Saccharomyces cerevisiae - University of Calgarytheses.ucalgary.ca/bitstream/11023/1305/4/ucalgary_2014_tambellini... · UNIVERSITY OF CALGARY Metabolomic and lipidomic profiling

UNIVERSITY OF CALGARY

Metabolomic and lipidomic profiling of the effect of edelfosine treatment on

Saccharomyces cerevisiae

by

Nicolas Pietro Tambellini

A THESIS

SUBMITTED TO THE FACULTY OF GRADUATE STUDIES

IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE

DEGREE OF MASTER OF SCIENCE

DEPARTMENT OF BIOLOGICAL SCIENCES

CALGARY, ALBERTA

JANUARY, 2014

© Nicolas Pietro Tambellini 2014

ii

Abstract

Edelfosine is a lysophosphatidylcholine analogue and the prototype of a new class of

compounds being investigated for their potential as highly selective chemotherapeutic

agents. Edelfosine has been implicated as affecting numerous different metabolic

pathways, though its mechanism of action is not well understood at this time. To gain

further insight into edelfosine’s mechanism of action we carried out mass spectrometry

based metabolomic and lipidomic profiling of yeast exposed to a cytostatic

concentrations of edelfosine. Using multivariate projection methods and statistical

analysis, we determined that edelfosine exerts a significant effect on many aspects of

yeast metabolism. Metabolic pathways that were found to be perturbed included those

involved with amino acid metabolism, sugar metabolism, the TCA cycle, fatty acid

biosynthesis, sphingolipid metabolism, glycerophospholipid metabolism and glycerolipid

metabolism. It was also observed that there is a kinetic difference in the response of polar

and non-polar metabolites to edelfosine treatment in yeast.

iii

Acknowledgments

I would like to first thank Dr. Ray Turner for agreeing to be my supervisor. You

have helped me grow immensely as a person and a scientist. The skills and ideologies

you have developed and instilled in me will be instrumental as I go forward and explore

new academic and career opportunities. Your encouragement to find balance inside and

outside of my academic pursuits has been key in my success and you are everything and

more a student could ever ask for as a supervisor.

I would also like to thank my committee members Dr. Aalim Weljie and Dr.

Elmar Prenner. Dr. Weljie, you have been an excellent mentor and your patience,

knowledge and support has been extremely important. Dr. Prenner I have learned so

much from you both as an instructor and as a scientist. The insights and ideas both of you

have provided throughout my project have been extremely helpful. Additionally I would

like to thank Dr. Vanina Zaremberg for starting me on this project and for her help and

thoughts throughout the last two and a half years. Furthermore, I would also like to thank

Dr. David Schriemer for agreeing to be my internal-external examiner. I learned so much

from you about mass spectrometry through your teaching and discussion which has been

extremely helpful during the course of my project.

I would also like to past and present members of both the Turner and Weljie labs.

You are a great group of people and fun to work with and be around. I wish you all

success in your future endeavours and am positive you will be very successful as you are

all very intelligent and driven individuals.

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Finally I would like to all my family and friends (too many to name) who have

been with me through all of my ups and downs. Your support and generosity have

encouraged me to continue working towards the achievement of my goals and dreams

and for that I will be forever grateful.

v

Dedication

To my mom, thank you for all of your encouragement, love and sacrifices that have

allowed me to be where I am today.

vi

Table of Contents

Abstract ............................................................................................................................... ii

Acknowledgements ............................................................................................................ iii

Dedication ........................................................................................................................... v

Table of Contents ............................................................................................................... vi

List of Tables ..................................................................................................................... xi

List of Figures ................................................................................................................... xii

List of Symbols, Abbreviations and Nomenclature ......................................................... xiv

Chapter One: Introduction .............................................................................................. 1

1.1 Lipid based cancer drugs ........................................................................................... 1

1.1.1 Phosphatidylinositol ether lipid analogues (PIA’s) ............................................ 1

1.1.2 Anti-tumour lipid analogues (ATL’s)................................................................. 2

1.2 Background on edelfosine ......................................................................................... 4

1.2.1 Pathways proposed to be affected by edelfosine treatment ................................ 5

1.2.2 Uptake of edelfosine ........................................................................................... 8

1.2.3 Clinical applications of edelfosine ..................................................................... 9

1.3 Yeast as a model system for cancer ........................................................................ 10

1.3.1 Drug studies in yeast......................................................................................... 10

1.3.2 Edelfosine studies in yeast ................................................................................ 11

1.4 Metabolomics .......................................................................................................... 13

1.4.1 Metabolomics methodology ............................................................................. 13

1.4.2 Lipidomics: A subspecialty of metabolomics .................................................. 16

vii

1.5 Multivariate analysis ............................................................................................... 17

1.6 Research goals ......................................................................................................... 20

Chapter Two: Materials and Methods .......................................................................... 21

2.1 Yeast growth and sample harvesting ....................................................................... 21

2.2 Metabolite extraction............................................................................................... 21

2.3 GC-MS analysis ...................................................................................................... 22

2.3.1 Sample preparation and derivitization for GC-MS analysis ............................. 22

2.3.2 GC-MS data acquisition ................................................................................... 23

2.3.3 GC-MS data processing .................................................................................... 24

2.4 LC-MS analysis ....................................................................................................... 25

2.4.1 Addition of internal standards to LC-MS samples ........................................... 25

2.4.2 UPLC-TOF-MS data acquisition ...................................................................... 25

2.5 Multivariate statistical analysis ............................................................................... 28

2.6 Pathway analysis and metabolic network construction ........................................... 29

Chapter Three: Optimization of Metabolite and Fatty Acid Extraction from

Saccharomyces cerevisiae ................................................................................................ 31

3.1 Introduction ............................................................................................................. 31

3.2 Results and discussion ............................................................................................. 32

3.2.1 Unsupervised analysis clearly differentiates extraction method ...................... 34

3.2.2 Supervised analysis identifies 36 metabolites and four fatty acids

differentiating the extraction methods ....................................................................... 34

3.2.3 Comparison of FAME and aqueous metabolite profiles obtained ................... 39

3.2.4 Summary ........................................................................................................... 44

3.3 Experimental section ............................................................................................... 44

viii

3.3.1 Yeast growth and harvesting ............................................................................ 44

3.3.2 Metabolite extraction ........................................................................................ 44

3.3.3 Derivatization and sample preparation ............................................................. 46

3.3.4 GC-MS data acquisition .................................................................................. 47

3.3.5 Data processing and interpretation .................................................................. 47

3.4 Conclusions ............................................................................................................. 48

3.5 Contributions ........................................................................................................... 48

Chapter Four: Polar Metabolite and Fatty Acid Profiling of Edelfosine Treated

Saccharomyces cerevisiae ............................................................................................... 49

4.1 Introduction ............................................................................................................. 49

4.2 Experimental methods ............................................................................................. 50

4.2.1 Yeast and edelfosine growth curves ................................................................. 50

4.2.2 Yeast sample growth and sample harvesting .................................................... 51

4.2.3 Sample extraction and derivitization ................................................................ 51

4.2.4 GC-MS data acquisition ................................................................................... 52

4.2.6 Metabolite modelling and pathway analysis .................................................... 52

4.3 Results ..................................................................................................................... 52

4.3.1 Growth with edelfosine .................................................................................... 52

4.3.2 OPLS-DA modelling differentiates edelfosine treated and untreated samples 54

4.3.3 22 polar metabolites and 8 fatty acids altered by edelfosine treatment ............ 58

4.3.4 Metabolic pathway analysis .............................................................................. 61

4.4 Discussion ............................................................................................................... 61

3.5 Contributions ........................................................................................................... 72

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Chapter Five: Lipidomic Profiling using UPLC-TOF-MS of Edelfosine Treated

Saccharomyces cerevisiae ................................................................................................ 74

5.1 Introduction ............................................................................................................. 74

5.2 Experimental methods ............................................................................................. 75

5.2.1 Sample preparation ........................................................................................... 75

5.2.2 UPLC-TOF-MS analysis .................................................................................. 75

5.2.3 Data analysis and multivariate projection modelling ....................................... 75

5.2.4 Lipid identification ........................................................................................... 76

5.3 Results ..................................................................................................................... 77

5.3.1 Initial analysis reveals magnitude of edelfosine treated samples is higher than

untreated samples ...................................................................................................... 77

5.3.2. Multivariate projection modelling differentiates edelfosine treated and

untreated yeast samples from lipidomic profiling ..................................................... 81

5.3.3. 28 Lipids from 7 major lipid classes identified to be altered by edelfosine

treatment .................................................................................................................... 81

5.4 Discussion ............................................................................................................... 87

5.5 Contributions ........................................................................................................... 91

Chapter Six: Concluding Remarks and Future Directions ........................................ 93

6.1 Summary of research objectives and implications .................................................. 93

6.1.1 Evaluation of extraction protocols for yeast ..................................................... 93

6.1.2 Analysis of changes in the metabolome and fatty acid profile of yeast induced

by edelfosine treatment. ............................................................................................. 94

6.1.3 Analysis of changes in the lipidome of yeast induced by edelfosine treatment.

................................................................................................................................... 95

6.1.4 Secondary analysis and biological interpretation of the metabolomics data .... 96

x

6.2 Future directions ...................................................................................................... 99

6.2.1 Further metabolomics studies ........................................................................... 99

6.2.2 Confirming our biological interpretations ...................................................... 101

xi

List of Tables

Table 1.1. Steps and methods from a typical metabolomics experiment.......................... 14

Table 2.1. Internal standards added to samples for ultra perfomance liquid

chromatography time-of-flight mass spectrometry (UPLC-TOF-MS) analysis. ............. 26

Table 3.1. Metabolites identified to have a VIP score greater than 1 through OPLS-DA

modelling of aqueous metabolite and FAME extraction data and the corresponding

coefficient values for each extraction protocol. ................................................................ 40

Table 4.1. Summary of parameters for the assessment of the quality of OPLS-DA models

comparing edelfosine treated and untreated yeast samples. ............................................. 57

Table 4.2. Polar metabolites and fatty acids identified to have a VIP score greater than 1

through OPLS-DA modelling and the corresponding coefficient values for edelfosine

treated samples compared to untreated samples. .............................................................. 59

Table 4.3. Identified metabolites that were found to be not significantly perturbed by

edelfosine treatment through OPLS-DA modelling and have VIP scores of less than 1. 62

Table 4.4. Pathway analysis results from edelfosine treatment of yeast using

MetaboAnalyst 2.0. ........................................................................................................... 63

Table 5.1. Lipids identified as altered by edelfosine treatment, their m/z values, retention

times and the adduct used for identification. .................................................................... 84

xii

List of Figures

Figure 1.1: Names and chemical structures of lysophosphatidylcholine, the synthetic

alkylyphospholipid edelfosine and its derivatives. ............................................................. 3

Figure 1.2. Pathways that have been proposed to be affected by edelfosine treatment and

the resulting cell survival, proliferation and pro-apoptotic processes affected. ................. 6

Figure 1.3. Suggested working mode of action for edelfosine in yeast. ........................... 12

Figure 1.4 Projection methods simplify all observations for a sample into a single point to

allow for easy visualization and comparison. ................................................................... 18

Figure 3.1. Models for polar metabolites extracted from S. cerevisiae by one of three

chloroform/methanol/water based extraction protocols. ................................................... 36

Figure 3.2. Models for fatty acid metabolites extracted from S. cerevisiae by one of three

chloroform/methanol/water based extraction protocols. ................................................... 37

Figure 3.3. Shared and unique structure (SUS) plots of fatty acid and polar metabolites

from S. cerevisiae by one of three chloroform/methanol/water based extraction protocols.

........................................................................................................................................... 42

Figure 4.1. Yeast and edelfosine treatment growth curves. .............................................. 53

Figure 4.2. OPLS-DA models using cross-validated latent variables (tcv) and cross-

validated orthogonal latent variables (tocv) comparing the polar metabolite profiles of

untreated and edelfosine treated S. cerevisiae samples at 4 timepoints after treatment ... 55

Figure 4.3. OPLS-DA models comparing the fatty acid profiles using tcv’s and tocv’s for

FAME analysis of untreated and edelfosine treated S. cerevisiae samples at 4 timepoints

after treatment .................................................................................................................. 56

xiii

Figure 4.4. MetaboAnalyst 2.0 pathway analysis summary of perturbations caused by

edelfosine treatment of yeast samples............................................................................... 64

Figure 4.5. Examples illustrating the different kinetic responses from 0 to 6 hours after

edelfosine treatment observed for polar metabolites and fatty acids. ............................... 67

Figure 4.6. Schematic overview of polar metabolites, fatty acids and metabolic pathway

affected by edelfosine treatment. ...................................................................................... 69

Figure 5.1. Retention time deviation observed for 8 untreated and 10 edelfosine treated

yeast samples uploaded to XC-MS Online for analysis and peak detection. .................... 78

Figure 5.2. Cloud plot obtained from XC-MS Online analysis of 10 edelfosine treated and

8 untreated yeast samples.................................................................................................. 79

Figure 5.3. Total ion chromatograms for 8 untreated and 10 edelfosine treated yeast

samples uploaded to XC-MS Online for analysis and peak detection. ............................. 80

Figure 5.4. Pareto scaled PCA and OPLS-DA models of 8 untreated and 10 edelfosine

treated yeast samples from lipidomic profiling ................................................................ 82

Figure 5.5. S-plot of 8 untreated and 10 edelfosine treated yeast samples from lipidomic

profiling to identify lipids decreased or increased by edelfosine treatment ..................... 83

Figure 6.1. Schematic overview of polar metabolites, fatty acids and lipids identified to

be affected by edelfosine in yeast through metabolomic and lipidomic profiling. ........... 98

xiv

List of Symbols, Abbreviations and Nomenclature

Symbol or Abbreviation Definition

APL alkylphospholipid

ASK1 apoptosis signal-regulating kinase 1

ATP adenosine triphosphate

ATL anti-tumour lipids

CDP cytidine-diphosphate

Cer ceramide

CK choline kinase

CL cardiolipin

CPT choline phosphotransferase

CT CTP:phosphocholine cytidyltransferase

CV-ANOVA cross-validated analysis of variance

DAG diacylglycerol

DNA deoxyribonucleic acid

EI electron ionization

ER endoplasmic reticulum

FAME fatty acid methyl ester

GABA γ-aminobutyric acid

GC-MS gas-chromatography mass spectrometry

GC-TOF-MS gas chromatography time-of-flight mass

spectrometer

LC-MS liquid-chromatography mass spectrometry

LV latent variable

LPI lysophosphatidylinositol

LPC lysophosphatidylcholine

MALDI matrix-assisted laser desorption/ionization

MAPK/ERK mitogen-activated protein kinase/extracellular-

signal regulated kinases

xv

MS mass spectrometry

MSTFA N-methyl-N-(trimethylsilyl) trifluoroacetamide

mTOR mammalian target of rapamycin

m/z mass to charge ratio

S/N signal to noise

NMR nuclear magnetic resonance

OD600 optical density at 600 nm

OPLS-DA orthogonal partial least squares-discriminant

analysis

p magnitude of a variable

p(corr) reliability of a variable

PA phosphatidic acid

PC1 prinicipal component 1

PC phosphatidylcholine

PCA principal component analysis

PDK phosphoinositide-dependent kinase

PE phosphatidylethanolamine

PG phosphatidylglycerol

PGP phosphatidylglycerol phosphate

PIA phosphatidylinositol ether lipid analogues

PI3K phosphatidylinositol 3ˈ - kinase

PIP2 phosphatidylinositol-4,5 bisphosphate

PIP3 phosphatidylinositol-3,4,5 triphosphate

PKB protein kinase B

PKC protein kinase C

PKD protein kinase D

PLC phospholipase C

PLD phospholipase D

PM plasma membrane

Q2 predictive quality of the model

Q-TOF-MS quadropole time-of-flight mass spectrometry

xvi

R2 fit of the data

RasGRP Ras guanine-releasing protein

Req. score required similarity score

RI retention index

ΔRI change in retention index

ROS reactive oxygen species

SAPK/JNK stress-activated protein kinase/c-Jun NH2-terminal

kinase

SD selective defined

SM sphingomyelin

SMS sphingomyelin synthase

SUS shared and unique structure

TAG triacylglycerol

TCA tricarboxylic acid

tocv cross-validated orthogonal latent variables

tcv cross-validated latent variables

μl microliter

UPLC-TOF-MS ultra performance liquid chromatography time-of-

flight mass spectrometry

VIP variable influence on projection

YNB yeast nutrient broth

YPD yeast extract-peptone dextrose

1

Chapter One: Introduction

1.1 Lipid based cancer drugs

As the search for novel therapeutic approaches for cancer treatment continues to

progress, new strategies are coming to the forefront. Of the new strategies being explored

including bioactive peptides (1), non-pathogenic bacteria (2) and oncolytic viruses (3),

one approach that has gained increased interest is the use of lipid analogues as potential

therapeutic agents for cancer. Lipid analogues show promise as they do not target DNA

or DNA synthesis as is the case with traditional chemotherapeutic agents (4), potentially

leading to less toxic side effects. There are two main types of synthetic lipid analogues

currently being explored for their potential use as anti-cancer compounds;

phosphatidylinositol ether lipid analogues (PIA’s) and synthetic anti-tumour lipids

(ATL’s).

1.1.1 Phosphatidylinositol ether lipid analogues (PIA’s)

A review written in 2004 by Gills and Dennis (5) discusses in detail the

development of PIA’s and their biological activities. Briefly, PIA’s inhibit Akt

translocation, phosphorylation and kinase activity (5) and were developed based on the

observation that D-3-deoxy-3-substituted myo-inositol analogues inhibited cell growth of

oncogene transformed cells but were antagonized by myo-inositol itself (6). Akt, also

known as protein-kinase B (PKB) is involved in the phosphatidylinositol 3’-kinase

(PI3K) signalling pathway which is thought to be involved in the control of key processes

involved with cancer (5). A follow up study found that PIA’s are less potent but more

cytotoxic than other PI3K/Akt/mTOR (mammalian target of rapamycin) inhibitors and

2

biologically distinct from these inhibitors in their modes of action (7). Further studies

found that PIA’s activate p38α which is involved in the p38 pathway that responds to cell

stress and induces apoptosis (8). It was also found that PIA’s caused increased expression

of tumour suppressor genes as Akt-independent effects that likely contributed to the

increased cytotoxicity observed for PIA’s (9).

1.1.2 Anti-tumour lipid analogues (ATL’s)

Most ATL’s, more commonly referred to as alkylphospholipids (APL’s), are

derived from edelfosine which is a metabolically stable analog of

lysophosphatidylcholine and will be discussed more in depth below. An in depth review

written in 2008 of edelfosine and some of its derivatives including ilmofosine,

erucylphosphocholine, miltefosine and perifosine (Figure 1.1) by van Blitterswijk and

Verheij (10) discusses what is known about the mechanisms of action, cellular sensitivity

and clinical prospects of APL’s. A brief synopsis of some of the uses and prospects of

these compounds is discussed below, though interestingly it seems that they all have

similar modes of action based on what is currently understood.

Ilmofosine varies from edelfosine in that it has a thioether linkage as opposed to

an ether linkage. Ilmofosine initially showed promising results as it was able to induce

apoptosis in the Lewis-Lung carcinoma model (11) and neuroblastoma cells (12) and

was effective in pre-clinical trials in vivo (13). However during clinical trials much less

promise was shown (10) and little follow up work has been done since.

Miltefosine differs from most other APL’s in that it is not metabolically stable

and can be metabolized by phospholipases (14). Despite this fact, it has shown

antitumour activity in vitro (14) and differs from edelfosine in that it lacks a glycerol

3

Figure 1.1: Names and chemical structures of lysophosphatidylcholine, the synthetic alkylyphospholipid edelfosine and its

derivatives. Figure adapted from (10).

LysoPC Edelfosine Ilmofosine Miltefosine Erucylphosphcholine Perifosine

4

backbone, making it the simplest structure in the APL class of compounds that still

demonstrates antitumour activity (15). Due to its hemolytic nature when administered

intravenously (16), miltefosine is more commonly used to treat leishmaniasis (17), in

addition to being used as a topical agent for breast cancer skin metasteses (18) and

cutaneous lymphoma (19). These applications make it the most clinically used APL

compound to date. As such another edelfosine derivative, erucylphosphocholine has been

developed. Erucylphosphocholine also lacks a glycerol backbone and differs from

miltefosine only due to having a longer alkyl chain and the presence of a double bond

making it more hydrophobic and eliminating its hemolytic nature (20), allowing for

intravenous use (21). Due to these properties, erucylphosphocholine has shown promise

for the treatment of brain tumours (21) as it is able to pass the blood brain barrier.

Perifosine is another APL with a unique structure that is similar to miltefosine,

with the only difference being that the choline headgroup has been replaced with a

heterocyclic piperidin group (22). Perifosine has also shown very promising signs for its

use clinically. Firstly, it was able to induce apoptosis in patient derived multiple myeloma

cells that were resistant to conventional treatment in addition to human multiple myeloma

cell lines (23). Furthermore, it has been shown that perifosine can enhance

radiosensitivity of two carcinoma tumour types without the resulting bone marrow

toxicity that is commonly seen with current treatment strategies (24).

1.2 Background on edelfosine

As mentioned previously, edelfosine (1-O-octadecyl-2-O-methyl-rac-glycero-3-

phosphocholine, Et-18-OCH3), is the prototype for the ATL group of compounds and is a

lysophosphotidylcholine analog (Figure 1.1). It was originally synthesized in the 1960’s,

5

along with other ether lipids, while searching for novel immune modulators that were

made to be metabolically stable and resistant to acyltransferases and phospholipases

through modifications of the glycerol backbone at the C1 and C2 positions (25). In

addition to being immune modulators, many of these ether lipids were found to have

selective antitumour activities in vitro and in vivo (26,27) and the ability to induce

apoptosis in cells (28,29).

1.2.1 Pathways proposed to be affected by edelfosine treatment

Numerous pathways have been suggested to be affected by treatment with

edelfosine, with cell type potentially dictating the most important molecular target/targets

(10). The different pathways proposed to be affected by edelfosine can be seen in Figure

1.2 and the evidence for each will be briefly stated, with a more extensive discussion

found in the review by van Blitterswijk and Verheij (10).

Strong evidence exists that edelfosine has an effect on phosphatidylcholine (PC)

biosynthesis through inhibition of the endoplasmic reticulum (ER) enzyme

CTP:phosphocholine cytidylyltransferase, which is the rate limiting step for the

biosynthesis of PC (30,31). Furthermore, it has been observed that edelfosine is able to

inhibit the phospholipase D (PLD) mediated breakdown of PC to phosphatidic acid (32)

and phospholipase C (PLC) mediated breakdown of PC to diacylglycerol (DAG) in

small cell lung carcinoma cells due to inhibition of phospholipase C-β1 with its direct

activator (33). Through PLC and PLD inhibition, edelfosine has been suggested to exert

an effect on the mitogen-activated protein kinase/extracellular-signal regulated kinases

(MAPK/ERK) pathway which is involved with cell proliferation (34).

Another pathway edelfosine has been shown to exert an effect on is the

6

PC

PLD PLC

PA Choline

Bad, caspase 9,

Mdm2/p53

SMSSM

Ceramide

DAG

PC

P-Choline

CDP-Choline

DAG

c-Raf Ras

Edelfosine

rafttransporter

RasGRPPKC

PKD

MAPK/ERK

Proliferation

PIP2

Survival ER Stress, ROS, ASK1, SAPK/JNK

Apoptosis

PKB/Akt

mTOR

PI3K

PIP3 PDK

CK

CT

CPT

7

Figure 1.2. Pathways that have been proposed to be affected by edelfosine treatment and the resulting cell survival, proliferation

and pro-apoptotic processes affected. List of abbreviations: ASK1 (apoptosis signal-regulating kinase 1), APL (alkylphospholipid), CK

(choline kinase), CPT (choline phosphotranferase), CT (CTP:phosphocholine cytidyltransferase), DAG (diacylglycerol), MAPK/ERK

(mitogen-activated protein kinase/extracellular-signal regulated kinases), PA (phosphatidic acid), PC (phosphatidylcholine), PDK

(phosphoinositide-dependent kinase), PLC (phospholipase C), PLD (phospholipase D), PKB/Akt (protein kinase B/Akt), PKC (protein

kinase C), PKD (protein kinase D), PI3K (phosphatidylinositol-3-kinase), PIP2 (phosphatidylinositol-4,5 bisphosphate), PIP3

(phosphatidylinositol-3,4,5 triphosphate), RasGRP (Ras guanine-releasing protein), ROS (reactive oxygen species), SAPK/JNK (stress-

activated protein kinase/c-Jun NH2-terminal kinase), SMS (sphingomyelin synthase). Figure adapted from (10).

8

PI3K-Akt/PKB survival pathway with dose-dependent inhibition seen in A431 and HeLa

epithelial carcinoma cells seen (35). Additionally it was found that inhibition of the

PI3K/Akt pathway resulted in activation of the pro-apoptotic stress-activated protein

kinase/c-Jun NH2-terminal kinase (SAPK/JNK) pathway (35,36). The SAPK/JNK

pathway can be activated by Fas/CD95 (37), a death receptor on the surface of cells that

leads to apoptosis, stimulation and cellular stresses (38). This supports observations that

Fas/CD95 death receptor is involved in inducing apoptosis in human leukemic cells

treated with edelfosine (39) and that edelfosine induced ER stress leads to apoptosis

(40,41). Further supporting the induction of cellular stress by edelfosine and the role of

the SAPK/JNK pathway in apoptosis, it has been shown that Jurkat cells treated with

edelfosine showed enhanced productions of reactive oxygen species (ROS) (42).

1.2.2 Uptake of edelfosine

There is strong evidence that edelfosine is able to easily incorporate into the

plasma membrane (43). As discussed above the targets of edelfosine are located in the

ER, on the cytoplasmic side of the plasma membrane or the membranes of endosomes,

dictating that edelfosine has to be internalized after insertion in the plasma membrane

(10). Two modes of internalization have so far been suggested; either movement from the

outer leaflet to inner leaflet of the bilayer or internalization through lipid-raft mediated

endocytosis (Figure 1.2). As spontaneous flipping of edelfosine across the bilayer is

probably very energetically unfavourable, it seems more likely that a lipid transporter is

involved (10). Though no specific lipid transporter has been found thus far in human or

tumour cells, there is evidence to support this method of internalization. It was observed

that KB epidermal carcinoma cells were highly dependent on intracellular adenosine

9

triphosphate (ATP) and ambient temperature for APL uptake and the uptake was not

affected by treatment with an inhibitor of raft-mediated endocytosis (44). These results

showed that APL uptake was via an energy-dependent and endocytosis-independent

process, suggesting the need for a transporter (44). This conclusion was also supported by

an independent study (45).

It has been definitively shown that after insertion into the plasma membrane,

edelfosine accumulates in lipid rafts and is internalized through a lipid raft dependent

endocytosis pathway (31,46). The importance of this lipid raft-mediated endocytosis was

confirmed by experiments showing that pre-treatment of cells with raft-disrupting agents

resulted in reduced APL uptake and apoptosis (47). Furthermore, observations that the

inability to synthesize sphingomyelin (SM) due to downregulated sphingomyelin

synthase (SMS) and disruption of cholesterol trafficking to the trans-Golgi network

caused edelfosine resistance (48) suggest the importance of these components for lipid-

raft dependent uptake of edelfosine. It should be noted that SMS is involved with the

conversion of ceramide (Cer) to SM (Figure 1.2) and that increased levels of ceramide

were proposed to mediate apoptosis upon treatment with miltefosine (49), suggesting

conflicting results and that further work in this area is still needed.

1.2.3 Clinical applications of edelfosine

Despite its status as the prototype compound for the ATL family of compounds,

the only current clinical use for edelfosine is in the purging of leukemic bone marrow

cells (50). During the early 1980’s phase I clinical trials of edelfosine showed early

tumour and leukemia response with antineoplastic activity being observed, suggesting it

could potentially have clinical value (51). Phase II clinical trials of 116 non-small-cell

10

lung carcinoma patients treated with edelfosine demonstrated very little promise with

only 2 showing partial remission of the 81 patients who tolerated the treatment (52). In

addition to anti-cancer applications edelfosine has also been used for other therapeutic

purposes. Among these uses, edelfosine promoted improved clinical symptoms in a trial

with a limited number of multiple sclerosis patients (53). Edelfosine and some analogues

have also been reported to inhibit human immunodeficiency virus (HIV) reverse

transcriptase suggesting a potential future as an anti-HIV drug (54).

1.3 Yeast as a model system for cancer

Saccharomyces cerevisiae has a long history as an extremely beneficial model

organism due to the high degree of conservation in the basic cellular processes and

pathways found in yeast and higher eukaryotic organisms in addition to the advantages of

yeast genetics (55). To this end, yeast has been a very important tool for understanding

processes including DNA repair mechanisms (56) and the cell cycle (57).

1.3.1 Drug studies in yeast

Yeast also has a very successful history in the development of compounds for

pharmaceutical uses and an excellent review discussing the many advantages of using

yeast as a model organism for anticancer drug discovery are discussed (58). Among

them are its very simple growth requirements, rapid cell division and the ease with which

genetic manipulations and screens can be done (58). A number of yeast genomic assays

have been developed for drug and target discovery including drug-induced haploid

deficiency profiling, haploid deletion chemical genetic profiling, multi-copy suppression

profiling and comparative expression profiling as discussed by Smith et al. (59). One

well known example of yeasts use to uncover a mode of action is rapamycin, which was

11

instrumental in uncovering the molecular target of rapamycin (60). Another example of

how S. cerevisiae can be used to investigate the mode of action of compounds is

tamoxifen, a drug used for the treatment of breast cancer that was found to disrupt

calcium homeostasis through chemical-genetic profiling of 82 compounds and natural-

product extracts with yeast haploid deletion mutants (61).

1.3.2 Edelfosine studies in yeast

In addition to yeasts successful use for the study of different aspects of cancer, it

has also been successfully used to uncover aspects of the mode of action of edelfosine.

As previously mentioned, it has been suggested that one of two ways through which

edelfosine was internalized was likely through a lipid transporter though it has not yet

been identified. Using a combined mutant selection and screen in yeast, it was

determined that the Lem3, a plasma membrane protein, was required for normal transport

of phosphatidylcholine and APL’s including edelfosine (62). Another genetic screen

using yeast showed that edelfosine treatment resulted in Pma1, a plasma membrane

ATPase, selectively partitioning out of lipid rafts and being localized to vacuoles (63).

Additionally, this study also found that yeast cells with deficient endocytosis and

vacuolar protease activities prevented sterol movement out of the plasma membrane

(PM), in addition to preventing Pma1 loss from lipid rafts and apoptosis (63). A follow

up studying examining the protective effect exhibited by vitamin E on edelfosine treated

cells showed it is a result of both its antioxidant activity and lipophilic nature and results

in inhibition of the oxidative stress response induced by edelfosine (64). Furthermore, the

authors put forward a working model for the mode of action in yeast that can be seen in

Figure 1.3 involving the insertion of edelfosine into the plasma membrane followed by

12

Edelfosine

Sterols

Pma1p

Vacuole

51

3

2

4

6

Figure 1.3. Suggested working mode of action for edelfosine in yeast. 1) Edelfosine inserts into the plasma membrane and is flipped by

a Lem3p regulated flippase. 2) Interaction of edelfosine with the plasma membrane induces sterol internalization. 3) Displacement of the

essential proton pump, Pmap1, from lipid rafts. 4) Pmap1 is endocytosed followed by. 5-6) Degradation in the vacuole. Figure obtained

from (64).

13

flipping to the inner leaflet by a Lem3 regulated flippase and sterol internalization (64).

Pma1 is also displaced from lipid rafts, endocytosed and degraded in the vacuole (64).

1.4 Metabolomics

Metabolomics is a rapidly emerging technique that follows on the heels of other

omics technologies such as genomics, transciptomics and proteomics. It has quickly seen

widespread use across multiple disciplines as metabolites can serve as direct monitors of

biochemical activity at a given point in time or under a defined condition and are not

subject to genetic regulation or post-translational modification as is the case with genes

and proteins (65). Metabolites are defined as small molecules that are involved in cellular

processes or the regulation of them and include compounds such as organic acids, amino

acids, sugars, lipids and alcohols among others.

1.4.1 Metabolomics methodology

Metabolomics analysis requires many unique and specific methods for the various

steps involved (66). An excellent review by Dettmer et al. discusses many of the steps

required for mass-spectrometry based metabolomics including sampling, sample

preparation, separation, mass spectrometric analysis, data export and analysis, and

metabolite identification (66). Wilcoxen et al. (67) have summarized in a table the 3 main

stages of a metabolomics experiment workflow; sample preparation, sample analysis and

data analysis with the commonly used methods for each step and their advantages and

disadvantages (Table 1.1).

Sample preparation is very dependent on the type of sample being analyzed and

platform being used for the analysis. For instance yeast has cells walls that must be

disrupted by means other than sonication for efficient extraction, and bacteria have rapid

14

Table 1.1. Steps and methods from a typical metabolomics experiment. Table obtained and modified from (67)

Steps Methods Advantages Disadvantages

Sample preparation Sample quenching Minimizes formation/degradation of

metabolites due to enzymatic activity

Possible analyte loss due to cell leaching;

buffers cause ion suppression (MS)

Tissue/cell

homogenation

Necessary to obtain efficient

metabolite extraction

Potential loss of analytes

Liquid–liquid extraction Enrichment of metabolite classes by

physiochemical properties


Solid phase extraction Focused collection of analytes by

varying material and eluant


Derivatization Allows analysis of polar metabolites

(necessary for GC-MS)

Not suitable for analytes with poor

thermal stability

No modification No analyte loss and short analysis time Significant ion suppression (MS), only

abundant species identity (NMR)

Sample analysis NMR spectroscopy Quantitative, versatile, rapid, databases

for metabolite ID

Lack of sensitivity, requires large sample

volumes

LC-MS Quantitative, excellent sensitivity,

minimal sample size, databases for

metabolite ID

Expensive instrumentation, destruction of

sample, longer sample analysis times

GC-MS Quantitative, good sensitivity,

moderate sample size, databases for

metabolite ID

Requires derivatization, destruction of

sample, longer sample analysis times

Data analysis Metabolomic profiling Selected metabolite family,

quantitative, metabolite ID achievable

Not global analysis (biased)

Metabolomic

fingerprinting

Provides pattern of all metabolites,

metabolite ID unnecessary

Limited to classification tool, poor

metabolite identification

15

metabolite turnover rates so quenching is required for these sample types. Another

examples is the case of derivitization which is required for analysis by gas-

chromatography mass spectrometry (GC-MS) instruments but is always necessary for

liquid-chromatography mass spectrometry (LC-MS) analysis.

Sample analysis for metabolomics is most often done on one of 3 platforms;

nuclear magnetic resonance (NMR) spectroscopy, GC-MS or LC-MS. Many reviews are

available for each platform that discuss their applications with fairly recent reviews for

GC-MS (68), LC-MS (69) and NMR (70) of note. Additionally, a summary of the major

advantages and drawbacks for each of the 3 major metabolomics platforms can be seen in

Table 1.1.

The data analysis steps for metabolomics can be very extensive, depending on the

processing steps required and the type of analysis being done. Many instrument

manufacturers provide software that can be used for peak identification and analysis,

however more often than not the software can only be used for samples run on that

particular instrument. However, downloadable software including MET-IDEA (71),

MetaboliteDetector (72) and XC-MS (73) are available to be used for the peak detection

and identification steps and are not restricted to a specific instrument. Furthermore

commercially available software such as SIMCA (Umetrics AB, Umea Sweden) and

downloadable software such as MetaboAnalyst 2.0 (74) are also available to carry out the

multivariate analysis steps that are discussed below.

There are two main types of metabolomics approaches currently used known as

targeted or untargeted profiling. With targeted profiling a specific class of compounds or

pathway(s) are analyzed (65). Conversely untargeted profiling is a global approach which

16

aims to detect and identify as many metabolites as possible and examine sample wide

metabolism (65). Several commercial and downloadable programs are available to help

with the secondary analysis of metabolomics data as reviewed by Booth et al. (75). From

a list of altered metabolites identified using metabolomics profiling, these programs are

able to carry out enrichment analysis which identifies significantly altered metabolic

pathways, or pathway analysis which allows for the visualization of the network of

affected metabolites and puts it into a metabolic context (75). Both of these approaches

aid in the biological interpretation of metabolomics data.

1.4.2 Lipidomics: A subspecialty of metabolomics

Lipidomics is a subclass of metabolomics that focuses solely on the detection and

analysis of lipids. This speciality has recently become more prevalent as it is increasingly

recognized that lipids play essential roles in cell structure and organization, signalling

and trafficking (76). The main problem associated with lipidomic analysis is the diversity

displayed by lipids. A review of the different lipid classes and their cellular functions by

Khalil et al. (76) demonstrates the sheer magnitude of the different types of lipids that

exist. Much of the work in lipidomics up to this point has focused on trying to expand

upon the number of lipid classes that can be identified within a single sample. Several

recent reviews discussing the progress that has been made in the field of lipidomics and

the advantages and disadvantages for lipidomic profiling are available (77-79). GC-MS

shows the most promise for analysis of fatty acids and its derivatives, but is not ideal for

analysis of larger lipids due to the requirement of derivitization with GC-MS analysis

(77). Developments related to tandem MS, matrix-assisted laser desorption/ionization

(MALDI), shotgun and imaging mass spectrometry techniques have all greatly aided in

17

the advancement of lipidomic analysis (79). An example of a success story that shows

just how far lipidomic analysis methods have come is a study that was able to absolutely

quantify 95% of the lipidome of yeast covering 21 major lipid classes using a shotgun

approach, where a total lipid extract sample is directly injected into the instrument for

ionization without separation (80).

1.5 Multivariate analysis

Due to the immense amounts of data obtained through metabolomics analysis,

traditional statistical methods are not able to effectively analyze the data obtained.

Therefore multivariate statistical analysis methods are needed to extract the information

from the data. Multivariate analysis uses projection based modelling methods (Figure

1.4), which involve expressing the metabolite levels in each sample as a single point to

allow for comparison between samples and to summarize and simplify data to a point

from which meaningful information can be obtained (81).

In order for the modelling methods to be successfully carried out the data may need to be

pre-processed. Such steps may include normalization, scaling and mean centering of the

data. Normalization is done to account for small difference in dilution between samples

that can affect the data quality. Scaling is also done to account for the fact that different

metabolites will have different ranges, which if left as is can cause problems for

modelling and interpretation (81). Additionally mean centering is also carried out in order

to give all the variables (metabolites) the same reference point, allowing for the

simplified comparison of different samples.

After processing of the data, projection methods can be used to summarize the

data and allow for analysis and comparison (Figure 1.4). Two types of modelling

18

Figure 1.4 Projection methods simplify all observations for a sample into a single point to allow for easy visualization and

comparison. Figure adapted from (81).

19

methods are most commonly used for multivariate analysis of metabolomics data.

Principal component analysis (PCA) is an unsupervised projection method commonly

used to examine the dataset for outliers, trends and for pattern recognition (81).

Orthogonal partial least squares-discriminant analysis (OPLS-DA) modelling is a

supervised method that is used to identify and explain the differences between two or

more defined sample groups (81). To aid with interpretation parameters such as variable

influence on projection scores (VIP) scores can be used. VIP scores estimate the amount

different metabolites contribute to the separation of the different sample groups, with a

score of greater than 1 suggesting a significant contribution. Additionally coefficient

scores can be used to determine if individual metabolites are elevated in one sample

group compared to another. Two main types of plots are used for the analysis of

metabolomics data after modelling. Scores plots are used to summarise the samples, and

to observe patterns and trends (81). Loadings plots are used to summarise the metabolites

and how they relate to the samples (81).

After construction, models are evaluated for quality through fit of the data (R2)

and predictive quality of the model (Q2) parameters (81). The Q

2 parameter is calculated

using cross-validation which involves splitting of the data into 7 sets and using 6 of the

sets to build a model and using the 7th

to test it, and this is repeated for all the iterations.

A good model will have R2 and Q

2 scores both above 0.5, with a difference of no greater

than 0.3 between them. Additionally cross-validated analysis of variance (CV-ANOVA)

p- values can be calculated for OPLS-DA models, with a score of less than 0.05

considered to be significant and indicative of separation between the sample groups being

modelled.

20

One such program that is able to carry out such multivariate statistical analysis

and modelling is the commercial software SIMCA (Umetrics AB, Umea Sweden).

1.6 Research goals

As edelfosine is the prototype of the ATL group of compounds and its mode of

action is not well understood with different and sometimes conflicting observations

published in the literature, we hypothesize that the use of metabolomic analysis methods

with the model system S. cerevisiae will provide insight into the mode of action of

edelfosine. This hypothesis will be addressed in multiple steps using different

metabolomics technologies and analysis techniques:

1) Optimization of polar metabolite and lipid extraction from yeast cells.

2) GC-MS analysis of the changes in the metabolome and fatty acid profile of

yeast induced by edelfosine treatment.

3) LC-MS analysis of the changes in the lipidome in yeast induced by edelfosine

treatment.

4) Secondary analysis and biological interpretation of the metabolomics data.

By combining all of this metabolomics information and trying to analyze it as whole, I

aim to gain a broad data set with which to study the metabolism-wide effects of

edelfosine in yeast. Ultimately the goal of this research is to build upon the previous work

done with edelfosine characterization in yeast and expand up the current working mode

of action.

21

Chapter Two: Materials and Methods

2.1 Yeast growth and sample harvesting

Yeast strain BY4741 (MATa; his3∆1, leu2∆0, met15∆0 and ura3∆0) a commonly

used wild-type lab strain that has been used in previous studies with edelfosine (63) was

grown in minimal selective defined (SD) liquid media composed of 0.67% (w/v) yeast

nutrient broth (YNB) with ammonium sulphate (MP Biomedical, Solon OH, USA), 2%

(w/v) dextrose, 0.002% (w/v) histidine, 0.003% (w/v) leucine, 0.002% (w/v) methionine

and 0.002 % (w/v) uracil. A SD media was used so that all components of the media had

consistent quantified levels as opposed to rich media, in this case yeast extract-peptone

dextrose (YPD), which varies from batch to batch. Yeast cultures were grown in an

incubated shaker at 30⁰C or 37⁰C with a rotation speed of 150 rpm to a log phase OD600

of 0.2/ml. Each sample harvested consisted of approximately 10 OD600 total of pelleted

cells. The pellets were washed twice with water to remove all growth media, flash frozen

in liquid nitrogen to prevent further growth and/or metabolite turnover and stored at -

80⁰C.

2.2 Metabolite extraction

In order to effectively carry out metabolic profiling studies, consideration must be

given to the protocol used as the metabolite recovery process affects all downstream

analysis and interpretation steps. Furthermore, if multiple metabolite types are being

considered it is best to carry out the different types of analysis on the same sample so as

to avoid introducing non-biological variation. Given these considerations,

chloroform/methanol/water metabolite extraction methods were used as they have had

22

success with both lipid and polar metabolite extractions, have good metabolite recovery

across different metabolite classes and are reproducible.

The different chloroform/methanol/water metabolite extraction methods explored

are discussed in chapter 3.

2.3 GC-MS analysis

GC-MS analysis is a technique that allows for metabolic profiling of polar

metabolites or fatty acids from biofluid, tissue, or cell samples. In order for GC-MS

analysis to be carried out the metabolites being analyzed must first be derivitized to allow

for their detection. Metabolomic profiling using GC-MS can be quantitative and can be

done using either a targeted or untargeted approach. With a targeted approach such as

FAME (fatty acid methyl ester) profiling of fatty acids, standards are run and used for the

detection of those compounds in the samples being analyzed using their specific m/z

(mass to charge ratio) signature and retention time. With untargeted analysis, all detected

peaks are considered and compounds are identified through matching the m/z value to

those of known compounds from a database.

2.3.1 Sample preparation and derivitization for GC-MS analysis

Aqueous samples were prepared for GC-MS analysis by derivatization with

methoxyamine and MSTFA (N-methyl-N-(trimethylsilyl) trifluoroacetamide) using a

previously described protocol (82). To each dried down aqueous phase sample, 50 μl of a

20 mg/ml solution of methoxylamine-hydrochloride in pyridine was added. After

addition of methoxylamine-hydrochloride the samples were shaken at 37 °C for 2.5

hours. After shaking, 50 μL of MSTFA was then added and followed by 45 min of

additional shaking at 37 °C. Each sample was diluted with 500 μL of hexane and

23

centrifuged at 14,000 rpm with an Eppendorf 5415 C Centrifuge for 4 minutes in order to

remove any solid particulate in preparation for GC-MS analysis. After centrifugation was

complete, 200 μL of the samples was transferred to a GC-MS analysis vial with a glass

insert.

Organic samples were prepared for GC-MS FAME analysis by derivitzation with

BF3/methanol using a previously described protocol (83). The dried down organic phase

samples were dissolved in 750 μl of 1:1 (CHCl3:MeOH ) under sonication for 15

minutes. This was followed by the addition of 50 μl of 200μM D-25 tridecanoic acid

which was the internal standard. Next 125 μl of BF3/methanol was added and the samples

were incubated in glass vials at 80 °C for 90 min. After cooling, 300 μl of H2O and 600 μl

of hexane were added to each sample and the contents were vortexed to mix and allow

for separation of the aqueous phase and the organic phase which contained the fatty acid

methyl esters. The aqueous and organic layers were then isolated and placed into separate

eppendorf tubes and the organic phase was evaporated to dryness overnight in a fume

hood. Prior to GC-MS analysis the samples were reconstituted in 200 μL of hexane and

transferred to GC-MS analysis vials with glass inserts.

The derivitization methods described above were used for sample preparation as

they are the protocols most commonly found in literature and are well established.

2.3.2 GC-MS data acquisition

GC-MS acquistion was carried out using a Waters GCT Premier GC-TOF-MS

(gas chromatography time-of-flight mass spectrometer). For aqueous metabolite analysis

an EI (electron ionization) source was used with a DB-5MS 30 m x 0.25mm column

(Agilent Technologies, Mississauga Ontario) and a 0.25um filament size. For FAME

24

analysis an EI source was used with a DB-23 60m x 0.25mm column (Agilent

Technologies, Mississauga Ontario) and a 0.15um filament size. The settings on the GC-

MS were 275⁰C and 240⁰C injector temperature for the aqueous column and FAME

columns respectively with a flow rate of helium (carrier gas) of 1.2 ml/min. A blank

followed by the standards (n-alkane mix (Sigma-Aldrich, Oakville Ontario) for aqueous

metabolite samples, and a 37 FAME standard mix (Sigma-Aldrich, Oakville Ontario) for

the FAME analysis) were run between the analysis of every 10 samples to monitor

instrument and column stability throughout the course of the data acquisition. Samples

were run in a randomized order in order to avoid bias.

2.3.3 GC-MS data processing

Raw GC-MS data from polar metabolite profiling was imported to

MetaboliteDetector (72) for peak detection and compound identification using an

untargeted approach. Briefly the ΔRI, Pure/Impure, required similarity score (Req. Score)

and compound reproducibility parameters were varied with iterations of the different

value combinations carried out. The set of values that resulted in the most identified

compounds while limiting the overall number of unidentified ions was then used in each

case for further analysis.

Peak detection and identification for FAME analysis was done with

AMDIS/MetIdea using a targeted approach with a 37 FAME standard (Oakville, Ontario

Canada) serving as the dataset from which identifications were made. Briefly, AMDIS

(www.amdis.net) was used to identify the 37 FAME standard peaks and to assign

retention times and unique m/z signatures. MetIdea (71) was used for calibration of the

sample peaks and to detect the amount of the FAME’s present in the samples.

25

The data were then normalized using Excel 2010 (Microsoft, Redmond, WA,

USA) in order to account for different dilutions of the samples being analyzed. For

targeted FAME profiling normalization to the internal standard, D-25 Tridecanoic Acid,

occurred first and was followed by integral normalization. In the case of untargeted

aqueous metabolite profiling, no internal standard was used so only integral

normalization occurred.

2.4 LC-MS analysis

LC-MS analysis like GC-MS analysis is a technique that allows for metabolic

profiling. One difference between GC-MS and LC-MS profiling methods is that with LC-

MS derivitization of metabolites is not always needed, thus allowing for profiling of

intact metabolites and lipids. However, LC-MS is not very quantitative without the

extensive use of standards. As untargeted sample analysis with LC-MS methods can be

time consuming care must be taken to ensure that samples are carefully chosen for before

they are analyzed so resources and instrument availability can be conserved.

2.4.1 Addition of internal standards to LC-MS samples

Internal standards for different types of lipid species were added to each sample in

order to allow for quantification. The lipids standards (Avanti Polar Lipids Inc., Alabaster

Alabama) used were chosen as they are not naturally occurring in yeast and allow for

monitoring. The lipids standards, their ID number, concentration and mass can be seen in

Table 2.1.

2.4.2 UPLC-TOF-MS data acquisition

Dried organic extracts were dissolved in injection solvent, with initial gradient

conditions of 60% solvent A, 40% solvent B, and injected onto a 1.8 μm particle, 150 x

26

Table 2.1. Internal standards added to samples for ultra perfomance liquid chromatography time-of-flight mass spectrometry

(UPLC-TOF-MS) analysis.

Lipid Standard ID # Concentration Exact Mass Supplier

PE (17:0/14:1) LM 1104 10.90ug/1mL 675.4839 Avanti Polar Lipids

PS (17:0/20:4) LM 1302 9.57ug/1mL 797.5207 Avanti Polar Lipids

PA (17:0/14:1) LM 1404 10.34ug/1mL 422.24 Avanti Polar Lipids



PI (17:0/20:4) LM 1502 10.04ug/1mL 872.54 Avanti Polar Lipids

PG (17:0/14:1) LM 1204 10.93ug/1mL 706.48 Avanti Polar Lipids

PC (17:0/20:4) LM 1002 8.7ug/1mL 795.58 Avanti Polar Lipids

PC (13:0/ 0:0) LM 1600 9.67ug/1mL 453.29 Avanti Polar Lipids




D5-DAG Mix LM 6004 4uM each 569.51; 629.6; 625.57; 621.54 Avanti Polar Lipids

D5-TAG Mix LM 6000 4uM each 975.74; 753.69; 809.75;

839.8; 851.8; 839.8; 977.94;

937.81; 931.77

Avanti Polar Lipids

27

2.1 mm id Waters ACQUITY HSS T3 column (Waters, Milford Massachusetts)

which was heated to 40 °C in the column oven. Mobile solvent phase A consisted of

40% HPLC grade acetonitrile (Fisher Optima, Pittsburgh Pennsylvania) and 60%

Milli-Q H2O (Millipore, Billerica Massachusetts), 10mM ammonium formate (Sigma-

Aldrich, St. Louis Missouri). Mobile solvent phase B consisted of 10% acetonitrile

and 90% HPLC grade isopropanol (Fisher Optima, Pittsburgh, Pennsylvania), 10mM

ammonium formate (Sigma-Aldrich, St. Louis Missouri). A linear gradient was used

(curve 6) over a total run time of 18 minutes. Initial conditions of 60% solvent A and

40% solvent B were held for 1 minute. The gradient was ramped up in a linear fashion

over the next 10 minutes to 96% solvent B where it was held for 2 minutes. The

column was then re-equilibrated at initial conditions for 5 minutes before the next

sample injection. The flow rate used was 0.3 ml/minute and the injection volume was

10 μL.

The ACQUITY UPLC system (Waters, Milford Massachusetts) was coupled

to a Xevo G2-S time-of-flight mass spectrometer (Waters MS Technologies,

Manchester, United Kingdom). Electrospray positive ionization mode was used in

resolution mode. A capillary voltage of +3 kV and a cone voltage of +35 V were used.

The desolvation gas flow was set to 700 L/hr at a temperature of 400 °C. Nitrogen

was used as the desolvation gas. MSE centroid mode was used for data acquisition

over the mass range of 100-1500 Da, with a scan time of 1 second. For the MSE

settings, the low energy function was set to a collision energy of 6 V, and the high

energy function was set to a ramp collision energy from 20 – 30 V. Argon gas was

used as the collision gas. The mass spectrometer was equipped with a LockSpray

exact mass ionization source that automatically collected a reference scan of the

lockmass compound every 30 seconds lasting 1 second. Leucine encephalin (Sigma-

28

Aldrich, St. Louis Missouri) was used as the lockmass reference, and exact mass

correction was applied as data was acquired based on the mass, 556.2771, of leucine

encephalin.

2.5 Multivariate statistical analysis

After normalization and compound detection, the data were exported to

SIMCA-P13 (MKS Umetrics AB, Umea, Sweden), a commercial multivariate

statistical analysis software that has been used for various metabolomics studies

including characterization of colorectal cancer using NMR (84), metabolomics

analysis of renal failure using quadropole time-of-flight mass spectrometry (Q-TOF-

MS) (85), and to identify metabolic responses to metal stress in bacteria using NMR

and GC-MS (82). In SIMCA-P, univariate scaling (shifts all variables to the same

range) and mean centering (gives all variables the same reference point) were applied

before the model construction and validation steps. PCA models were prepared,

through unsupervised modelling, in order to examine the data for outliers using a 95%

confidence interval. Additionally PCA modelling was used to examine the data for

non-biological biases that could result for things such as extraction, derivitization or

analysis batch. OPLS-DA models were constructed to identify and highlight the

differences between distinct sample types using supervised modelling. Model

construction was done using the autofit routine of SIMCA-P13, to avoid overfitting of

the data for models. The models were evaluated for quality and reliability through R2

and Q2 scores, with a good model considered to have scores over 0.5 (from a range of

0 to 1) for both parameters and values that are close to each other. OPLS-DA models

were also validated using CV-ANOVA p-values with a value of less than 0.05

considered to be significant.

From the OPLS-DA modelling, metabolites that contribute significantly to the

29

separation between sample groups can be identified. This was done using VIP scores

that are calculated by SIMCA-P13 with the cutoff value set at 1, as this provides a

relatively high level of confidence that the identified metabolites are significantly

contributing to the separation of the sample groups and is a value that has been used

commonly in the literature. Additionally, coefficient values that can be used to

identify whether specific metabolites are increased in one sample group compared to

another were calculated by SIMCA-P13 and were obtained after construction of an

OPLS-DA model. Using the VIP and coefficient scores information, a picture of the

overall differences between sample groups can be obtained to aid in biological

interpretation.

2.6 Pathway analysis and metabolic network construction

Metabolites identified to have VIP scores greater than one through OPLS-DA

modelling can be subjected to secondary analysis which usually involves enrichment

analysis or pathway analysis. The different types of methods for secondary analysis of

metabolomics data and the programs available to do these types of analysis are

discussed in a review by Booth et al. (75). The MetaboAnalyst 2.0 server (74) was the

program chosen to be used for secondary analysis of the metabolites as it has the

capability to perform both enrichment and pathways analysis and to select organism

specific metabolic pathway sets. Impact and p-value scores were used to trim and

identify the pathways that were significantly perturbed between the sample types.

These values were calculated by the server, with a p-value less than 0.05 and an

impact score greater than 0.1 considered to significant. The impact score is calculated

based on a sum of the impact scores for each metabolite identified in a pathway that

are based on the importance of the metabolites to each given pathway. Using the

information of the affected pathways and metabolites, a metabolic network of the

30

changes induced can be constructed to give an overall summary of the various

processes being affected and aid in the biological interpretation of the profiling data.

31

Chapter Three: Optimization of Metabolite and Fatty Acid Extraction from


3.1 Introduction

The field of metabolomics is rapidly seeing more widespread use for the

determination of system level metabolic changes caused by influences such as diet,

environmental stress and disease (82,86-89). However, to accurately determine the

changes in a metabolite profile caused by these influences, care must be taken in order to

optimize the different factors that affect data quality and reproducibility. Of paramount

importance in this regard is the metabolite extraction step, as it affects both the number of

different metabolites available for analysis as well as the reproducibility and reliability of

the data obtained.

Studies looking at optimal polar metabolite extraction protocols for biological

compounds including sugars, amino acids and water soluble metabolic precursors or

intermediates have been carried out for single platforms including NMR (90,91), GC-

MS (92-95), and LC-MS (96-98). Additional studies have focused on a polar

metabolite extraction for multi-platform use (99,100). Optimal extraction protocols

have been tested for biological samples including serum (97)

and plasma (92), and for different model organisms such as Escherichia coli (93), S.

cerevisae (101,102) and Caenorhabditis elegans (100). Furthermore, some effort has also

gone into determining the best extraction method for non-polar metabolites, such as fatty

acids and other lipids (103-105), with these types of studies becoming more frequent as the

subfield of metabolomics (also known as lipid profiling or lipidomics) has become more

popular. For instance, detailed protocols on how to extract and analyse lipids from yeast

(106), as well as body fluids and tissues (107) are now available.

32

Though much work has gone into establishing protocols to look at either polar

(water soluble) or non-polar (soluble in organic solvents) metabolites, little work has

gone into finding a protocol that is effective at simultaneously extracting both polar

and non-polar metabolites. Analysis of both polar and non-polar metabolites from the

same samples could be extremely beneficial in future metabolomic studies, as it will

avoid much of the variation that can occur when trying to combine both types of

metabolite information from separate samples. Additionally, much of the previous work

with optimizing extraction of metabolites has centered on comparing different types of

quenching and extraction solvents, while focusing mainly on optimal metabolite

recovery as opposed to reproducibility.

Here we investigated three different chloroform/methanol/water based

metabolite extraction protocols found in the literature on S. cerevisiae for the ability

to reproducibly extract high levels of polar and non-polar fatty acid metabolites.

Chloroform/methanol/water based protocols were explored as they are generally the

standard for classical lipid/fatty acid extractions and (108,109) have had success

extracting polar metabolites in yeast (102), among other sample types. Yeast was used

as it is accepted as a suitable fungal representative of the microbial community, and as

a model system for eukaryotic organisms. Additionally, it is unique in containing only

mono-unsaturated and even numbered fatty acid chain lengths, thus simplifying

analysis. We were able to successfully identify a protocol using chemoinformatics and

multivariate projection methods that was able to reproducibly extract comparatively

high levels of both polar metabolites and non-polar fatty acids.

3.2 Results and discussion

Cheminformatics is a field that is growing rapidly and will be of great use for

metabolomic studies, especially as compound databases continue to expand. This will

33

allow for untargeted analysis of many different sample types to be carried out. As

more untargeted metabolomic studies are done, it becomes necessary to use

multivariate projection methods such as those discussed below to help interpret the

complex data collected. Using this approach we can apply metabolomics in many

different areas, including biomarker studies, drug monitoring, identifying effects of

diet and responses to external stresses or stimuli.

As metabolomics becomes a more prevalent tool across multiple scientific

disciplines, it has become clear that the extraction protocol used has a large influence

on the quality and reliability of the data obtained. As such, numerous studies have

tried to improve upon previously accepted metabolite extraction methods, and identify

an optimal extraction protocol for various platforms (90-98) and sample types

(92,93,97,100-102) though few, if any, have attempted to systematically identify an

extraction protocol that is able to obtain both polar and fatty acid/lipid metabolites

simultaneously. Additionally, most of these studies have focused on optimizing the

percent metabolite recovery through the use of internal standards, with less focus

placed on the reproducibility of the extraction protocol being utilized. As there are

now a large number of validated optimal extraction protocols suggested for various

platforms and biological samples, it must be recognized that obtaining reproducible

extraction from sample to sample is also of significant concern and should be

explored further. We also believe that it is important to recognize that biological

stresses and disease affect both polar and non-polar metabolites in organisms or

biological samples, and extraction of both types of metabolites from the same sample

will overcome much of the variation and drift problems associated with combining

data from different samples. Typical problems leading to these undesirable effects can

include small changes in atmospheric pressure from day to day, temperature and

34

growth media composition that occur between cultures and can lead to so called batch

effects. Additionally, the use of different extraction techniques and solvents can lead

to metabolite loss and/or variation or drift between samples reducing reliability and

reproducibility.

3.2.1 Unsupervised analysis clearly differentiates extraction method

In order to assess the influence of the metabolite extraction process on both polar

and non-polar metabolites, three separate procedures based on

chloroform/methanol/water partitioning that have been used previously in the literature

(63,80,83) were compared using multivariate modelling. The multivariate modelling was

carried out with the polar metabolite extracts having a dimensionality of 33 samples with

322 total ion hits obtained, and lipid extracts having a dimensionality of 48 samples

with 12 fatty acids monitored. Unsupervised PCA is a tool which provides a

multivariate overview of the data based on the underlying variance between the

metabolite profiles of the samples without specifying the different sample types.

Analysis of this type is useful for screening of outliers and overviewing the metabolite

patterns of the different sample types. Samples extracted for polar metabolites were

screened for outliers with PCA (Figure 3.1A). The overall sample pattern shows

clustering by extraction protocol and not by growth temperature, with R2

and Q2

values of 0.557 and 0.438 respectively for the model, which is within the acceptable

range for a biological model. Samples extracted for fatty acids using FAME analysis

were also examined for outliers using PCA (Figure 3.2A). The model consists of one

component with R2 and Q

2 values of 0.325 and 0.176 respectively, shows no outliers

and also shows loose clustering of the samples based on the extraction protocol used.

3.2.2 Supervised analysis identifies 36 metabolites and four fatty acids differentiating

the extraction methods

35

(A)

(B) (C)

36

Figure 3.1. Models comparing the polar metabolites extracted from S. cerevisiae by one of three chloroform/methanol/water based

extraction protocols. An overview of the data confirms that there are no outlying samples within a 95% confidence interval. (A) PCA scores

plot model with R2 = 0.557 and Q

2 = 0.438 values. Green circles represent samples extracted using method 1, blue squares represent samples

extracted using method 2 and red triangles represent samples extracted using method 3. (B) OPLS-DA scores plot model of predictive latent

variables (LV) 1 and 2 showing separation based on extraction method with R2(X) = 0.704, R

2(Y) = 0.933, Q

2 = 0.881 and CV-ANOVA p = 4.20 ×

10−7

values. Green circles represent samples extracted using method 1, blue squares represent samples extracted using method 2 and red triangles

represent samples extracted using method 3. (C) OPLS-DA scores plot model of predictive LV 2 and 3 showing separation based on growth

temperature with R2(X) = 0.704, R

2(Y) = 0.933, Q

2 = 0.881 and CV-ANOVA p = 4.20 × 10

−7 values. Green circles represent samples grown at 30

°C, blue squares represent samples grown at 37 °C.

37

(A)

(B)

Figure 3.2. Models comparing the fatty acid metabolites extracted from S. cerevisiae by one

of three chloroform/methanol/water based extraction protocols. FAME’s were identified

through comparison to a 37 FAME standard. An overview of the data confirms that there are no

outlying samples within a 95% confidence interval. Green circles represent samples extracted

using method 1, blue squares represent samples extracted using method 2, red triangles represent

apolar (17:1 CHCl3:MeOH) organic fraction samples extracted using method 3 and yellow upside

down triangles represent polar (2:1 CHCl3:MeOH) organic fraction samples extracted using method

3. (A) PCA scores plot model with R2 = 0.325 and Q

2 = 0.176 values. (B) OPLS-DA scores plot

model showing separation based on extraction method with R2(X) = 0.616, R

2(Y) = 0.517, Q

2 =

0.411 and CV-ANOVA p = 1.48 × 10−6

values.

38

In order to specifically identify which metabolites significantly contributed to the

separation between sample groups, OPLS-DA was also performed on the dataset. OPLS-

DA is a supervised analysis method to cluster multivariate data by maximizing the

variance between different sample groups. OPLS-DA modelling of polar metabolite data

produced an excellent three component model with R2(X) = 0.704, R

2(Y) = 0.933, Q

2 =

0.881 and CV-ANOVA p = 4.20 × 10−7

, with all samples clustering into their respective

extraction condition (Figure 3.1B). The first latent variable (LV) shows the separation of

samples from Extraction 1 and the samples of Extractions 2 and 3, and the second latent

variable shows separation of samples from Extractions 2 and 3. The third latent variable

separates the samples based on their growth temperatures of 30 °C and 37 °C, with the

samples of the same growth temperature clustering based on the protocol by which they

were extracted (Figure 3.1C).

The OPLS-DA model for FAMEs shows the separation of Extraction 2 samples from

Extraction 3 Polar and Extraction 3 Apolar samples with the first latent variable, while the

second latent variable shows separation of Extraction 1 samples from Extraction 2 and

Extraction 3 Apolar samples (Figure 3.2B). The model has a R2(X) = 0.616, R

2(Y) = 0.517, Q

2

= 0.411 and CV-ANOVA p = 1.48 × 10−6

, and shows loose clustering of the samples based

on the protocol with which they were extracted, with the exception of the Extraction 3

Polar samples which mix with each of the samples from the other extractions.

From OPLS-DA modelling, additional information can be obtained with regard to

the metabolites contributing to the separation of sample groups and comparative

metabolite levels using VIP scores and coefficients. Comparing metabolites with a VIP

score above 1 with the corresponding coefficient values for these metabolites from each

39

extraction method gives a basis to compare the differences in metabolite levels extracted.

Metabolites with a VIP greater than 1 (as identified by OPLS-DA modelling of polar

metabolites and FAME’s) and their corresponding coefficients can be seen in Table 3.1.

Thirty-six polar metabolites were found to have a VIP greater than 1, with Extraction 2

samples having higher coefficients in the majority of cases, indicating comparatively

higher levels of those metabolites (Table 3.1). Samples for Extraction 1 had positive

coefficients for about half of these metabolites and Extraction 3 samples had negative

coefficients in the majority of cases. Four fatty acids were found to have a VIP greater

than 1, with Extraction 1 samples having positive coefficients for all 4 of the FAME’s,

Extraction 2 samples have two positive and two negative coefficients and Extraction 3

samples having three negative coefficients (Table 3.1). Shared and unique structure (SUS)

plots were also generated to compare similarities and differences in metabolites extracted by

protocols 1 and 2 using samples from Extraction 3 as a common profile (Figure 3.3). The

SUS plots for both the polar and FAME metabolites show that all metabolites identified to

have a VIP > 1 score vary in the same direction and that neither protocol 1 or 2 extracts

any unique metabolites compared to the other. This indicates that, other than differences

in the levels of the metabolites extracted by the protocols, there is no difference in the

breadth of polar and fatty acid metabolites recovered between the two extraction

protocols.

3.2.3 Comparison of FAME and aqueous metabolite profiles obtained

PCA modelling of the FAME data from the organic layer(s) of the extraction

protocols produced a relatively weak model based on the statistics R2

and Q2. This can

perhaps be attributed to the fact that S. cerevisae does not have an overly complex fatty

40

Table 3.1. Metabolites identified to have a VIP score greater than 1 through OPLS-

DA modelling of aqueous metabolite and FAME extraction data and the

corresponding coefficient values for each extraction protocol. Values with a positive

coefficient indicate higher comparative levels, while values with a negative coefficient indicate

lower comparative levels.

Metabolite VIP Coefficient

Extraction 1

Coefficient

Extraction 2

Coefficient

Extraction 3

Threonine 1.832 0.013 0.046 −0.059

Glycerol 1.703 −0.012 −0.042 0.054

Phenylalanine 1.678 0.030 0.034 −0.064

Alanine-3cyano 1.666 0.037 0.009 −0.046

Methionine 1.649 0.014 0.041 −0.055

Proline 1.630 −0.001 0.029 −0.028

Sorbitol 1.603 0.002 0.023 −0.024

Phosphoric Acid 1.575 −0.008 0.052 −0.043

Homoserine 1.531 −0.001 0.049 −0.048

Pyroglutamic Acid 1.496 0.002 0.014 −0.016

Alanine 1.476 −0.023 0.025 −0.002

Ornithine 1.461 −0.011 −0.046 0.057

Serine-O acetyl 1.382 −0.029 −0.035 0.064

Fumaric Acid 1.372 0.002 0.004 −0.006

Trehalose-alpha,alpha’-D 1.340 0.013 0.002 −0.015

Alanine-beta 1.327 0.007 0.006 −0.013

Succinic Acid 1.310 −0.006 −0.011 0.017

Malic acid, 2-isopropyl 1.299 0.016 0.020 −0.035

Decan-1-ol, n- 1.296 0.003 −0.004 0.002

Glycine 1.271 −0.027 −0.014 0.041

Valine 1.267 −0.010 0.033 −0.023

Aspartic Acid 1.261 0.016 0.012 −0.028

Arginine [-NH3] 1.259 0.030 0.003 −0.033

Glutamic Acid 1.213 0.009 0.007 −0.016

Hexadecane, n- 1.210 −0.016 0.036 −0.019

Malic Acid 1.190 0.005 0.002 −0.006

41

Uracil 1.189 −0.039 −0.017 0.056

Isoleucine 1.162 −0.009 0.019 −0.011

Glutamine, DL- 1.156 0.029 −0.023 −0.007

Octylamine 1.131 −0.005 0.005 −0.001

Tyramine 1.119 0.014 0.012 −0.026

Butanoic Acid 1.063 −0.010 0.019 −0.008

Serine 1.062 −0.016 0.036 −0.020

Pentadecane, n- 1.029 −0.015 0.022 −0.008

Citric Acid 1.024 0.018 0.005 −0.023

Heptadecan-1-ol 1.010 −0.011 0.011 −0.001

Palmitic Acid 1.950 0.581 −0.372 −0.182

Palmitoleic Acid 1.273 0.173 0.082 −0.221

Oleic Acid 1.239 0.123 0.066 −0.164

Stearic Acid 1.139 0.058 −0.197 0.121

42

(A)

(B)

Figure 3.3. Shared and unique structure (SUS) plots of fatty acid and polar

metabolites from S. cerevisiae by one of three chloroform/methanol/water based

extraction protocols. Extraction 1 and Extraction 2 are plotted against Extraction 3 in

order to observe the shared and unique metabolites obtained with Extractions 1 and 2. Red

metabolites are those with a VIP greater than 1 as identified by OPLS-DA modelling. (A)

SUS plot of polar metabolites. (B) SUS plots of FAME metabolites.

43

acid profile as it usually produces only even numbered chain length fatty acids and only

three unsaturated fatty acids all of which are monounsaturated (80). Despite this, some

clustering was observed in this study although the clustering is not as tightas that seen

with the polar metabolites extracted (Figure 3.2). This is to be expected, as the fatty acid

profile of yeast is not nearly as complex as that of its polar metabolites and small

differences in levels between samples may be magnified leading to reduced variance.

Somewhat surprisingly, the primary separation of the samples is based on extraction

method used, as opposed to growth temperature—as was the case with the polar

metabolite profiles. One could expect to see changes in the fatty acid composition as a

result of the increase in growth temperature, although the temperature increase may not

have been dramatic enough to cause the anticipated changes. Four fatty acids were

identified as significantly altered (VIP ≥ 1), and examination of the corresponding

coefficient values for each protocol suggests that samples from Extraction 1 have

comparatively higher levels, while samples from Extraction 3 have comparatively lower

levels, with samples from Extraction 2 falling in the middle.

With only four fatty acids showing differences between the 3 extraction protocols

tested, we conclude that the aqueous component of the samples is more sensitive to the

extraction system composition, although this may be a result of the diverse polar

compounds analyzed and relatively narrow class of non-polar compounds since our

analytical evaluation was not comprehensive. For example, the organic component of the

extract may not be suitable for analysis of all lipid species due to the wide variety of

headgroup chemistries and, likewise, highly polar compounds such as phosphorylated

nucleotides may be measured sub-optimally from the aqueous phase. Furthermore, our

44

analysis was limited to GC-MS analysis under specific derivatization conditions,

providing some analytical constraints. In spite of these limitations, we estimate that this

evaluation provides some guidance for a first-approach towards analysis of sample with

mixed physiochemical analytes of interest.

3.2.4 Summary

Considering the two best procedures (Extractions 1 and 2) as identified through

multivariate projection methods neither protocol extracts any unique metabolites and

essentially all metabolites vary in the same direction for both protocols (Figure 3.3). This

suggests that both protocols could serve as viable options for extraction of both polar and

non-polar metabolites, though one would tend to prefer Extraction protocol 2, as the polar

metabolite profile is more complex than that of the fatty acid profile and Extraction

protocol 2 seems to be able to extract comparatively higher levels of these metabolites in

a more reproducible manner. Furthermore, we were able to show that the use of

multivariate projection methods is a viable method to compare and evaluate established

extraction protocols for reproducibility and relative amount/breadth of metabolites

recovered.

3.3 Experimental section

3.3.1 Yeast growth and harvesting

Yeast S. cerevisiae strain BY4741 was grown and harvested as described in section

2.1.

3.3.2 Metabolite extraction

3.2.1 Extraction protocol 1

45

Cells were extracted using a modified version of an extraction previously

described by Zaremberg et al. (63). Briefly, yeast cell pellets were re-suspended in 1 mL of

CHCl3:MeOH (1:1) and transferred to 2 mL bead beater vials 1/8 filled with 0.5 mm acid

washed beads. Bead beating was sustained for 60 seconds at 4 °C using the homogenize

setting to break the cell walls, followed by transfer of the lysate to a 15 mL falcon tube.

Beads were rinsed with 1 mL CHCl3:MeOH (2:1) which was then combined with the

previously obtained cell lysate. Next 0.5 mL CHCl3:MeOH (2:1), 0.5 mL CHCl3 and 1.5 mL

H2O were added, the contents vortexed and centrifuged at 2500 rpm with an Eppendorf

5415 C Centrifuge for 10 minutes at 4 °C. The aqueous layer was then collected, the protein

layer aspirated off and the tube spun again at 2500 rpm for 5 min at 4 °C. Remaining protein

was aspirated and the organic layer was collected and retained. Organic and aqueous

fractions were stored at −80 °C after being dried overnight in a speed vacuum or fume

hood respectively.

3.3.2.2 Extraction protocol 2

Cells were extracted using a modified version of an extraction previously

described by McCombie et al. (83). Briefly yeast cell pellets were re-suspended with 300

μL CHCl3:MeOH (1:2) and transferred to a 2 mL bead beater vial 1/8 filled with 0.5 mm

acid washed beads. Bead beating for 60 seconds at 4 °C using the homogenize setting to

break the cell walls was followed by transfer of the lysate to a microcentrifuge tube and

the beads were rinsed with 300 μL CHCl3:MeOH (1:2) which was combined with the

previously obtained cell lysate. Next, 200 μL each of CHCl3 and H2O were added and the

contents vortexed, followed by centrifugation at 14,000 rpm with an Eppendorf 5415 C

Centrifuge for 7 min at 4 °C. The aqueous layer was isolated and transferred to a new

46

microcentrifuge tube, the protein layer aspirated off and the organic phase collected and

saved. The aqueous layer was then centrifuged at 14,000 rpm with an Eppendorf 5415 C

Centrifuge for 7 min at 4 °C and saved. Organic and aqueous fractions were dried down and

stored as described above.

3.3.2.3 Extraction protocol 3

Cells were extracted using a modified version of an extraction previously described

by Ejsing et al. (80). Briefly yeast cell pellets were re-suspended with 300 μL 150 mM

NH4HCO3 and transferred to a 2 mL bead beater vial 1/8 filled with 0.5 mm acid washed

beads. Bead beating for 60 seconds at 4 °C using the homogenize setting to break the cell

walls was followed by transfer of the lysate to an microcentrifuge tube and the beads

rinsed with 300 μL 150 mM NH4HCO3. This was combined with the previously obtained

cell lysate. Next 990 μL CHCl:/MeOH (17:1) was added to the microcentrifuge tube and

subject to passive extraction (extraction on a benchtop at room temperature without any

centrifugation) for 2 h at 20 °C. The upper phase was subsequently isolated and transferred

to another microcentrifuge tube while the lower organic phase was collected and saved.

This followed by passive extraction of the isolated upper phase for 2 h at 20 °C with 990

μL CHCl3:MeOH (2:1). The aqueous and organic layers were isolated and placed in

separate microcentrifuge tubes, resulting in a total of two different organic fractions and

one aqueous fraction. Organic and aqueous fractions were dried down and stored as

described above.

3.3.3 Derivatization and sample preparation

Derivatization and sample preparation for GC-MS analysis were carried out as

described in section 2.3.1.

47


GC-MS data acquisition was carried out as described in section 2.3.2.

3.3.5 Data processing and interpretation

Raw GC-MS data was imported to MetaboliteDetector (72) for peak detection.

The data were first normalized using Excel 2010 (Microsoft, Redmond, WA, USA) to the

internal standard, D-25 Tridecanoic Acid, in the case of targeted FAME profiling,

followed by integral normalization. For the untargeted polar metabolite profiling integral

normalization was carried out. In MetaboliteDetector, for compound detection the peak

threshold, minimal peak height and bins/scan parameters were all set at 2.00 and the

deconvolution width was set at 1.90. For metabolite detection the values were set as 15.0,

0.30 and 0.70 for Maximum retention index difference (ΔRI), Pure/Impure Composition

and Cutoff score respectively. Lastly, using the batch quantification tool integrated GC-

MS analysis was done. For this step the parameters for compound matching were set as

5.0, 0.30 and 0.85 for ΔRI, Pure/Impure and Req. Score) respectively, for identification

15.0 and 0.30 for ΔRI and Pure/Impure and for compound filter the signal to noise (S/N)

parameter was set as 0.30. After normalization and compound detection, the data were

exported to SIMCA-P-13 (MKS Umetrics AB, Umea, Sweden), a multivariate statistical

analytical software, where univariate scaling and mean centering was applied before the

model construction and validation step. Model construction was done using the autofit

routine of SIMCA-P, to avoid overfitting of the data, and the OPLS-DA models were

validated with CV-ANOVA p-values.

48

3.4 Conclusions

Choosing the correct extraction protocol for a given organism or biological system is of

fundamental importance when designing metabolomic studies as the metabolite

extraction step has a direct effect on all subsequent steps of data collection and analysis.

Using multivariate projection methods, we were able to compare three established

chloroform/methanol/water partitioning metabolite extraction methods for the ability to

reproducibly extract both polar and non-polar yeast metabolites. Using this approach a

highly reproducible method was identified that was able to extract comparatively higher

amounts of polar metabolites—all three protocols were able to obtain comparable

metabolite breadths. We were able to confirm the effectiveness of chemoinformatics and

multivariate projection methods to efficiently give a comparison of different extraction

protocols for a given organism. This approach should prove an efficient way to compare

other established extraction protocols for other systems against each other, as well as

providing a quicker and more cost effective way of comparing new extraction protocols to

previously established ones.

3.5 Contributions

This chapter was published as a manuscript in the open access journal Metabolites,

“Tambellini, N.P., Zaremberg V., Turner, R.J., and Weljie A.M. (2013) Evaluation of

Extraction Protocols forSimultaneous Polar and Non-Polar Yeast Metabolite Analysis

using Multivariate Projection Methods. Metabolites 3, 592-605” and is formatted in a

manner consistent with the journal formatting. The experiments and data analysis in this

chapter were designed and carried out by me under the supervision of Dr. Ray Turner and

Dr. Aalim Weljie and with some discussional input from Dr. Vanina Zaremberg.

49

Chapter Four: Polar Metabolite and Fatty Acid Profiling of Edelfosine Treated


4.1 Introduction

As discussed previously the precise mechanism of edelfosine is not fully

understood but evidence exists that it may induce apoptosis through a number of

processes including the MAPK/ERK pathway (34), the Fas/CD95 death receptor (39),

endoplasmic reticulum stress (40), c-jun NH2 terminal kinase activation (36) and

inhibition of CTP:phosphocholine cytidylyltransferase, the rate limiting step for the

biosynthesis of phosphatidylcholine (30,31).

Additionally it was also discussed that two uptake methods for edelfosine have

been suggested one of which involves edelfosine insertion into the plasma membrane,

accumulation in lipid rafts and internalization through a lipid raft dependent endocytosis

pathway (31,46). The importance of lipid rafts in edelfosine uptake is highlighted by the

fact that pre-treatment of cells with raft-disrupting agents lead to reduced

alkylphospholipid uptake and apoptosis (47). The other method for edelfosine uptake,

which has been heavily implicated in yeast cells, is through an ATP-dependent flippase

moderated mechanism after insertion into the plasma membrane (62,110).

A previous metabolic flux study using 13

C-labeled glucose (42) was able to

identify some changes in metabolism induced by edelfosine, though it was targeted to the

tricarboxylic acid (TCA) cycle and de novo nucleic acid synthesis. As there is strong

evidence that edelfosine treatment leads to an altered metabolism, a metabolomics

approach could be ideal to try and elucidate more information about its mode of action.

As metabolomics has emerged as a viable tool, it has been used extensively in drug

50

discovery and development in addition to many other fields (111). Metabolomic studies

have been successfully used to uncover the mechanisms by which drugs act including the

exploration of the mechanisms of action for hydrazine induced hepatotoxicity (112) and

the xenobiotic carbon tetrachloride (113). Furthermore, changes in the fatty acid and/or

lipid profile of yeast caused by growth temperature and defective lipid biosynthesis (80)

as well as the different responses of four strains of Saccharomyces cerevisiae to furfural,

phenol and acetic acid stresses (114) have been successfully carried out using a

metabolmics approach.

For these reasons I carried out untargeted profiling of the polar metabolites and

targeted fatty acid analysis of yeast treated with edelfosine at concentrations that induce a

cytostatic effect and identified perturbations induced by the treatment through

comparison to untreated yeast. S. cerevisiae was used as it has been shown to be an

excellent model organism for studying lipid metabolism and its regulation in eukaryotes

as the regulatory structures are highly conserved between yeast and mammals (115).

Additionally yeast are susceptible to similar concentrations of edelfosine to those used

with mammalian cells (63) and edelfosine can kill and/or prevent growth of yeast cells

within two doubling cycles and has previously been used to study the effects of

edelfosine with success (62,116,117). Furthermore, we have not observed any

morphological effects caused by edelfosine treatment of yeast cells.

4.2 Experimental methods

4.2.1 Yeast and edelfosine growth curves

Yeast strain BY4741 (MATa; his3∆1, leu2∆0, met15∆0 and ura3∆0) was grown

in 50mL cultures in liquid YNB with ammonium sulphate (MP Biomedical, Solon OH,

51

USA) using the culture composition and method described in section 2.1. Log phase cultures

were grown to an OD600 of approximately 0.2/mL and edelfosine was then added.

Edelfosine was dissolved in anhydrous ethanol and added at concentrations of 2, 4, 8 and

16 μg/ml to separate flasks with ethanol only added to the control flask. OD600 readings

were taken every 90 minutes after edelfosine addition for 810 minutes (13.5 hours) and a

final reading was taken at 1920 minutes (32 hours) to determine if recovery or death of

the yeast culture had occured. Two sets of triplicate readings were taken for each

concentration of edelfosine and the control in order to ensure sufficient replicates. The

mean and standard error for each concentration were then graphed using Excel 2010

(Microsoft, Redmond, WA, USA).

4.2.2 Yeast sample growth and sample harvesting

Yeast S. cervisisae strain BY4741 was grown in two cultures, edelfosine treated

and untreated, with 1.75L of liquid YNB with ammonium sulphate (MP Biomedical,

Solon OH, USA) using the culture composition and method described in section 2.1. Log

phase cultures were grown to an OD600 of 0.2/mL. At this time 2μg/mL of edelfosine

(dissolved in anhydrous ethanol) was added to the edelfosine treated culture and an equal

volume of anhydrous ethanol was added to the untreated culture. Samples were harvested in

multiples of six at each timepoint of 0, 2, 4 and 6 hours after edelfosine addition from both

the edelfosine treated and untreated culture. Each samples was then harvested as described in

section 2.1.

4.2.3 Sample extraction and derivitization

Cells were extracted using the extraction protocol described in section 3.3.2.2.

Derivatization and sample preparation for GC-MS analysis were carried out as described

52

in section 2.3.1.


GC-MS data acquisition was carried out as described in section 2.3.2.

4.2.5 Data processing and multivariate statistical analysis and projection modelling

GC-MS data processing was carried out as described in section 2.3.3 Multivariate

statistical analysis and projection modelling was carried as described in section 3.3.5.

4.2.6 Metabolite modelling and pathway analysis

Polar metabolites and fatty acids identified to have VIP scores greater than one

through OPLS-DA modelling of edelfosine treated and untreated yeast cells were

subjected to pathway analysis using the MetaboAnalyst 2.0 (74) server. Pathway analysis

was done with 22 polar metabolites and 8 fatty acids using the S. cervisisae pathway

library. Hypergeometric test and relative-betweeness centrality algorithms were selected

as the options for the over representation analysis and pathway topology analysis portions

respectively.

4.3 Results

4.3.1 Growth with edelfosine

In order to assess the effect on the metabolome and fatty acid composition of the

model organism S. cerevisiae induced by treatment with edelfosine, comparative

metabolomics was done. Growth curves with yeast and different concentrations of

edelfosine added were constructed in order to identify the concentration of the compound

that was able to inhibit growth of the yeast culture without killing the cells to allow for

effective metabolic profiling (Figure 4.1). It was determined that 2 μg/mL of edelfosine

53

Figure 4.1. Yeast and edelfosine treatment growth curves. Yeast strain BY4741 was grown in

50mL cultures in liquid YNB with ammonium sulphate. Each culture was grown to an initial

OD600 of approximately 0.2/mL from a start point of approximately 0.05/mL and edelfosine

dissolved in ethanol was then added at 2, 4, 8 and 16 μg/mL. Two sets of triplicate readings

were taken and the mean and standard error for each concentration were calculated and graphed

using Excel 2010 (Microsoft, Redmond WA, USA). A culture of yeast strain BY4741 without

ethanol was also grown (not shown) to confirm ethanol addition had no effect on culture growth

or viability.

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

0 500 1000 1500 2000

OD

60

0n

m (

op

tica

l de

nsi

ty a

t 6

00

nm

)

Time (min)

BY4741 + EtOH

BY4741 + 2ug/ml Edel




54

added to culture at an OD600 of approximately 0.2/mL achieved the desired effect as seen

by the halting of growth during the first six hours after edelfosine treatment followed by

culture recovery overnight.

4.3.2 OPLS-DA modelling differentiates edelfosine treated and untreated samples

Yeast treated with edelfosine was compared to untreated yeast 0, 2, 4 and 6 hours

after the addition of edelfosine in order to monitor the effects induced during the two

doubling cycles in which it has been reported that edelfosine is able to prevent further

growth. Polar metabolites and fatty acids were extracted from each sample and analyzed

using GC-MS and multivariate projection methods to model the differences between the

treated and untreated samples. OPLS-DA modelling was performed on samples extracted

for both polar metabolites (Figure 4.2) and fatty acids (Figure 4.3). The OPLS-DA

models for polar metabolites show a significant separation between the treated and

untreated samples 2 and 4 hours after the addition of edelfosine as evidenced by the high

Q2 values of greater than 0.5 and low CV-ANOVA (cross-validated analysis of variance)

p-values of less than 0.05 (Table 4.1). Conversely the low Q2 value 0 hours after the

addition of edelfosine (Q2

= 0.005), demonstrates that there is no predictive value of the

model and suggest little difference between the treated and untreated samples. This is an

expected result as edelfosine is not known to instantaneously induce measurable changes

in metabolism. Additionally, 6 hours after the addition of edelfosine there was some

separation of the treated and untreated polar metabolite profiles (Figure 4.2D) with a Q2

value of 0.464. This timepoint falls just after two doubling cycles of untreated yeast and

it has been previously reported that edelfosine treatment of yeast will prevent further

growth within this time period (63). Fatty acid profiling using FAME analysis revealed

55

Figure 4.2. OPLS-DA models using cross-validated latent variables (tcv) and cross-

validated orthogonal latent variables (tocv) comparing the polar metabolite profiles of

untreated and edelfosine treated S. cerevisiae samples at 4 timepoints after Edelfosine

treatment. Blue circles represent untreated samples and green circles represent edelfosine

treated samples. (A) 0 hours after treatment model with R2X = 0.237, R

2Y = 0.388, Q

2 =

0.005 and CV-ANOVA (cross-validated analysis of variance) p = 1.000 values. (B) 2 hours

after treatment model with R2X = 0.607, R

2Y = 0.992, Q

2 = 0.847 and CV-ANOVA p =

0.004 values. (C) 4 hours after treatment model with R2X = 0.696, R

2Y = 0.989, Q

2 = 0.918

and CV-ANOVA p = 7.078 x 10-5

values. (D) 6 hours after treatment model with R2X =

0.202, R2Y = 0.573, Q

2 = 0.464 and CV-ANOVA p = 0.003 values.

A B

D C

56

Figure 4.3. OPLS-DA models comparing the fatty acid profiles using tcv’s and tocv’s

for FAME analysis of untreated and edelfosine treated S. cerevisiae samples at 4

timepoints after edelfosine treatment covering. Blue circles represent untreated samples

and green circles represent edelfosine treated samples. (A) 0 hours after treatment model

with R2X = 0.897, R

2Y = 0.489, Q

2 = 0.156 and CV-ANOVA p = 0.825 values. (B) 2 hours

after treatment model with R2X = 0.768, R

2Y = 0.655, Q

2 = 0.293 and CV-ANOVA p =

0.401 values. (C) 4 hours after treatment model with R2X = 0.751, R

2Y = 0.830, Q

2 = 0.772

and CV-ANOVA p = 0.002 values. (D) 6 hours after treatment model with R2X = 0.59,

R2Y = 0.762, Q

2 = 0.661 and CV-ANOVA p = 4.101 x 10

-4 values.

A B

D C

57

Table 4.1. Summary of parameters for the assessment of the quality of OPLS-DA models

comparing edelfosine treated and untreated yeast samples. R2X and R

2Y are the modeled

variation in the X and Y matrix respectively, Q2 is the predicted variation and CV-ANOVA p-

value is obtained from the cross-validated analysis of variance of the OPLS-DA model.

Time After Edelfosine

Treatment Model Group R2X R

2Y Q

2

CV-ANOVA

p-value

0 hours Polar Metabolites 0.237 0.388 0.005 1.000

0 hours FAME's 0.897 0.489 0.156 0.825


2 hours FAME's 0.768 0.655 0.293 0.401

4 hours Polar Metabolites 0.696 0.989 0.918 7.078 x 10-5

4 hours FAME'S 0.751 0.830 0.772 0.002


6 hours FAME'S 0.59 0.762 0.661 4.101 x 10-4

58

significant separation between treated and untreated samples 4 and 6 hours after

edelfosine addition that can be confirmed by the Q2 and CV-ANOVA p- values meeting

the significance threshold of p<0.05 (Table 4.1). The low Q2 and insignificant CV-

ANOVA p-values obtained at the 0 (Q2 = 0.156and p = 0.825) and 2 hours (Q

2 = 0.293

and p = 0.401) after treatment time points suggest there is little difference in the fatty acid

profiles of treated and untreated samples. The observation that significant separation of

untreated and edelfosine treated samples is seen at the 4 and 6 hour timepoints for FAME

analysis, is in contrast to polar metabolite profiling which showed significant separation

at the 2 and 4 hours timepoints.

4.3.3 22 polar metabolites and 8 fatty acids altered by edelfosine treatment

Using the 2 and 4 hour timepoints from OPLS-DA modelling of polar metabolites

and the 4 and 6 hour timepoints from modelling of the FAME profiling a list of

metabolites and fatty acids significantly contributing to the separation between the

edelfosine treated and untreated was identified using a cutoff of VIP scores greater than

1, with a higher score indicating a greater influence on the separation of the two sample

groups (Table 4.2). The 2 and 4 hour timepoints from polar metabolite profiling and the 4

and 6 hour timepoints from FAME profiling were the models chosen as they were the

timepoints that displayed Q2

values greater than 0.5 and CV-ANOVA p-values of less

than 0.05, indicating significant separation between the untreated and edelfosine treated

sample groups. Additionally coefficient scores for the metabolites and fatty acids

contributing to the separation of the treated and untreated samples were obtained from the

OPLS-DA modelling and were used to identify if the levels were increased or decreased

in the edelfosine treated samples compared to the untreated samples (Table 4.2). In total,

59

Table 4.2. Polar metabolites and fatty acids identified to have a VIP score greater than 1

through OPLS-DA modelling and the corresponding coefficient values for edelfosine

treated samples compared to untreated samples. Values with a positive coefficient indicate

higher comparative levels, while values with a negative coefficient indicate lower comparative

levels.

Time After

Edelfosine Treatment Profiling Method Metabolite VIP

Coeff of Edelfosine

Treated Compared

to Untreated

2 hours Polar Metabolite myo-Inositol 2.156 0.105

2 hours Polar Metabolite α,α,D1-Trehalose 1.624 0.093

2 hours Polar Metabolite Malic Acid 1.458 0.148

2 hours Polar Metabolite Proline 1.013 0.002

2 hours Polar Metabolite Glycine 2.038 -0.080

2 hours Polar Metabolite Phosphoric Acid 1.990 -0.118

2 hours Polar Metabolite Fumaric Acid 1.588 -0.027

2 hours Polar Metabolite Octadecanoic Acid 1.580 -0.038

2 hours Polar Metabolite D-Glucopyranose 1.203 -0.070

2 hours Polar Metabolite Lactic Acid 1.201 -0.053

2 hours Polar Metabolite L-O-methyl-Threonine 1.042 -0.005

2 hours Polar Metabolite Aspartic Acid 1.015 -0.069

4 hours Polar Metabolite myo-Inositol 2.482 0.113

4 hours FAME Myristoleic Acid (C14:1) 2.344 0.434

4 hours Polar Metabolite Glucose 2.090 0.068

4 hours Polar Metabolite Galactose 1.926 0.056

4 hours Polar Metabolite Glucose-6-phosphate 1.914 0.061

4 hours Polar Metabolite Alanine 1.77 0.050

4 hours Polar Metabolite 4-Amino-Butanoic Acid 1.354 0.015

4 hours FAME Myristic Acid (C14:0) 1.334 0.17

4 hours FAME Lauric Acid (C12:0) 1.307 0.218

4 hours Polar Metabolite Sorbitol 1.101 0.038

4 hours Polar Metabolite L-O-methyl-Threonine 2.624 -0.114

4 hours Polar Metabolite Glycine 2.115 -0.052

4 hours Polar Metabolite Phosphoric Acid 2.067 -0.079

4 hours FAME Palmitic Acid (C16:0) 1.634 -0.364

4 hours Polar Metabolite Aspartic Acid 1.449 -0.031

4 hours Polar Metabolite Glutamic Acid (3TMS) 1.235 -0.026

4 hours Polar Metabolite Ornithine (3TMS) 1.165 -0.018

4 hours Polar Metabolite Glutamic Acid (2TMS) 1.163 -0.057

60

4 hours Polar Metabolite Lactic Acid 1.146 -0.074

4 hours Polar Metabolite Ornithine (4TMS) 1.022 -0.044

4 hours Polar Metabolite Citric Acid 1.019 -0.017

6 hours FAME Myristoleic Acid (C14:1) 1.764 0.292

6 hours FAME Lauric Acid (C12:0) 1.093 0.183

6 hours FAME Lignoceric Acid (C24:0) 1.600 -0.188

6 hours FAME Palmitic Acid (C16:0) 1.379 -0.188

6 hours FAME Eicosanoic Acid (C20:0) 1.049 -0.014

6 hours FAME Docosanoic Acid (C22:0) 1.036 -0.056

6 hours FAME Decanoic Acid (C10:0) 1.036 -0.168

61

22 different polar metabolites from the 2 and 4 hour timepoints and 8 different fatty acids

from the 4 and 6 hour timepoints were identified to have statistically significant changes

in the treated samples when compared to the untreated samples. Additionally, metabolites

that were detected by GC-MS analysis and identified but were found to not be perturbed

by edelfosine treatment through OPLS-DA modelling and had VIP scores of less than 1

are listed (Table 4.3).

4.3.4 Metabolic pathway analysis

Using the 22 polar metabolites and 8 fatty acids identified to be perturbed by

edelfosine treatment, pathway analysis was carried out. Six metabolic pathways were

found to be significantly perturbed with the criteria of having an impact of 0.1 and p-

value of less than 0.05 as determined by the MetaboAnalyst 2.0 software (74) (Figure 4.4

and Table 4.4). The pathways identified to be potentially altered by edelfosine treatment

involved amino acid metabolism (alanine, aspartate and glumate metabolism and arginine

and proline metabolism), sugar metabolism (galactose metabolism and starch and sucrose

metabolism) as well as TCA cycle metabolism and glutathione metabolism.

4.4 Discussion

Recent advances in metabolomics technologies and data processing have allowed

for studies that encompass the global cellular metabolism in contrast to previous studies

that targeted specific metabolite classes or metabolic pathways. This untargeted approach

has recently been used with great success in our lab to study the effects of metal toxicity

on bacteria (82,118) and cancer hypoxia (119) and in other research groups for studies

including evaluation of the HIV-1 Tat protein pathogenic mechanism (120). Furthermore

studies have successfully used FAME profiling to examine changes to fatty acid

62

Table 4.3. Identified metabolites that were found to be not significantly perturbed by

edelfosine treatment through OPLS-DA modelling and have VIP scores of less than 1. Note

this does not include peaks that could not be identified or did not have a confirmed match.

Metabolite Profiling Method

Laminaribiose Polar Metabolites

n-Heptadecane Polar Metabolites

Urea Polar Metabolites

Glycerol Polar Metabolites

Pyroglutamic Acid Polar Metabolites

Isoleucine Polar Metabolites

Threonine Polar Metabolites

Lysine Polar Metabolites

Hydroxylamine Polar Metabolites

Hexanoic Acid (C6:0) FAME

Octanoic Acid (C8:0) FAME

Palmitoleic Acid (C16:1) FAME

Stearic Acid (C18:0) FAME

Oleic Acid (C18:1) FAME

63

Table 4.4. Pathway analysis results from edelfosine treatment of yeast using MetaboAnalyst

2.0. Raw p is the p-value calculated from the enrichment analysis and the impact score is the

pathway impact calculated from the pathway topology analysis. Significantly perturbed pathways

are defined as having a raw p-value of less than 0.05 and an impact score of greater than 0.1 and

are highlighted with bold and italics.

Pathway Name

Total

Compounds Hits Raw p -log (p) Impact

Arginine and proline metabolism 37 6 0.001 7.119 0.316

Nitrogen metabolism 8 3 0.002 6.453 0.000

Alanine, aspartate and glutamate metabolism 20 4 0.003 5.776 0.637

Biosynthesis of unsaturated fatty acids 42 5 0.009 4.660 0.000

Galactose metabolism 17 3 0.016 4.155 0.308

Starch and sucrose metabolism 18 3 0.018 3.995 0.254

Citrate cycle (TCA cycle) 20 3 0.025 3.704 0.183

Glutathione metabolism 23 3 0.036 3.330 0.169

Cyanoamino acid metabolism 10 2 0.039 3.232 0.000

Glyoxylate and dicarboxylate metabolism 14 2 0.074 2.608 0.225

Butanoate metabolism 17 2 0.104 2.266 0.286

Aminoacyl-tRNA biosynthesis 67 4 0.169 1.776 0.000

Pyruvate metabolism 23 2 0.171 1.765 0.116

Glycolysis or Gluconeogenesis 24 2 0.183 1.697 0.033

Amino sugar and nucleotide sugar metabolism 24 2 0.183 1.697 0.167

Glycine, serine and threonine metabolism 26 2 0.207 1.573 0.211

beta-Alanine metabolism 7 1 0.208 1.569 0.000

Methane metabolism 11 1 0.308 1.178 0.000

Fructose and mannose metabolism 17 1 0.435 0.833 0.047

Pentose phosphate pathway 18 1 0.454 0.790 0.066

Lysine biosynthesis 19 1 0.472 0.751 0.000

Inositol phosphate metabolism 19 1 0.472 0.751 0.164

Steroid biosynthesis 23 1 0.539 0.617 0.000

Fatty acid metabolism 28 1 0.612 0.491 0.000

Cysteine and methionine metabolism 33 1 0.673 0.395 0.000

Fatty acid biosynthesis 37 1 0.716 0.335 0.000

64

Figure 4.4. MetaboAnalyst 2.0 pathway analysis summary of perturbations caused by

edelfosine treatment of yeast samples. a) alanine, aspartate and glutamate metabolism, b)

arginine and proline metabolism, c) galactose metabolism, d) starch and sucrose

metabolism, e) TCA cycle, f) glutathione metabolism.

a

c

b

e

d

f

65

composition induced by stress including different atmospheric conditions and phenethyl

alcohol in Mucor rouxii (121) as well heavy metal contamination on soil communities

(122).

In this study we combined untargeted profiling of polar metabolites and targeted

profiling of fatty acids to study the cytostatic effects of edelfosine treatment on yeast

metabolism. A cytostatic concentration of edelfosine was chosen due to the problems that

can be encountered in metabolomics studies when biological variation is introduced

through cell death as would be found if cytotoxic concentrations were used. Though

cytotoxic concentrations are more often used for studies with edelfosine due to its clinical

relevance, we hypothesize that the same alterations to metabolism seen with cytostatic

concentrations would be found at cytoxic concentrations of the compound. By subjecting

both the aqueous (polar metabolites) and organic (fatty acids) fractions of yeast samples

to GC-MS analysis we were able to study a wider range of the effects induced by

edelfosine as several different metabolic pathways have been implicated in previous

studies, while also exploring any changes to the fatty acid profile induced by its insertion

into the plasma membrane. Yeast was used as opposed to cell lines due to the ease with

which it can rapidly be grown reproducibly, the success of prior edelfosine studies in

yeast (62-64), availability of genetic screen information for edelfosine treated yeast

(116,117) and the fact that yeast is a model system for eukaryotic metabolism.

Using the 22 polar metabolites and 8 fatty acids (Table 4.2) identified to be

affected by edelfosine treatment as well as the 6 metabolic pathways that were found to

be perturbed using MetaboAnalyst 2.0 (74) (Figure 4.4 and Table 4.4), a schematic of the

perturbed metabolites and metabolic pathways was constructed to provide an overview of

66

the effects induced by edelfosine treatment (Figure 4.6) Interestingly, it also appears that

there is a kinetic difference between the polar metabolite and fatty acids responses to

edelfosine (Figure 4.5). Polar metabolites show a response to edelfosine treatment within

2 hours which lessens or is negligible by the 6 hours after treatment timepoint that covers

two doubling cycles of untreated yeast (Figure 4.5A and B). In contrast, fatty acids show

an initial response to edelfosine treatment at 2 hours that continues to strengthen through

turnover rates for fatty acids compared to polar metabolites, or could indicate a

differential response by polar metabolites and non-polar metabolites such as lipids and

fatty acids in the membrane. The major metabolic pathways and patterns as well as

potential biological interpretations for the changes induced by edelfosine treatment are

discussed below.

Proline, glutamic acid, aspartic acid, ornithine and γ-aminobutyric acid (GABA)

are all amino acids, though GABA is not an alpha amino acid and is not incorporated into

proteins, involved with the arginine and proline metabolism pathway. Our comparative

profiling found proline and GABA were increased and glutamic acid, ornithine and

aspartic acid were decreased in edelfosine treated samples compared to untreated ones.

Physiologically the arginine and proline metabolism pathway in yeast has been suggested

to be involved with stress response through an antioxidative mechanism (123) which

would support previous assertations that edelfosine induces oxidative stress (64,124). Of

further interest, arginine and proline metabolism was found to be altered in a

metabolomics study of liver cancer (125).

Alanine, aspartate and glutamate metabolism is involved with what are considered

to be non-essential amino acids. However, as it has become apparent that glutamine plays

67

Figure 4.5. Examples illustrating the different kinetic responses from 0 to 6 hours

after edelfosine treatment observed for polar metabolites and fatty acids. A) Response

of myo-inositol to edelfosine treatment. B) Response of glycine to edelfosine treatment. C)

Response of myrisotelic acid to edelfosine treatment. D) Response of lignoceric acid to

edelfosine treatment.

0

1

2

3

4

5

6

7

0h 2h 4h 6h

Rel

ati

ve

Am

ou

nt

of

my

o-I

no

sito

l

Time After Edelfosine Treatment

0

0.5

1

1.5

2

2.5

3

0h 2h 4h 6h

Rel

ati

ve

Am

ou

nt

of

Gly

cin

e


0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

0h 2h 4h 6h

Rel

ati

ve

Am

on

t o

f

My

rist

ole

ic A

cid


0

0.5

1

1.5

2

2.5

0h 2h 4h 6h

Rel

ati

ve

Am

ou

nt

of

Lig

no

ceri

c A

cid


C D

A B

68

TCA Cycle

4-aminobutanoate

L-Glutamate

Urea Cycle

L-Glutamine

Fumarate

Proline

Ornithine

Aspartate

2-oxoglutarate

Glutathione

Metabolism

Succinate

Malate

Oxaloacetate

Arginine

Alanine

Citrate

Pyruvate Acetyl-CoA

Starch and Sucrose

Metabolism

Arginine and Proline

Metabolism

Fatty Acid

Biosynthesis

and Fatty Acid

Metabolism

Arginine

Succinate

Citrulline

Alanine, Aspartate

and Glutamate

Metabolism

Glycolysis Galactose Metabolism

Glycerophospholipid

Metabolism

myo-Inositol

D-Glucopyranose

Glucose

Glucose-6-phosphatec

Trehalose

Galactose

Sorbitol

Glycine

Lauric Acid (C12:0)

Myristic Acid (C14:0)

Myristoleic Acid (C14:1)

Decanoic Acid (C10:0)

Palmitic Acid (C16:0)

Eicosanoic Acid (C20:0)

Docosanoic Acid (C22:0)

Lignoceric Acid (C24:0)

Octadecanoic Acid

Phosphoric Acid

L-O-Methyl-Threonine

Lactate

69

Figure 4.6. Schematic overview of polar metabolites, fatty acids and metabolic pathway affected by edelfosine treatment. Open

circles indicate metabolites were not detected, green filled circles indicate the metabolite has lower levels in edelfosine treated samples

compared to untreated samples and red filled circles indicate the metabolite has higher levels in treated compared to untreated samples.

Metabolic pathways highlighted with larger font and those that are bolded represent pathways identified using MetaboAnalyst 2.0 (74)

with p-values of less than 0.05 and impact scores greater than 0.1.

70

a significant role in cancer, with several reviews and research highlights written on the

topic (126-128). As there are high levels of glutaminase in tumour cells, glutamate is

produced in abundance and can shuttle into the TCA cycle as well as aspartate through

the malate-aspartate shuttle (126). Glutamine was not detected during our polar

metabolite analysis, likely due to the fact that the derivitization required GC-MS analysis

can deaminate glutamine to glutamate (129). However, it was found that four metabolites

involved with alanine, aspartate and glutamate metabolism were perturbed by edelfosine

treatment. Glutamic acid, fumaric acid and aspartic acid all showed decreased levels in

treated compared to untreated cells, while as previously stated GABA showed increased

levels. This result could be indiciative of indicate a depletion of glutamine in the

edelfosine treated samples or at the very least disruption of the TCA cycle which is

needed for cell growth, and was found in a previous metabolic flux study with edelfosine

treated jurkat cells (42). Our pathway analysis data also seems to supports this result as

the TCA cycle was one of the metabolic pathways found to be perturbed by edelfosine

treatment with citric acid and fumaric acid levels lower in treated samples compared to

treated ones and malic acid levels higher. This may suggest a shift towards respiration

metabolism due to edelfosine treatment as yeast ferment in the presence of glucose and it

has been found that yeast mutants with impaired mitochondrial function are resistant to

edelfosine (116). Further adding to the potential significance of the glutamate findings, it

is known that glutathione metabolism is a highly responsible for antioxidant defense in

cell and promotes cells survival (130). Our polar metabolite profiling identified

glutathione synthesis as a pathway highly perturbed by edelfosine treatment with

glutamic acid, glycine and ornithine showing decreased levels in treated samples when

71

compared to untreated samples. This result ties in with our findings above suggesting that

edelfosine may promote oxidative stress in the cell. Furthermore it has been suggested

that the glutathione pathway can actually be detrimental to cancer treatment as it

interferes with and binds chemotherapeutic drugs as reviewed by Yang et al.(131). Our

results taken as a whole could suggest that edelfosine’s ability to induce oxidative stress

and potentially interfere with the glutathione pathway, may have the added effect of

promoting apotosis through a build-up of reactive oxygen species as previously suggested

(64).

The final two pathways identified to be significantly altered by edelfosine

treatment in yeast involve sugar metabolism with galactose metabolism and starch and

sucrose metabolism being affected. Galactose, glucose, glucose-6-phosphate and

trehalose were found to have higher levels in edelfosine treated samples compared to

untreated samples. This suggests that perhaps yeasts ability to utilize sugar for further

growth is affected. Alternatively it may suggest that gluconeogenesis is taking place and

could possibly explain why malic acid levels were seen to increase in edelfosine treated

cells compared to untreated cells as mentioned above.

Another potentially interesting finding from the pathway analysis found that

unsaturated fatty acid biosynthesis had a significant p-value though the impact was

calculated to be 0.000 (Table 4.4). Upon further investigation it was found that there were

no impact scores for any of the metabolites involved in the pathway defined by

MetaboAnalyst 2.0, explaining the impact score of 0 obtained. Despite this fact, the result

could still be interesting and a significant finding as 8 fatty acids were found to be

different in treated and untreated samples with the overall trend suggesting a decrease in

72

long chain fatty acids and an increase in shorter chain fatty acids and myristoleic acid, an

unsaturated fatty acid. This suggests edelfosine treatment may change the membrane

fluidity through its insertion in the membrane. Furthermore, inositol is the master

regulator of glycerophospholipid biosynthesis in yeast (132) making it of particular

interest that my-inositol was found to be increased in edelfosine treated compared to

untreated yeast samples (Table 4.2) as glycerophospholipids are a major component of

cellular membranes. This observation could further support an effect on the membrane

composition induced by edelfosine treatment and is of note as lipid metabolism has been

implicated in signalling of stress response in yeast (133).

In conclusion, the use of a metabolomics approach to study the effects of

edelfosine treatment through polar metabolite and fatty acid profiling using GC-MS

analysis identified 22 polar metabolites, 8 fatty acids and 6 metabolic pathways that were

statistically altered. Additionally, the kinetic responses of polar metabolites and non-polar

fatty acids to edelfosine treatment were found to be different. The perturbed metabolites

were suggested to be involved in alanine, aspartate and glutamate metabolism, arginine

and proline metabolism, galactose metabolism, starch and sucrose metabolism,

glutathione metabolism and the TCA cycle. The possible biological interpretations of the

effects seen in this study supports and expand upon previous findings about the

mechanism of edelfosine and are in line with the current understanding of cancer

metabolism in general.

3.5 Contributions

The experiments and data analysis in this chapter were designed and carried out

by me under the supervision of Dr. Ray Turner and Dr. Aalim Weljie and with some

73

discussional input from Dr. Vanina Zaremberg. This work is currently being prepared to

be submitted to the Journal of Biological Chemistry for publication.

74

Chapter Five: Lipidomic Profiling using UPLC-TOF-MS of Edelfosine Treated


5.1 Introduction

As previously discussed, edelfosine is known to insert into the plasma membrane

and has previously been shown to inhibit CTP:phosphocholine cytidylyltransferase which

is a key enzyme and the rate limiting step for the biosynthesis of phosphatidylcholine

(30,31). Additionally we have shown that edelfosine treatment in yeast leads to altered

fatty acid levels through GC-MS based FAME analysis as discussed in chapter 4. We

also found that myo-inositol levels were increased in edelfosine treated compared to

untreated yeast samples, which is of note as inositol is the master regulator of

glycerophospholipid biosynthesis in yeast (132). This is of particular interest as

glycerophospholipids are a major component of cellular membranes.

Whereas GC-MS analysis requires chemical derivitization that restricts analysis

of lipids to primarily fatty acids due to the cleavage of head groups, LC-MS generally

does not require derivitization of lipids for analysis thus allowing for detection of intact

phospholipids. A study using a shotgun lipidomics approach was able to absolutely

quantify approximately 95 percent of the yeast lipidome (80). Additionally, lipidomic

profiling has been successfully employed to study acetic acid tolerance in S. cerevisiae

and Zygosaccharomyces bailii with electrospray ionization multiple-reaction-monitoring

mass-spectrometry (134) and for lipidomic profiling and lipid biomarker discovery of

microalgael response to salt stress using UPLC-TOF-MS (135).

For these reasons, we carried out untargeted lipid profiling of edelfosine treated

yeast and compared it with untreated yeast to uncover changes in lipids induced by

75

edelfosine treatment. As we were only looking for comparative changes, the extensive

use of internal standards was not required as would be the case for absolute

quantification.

5.2 Experimental methods

5.2.1 Sample preparation

75μl aliquots from the organic phase of the edelfosine treated and untreated

samples grown and harvested as described in section 4.2.2, were taken and transferred to

1.5mL microcentrifuge tubes after extraction via the protocol described in section 3.2.2.2

had occured. After transfer to the microcentrifuge tube, samples were dried down in a

fume hood overnight and then frozen at −80 °C for later analysis if needed. Due to

instrument availability and cost, only samples from the 4 hour timepoint after edelfosine

treatment were analyzed.

5.2.2 UPLC-TOF-MS analysis

LC-MS analysis was carried out as described in section 2.4.

5.2.3 Data analysis and multivariate projection modelling

Raw LC-MS data files were converted to .mzXML format using masswolf with

the high energy scan (func 002) files removed. The data from 10 edelfosine treated and 8

untreated yeast samples was uploaded to XC-MS online (136). Analysis and peak

detection was then carried out using the HPLC/Waters TOF parameters built into the

server with [M+H], [M+NH4], [M+Na], [M+H-H20], [M+K], [M+ACN+H] and

[M+ACN+Na] adducts detected. Upon initial examination of the resulting data, it was

observed that edelfosine treated samples had approximately 5-fold higher total ion counts

than untreated samples for an unknown reason. As such the data was exported out of

76

XC-MS online and normalized using total integral normalization to allow for comparison

of treated and untreated samples using multivariate projection modelling and statistical

analysis in SIMCA-P13 (Umetrics AB, Umea Sweden). Edelfosine (523.73 g/mol)

adducts and isotope peaks were identified and removed before normalization as they

could affect the normalization and modelling steps.

After import into SIMCA-P13 mean centering and pareto scaling were applied to

the data followed by PCA and OPLS-DA modelling as described in section 3.3.5.

Additionally an S-plot was constructed to identify significantly increased or decreased

lipids as the use of VIP scores for screening is not ideal with a high number of variables,

in this case thousands. S-plots combine the modelled variance and modelled correlation

from the OPLS-DA plot using the magnitude of each variable (p) and reliability of each

variable (p(corr)) to screen out metabolite peaks that have low magnitudes of change or

intensities.

5.2.4 Lipid identification

Lipid peaks identified to be increased or decreased by edelfosine treatment from

the S-plot were identified using mzMine (137). The raw data from a representative

edelfosine treated sample with high intensity peaks was imported to mzMine and mass

detection was carried out using centroid, 1 MS level, and noise level of 1x 102

parameters. Chromatogram builder was then used with minimum time span of 0.02

minutes, minimum height of 1 x 103 and m/z tolerances of 0.005 or 5ppm used. This was

followed by use of the isotope peak builder function with m/z tolerance of 0.002,

retention time of 0.05, maximum charge of 1 and most intense representative isotope

parameters used. Finally, the Lipid Map database was used to for identification of the

77

peaks based on m/z and isotope pattern scores with a minimum absolute intensity of 1e2

and isotope pattern score of 70% required and m/z and isotope m/z tolerances of 0.1.

Matches were made based on the smallest difference between the expected and measured

mass and highest isotope pattern score.

5.3 Results

In order to assess the effects of edelfosine on the lipid composition of yeast,

untargeted lipidomic profiling was carried out on untreated and treated yeast samples 4

hours after treatment. The data from 8 untreated and 10 edelfosine treated samples was

uploaded to the XC-MS online (136) server to assess the quality of the data and for peak

detection.

5.3.1 Initial analysis reveals magnitude of edelfosine treated samples is higher than

untreated samples

An initial look at data quality revealed approximately 5,000 features and very

little retention time deviation (Figure 5.1). A cloud plot showed 1644 features with a fold

change of greater than 1.5 and p-value of less than 0.01 (Figure 5.2). However, all but 12

of these features were suggested to be increased in edelfosine treated samples compared

to untreated samples. The largest increases were observed at a retention time of 5.7-5.9

minutes and had m/z values consistent with edelfosine (523.73 g/mol) adducts. Looking

at the total ion chromatograms of the samples, a distinct magnitude difference was

observed between the edelfosine treated and untreated samples, indicating the need for

normalization to be carried out to allow for comparison between the sample types (Figure

5.3). As such, the feature information was exported from XC-MS online in order to allow

for integral normalization to be carried out.

78

Figure 5.1. Retention time deviation observed for 8 untreated and 10 edelfosine treated yeast samples uploaded to XC-MS Online

for analysis and peak detection. Samples 002, 005, 008, 011, 012, 015, 019 and 022 are untreated samples. Samples 001, 004, 007, 009,

010, 013, 016, 018, 020, 021 are edelfosine treated samples.

79

Figure 5.2. Cloud plot obtained from XC-MS Online analysis of 10 edelfosine treated and 8 untreated yeast samples. Features

shown have a fold change of greater than 1.5 and p-value of less than 0.01. The size of the circle for each feature corresponds to the

magnitude of the fold change. Red features are decreased in edelfosine treated samples compared to untreated samples and green features

are increased.

80

Figure 5.3. Total ion chromatograms for 8 untreated and 10 edelfosine treated yeast samples uploaded to XC-MS Online for

analysis and peak detection. Samples 002, 005, 008, 011, 012, 015, 019 and 022 are untreated samples. Samples 001, 004, 007, 009, 010,

013, 016, 018, 020, 021 are edelfosine treated samples.

81

5.3.2. Multivariate projection modelling differentiates edelfosine treated and untreated

yeast samples from lipidomic profiling

After integral normalization and trimming out of features consistent with

edelfosine treatment, PCA and OPLS-DA modelling was performed on the edelfosine

treated and untreated yeast samples from 4 hours after treatment (Figure 5.4). PCA

modelling showed separation of edelfosine treated and untreated samples with R2 = 0.716

and Q2 = 0.524 values and no outliers (Figure 5.4A). OPLS-DA modelling also showed

significant separation of treated and untreated samples as evidenced by strong model

parameters of R2(X) = 0.454, R

2(Y) = 0.876, Q

2 = 0.717 and CV-ANOVA p-value = 1.54

x 10-3

(Figure 5.4B). An S-plot was constructed to identify features that were noticeably

increased or decreased by edelfosine treatment in yeast (Figure 5.5). It was observed that

23 features were decreased and 46 increased by edelfosine treatment, with these features

highlighted in red (Figure 5.5).

5.3.3. 28 Lipids from 7 major lipid classes tentatively identified to be altered by

edelfosine treatment

Using the 23 decreased and 46 increased features identified from the S-plot,

mzMine (137) was used in combination with the Lipid Map database to tentatively

identify these features using their m/z and retention times. In total 28 different lipids from

7 major lipid classes were tentatively identified as well as 10 peaks that could not be

assigned an identity using this approach (Table 5.1). The major lipid classes proposed to

be affected by edelfosine treatment included ceramides (Cer’s), diacylglycerols (DAG’s),

triacylglycerols (TAG’s), phosphatidylcholines (PC’s), phosphatidylethanolamines

(PE’s), phosphatidylglycerols (PG’s) and lysophosphatidyl inositols (LPI’s). Identities

82

Figure 5.4. Pareto scaled PCA and OPLS-DA models of 8 untreated and 10 edelfosine treated yeast samples from lipidomic

profiling. Blue circles represent untreated samples and green circles represent edelfosine treated samples. (A) PCA scores plot

using cross-validated principal components (t[]cv[]) with R2X = 0.716 and Q2X = 0.524 values. (B) OPLS-DA model using tcv and

tocv variables with R2X = 0.454, R2Y = 0.876, Q2 = 0.717 and CV-ANOVA p = 1.54 x 10-3

values.

A B

83

Figure 5.5. S-plot of 8 untreated and 10 edelfosine treated yeast samples from lipidomic profiling to identify lipids decreased

or increased by edelfosine treatment. The magnitude of each variable (p) and reliability of each variable (p(corr)) are used to

screen out metabolite peaks that have low magnitudes of change or intensities.

84

Table 5.1. Lipids tentatively identified as altered by edelfosine treatment, their m/z values, retention times and the adduct used for

the proposed identifications. Tentative identifications were made using mzMine (137) and the Lipid Map database using m/z difference

between the calculated and measured masses and isotope pattern scores as determined by mzMine. 10 peaks were not identified and are

listed at the bottom of the table.

m/z Retention

Time (min) Adduct Proposed Identity

Relative Amount

Compared to Untreated

Yeast

Mass Difference Between

Expected and Measured

(m/z)

Isotope

Pattern Score

341.3052

342.3086 11.9

[M+H]

[M+H]+1 docosanoic acid Increased 0.0362 98%

311.2586 9.8 [M+H] octadecenyl acetate Increased 0.0358 97%

494.5662 10.3 [M+NH4]

myristoleyl oleate,

palmitoyl

palmitoleate, oleyl

myristoleate

Increased 0.0731 98%

522.5976 10.9 [M+NH4] palmitoleyl oleate,

oleyl palmitoleate Increased 0.0732 98%

480.5141 11.0 [M+H] ceramide (30:2) Increased 0.0730 98%

508.5458

509.6046

1016.0850

11.6

[M+H]

[M+H]+1

[2M+H]

ceramide (32:2) Increased 0.0734 94%

536.5774

537.5804

1072.1484

1074.1496

12.1

[M+H]

[M+H]+1

[2M+H]

[2M+H]+1

ceramide (34:2) Increased 0.0737 81%

582.5106

583.5134 9.8

[M+NH4]

[M+NH4]+1 DAG (32:2) Increased 0.0014 99%

587.4657 9.8 [M+Na] DAG (32:2) Increased 0.0011 98%

85

579.5356

580.5389

597.5458

11.5

[M+H-H20]

[M+H-

H20]+1

[M+H]

DAG (34:0) Increased 0.0005 89%

614.5723 11.5 [M+NH4] DAG (34:0) Increased 0.0005 99%

619.5277

1216.0667 11.5

[M+Na]

[2M+Na] DAG (34:0) Increased 0.0005 98%

610.5413 10.4 [M+NH4] DAG (34:2) Increased 0.0008 98%

615.496 10.4 [M+Na] DAG (34:2) Increased 0.0001 98%

607.5673

608.5707

609.5733

11.9

[M+H-H20]

[M+H-

H20]+1

[M+H-

H20]+2

DAG (36:0) Increased 0.0022 96%

642.6034

643.6073 11.9

[M+NH4]

[M+NH4]+1 DAG (36:0) Increased 0.0003 90%

647.5595

1272.1284

1273.1315

11.9

[M+Na]

[2M+Na]

[2M+Na]+1

DAG (36:0) Increased 0.001 95%

818.7244 12.8 [M+NH4] TAG (48:3) Increased 0.0012 89%

494.3250

495.3280 2.6

[M+H]

[M+H+1] LPC (16:1) Decreased 0.0009 94%

522.3563 3.7 [M+H] LPC (18:1) Decreased 0.0009 93%

702.5082 7.6 [M+H] PC (30:2) Increased 0.0013 87%

730.5400

731.5431 8.5

[M+H]

[M+H]+1 PC (32:2) Decreased 0.0018 75%

752.5223 8.5 [M+Na] PC (32:2) Decreased 0.0022 86%

758.5705

758.5739 9.4

[M+H]

[M+H]+1 PC (34:2) Decreased 0.001 81%

653.4412 7.7 [M+NH4] PE (28:0) Increased 0.0452 91%

654.3339

670.4673 3.9

[M+Na]

[M+K] PE (28:2) Increased

0.0766

0.0828

91%

87%

86

681.4755

682.4771

698.5009

699.5037

8.6

[M+NH4]

[M+NH4]+1

[M+2NH4]

[M+2NH4]+1

PE (30:0) Increased 0.0422 77%

688.4934 8.5 [M+H] PE (32:2) Decreased 0.0022 88%

716.5248

717.5271

718.5294

9.3

[M+H]

[M+H]+1

[M+H]+2

PE (34:2) Decreased 0.0023 87%

744.5559

745.5584 9.4

[M+H]

[M+H]+1 PE (36:2) Decreased 0.0021 96%

663.4548

680.4813 10.2

[M+H]

[M+NH4] PG (28:2) Decreased 0.0316 89%

740.5444 8.6 [M+NH4] PG (32:0) Increased 0.0008 97%

801.5521 7.3 [M+Na] PG (36:0) Increased 0.0095 78%

851.3964

852.4003 1.8

[M+Na]

[M+Na]+1 PGP (34:1) Decreased 0.0846 86%

621.3099

637. 3006

6.6

3.9

[M+Na]

[M+K] LPI (18:1) Increased

0.0089

0.0316

91%

75%

531.2753 3.5 n/a n/a Increased n/a n/a




119.1678 1.6 n/a n/a Decreased n/a n/a






87

were proposed based on differences between calculated and expected m/z and isotope

pattern score matches as determined by mzMine.

5.4 Discussion

We previously determined that edelfosine treatment altered fatty acids levels in

yeast and found that myo-inositol was increased in edelfosine treated yeast. Combined

with the fact that inositol is a master regulator of glycerophospholipid biosynthesis (132)

in yeast and previous studies have established that edelfosine treatment disrupts the

kennedy pathway (30), there was compelling evidence to further study the effects of

edelfosine treatment on lipids in yeast.

In this study we used UPLC-TOF-MS for untargeted lipidomic profiling of

edelfosine treatment on yeast 4 hours after the compound was added to the yeast culture.

The 4 hour after treatment timepoint was chosen as this timepoint demonstrated a

response to both polar metabolites and fatty acids in our previous GC-MS study (Chapter

4). Furthermore, by using aliquots of the organic phase from the samples used in the GC-

MS study we can reliably integrate the results from both GC-MS and LC-MS analysis as

was successfully done by Liao et al. when they evaluated the HIV-1 Tat protein

pathogenic mechanism (120). Using multivariate projection methods, namely an S-plot

(Figure 5.5), we were able identify 23 features decreased by edelfosine treatment and 46

features that were increased that were then tentatively identified through the use of

mzMine and the Lipid Map database. Pareto scaling was used as opposed to univariate

scaling as univariate scaling gives equal weight to all variables whereas pareto scaling

gives more weight to larger variables. With the number of features obtained

(approximately 5,000) and the fact that an S-plot was used for identification of altered

88

lipids, pareto scaling was the more appropriate choice for this study. Using this approach

we were able to identify 28 lipids from 7 major classes that included ceramides, DAG’s,

a TAG, PC’s, PE’s, PG’s and a LPI. Some of the proposed lipid species identified and

possible biological interpretations of these results are discussed below.

Ceramide is a metabolically active cleavage product of sphingomyelinases, and is

a lipid second messenger that can induce a number of signalling pathways through

cytokines, tumor necrosis factor and interleukin-1 that can lead to cell death (138).

Increased levels of ceramides have previously been proposed to mediate apoptosis upon

treatment with miltefosine, an APL related to edelfosine (49). However this contradicted

the observation that reduced sphingomyelin (SM) biosynthesis due to downregulated

sphingomyelin synthase (SMS), which is involved in the conversion of ceramide to

sphingomyelin, resulted in edelfosine resistance (48). Due to these contradicting facts it is

very interesting that lipidomic profiling suggested the increase of 3 different ceramide

species in yeast (Cer 30:2, Cer 32:2 and Cer 34:2) upon edelfosine treatment. These

ceramides would constitute C14, C16 and C18 chain lengths suggesting they are

phytoceramides (139). Interestingly, ceramides have previously been found to increase as

part of the heat stress response of S. cerevisiae (140). It was recently discovered that the

ceramides that were increased were part of the phytoceramide family and included C14,

C16 and C18 chain lengths (139). As yeast does not produce SM, the increase in

ceramide observed must be due to a mechanism other than downregulation of SMS. This

mechanism could be sphingolipid turnover by ISC1 a gene that is involved with

sphingolipid metabolism and ceramide production in yeast (116). Of great interest in this

regard, it was found that the ISC1 deletion mutant is hyper-resistant to edelfosine

89

providing support for this assertion. Combined with observations that a ceramide-

activated protein phosphatase mediates ceramide-induced G1 arrest of yeast (141) and the

discovery that ceramides induced mitochondrial cell death in yeast through ROS (142),

there is evidence that edelfosine could at least partially induce cell death through

ceramide signalling in yeast. This could also be a potential mechanism in eukaryotes as a

study showed co-adminstration of histone deacetylase inhibitors coadministered with

perifosine, another APL related to edelfosine, resulted in Akt and MAPK/ERK disruption

in addition to increased ceramide and ROS production (143).

Another lipid second messenger that was proposed to be increased by edelfosine

treatment was diacylglycerol with DAG 32:2, DAG 34:0, DAG 34:2 and DAG 36:0 all

found to be increased. DAG is known to activate PKC which also responds to tumour

necrosis factor and interleukin-1 (138). An increase in DAG could be explained by

disruption of the Kennedy pathway which is known to be a result of edelfosine treatment

through inhibition of CTP:phosphocholine cytidylyltransferase which is the rate limiting

step for the biosynthesis of PC (30,31). Of note in this regard is the fact that PC 32:2 and

PC 34:2 were suggested to be decreased by edelfosine treatment though PC 30:2 was

increased. The decrease in PC is could be expected due to the disruption of the Kennedy

pathway, however this would lead to other biological effects to counter the loss of PC

which is the major membrane phospholipid in yeast (144). One possible mechanism to

counter PC depletion is methylation of PE usually through PC 32:2 (144). Additionally, it

has been observed that PC depletion in yeast leads to shortening and increased saturation

of lipid acyl chains and could be an attempt to overcome the non-bilayer propensity of PE

due to its tendency to cause negative curvature (144). Our results seem to support this

90

conclusion as PE 28:0, PE 28:2, and PE 30:0 were all suggested to be increased in

edelfosine treated yeast while PE 32:2, PE 34:2 and PE 36:2 were all suggested to be

decreased. As a result of this acyl chain remodelling, the increase of PC 30:2 could then

be explained as a part of the mechanism to mitigate PC depletion through PE

methylation.

Further supporting the increase in DAG due to inhibition of the Kennedy pathway

is our observations that TAG 48:3 increased in edelfosine treated yeast. This suggested

increase could be a result of the excess DAG being turned over to TAG (145) by Dga1p

(146) or Lro1p (147), though increases in more than one TAG would provide stronger

support for this conclusion. Further supporting excess DAG and potentially explaining

the increase of only a single TAG, is the fact that cytidine-diphosphate (CDP)-DAG can

be converted to phosphatidylglycerol phosphate (PGP) by Pgs1p in the mitochondria

(148). As PGP formation in the mitochondria is the committed step in the biosynthesis of

phosphatidylglycerol (PG) and cardiolipin (CL) this could explain our observations that

PGP 34:1 was decreased and PG 32:0 and PG 36:0 were increased by edelfosine

treatment as PGP is dephosphorylated to PG by Gep4p (149). Although PG 28:2 was

decreased by edelfosine treatment, this could again be a result of acyl chain remodelling.

Finally LPI (18:1) was proposed to be increased by edelfosine treatment, while

LPC (16:1) and LPC (18:1) were decreased. However, without more information it is

hard to draw any concrete biological conclusions from these results though the observed

LPC decrease may be due to the presence of edelfosine, which has an LPC-like structure.

In conclusion, 23 features were found to be decreased by edelfosine treatment of

yeast after 4 hours and 46 features were found to be increased. These features were

91

tentatively identified to comprise 28 lipids from 7 major lipid classes. Although standards

were not used to support our identifications due to the significant cost associated with

such an approach, we believed that as yeast only generates even numbered acyl chains

and single unsaturations on C14, C16 and C18 carbon length chains there was a lower

likelihood for false identifications. However in some cases, particularly with the

ceramides and phosphatidylethanolamines, the m/z differences between were in excess of

200ppm which brings into questions their validity. These differences could be due to

fragmentation patterns, or simply down to the fact that the compound was not in the Lipid

Maps Database but was closely related to the identity we proposed. Combined with the

unknown origin of the approximately 5-fold total ion count magnitude difference

between edelfosine treated and untreated samples, deeper analysis of these samples is

likely necessary. Despite these limitations potentially interesting conclusions were

generated from the lipidomic profiling of edelfosine treatment that are consistent with

and supported by current understanding in the field of cancer and our polar metabolite

and fatty acid profiling study. Biological interpretation of these results suggests possible

roles for the lipid second messengers ceramide and DAG as key players in edelfosine’s

ability to stop cell growth, possibly through tumour necrosis factor and interleukin-1.

Additionally shortening and increased saturation of fatty acyl chain lengths of the major

membrane lipids PC and PE was observed.

5.5 Contributions

The experiments and data analysis in this chapter were designed and carried out

by me under the supervision of Dr. Ray Turner and Dr. Aalim Weljie and with some

discussional input from Dr. Vanina Zaremberg. This work is currently being prepared to

92

be submitted with the work done from chapter 4 to the Journal of Biological Chemistry

for publication or possibly as its own manuscript.

93

Chapter Six: Concluding Remarks and Future Directions

6.1 Summary of research objectives and implications

When this research project was started, the mode of action of edelfosine was not

well understood, with several different metabolic pathways reported to be affected and

two uptake mechanisms suggested. Using metabolomics techniques and methodology, we

set out to understand the metabolism wide effects edelfosine induces in yeast and build

upon the working mechanism of action that has been proposed. More specifically

perturbations induced to polar metabolites, fatty acids and lipids were examined in order

to generate new hypotheses about edelfosine’s mechanism of action.

6.1.1 Evaluation of extraction protocols for yeast

The first objective involved optimization of simultaneous extraction of polar

metabolites and lipids simultaneously from a sample. GC-MS analysis of aqueous and

organic fractions from 3 chloroform/methanol/water based extraction protocols used in

the literature was carried out (Chapter 3). Using multivariate projection methods and

multivariate statistical modelling, the extraction protocols were compared for their ability

to effectively and reproducibly extract high amounts of both types of metabolites (Section

3.2).

Using this approach comparison, of the different extraction protocols was

efficiently and economically carried out. Multivariate projection methods and statistical

modelling showed that although all 3 extraction protocols were able to extract the same

metabolites, they differed in their reproducibility and metabolite recovery capabilities

(Table 3.1). A protocol was identified that was able to reproducibly extract high levels of

94

polar metabolites and fatty acids for metabolomics studies in yeast. Additionally, we

were able to show the usefulness of a multivariate projection based method for

comparison of different metabolite extraction protocols in an efficient manner without the

extensive use of standards.

6.1.2 Analysis of changes in the metabolome and fatty acid profile of yeast induced by

edelfosine treatment.

Achieving this objective involved the use of GC-MS analysis to identify changes

to the polar metabolite and fatty acid profiles of yeast 0, 2, 4 and 6 hours after edelfosine

treatment through comparison of edelfosine treated and untreated yeast samples (Chapter

4). It was key to establish concentration and exposure timing for these experiments

(Section 4.3.1)

With the aid of multivariate projection methods and statistical modelling 22 polar

metabolites and 8 fatty acids were found to be perturbed by edelfosine treatment in yeast

(Section 4.3.3). Furthermore, differences in the kinetic response of polar metabolites and

non-polar fatty acids to edelfosine treatment were observed (Figure 4.5). Combined, these

observations indicate a strong physiological response of the cells is induced upon

edelfosine treatment in yeast.

Proposed effects include a modulation of fatty acid composition and a shift

towards a more respirative than fermentative metabolism. Evidence for the possible

metabolic changes induced by edelfosine treatment include a decrease in lactate and

increase in glucose, glucose-6-phosphate, trehalose and other sugars which could be an

indication that there is a shift away from a fermentative metabolism. In the context of

tumour cells these results could suggest that edelfosine may be able to counter the

95

Warburg effect, a fermentative like metabolism that is known to be prevalent in various

types of tumours. Additionally we propose that edelfosine treatment exerts an effect on

the fatty acid composition as evidence by a shortening of acyl chain lengths and an

increase in the unsaturated fatty acid myristoleic acid in addition to an increase in myo-

inositol which is a master regulator of glycerophospholipids in yeast. Furthermore effects

on amino acid and TCA cycle metabolism are also proposed to be caused by edelfosine.

These edelfosine influenced changes could further compound the cell death or halting of

growth that edelfosine is known to cause or conversely may be a by-product of these

effects.

6.1.3 Analysis of changes in the lipidome of yeast induced by edelfosine treatment.

Using UPLC-TOF-MS and aliquots of the 4 hour after edelfosine timepoint

samples from edelfosine treated and untreated yeast, untargeted lipidomic profiling was

done in order to achieve this objective (Chapter 5). By using the same samples from polar

metabolite and fatty acid profiling, these observations can be integrated and interpreted

together.

With the aid multivariate projection methods and statisitical modelling, an S-plot

was constructed that identified 23 features decreased and 46 features increased by

edelfosine treatment, indicating that lipid metabolism is strongly effected by edelfosine

treatment in yeast (Figure 5.5). Tentative identification of these features proposed that 28

lipid species involved primarily with lipid signalling and membrane architecture were

perturbed by edelfosine treatment (Section 5.3.3). Though care must be taken to confirm

these lipid identifications more concretely, if they hold true it would represent a

significant step forward in our understanding of edelfosine’s mechanism of action.

96

6.1.4 Secondary analysis and biological interpretation of the metabolomics data

In order to propose specific metabolic pathways in yeast perturbed by edelfosine

treatment and allow for increased biological interpretation of the proposed effects

observed, pathway analysis was carried out on the 22 polar metabolites and 8 fatty acids

identified through GC-MS analysis using MetaboAnalyst 2.0 (74) (Section 4.3.4). This

approach identified alanine, aspartate and glutamate metabolism, arginine and proline

metabolism, galactose metabolism, starch and sucrose metabolism, glutathione

metabolism and the TCA cycle to be significantly perturbed by edelfosine treatment.

Using these perturbed metabolic pathways in combination with the 22 polar metabolites

and 8 fatty acids involved, a schematic of the wide ranging effects of edelfosine treatment

was constructed (Figure 4.6) that also implicated glycerophospholipid metabolism as well

as fatty acid biosynthesis and fatty acid metabolism as being perturbed.

Additionally biological interpretation of the 28 lipid species affected by

edelfosine treatment was done (Section 5.4). The lipid second messengers ceramide and

DAG were suggested to be strongly affected by edelfosine treatment. Furthermore it was

proposed that shortening and increased saturation of PC and PE acyl chain lengths

resulted from edelfosine treatment and this novel observation could be a result of or

response to edelfosine’s previously reported inhibition of the Kennedy pathway.

Our proposed biological interpretations of these effects supported and expanded

upon previous findings about edelfosine, aiding efforts to better understand its

mechanism of action through the generation of new hypotheses that can now be explored.

Additionally, a figure summarizing the proposed effects observed from metabolomic and

lipidomic profiling of edelfosine was constructed to aid further studies (Figure 6.1).

97

4-aminobutanoate

L-Glutamate

rea Cycle

L-Glutamine

Aspartate

Glutathione Metabolism

TCA Cycle

Fumarat

2-oxoglutarate

Oxaloacetate

Starch and Sucrose

Metabolism

Arginine and Proline Metabolism

Fatty Acid

Biosynthesis and Fatty Acid

Metabolism

Alanine, Aspartate

and Glutamate

Metabolism

Alanine

Pyruvate Acetyl-CoA

Glycolysis Galactose Metabolism

D-Glucopyranose

Glucose

Glucose-6-phosphate

Trehalose

Galactose

Sorbitol

Glycine

Lauric Acid (C12 0)

Myristic Acid (C14 0)

Myristoleic Acid (C14 1)

Decanoic Acid (C10 0)

Palmitic Acid (C16 0)

Eicosanoic Acid (C20 0)

Docosanoic Acid (C22 0)

Lignoceric Acid (C24 0)

Octadecanoic Acid

Lactate

Serine

Succinate

Malate

Citrate

Ornithine

Arginine

Arginine

Succinate

Citrulline

Proline

Glycerophospholipid

Metabolism

Glycerolipid Metabolism

Sphingolipid

Metabolism

Ceramide (30 2)

Ceramide (32 2)

Ceramide (34 2)

LPC (16 1)

LPC (18 1)

PC (30 2)

PC (32 2)

PC (34 2)

PE (28 0)

PE (28 2)

PE (30 0)

DAG (32 2)

DAG (34 0)

DAG (34 2)

DAG (36 0)

TAG (48 3)

Phosphoric Acid

L-O-Methyl-Threonine

Palmitoleyl oleate

LPI (18 1)

Octadecenyl acetate

Oleyl myristoleate

PG (28 2)

PG (32 0)

PG (36 0)

PGP (34 2) Myo-inositol PE (32 2)

PE (34 2)

PE (36 2)

98

Figure 6.1. Schematic overview of polar metabolites, fatty acids and lipids identified to be affected by edelfosine in yeast

through metabolomic and lipidomic profiling. Open circles indicate metabolites were not detected, green filled circles indicate

the metabolite has lower levels in edelfosine treated samples compared to untreated samples and red filled circles indicate the

metabolite has higher levels in treated compared to untreated samples.

99

6.2 Future directions

6.2.1 Further metabolomics studies

6.2.1.1 LC-MS analysis of polar metabolite perturbations

Though GC-MS analysis is an excellent and commonly used technique for

metabolomics profiling, it does have some limitations including the requirement for

chemical derivitization of compounds for analysis. This results in some bias in terms of

the number and types of compounds that can be analyzed as evidenced by our detection

of 31 polar metabolites despite that fact that yeast contains many more polar metabolites

than this. One example that was discussed previously is the conversion of glutamine to

glutamate during derivitization (129). Additionally, GC-MS cannot easily be used for the

detection of many large highly polar metabolites as they are not very volatile or stable

(150), resulting in a potential loss information and metabolic effects.

Combining GC-MS analysis with LC-MS analysis can often help overcome some

of these limitations and can be used to complement GC-MS. To follow up on our

profiling of polar metabolites using GC-MS analysis, LC-MS analysis on edelfosine

treated yeast could also be undertaken. This would likely have to be done on certain

classes of metabolites or targeted to specific metabolic pathways as LC-MS analysis

often requires extensive use of standards and can have very long sample analysis times

when global metabolite profiling is carried out. However, as GC-MS based profiling was

able to identify pathways significantly perturbed by edelfosine treatment, this could be an

excellent approach to provide a more in depth picture of how exactly the pathway is

being perturbed and what metabolites or branches within a given metabolic network are

the most affected.

100

6.2.1.2 Edelfosine resistant yeast mutants

Another set of potentially informative follow up experiments that could be

performed would be to carry out metabolomic profiling of edelfosine resistant mutants

that have been identified through chemical-genomic screens (116). As edelfosine

resistant mutants have been identified for a number of cellular processes including

uptake, endocytosis and retrograde transport (116), the different effects induced by

edelfosine at various points in the cell could be identified and analyzed. An example

would be metabolomic profiling of the Lem3p mutant of edelfosine as Lem3p has been

identified as being essential for the uptake of edelfosine itself (62). Metabolomic

profiling of this mutant would then allow for the metabolic changes induced by

edelfosine uptake to be identified. In this manner a series of mutants could be profiled

and the specific roles of each in the variety of effects induced by edelfosine could be

unravelled systematically.

6.2.1.3 Profiling of the effects of other APL’s in yeast

As mentioned previously, edelfosine is the prototype for the APL class of

compounds which also includes other potential chemotherapeutic agents including

ilmofosine, miltefosine, erucylphosphocholine and perifosine. These compounds are

thought to act through the same or similar modes of actions as edelfosine (10). As a

workflow to use metabolomic profiling to study this class of compounds in yeast has now

be tested and proven effective, the similarities and differences (if any) could be explored

and identified using this type of approach in the future.

101

6.2.2 Confirming our biological interpretations

As metabolomics has grown as a field and seen more widespread use, one

potential shortcoming that has been exposed is that the metabolic effects seen could

happen through a number of mechanisms or may be artifacts despite ones best efforts to

avoid such a result. These artifacts sometimes arise due to instrumentation or statistical

biases that are often unavoidable with metabolomics approaches. As such, it is often a

good idea to consider metabolomic profiling to be a hypothesis generating technique that

needs to be followed up on and confirmed biologically in some cases. One such method

to do this is to use classical biochemistry techniques to confirm the suggestions put

forward.

As yeast is a well-studied model system, genetic, mRNA and protein information

is readily available and its metabolism is for the most part well understood. Furthermore

as genetic screens have already been carried out and found edelfosine resistant mutants,

many of the genes of interest have already been identified. To this effect, it would be

possible to identify the proteins involved in the metabolic pathways implicated to be

perturbed by edelfosine treatment from our metabolomics profiling. Classical

biochemistry techniques such as western blotting could then be used to monitor levels of

the enzymes involved in the conversion of the metabolites that have been identified to be

altered to see if their levels are modulated as would be expected when yeast is treated

with edelfosine. Another complementary approach would be to use quantitative reverse

transcription polymerase chain reaction (qRT-PCR) to determine if the expression levels

of the enzyme in questions changed in a manner that would be consistent with what is

expected upon edelfosine treatment based on the observations made from the

102

metabolomics profiling data. In this way, further evidence could be obtained to back up

the suggestions made using the metabolmics data. An example of this type of approach

was the successful use of qRT-PCR to corroborate observations made from GC-MS and

LC-MS metabolomics data that uncovered the pathogenic mechanism of HIV-1 Tat

protein in Jurkat cells (120).

Despite the need for follow up experiments, metabolomics and lipidomic profiling

applied to exploring edelfosine’s mechanisms action was still able to provide valuable

insight as well as generate several novel hypotheses that can be explored in the future.

Furthermore, it was unique in allowing simultaneous exploration of a range of metabolic

effects and we can take comfort in that fact that many of our observations were supported

by current knowledge in the field of cancer as well as being in line with and expanding

upon previous studies carried out with edelfosine.

103

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