Proteomic analysis of saccharomyces cerevisiae KAY446 under very high gravity conditions

Wright, “The proteomic response of Saccharomyces cerevisiae in very high glucose conditions with amino acid supplementation”, Journal of Proteome Research, 7 11, 2008, 4766-4774... The

P ROTEOMIC A NALYSIS OF

Trong Khoa PHAM (ME)

Thesis submitted for the degree of Doctor of Philosophy (PhD)

to The University of Sheffield, Sheffield, UK

On completion of research in the Biological and Environmental Systems Group within

the Department of Chemical and Process Engineering

May, 2008

This copy of the thesis has been submitted with the condition that anyone who consults it is understood to recognise that the copyright rests with its author No quotation and information derived from this thesis may be published without prior written consent to the author or the University (as may be appropriate)

This thesis is dedicated to my parents, my wife and my daughter.

Declaration

This is to declare that I am the sole author of this thesis with work performed and completed in The University of Sheffield, UK, except where acknowledgements are made This work has not been submitted for any other degrees

Trong Khoa PHAM (Candidate)

Acknowledgments

I would like to express my sincere appreciations and acknowledgments to all the people who have been helping me to contribute this thesis I have really been surrounded by many nice people!

• My deepest appreciation to my supervisor Prof Phillip C Wright for his excellent supervision, invaluable guidance, encouragement and inspiration

• I would like to express my gratitude to Dr (Shirly) Poh Kuan Chong, Dr Chee Sian Gan for their teaching during the early years of my research, for their collaborator work, as well as for their endless help when I needed them

• I thank Dr Bram Snijders, and Dr Martin Barrios-Llerena for their technical advice Many thanks to Mark Scaife for his advice and discussions

• I would like to acknowledge Prof Katherine Smart (from Division of Food Sciences, University of Nottingham) and Dr Mark Dickman (from Department of Chemical and Process Engineering, University of Sheffield) for being my external and internal examiner, as well as a special thanks to Dr Mark Dickman for his technical advice

• I also gratefully acknowledge the Ministry of Education and Training of Vietnam for financial support

• I thank to Miss Thi Hong Nhan Le who bring a home atmosphere to me and my family

• Last but not least, I would like to thank my family: my parents, Mr Hau Phuc Pham and Mrs Hoa Thi Trinh who put their hope into my name “Trong Khoa”, for giving

me the life, for educating me, for unconditional support and encouragement My wife, Kim-Quyen T Huynh, for her sacrifice, patience, understanding and encouragement for all three years My daughter, Bao-Nhi H Pham, for her excellent performance in my spirit

List of Publications

Parts of the work in this thesis have been published as below:

(J – Journal paper, O – Oral presentation, P – Poster presentation)

(J1) P.K Chong, C.S Gan, T.K Pham, and P.C Wright, “Isobaric Tag for Relative and

Absolute Quantitation (iTRAQ) reproducibility: Implication of multiple

injections”, Journal of Proteome Research, 5 (5), 2006, 1232-1240

(J2) T.K Pham, P.K Chong, C.S Gan, and P.C Wright, “Proteomic analysis of

Saccharomyces cerevisiae under high gravity fermentation conditions”, Journal of Proteome Research, 5 (12), 2006, 3411-3419

(J3) C.S Gan, P.K Chong, T.K Pham, and P.C Wright, “Technical, experimental, and

biological variations in Isobaric Tag for Relative and Absolute Quantitation

(iTRAQ)”, Journal of Proteome Research, 6 (2), 2007, 821-827

(J4) T.K Pham and P.C Wright, “Review: Proteomic analysis of Saccharomyces

cerevisiae”, Expert Review of Proteomics, 2007, 4(6), 793-813

(J5) T.K Pham and P.C Wright, “Proteomic analysis of calcium alginate immobilized

Saccharomyces cerevisiae under high gravity fermentation condition”, Journal of Proteome Research, 7 (2), 2008, 515-525

(J6) T.K Pham and P.C Wright, “The proteomic response of Saccharomyces cerevisiae

in very high glucose conditions with amino acid supplementation”, Journal of

Proteome Research, 7 (11), 2008, 4766-4774

(J7) T.K Pham and P.C Wright, “Proteomic analysis of Saccharomyces cerevisiae

during continuous fermentation under VHG conditions: Regulation of proteomics versus oscillation of fermentation process”, In preparation

(J8) T.K Pham and V.M Le-Van, “Fermentation for ethanol production using

immobilized yeast cells in alginate gel” Proceeding of the 8 th ASEAN Food Conference, Nov 2003, Hanoi-Vietnam

(J9) T.K Pham and V M Le-Van, “Using Saccharomyces cerevisiae immobilized in

calcium alginate gel for ethanol fermentation”, Proceeding of the 8 th Conference on Science and Technology, Apr 2002, HoChiMinh-Vietnam

(O1) P.K Chong, C.S Gan, T.K Pham and P.C Wright, “Application and reliability of

isobaric mass tagging technology in proteomics”, 232nd ACS National Meating, San Francisco, CA, Sep 10-14th, 2006

(O2) P.K Chong, C.G Gan, T.K Pham and P.C Wright, “Assessing iTRAQ’s reliability,

quantitative proteomics, Applications in Biology and Medicine, Sheffield, Oct

2006

(P1) T.K Pham and P.C Wright, “Proteomic analysis of Saccharomyces cerevisiae under

high gravity conditions”, 3rd Joint BSPR/EBI Meeting, Cambridge, UK, Jul 12-14th,

2006

Table of Contents

Declaration iii

Acknowledgments iv

List of publications vi

Table of contents viii

List of tables xiv

List of figures xvi

Abbreviations xxii

Abstract xxv

Chapter 1 26

1.1 Introduction 27

1.2 Aims of this thesis 28

1.3 Thesis overview 28

Chapter 2 31

2.1 Abstract 32

2.2 Literature review of Saccharomyces cerevisiae proteomic analysis 32

2.2.1 The importance of proteomic investigations in Saccharomyces cerevisiae

32

2.2.2 Proteomics as a tool for the identification and quantitation of S cerevisiae proteins 34

2.2.3 The identification and quantitation of S cerevisiae proteins in proteomics experiments 43

2.2.4 Proteomics in the study of S cerevisiae networks 57

2.2.5 Proteomic analysis of S cerevisiae protein modifications 62

2.2.6 Discussion 65

2.2.7 The outlook of S cerevisiae proteomics analysis 68

2.3 Bioethanol fermentation process and methods to improve this process 71

2.3.1 Bioethanol fermentation process 71

2.3.2 Methods (techniques) to improve bioethanol fermentation 78

Chapter 3 82

3.1 Abstract 83

3.2 Introduction 83

3.3 Materials and Methods 85

3.3.1 Sample preparation 85

3.3.2 Protein preparation 86

3.3.3 Experimental design 86

3.3.4 Isobaric peptide labeling 87

3.3.5 Strong cation exchange fractionation 88

3.3.6 Mass spectrometric analysis 88

3.3.7 Data analysis 89

3.4 Results and discussion 90

3.4.1 Multiple injections 90

3.4.2 Technical variation 97

3.5 Conclusions 99

Chapter 4 100

4.1 Abstract 101

4.2 Introduction 101

4.3 Materials and methods 104

4.3.1 Fermentation conditions 104

4.3.2 Measurements of fermentation parameters 105

4.3.3 Cell extraction, labeling, mass spectrometry and data analysis 105

4.4 Results and discussion 106

4.4.1 Ethanol fermentation as a function of glucose concentration 106

4.4.2 Relative protein expression under different glucose concentrations 108

4.4.3 The glycolysis pathway 110

4.4.4 Storage carbohydrates 114

4.4.5 The requirement for redox balance led to secondary products generation 115

4.4.6 Tricarboxylic acid cycle (TCA cycle) 118

4.4.7 Amino acids metabolism 118

4.4.8 Proteins involved in eIF (eukaryotic initiation factor), and heat-shock proteins 120

4.5 Conclusions 123

Chapter 5 125

5.1 Abstract 126

5.2 Introduction 126

5.3 Materials and methods 129

5.3.1 Growth conditions 129

5.3.2 Measurements of fermentation parameters 130

5.3.3 Cell viability determination 130

5.3.4 Dry weight determination 130

5.3.5 Glycogen and trehalose determination 130

5.3.6 Intracellular amino acids determination 131

5.3.7 Labeling, mass spectrometry and data analysis 132

5.4 Results and discussion 133

5.4.1 The cell growth during lag phase under stress condition 133

5.4.2 The fluctuations in intracellular amino acids concentrations 134

5.4.3 The identification and classification of detected proteins 142

5.4.4 The expression of proteins related to the biosynthesis of amino acids 149

5.4.5 Response of heat-shock proteins to VHG conditions with amino acid supplementation 152

5.4.6 The expression of proteins related to translation processes

(aminoacyl-tRNA biosynthesis) and yeast growth 155

5.4.7 The expression of proteins related to cell division, cycle, and homeostasis 156

5.4.8 The expression of proteins related to the stress condition, trehalose and glycogen biosynthesis 157

5.4.9 The expression of proteins related to ethanol fermentation 159

5.4.10 The expression of proteins relating to energy metabolism and nucleotide metabolism 161

5.4.11 Comparison of ethanol fermentation process 161

5.5 Conclusions 162

Chapter 6 164

6.1 Abstract 165

6.2 Introduction 165

6.3 Materials and methods 168

6.3.1 Cell growth conditions and immobilization 168

6.3.2 Measurement of fermentation parameters 169

6.3.3 Immobilized cell extraction 170

6.3.4 Labeling, mass spectrometry and data analysis 170

6.4 Results and discussion 171

6.4.1 The behaviour of immobilized cells, and the kinetic fermentation parameters 171

6.4.2 The identification and quantitation of protein expression 177

6.4.3 The Ras/cAMP pathway 178

6.4.4 The glycolysis pathway 180

6.4.5 Utilization of ATP, and the synthesis of trehalose and glycogen 182

6.4.6 Differential expression of ribosomal proteins 185

6.4.7 Proteins essential for viability 186

6.4.8 Proteins related to de novo biosynthesis of amino acids and

aminoacyl-tRNA biosynthesis 187

6.4.9 Heat-shock proteins 188

6.4.10 Comparison of ethanol fermentation processes 189

6.5 Conclusions 190

Chapter 7 192

7.1 Abstract 193

7.2 Introduction 193

7.3 Materials and methods 196

7.3.1 Fermentation system 196

7.3.2 Cell growth conditions, immobilization, measurements of fermentation parameters, and proteomic analysis 197

7.3.3 ATP and ADP measurements 198

7.3.4 Ethanol yield determination 198

7.4 Results and discussion 199

7.4.1 The fluctuation of parameters 199

7.4.2 Ethanol yield oscillations 204

7.4.3 The rhythm of oscillations 205

7.4.4 Expression of proteins in generating the rhythm of the oscillations 206

7.4.5 Comparison of ethanol fermentation process 221

7.5 Conclusions 223

Chapter 8 225

8.1 Conclusions 226

8.2 Recommendations 230

References 236

Appendices 289

Appendix A 290

Appendix B 291

Appendix C 292

Appendix D 301

Appendix E 310

Appendix F 317

Appendix G 328

Appendix H 333

List of Tables

Table 2.1 An introduction and comparison of methods for quantitation 38

Table 3.1 Summary of iTRAQ labeled samples for all three organisms studied 87

Table 3.2 The protein distribution for both the mixed genome and single genome database

Here, alien proteins were referred to those false identifications identified in the database, but do not belong to the respective model of organism The ‘Overlapping proteins’ are defined as the proteins being found in both databases (mixed genome and single genome) and ‘Non-overlapping proteins’ were those proteins identified only in either one database 91

Table 3.3 Total unique peptides identified in mixed genome from replicate analyses for all

the organisms Here, only the proteins identified for the corresponding organism were taken into account (excluding alien proteins) 92

Table 3.4 The coefficient of variation (CV) calculated for the 10 experiments carried out

Only proteins found repeatedly in all three injections were taken into account in the CV calculation to investigate the MS and iTRAQ variance in this study 96

Table 3.5 The percentage of variation obtained from technical replicates in iTRAQ

experiments 97 Table 5.1 The list of amino acids detected by GC-MS 135

Table 5.2 Relative expression levels of proteins discussed here 143

Table 6.1 The fermentation kinetic parameters using free and immobilized cells with

various media 174

Table 7.1 The fluctuations of ethanol, glycerol, and residual glucose concentrations, as

well as biomass in the bioreactor during continuous fermentation under VHG conditions with different dilution rates 200

Table 7.2 The fluctuations of ethanol, glycerol, and residual glucose concentrations, as

well as biomass in the column during continuous fermentation under VHG conditions with different dilution rates 201

Table 7.3 The fluctuations of ethanol yields corresponding to three different dilution rates.

204

Table 7.4 Concentrations of ATP and ADP, and ratio of ATP/ADP versus times 209

List of Figures

Figure 1.1 The diagram illustrates the relationship between chapters in this thesis 30

Figure 2.1 The relationship of DNA, RNA, and protein in terms of “-omics” The Figure also illustrates the main functions of proteomics in the study of S cerevisiae 33

Figure 2.2 The identification and quantitation methods which have been applied to the proteomic analysis of S cerevisiae 35

Figure 2.3 An overview of the workflows involved in the various labeling methods 36

Figure 2.4 Structure of reagents used for iTRAQ (A) or ICAT (B) techniques 42

Figure 2.5 The combination of nano-LC-MS/MS (A) or MALDI-MS (B) 44

Figure 2.6 Illustration of the total ion intensity (A), the mass spectrum of isolated peptide determined by MS (B), the figure inset illustrates the mass-to-charge values around the ion peptide of interest (see the text for details), and the fragments of the selected peptide (C)

46

Figure 2.7 The fragmentation of protonated peptide ions, and correlated ions that can be formed in tandem MS/MS 47

Figure 2.8 Illustration of the matching of mass spectra data with protein (peptide) database While (A) is used for MALDI-TOF instrument, (B) for LC-MS/MS instrument

28

Figure 2.9 Illustration of algorithm searching for peptide (protein) identification 50

Figure 2.10 Number of publications related to the proteomic analysis of S cerevisiae 51

Figure 2.11 The reconstruction of central carbon metabolism in relationship to the TCA cycle, starch and sucrose metabolism, and the biosynthesis of amino acids 59

Figure 2.12 The glycolysis pathway for ethanol and glycerol production 72

Figure 3.1 (A) The overall percentage increment in the total number of unique proteins,

unique peptides and spectra identified from the mixed genome database (excluding alien

proteins) for each iTRAQ experiments via three analyses The overall increment was

calculated based on the result obtained from all three injections compared to the result obtained in first injection (B) The average percentage increment in total number unique proteins, unique peptides and spectra identified in (i) two analyses, which was calculated by comparing the result obtained from two injections (1st and 2nd injection) to the first injection; and (ii) three analyses, the comparison between the result obtained in all the three injections to the total result obtained from the first two injections (1st and 2nd injection) 94

Figure 3.2 The average percentage of coverage at different levels for technical replicates

A total of 5 replicates were used with 1 set from S cerevisiae, and 2 each from

Synechocystis sp and S solfataricus, respectively 98

Figure 4.1 The factors that can inhibit yeast cells 102

Figure 4.2 Residual glucose, ethanol, and glycerol concentrations, as well as the growth of

S cerevisiae in the broths under different glucose concentrations: 120 g/L (U), 210 g/L

(…), and 300 g/L ({) Filled symbols are representative for (A) OD650, or (B) glycerol, open symbols are representative for (A) glucose, or (B) ethanol Experiments were performed in triplicate 107

Figure 4.3 The number of protein showing either up- or down-regulation with respect to

their functional groups involved in primary and secondary metabolic pathways 109

Figure 4.4 Relative protein expression levels of glycolysis related proteins in the 210 g/L

and 300 g/L glucose conditions compared to the 120 g/L glucose concentration 109

Figure 4.5 The glycolysis pathway shown in relationship to other metabolic pathways,

such as the citrate cycle; alanine and aspartate metabolism; glutamate metabolism; lysine biosynthesis; phenylalanine, tyrosine and tryptophan biosynthesis; and starch and sucrose metabolism (with reference to KEGG) Proteins shown in bold format with blue background and ratio in red is representative for up (+) regulation; proteins in bold format with green background and ratio in blue is representative for down (-) regulation in this study The expression ratios of these proteins are also shown in Table 1 in Appendix C 111

Figure 4.6 The central model of redox balance, and bioenergetics in glycolysis 117

Figure 4.7 Relative protein expression levels of heat-shock proteins in the 210 g/L and 300

g/L glucose conditions compared to the 120 g/L glucose concentration 121

Figure 5.1 The growth of S cerevisiae under standard conditions (20 g/L of glucose)

(control samples) without (…) or with („) a dilution of OD650 (see the text for details), and VHG conditions (300 g/L of glucose) without ({) with (z) amino acid supplementation All experiments were performed in triplicate The numbers in the dashed boxes indicate sampling times for iTRAQ proteomic analysis 134

Figure 5.2 The ion chromatograph of a synthetic amino acid mixture analysed by GC/MS

The details of compound-specific ions are displayed in Table 5.1, as well as the peptide codes are also shown in Table 5.1 The inserted Figure illustrates the peak area was used to calculate the peptide concentration 136

Figure 5.3 The concentrations of intracellular amino acids in cells grown with standard

(…) and VHG conditions with (z) or without ({) amino acid supplementation All experiments were performed in triplicate 137

Figure 5.4 The expression of proteins at 2, 10, and 12 h under VHG conditions with amino

acid supplementation compared to standard conditions (at 0 h) Red, green and yellow colours are representatives for numbers of down-, up-, un- regulated proteins, respectively 148

Figure 5.5 The relationship of most amino acid metabolisms in relationship with proteins

detected here (modified from [309]) 151

Figure 5.6 A representation of the response of yeast to osmotic stress Bold arrows are

representative for flux of molecules (such as water, ions, trehalose, glycogen), as well as physical forces (such as turgor pressure) Dashed and plain arrows are representative for putative pathways, interactions, chains of events that may be triggered, enhanced (+) or negatively affected (-) by osmotic stress 154

Figure 5.7 Concentrations of trehalose ({) and glycogen (…) before (at 0 h) and after (2,

10 and 12 h) stress conditions were applied 159

Figure 6.1 Numbers of cells during fermentations All experiments were performed in

triplicate 172

Figure 6.2 Residual glucose concentrations in the broths with free cells (U) and

immobilized cells without (c) or with (…) an amino acid supplementation Experiments were performed in triplicate Figure inset illustrates the fluctuation of the glucose concentration in the broths during the early hours of fermentation 173

Figure 6.3 (A) Concentrations of ethanol (open symbols), and (B) glycerol (filled

symbols) in the broths with free cells (U) and immobilized cells under conditions of without (c) or with (…) an amino acid supplementation Experiments were performed in triplicate 173

Figure 6.4 Dependency of reaction rates on glucose concentration (U) free cells, (c) and

(…) are immobilized cells without, or with an amino acids supplement respectively 175

Figure 6.5 A Hill plot representing the activity of a particular key enzyme in free cells and

immobilized cells (U) represents free cells, while (c) and (…) represent immobilized cells without or with an amino acid supplementation respectively 176

Figure 6.6 The expression of proteins in immobilized cells compared to free cells (c), and

immobilized cells with an amino acid supplementation compared to free cells (…) All data are presented in log10 space 177

Figure 6.7 The glycolysis pathway is depicted in relationship to the Ras/cAMP pathway,

starch and sucrose pathway, amino acid biosynthesis, and aminoacyl t-RNA biosynthesis Metabolites are shown with a blue background (except for energy compounds in red), while up-regulated proteins are depicted with a green background The blue line shows the interaction of transmission of signals to activities of proteins 179

Figure 6.8 A model to describe the activation of Ras/cAMP pathway for the transition of

signals in immobilized cells compared to freely suspended cells Its activation indirectly accelerated alcohol fermentation under the conditions studied here 183

Figure 6.9 Concentrations of trehalose (A) and glycogen (B) at the 48th and 60th h sampling time for iTRAQ experiment ( free cells, immobilized cells, immobilized cells with an amino acid supplementation) All experiments were performed in triplicate 184

Figure 7.1 The system consisting of a stirrer bioreactor, and a column bioreactor was used

for continuous fermentation under VHG conditions 1 and 5 – Media and broth bottles, 2 – Peristaltic pumps, 3– Stirred bioreactor 4 – Column bioreactor, 6 – Valves, 7 – Computer and controller 197

Figure 7.2 The concentrations of ethanol (‘), glycerol ({), and residual glucose (¡) as

well as biomass (z) for continuous fermentation under VHG conditions at a dilution rate of

D1 = 0.020 h-1 (A) is representative for the stirred bioreactor, and (B) for the column bioreactor 199

Figure 7.3 The concentrations of ethanol (‘), glycerol ({), and residual glucose (¡) as

well as biomass (z) for continuous fermentation under VHG condition at dilution rate D2 = 0.025 h-1 (A) is representative for the bioreactor, and (B) for the column 199

Figure 7.4 The concentrations of ethanol (‘), glycerol ({), and residual glucose (¡) as

well as biomass (z) for continuous fermentation under VHG condition at dilution rate D3 = 0.030 h-1 (A) is representative for the bioreactor, and (B) for the column 200

Figure 7.5 The regulation of proteins relating to the fluxes of glycolysis (A), F-ATPases

and V-ATPases (B), and other proteins (C) at the sampling time of 132 h, 144 h, and 165 h compared to 120 h 208

Figure 7.6 The figure is reproduced from Figure 7.3.A to illustrate the sampling times as

well as the labeling samples for proteomic analysis The concentrations of ethanol (‘), glycerol ({), and residual glucose (¡) as well as biomass (z) for continuous fermentation under VHG condition at dilution rate D2 = 0.025 h-1 210

Figure 7.7 The regulation of pO2 in the bioreactor 210

Figure 7.8 The diagram illustrates the formation and consumption of ATP, ADP, NAD+, NADH in glycolysis and other pathways The effects of metabolites on activity of proteins functioned in the regulator of glycolytic flux are also included The green dotted line is

representative for the activation of a metabolite on the protein activity, and red dotted line

is for the de-activation of a metabolite on the protein activity 213

Figure 7.9 The structures of F- and V-ATPases in the relationship with proteins detected

in this study The Figure was modified from [444, 445] For F-ATPase, the synthesis of ATP occurs in F1 coupling with the transport of proton through F0 from lumen/extracellular For the For V-ATPase, the V1 domain is response for ATP hydrolysis and drives proton transport through the V0 domain from cytoplasm 216

Figure 7.10 The summarization of continuous fermentation fluctuation Data were taken

from duplicate fermentation experiments 221

Abbreviations

1-DE: One dimensional gel electrophoresis

2-DE: Two dimensional gel electrophoresis

ACN: Acetonitrile

ADP: Adenosine diphosphate

AMP: Adenosine monophosphate

ATP: Adenosine triphosphate

cAMP: Cyclic adenosine monophosphate

CPY: Carboxypeptidase Y

CV: Coefficient of variation

D: Dilution rate

DIGE: Difference gel electrophoresis

DNA: Deoxyribonucleic acid

DTT: Dithiothreitol

ECD: Electron capture dissociation

EDC: 1-(3-dimethylaminopropyl)-3-ethylcarbodiimide hydrochloride

EDTA: Ethylenedinitrilotetraacetic acid

EF: Error factor

eIF: Eukaryotic initiation factor

ESI: Electrospray ionization

ETD: Electron transfer dissociation

FDA: Food and drug administration

FT-ICR: Fourier transform ion cyclotron resonance

GDP: Guanosine diphosphate

GEF: Guanine nucleotide-exchange factor

GRAS: Generallyregarded as safe

GTP: Guanosine triphosphate

HOG-MAP: High osmolarity glycerol response – Mitogen actived protein

HPLC: High-performance liquid chromatography

HSP: Heat-shock protein

ICAT: Isotope-coded affinity tags

iTRAQ: Isotope tags for relative and absolute quantification

KEGG: Kyoto encyclopedia of genes and genomes

LC: Liquid chramatography

m/z: Mass-to-charge ratio

MALDI: Matrix-assisted laser-desorption ionization

MBDSTFA: N-methyl-N-t-butyldimethylsilyl-triflouroacetamide

MCAT: Mass-coded abundance tagging

MES: 2-Morpholinoethanesulfonic acid

MFA: Metabolic Flux Analysis

mRNA: Messenger ribonucleic acid

MS/MS: Tandam mass spectrometry

MS: Mass spectrometry

NAD+ (NADH): Nicotinamide adenine dinucleotide

NADH: Nicotinamide adenine dinucleotide

NCBI: National center for biotechnology information

OD: Optical density

PAGE: Polyacrylamide gel electrophoresis

PTM: Post-translational modification

RNA: Ribonucleic acid

RP: Reverse phase

SCX: Strong cation exchange

SILAC: Stable isotope labeling of amino acids in culture

TAP: Tandem affinity purification

tBOC: Tert-butoxycarbonyl

TCA: Tricarboxylic acid cycle

TEAB: Tetraethylammonium borohydride

TFA: Trifluoroacetic acid

TOF: Time of flight

tRNA: Transport ribonucleic acid

VHG: Very high gravity

Abstract

Enhanced ethanol fermentation is one of the current issues in fermentation technology; this

also is a main aim that goes through this thesis In this thesis, S cerevisiae KAY446 was

used for ethanol fermentation under very high gravity (VHG) conditions This strain showed an ability to ferment under these conditions since an average ethanol concentration

of 117.5 g/L was determined for continuous fermentation In terms of deeper biological

aspects, a proteomic analysis of S cerevisiae was also performed to gain an understanding

of this micro-organism in responding to stress conditions due to high glucose concentrations in the broths Therefore, the iTRAQ technique was used as a proteomics tool

for this purpose The reliability of this technique was analysed via three different domains

of life, including S cerevisiae (eukaryotes), S solfataricus (archaea) and Synechocystis sp

(bacteria) Based on multiple replicate analyses, an increment of peptides (proteins) was found, as well as a technical replicate to confirm the reliability of this method The main functions of the proteomic analyses in this thesis were to discover three crucial objectives:

firstly, to investigate the cellular response of S cerevisiae to osmotic stress generated by

VHG conditions, as well as to immobilization of cells; secondly, to examine the response of this micro-organism to VHG conditions with an amino acid supplementation; and finally, to investigate the main triggers for the fluctuation of fermentation parameters observed during continuous fermentation process

Chapter 1

Introduction

1.1 Introduction

In this thesis, Saccharomyces cerevisiae KAY446 was used for ethanol fermentation under

very high gravity (VHG) conditions in order to improve ethanol fermentation This organism is widely used as a biological production organism, as well as aeukaryotic model system

micro-Ethanol is an important industrial solvent or chemical feedstock with two broad production routes; these being chemical (from the catalytic conversion of ethylene), or biological (fermentation using micro-organisms) During the 1970s energy crisis, many attempts focused on the development of alternative energy resources, with ethanol being one of the foci Consequently, a large number of cars use either Gasohol (76% gasoline and 24% ethanol) or pure ethanol as a fuel [1] The current refocus on alternative energy in light of global warming issues has focussed attention on ethanol as one of the potential solutions

Furthermore, fermentative ethanol production by S cerevisiae is still currently the most

economic method, with many attempts being made to achieve higher ethanol concentrations [2-4] As an example, VHG fermentation technologies have been used to increase the ethanol productivity [2] The application of this technique is most promising for commercial purposes

However, in terms of proteomics, an understanding of S cerevisiae in responding to this

stress conditions (because of VHG conditions) has not yet been reported, and the proteomes

of brewing yeast have still largely unknown [3] Therefore, in this thesis, proteomic

analyses of S cerevisiae were performed to understand how the wild-type S cerevisiae

KAY446 strain adapts to stress conditions generated by the VHG conditions Moreover, the improvement of ethanol fermentation was also performed to achieve high ethanol production

1.2 Aims of this thesis

The main aims of this thesis are:

• To gain knowledge of yeast physiology during VHG fermentation processes using proteomics as a tool

• To investigate how the proteomes of S cerevisiae change from normal to VHG

conditions, and from suspended to immobilized cells during fermentation

• To determine main triggers for the oscillation of fermentation parameters observed during the continuous fermentation process

• To enhance ethanol fermentation that is evaluated via ethanol yield and final

ethanol concentration in the broths

1.3 Thesis overview

This thesis contains eight chapters and is organised as following (see also Figure 1.1):

• Chapter 1: This chapter introduces the background and the aims of this thesis

• Chapter 2: A literature review of the application of proteomics applied to S

cerevisiae is presented in this chapter The present approaches and issues in

proteomic analyses of S cerevisiae as well as bioethanol fermentation are

addressed and discussed here

• Chapter 3: The application of the iTRAQ shotgun proteomics tool is performed where multi injections and technical replicates are discussed in detail The work in this chapter was carried out based on analysing iTRAQ data from three different

domains of life including S cerevisiae (eukaryotes), S solfataricus (archaea) and

Synechocystis sp (bacteria) The evaluation of this technique is also included

• Chapter 4: An understanding of S cerevisiae proteomes under elevated glucose

concentrations is presented in this chapter This is first time a global proteome

analysis of S cerevisiae using iTRAQ has been carried out to understand the

responses of yeast to osmotic stress condition (high concentrations of glucose)

• Chapter 5: This chapter investigates the benefits of amino acid supplementation in

VHG media for ethanol fermentation Proteomic analysis of S cerevisiae was

performed during the lag and early exponential phase to illustrate the benefits of amino acids addition

• Chapter 6: The application of two techniques, immobilized and improved quality of VHG media using an amino acid supplementation, were applied for ethanol fermentation As a result, an improvement of the specific ethanol yield was observed, as well as time for fermentation was also reduced The differences of protein expressions between immobilized and suspended cells are also investigated and discussed in this chapter

• Chapter 7: This chapter offers a combination of suspended and immobilized cells for continuous fermentation under VHG conditions with an amino acid supplementation This combination helped enhance ethanol fermentation, since an average ethanol concentration of 117.5 g/L was determined A proteomic analysis

of S cerevisiae was also carried out in this chapter to explain as well, which is

aimed at elucidating the main triggers for the fluctuation of fermentation parameters observed during the continuous fermentation process

• Chapter 8: This chapter summarises the whole research, as well as presenting the main conclusions drawn across the preceeding Suggestions and recommendations for future work are also included

Figure 1.1 The diagram illustrates the relationship between chapters in this thesis

This work contributes to the field of ethanol production in S cerevisiae by implementing

reliable post-genomics approaches using quantitative shotgun proteomics The deeper

understand of S cerevisiae’s metabolism was obtained by examining its responses to stress conditions at the protein level via the iTRAQ technique This thesis provides a new

contribution to fermentation by studying the metabolism of glycolysis pathway and other

processes via the global protein expression changes of S cerevisiae (under different

conditions) as well as measurements of metabolites Finally, the enhanced ethanol fermentation under VHG conditions was achieved by applying media and process condition changes to overcome issues that were highlighted from an analysis of the proteomic data Thus this thesis demonstrates that proteomics can be used to improve a bioprocess

Chapter 2 Review: Proteomic Analysis of

Saccharomyces cerevisiae and Bioethanol

Fermentation

The contents of this chapter were published in Expert Review of Proteomics, 2007, 4(6),

2.1 Abstract

Nowadays, proteomics is recognised as one of the fastest growing tools in many areas of

research This is especially true for the study of Saccharomyces cerevisiae, as it is

considered as a model organism for eukaryote cells Proteomic analysis provides an insight into global protein expressions from identification to quantitation, from localization to function, from individual to network systems Moreover, recently, many methods for identification and quantitation of proteins based on tandem MS/MS workflows have been

developed and widely applied in S cerevisiae The current methods and current issues in the proteomic analysis of S cerevisiae are reviewed Furthermore, the bioethanol fermentation using S cerevisiae as well as current issuses to improve ethanol fermentation

are also surveyed here

2.2 Literature review of Saccharomyces cerevisiae proteomic analysis

2.2.1 The importance of proteomic investigations in Saccharomyces cerevisiae

The proteomic analysis of S cerevisiae is the next step in the analysis of biological systems

of this domain, after genomics and transcriptomics The global analysis of proteins is now receiving significant attention over that of genes, since the expression of proteins directly provides an understanding of function and regulation of cells in response to their environments In comparison to gene expression analysis at the mRNA level, proteomic expression analysis provides deeper information on biological systems, as well as pathways since the measurements concentrate on the actual biological effector molecules [4] Moreover, genomics/transcriptomics has some disadvantages, such as (i) the gene transcription levels only provide a rough estimate of their expression level in terms of proteins, as the mRNA molecules may be degraded rapidly, or translated inefficiently, resulting in discordance with the protein abundance [5] (ii) Some transcripts generate more

than one protein via alternative post-translational modifications, and alternative splicing

(iii) Some of proteins only reveal their functions after phosphorylation (iv) Some proteins

interact with other proteins, and only demonstrate functionality in the presence of these additional molecules The relationship between the DNA, RNA, and protein levels are shown in Figure 2.1 DNA is known as an information storage level that instructs the later construction of RNA and proteins; RNA plays a key role in translating the genetic information from DNA to proteins; and finally, proteins play important roles in the cell activities from constructing cells to metabolic pathways and other networks

Figure 2.1 The relationship of DNA, RNA, and protein in terms of “-omics” The Figure

Briefly, the main aims of proteomics in studying S cerevisiae include (see Figure 2.1): (i)

identification of proteins as well as comparison of protein expression changes in response

to different states or environment changes; then, (ii) characterization of cellular functions of each protein(s); subsequently, (iii) interaction of proteins in the regulatory networks to reconstruct some metabolic pathways, or to establish the protein-protein interaction networks; finally, (iv) this tool can be also used in study of post-translational modifications

such as glycosylation, phosphorylation, and lipid modification etc In terms of S cerevisiae

protein analysis, the most important initial issue is the identification and quantitation of proteins in different states or conditions

2.2.2 Proteomics as a tool for the identification and quantitation of S cerevisiae

proteins

2.2.2.1 Introduction

Since protein identification is also a key part of the quantitation procedure (for shotgun proteomics in particular), this chapter aims to focus on quantitative methods more than identification methods To date, traditional quantitative proteomics has been carried out in the main by 2-DE (two dimensional gel electrophoresis) Recently, various techniques for protein quantitation by mass spectrometry have been developed, ranging from label-free quantitation [6, 7] to label-based quantitation [8-11] Proteomic quantitation can be

performed either in vivo (metabolic labeling) or in vitro (protein and peptide labeling)

Basically, the label-based proteomic quantitation can be performed at 3 levels: cell, protein, and peptide levels The cell labeling approach is usually known as a metabolic labeling, and includes: metabolic labeling (15N, or 13C), and stable isotope labeling of amino acids in culture (SILAC) [8, 9] The labeling approach at the protein and peptide levels are mostly based on employing stable isotopic tags, such as trypsin digestion of protein in [18O-water] [10], isotope-coded affinity tags (ICAT) [4], or isobaric tags for relative and absolute quantitation (iTRAQ) [11] (see Figure 2.2) The majority of the proteomic quantitation work carried out to date has been relative quantitation Although, clearly, absolute

quantitation is highly desirable ultimately for a deeper understanding, and several emerging techniques are meeting with success in this regard, such as absolute quantitation (AQUA) [12, 13] and QConCat [14, 15] workflows (see Table 2.1 for details) The advantages of the labeling techniques are the ability to simultaneously identify and quantity simultaneously (and hopefully automatically) targets from complex protein mixtures [9]

Identification

Quantitation

2D-gels Shot- gun

Based on gels Labeling technique

O]-Free-label

Peak intensity

of peptides

Number

of MS/

MS spectra

Absolute

Proteomic analysis of

S cerevisiae

Figure 2.2 The identification and quantitation methods which have been applied to the

proteomic analysis of S cerevisiae

With metabolic labeling, the labeling step is performed by incorporation of the isotopes during cell growth Proteins in cells are labeled with a stable isotope labeled carbon source,

or nitrogen source, for example 13C-Glucose [16, 17], 15N-ammonium sulphate [172, 173],

or via the SILAC method [18] In practice, cells are grown under different conditions

containing either compounds with the natural isotope abundance, or those containing the heavy isotope The cells from different conditions are then mixed, and protein extraction carried out (it is possible to do an extraction first, with the proteins mixed after extraction, but this is more likely to create errors) (see Figure 2.3 for details) A traditional 1-DE or 2-

DE gel-based workflow or shotgun proteomics workflow can then be followed Peptides (usually) are then identified using mass spectrometry The relative abundance of a peptide

is then calculated based on the areas under the heavy and the light versions of the same peptide fragmented by MS

Peptides labeling

Labeling Labeling

SCX Digestion Mixed proteins Labeling Labeling

Proteins labeling

Mixed proteins

Protein extraction extraction Protein extraction Protein

Protein

extraction extraction Protein

Cells in sample 1

Cells in sample 2 sample 1 Cells in sample 2 Cells in

In vivo

labeling

Metabolic labeling

Cells in sample 2

In vivo

labeling

Cells in

sample 1

Figure 2.3 An overview of the workflows involved in the various labeling methods

A favourable alternative technique to metabolic labeling is the use of a post-growth labeling strategy In this case, all cells are grown in normal media with no heavy isotope enrichment The proteins or peptides are then labeled with tags A typical method for protein labeling is ICAT [4], see Table 2.1 for detail For peptide labeling, the labeling step

is carried out after the digestion step via a number of potential methods, for example

iTRAQ [11], or during digestion step using 18O labeled water Then these peptides are fractionated and analysed by tandem mass spectrometry

2.2.2.2 Comparison of methods

The 2-DE approach is the classical technique for the identification and quantitation of proteins for proteomics applications 2-DE works by comparing both the positions and intensities of spots on a series of gels Since the workflow is based on gel-to-gel comparisons it may be affected by spot position and lead to problems in detecting the differences between gels [19] To meet the requirements of data validity, numerous repeated gel experiments should be performed to ensure the statistical significance of results [20] To improve the power of this technique, both technical solutions and analysis software platforms have been developed One of the solutions for the technical aspects is the application of DIGE (difference gel electrophoresis), an approach that allows for the comparison of two samples in the same gel [21] Briefly, proteins in two samples are labeled with one of two fluorescent dyes and then mixed with a third labeled mixture of two samples as an internal calibration [21] After separation on 2-DE gels, proteins are detected

by different lasers, and then images are overlapped to reveal changes in proteins abundance The most significant advantage of this method is increased ease of comparison, since the samples are run together, resulting in the elimination of gel-to-gel variation [22]

For in vivo labeling with 14N and 15N, there is a mass shift in the resultant peptides from the two different (labeled and unlabeled) media observed during MS analysis The N-label can

be found in both the backbone and side chain nitrogen atoms, therefore the mass shift cannot (easily) be used to predict peptides from unknown sequences [23] As a result, this method is not advantageous for highly complex samples such as cell lysates, since having heavy isotope containing compounds essentially doubles the complexity of the sample submitted to the mass spectrometer However, this method provides a fractionation step (for example cysteine capture), which is used to reduce the complexity of samples [24]

Table 2.1 An introduction and comparison of methods for quantitation

Time 2-DE Gel

Fluorescent protein stains which are used to increase the quantitative accuracy

Recently, stains specific for post-translational

modifications have been developed

The mixture of proteins are separated firstly by isoelectric point on gel strip, and then by molecular weight on SDS-PAGE gels

The comparison is based on the position and intensities of spots on 2-DE gels

Accuracy in comparison due to at least triplicate for each phenotype Ease of performance

Sequential, labour intensive Difficult to automate Not sensitive for basic, hydrophobic, and large molecular mass proteins

At least triplicate gels for each sample, therefore, cost time and laborious to analyse and perform

Using a heavy or light form of

an essential amino acid as a

]-leucine

The isotopic label into peptides via metabolic labeling in

processing steps as well

as purification of protein samples

Not always suitable for all experimental samples (requires growth on isotope enriched substrate)

contains eight deuterium) and

a light isotope (e.g contains

no deuterium) Each reagent includes 3

- An affinity tag (biotin) which is used to isolate ICAT-labeled peptides

- A linker which is incorporated stable isotopes

Number of samples for one ICAT experiment: 2 samples

Each sample is denatured, reduced, and labeled with isotopic reagent Protein samples are then mixed and trypsin digested

Cysteine-tagged peptides are enriched by affinity chromatography of the biotin tag using an avidin column Enriched-peptides are then fractionated by RP-HPLC alone

or in combination with SCX Peptides fractions are submitted to the mass spectrometer for identification and quantitation of peptides

The comparison is based on isotopic tagging of cysteine residues which can be seen on MS/MS

Good for complex samples due to only few peptides per proteins being used for analysis

Only two samples can be used for one experiment The excess of biotin in the sample matrix (e.g serum) may reduce the affinity column because of binding sites competition

Sometimes, the isobaric between ICAT peptides and non-tagged peptides eluting from affinity chromatography results in

positive and

false-1999 by

Gygi et al

[4]

Names Reagents - Functions Methods Advantages Disadvantages Release

Time

- An iodoacetamide reactive group which is reacted with thiol groups (cysteines) Versions:

Because of peptide selective strategies, a loss of peptide redundancy of a given protein can be found Possible difficulties for PTM analysis in the case of only one or few peptides containing low-abundance cysteine, which is used as a representative for protein identification

116, 117, 118, 119, 121 Each reagent include 3

Reporter group (mass 114 -

117 for 4 plex, or 114 - 119 and 121 for 8 plex) which is used quantify their respective samples

Balance group (mass 31-28 for

4 plex, or 92 - 86 and 84 for 8 plex) which is used to

Number of samples for one iTRAQ experiment: 4 samples, and soon 8 samples

Each sample is denatured, reduced, alkylated, and trypsin digested Subsequently, peptides in each sample are labeled with iTRAQ reagents, and then combined

The labeled-peptides mixture is then fractionated using SCX chromatography Fractions are introduced to tandem MS/MS for identification and quantitation of peptides and then proteins

The label is cleaved in the MS before quantification

Quantitative comparison can be performed up to

4 samples and soon 8 samples within a single experiment

High validation and good for quantitative comparison of PTM and sub-proteome

The signal intensity is increased due to the

Not so good for identification and quantitation of low abundance proteins, as well

as samples with high complexity

Time-consuming The noise of un-tagged isobaric chemicals may confuse MS sequencing of the labeled-peptides

Problem of protein variants

2004 by

Ross et al

[11]

Names Reagents - Functions Methods Advantages Disadvantages Release

isotopically-labeled peptides are isobaric and all contribute to one ion species that is used for CID and observed in the

MS

16

termini of trypsin digested peptides Two samples are then combined, and analysed by MS/MS The quantitation is performed based on the relative intensity

of paired peptides with 4 Da mass difference detected by MS/MS

Simple procedure, high efficiency

Less cost for experiments compared

to other labeling methods

The 4 Da mass difference is not big enough to detect by ESI ion trap MS/MS Because of peptide selective strategies, a loss of peptide redundancy of a given protein can be found

The quantitation can be approached based on:

i) The peak intensity measurement of peptides ii) The number of MS/MS spectra per protein detected

Lower cost for experiments compared

to other methods

The quantitation is affected

by ionization efficiency and chromatography conditions

An selected isotope labeled

an internal standard Each AQUA peptide is a synthetic tryptic peptide incorporating one stable isotope labeled amino acid,

An optimal tryptic peptide corresponding to an interested protein is selected and then synthesised incorporating stable

The known quantity of AQUA peptide is subsequently added to protein sample extracted from biological sample

Sample is then proteolysised with enzyme Peptide mixture is analysed by tandem MS/MS

High sensitivity, and more accurate compared

to quantitative PCR or Northern blotting Suitable for study of gene silencing in low abundance proteins

Each protein to be quantified requires at least one stable isotope labeled peptide synthesised independently, therefore high cost is required

Moreover, each peptide

2003 by Gygi [13]