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Distinct proteome remodeling of industrial Saccharomyces cerevisiae in response to prolonged thermal stress or transient heat shock Weidi Xiao, Xiaoxiao Duan, Yuping Lin, Qichen Cao, Shanshan Li, Yufeng Guo, Yuman Gan, Xianni Qi, Yue Zhou, Li-hai Guo, Peibin Qin, Qinhong Wang, and Wenqing Shui J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/acs.jproteome.7b00842 • Publication Date (Web): 03 Apr 2018 Downloaded from http://pubs.acs.org on April 4, 2018

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Journal of Proteome Research

Distinct

proteome

remodeling of

industrial Saccharomyces

cerevisiae in

response to prolonged thermal stress or transient heat shock Weidi Xiao1,2, Xiaoxiao Duan2,3, Yuping Lin3, Qichen Cao3, Shanshan Li1, Yufeng Guo3, Yuman Gan3, Xianni Qi3, Yue Zhou4, Lihai Guo5, Peibin Qin5, Qinhong Wang3,*, Wenqing Shui1,6*

1

iHuman Institute, ShanghaiTech University, Shanghai 201210, China

2

College of Life Sciences, Nankai University, Tianjin 300071, China

3

Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin

300308, China 4

Demo Laboratory of Thermofisher Scientific China, Shanghai 200120, China

5

AB SCIEX, 1# Building, 24# Yard,Jiuxianqiao Mid Road, Chaoyang District, Beijing

100015, China 6

School of Life Science and Technology, ShanghaiTech University, Shanghai, 201210,

China

Correspondences: Qinhong Wang, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China; Tel: 86-22-84861950; email: [email protected] Wenqing Shui, iHuman Institute, ShanghaiTech University, Shanghai 201210, China; Tel: 86-21-20685595; email: [email protected]

1

ACS Paragon Plus Environment

Journal of Proteome Research 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Keywords: Industrial yeast, thermotolerance, heat shock response, Transcription factor, DIA, SWATH

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Abstract To gain deep understanding of yeast cell response to heat stress, multiple laboratory strains have been intensively studied by genome-wide expression analysis for mechanistic dissection of classical heat shock response. However, robust industrial strains of S. cerevisiae have hardly been explored in global analysis for elucidating the mechanism of thermotolerant response (TR) during fermentation. Herein, we employed DIA/SWATH–based proteomic workflows to characterize proteome remodeling of an industrial strain ScY01 responding to prolonged thermal stress or transient heat shock. By comparing the proteomic signatures of ScY01 in TR vs HSR, as well as HSR of the industrial strain vs a laboratory strain, our study revealed disparate response mechanisms of ScY01 during thermotolerant growth or under heat shock. In addition, through proteomics data-mining for decoding transcription factor interaction networks followed by validation experiments, we uncovered the functions of two novel transcription factors, Mig1 and Srb2, in enhancing thermotolerance of the industrial strain. This study has demonstrated that accurate and high-throughput quantitative proteomics not only provides new insights into the molecular basis underlying complex microbial phenotypes but also pinpoints upstream regulators that can be targeted for improving desired traits of industrial microorganisms.

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Introduction Saccharomyces cerevisiae is the most widely used microorganism for large-scale ethanol production in food and beverage industry, and more recently, biofuel industry 1-3

. Yeast industrial strains are renowned for their high ethanol yield and productivity

as well as general robustness. However, due to the increasing demand of producing larger and cheaper ethanol volumes worldwide, S. cerevisiae has been challenged with new process requirements. Specifically, yeasts with higher thermotolerance are needed to fulfill fermentation at temperature above 40 °C which will largely reduce cooling costs and help preventing contamination 4. High operating temperature will also facilitate synchronization of the processes of saccharification (45-50°C) and fermentation (30-35°C), leading to substantial increase of ethanol productivity

4-6

.

Hence, significant cost reduction and productivity improvement would be achieved in fermentation with yeast strains of enhanced thermotolerance. To gain insights into yeast cell response to heat stress, multiple laboratory strains have been intensively characterized by genome-wide expression analysis for mechanistic dissection of classical heat shock response

7-10

. However, robust yeast

industrial strains have hardly been explored in global analysis for elucidating the molecular basis of long-term thermotolerance during fermentation. Notably, the Nielsen

group

has

recently

conducted

genome-wide

RNA sequencing

of

thermotolerant yeast strains to reveal a change in sterol composition and induced expression of genes involved in sterol synthesis during adaptive evolution upon thermal stress

11

. In our previous proteomic survey of two yeast industrial strains of

superior thermotolerance (ScY and ScY01), we identified an array of altered cellular 4


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functions such as central carbon metabolism, amino acid metabolism, protein folding/degradation, and anti-oxidant defense in response to prolonged thermal stress 12

. Moreover, our study revealed the dramatic difference between the adapted

thermotolerant response (TR) of industrial strains and the transient heat shock response (HSR) of a laboratory strain based on distinct proteomic profiles

12

.

Nevertheless, it remains unclear whether there is significant disparity in the proteome landscape of TR vs HSR from the same industrial strain. More importantly, how we mine the omics-type data effectively to discover key regulatory factors that could be targeted for metabolic engineering of industrial yeast strains has posed a great challenge to proteomic studies. Depicting cellular proteomes perturbed by environmental insults relies on advanced mass spectrometry (MS) techniques to enable qualitative and quantitative profiling of the full repertoire of proteins with sufficient accuracy and consistency

13

.

The recent development of the sequential window acquisition of all theoretical fragment ion spectra (SWATH) technique has gained wide interest because this new approach overcomes the intrinsic weakness of conventional data dependent acquisition (DDA) methods and affords higher throughput and reproducibility for proteome-wide quantification

14-15

. Furthermore, SWATH MS is reported to achieve

the quantification accuracy to the level of quality delivered by SRM quantification of targeted proteins

15

. An increasing number of studies have demonstrated the great

potential of SWATH MS in large-scale quantitative proteomic research including interrogation of dynamics of the human interactome

16-17

, comprehensive mapping of

5



18-19 20

mouse tissue proteome and human plasma proteome genome-wide absolute protein concentrations

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, and determination of

21-22

. While SWATH MS was

predominantly implemented on TripleTOF mass spectrometers

14-17, 20-22

, recent

development of a SWATH-like DIA workflow has extended the instrument platform to Orbitrap systems 18-19, 23. Compared to the isobaric labeling-based technique iTRAQ 24 adopted in our previous proteomic study of industrial yeast strains, we speculate the application of SWATH/DIA-based quantification techniques would enable more accurate and consistent measurement of proteome dynamics. In the present study, we employed SWATH and DIA–based proteomic workflows to characterize proteome remodeling of an industrial strain ScY01 responding to prolonged thermal stress. In addition, we performed SWATH-based proteomic survey of the same strain exposed to transient heat shock. By comparing the proteomic signatures of ScY01 in TR vs HSR, as well as HSR of the industrial strain vs a laboratory strain, our study revealed disparate response mechanisms of ScY01 exposed to prolonged thermal stress or transient heat shock. Using these highly accurate proteomic quantification data, we then predicted distinct PPI networks of transcription factors (TFs) that potentially regulate the global proteome patterns of ScY01 in TR compared to HSR. The identified TFs specifically associated with TR were tested experimentally by single gene overexpression and deletion, which led to discovery of two new TFs promoting thermotolerance and ethanol biosynthesis of ScY01 at elevated temperature.

6


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Experimental Procedure Experimental Design and Statistical Rationale Two biological replicates were prepared for the control and heat-stressed conditions of both TR and HSR experiments. Sample processing duplicates were generated for each biological replicate. In the TR experiment, mixed samples from the heat-stressed and control cells were injected in triplicate for DDA analysis. Then samples from two processing replicates at each condition was pooled and analyzed in technical triplicate with either DIA or SWATH workflow. In the HSR experiment, mixed samples from two conditions were subjected to 2D-LC-MS/MS for DDA analysis. Then two processing replicates from each biological replicate of the control or heat shock sample were separately injected for SWATH analysis, resulting in four acquisitions for each condition.

Yeast cell culture for proteomic analysis. Strains used in this study are listed in Table S6. Cell culture media and media supplements were purchased from Invitrogen (USA). S. cerevisiae ScY01 cells were first grown at 30 °C to mid-log phase in YPD medium containing 100 g/L glucose, then inoculated into new YPD medium containing higher glucose concentration (200 g/L) with the initial OD of 0.1. In the TR experiment, cells were cultivated at 40 °C and 30 °C separately for 14 h, and harvested by centrifugation at 1500 × g for 5 min. In the HSR experiment, ScY01 cells were cultivated at 30 °C to early-log phase, and heat shock was induced by instantaneously shifting temperature from 30 °C to 50°C by adding an equal volume of pre-warmed 7



YPD medium (70 °C). Cells were collected at t = 0 and 30 min after incubation at 50 °C for 30 min.

Protein extraction and digestion. Cell pellets were resuspended in lysis buffer containing 5 mM DTT, 8 M Urea and 1× protease inhibitors cocktail (Roche, Mannheim, Germany) in 0.1 M NH4HCO3 before lysed with the glass bead-beating method 25. Protein extracts from total cell lysis was clarified by centrifugation at 13,000 × g for 10 min. Protein concentration was determined using Bradford assay kit (Solarbio, China). Then the protein extract was reduced with 20 mM DTT for 4 h at 37 °C and alkylated with 40 mM iodoacetamide for 40 min at room temperature in darkness. The total cell extract was diluted with 0.1 M NH4HCO3 to a final concentration of 1 M urea and digested with sequencing-grade modified trypsin (Promega, Fitchburg, USA) at a final enzyme: substrate ratio of 1: 50 overnight at 37 °C. Digestion was quenched by formic acid (final concentration 1 %). After desalting using Oasis HLB Extraction Cartridge (Waters, Milford, USA), the peptides were lyophilized under vacuum and re-suspended in 0.1% formic acid (FA) prior to nanoLC-MS/MS analysis.

Mass spectrometric instrumentation and data acquisition. Orbitrap QE HF system Peptides from one biological replicate set of the TR experiment were analyzed on Easy-nLC 1000 system connected to QE HF mass spectrometer (Thermo Scientific).

8


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Peptides (1 µg of digests) were loaded onto a self-packed C18 column (75 µm x 15 cm, Reprosil C18, 3 µm 120 Å, Dr. Maisch GmbH, Germany) with 98% mobile phase A (0.1% FA in H2O), and eluted with a gradient of 3-10% mobile phase B (0.1% FA in acetonitrile) in 5 min at 300 nl/min, followed by 10-22% B in 54 min, 22-30% in 13 min, 30-90% in 5 min and 90% for 4 min. In the DDA mode, the full scan was performed within m/z 350-1500 with a resolution of 60,000 (at m/z 200). The top 20 most abundant ions were selected for MS/MS analysis under 30% normalized collision energy (NCE) with a resolution of 15,000 (at m/z 200). The precursor isolation window was set to 1.6 Da and the dynamic exclusion time was set to 30 sec. The maximum injection time was set to 20 ms for full scan and 100 ms for MS/MS scan. Equally mixed samples from two conditions (30 °C and 40 °C) were injected in triplicate for DDA analysis. In the DIA mode, a survey scan was acquired within 350-1200 m/z with 60,000 resolution (at m/z 200), AGC target 3e6, and maximum inject time 20 ms. After each full scan, 30 MS/MS scans were acquired with variable isolation window (average 28.3 m/z wide), 30,000 resolution, AGC target 1e5, auto maximum injection time and NCE 27%. The MS/MS spectra were recorded within 350-1200 m/z. The cycle time of DIA analysis was roughly 2.3 s. Samples from control or heat-stressed cells were separately injected in triplicate for DIA analysis. Before LC-MS/MS analysis, peptide samples were spiked with the iRT standard peptides (Biognosys, Switzerland) for retention time calibration according to the manufacture instruction.

TripleTOF 6600 system 9



Peptides from the other biological replicate set of the TR experiment were analyzed on an Eksigent NanoLC system (Eksigent, Dublin, CA) coupled to TripleTOF 6600 mass spectrometer (AB SCIEX). Samples (1 µg of digests) were loaded onto an Eksigent column (75 µm×15 cm, ChromXP C18-CL 3 µm 120 Å) at a flow rate of 300 nl/min with 95% mobile phase A (98% water, 2% acetonitrile, 0.1% FA) and 5% mobile phase B (98% acetonitrile, 2% water, 0.1% FA). Peptides were then separated with a 85 min gradient as follows: 0-0.5 min, 5-8% B; 0.5-1 min, 8-10% B; 1-60 min, 10-22% B; 60-75 min, 22-30% B; 75–80 min, 30-80% B; 80-85 min 80% B. In the DDA mode, a 250 ms full scan was performed with an m/z range of 350-1500, and the top 40 precursors were selected for MS/MS experiments employing an accumulation time of 50 ms. The precursor ions were selected based on the following criteria: ions intensity in the full scan more than 120 counts per second, charge stage between +2 to +4 and mass tolerance 50 mDa. Ions were fragmented according to defined collision energy (CE) equations with an additional CE spread of 15 eV. Equally mixed samples from two conditions were injected in triplicate for DDA analysis. In the SWATH mode, a 250 ms full scan was performed with an m/z range of 350-1500, followed by 60 SWATH scans covering the mass range of 400-1250 m/z. Variable isolation window was implemented with an accumulation time of 50 ms. CE applied to each window was based on the setting for a putative 2+ ion centered in the respective window (CE equation: 0.045 × m/z + 4). The duty cycle of SWATH MS analysis was 3.3 s. Samples from control or heat-stressed cells were separately injected in triplicate for SWATH analysis. 10


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TripleTOF 5600 system Peptides from two biological replicate sets of the HSR experiment were analyzed on an Eksigent NanoLC system (Eksigent, Dublin, CA) coupled to TripleTOF 5600 mass spectrometer (AB SCIEX). To build an extensive reference spectral library, peptides pooled from all replicates of the control and heat shock samples were pre-fractioned by high-pH RPLC with a Durashll-C18 column (C18, 3 µm resin, 4.6 mm × 250 mm, Agela, China) on the Nexera UHPLC system (SHIMADZU, Japan). Peptides were dissolved in mobile phase A (water-acetonitrile-NH4OH = 98: 2: 0.014, v/v/v), and eluted by mobile phase B (water-acetonitrile-NH4OH = 2: 98: 0.014, v/v/v) using a gradient of 5-40 min 8-18% B, 40-62 min 18-32% B, and 62-64 min 32-95% B. The fractions were collected and pooled into 15 fractions before lyophilization under vacuum. Before LC-MS/MS analysis, all peptide samples were spiked with the iRT standard peptides according to the manufacture instruction. Each peptide sample (1 µg of digests) was loaded onto a self-packed C18 column (75 µm x 15 cm, Magic C18 AQ 3 µm 120 Å, Dr. Maisch GmbH, Germany) with 98% mobile phase A (0.1% FA in H2O) and 2% mobile phase B (0.1% FA in 98% acetonitrile), and eluted with the following gradient: 0-1 min, 5-7% B; 1-75 min, 7-24% B; 75-97 min 24-36% B; 97-105 min 36-85% B; 105-109 min, 85-94% B. In the DDA mode, the full scan was performed in an m/z range of 350-1500 with an accumulation time of 250 ms, and the top 40 precursor were selected for MS/MS experiments employing an accumulation time of 50 ms. Precursor ion selection was based on the 11



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same criteria employed on the TripleTOF 6600 system. Ions were fragmented according to defined collision energy (CE) equations with an additional CE spread of 15 eV. In the SWATH mode, a 250 ms full scan was performed with an m/z range of 350-1500, followed by 33 SWATH scans covering the mass range of 400-1000 m/z and implementing a fixed isolation window of 21 m/z width (containing 1 m/z for the window overlap). The accumulation time for MSMS was 80 ms, and CE setting principle was the same as for 5600. The raw MS data were deposited to ProteomeXchange database (userame: [email protected], pass word: PsgTrgGJ)

DDA data searching and spectral library generation. DDA raw data for the TR sample acquired on QE HF was processed with Proteome Discoverer (v2.0, Thermo Scientific, Germany) using Sequest HT engine. Each file was searched against proteome sequences from Saccharomyces Genome Database (release 09 Nov. 2011, 6771 entries) appended with iRT peptides and common contaminants. After peak area normalization, MSstats estimate protein fold changes and p-values using a linear mixed-effects model 26. Database search parameters were as follows: mass tolerance of 20 ppm for MS, 0.05 Da for MS/MS, trypsin as enzyme, allowing two missed cleavages, Cys carbamidomethylation as fixed modification; Met oxidation and Lys/N-terminal acetylation as variable modifications. Results were filtered at 1% FDR of both protein and peptide levels. FDR was calculated by the software based on identifications of proteins and peptides from the forward and reverse database 12


27

.The

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search result file was imported into Spectronaut (v7.0, Biognosys, Schlieren, Switzerland) to build a spectral library with default settings. Spectronaut further filtered the library spectra to ensure > 99% confidence in spectral assignment. DDA raw data for the TR sample acquired on TripleTOF 6600 were processed with ProteinPilotTM software (v5.0, AB SCIEX, Framingham, US) using the Paragon algorithm. Each file was searched against the same SGD protein sequence database with similar parameters defined above for modification and enzyme specificity. The “Thorough ID” mode was selected which automatically adjusted mass tolerance to fit the high-resolution MS and MS/MS data

12

. Results were filtered at 1% FDR of both

protein and peptide levels using the forward/reverse database search strategy 27. The search output file was imported into SWATH micro App 2.0 in PeakView (AB SCIEX, Framingham, US) to build a spectral library with default settings. DDA raw data for the pre-fractionated HSR sample acquired on TripleTOF 5600 were searched against the same SGD protein sequence database using MaxQuant (v1.5.0.30). Search parameters were defined as follows: fixed modification, carbamidomethyl (C); variable modifications, oxidation (M), acetyl (K) and acetyl (N-terminus); fragment ion mass tolerance, 40 ppm; parent ion tolerance, 0.01 Da. Results were filtered at 1% FDR of protein and peptide levels using the forward/reverse database search strategy 27. The search result file was imported into Spectronaut (v7.0) to generate a spectral library with default settings.

DIA/SWATH MS data extraction, statistics and TF prediction. Peptide peak 13



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extraction of both the DIA data from QE HF and the SWATH data from TripleTOF 5600 was performed using Spectronaut (v7.0) with default settings. iRT reference peptides spiked into each sample were used to calibrate the retention time of extracted peptide peaks using Spectronaut. Peptide identification results were filtered with q-value< 0.01 which controlled the estimated peptide FDR below 1% using the error rate algorithm originally from mProphet

14

. Transitions from a given peptide

quantified in all replicates of DIA/SWATH analysis for pairwise samples were summed to derive the peptide intensity. SWATH Micro App (v2.0) in PeakView was used to extract peptide peaks from SWATH data obtained on TripleTOF 6600 with regular parameters: 75 ppm m/z tolerance for the targeted transition, six peptides per protein, six transitions per peptide, peptide confidence> 99%, and excluding shared peptides. Several abundant endogenous peptides were manually selected for retention time calibration using PeakView. Peptide identification in PeakView was also controlled at < 1% FDR based on a scoring strategy similar to mProphet. Peak extraction output data matrix from either Spectronaut or PeakView was imported into MSstats (v2.3.5) for data normalization and relative protein quantification 28. Proteins with a fold change > 1.5 and statistical p-value < 0.05 given by MSstats were regarded differentially expressed under the stress condition vs control in both TR and HSR experiments. For transcription factor prediction, the YEASTRACT database

29

was solicited to

identify TFs of documented associations with differentially expressed proteins found 14


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in our TR or HSR datasets. YEASTRACT automatically classified the predicted TFs according to a p-value which denotes the overrepresentation of regulations of the given TF-targeting genes in the user list relative to the regulations of that TF-targeting genes in the whole YEASTRACT database

29

. The first-tier TFs of most significant

overrepresentation (p< 2.0E-5 for TR dataset, p< 3.5E-5 for HSR dataset, both corresponding to Bonferroni corrected FDR< 1%) were retained for further analysis. These TR- and HSR-associated TFs were separately submitted to STRING 9.0 (http://string-db.org) for PPI analysis. Only TFs with confidence score for each interaction pair > 0.9 and at least two nodes in the PPI network were considered core TFs potentially regulating TR or HSR of ScY01 (Table S4).

Real-time qPCR analysis. ScY01 cells were cultured as described above for the proteomic analysis, TR samples were harvested at 12 h and 14 h of cultivation, and HSR samples were harvested at 20 min and 30 min post-stress. Biological triplicates were prepared for each sample. Cells were harvested by centrifugation at 12000 rpm for 1 min, and total RNA was isolated with the EASYspin yeast RNA extraction kit (Aidlab, China) according to the manufacturer’s protocol. Total yeast RNA concentration was determined using NanoDrop 2000 (Thermo Fisher, USA) and the RNA quality was assessed through 1% (w/v) agarose gel electrophoresis. Synthesis of cDNA from 500 ng total RNA was conducted using the TRUEScript 1st Strand cDNA Synthesis Kit (Aidlab, China). Real-time quantitative PCR reactions were performed in 96-Well Optical Reaction Plates (Applied bioscience, USA) using the 15



SYBR Green Real time PCR Master Mix (TOYOBO, Japan) and analyzed on ABI 7500 Fast Real-Time PCR System using Comparative CT (△△CT) quantitation method. ACT1 gene was used as internal control. Primers used are listed in Table S7.

Construction of TF deletion and overexpression strains. The 25 TF deletion strains of BY4743 used in this study were purchased from EUROSCARF (Frankfurt, Germany). To delete MIG1 or SRB2 in strain ScY01a (ScY01 haploid), a PCR-based gene disruption method was used

30

. The KanMX expression cassettes were

amplified using TF-specific primers from the plasmid pUG6A, and fused with up- and downstream 500-bp fragments of each TF that were PCR amplified from the ScY01 genome. About 1 µg of purified PCR products and 50 µg ssDNA were transformed into ScY01a using electrotransformation. The transformed cells were plated on YPD medium supplemented with G418, and viable clones were selected and verified using PCR. Yeast genome was extracted with the TIANprep Yeast DNA Kit (TIANGEN) and used as PCR template. Overexpression of SRB2, MIG1 and MSN4 was performed using a low-copy commercial plasmid pRS316. Protein-coding regions were cloned from ScY01 genome into the plasmid. Cloned genes contained their own ~ 500 bp upstream promoter regions and ~ 500 bp downstream terminator regions. Transformation of the ScY01 haploid uracil-auxotroph strain ScY01a with the constructed plasmids or the control plasmid was performed as described 31. The transformed cells were plated on SC minimal medium supplemented with essential amino acids and bases required for 16


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plasmid maintenance. Viable clones were selected and verified using PCR.

Evaluation of yeast glucose fermentation capacity. Fermentation of TF deletion strains were performed in YPD medium containing 100 g/l or 200g/L glucose at 30 °C or 40 °C as specified in the text. Fermentation of TF overexpression strains were performed in SD medium (FunGenome, China) with 200 g/L glucose at 30°C or 40°C. Cells were collected at different time points during fermentation, and centrifuged at 10,000 g for 5 min at 4 °C. The supernatants were diluted with ddH2O and filtered through 0.22 µm Nylon Syringe filters prior to HPLC analysis. Glucose and ethanol concentrations were determined using Agilent 1200 HPLC system, equipped with an Aminex HPX-87 ionexclusion column (Bio-Rad) maintained at 63°C. Glucose and ethanol were eluted from the column by 5 mM sulfuric acid solution at a flow rate of 0.6 ml/min. Fermentation rates including maximum maximum growth rates (µmax), maximum glucose consumption rates (qsmax) and ethanol productivities (PEtOH) were calculated as described previously 32.

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Results Defining conditions that induce thermotolerant response (TR) or heat shock response (HSR) of the industrial yeast strain ScY01 The S. cerevisiae strain ScY01 obtained through adaptive evolution of a parental industrial strain and characterized in our previous study

12

exhibited superior

thermotolerance when cultivated at 40 °C and produced significant amount of ethanol (80 g/L in 24 h culture) with high glucose consumption (Figure S1). To profile the proteomic signature associated with the long-term thermotolerant response (TR) of the industrial strain, we harvested cells grown to mid-log phase at normal temperature (30 °C) or elevated temperature (40 °C). Considering the superior heat stress endurance of ScY01, we tested several temperatures for short-term heat shock which lasted 30 min. Stress temperature from 40 °C to 50 °C did not significantly impair cell viability yet severe growth defect was observed when cells were exposed to a sudden heat shock at 55 °C (Figure S2). Therefore, cells treated with the highest endurable heat stress (50 °C) or the normal condition (30 °C) were harvested in order to acquire the proteomic profile for heat shock response (HSR) of ScY01. It is noteworthy that laboratory S. cerevisiae strains are typically subjected to stress at 37-39 °C in HSR experiments whereas they normally grow at 25-30 °C

33-34

, indicating much weaker

thermotolerance than the industrial strain investigated in this work.

DIA/SWATH-based proteomic analysis of strain ScY01 during TR and HSR Proteomic samples from TR and HSR experiments were prepared and analyzed using either DIA or SWATH-based workflows for quantitative proteomic surveys 18


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(Figure 1). Specifically, in the TR experiment, mixed samples from control and heat-stressed cells were first injected to either the Orbitrap QE HF system or the TripleTOF 6600 system to generate a platform-specific spectral library. Then TR samples from one biological replicate were analyzed on Orbitrap QE HF in the DIA mode (TR_DIA dataset), and TR samples from the other biological replicate were analyzed on TripleTOF 6600 in the SWATH mode (TR_SWATH dataset), both using variable isolation windows (Figure 1). In the HSR experiment, peptide samples from biological duplicates were analyzed on TripleTOF 5600 in the SWATH mode using fixed isolation windows (HSR_SWATH dataset). An extended spectral library was first generated by 2D LC-MS/MS analysis of the mixed sample from control and heat shock cells. Different combinations of software tools were employed for database searching, spectral library construction and DIA/SWATH peak extraction from three datasets acquired on different platforms (Figure 1). These data processing tools for label-free proteome quantification have been extensively evaluated in a recent study by Tenzer group to reveal their comparable performance and robustness

35

. Finally,

peptide quantification data for TR and HSR sample sets were imported into MSstats 28 to derive protein ratios that reflect expression changes at stressed vs normal conditions (Figure 1). Our DIA/SWATH-based proteomic analysis resulted in quantification of 2915, 2559 and 1448 proteins from the TR_DIA, TR_SWATH and HSR_SWATH datasets respectively (Figure 2A). The full sets of protein and peptide quantification results are provided in Table S1. Proteins and peptides reported here were detected and 19



quantified in all experimental replicates (n=6 or 8) across different conditions. Compared to 20-30% missing values between replicate runs observed in our DDA analysis, less than 10% missing values were found in the original DIA/SWATH data reports, which demonstrates the remarkable repeatability of DIA/SWATH-based quantification. Increased protein quantification throughput in the TR experiment compared to the HSR experiment could be attributed to multiple factors such as enhanced instrument sensitivity, faster scan speed and application of variable isolation windows rather than fixed-width windows in DIA/SWATH data acquisition 36. To assess the quantification precision, we determined the coefficients of variation (CVs) of the peptide precursor peak areas across replicates for each condition of TR and HSR experiments. Median CVs were between 6.5% to 9.5% for different conditions from TR_DIA and TR_SWATH datasets, and 10.4%, 11.2% for two conditions from the HSR_SWATH dataset (Figure 2B). Similar distribution of peptide peak area CVs for TR_DIA and TR_SWATH datasets suggested equally excellent precision of quantification on both platforms. Slight increase of peptide peak area CVs in the HSR_SWATH dataset may imply the influence of instrument sensitivity and isolation window setup on quantification performance. Nevertheless, over 80% of quantified peptide precursors showed CV 1.5 and p-value< 0.05 determined with a linear mixed-effects model

26

. In the TR experiment,

418 up- and 424 down-regulated proteins were discovered on the DIA platform whereas 305 up- and 283 down-regulated proteins were reported on the SWATH platform (Table S2). For the 271 proteins co-identified on both platforms to be differentially expressed in TR in the same direction, their protein ratios showed good correlation (Pearson correlation R = 0.94, Figure S4B), indicating quantification consistency between two instrument platforms. Cross-validating quantification results from independent platforms and different biological replicates strengthened our confidence of filtering differentially expressed proteins. Surprisingly, only 100 proteins were found to be differentially regulated in ScY01 upon heat shock according to the same filtration criteria, which constituted 6.9% of the 1448 quantified proteins in the HSR experiment. Similarly, a previous proteomic study of a laboratory yeast strain W303 by Mann group revealed 97 differential proteins (3.1% of the total quantified proteins) changing their abundances upon heat shock at 37 °C if applying the same fold-change cutoff adopted in our study

10

. These results are in

drastic contrast with the finding of 842 (28.9%) and 588 (23.0%) differential proteins in the TR_DIA and TR_SWATH experiments respectively for strain ScY01. It is clear that TR of strain ScY01 induced the reprogramming of a significantly higher percentage of 22


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the total proteome than HSR of both strain ScY01 and strain W303. Functional classification of differentially regulated proteins in three separate datasets revealed distinct characteristics of strain ScY01 during TR vs HSR, as well as significant disparity between the industrial strain and the laboratory strain during HSR (Table 1, complete GO classification and protein ratios in Table S3). Among the 271 proteins of differential expression in TR, 143 were up-regulated and 133 were down-regulated in cells cultivated under prolonged heat stress vs the normal condition. By contrast, only 51 up-regulated and 49 down-regulated proteins were found in the same strain upon heat shock, and 93 up-regulated and 4 down-regulated proteins were reported in the previous HSR experiment of the laboratory strain W303 (Table 1). Furthermore, greater amplitude of changes in protein expression was observed in multiple functional categories in the TR dataset than the HSR datasets (Figure 3). TR of strain ScY01 resulted in substantial perturbation of abundances for proteins mostly involved in metabolism, protein folding/sorting/degradation, transport and organelle organization, and translation whereas HSR of the same strain only induced modest changes of a much smaller set of proteins in these cellular processes (Table 1, Figure 3). Interestingly, several proteins even showed an opposite trend of change in TR vs HSR. For example, Cho1 functioning in phospholipid biosynthesis and Sec61 important for protein transport in ER reduced abundances in TR yet increased expression in HSR. A couple of proteins involved in oxidative stress response (Ccp1, Prx1 and Mxr2) increased abundances in TR and suppressed expression in HSR (Table S3). Surprisingly, we observed severe down-regulation of 23



protein clusters for lipid metabolism and cell wall biogenesis in TR which is seemingly contradictory to the known influence of membrane integrity and fluidity on yeast stress resistance 11, 33, 39 (Figure 3, Figure S5). Comparison of HSR-induced proteome remodeling between ScY01 and W303 revealed remarkably disparate response patterns of the two strains. W303 responded to heat shock by upregulating 93 proteins involved in carbohydrate metabolism, protein folding, transport and organelle organization, and oxidative stress response, which was also observed in the proteomic survey of TR of strain ScY01. However, HSR of ScY01 mainly elicited the expression of proteins in the chaperon network and reduced abundances of 11 components of the respiratory chain for energy metabolism (Table 1). Thus our results imply an unexpected finding that the proteome dynamics of the industrial strain ScY01 in response to prolonged thermal stress shared a pattern more similar to that of the laboratory strain than to itself in heat shock response (Figure 3).

Transcription factors associated with proteomic regulation in TR and HSR of strain ScY01 Previously we predicted transcription factors (TFs) associated with differential protein expression in TR of ScY01 and HSR of W303 based on our iTRAQ quantification data for TR12 and reference data for W303

10

. Here we relied on DIA/SWATH-based

quantification of ScY01 proteomes in TR and HSR to identify upstream TFs mediating a specific response. Using YEASTRACT database and applying a stringent threshold 24


Page 24 of 52

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for TF overrepresentation, we first identified 97 TFs significantly related to the regulation of the 271 TR differential proteins and 35 TFs closely related to the regulation of the 100 HSR differential proteins. To narrow down our candidate TFs, we then filtered this list using STRING database to search for core TFs with highest confidence and multi-node interaction connections (see Methods for details). At the end, we obtained 47 core TFs and 12 core TFs significantly associated with proteomic regulation in TR and HSR of ScY01 respectively (Figure 4A, a complete TF list in Table S4). Interestingly, the larger network of TR core TFs contained all HSR core TFs except for Pgd1 (Figure 4B). In addition, Hsf1, Msn 2 and Msn 4, three key TFs long recognized to activate conventional HSR or general stress response

33-34

, were

among the 11 TFs shared by TR-specific and HSR-specific TF networks. Two main regulators of oxidative stress response (Yap1, Skn7)

33, 40

and multiple subunits of the

RNA polymerase II mediator complex (Srb2, Sin4, Nut1, Cse2, Ssn2, Srb8) were only associated with proteome remodeling in TR, indicating characteristic regulatory mechanisms for ScY01 response to long-term heat stress (Figure 4B). Among the 36 predicted TFs exclusively associated with TR, we selected 27 TFs mostly present in different sub-complexes to examine the corresponding gene transcription under two different stress conditions. We collected ScY01 cells cultivated at 40 °C or 30 °C for 10 h and 14 h (TR conditions), and cells exposed to heat shock at 50 °C for 20 min and 30 min (HSR conditions) for RT-qPCR analysis. Twenty-three out of the 27 TF genes were up-regulated by above 2 fold (p

Distinct proteome remodeling of industrial ... - ACS Publications

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