. In external validation additional

samples that were not involved in model calculations are used in the validation

step while the original model data itself is used in internal validation177.

Validation using an external dataset is preferred but not always possible due to

sample limitations. In this work, internal validation has mainly been used.

However, in Paper II a validation dataset was included and resulting

metabolites (potential biomarkers) have been compared across datasets which

will provide a type of external validation.

3.6.2. Cross-validation

Cross-validation (CV)183,184 is a type of internal validation used in multivariate

models to assess the predictive ability of the model and to determine the

suitable number of model components. In CV (here in the case of OPLS/OPLS-

DA models) the data is divided into several groups (here seven) and one group is

excluded and the model of the remaining groups is used to predict the response

values of the samples in the excluded group. These predicted values are then

compared to the samples’ original values where a model with good predictive

ability gives predicted values close to the true response values. This procedure is

repeated until all groups (i.e. all samples) have been excluded and their

response values predicted. The prediction error sum of squares (PRESS) is a

summarized measure of the squared differences in predicted and true response

values for all samples. The PRESS value is then used to calculate the Q2 value

which represents how much of the total variation in the response, Y, the model

can predict. The Q2 value can be used to determine the suitable number of

37

model components. A component can be considered significant if the Q2 value

increases by its addition. Q2 values are reported for all OPLS-DA models in this

work.

Q2 = 1 – PRESS/SS(Y)

The model score values are considered more reliable if the cross-validated

version is used. Thus the majority of the models in this work are visualized as

cross-validated scores. Another use of the cross-validated data is in the

significance testing of the multivariate models, using a CV version of analysis of

variance (CV-ANOVA)185. In CV-ANOVA the residuals (differences between true

and predictive response values) from the cross-validation is used in an F-test to

determine if these residuals are smaller compared to the residuals resulting

from variation around the global response average. A model is considered

significant if the resulting p-value from this test is less than 0.05. CV-ANOVA p-

values are reported for all OPLS-DA models in this work.

3.6.3. Investigation of raw data

Investigation of the raw data is an important step in the validation and

interpretation of the results from metabolomics analyses, especially when

highlighting specific metabolites of interest. An example of this, from Paper I,

is shown in Figure 8. Here the raw data of one metabolite significant in the

three pairwise comparisons of the sample groups in Paper I (patients with S.

Typhi infection, S. Paratyphi A infection and afebrile controls) was investigated

in three samples, one from each sample group (highlighted in Figure 8A).

Since the data was analysed with GCxGC-TOFMS both one- (Figure 8B) and

two-dimensional chromatographic peaks (Figure 8C-E) were investigated and

in both chromatograms a difference in peak area between the three groups

could be seen.

38

Figure 8. Example of raw data investigation from Paper I. The raw data for one metabolite (phenylalanine) that was significant for separating samples from enteric fever patients from afebrile controls, and also for separating S. Typhi infection samples from S. Paratyphi A infection samples was investigated. A) Score plot of a 3-class OPLS-DA model highlighting three samples selected for raw data investigation (enlarged symbols with blue outline). B) One-dimensional chromatographic peak of phenylalanine (mass: 218) with first and second retention times (RT1 & 2) along the x-axis and peak intensities along the y-axis showing differences in peak intensities between the sample groups. C-E) Two-dimensional chromatographic peaks of phenylalanine (mass 218) with first- (RT1) and second-dimension retention times (RT2) along the x- and y-axes respectively and peak areas (with colour intensities) along the z-axis for the S. Typhi sample (C), the S. Paratyphi A sample (D) and for the afebrile control (E) showing differences in peak areas between the sample groups.

3.6.4. Receiver operating characteristic curves

Receiver operating characteristic (ROC) curves is a useful tool for validating

diagnostic tests both clinically and in biomarker discovery research124,186. ROC

curves visualizes the sensitivity and specificity of the investigated diagnostic test

(in metabolomics studies one or several metabolites) for all possible cut-off

values for the classification of interest (discrimination between cases and

controls or similar)124. The sensitivity can be explained as the proportion of true

positive results among all test positive results and the specificity as the

proportion of true negative results among all test negative results. Depending on

the test application there can be different requirements for sensitivity and

specificity. A common way of interpreting ROC curves is to calculate the area

under the curve (AUC), where a value of 1.0 represents a perfect test and a value

of 0.5 represents a test that performs randomly. The AUC value in a ROC curve

has great similarities to the Mann-Whitney U-test187. In this work ROC curves

have been created for OPLS-DA model score values and for individual

metabolites. An example of a ROC curve from Paper I is shown in Figure 9.

39

3.6.5. Significant metabolites

Variable selection is a widely discussed subject within metabolomics analysis

and many different approaches exist, including both multivariate and univariate

methods188–191. Different aspects of univariate and multivariate methods are

reviewed by Saccenti et al. and one main conclusion is that multivariate and

univariate methods can be used complementary to determine metabolite

significance192. One important advantage of multivariate methods is that the

interaction or co-variation of metabolites is taken into consideration. In this

work, the main strategy has been to use multivariate models to highlight

significant metabolites. Univariate p-values have also been calculated but

generally not used in the variable selection. For significance, model covariance

loadings, w*, from 2-class OPLS-DA models have been used. The model

covariance loadings relate the measured variables to the true response (Y). In

Papers I and II, the significance limit of w* was either a fixed value or

calculated from the average and standard deviation for each model. In Paper

III, the recently developed latent significance approach was used (Jonsson et

al., in preparation). In the latent significance approach the unique benefit of

OPLS, the ability to model and subtract orthogonal variation, is utilized also in

the variable selection making this truly multivariate. Latent model covariance

loadings, wlatent, can be calculated by removing the orthogonal variation, found

through OPLS, to focus on the variation relevant to the response. An additional

advantage in the latent significance approach is that statistical limits for wlatent

can be used for determining significant metabolites, something that cannot be

done with the original model covariance loadings. For the univariate tests, the

Student´s t-test and/or the Mann-Whitney U-test have been used depending on

the sample size and distribution (both with a significance limit of 0.05). When

significant metabolites have been highlighted these metabolites have been

Figure 9. Example of ROC curves with data from Paper I. ROC curves for cross-validated scores from OPLS-DA models of S. Paratyphi A compared to afebrile controls based on 46 and 6 metabolites along with the best individual metabolite. False positive rates (i.e. 1-specificity) and true positive rates (i.e. sensitivity) are shown along the x- and y-axes respectively. In general, the closer the curve is to the upper left corner the better. AUC values for the respective curves are, 46 met: 0.999, 6 met: 0.948, top met: 0.884.

40

compared to other metabolites from other models within the same cohort or to

models in other cohorts (see chapter 3.7. Comparison of metabolites between

studies). Further, models with a reduced number of metabolites have mainly

been based on metabolites common between models/cohorts.

3.6.6. Biological interpretation

It is a complex task to provide a relevant biological interpretation of

metabolomics data. Most metabolites are involved in a number of biochemical

processes and it is often difficult to obtain a complete overview. There are many

databases or software available for different types of pathway analysis that can

be used as tools for the biological interpretation. In this work, the focus has not

been on making thorough biological interpretations of the metabolites and their

interactions. Instead the focus has been on showing the potential of the used

methodology and to compare metabolites between different cohorts. Therefore,

pathway analysis has only been performed to a low degree and the metabolites

highlighted as important in the studies have only been briefly investigated.

However, the work and results presented here provides great opportunities for

further, more detailed, biological interpretation of metabolite patterns related to

enteric fever. Such information would be useful in the development of

diagnostic tests but also to provide more insight into the mechanisms of disease.

3.7. Comparison of metabolites between studies

It can be of great value to investigate the robustness of detected metabolites.

One way of doing this is to compare metabolites between similar

studies/cohorts/models. In this work, the interesting metabolites have been

compared both within and between cohorts. For comparing metabolites

between cohorts analysed with GC-TOFMS or GCxGC-TOFMS, the initial step

has been to include the metabolite mass spectra for the previous cohorts in the

library search during the identification process to be able to find matches

between both identified and unidentified metabolites between the cohorts.

Metabolites found significant have then been compared to the metabolites from

cohorts of interest looking at degree of significance and direction of change. All

comparisons made between the studies/cohorts in this thesis are listed in Table

4. Metabolites were also compared within cohorts, for example the cohorts in

Papers I and III including three pairwise comparisons (S. Typhi vs. Control, S.

Paratyphi A vs. Control and S. Typhi vs. S. Paratyphi A). If or when a set of

common metabolites expressing the same alteration in several cohorts have

been detected corresponding OPLS-DA models can be calculated based on these

common metabolites in order to investigate the robustness of the detected

metabolite pattern. Examples of such models can be found in Paper II.

41

Table 4. Comparison of significant metabolites between cohorts.

Paper/cohort Paper/cohort compared to Model compared to

Paper II: Acute typhoid.

Bangladeshi cohort

Paper I: Acute enteric. Nepali cohort S. Typhi vs. Control

Paper II: Acute typhoid.

Bangladeshi + Senegalese

validation cohort

Paper I: Acute enteric. Nepali cohort

Paper II: Acute typhoid. Bangladeshi

cohort

S. Typhi vs. Control

S. Typhi vs. Control

Paper III: Chronic

carriers. Nepali cohort

Paper I: Acute enteric. Nepali cohort S. Typhi+S. Paratyphi A

vs. Control

42

4. Results

4.1. Paper I

Salmonella Typhi and Salmonella Paratyphi A elaborate

distinct systemic metabolite signatures during enteric

fever

There is a need for improved diagnostic tests for enteric fever3. The hypothesis

is that the host-pathogen interactions can be responsible for generating specific

metabolite patterns in the blood of enteric fever patients that potentially could

be used to distinguish the enteric fever patients from patients with other

infections. Salmonella Typhi and Salmonella Paratyphi A are closely related

bacteria that cause a very similar disease in humans. However, due to

differences in epidemiology, antimicrobial susceptibility and ability of

vaccination it would be useful to be able to distinguish between them9,10,83. Due

to their genetic and biochemical differences these two bacteria may potentially

give rise to unique metabolite patterns in the blood making it possible to also

differentiate between the causative agents of enteric fever.

4.1.1. Objective

The objective of this first metabolomics study targeting enteric fever was to

investigate the metabolite patterns in plasma samples from patients in Nepal

with blood culture-confirmed acute S. Typhi infection, acute S. Paratyphi A

infection along with afebrile controls. The study setup and a brief overview of

the methods used are shown in Figure 10.

Figure 10. Overview of study design and methods used in Paper I.

43

4.1.2. Results

OPLS-DA modelling comparing metabolite patterns for acute enteric fever and

afebrile controls revealed a significant separation for both S. Typhi and S.

Paratyphi A from control (Figure 11A and B). A separation, although not as

clear, could also be found comparing the two causative agents of enteric fever, S.

Typhi and S. Paratyphi A (Figure 11C).

Figure 11. Cross-validated scores for the first predictive component (tcv[1]p) in 2-class OPLS-DA models of acute enteric fever and afebrile controls based on 695 metabolites (error bars: mean score value with 95% confidence interval). A) S. Typhi samples significantly separated from afebrile controls (Q2= 0.824, p=4.1*10-20). B) S. Paratyphi A significantly separated from afebrile controls (Q2= 0.815, p=4.2*10-18). C) S. Typhi samples separated from S. Paratyphi A samples (with some overlapping samples) (Q2= 0.140, p=0.062).

The metabolites contributing to the detected separations were investigated for

the three pairwise comparisons and metabolites significant in i) S. Typhi versus

S. Paratyphi A and ii) S. Typhi and/or S. Paratyphi A versus afebrile controls

were highlighted. This comparison resulted in 46 metabolites that could

separate acute enteric fever from afebrile controls but also S. Typhi from S.

Paratyphi A. OPLS-DA models of the three pairwise comparisons based on these

46 metabolites all resulted in significant separations, even for S. Typhi versus S.

Paratyphi A (Q2=0.420, p=2.6*10-6).

To further investigate the diagnostic potential of the obtained metabolite

pattern, ROC curves were constructed using the scores (estimated and cross-

validated) from the OPLS-DA models based on 46 metabolites, along with the

individual metabolites, for each comparison. The ROC curves (Figure 12)

showed that the detected metabolite pattern gave AUC values ≥0.9 in all

comparisons, indicating a high diagnostic potential. The metabolite pattern gave

higher AUC values compared to the individual metabolites (the metabolite with

the highest AUC value for each comparison is shown in Figure 12). Finally, a

combination of 6 putatively identified or classified metabolites (ethanolamine,

gluconic acid, monosaccharide, phenylalanine, pipecolic acid and saccharide)

from the 46 was shown to retain a high diagnostic potential, with significant

OPLS-DA models and AUC values ≥0.8 in all comparisons (Figure 12).

44

Figure 12. ROC curves for cross-validated scores from 2-class OPLS-DA models based on 46 and 6 metabolites along with the best individual metabolite. A) S. Typhi vs. afebrile control, AUC values for CV-scores 46 met: 0.996, CV-scores 6 met: 0.923 and top metabolite (phenylalanine): 0.925. B) S. Paratyphi A vs. afebrile control, AUC values for CV-scores 46 met: 0.999, CV-scores 6 met: 0.948 and top metabolite (phenylalanine): 0.884. C) S. Typhi vs. S. Paratyphi A, AUC values for CV-scores 46 met: 0.898, CV-scores 6 met: 0.796 and top metabolite (2-phenyl-2-hydroxypropanoic acid): 0.693.

4.1.3. Discussion and Comments

The objective of this first metabolomics study, targeting enteric fever, was to

investigate the metabolite patterns related to acute enteric fever to see if

differences could be detected in plasma samples of patients with acute disease

compared to afebrile controls. After analysis with GCxGC-TOFMS and OPLS-

DA significant differences could be detected between enteric fever and the

afebrile controls (for S. Typhi and S. Paratyphi A respectively). Interestingly a

separation, although weaker, could be detected between the S. Typhi and S.

Paratyphi A infections. Decreasing the number of metabolites gave significant

separations between S. Typhi and S. Paratyphi A (in models based on 46 and 6

common metabolites). Although S. Typhi and S. Paratyphi A cause a similar

disease from a clinical perspective there are differences between the serovars

that suggests that it would be valuable to effectively discriminate between them.

There has been reports on differences in antimicrobial susceptibility between S.

Typhi and S. Paratyphi A with higher MICs, and higher likelihood for resistance

reported for S. Paratyphi A9,83. Another important difference between S. Typhi

and S. Paratyphi A is that there is no licenced vaccine against S. Paratyphi A92.

Apart from clinical isolation of the organisms there has been few other ways of

separating S. Typhi from S. Paratyphi A and therefore the results presented in

this study are promising. To further investigate and validate the differences in

metabolite patterns between S. Typhi and S. Paratyphi A additional, larger

studies would be required.

A large number of metabolites were found significant for separating the enteric

fever samples from afebrile controls (for both Salmonella serovars) and a

smaller number of metabolites were significant for separating S. Typhi from S.

Paratyphi A. In order to highlight more Salmonella-specific markers, the

45

metabolites with the ability to distinguish between patients with infections

caused by the two Salmonella serovars were selected initially and among them

the metabolites able to distinguish enteric fever patients from afebrile controls

were included in the final set. These 46 metabolites included metabolites with a

putative identity, a putative metabolite class and also unidentified metabolites.

ROC curves were constructed for the metabolite panels (from OPLS-DA model

scores) in the three pairwise comparisons as well as for the single metabolites.

This showed that the metabolite panels in general had higher diagnostic

potential compared to the single best metabolite, which visualized the strength

of using a combination of correlated metabolites, i.e. a latent biomarker, instead

of individual metabolites. As a final step, a metabolite panel consisting of a

smaller number of metabolites that still had a high diagnostic potential was

searched for among the 46 highlighted metabolites. A panel of 6 metabolites

was found (using only putatively identified or classified metabolites) which

resulted in significant OPLS-DA models as well as ROC curves with AUC values

above 0.8 for the enteric fever samples compared to controls and an AUC value

of 0.8 for the comparison of S. Typhi and S. Paratyphi A.

In future studies it would be of great interest to include other febrile diseases

with clinical representation similar to enteric fever to be able to find biomarkers

more specific to typhoidal Salmonella. A strength in this study is the fact that

the two closely related bacteria S. Typhi and S. Paratyphi A could be

distinguished. This further highlights the potential of the used methodology. In

summary, this study has shown the possibilities of using metabolite patterns to

distinguish between plasma samples from patients with acute enteric fever and

afebrile controls and also for separating S. Typhi from S. Paratyphi A. In

addition, the strength of using a combination of metabolites as a biomarker

pattern compared to single metabolites was highlighted. This proof-of-concept

study for metabolomics targeting enteric fever is a first step towards using

metabolomics in the search for diagnostic biomarkers for enteric fever.

46

4.2. Paper II

Reproducible diagnostic metabolites in plasma from

typhoid fever patients in Asia and Africa

The results from Paper I showed that it was possible to find differences in

metabolic patterns in relation to acute enteric fever. To further explore the

metabolic patterns, a second study was conducted, this time focusing only on

Salmonella Typhi. The current gold standard method of diagnosing enteric fever

is blood culture. However, the sensitivity is varying and often too low (from 40

to 80%52,102). Thus, it would be of interest to investigate how samples from

patients with a negative blood culture but with clinically suspected typhoid

behaves in relation to samples from culture-positive typhoid patients.

4.2.1. Objective

The objectives of the second study was to i) investigate metabolite patterns in

plasma from Bangladeshi patients with culture confirmed acute typhoid,

patients culture-negative but with clinically suspected typhoid and febrile

controls, ii) validate findings from Paper I and iii) investigate the diagnostic

potential in corresponding urine samples. In addition, a separate validation

cohort, including samples from Bangladesh and Senegal with a malaria

subgroup as part of the controls, was investigated. The study setup and a brief

overview of the methods used are given in Figure 13.

Figure 13. Overview of study design and methods used in Paper II.

47

4.2.2. Results

Plasma samples from patients culture-positive for typhoid were significantly

separated from the febrile controls based on their metabolite patterns in an

OPLS-DA model (Figure 14A). Using the model to predict the culture-

negative/clinically suspected typhoid samples resulted in a diverse picture with

five out of nine culture-negative typhoid samples predicted as culture-positive

typhoid and three out of nine as controls (one indifferent) (Figure 14B).

Possibly, indicating which of the culture-negative samples that were true

typhoid samples. Interestingly, three of the five samples predicted as culture-

positive typhoid also had a positive PCR amplification for S. Typhi. Urine

samples were also analysed giving similar results as the plasma samples

regarding the separation of culture-positive S. Typhi from febrile controls,

although with some differences in the prediction of the culture-negative typhoid

samples (see supplementary material for Paper II).

Figure 14. 2-class OPLS-DA model of culture-positive typhoid and febrile controls with prediction of culture-negative/clinically suspected typhoid from the Bangladeshi cohort based on 236 plasma metabolites. A) Cross-validated scores for the first predictive component (tcv[1]p) showing a significant separation of culture-positive S. Typhi from febrile controls (Q2=0.598, p=5.9*10-3). B) Predicted scores for the first predictive component (tPS[1]) showing 5/9 clinically suspected S. Typhi samples clearly resembling culture-positive S. Typhi and 3/9 more resembling febrile controls. Clinically suspected S. Typhi samples with a positive PCR amplification are marked with * inside the symbol.

The metabolites separating the culture-positive typhoid samples from the febrile

controls in the Bangladeshi cohort were compared to the metabolites separating

the S. Typhi samples from the afebrile controls in the Nepali cohort from Paper

I. Significant metabolites with the same direction of change in the two studies

were highlighted. Two criteria for significance were used in Paper II, w*>0.03

and w*> ±SD. For the first criteria, 33 metabolites were highlighted as

consistently regulated between the studies. For the second, stricter criteria 15

metabolites were retained. OPLS-DA models based on these 15 metabolites

resulted in significant separations between the culture-positive typhoid samples

and the febrile controls from the Bangladeshi cohort (Figure 15A) and the

afebrile controls from the Nepali cohort (Figure 15B).

48

Figure 15. Cross-validated scores for the first predictive component (tcv[1]p) in 2-class OPLS-DA models of culture-positive typhoid and febrile/afebrile controls based on 15 plasma metabolites consistently regulated in the Bangladeshi cohort and the Nepali cohort (from Paper I) (error bars: mean score value with 95% confidence interval). A) S. Typhi samples significantly separated from febrile controls in the Bangladeshi cohort (Q2=0.535, p=0.016). B) S. Typhi samples significantly separated from afebrile controls in the Nepali cohort (Q2=0.722, p=4.8*10-16).

In addition to the Bangladeshi cohort, a validation cohort with patient samples

from both Bangladesh and Senegal including culture confirmed S. Typhi

infection and febrile controls with malaria or infections caused by other

pathogens was analysed. The febrile controls were combined into one class and

compared to the S. Typhi samples which resulted in an OPLS-DA model where

the typhoid samples could be significantly separated from the febrile controls,

although with one sample from the other pathogens group clearly overlapping

with the typhoid group (Figure 16A). Since malaria has clinical features often

resembling typhoid fever, it is of great importance to be able to distinguish

patients with typhoid fever from malaria patients. Therefore, these two groups

were compared in a separate OPLS-DA model resulting in a significant

separation of the S. Typhi samples from the malaria samples (Figure 16B).

As a final step, the metabolites significant for separating i) the S. Typhi samples

from febrile controls (including malaria and infections by other pathogens) in

the Bangladeshi/Senegalese validation cohort, ii) the S. Typhi samples from

febrile controls in the primary Bangladeshi cohort and iii) the S. Typhi samples

from afebrile controls in the Nepali cohort from Paper I were compared.

Comparing degree of significance and direction of change for the metabolites

resulted in 24 metabolites being consistently regulated in the

Bangladeshi/Senegalese validation cohort and the primary Bangladeshi cohort

and/or the Nepali cohort. OPLS-DA models of the consistently regulated

metabolites resulted in a significant separation of S. Typhi from the respective

controls samples in the Bangladeshi/Senegalese cohort (with some overlap)

(Figure 17A) and in the Nepali cohort (Figure 17C) and a weaker model in the

primary Bangladeshi cohort (Figure 17B).

49

Figure 16. Cross-validated scores for the first predictive component (tcv[1]p) in 2-class OPLS-DA models of culture-positive typhoid and febrile controls (including malaria and infections from other pathogens) from the Bangladeshi and Senegalese validation cohort based on 104 plasma metabolites (error bars: mean score value with 95% confidence interval). A) S. Typhi samples significantly separated from febrile controls including malaria and infections from other pathogens (with one clearly overlapping other pathogen sample) (Q2=0.404, p=7.2*10-5). B) S. Typhi samples significantly separated from malaria samples (Q2=0.613, p=1.3*10-4).

Figure 17. Cross-validated scores for the first predictive component (tcv[1]p) in 2-class OPLS-DA models of culture-positive typhoid and febrile/afebrile controls based on plasma metabolites consistently regulated in the primary Bangladeshi cohort, the Bangladeshi and Senegalese validation cohort and the Nepali cohort (from Paper I) (error bars: mean score value with 95% confidence interval). A) S. Typhi samples significantly separated from febrile controls including malaria and infections from other pathogens in the Bangladeshi and Senegalese validation cohort (with a few overlapping samples) (Q2=0.404, p=7.2*10-5). B) S. Typhi samples separated from febrile controls in the Bangladeshi cohort (with some overlapping samples) (Q2=0.227, p=0.39). C) S. Typhi samples significantly separated from afebrile controls in the Nepali cohort (Q2=0.598, p=2.5*10-11).


The first objective of this study was to investigate the metabolite patterns of

culture-positive typhoid fever and febrile controls and to see how a group of

culture-negative, but clinically suspected typhoid fever samples, would behave

since this is a challenging group to diagnose. Analysis by GCxGC-TOFMS and

multivariate data analysis resulted in a significant separation of the culture-

positive typhoid samples from the febrile controls. Through prediction, a

heterogeneity in the behaviour of the culture-negative typhoid samples was

found with some culture-negative samples behaving similarly to culture-positive

typhoid and others behaving similarly to controls. Although the true nature of

these samples is difficult to confirm in retrospect the theory of the typhoid-

50

predicted culture-negative samples being true typhoid samples was somewhat

strengthened by a positive PCR amplification of S. Typhi for three of the

samples. Due to the low sensitivity of blood culturing many of the culture-

negative typhoid samples could be true typhoid cases but they could also be

affected by other infections. Studies from Nepal have reported Rickettsia spp. as

a common cause of infection in culture-negative typhoid cases193. Rickettsial

infections were also suspected as the cause of some of the culture-negative

typhoid cases in other Nepali studies where differences in treatment efficacy

between two antimicrobials were detected comparing culture-positive and

culture-negative typhoid cases83,85. In addition, differences in frequencies of

certain clinical features between culture-positive and negative typhoid samples

were also observed83 something that was not clearly seen in the investigation of

the clinical data in this study. The obvious heterogeneity of the culture-negative

typhoid population makes it a difficult but important group to investigate.

The second objective was to validate the findings from Paper I by comparing

metabolites significant for the separation of S. Typhi samples from

febrile/afebrile controls. A large number of consistently regulated metabolites

were found in the two studies. The six highlighted metabolites in Paper I were

not discussed more in comparison to the metabolites in Paper II due to the fact

that these metabolites were selected to separate S. Typhi from S. Paratyphi A as

well as enteric fever from control, thus they were shifted somewhat towards the

Typhi/Paratyphi comparison. Only the metabolites significant in the S. Typhi vs.

control model in Paper I were used in the comparison. In addition to the

consistently regulated metabolites found during the comparison there were also

inconsistently regulated metabolites, i.e. a metabolite could have a higher

relative concentration in the S. Typhi group in one cohort and in the control

group in the other cohort. The presence of inconsistently regulated metabolites

might be related to the difference in the control groups (febrile controls in

Paper II and afebrile in Paper I). Taking this into account there is a strength

in the consistently regulated metabolites since they are significant for separating

S. Typhi samples from controls for two different types of control groups. It

should also be noted that different data processing methods were used in

Papers I and II (see chapter 3.4. Processing of metabolomics data), however

this will probably not have a great impact on the result as a whole but can cause

small changes in individual metabolites.

As a second validation step, a separate validation cohort from Bangladesh and

Senegal was analysed, this time with GC-TOFMS. The cohort included culture-

confirmed S. Typhi samples along with febrile controls, including both malaria

and infections caused by other pathogens. Again, a number of consistently

regulated metabolites were found with the criteria of being significant in the

51

Bangladeshi/Senegalese validation cohort and the primary Bangladeshi cohort

and/or the Nepali cohort. A limitation of the study is the small size of the

cohorts but at the same time there is a strength in verifying metabolite patterns

in several cohorts with different characteristics. In addition, the presence of a

malaria control group in the Bangladeshi/Senegalese validation cohort was used

to investigate the difference in metabolite patterns between typhoid and malaria

samples. It is of importance to investigate the metabolite patterns of enteric

fever in relation to additional febrile illnesses with clinical features resembling

typhoid fever. The S. Typhi samples could be differentiated from the malaria

samples. An interesting observation in the typhoid versus malaria model was

the fact that three of the five typhoid samples from Senegal were overlapping

somewhat into the malaria group (Figure 16B). This overlap could possibly be

indicative of a co-infection of typhoid and malaria however despite further

investigation this could not be verified. See chapter 4.4.2. Comparing enteric

fever and malaria, for more discussion about the metabolites in the

typhoid/malaria comparison.

A third, smaller objective was to investigate the diagnostic potential for typhoid

fever in urine. Therefore, urine samples from the primary Bangladeshi cohort

were analysed with UHPLC-QTOFMS. A significant separation of culture

positive typhoid samples from febrile controls was obtained similar to what was

seen in the plasma samples. Although the prediction of the culture-

negative/clinically suspected samples differed somewhat compared to plasma.

Since the main focus here was on the plasma samples and due to the more

complicated identification process for LC-MS data the metabolites for the urine

samples were not investigated in more detail. However, it would be of interest to

compare the metabolite patterns in plasma and urine in the context of typhoid

fever diagnosis. Urine sampling would be beneficial for use in diagnostic tests in

areas where there may be a negative cultural attitude towards blood sampling.

In summary, this study has i) shown the possibility of using metabolite patterns

to separate patients with culture confirmed typhoid fever from febrile controls

(also including malaria samples), ii) shown a novel approach to investigate

culture-negative but clinically suspected typhoid cases and iii) validated a set of

metabolites across different patient cohorts with the potential of becoming

diagnostic biomarkers for acute enteric fever. Taken together these results

represent a second step on the way towards improving the diagnostic methods

of acute enteric fever using metabolite patterns.

52

4.3. Paper III

Metabolite biomarkers of typhoid chronic carriage

A distinctive feature of S. Typhi and S. Paratyphi A infections is that they can

cause an often asymptomatic chronic infection22,194 following an episode of acute

enteric fever or even with no documented history of acute disease13. A common

site for the colonization of the bacteria during chronic infection is the

gallbladder195 which has been shown to be associated with biofilm formation on

gallstones27. Carriers of the chronic infection can shed bacteria through the

faeces and thereby contribute to the transmission of enteric fever195. Being able

to detect these chronic carriers is therefore of great importance196. Existing

methods for detection of chronic carriers of S. Typhi and S. Paratyphi A

infection suffer from limitations regarding sensitivity, usefulness in endemic

areas and high invasiveness20.

4.3.1. Objective

The objectives of the third study were to i) investigate the metabolite patterns in

plasma samples from patients in Nepal undergoing cholecystectomy with

confirmed gallbladder carriage of either S. Typhi or S. Paratyphi A together with

non-carrier controls and ii) compare the metabolite patterns during chronic

carriage compared to the patterns related to acute enteric fever presented in

Paper I. The study setup and a brief overview of the methods used are given in

Figure 18.

Figure 18. Overview of study design and methods used in Paper III.

53

4.3.2. Results

In the initial OPLS-DA modelling of the plasma metabolite patterns, the two

carrier groups were combined into one and compared to the non-carriage

controls. This resulted in a significant separation of the Salmonella carriage

samples from the non-carriage controls (Figure 19A). The S. Typhi and S.

Paratyphi A carriage samples were then compared to the non-carriage controls

in individual models also resulting in significant discrimination (Figure 19C

and D). Further the two carrier groups were compared to each other indicating

a difference between the S. Typhi and S. Paratyphi A carriage (Figure 19B).

However, this model was weaker as compared to the other models and the small

sample number in the S. Paratyphi A group should be considered.

Figure 19. Cross-validated scores for the first predictive component (tcv[1]p) in 2-class OPLS-DA models of Salmonella carriage and non-carriage controls based on 195 metabolites (error bars: mean score value with 95% confidence interval). A) Salmonella carriage samples significantly separated from non-carriage controls (Q2=0.613, p=2.8*10-6). B) S. Typhi carriage samples separated from S. Paratyphi A carriage samples (with some overlapping samples) (Q2=0321, p=0.070). C) S. Typhi carriage samples significantly separated from non-carriage controls (Q2=0.607, p=3.1*10-5). D) S. Paratyphi A carriage samples significantly separated from non-carriage controls (Q2=0.630, p=3.7*10-4).

Investigating the metabolite distribution in the individual carriage models,

revealed some interesting patterns. In the model comparing S. Typhi carriage

samples and non-carriage controls the distribution of metabolites were

54

somewhat shifted towards the non-carriage controls, meaning that more

metabolites had a higher relative concentration in the non-carrier controls

compared to the S. Typhi carriage samples (Figure 20A). In the model

comparing S. Paratyphi A carriage samples to non-carriage controls a more even

distribution of the metabolites was seen between the two sample groups

(Figure 20B).

Figure 20. Model covariance loadings for the first predictive component (w*[1]) in 2-class OPLS-DA models of Salmonella carriage and non-carriage controls based on 195 metabolites. A) Covariance loadings showing a shift towards the non-carriage controls compared to the S. Typhi carriage samples. B) Covariance loadings showing a more equal distribution between the S. Paratyphi A carriage samples and the non-carriage controls.

A comparison was made between the metabolites significant for separating

Salmonella carriage samples from non-carriage controls to the metabolites

significant for separating the acute enteric fever samples from afebrile controls

in Paper I. The degree of significance and direction of change was compared

and the Venn diagram in Figure 21A summarizes the metabolites and their

alterations in the different comparisons. A number of metabolites were

consistently regulated in carriage and acute infection (with three being

increased in the Salmonella group and seven being increased in the control

group). A larger number of metabolites were inconsistently regulated in carriage

and acute infection (with 16 being increased in the Salmonella group in acute

infection and in the control group in the carriage samples). Most of the

metabolites where significant in only one of the studies. Of those we highlighted

five metabolites that were only significant in the carriage samples and increased

in the Salmonella group (Figure 21B). Two of the metabolites could be

assigned a putative annotation, glutaric acid and hexanoic acid, while the others

were considered unknown. In an OPLS-DA model using only the five

metabolites, the Salmonella carriage samples could be significantly

distinguished from the non-carriage controls although with a few overlapping

samples (Figure 21C).

55

Figure 21. Comparison of metabolites between acute infection and carriage. A) Venn diagram showing consistently regulated metabolites in acute infection (Paper I) and carriage in the overlapping circles, inconsistently regulated metabolites in acute infection and carriage in the overlapping ellipses and metabolites only significant in either study in the reminder part of the circles. The squared box highlights metabolites of particular interest by being significant (2) or present (3) only in the carriage dataset. B) Table of the five highlighted metabolites from the Venn diagram. RI: first-dimension retention indexes, Chronic: T/P indicating an increase in relative concentration in the Salmonella carriage samples compared to the non-carriage controls, Acute: n.s. indicating a non-significant metabolite and “-“ a metabolite not present. C) Cross-validated scores for the first predictive component (tcv[1]p) in 2-class OPLS-DA models showing a significant separation of Salmonella carriage samples from non-carriage controls based on five metabolites (Q2=0.604, p=1.4*10-7). Error bars represent mean score value with 95% confidence interval.


The first objective of this study was to investigate metabolite patterns related to

chronic Salmonella carriage in the gallbladder. Plasma samples analysed with

GCxGC-TOFMS and OPLS-DA revealed a significant separation of Salmonella

carriage samples from non-carriage controls both with the S. Typhi and S.

56

Paratyphi A carriage groups combined and separated. A model indicating a

separation between the S. Typhi and S. Paratyphi A carriage samples was also

obtained, although not as strong as the models comparing carriage samples and

non-carriage controls. A similar pattern was seen in Paper I with significant

separations of acute S. Typhi and S. Paratyphi A infection respectively from

controls and a weaker separation of S. Typhi infection from S. Paratyphi A

infection. However, due to the very small sample size in the S. Paratyphi A

carriage group the results of the models with separate carriage groups should be

interpreted carefully.

Differences in the distribution of metabolites in the separate S. Typhi and S.

Paratyphi A models were detected. More metabolites had a higher relative

concentration in the non-carriage group compared to the S. Typhi group and

this pattern was not seen in the S. Paratyphi A model. A speculation is that this

metabolite shift can be related to a Vi polysaccharide capsule that is present in

S. Typhi bacteria but absent in S. Paratyphi A bacteria. Studies have shown a

relationship between the presence of the Vi capsule and a suppression of the

inflammatory response during infection48,197 something that can be related to

the stealth features of S. Typhi and the ability to cause a chronic carrier state.

The decrease in relative concentration of many metabolites in the S. Typhi

carriage samples compared to the non-carriage controls could possibly be

related to this suppression. Despite this difference between S. Typhi and S.

Paratyphi A, both do cause a similar acute disease and both can establish a

chronic carriage state. The use of the Vi capsule is only one of the possible

mechanisms unique for the typhoidal Salmonella bacteria. It is interesting that

these differences may possibly be reflected in the metabolite patterns.

The second objective of this study was to compare the metabolite patterns

observed during chronic carriage to the patterns related to acute enteric fever in

Paper I. For this, the latent significance method was used for highlighting

significant metabolites in the carrier data as well as re-applied to the data from

Paper I to obtain a reliable comparison. In the latent significance approach

(see chapter 3.6.5. Significant metabolites), the orthogonal variation is removed

which gives more focus on the variation related to the sample groups. Using the

latent significance approach a statistical significance limit can be set on the

latent model covariance loadings. The models with the S. Typhi and S. Paratyphi

A groups combined were used both for chronic carriage and acute infection. The

comparison resulted in a small number of consistently regulated metabolites,

somewhat more inconsistently regulated metabolites and a larger number of

metabolites only significant in acute enteric fever or in chronic carriage. All of

the inconsistently regulated metabolites showed a higher relative concentration

in the Salmonella group during acute enteric fever but a higher relative

concentration in the control group during chronic carriage. Using the individual

57

models for S. Typhi and S. Paratyphi A for the same comparison showed that

the majority of the inconsistently regulated metabolites originated from the S.

Typhi model. In general, there seem to be a reduced level of many metabolites

in the S. Typhi carriage group both in relation to the S. Paratyphi A carriage

group and to the acute S. Typhi infection group. Again, this might be related to

the Vi capsule/inflammatory response suppression discussed above, considering

that a suppressed inflammatory response would be of benefit for S. Typhi

during the “silent” chronic stage. It should be noted that this is merely a

speculation that needs further detailed investigation.

The five metabolites significant during chronic carriage only and increased in

the Salmonella group were of particular interest. These five metabolites

represent possible diagnostic biomarkers for Salmonella carriage and therefore

an OPLS-DA model based only of these five metabolites was calculated.

Although there were some overlap between the sample classes, the model was

able to significantly separate Salmonella carriage samples from non-carriage

controls.

In summary, this study has shown the possibility of detecting differences in

metabolite patterns between patients undergoing cholecystectomy with and

without confirmed Salmonella gallbladder carriage. A small set of metabolites

were highlighted as potential diagnostic biomarkers for Salmonella chronic

carriage that were unique to chronic carriage compared to acute enteric fever.

This small study represents a first step towards improving the diagnostic

methods for detecting chronic Salmonella carriers. Here the study population

consisted of patients with a gallbladder condition and it cannot be ruled out that

this or other underlying factors might affect the detected metabolite patterns.

The condition was however the same for both carriers and non-carriers and

since there is a higher risk for Salmonella carriage with for example

cholecystitis28 there is a value in investigating carriers in this setting. Using a

similar population, it would be of interest to include patients with growth of

other pathogens in their gallbladder for detection of more Salmonella-specific

metabolites. Ideally, asymptomatic carriers should be the target group but then

a larger population would need to be screened for detection of chronic carriers.

It can be debated if it is practically feasible to detect and treat all chronic

carriers, considering the difficulties in screening an asymptomatic population,

the large amounts of antimicrobials needed for treatment and the invasive

nature of gallbladder removal. However, it is clear that the chronic carriers are

an important group to consider since they contribute to disease transmission.

58

4.4. Metabolites and metabolite patterns

A few additional aspects of the metabolite patterns of interest from Papers I-III will be presented here. 4.4.1. Metabolites affected during acute enteric fever

Significant metabolite patterns during acute enteric fever have been found in

both Papers I and II. Here metabolites consistently regulated in at least two of

the cohorts are considered. Among the consistently regulated metabolites, the

majority had a higher relative concentration in the enteric fever group

compared to the controls. For individual metabolite classes a few general trends

could be seen. For carbohydrates/saccharides there was a general decrease in

the enteric fever group, something that might be related to the high-energy

requirements during infection. There was a general increase in the levels of

many fatty acids in the enteric fever group (with stearic acid being increased in

all three cohorts). An increase in fatty acids has also been reported in the studies

of other infections, such as dengue, malaria and sepsis128,129,133. Even though this

increase in fatty acids might be a sign of general infection, the individual

metabolites changed and the levels might differ for different infections. For the

amino acids, most were increased in the enteric fever group (such as

phenylalanine, leucine, isoleucine and valine) while others were decreased

(tryptophan and lysine). Tyrosine was inconsistently regulated. Increased levels

of phenylalanine and valine and decreased levels of tryptophan were also seen in

the studies of other infections. Usually a trend of decreasing amino acid levels is

seen during infection, however the changes in amino acids can differ depending

on the type of infection198. Phenylalanine is known to increase during many

infections and the decrease of tryptophan has been seen previously in enteric

fever198. Examples of other consistently regulated metabolites are glycerol-3-

phosphate, glyceric acid and 1-monostearoylglycerol. There are several

interesting metabolites related to acute enteric fever that would be of interest to

investigate further, together with several unidentified metabolites that might be

of interest for future studies.

4.4.2. Comparing enteric fever and malaria

A significant separation in metabolite patterns between acute enteric fever and

malaria was found in Paper II. There were some similarities in the observed

metabolite pattern compared to the models of acute enteric fever versus control

seen previously (in the cohorts in Papers I and II). As discussed above there

were several metabolites found increased during acute enteric fever that were

also found in studies of other infections, including malaria. A few of these

metabolites were however found increased in the S. Typhi group compared to

malaria. Two examples are phenylalanine and stearic acid that were shown to

increase during malaria in previous studies129,130 but increased in the enteric

59

fever group both compared to controls and malaria alone in this work. A few

metabolites that were decreased in the malaria groups in the studies mentioned

above were also decreased in the malaria group in the model comparing enteric

fever and malaria. One example is alanine. There were also differences in the

metabolite pattern in the enteric fever versus malaria model compared to the

other models of acute enteric fever in this work. Among them were glucose- and

fructose-6-phosphate, that were increased in the enteric fever group compared

to malaria and non-significant in the comparison to the other control subgroup

in the same cohort. The similarities in metabolite patterns between the models

comparing enteric fever to malaria and to the other controls shows that there

are metabolites with the possibility of distinguishing enteric fever from febrile

controls including malaria. The differences in metabolite patterns between

enteric fever and malaria could be of interest for further investigations into

specific differences between the infections.

4.4.3. Comparing acute enteric fever and chronic carriage

In the comparison of metabolites related to acute enteric fever and chronic

carriage, a general trend could be seen for fatty acids. Shorter-chain fatty acids

were either inconsistently regulated or significant during chronic carriage only

while many longer-chain and unsaturated fatty acids where significant during

acute infection only. Among the metabolites consistently regulated in acute

infection and chronic carriage glycerol-3-phosphate showed a relative higher

concentration in the Salmonella group, not only in this comparison but in all

cohorts included in this work. An unidentified metabolite was also consistently

regulated across all studies but had a higher relative concentration in the

control group. Among the metabolites only significant during chronic carriage

the most interesting where the five metabolites mentioned previously, including

glutaric acid, hexanoic acid and three unidentified. A possible connection to

fermentation of the gut microbiome could be made for hexanoic acid and

glutaric acid. There were several metabolites only significant during chronic

carriage but with a higher relative concentration in the non-carriage controls. A

majority of these metabolites were unidentified but among the identified or

classified there were a few monosaccharides. Many of the metabolites found

increased in the Salmonella group during acute infection (in Papers I and II)

were not found significant during chronic carriage (in Paper III). This suggests

that there are differences in metabolite responses during acute enteric fever and

chronic carriage.

60

5. Conclusions and Future perspectives

There is a need for improved diagnostic methods for enteric fever with emphasis

on diagnosing patients with acute enteric fever and for detecting chronic

carriers. Metabolomics has become a popular tool in the search for diagnostic

biomarkers. One underlying hypothesis within metabolomics is that a particular

disease, in this case enteric fever, will give rise to a specific metabolite pattern

that can be detected in biological samples. In this thesis, a mass spectrometry-

based metabolomics approach has been used to investigate the metabolite

patterns in plasma and urine from patients with acute enteric fever along with

chronic Salmonella carriers. The results summarized in this work highlight the

usefulness of applying metabolomics to enteric fever.

In Papers I and II, differences in metabolite patterns between acute enteric

fever and afebrile/febrile controls were detected. The presence of consistently

regulated metabolites between the cohorts suggests that there is a shift in

several metabolites during acute enteric fever that is independent of the patient

cohort. In Paper II, the novel approach to investigate the challenging patient

group of blood culture-negative but clinically suspected enteric fever highlights

the problems related to blood culture diagnosis. In addition, the detected

differences between enteric fever and malaria puts focus on the importance of

including samples from febrile diseases with clinical features similar to enteric

fever in the search for Salmonella-specific metabolite biomarkers.

In Paper III, differences in metabolite patterns between Salmonella carriage

samples and non-carriage controls were detected. The results of comparing

metabolite patterns between Papers I and III suggests that there are more

differences than similarities in the metabolite patterns during acute enteric

fever and chronic carriage. In Papers I and III, differences in metabolite

patterns between the two causative agents of enteric fever, S. Typhi and S.

Paratyphi A, were detected. This could be of interest in relation to the reported

regional differences in antimicrobial susceptibility for the two serovars and the

absence of a vaccine for S. Paratyphi A. The presence of the Vi capsule in S.

Typhi but not in S. Paratyphi A was possibly reflected by differences in the

distribution of the metabolites in the separate S. Typhi and S. Paratyphi A

models compared to controls in Paper III.

A key factor for future studies is to include additional febrile diseases with

clinical representation similar to enteric fever. This would increase the

possibility of finding enteric fever-specific metabolites. In this thesis the main

sample groups have been enteric fever and different types of controls. However,

in Paper II there was a control subgroup of malaria patients, which could be

61

differentiated from enteric fever. In addition, the S. Typhi and S. Paratyphi A

samples could be differentiated, which can be considered a strength of the used

methodology. For the future, larger studies in terms of sample numbers would

be preferred. However, the study design is crucial and a well-designed study can

often afford a smaller sample size. For detection of chronic carriers, a large

asymptomatic population would preferably be screened instead of only focusing

on carriers with a gallbladder condition. This would however be a challenging

task. It would also be of interest to include carriers of other pathogens. In this

thesis, non-targeted metabolomics has been used. Further non-targeted

metabolomics studies of enteric fever could be of interest but it would also be

interesting to use a targeted approach based on the results presented in this

thesis. One specific area could be to investigate the effect of the S. Typhi Vi

capsule.

In summary, the work presented in the thesis has i) highlighted the potential of

using metabolomics in the search for diagnostic biomarker patterns for enteric

fever, focusing both on acute infection and chronic carriers, ii) suggested

interesting metabolite patterns that can be considered as potential diagnostic

biomarkers, although more work is required to investigate and validate these

findings, and iii) provided clues for further investigations of the mechanisms of

enteric fever. In addition, the strength of using a combination of metabolites as

a latent biomarker compared to single biomarkers has been emphasized.

Altogether, this thesis has contributed to the work towards improved diagnostic

methods for different stages of enteric fever.

62

6. Acknowledgements Tack till alla som på något sätt bidragit till att jag kunnat göra den här

avhandlingen. Jag skulle vilja rikta ett speciellt tack till:

Henrik för att du gav mig möjligheten att vara en del av din forskningsgrupp

först genom exjobb och projektarbete och sedan som doktorand. Du har alltid

uppmuntrat mig och trott på mig även om jag inte alltid gjort det själv. Jag har

lärt mig otroligt mycket under de här åren. Anders för att du har varit min

biträdande handledare. Du har lärt mig mycket om infektionssjukdomar och

trots att vi inte alltid har träffats så ofta så har det känts bra att diskutera

projektet med dig och få nya perspektiv på saker. Stephen, thank you for

introducing me to the fascinating world of enteric fever and for letting us

collaborate with you on this interesting project. I am also grateful for your help

in turning our sometimes a bit “boring” manuscript texts into better versions.

Nuvarande och tidigare medlemmar i Henriks grupp: Lina för att vi har följts åt

under en stor del av doktorandtiden, undervisat på många kurser, turistat i

London och framför allt delat kontor under alla år. Anna för att du är en sådan

engagerad person och för att man alltid lär sig något nytt när man pratar med

dig. Vi hade trevligt när vi delade kontor på plan 5. Elin C och Elin T för att ni

har varit bra förebilder. Som Elin nummer tre i gruppen blev jag lill-Elin för att

jag var nyast och även kortast. Pär för hjälp med processering,

modelleringsidéer och när jag försökt göra egna Matlab-skript. Benny för

intressanta diskussioner om GCxGC bland mycket annat när vi delade kontor.

Kristina, min nuvarande rumskompis, för trevliga samtal, även om jag inte

alltid har varit så pratsam mitt uppe i avhandlingsskrivandet. Olena, Magnus

och Tommy för trevliga diskussioner. Also thanks to Ilona, it is nice to have

you at our group meetings and we had a great time sightseeing in SF.

Alla på SMC som hjälpt till på olika sätt: Inga-Britt och Krister som visat allt

från extrahering till hur man kör instrumenten. Jonas och Annika för hjälp

med planering av analyser och med instrumenten. Hans för förklaring av

processeringsmetoder. Tack även till Thomas, Maria, Jenny, Pernilla och

Joakim. Alla nuvarande och tidigare medarbetare i ”kemometrikorridoren”:

Matilda, Frida, Daniel, Tomas, Rickard, David, Izabella, Beatriz och

Johan.

Peter Haglund för möjligheten att analysera prover på GCxGC-instrumentet

och för hjälp vid analyserna. Thanks to Konstantin Kouremenos for

teaching me a lot about GCxGC and for all the time we spend with “Peggy”. Alla

trevliga medarbetare på kemiska institutionen. Ett speciellt tack till

administratörer och tekniker för hjälp med allt från kurser, intyg,

anställningsfrågor och reseräkningar till datorer: Maria, Lars-Göran, Rosita,

63

Sara, Carina, Barbro m.fl. Madeleine Ramstedt och Jan Larsson, i min

referensgrupp, för stöd och visat intresse under alla uppföljningsmöten.

Andra medarbetare: Thanks to the group in Oxford for collaboration on the

typhoid challenge-project (although not included in this thesis), a special thanks

to Claire for the nice dinner in Boston and Helene for nice discussions about

the data. Maria, Hans och Anna på molekylärbiologin för samarbetet med

MRSA/MSSA-studien under mina exjobb. Alicia, Anna och Daniel på klinisk

mikrobiologi för diskussioner om metabolomik och för trevligt sällskap på

konferenser. Thank you to all of the co-authors on the papers. Thank you to

everyone who has read and commented my thesis during the writing process.

Alla som började kemistprogrammet 2007, vi var inte så många men vi hade

trevligt tillsammans: Cecilia för att du har varit min vän sedan första dagen vi

träffades för tio år sedan, för att vi är lika på så många sätt, inte minst att vi

båda kommer från Jämtland och för allt roligt vi har haft under studenttiden,

under doktorandtiden och utanför universitetet. Mari för att du var den första

jag pratade med den första dagen på universitetet och för all den roliga tid som

följde, speciellt när vi bodde i samma studentkorridor. Det är alltid lika roligt att

ses trots att det inte blir så ofta nu. Marcus (mer längre ner), Kalle, Viktor

och John för alla timmar i föreläsningssalar, på labb och på roliga aktiviteter

under studenttiden. Tack även till övriga kemister och TNK:are, speciellt

Jennifer för det roliga första året och för att vi hade det trevligt när vi träffades

igen efter många år, nu tillsammans med våra småttingar.

Vännerna från Strömsund: Marlene för att vi blev bästa vänner i första klass

och har hängt ihop sedan dess, för ditt sällskap under din tid i Umeå och för att

det alltid är lika kul att ses när jag är ”hemma”, nuförtiden blir det full fart med

våra små tjejer. Therese för den trevliga tiden under gymnasiet och alla

pratstunder, nu blir det dock inte lika mycket tid till prat när vi ses på grund av

tre små busfrön. Lina för din positiva energi och för att du skämtat om

Nobelpris (det har i alla fall blivit en avhandling!).

Släkten: Annbritt och Bernt, Madelene, Sture, Ronny, Irene, Catrine

och Johnny med familjer för alla trevliga sammankomster innehållande

mycket kortspel. Irene, Sven-Olov, Simon och Alexander i Finspång för att

ni välkomnat mig till er familj.

Mamma och Pappa för att ni alltid finns där, för ert stöd, för att ni peppar mig

på ert eget sätt (”räkna inte med att det kommer gå så här bra sen”) och för allt

roligt vi har tillsammans. Marcus för ditt stöd, för att du kan ta beslut när jag

velar, för att du har en inställning att allt ordnar sig och för att vi passar så bra

ihop. Matilda, vår lilla solstråle, för att du finns, för att du får oss att skratta

och häpna varje gång du lär dig något nytt.

64

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