Multi-platform metabolomics assays to study the responsiveness of the human plasma and lung lavage metabolome

Masoumeh Karimpour

Doctoral thesis, Department of Chemistry,

Umeå University, 2016

This work is protected by the Swedish Copyright Legislation (Act 1960:729)

ISBN: 978-91-7601-506-3

Cover picture: online word cloud generator (Tagul)

Electronic version available at http://umu.diva-portal.org/

Printed at the KBC Service Centre, Umeå University

Umeå, Sweden, 2016

i

Table of Contents

ABSTRACT ........................................................................................................................................................ III

SAMMANFATTNING (SUMMARY IN SWEDISH) ................................................................................ V

LIST OF ABBREVIATIONS ......................................................................................................................... VII

LIST OF PUBLICATIONS ........................................................................................................................... VIII

BACKGROUND ................................................................................................................................................ 1

THE HISTORY OF METABOLOMICS ............................................................................................................................. 1 DEFINITION OF KEY CONCEPTS .................................................................................................................................. 2 AIM OF THE WORK ..................................................................................................................................................... 3

METHODS ......................................................................................................................................................... 4

MULTI-PLATFORM METABOLOMICS .......................................................................................................................... 4 Non-targeted metabolomics ............................................................................................................................. 4 Targeted metabolomics ..................................................................................................................................... 5 Advantages and disadvantages of different metabolomics assays ........................................................... 5

ANALYTICAL WORKFLOW ........................................................................................................................................... 6 Biofluids, sampling and exposure setup ......................................................................................................... 8 Chromatography techniques coupled to mass spectrometry .................................................................... 11

Gas chromatography-time-of-flight mass spectrometry (GC-TOF-MS) ................................................................. 11 Liquid chromatography-quadrupole time-of-flight mass spectrometry (LC-QTOF-MS) ..................................... 12 Liquid chromatography-tandem mass spectrometry (LC -MS/MS) ....................................................................... 13

Nuclear magnetic resonance (NMR) spectroscopy .................................................................................... 13 DATA ANALYSIS ........................................................................................................................................................ 15

Univariate analysis (UVA) ............................................................................................................................. 15 Multivariate analysis (MVA) ......................................................................................................................... 16

Principal component analysis (PCA) ........................................................................................................................... 16 Orthogonal projections to latent structures-discriminant analysis (OPLS-DA) .................................................... 18

Statistical validation of multivariate models .............................................................................................. 18 Receiver operating characteristic (ROC) curve .......................................................................................... 20

RESULTS AND DISCUSSION .................................................................................................................... 22

A METABOLOMICS PILOT STUDY OF THE POSTPRANDIAL PHASE (PAPER I) ............................................................. 22 Objective .......................................................................................................................................................................... 22 Main findings and discussion of results in Paper I .................................................................................................... 23

AIR POLLUTION EXPOSURE STUDIES (PAPER II, III AND IV) .................................................................................. 24 Paper II .............................................................................................................................................................. 26

Objective .......................................................................................................................................................................... 27 Main findings and discussion of results in Paper II .................................................................................................. 27

Paper III ............................................................................................................................................................ 28 Objective .......................................................................................................................................................................... 29 Main findings and discussion of results in Paper III ................................................................................................. 30

Paper IV ............................................................................................................................................................. 30 Objective .......................................................................................................................................................................... 30 Main findings and discussion of results in Paper IV ................................................................................................. 30

CONCLUSION AND FUTURE PERSPECTIVES .................................................................................... 32

ii

FUTURE PERSPECTIVES ............................................................................................................................................ 33

ACKNOWLEDGMENT .................................................................................................................................. 34

REFERENCES ................................................................................................................................................. 35

iii

Abstract

Metabolomics as a field has been used to track changes and perturbations in the human body by

investigating metabolite profiles indicating the change of metabolite levels over time and in

response to different challenges. In this thesis work, the main focus was on applying

multiplatform-metabolomics to study the human metabolome following exposure to

perturbations, such as diet (in the form of a challenge meal) and exhaust emissions (air pollution

exposure in a controlled setting). The cutting-edge analytical platforms used for this purpose were

nuclear magnetic resonance (NMR), as well as gas chromatography (GC) and liquid

chromatography (LC) coupled to mass spectrometry (MS). Each platform offered unique

characterization features, allowing detection and identification of a specific range of metabolites.

The use of multiplatform-metabolomics was found to enhance the metabolome coverage and to

provide complementary findings that enabled a better understanding of the biochemical processes

reflected by the metabolite profiles. Using non-targeted analysis, a wide range of unknown

metabolites in plasma were identified during the postprandial stage after a well-defined challenge

meal (in Paper I). In addition, a considerable number of metabolites were detected and identified

in lung lavage fluid after biodiesel exhaust exposure compared to filtered air exposure (in Paper

II). In parallel, using targeted analysis, both lung lavage and plasma fatty acid metabolites were

detected and quantified in response to filtered air and biodiesel exhaust exposure (in Paper III

and IV).

Data processing of raw data followed by data analysis, using both univariate and multivariate

methods, enabled changes occurring in metabolites levels to be screened and investigated. For the

initial pilot postprandial study, the aim was to investigate the plasma metabolome response after

a well-defined meal during the postprandial stage for two types of diet. It was found that

independent of the background diet type, levels of metabolites returned to their baseline levels

after three hours. This finding was taken into consideration for the biodiesel exhaust exposures

studies, designed to limit the impact of dietary effects. Both targeted and non-targeted approaches

resulted in important findings. For instance, different metabolite profiles were detected in

bronchial wash (BW) compared to bronchoalveolar lavage (BAL) fluid with mainly NMR and LC-

MS. Furthermore, biodiesel exhaust exposure resulted in different metabolite profiles as observed

by GC-MS, especially in BAL. In addition, fatty acid metabolites in BW, BAL, and plasma were

shown to be responsive to biodiesel exhaust exposure, as measured by a targeted LC-MS/MS

iv

protocol. In summary, the new analytical methods developed to investigate the responsiveness of

the human plasma and lung lavage metabolome proved to be useful in an analytical perspective,

and provided important biological findings. However, further studies are needed to validate these

results.

v

Sammanfattning (Summary in Swedish)

Metabolomik har använts för att spåra förändringar och störningar i kroppens funktioner genom

undersökning av metabolit-profiler. I detta avhandlingasarbete har huvudfokus varit på

tillämpning av flera olika analytiska plattformar för metabolomikstudier av det mänskliga

metabolomet efter exponering för olika kost och avgasutsläpp från biodieselbränsle. De

sofistikerade analytiska plattformarna som användes för detta ändamål var kärnmagnetisk

resonans (NMR), samt gaskromatografi (GC) och vätskekromatografi (LC) kopplat till

masspektrometri (MS). Varje plattform erbjöd unika karakteriseringsmöjligheter med detektion

och identifiering av specifika grupper av metaboliter. Användningen av multipattform-

metabolomik förbättrade täckningen av metabolomet och genererade kompletterande resultat

som möjliggjorde en bättre förståelse av de biokemiska processer som reflekteras av

metabolitprofilerna. Med hjälp av breda analyser har ett stort antal okända metaboliter i plasma

identifierats under den postprandial fasen efter en väldefinerad måltid (i Paper I). Dessutom har

ett stort antal metaboliter påvisats och identifierats i lungsköljvätska efter exponering av

biodieselavgaser jämfört med kontollexponering med filtrerad luft (i Paper II). Parallellt med

dessa breda analyser har också riktade analyser genomförts av både lungsköljvätska och plasma.

Därigenom har bioaktiva lipider detekterats och kvantifieras efter avgasexponering och

resultaten har jämförts med filtrerad luft som kontrollexponering (Paper III och IV).

Processning av rådata följt av dataanalys, med både univariata och multivariata metoder

möjliggjorde screening och fördjupad undersökning av förändringen i metabolitnivåer. I den

första pilotstudien av postprandiala nivåer var syftet att undersöka responsen i

plasmametabolomet efter en väldefinierad måltid under den postprandiala fasen vid två olika

typer av kost. Resultaten visade att oberoende av kosten, så återvände metabolitnivåerna till sina

baslinjenivåer tre timmar efter måltiden. Detta togs i beaktande vid exponeringsstudierna för

biodieselavgaser, som designades så att dietens inverkan minimerades. Både breda och riktade

analyser resulterade i viktiga resultat. Exempelvis så detekterades olika metabolitprofiler i

bronkiell sköljvätska (BW) jämfört med bronkoalveolär sköljvätska (BAL), speciellt med NMR och

LC-MS. Dessutom resulterade avgasexponering i förändrade metabolitprofiler, observerade med

GC-MS, särskilt i BAL. Dessutom uppvisade fettsyrametaboliter i BW, BAL och plasma

förändrade halter efter avgasexponering, uppmätt genom en riktad LC-MS/MS-analys.

Sammanfattningsvis så visade sig de nya metoderna som utvecklats för att undersöka

vi

förändringar i metabolithalterna i plasma och lungsköljvätska fungera väl ur ett analytiskt

perspektiv och resulterade i viktiga biologiska fynd. Fördjupade studier behövs dock för att

validera resultaten.

vii

List of Abbreviations

AUC Area Under the Curve

CV-ANOVA Cross Validation- ANalysis Of VAriance

EDTA Ethylene Diamine Tetraacetic Acid

GC-MS Gas Chromatography-Mass Spectrometry

LC-MS Liquid Chromatography-Mass Spectrometry

MVA MultiVariate Analysis

NMR Nucleic Magnetic Resonance

OPLS Orthogonal Projections to Latent Structures

OPLS-DA Orthogonal Projections to Latent Structures - Discriminant Analysis

PCA Principal Component Analysis

RME Rapeseed Methyl Ester

ROC Receiver Operating Characteristic

RSD Relative Standard Deviations

SMC Swedish Metabolomics Centre

SUS Shared and Unique Structure

TOF Time Of Flight

UVA UniVariate Analysis

UPSC Umeå Plant Science Centre

viii

List of publications This thesis is based on the following papers, which will be referred to by Roman numerals.

I. Karimpour M, Surowiec I, Wu J, Gouveia-Figueira S, Pinto R, Trygg J, Zivkovic A.L, L.

Nording M.L. Postprandial metabolomics: a pilot mass spectrometry and NMR study of

the human plasma metabolome in response to a challenge meal. Analytica Chimica Acta.

2016 Feb; 908: 121-131.

II. Surowiec I*, Karimpour M*, Gouveia-Figueira S, Wu J, Unosson J, Bosson J.A,

Blomberg A, Pourazar J, Sandström T, Behndig A.F, Trygg J, Nording M.L. Multi-platform

metabolomics assays for human lung lavage fluids in an air pollution exposure study. Anal

Bioanal Chem. 2016 Apr 25. [Epub ahead of print]

III. Gouveia-Figueira S, Karimpour M, Bosson J.A, Blomberg A, Unosson J, Pourazar J,

Sandström T, Behndig A.F, Nording M.L. Mass spectrometry profiling of oxylipins,

endocannabinoids and N-acylethanolamines in human lung lavage fluids reveal

responsiveness of prostaglandin E2 and associated lipid metabolites to biodiesel exhaust

exposure. (Submitted manuscript)

IV. Gouveia-Figueira S, Karimpour M, Bosson J.A, Pourazar J, Blomberg A, Unosson J,

Sandström T, Behndig A.F, Nording M.L. Effect of controlled exposure to biodiesel exhaust

on human plasma bioactive lipid profiles. (Manuscript)

*these authors contributed equally to this work

The published papers have been reprinted with the kind permission from the original

publishers.

1

“The body and its parts are in a continuous state of dissolution and nourishment, so they are

inevitably undergoing permanent change”

Ibn al-Nafis (1213–1288)

Background

The word metabolism originates from the Greek word “μεταβολισμός” (metavolismós), which

means “change”. Input from the environment and lifestyle can affect human metabolic pathways.

Hence, the study of metabolism and its constituents, the metabolites, is a powerful way to elucidate

factors behind the influence of the environment on humans [1]. Furthermore, integrative analysis

of metabolites plays an important role in describing the biochemical and biological mechanisms

in any complex regulatory system [1-3]. Thus, metabolomics is an emerging field of “omics”

science aimed at comprehensive detection and identification of the enormous metabolite content

in biological samples by using advanced analytical techniques and data analysis methods. Thereby,

metabolomics is an important tool for the identification of disease biomarkers, as well as drug

discovery, in addition to the elucidation of environmental influences.

The history of metabolomics

The first study to investigate a number of metabolites simultaneously was published in 1966 by

Dalgliesh et al., who applied gas chromatography (GC) with flame ionization detection (FID) for

the separation of a wide range of metabolites [4]. Mamer and Crawhall [5] and Horning and

Horning [6] performed the first mass spectrometry (MS) based metabolomics experiments. These

studies represented the beginning of the metabolomics development process, but the name

“metabolomics” had not yet been conceived. In 1971, Horning and Horning used GC-MS for

metabolite profiling and identification of metabolites in human samples [6]. In the same year,

Pauling et al. published a quantitative and qualitative analysis of human breath, which resulted in

a rich dataset of metabolites that were integrated by means of a computer program [7]. Although,

the latter study was not labeled as a metabolomics study at the time, this paper is generally

considered to be one of the first to give birth to the concept of metabolomics.

2

Metabonomics was introduced as a scientific field in 1999 by Nicholson et al. [3], and shortly after,

the term “metabolomics” was coined by Fiehn and colleagues to refer to investigations of the

metabolome [8]. Today the term “metabonomics” is widely used to describe multiple metabolic

changes caused by a biological perturbation. Metabolomics has a similar definition. However, it

places a greater emphasis on comprehensive metabolic profiling of various species [3, 8-10].

Definition of key concepts

The metabolome comprises a large number of small molecular weight compounds, such as lipids,

amino acids, nucleotides, organic acids, etc. Metabolome components span a diverse range of

compounds with different properties. For example, within lipids alone, there are high abundance

compounds, such as fatty acids, triglycerides or phospholipids, but other compounds with lower

abundance, such as eicosanoids derived from arachidonic acid, also have significant regulatory

effects [11, 12]. Owing to the large diversity of physicochemical properties and abundance levels of

metabolites, metabolomics requires a broad range of instrumentation and special protocols and

techniques for sample preparation, separation and detection.

Analytical techniques have been developed for metabolomics analysis of endogenous and

exogenous metabolites in many types of biological samples, such as human plasma, saliva, serum,

urine and lung lavage fluids, enabling identification of key metabolites and mechanisms behind

different pathologies [4, 13-20]. For instance, lung lavage analysis was successfully performed to

investigate lung inflammation in air pollution studies and pulmonary diseases, such as cystic

fibrosis, asthma, and respiratory distress syndrome [21-26]. Both non-targeted and targeted

analytical protocols have been used extensively under names such as untargeted metabolomics,

global metabolomics, metabolic fingerprinting, lipidomics, targeted metabolomics, MRM

metabolomics, etc., as reviewed by Wishart [1], Griffiths [10] and Lu [27].

3

Aim of the work

In this thesis work, the general goal was to study the human metabolome in plasma and lung

lavage fluid following different interventions, such as a challenge meal or exposure to air

pollutants (biodiesel exhaust). We hypothesized that applying multi-platform metabolomics

would enable detection and identification of a wide swath of metabolites and that targeted/non-

targeted analysis of the human metabolome during the postprandial stage and/or post exposure

would deepen the knowledge on systemic and pulmonary responses in these situations.

The specific aims were as follows:

I. To investigate the human plasma metabolome during the postprandial stage.

II. To evaluate human lung lavage metabolite profiles after filtered air and biodiesel exhaust

exposure.

III. To investigate bioactive lipid (oxylipins, endocannabinoids and N-acylethanolamines)

responses in human lung lavage fluid after exposure to filtered air and biodiesel exhaust.

IV. To explore bioactive lipid profiles in human plasma at different time points pre- and post-

exposure to filtered air and biodiesel exhaust.

4

Methods

This chapter presents and explains the methods used throughout this thesis work. The focus is on

describing all of the metabolomics steps used in the described studies. In addition, examples will

be given from Papers I-IV.

Multi-platform metabolomics

At present, there is no single-instrument platform that can fully analyze all metabolites in a given

sample. Both MS and nuclear magnetic resonance (NMR) are advanced techniques suitable for

metabolomics analysis. However, they have different analytical advantages and disadvantages. To

date, different protocols and methods have been introduced in metabolomics and it has been

shown that depending on the specific aims and hypotheses, different platforms can be employed.

Each platform has specific characterization features, which enables analysis of a particular range

of metabolites. Therefore, applying several platforms has the potential for obtaining more

comprehensive metabolite coverage [9, 28-31].

Non-targeted metabolomics

Non-targeted metabolomics, or global metabolome analysis, is a powerful approach that aims to

discover and identify a wide range of both unknown and known metabolites in a biological sample

[32]. Therefore, non-targeted metabolomics can allow discovery of novel metabolite species and

analysis of pathways and metabolism in biological and complex systems. The most used platforms

in non-targeted metabolomics are NMR, GC-MS and liquid chromatography-MS (LC-MS).

However, wide-range metabolomics analysis of samples is often challenging owing to the huge

number of compounds present from different chemical classes and at different concentrations.

Consequently, platforms such as quadrupole time-of-flight-MS (QTOF-MS) could be useful for

detecting metabolites of different chemical classes [20, 33-36]. In non-targeted metabolomics,

determination is achieved by using a semi-quantitative or quantitative concentration measure for

each metabolite. Identification of a metabolite, in addition to de novo structure elucidation, entails

the integrated analysis of exact mass, MS/MS fragmentation patterns and searches in libraries and

databases to find the right structure [10, 14].

5

Targeted metabolomics

Targeted metabolomics aims to measure a set of specific and predefined metabolites. Knowledge

of metabolites and their biochemical pathways plays a pivotal role in the discovery of novel

biomarkers [37, 38]. Based on the aims and hypotheses, special instruments can be selected for

analysis and specific analytical methods have been developed. LC-MS-based targeted

metabolomics analysis is performed using a triple quadrupole (QqQ)-MS, which enables multiple

reaction monitoring (MRM) of specific product ions and their unique one or two fragment ions to

achieve a high sensitivity and selectivity of detection [37, 39, 40]. To date, MRM targeted analyses

have been developed and optimized for a set of desired metabolites to enable analysis of low

abundant metabolites and provide a high sensitivity and wide dynamic range [12, 13, 41]. The

acquired data from targeted metabolomics is quantitative and can be used for biomarker discovery

and validation, and for constructing pathway maps [10]. MRM targeted analysis has some

limitations, e.g., it only measures known metabolites [37]. However, MRM-targeted analysis has

been used for the quantitative analysis of analytes such as oxylipins and endocanabinoids in

human plasma and lung lavage fluid [13, 21, 41, 42].

Advantages and disadvantages of different metabolomics assays

NMR has an important role in metabolomics owing to its easy and rapid sample preparation, non-

destructiveness, no need for chromatographic separation and high degree of reproducibility [18,

22]. NMR based metabolomics has been extensively used in drug toxicity studies [13],

environmental assessment [14], pharmacological drug discovery [15, 16] and pulmonary and

nutrition research [24, 29, 43]. However, it has lower sensitivity and resolution compared to MS-

based techniques.

MS-based metabolomics platforms combined with GC or LC separation methods have been

frequently used in metabolomics [10, 15, 20, 44-47] because of their higher sensitivity to a wide

range of compounds compared with other detection techniques, wide metabolome coverage and

possibility for metabolite identification. In metabolite profiling, LC-MS has several advantages

over GC-MS, e.g., no need for sample derivatization and capabilities for analyzing more polar and

higher molecular weight compounds. In contrast, compound deconvolution and compound

identification are more challenging in LC-MS analysis. GC-MS electron impact libraries are

standardized and comparable between different instruments, and retention indices are to some

6

extent comparable between different laboratories. In contrast, in LC, retention times depend on

the column used and MS/MS fragmentation spectra can differ between different instruments.

Hence, LC-MS libraries are best acquired on the same instrument as used for the metabolomics

analysis.

Analytical workflow

A typical metabolomics workflow is shown in Figure 1. It starts with sampling (A) and sample

preparation (B), continues with metabolite separation and detection (C) and data processing (D),

and ends with data analysis (E).

7

*gas chromatography-time of flight-mass spectrometry

**liquid chromatography-tandem mass spectrometry ***nuclear magnetic resonance

Figure 1. General overview of the metabolomics workflow.

(E) Data analysis

Univariate analysis Multivariate analysis Validation

(D) Data processing

Quantification Identificaion Normalization

(c) Metabolite separation and detection

Targeted metabolomics

(LC-MS/MS**)

Non-taregetd metabolomics

(GC-TOF-MS, NMR***, LC-TOF-MS)

(B) Sample preparation

Methanol/water extraction Solid phase extraction Derivitization ( for GC-TOF-MS*)

(A) Sampling

8

As mentioned above, the most widely used techniques in metabolomics are based on XC-MS,

where X is a chromatographic separation method, or NMR [9, 45, 48]. The studies in this thesis

are also based on these types of assays, as discussed in more detail below.

Biofluids, sampling and exposure setup

Almost 60 percent of the human body is made up of water, circulating between different body

compartments and units. The circulation plays a vital role in human life in delivering nutrients to

all cells in the body. Most human metabolomics measurements to date have been carried out on

biofluids because they contain physiological information due to their circulation. Plasma, serum,

saliva, urine, cerebrospinal fluid (CSF) and respiratory tract lining fluid (RTLF) are some

examples of human biofluids. Blood is a special biofluid owing to its central circulation, which

means it reflects processes taking place in organs [48].

Plasma is an extracellular matrix of blood cells that consists of dissolved proteins, clotting factors,

hormones, electrolytes, carbon dioxide, etc. Plasma is often collected by venipuncture into

standard vials containing anti-coagulant, e.g., ethylene diamine tetra acetate (EDTA), heparin or

citrate. Thus, extra resonances may be observed in NMR spectra due to the formation of complexes

between the anti-coagulants, such as EDTA, and ions, such as Ca2+ and Mg2+, in plasma [49].

Plasma was used in Paper I to investigate the postprandial response after a defined meal by

means of multiplatform metabolomics, and in Paper IV to explore the response of bioactive lipids

following exposure to biodiesel exhaust (rapeseed methyl ester (RME)-biodiesel) compared to

filtered air.

RME-biodiesel exhaust was generated from an idling diesel engine. More than 90% of the exhaust

gas was shunted away, and the remainder was diluted with filtered air at a temperature of 20°C

(relative humidity 50%) before being fed into a whole-body exposure chamber (3.0 x 3.0 x 2.4 m).

The chamber was monitored continuously for pollutants. Figure 2 shows the exposure chamber

setup used in the studies in Paper II-IV. A detailed description of the exposure facility and

bronchoscopy sampling procedure used is given in Paper II-IV.

In Paper II and III, airway responses following exposure to RME-biodiesel exhaust and filtered

air were assessed. Subjects were exposed to RME-biodiesel exhaust and filtered air in a

randomized fashion, at least three weeks apart. During exposure, the subjects alternated between

15 minutes of exercise on a bicycle ergometer (ventilation of 20 L/m2 of body surface) and 15

9

minutes of rest. Bronchoscopy was performed six hours after each exposure. Bronchial wash (BW)

(2 × 20 mL) and bronchoalveolar lavage (BAL) (3×60 mL) with sterile sodium chloride were

extracted in either the middle lobe or lingual lobes. The aspirates recovered were collected on ice

and filtered and centrifuged. The resulting supernatants and cell pellets were separated and the

supernatant was used for metabolomics analysis. Bronchoscopy is a safe and widely used routine

procedure for the diagnosis of many pulmonary diseases, as well as for research to evaluate airway

responses following exposure to, e.g., air pollutants [50, 51]. The characterization of the

metabolome within this compartment provides a novel opportunity for identifying biomarkers and

pathways in order to, e.g., investigate adverse health effects of air pollution in the human

respiratory tract.

Figure 2. Exposure chamber facility (A) and the engine (B) at SMP Svensk Maskinprovning AB, Umeå,

Sweden. Photos by Karimpour, June, 2013.

A)

B)

10

The spatial resolution of sampling for different lung compartments during metabolite profiling

can be improved by using both BW and BAL samples: the contents of BW fluid reflect the

metabolite profile of the central airways, whereas BAL fluid reflects the metabolite profile of the

lungs’ more peripheral regions. This dual approach has previously been applied to investigate

compartment-specific fatty acid metabolite (oxylipin) profiles [51]. BAL and BW samples were

used in Paper II and III to investigate the effects of biodiesel exposure on the lung lavage

metabolite profile and compare them to results obtained after filtered air exposure. Figure 3

shows the procedure used for collecting the BAL and BW fluids.

Figure 3. Procedure for collecting bronchoalveolar lavage (BAL) and bronchial wash (BW) fluids using a

flexible video bronchoscope. Photo by Ester Roos-Engstrand, 2006.

11

Chromatography techniques coupled to mass spectrometry

Chromatography techniques coupled to MS are powerful approaches whereby compounds are first

separated based on their chemical properties by chromatography and then eluted compounds can

be identified based on their mass spectra and quantified based on peak intensities. Depending on

the instrument setup, a wide and diverse range of compounds can be reliably detected and

analyzed.

Gas chromatography-time-of-flight mass spectrometry (GC-TOF-MS)

GC-TOF-MS is a common technique for generating metabolomics data. For GC-TOF-MS analysis,

pre-treatment of samples (i.e., extraction and derivatization) is required to isolate metabolites and,

if needed, transfer them into volatile derivatives [20]. Furthermore, protein removal from plasma

samples is needed prior to analysis. Vaporization of the samples is carried out before injection to

the GC column. A carrier gas (helium) is used as a mobile phase to transport the sample

components through the column containing the stationary phase. The molecules interact with the

stationary phase during their passage and are separated based on their strength of interaction with

the column, which is dependent on the chemico-physical properties of the molecule and type of

stationary phase. The time for the molecules to pass through the column will be different for each

compound, resulting in separation of the compounds. GC ionization is usually carried out using a

hard ionization technique, known as electron impact (EI), in which analytes are ionized and

fragmented through a bombarding electron stream, generating radical cation species

characteristic of the ionized compound. In TOF-MS, generated ions are accelerated in an electric

field and vacuum, reaching the detector at different times dependent on the m/z ratio of the ion.

[52]. To identify specific molecules, e.g., metabolites in plasma, mass spectra as well as

information about the compound’s retention index are needed [53].

GC-TOF-MS analysis utilizes three dimensional data, including the intensity, spectral, and

chromatographic dimensions. A multivariate deconvolution method is applied to translate the

three dimensions into a two dimensional data table, i.e., integrated area under the resolved

chromatographic profile. In the work presented in this thesis, sample files from GC-TOF-MS

analysis were exported to MATLAB 8.1 (R20013a) (Mathworks, Natick, MA, USA) in NetCDF

format. Matlab-based scripts were successfully customized to accomplish alignment of

12

chromatograms, peak detection and identification based on maximum covariance between the

samples’ total ion current chromatograms, retention indices and full MS spectra from the in-house

mass spectra library established by the Umeå Plant Science Centre (UPSC) and Swedish

Metabolomics Centre (SMC) in Umeå, Sweden. The advantage of this method is that it allows for

the possibility of deconvoluting many samples in a short time, as well as subsequent identification

of important metabolites. Data were normalized in Excel by dividing analyte peak areas by the

peak area of the internal standard [30, 54].

Liquid chromatography-quadrupole time-of-flight mass spectrometry (LC-QTOF-MS)

In LC, analytes are injected into a column and then separated due to different interactions with

the stationary and mobile phases [55]. Eluted analytes are ionized by an electrospray ionization

(ESI) technique in an ionization source. It should be noted that ESI is one of many ionization

techniques that have been used in LC-MS studies [56]. ESI is a soft ionization technique, which

means that, contrary to EI ionization used in GC, analytes are not fragmented in the ion source.

Identification of metabolites is achieved by accurate mass measurement of their ionized molecular

ions and their subsequent fragmentation to obtain fragment ion patterns characteristic for each

molecule.

LC-QTOF-MS has been used for characterization, identification and quantification of small

molecules in complex biological samples and offers several advantages, such as higher sensitivity

for many compounds and improved resolution compared with other detection methods [38]. LC

is often coupled to QTOF-MS to deliver a wide dynamic range for non-targeted analysis, and ESI

is the most commonly used ionization method in LC-MS analysis [57]. In addition, tandem mass

spectrometry (MS/MS) offers improved selectivity of detection and possibility for identification of

unknown metabolites. Prior to LC-MS analysis, pre-treatment of samples (i.e., protein

precipitation in plasma samples) is needed. In specific applications, the sample complexity can be

reduced by customizing the extraction or matrix removal steps to enhance metabolite detection,

increasing the signal-to-noise ratio in the LC-MS data, as well as decreasing ion-suppression

effects.

13

Liquid chromatography-tandem mass spectrometry (LC -MS/MS)

Sample preparation for targeted and quantitative LC-MS/MS usually begins with solid phase

extraction (SPE), followed by evaporation of eluates and reconstitution with a solvent [41].

For quantitative analysis of small molecules and metabolites by LC-MS/MS, triple quadrupole

(QqQ) mass spectrometry (MS/MS) in MRM mode is usually used owing to its good sensitivity,

reproducibility and broad dynamic range [27, 45]. In QqQ, quadrupoles are arranged in series.

The first quadrupole selects the parent ion of interest to be further fragmented in a second

quadrupole that works as a collision cell. Selected product ions are then analyzed in the third

quadrupole. For analysis of multiple metabolites, this process is cyclically repeated for each

compound. Therefore, specificity and signal-to-noise ratios are increased.

Nuclear magnetic resonance (NMR) spectroscopy

NMR spectroscopy is one of the versatile technologies that can be used for biological samples.

NMR has been significantly advanced in metabolomics through developments in sample

preparation, spectral processing and multivariate analysis. In this thesis work, the focus was on

the application of 1H NMR spectroscopy to profile metabolite levels in human samples, i.e., plasma

and lung lavage [49].

Because both low and high molecular weight components may be present in some biofluids, e.g.,

plasma, 1H NMR spectra may have a wide range of signal line widths. Larger molecules, e.g.,

proteins and lipoproteins, contribute to broad bands, which are often overlaid by sharp peaks due

to smaller molecules. Therefore, microcentrifuge filters were used to remove proteins and

insoluble impurities in the plasma samples. A deuterated sodium salt of 3-trimethylsilylpropionic

acid (TSP) was used as a reference compound in the studies of Paper I and II. Water in all

biofluids has to be removed by appropriate standard NMR solvent suppression methods in order

to eliminate its large interference in the signal. Various techniques can be applied for monitoring

as well as quantification of different groups of metabolites in a given sample. For processing 1H

NMR spectra, spectral editing techniques, such as phase and baseline correction, as well as

interrogation of spectral databases followed by a data reduction step are crucial. NMR data

processing might involve normalization by the sum of the integrals of each spectrum. Each spectral

14

integral is helpful as a metabolic descriptor for determining similarities or differences in the

acquired data. Biomarkers can be identified in plasma and lung lavage fluid based on their

chemical shifts, signal multiplicities and effect of adding authentic material.

15

Data analysis

Metabolomics studies generate complex multivariate datasets. Therefore, in addition to univariate

analysis, chemometric and bioinformatic tools are required for data analysis, visualization and

interpretation [58]. Hence, the statistical analysis may consist of univariate analysis [59] and

several multivariate data analysis (MVA) methods, such as principal components analysis (PCA)

[60], partial least squares-discriminant analysis (PLS-DA) and orthogonal projections to latent

structures (OPLS) [61]. Chemometrics, is an interdisciplinary science incorporating tools from

chemistry, statistics and mathematics fields, that has been acknowledged and established in the

field of metabolomics [32, 62].

Univariate analysis (UVA)

A variety of statistical tests are used to show whether metabolites are at significantly different

levels between different studied groups. Student’s t-test is a parametric test for normally

distributed data. However, for non-normally distributed datasets, as typical for clinical

metabolomics studies, the Wilcoxon’s test is used [57]. In t-tests, each metabolite is investigated

separately to determine whether the two groups have significantly different mean values. The null

hypothesis for the test is:

H0: μgroup1 = μgroup2

If the test p-value is smaller than a cutoff value, usually 0.05, the null hypothesis is rejected. In

contrast, if the p-value is larger than the cutoff value, the null hypothesis is approved, meaning

that the mean values of the two groups are not significantly different and the investigated

metabolite is not able to differentiate them. For an individual metabolite t-test, a 0.05 cutoff value

is normally used. However, for evaluating a set of metabolites, a smaller cutoff value is

recommended because of the increased risk of false positive findings when multiple tests are

performed. Multiple comparison correction procedures can be used to control for the t-test errors.

Simple multiple comparison procedures, such as Bonferroni correction, do not work well for

datasets with a very large number of metabolites; the set cutoff value is too close to zero as the

number of tests increases and becomes a too conservative approach to be of practical relevance

(with higher risk of reporting false negative results) [63]. A simple 0.05 cutoff can be used if

16

metabolites with small p-values are further evaluated by building a discrimination model and t-

tests alone do not give conclusive results [64].

Multivariate analysis (MVA)

Multivariate analysis (MVA) methods are crucial in metabolomics studies because all metabolites

are considered at the same time, allowing trends to be detected between both samples and

metabolites, as well as within samples and metabolites. MVA is performed to investigate

metabolite patterns and their ability for classifying the dataset [57].

Principal component analysis (PCA)

Principal component analysis (PCA) [60] is an unsupervised projection method commonly used

in chemometrics to reduce multidimensional data complexity in order to visualize and interpret

relations between samples and between studied variables (e.g. levels of metabolites). This method

summarizes the variation within a dataset by a smaller number of variables, called principal

components (PCs). PCs are linearly weighted combinations of the original variables calculated in

such a way that each PC consecutively models the maximum variation in the data, and at the same

time is orthogonal to the other PCs. This summary into PCs results in a bi-linear decomposition,

represented by two matrices, known as the scores (T) and loadings (P); Equation (1). Scores, linear

combinations of the original variables (X), may be considered as new variables, whereas loadings

describe how the old variables are linearly combined to form the new variables. Each point in a

score plot and loading plot represents a single sample and a single variable (metabolite),

respectively [9, 65]. Figure 4 shows examples of PCA score and loading plots.

(1) 𝑿 = 𝑻𝑷′ + 𝑬

Equation (1) where P' is transposed P, and E represents the residuals, i.e., unexplained variation

in the data.

17

In this thesis work, PCA was used to screen the data and detect clustered samples for further data

analysis.

Figure 4. Principal component analysis (top) score plot and (bottom) loading plot for GC-TOF-MS

(Paper I) (R2X = 0.80, Q2 (cum) = 0.62) t[1] is the first component and explains the largest variation,

t[2] is independent of t[1] and explains the second largest variation, which is orthogonal to t[1]. p[1] and

p[2] display the loadings of the first and second components, respectively.

18

Orthogonal projections to latent structures-discriminant analysis (OPLS-DA)

Information about samples in the dataset (e.g. class or other parameter of interest) can be used as

a response variable (Y) in a supervised method, such as PLS, OPLS [66], PLS-DA, and OPLS-DA

[67]. In a two-class situation, Y is a binary dummy vector (1/0) that contains information about

the pre-defined sample class ID. In OPLS-DA, the Y is used to divide systematic variation in X into

two parts, related and unrelated (orthogonal) to Y, which simplifies data interpretation. The

mathematical relationship is described in Equation (2).

(2) 𝑿 = 𝑻𝒑𝑷𝒑′ + 𝑻𝒐𝑷𝒐′ + 𝑬

Equation (2) where Tp is the predictive score matrix for X, Pp is the predictive loading matrix for

X, To is the corresponding Y-orthogonal score matrix, Po is the corresponding Y-orthogonal

loading matrix, Pp' is transposed Pp, Po' is transposed Po, and E is the predictive matrix for X.

OPLS-DA is utilized to map the variable patterns in the X matrix that discriminate the pre-defined

classes. To build a multivariate model for discriminating the sample classes, correlation between

the metabolite data matrix (X) and (Y) is undertaken. Examples of OPLS-DA score and loading

plots from Paper I are illustrated in Figure 5.

Statistical validation of multivariate models

To build a reliable multivariate model, statistical validation step is needed. Two types of

validations are commonly performed: external and internal. In external validation, new data are

collected/measured and used, and for internal validation the data are either divided into two sets:

a training set for modeling and a test/validation set or a permutation technique is used. The

purpose of validation is to assess the predictive ability of the model. Preferably, both types of

methods should be used in parallel to confirm the reliability of the model.

19

Figure 5. Orthogonal projections to latent structures-discriminant analysis (OPLS-DA) score plot (top) and

loading plot (bottom) obtained from GC-TOF-MS spectra showing differences between usual and modified

diets (R2X = 0.37, Q2 (cum) = 0.72, CV-ANOVA p-value = 0.00004); t[1] shows the direction of class

separation; to[1] expresses within-class variability; p[1] displays the loadings of the predictive component;

po[1] displays the loadings of the orthogonal component.

20

Cross-validation techniques include leave-n-out and K-fold methods [68]. In K-fold cross-

validation, the dataset is partitioned into k sized subsets and then iteratively k−1 subsets are

combined as a training set, with the remaining subset functioning as a test set. In leave-n-out, the

data are divided into N choose-n subsets and each subset serves as a validation set in each iteration.

If the sample size is n, in leave-one-out, n−1 samples are used as a training set to fit a classification

model and the remaining sample is used as test data. Therefore, every sample functions as a test

set just once. The model built on n-1 samples has the same accuracy as a model built on all (n)

samples. The estimated error rate is based on the misclassified test data. Leave-one-out techniques

are computationally desirable because they involve fitting the classification model n times [59].

This process is repeated until all samples have been left out and predicted once. The prediction

error sum of squares (PRESS) is defined as the sum of the square of the differences between the

observed and predicted value for each sample; Equation (3).

(3) 𝑷𝑹𝑬𝑺𝑺 = ∑ (𝒚𝒊 − 𝒚�̂�)𝟐𝒏

𝒊=𝟏

Equation (3) where 𝑦 is the true value for each sample, 𝑦̂ is the predicted value, 𝑖 is the number

of samples and n is the total number of samples.

In this thesis, CV-score plots are shown based on CV values indicating the predicted values.

Receiver operating characteristic (ROC) curve

Receiver operating characteristic (ROC) curve analysis is a validation tool that does not assume a

normal distribution of the studied variable and measures a variable’s predictive accuracy by

showing the relation between the true positive rate (sensitivity) and true negative rate (specificity)

[59, 69, 70]. Sensitivity is defined as the fraction of positive observations correctly classified by

the model into the positive class. Specificity is defined as the fraction of negative observations

correctly assigned by the model to the negative class. The area under the curve (AUC) of a plot of

sensitivity vs. specificity indicates the diagnostic value of each analyzed feature. The AUC can be

used as a criterion for assessing the success of the classification model. The closer the area under

the ROC curve (AUC) to 1 (maximum value), the more successful the classification model is; a

21

value of 0.5 indicates no diagnostic value. In addition, the ability of the metabolite to differentiate

between two groups of samples can be investigated from the shape of the ROC curve. The most

desirable curve has a sharp increase in true positive rate and slight increase in false positive rate.

22

Results and discussion

In this chapter, the main findings from the papers included in the thesis are summarized and

discussed in chronological order (Papers I - IV). The studies were designed to investigate the

responsiveness of the metabolome following external challenges in the form of meal intake and air

pollution exposure. As described above, the human body may respond to any stimuli or change in

the environment and metabolomics platforms are capable of investigating the net result of the

response by detecting alteration of metabolic profiles.

A metabolomics pilot study of the postprandial phase (Paper I)

Nutrition plays a vital role in human life and is obviously closely linked to metabolism. Thus,

nutritional metabolomics has been extensively investigated for its application to therapeutics,

medical foods and dietary supplements and as a confounding factor in studies of other external

stimuli. Studies have occasionally focused on metabolite profiling and fingerprinting to track

changes during the postprandial phase, directly after a meal intake [17, 71]. Postprandial

metabolomics has been shown to be useful, e.g., for metabolite quantification and identification

in plasma in response to a challenge meal [30], and assessment of individual metabolic

responsiveness to a lipid challenge [72], altered metabolic pathways in diabetes [16] and

postprandial insulin demand in postmenopausal women [71]. Potentially, postprandial

metabolomics can provide a comprehensive health assessment and allow optimization to different

dietary challenges, thus helping in the prevention of diseases and disorders in humans.

Objective

In this study, we aimed to detect a wide range of analytes in the postprandial human plasma

metabolome after usual and modified diet regimens, and investigate the overlapping and unique

extracted features from each of the investigated non-targeted metabolomics assay (LC-TOF-MS,

GC-TOF-MS and NMR), as well as the postprandial response (Figure 6).

23

Figure 6. Graphical abstract illustrating the non-targeted metabolomics study steps in Paper I.

Main findings and discussion of results in Paper I

Among the detected compounds, 54 GC-TOF-MS detected metabolites and 36 NMR detected

metabolites were identified, with 19 common to both assays. As explained in the methods section,

identification of LC-TOF-MS features is challenging and we were not able to identify the unknown

compounds detected by LC-TOF-MS. Therefore, we performed data analysis using the

unannotated LC-TOF-MS data.

Metabolome analysis during the postprandial stage by NMR and GC-TOF-MS indicated that

independent of background diet, samples were clustered into two groups; samples at 0.5 and 1 h

were separated from the baseline and samples at 3 h. However, this trend was not obvious in the

corresponding LC-TOF-MS data. To further investigate the trend, we constructed OPLS-DA

models (by defining samples at 0.5 and 1 h as one class, and the baseline and samples at 3 h as a

second class) and explored the metabolite profiles. To validate the model, we applied a cross-

validation test, which indicated that the metabolite profiles were significantly different between

24

fasting (0 + 3 h), and response (0.5 h + 1 h) to both background diets and for both the NMR and

GC-TOF-MS assays. The 19 common metabolites showed more significant changes in the NMR

OPLS-DA model compared to the GC-TOF-MS, which may at least be partly explained by the

higher within group variation in the GC-TOF-MS data. Increased levels of amino acids, organic

acids and sugars at the response stage, as well as decreased levels of fatty acids, acetoacetate and

3-hydroxybutyric acid, were observed in the OPLS-DA models independent of background diet.

Higher levels of amino acids and sugars could be related to absorption and metabolism processes

following meal (banana) intake. The meal contained no lipids, which may have caused the

decreased levels of fatty acids. In addition, this decrease could be linked to insulin secretion in

adipose tissue, which can inhibit lipolysis. In the LC-TOF-MS analysis, only one significant OPLS-

DA model was obtained according to CV-ANOVA (for the modified diet in negative ionization

mode; 21 compounds were upregulated and seven compounds downregulated).

Metabolites showing common or unique behavior after the usual or modified diet during the

postprandial stage were explored by plotting the correlation loading vectors (p(corr)) from the

OPLS-DA models on shared and unique structure (SUS)-plots. In the GC-TOF-MS SUS plot,

cysteine upregulation during the postprandial stage was uniquely observed after the modified

background diet, which could be related to the high protein foods consumed, such as eggs, meat

and dairy products.

OPLS-DA models classifying the background diets were constructed for all three analytical

techniques. The GC-TOF-MS diet-dependent OPLS-DA plots showed higher linoleic acid, oleic

acid, glycine and 3-hydroxybutyric acid levels following the usual diet, whereas the modified diet

samples displayed higher cholesterol, tyrosine, phenylalanine and taurine levels. The univariate

analysis results were in agreement with those obtained from multivariate analysis, with similar

trends observed for the majority of the studied compounds.

Air pollution exposure studies (Paper II, III and IV)

Air pollution originating from fossil fuel combustion contributes considerably to the worldwide

increasing prevalence of respiratory and cardiovascular diseases [73-78]. Owing to the chemical

and toxicological properties of particulate matter (PM) generated from combustion, it can cause

substantial damage to the lungs if it penetrates deep into the respiratory tract during inhalation

25

[79]. Because of limited oil reserves and concerns about the adverse health effects and

environmental damage of fossil fuel combustion, there is an increasing trend toward replacing

fossil fuels with biofuels derived from renewable sources [80]. RME is considered renewable and

carbon dioxide neutral. Therefore, it is regarded as ecologically less damaging than petrodiesel.

However, there is currently a lack of studies investigating how biodiesel replacement can affect

human health [81].

The filtered air and biodiesel exhaust exposure study were performed at the Department of Public

Health and Clinical Medicine, Division of Respiratory Medicine and Allergy, Norrlands University

Hospital. The exposure chamber at SMP Svensk Maskinprovning AB, Umeå, Sweden was

connected to a Volvo engine (Volvo TD40 GJE, 4.0 L, 4 cylinders), which was designed to generate

biodiesel exhaust emissions from RME100 fuel (Figure 2). Many studies have been conducted

using this set-up to investigate the adverse health effects of exhaust exposure on cardiorespiratory

status and inflammatory processes [82-84]. In this thesis, we complement these studies applying

different analytical platforms (based on targeted and non-targeted metabolomics assays) to

analyze healthy human BW, BAL and plasma samples (Paper II-IV) gathered after bio-diesel

exhaust and filtered air exposure. The experimental pipeline applied in Paper II-IV is presented

in Figure 7.

26

Figure 7. Graphical abstract illustrating the air pollution exposure studies (Paper II, III and IV).

Paper II

Non-targeted metabolomics of BAL fluid has been successfully used to study human lung injuries

and pulmonary diseases, such as cystic fibrosis, asthma, and respiratory distress syndrome, as well

as for animal lung inflammation assessment [21, 24, 25, 85-87]. In such studies, key metabolic

markers were detected and identified to develop an understanding of the studied pathologies. It

was reported that LC-MS was able to detect and quantify the maximum number of 23 unique

metabolites in BAL samples [21]. Nevertheless, no study has yet applied GC-MS, LC-MS and NMR

together for analysis of aliquots of the same sample, which might be the reason for the limited

number of metabolites identified in previous studies. Metabolite profiling of lung lavage fluids

using multi-platforms, i.e., GC-TOF-MS, LC-TOF-MS and NMR, can provide more extensive

coverage of the lung metabolome. However, such studies are challenging because of the extremely

low metabolite levels and high salt content in this type of sample [26, 50].

27

Objective

In Paper II, we hypothesized that multi-platform metabolite profiling (GC-TOF-MS, LC-TOF-MS

and NMR) would enable wider detection and identification of metabolites in lung lavage fluids

and demonstrate biodiesel exhaust exposure effects on the human lung lavage metabolome. For

this purpose, we developed an extraction protocol to detect and identify a wide range of

metabolites in BW and BAL fluids collected from subjects exposed to biodiesel exhaust and filtered

air.

Main findings and discussion of results in Paper II

The developed and optimized extraction protocol allowed us to identify 53 metabolites in the GC-

TOF-MS assay and 14 in the LC-TOF-MS assay, with a signal-to-noise ratio above three (relative

to blanks). In parallel, Chenomx software was used for the NMR assay to identify 23 metabolite

omitting interferences related to the anesthetic process in the bronchoscopy. Interestingly, the

three assays yielded a wide range of metabolites, e.g., fatty acids, sugars, amino acids and small

organic acids, with no single molecule in common between all three platforms. Seven metabolites

were in common between the NMR and GC-TOF-MS assays (glucose, glutamate, glycine, lactic

acid, pyruvic acid, taurine and valine), and one between the LC-TOF-MS and NMR assays

(creatinine).

Pathway analysis was carried out using the MetaboAnalyst 3.0 software to investigate the pathway

coverage for the identified metabolites. A few pathways were significant according to the

calculated p-values, with most of the pathways connected to amino acid and fatty acid metabolism.

OPLS-DA modeling for all three platforms indicated that the metabolite profiles differed between

the BW and BAL samples after exposure to filtered air. By focusing only on samples after filtered

air exposure, variations between the lung compartments related to biodiesel exhaust exposure

were omitted.

In addition, univariate analysis revealed that the metabolite concentrations in the BW and BAL

samples were at different levels after biodiesel exhaust exposure. For instance, in the GC-TOF-MS

assay, pentadecanoic acid levels were significantly higher in the BW samples, whereas

ethanolamine, inosine and nonanoic acid were significantly higher in the BAL samples. Increased

levels of compounds such as ethanolamine, phosphate, glycerol-3-phosphate and unsaturated

fatty acids and decreased levels of some fatty acid glycerol esters after biodiesel exposure in the

28

BAL samples indicated that biodiesel exposure could have an effect on the lipid degradation of the

cell membrane. Further, the LC-TOF-MS assay analysis indicated that following biodiesel exhaust

exposure, chenodeoxycholic acid glycine conjugate levels were decreased in BW fluid, whereas

niacinamide was increased in BAL fluid. Increased levels of niacinamide, which is a component of

the coenzyme NAD, may be connected to changes in the redox environment in airway cells. In the

NMR assay analysis, only changes in lactic acid concentrations were significant and it had lower

levels after biodiesel exhaust exposure in BW samples. This trend was also observed in the GC-

TOF-MS analysis, but the results were not significant. Under a shortage of oxygen, lactic acid may

be produced in aerobic organisms to meet the need for its consumption. Some other processes,

e.g., glycolysis and/or gluconeogenesis, may also affect levels of lactic acid.

Furthermore, to monitor changes in the metabolite profiles induced by biodiesel exhaust exposure,

we used OPLS-DA modeling. The results indicated that the only assay capable of differentiating

between the filtered air exposure and biodiesel exhaust exposure was GC-TOF-MS. The OPLS-DA

models for the BW and BAL samples were built separately and both models were significant based

on cross-validation; the strongest model (CV-ANOVA p-value = 0.0009) was obtained for BAL

samples. Independent of the sample type (BW or BAL), most subjects, with only a few exceptions,

showed the same direction of the metabolic response after biodiesel exhaust exposure. To further

confirm the OPLS-DA models, we plotted ROC curves. The corresponding AUC (0.75) verified that

the OPLS-DA models were able to accurately classify the groups.

Paper III

In parallel to the non-targeted study of BW and BAL samples (as discussed in Paper II), for the

study in Paper III, we used a targeted LC-MS/MS approach to detect a wide range of oxylipins,

endocannabinoids, N-acylethanolamines and related compounds in lung lavage fluid after

exposure to filtered air and biodiesel exhaust exposure.

Bioactive lipid mediators play a significant role in pulmonary inflammation, i.e., in initiation,

propagation and resolution of inflammation [88, 89]. Oxidation of polyunsaturated fatty acids

(PUFA) leads to the biosynthesis of a group of compounds called oxylipins. Eicosanoids, such as

prostaglandin E2 (PGE2), PGD2, PGF2α and 15-hydroxyeicosatetraenoic acid (15-HETE), are

oxylipins produced by the oxidation of ω6 arachidonic acid (20:4n6). Oxidation of linoleic acid

29

(LA) leads to production of 9-hydroxy-octadecadienoic acid (9-HODE), 13-HODE, 9,10-

dihydroxy-12Z-octadecenoic acid (9,10-DiHOME) and 12,13-DiHOME, etc. Three possible

pathways for this oxidation are shown in Figure 8 based on cyclooxygenase (COX), lipoxygenase

(LOX) and cytochrome (CYP) P450 as oxylipin sources [89, 90]. Endocannabinoids are lipid

mediators that comprise ligands to the cannabinoid (CB) receptors, e.g., anandamide (AEA) and

2-arachidonoyl glycerol (2-AG) [91, 92]. N-acylethanolamines and glycerol fatty acid derivatives

are examples of endocannabinoid-related lipids which influence the activity of the CB1 and CB2

receptors [93].

Figure 8. Scheme showing oxylipins and their metabolic pathways.

Objective

In the study described in Paper III, we hypothesized that the lipid mediator profile (consisting

of eicosanoids, endocannabinoids and related lipids) in the human lung changes in response to

biodiesel exhaust exposure. To test this hypothesis, we performed a targeted analysis using LC-

MS/MS to profile the levels of bioactive lipid mediators in both BW and BAL fluids.

30

Main findings and discussion of results in Paper III

Both univariate and multivariate analyses indicated that there was a significant difference between

the BW and BAL samples, reflecting different locations in the lung (proximal or bronchial region

vs. distal or alveolar region). Investigating the exhaust exposure effect, it was found that nine

metabolites in the human lung lavage samples were altered following biodiesel exhaust exposure,

six in BAL samples and three in BW samples. Of these, three compounds (PGE2, 12,13-DiHOME

and 13-HODE) in the BAL samples and no compounds in the BW samples passed the Bonferroni

correction (p-value ≤ 0.0019), which however is a conservative approach (discussed above).

Finally, the exposure-dependent profile of the detected compounds was investigated by OPLS-DA

modeling of the data for the biodiesel exhaust samples vs. filtered air exposure samples. The

peripheral airways were most responsive to exhaust exposure, showing increased concentrations

of arachidonic acid (PGE2) and linoleic acid (12, 13-DiHOME and 13-HODE) derived oxylipins.

Paper IV

Objective

In the work presented in Paper IV, we extended the lipid mediator profiling by LC-MS/MS to

plasma samples from the same individuals at multiple time points (pre-exposure and 2, 6 and 24

h post-exposure) to follow temporal trends in plasma lipid mediators. We hypothesized that the

local effect on bioactive lipid profiles would be found at the systemic level, in the blood stream.

Main findings and discussion of results in Paper IV

Overall, 35/38 oxylipins (in addition to 13 endocanabinoids) were quantified, of which 20 were

present in all plasma samples. We used the quantified data for further analysis. Independent of

the exposure, compounds derived from the LOX pathway showed the highest concentrations. Five

metabolites from the arachidonic acid LOX pathway (5-HETE, 11-HETE, 12-HETE, 15-HETE and

5-oxo-ETE) as well as one from the linoleic acid LOX pathway (9-HODE) and one from the CYP

31

pathway (9,10-DiHOME) showed decreased levels at 24 h post-exposure, except 5-oxo-ETE,

which was decreased at 6 h. Additionally, 5-HETE was also decreased at 6 h post-exposure.

Furthermore, the arachidonic acid COX metabolite PGF2α was elevated at 6 h and the N-

acylethanolamine DEA was decreased at 2 h. The majority of the responsive fatty acid metabolites

were monohydroxy fatty acids, linked to cardiovascular outcomes [94]. Thus, we concluded that it

is possible to detect alterations in circulating bioactive lipids in response to biodiesel exhaust

exposure using LC-MS/MS by focusing on compounds with known or suspected effects on

cardiovascular health.

32

Conclusion and future perspectives

Using three different non-targeted analytical methods (GC-TOF-MS, NMR and LC-TOF-MS), a

wide range of metabolites were detected and identified in the studies of Paper I (in plasma) and

Paper II (in lung lavage fluid). Paper I presents a pilot study where all three assays showed a

stable postprandial response over time that was largely independent of the background diet. Both

multivariate and univariate analyses indicated that 0.5 h after food intake, levels of amino acids

were increased, whereas levels of fatty acids were decreased, but both types of metabolites

returned to baseline levels 3 h after the meal intake. Comparing the platforms used in the Paper

I work, GC-TOF-MS seemed to be the most appropriate owing to the wide range of detected

metabolites, well established and robust deconvolution approaches and straightforward

compound identification. For robust detection of more polar compounds, NMR may be the best

choice. Multivariate analysis yielded a variety of information about the studied system, including

information about trends in samples and variables and connections between them. Univariate

analysis was used as a complementary statistical method to the multivariate analysis. The study

discussed in Paper II showed for the first time that by applying complementary analytical

techniques, a large number of metabolites (82) could be detected and identified in BW and BAL

fluid samples after air and biodiesel exhaust exposure, providing important and novel information

on the metabolome of the respiratory tract. We observed that there were significant differences in

the levels of specific metabolites between BW and BAL samples using non-targeted metabolomics

assays, in line with previous targeted metabolomics protocols. Furthermore, metabolite profiles

of BW and BAL samples, particularly the BAL samples, changed after exposure to biodiesel

exhaust compared to those following exposure to filtered air. Notably, this responsiveness was

best monitored by GC-TOF-MS, while NMR and LC-TOF-MS provided better data for assessment

of differences between the BW and BAL metabolite profiles.

Since metabolite levels in BAL and BW fluids reflect the molecular status of the lung epithelium,

the alterations observed after exposure to air pollution derived from biodiesel exhaust may be

related to changes in the human lung condition. The techniques used for this study may serve as

a starting point for future metabolomics studies on the respiratory tract system and further

evaluation of air pollution effects on human health.

The work presented in Paper III represents the first investigation of bioactive lipid responses of

human lung lavage fluid following exposure to RME biodiesel exhaust. By applying a MS based

33

approach, we were able to detect and quantify multiple oxylipins, endocannabinoids and

endocannabinoid-related lipids (36), and found compartment-dependent responsiveness of

individual species after biodiesel exhaust exposure.

In Paper IV, among the 35 oxylipins and 13 endocannabinoids and related lipids detected in

plasma by LC-MS/MS, nine metabolites were affected, mainly at 24 h post-exposure. Thus, we

concluded that it is possible to detect alterations in circulating bioactive lipids in response to

biodiesel exhaust exposure using LC-MS/MS by monitoring compounds with known or suspected

effects on cardiovascular health. However, we were not able to detect responsiveness among the

same metabolites as those detected in BW and BAL from the same study.

Following the presented papers in this thesis, we performed non-targeted metabolomics profiling

of the plasma samples, in addition to the bioactive lipid profiling, to investigate the bloodstream

response to biodiesel exhaust exposure vs. filtered air exposure of the more abundant metabolites.

This is currently work in progress.

Future perspectives

It would be very interesting to monitor metabolite changes in plasma and lung lavage fluids after

diesel exhaust or other exposures to investigate whether the local effects are more evident than

systemic effects, and extend the knowledge to air pollutants in general. To that end, targeted and

non-targeted metabolomics investigations of plasma samples after exposure to biodiesel vs. diesel

exhaust exposure is planned, with the aim of exploring in more detail the local vs. systemic

response after biodiesel exhaust and diesel exhaust exposure. A follow-up validation study would

offer an excellent opportunity for further improvement and development of the applied

methodologies, as well as confirmation of the biological findings. Extension of metabolomics

strategies and analysis methods to detect and identify more metabolites and at lower levels will

help to elucidate the mechanisms behind the effects of interventions and environmental influences

on the human body. Specifically, exposure-related effects on cardiorespiratory or other similar

diseases could facilitate the identification of disease specific biomarkers, drug development and

illness treatment.

34

Acknowledgment

First of all, I would like to thank my supervisor, Malin L. Nording, for giving me the opportunity

to study as a PhD student at Umeå University and supporting me to experience new things. Special

thanks go to my co-supervisor, Izabella Suroweic, who helped and strongly supported me

throughout these years. I would also like to acknowledge my co-supervisors, Johan Trygg and

Annelie Behndig, for their helpful guidance and suggestions during my projects. Thanks too to Rui

for helping me start the data analysis and Kate for kind support.

Thanks to Sandra for her fantastic help and companionship during my projects. I am also grateful

to Junfang for her valuable comments and help. Huge thanks go to my co-authors at Norrland

University Hospital Umeå and the University of California, Davis, for their contribution to

publications. Acknowledgment also goes to Hans, Jonas, Krister and other colleagues at the

Swedish Metabolomics Centre (SMC) for their great help with the laboratory and data processing

works. I would also like to thank Patrik Andersson, Jerker Fick, and Richard Lindberg for giving

me the opportunity to be involved in lab work prior to my PhD.

I sincerely thank Yaser, Mar, Shery, Qiuju, Sandra, Joao, Junfang, Jana, Mirva, Matyas, Lan,

Marcus, Jin and other friends at MKL for their companionship and happy times we had together

throughout these years, making me feel awesome. Thanks to our Iranian friends here in Umeå for

all the great times we shared.

Finally, sincere thanks go to my family. Thanks to my parents for their great support and love

throughout my whole life. Thanks to my brother, Reza, and sister, Marisa, for the wonderful

moments and their unlimited kindness. Thanks too to my brother-in-law, Ashkan, for his sincerity.

A very special thanks to my dearest Mehdi for all the happy and sad moments we have experienced

together and all the support and amiability that made me feel lucky throughout our shared lives.

Lastly, thanks to Mehdi’s family for their tenderness.

35

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