A Transcriptional Regulator Sll0794 Regulates Tolerance to Biofuel Ethanol in Photosynthetic Synechocystis sp. PCC 6803
2014; Elsevier BV; Volume: 13; Issue: 12 Linguagem: Inglês
10.1074/mcp.m113.035675
ISSN1535-9484
AutoresZhongdi Song, Lei Chen, Jiangxin Wang, Yinhua Lü, Weihong Jiang, Weiwen Zhang,
Tópico(s)Photosynthetic Processes and Mechanisms
ResumoTo improve ethanol production directly from CO2 in photosynthetic cyanobacterial systems, one key issue that needs to be addressed is the low ethanol tolerance of cyanobacterial cells. Our previous proteomic and transcriptomic analyses found that several regulatory proteins were up-regulated by exogenous ethanol in Synechocystis sp. PCC6803. In this study, through tolerance analysis of the gene disruption mutants of the up-regulated regulatory genes, we uncovered that one transcriptional regulator, Sll0794, was related directly to ethanol tolerance in Synechocystis. Using a quantitative iTRAQ-LC-MS/MS proteomics approach coupled with quantitative real-time reverse transcription-PCR (RT-qPCR), we further determined the possible regulatory network of Sll0794. The proteomic analysis showed that in the Δsll0794 mutant grown under ethanol stress a total of 54 and 87 unique proteins were down- and up-regulated, respectively. In addition, electrophoretic mobility shift assays demonstrated that the Sll0794 transcriptional regulator was able to bind directly to the upstream regions of sll1514, slr1512, and slr1838, which encode a 16.6 kDa small heat shock protein, a putative sodium-dependent bicarbonate transporter and a carbon dioxide concentrating mechanism protein CcmK, respectively. The study provided a proteomic description of the putative ethanol-tolerance network regulated by the sll0794 gene, and revealed new insights on the ethanol-tolerance regulatory mechanism in Synechocystis. As the first regulatory protein discovered related to ethanol tolerance, the gene may serve as a valuable target for transcription machinery engineering to further improve ethanol tolerance in Synechocystis. All MS data have been deposited in the ProteomeXchange with identifier PXD001266 (http://proteomecentral.proteomexchange.org/dataset/PXD001266). To improve ethanol production directly from CO2 in photosynthetic cyanobacterial systems, one key issue that needs to be addressed is the low ethanol tolerance of cyanobacterial cells. Our previous proteomic and transcriptomic analyses found that several regulatory proteins were up-regulated by exogenous ethanol in Synechocystis sp. PCC6803. In this study, through tolerance analysis of the gene disruption mutants of the up-regulated regulatory genes, we uncovered that one transcriptional regulator, Sll0794, was related directly to ethanol tolerance in Synechocystis. Using a quantitative iTRAQ-LC-MS/MS proteomics approach coupled with quantitative real-time reverse transcription-PCR (RT-qPCR), we further determined the possible regulatory network of Sll0794. The proteomic analysis showed that in the Δsll0794 mutant grown under ethanol stress a total of 54 and 87 unique proteins were down- and up-regulated, respectively. In addition, electrophoretic mobility shift assays demonstrated that the Sll0794 transcriptional regulator was able to bind directly to the upstream regions of sll1514, slr1512, and slr1838, which encode a 16.6 kDa small heat shock protein, a putative sodium-dependent bicarbonate transporter and a carbon dioxide concentrating mechanism protein CcmK, respectively. The study provided a proteomic description of the putative ethanol-tolerance network regulated by the sll0794 gene, and revealed new insights on the ethanol-tolerance regulatory mechanism in Synechocystis. As the first regulatory protein discovered related to ethanol tolerance, the gene may serve as a valuable target for transcription machinery engineering to further improve ethanol tolerance in Synechocystis. All MS data have been deposited in the ProteomeXchange with identifier PXD001266 (http://proteomecentral.proteomexchange.org/dataset/PXD001266). Through combined strategies of strain improvement and process optimization, current fermentation production of ethanol employing microbes such as yeast Saccharomyces cerevisiae and bacterium Zymomonas mobilis has reached a very high level of productivity that more than 20% (v/v) of ethanol is produced in industrial yeast fermentation vessels from starch-derived glucose (1Antoni D. Zverlov V.V. Schwarz W.H. Biofuels from microbes.Appl. Microbiol. Biotechnol. 2007; 77: 23-35Crossref PubMed Scopus (393) Google Scholar). The technology progresses contributed significantly to the recent increase of worldwide ethanol production, from 17.0 billion liters in 2000 to more than 84.6 billion liters in 2011 (2Renewable Fuels Association"Acelerating Industry Innovation – 2012 Ethanol Industry Outlook.".Renewable Fuels Association. 2012; (8, 10, 22): 3-23Google Scholar). 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Expanding the metabolic engineering toolbox: more options to engineer cells.Trends Biotechnol. 2007; 25: 132-137Abstract Full Text Full Text PDF PubMed Scopus (180) Google Scholar). As a nonnative platform of ethanol production, autotrophic cyanobacteria have attracted significant attention in recent years, because of the concerns that increasing production of ethanol directly from agricultural crops would require diverting farmland and crops for biofuel production, competing with world food supply, and causing economic and ethical problems (18Wang B. Wang J. Zhang W. Meldrum D.R. Application of synthetic biology in cyanobacteria and algae.Front. Microbiol. 2012; 3: 344Crossref PubMed Scopus (151) Google Scholar). By expressing pyruvate decarboxylase (pdc) and alcohol dehydrogenase (adh) of Z. mobilis in cyanobacterium Synechococcus sp. PCC 7942, Deng and Coleman achieved up to 230 mg/L ethanol directly from CO2 within 4 weeks of growth (19Deng M. Coleman J. Ethanol synthesis by genetic engineering in cyanobacteria.Appl. Environ. Microbiol. 1999; 65: 523-528Crossref PubMed Google Scholar). By constructing a genome-scale Synechocystis sp. PCC 6803 metabolic network and simulating cell growths under various conditions, Fu improved the ethanol production in the cyanobacterial host up to 690 mg/L in a week (20Fu P. Genome-scale modeling of Synechocystis sp. PCC 6803 and prediction of pathway insertion.J. Chem. Technol. Biotechnol. 2009; 84: 473-483Crossref Scopus (67) Google Scholar). More recently, by systematic evaluation and selection of adh genes from different cyanobacterial sources and optimization of culturing conditions, Gao et al. obtained an engineered Synechocystis sp. PCC 6803 strain with significantly higher ethanol-producing efficiency of 212 mg/L per day and 5.50 g/L in 26 days, respectively (21Gao Z. Zhao H. Li Z.M. Tan X.M. Lu X.F. Photosynthetic production of ethanol from carbon dioxide in genetically engineered cyanobacteria.Energy Environ. Sci. 2012; 5: 9857-9865Crossref Scopus (280) Google Scholar). However, to fully realize the ethanol-producing potentials that photosynthetic systems can offer, an urgent issue needs to be addressed is the extremely low tolerance of cyanobacteria to ethanol (22Ducat D.C. Way J.C. Silver P.A. Engineering cyanobacteria to generate high-value products.Trends Biotechnol. 2011; 29: 95-103Abstract Full Text Full Text PDF PubMed Scopus (375) Google Scholar). In our previous investigations, we have found 1.0–2.0% ethanol was enough to cause growth inhibition of Synechocystis sp. PCC6803 (hereafter Synechocystis) (23Qiao J. Wang J. Chen L. Tian X. Huang S. Ren X. Zhang W. Quantitative iTRAQ LC-MS/MS proteomics reveals metabolic responses to biofuel ethanol in cyanobacterial Synechocystis sp. PCC 6803.J. Proteome Res. 2012; 11: 5286-5300Crossref PubMed Scopus (116) Google Scholar). To identify possible targets related to ethanol tolerance, quantitative iTRAQ-LC-MS/MS based proteomics and RNA-seq based transcriptomics were applied to determine the metabolic response of Synechocystis under ethanol stress, and the results showed that multiple transcriptional regulators were differentially regulated, providing potential gene targets for engineering transcriptional machinery in order to improve ethanol tolerance in Synechocystis (23Qiao J. Wang J. Chen L. Tian X. Huang S. Ren X. Zhang W. Quantitative iTRAQ LC-MS/MS proteomics reveals metabolic responses to biofuel ethanol in cyanobacterial Synechocystis sp. PCC 6803.J. Proteome Res. 2012; 11: 5286-5300Crossref PubMed Scopus (116) Google Scholar, 24Wang J. Chen L. Huang S. Liu J. Ren X. Tian X. Qiao J. Zhang W. RNA-seq based identification and mutant validation of gene targets related to ethanol resistance in cyanobacterial Synechocystis sp. PCC 6803.Biotechnol. Biofuels. 2012; 5: 89Crossref PubMed Scopus (65) Google Scholar). In this work, by constructing gene knockout mutants and conducting phenotypic analyses, we demonstrated that a transcriptional regulator Sll0794 was involved in ethanol tolerance. Further proteomic analysis along with electrophoretic mobility shift assays (EMSAs) 1The abbreviations used are:CCMCO2-concentrating mechanismEMSAselectrophoretic mobility shift assaysFACSfluorescence-activated cell sortingHPLChigh-performance liquid chromatographyIPTGIsopropyl β-D-1-thiogalactopyranosideiTRAQIsobaric tag for relative and absolute quantitationLC-MSLiquid chromatography-tandem mass spectrometryMSmass spectrometryPCRpolymerase chain reactionRT-qPCRquantitative real-time reverse transcription-PCRSDS-PAGEsodium dodecyl sulfate polyacrylamide gel electrophoresisTBETris/Borate/EDTATCAtricarboxylic acidFSCforward scatterSSCside scatter.1The abbreviations used are:CCMCO2-concentrating mechanismEMSAselectrophoretic mobility shift assaysFACSfluorescence-activated cell sortingHPLChigh-performance liquid chromatographyIPTGIsopropyl β-D-1-thiogalactopyranosideiTRAQIsobaric tag for relative and absolute quantitationLC-MSLiquid chromatography-tandem mass spectrometryMSmass spectrometryPCRpolymerase chain reactionRT-qPCRquantitative real-time reverse transcription-PCRSDS-PAGEsodium dodecyl sulfate polyacrylamide gel electrophoresisTBETris/Borate/EDTATCAtricarboxylic acidFSCforward scatterSSCside scatter. allowed the determination of possible Sll0794 regulatory network and the identification of several possible gene targets of Sll0794. The results uncovered that the ethanol-tolerance regulation in Synechocystis may be mediated by direct binding of Sll0794 transcriptional regulator to the upstream regions of sll1514, slr1838, and slr1512, which encode a 16.6 kDa small heat shock protein, a carbon dioxide concentrating mechanism protein CcmK, and a putative sodium-dependent bicarbonate transporter, respectively. As the first transcriptional regulator involved in ethanol tolerance, Sll0794 could be a useful target for further improving ethanol tolerance through transcriptional machinery engineering approach in Synechocystis (13Alper H. Moxley J. Nevoigt E. Fink G.R. Stephanopoulos G. Engineering yeast transcription machinery for improved ethanol tolerance and production.Science. 2006; 314: 1565-1568Crossref PubMed Scopus (658) Google Scholar, 25Salis H. Tamsir A. Voigt C. Engineering bacterial signals and sensors.Contrib. Microbiol. 2009; 16: 194-225Crossref PubMed Scopus (38) Google Scholar). CO2-concentrating mechanism electrophoretic mobility shift assays fluorescence-activated cell sorting high-performance liquid chromatography Isopropyl β-D-1-thiogalactopyranoside Isobaric tag for relative and absolute quantitation Liquid chromatography-tandem mass spectrometry mass spectrometry polymerase chain reaction quantitative real-time reverse transcription-PCR sodium dodecyl sulfate polyacrylamide gel electrophoresis Tris/Borate/EDTA tricarboxylic acid forward scatter side scatter. CO2-concentrating mechanism electrophoretic mobility shift assays fluorescence-activated cell sorting high-performance liquid chromatography Isopropyl β-D-1-thiogalactopyranoside Isobaric tag for relative and absolute quantitation Liquid chromatography-tandem mass spectrometry mass spectrometry polymerase chain reaction quantitative real-time reverse transcription-PCR sodium dodecyl sulfate polyacrylamide gel electrophoresis Tris/Borate/EDTA tricarboxylic acid forward scatter side scatter. Synechocystis sp. PCC 6803 and the knockout mutants constructed in this study were grown in BG11 medium (pH 7.5) under a light intensity of ∼50 μmol photons m−2 s−1 in an illuminating incubator of 130 rpm at 30 °C (HNY-211B Illuminating Shaker, Honor, China) (23Qiao J. Wang J. Chen L. Tian X. Huang S. Ren X. Zhang W. Quantitative iTRAQ LC-MS/MS proteomics reveals metabolic responses to biofuel ethanol in cyanobacterial Synechocystis sp. PCC 6803.J. Proteome Res. 2012; 11: 5286-5300Crossref PubMed Scopus (116) Google Scholar, 24Wang J. Chen L. Huang S. Liu J. Ren X. Tian X. Qiao J. Zhang W. RNA-seq based identification and mutant validation of gene targets related to ethanol resistance in cyanobacterial Synechocystis sp. PCC 6803.Biotechnol. Biofuels. 2012; 5: 89Crossref PubMed Scopus (65) Google Scholar). Cell density was measured on a UV-1750 spectrophotometer (Shimadzu, Japan) at OD730 or an ELx808 Absorbance Microplate Reader (BioTek, Winooski, VT) at OD630. For control growth and ethanol (1.5%, v/v) treatment, 10 ml fresh cells at OD730 of 0.5 collected by centrifugation and then were inoculated into 50 ml BG11 liquid medium in a 250-ml flask. Ethanol of analytical pure was purchased from Merck (Darmstadt, Germany). Growth experiments were repeated at least three times to confirm the growth patterns. Cells for proteomics analysis were collected by centrifugation at 8000 × g for 10 min at 4 °C. To reveal cell phenotype difference, flow cytometric analysis was performed to compare the wild type and the mutant cells under ethanol stress on a FACS Calibur fluorescence-activated cell sorting (FACS) cytometer (Becton Dickinson) as described before (23Qiao J. Wang J. Chen L. Tian X. Huang S. Ren X. Zhang W. Quantitative iTRAQ LC-MS/MS proteomics reveals metabolic responses to biofuel ethanol in cyanobacterial Synechocystis sp. PCC 6803.J. Proteome Res. 2012; 11: 5286-5300Crossref PubMed Scopus (116) Google Scholar). A fusion PCR based method was employed for the construction of gene knockout fragments (26Wang H.L. Postier B.L. Burnap R.L. Optimization of fusion PCR for in vitro construction of gene knockout fragments.BioTechniques. 2002; 33 (28, 30): 26Crossref PubMed Scopus (34) Google Scholar). Briefly, for the gene target selected, three sets of primers were designed to amplify a linear DNA fragment containing the chloramphenicol resistance cassette (amplified from a plasmid pACYC184) with two flanking arms of DNA upstream and downstream of the targeted gene. The linear fused PCR amplicon was used directly for transformation into Synechocystis by natural transformation. The chloramphenicol-resistant transformants were obtained, confirmed for the gene knockout event by PCR and sequencing, and then passed several times on fresh BG11 plates supplemented with 10 μg/ml chloramphenicol to achieve complete chromosome segregation (confirmed by PCR). Three transcriptional regulator encoding genes, sll0792, sll0794, and sll1423 that were found differentially regulated by ethanol exposure either at protein or RNA levels according to previous studies (23Qiao J. Wang J. Chen L. Tian X. Huang S. Ren X. Zhang W. Quantitative iTRAQ LC-MS/MS proteomics reveals metabolic responses to biofuel ethanol in cyanobacterial Synechocystis sp. PCC 6803.J. Proteome Res. 2012; 11: 5286-5300Crossref PubMed Scopus (116) Google Scholar, 24Wang J. Chen L. Huang S. Liu J. Ren X. Tian X. Qiao J. Zhang W. RNA-seq based identification and mutant validation of gene targets related to ethanol resistance in cyanobacterial Synechocystis sp. PCC 6803.Biotechnol. Biofuels. 2012; 5: 89Crossref PubMed Scopus (65) Google Scholar), were selected for construction of gene knockout mutants. The successful knockout mutants were confirmed by PCR and sequencing analysis. PCR primers for mutant construction and validation were listed in supplemental Table S1. Comparative growth analysis of the wild-type Synechocystis and the mutants were performed in 100-ml flasks each with 10 ml BG11 medium with or without 1.5% (v/v) ethanol. Cultivation conditions are the same as described above. Growth analysis was performed in biological triplicates. 1) Protein preparation and digestion: for each sample, 10 mg of cells were frozen by liquid nitrogen immediately after centrifugation and washed with phosphate buffer (pH 7.2). The cells were broken with sonication cracking at low temperature, and then centrifuged for 20 min at 20,000 × g to collect the supernatant. In addition, cell debris was then resuspended in a lysis buffer (8 m urea, 4% 3-[(3-cholamidopropyl) dimethylammonio]-1-propanesulfonate (CHAPS), 40 mm Tris-HCl), with 1 mm PMSF, and 2 mm Ethylenediaminetetraacetic acid (EDTA) (final concentration). After 5 min of vigorously vortex, dithiothreitol was also added to a final concentration of 10 mm. After mix, the sample were centrifuged for 20 min at 20,000 × g, and the supernatant was mixed well with ice-cold acetone (1:4, v/v) with 30 mm dithiothreitol. After repeating this step twice, all supernatants were combined and precipitated at −20 °C overnight, and stored at −80 °C prior to sample cleanup if not for immediate use. For digestion, protein pellet from previous step was resuspended in digestion buffer (100 mm triethylammonium bicarbonate TEAB, 0.05% w/v sodium dodecyl sulfate, SDS) to a final concentration of 1 mg/ml (total protein measured by bicinchonic acid assay (Sigma, St. Louis, MO)). Equal aliquots (500 μg) from each lysate were then digested with trypsin overnight at 37 °C (Sigma; 1:40 w/w added at 0 and 2 h) and lyophilized; 2) iTRAQ Labeling: the iTRAQ labeling of peptide samples derived from each of the wild-type control and the gene knockout mutant samples were performed using iTRAQ Reagent 8-plex kit (Applied Biosystems, Foster City, CA) according to the manufacturer's protocol. Four samples (two biological replicates for the wild-type control and two biological replicates for the mutant, respectively) were iTRAQ individually labeled. The 113-, 114-, 119-, and 121-iTRAG tags are for the wild-type control replicate 1 and 2, and the Δsll0794 mutant replicate 1 and 2, respectively. The peptides labeled with respective isobaric tags, incubated for 2 h and vacuum centrifuged to dryness. The iTRAQ labeled peptides in Buffer A (10 mm KH2PO4, 25% acetonitrile, pH 2.85) were fractionated using PolySULFOETHYL ATM Strong Cation Exchange Choematography (SCX) column (200 × 4.6 mm, 5 μm particle size, 200 A° pore size) by HPLC system (Shimadzu, Japan) at flow rate 1.0 ml min-1. The 50 min HPLC gradient consisted of 100% buffer A (10 mm KH2PO4, 25% acetonitrile, pH 2.85) for 5 min; 0–20% buffer B (10 mm KH2PO4, 25% ACN, and 500 mm KCL, pH 3.0) for 15 min; 20–40% buffer B for 10 min; and 40–100% buffer B for 5 min followed by 100% buffer A for 10 min. The chromatograms were recorded at 218 nm. A total of twenty fractions of labeled peptides were collected and then individually desalted with Sep-Pak® Vac C18 cartridges (Waters, Milford, MA), concentrated to dryness using vacuum centrifuge and reconstituted in 0.1% formic acid for LC-MS/MS analysis; 3) LC-MS/MS proteomic analysis: the mass spectroscopy analysis was performed using an AB SCIEX TripleTOF™ 5600 mass spectrometer (AB SCIEX, Framingham, MA), coupled with online micro flow HPLC system (Shimadzu, JAPAN) as described before (23Qiao J. Wang J. Chen L. Tian X. Huang S. Ren X. Zhang W. Quantitative iTRAQ LC-MS/MS proteomics reveals metabolic responses to biofuel ethanol in cyanobacterial Synechocystis sp. PCC 6803.J. Proteome Res. 2012; 11: 5286-5300Crossref PubMed Scopus (116) Google Scholar, 24Wang J. Chen L. Huang S. Liu J. Ren X. Tian X. Qiao J. Zhang W. RNA-seq based identification and mutant validation of gene targets related to ethanol resistance in cyanobacterial Synechocystis sp. PCC 6803.Biotechnol. Biofuels. 2012; 5: 89Crossref PubMed Scopus (65) Google Scholar). The peptides were separated using nanobored C18 column with a picofrit nanospray tip (75 μm ID x 15 cm, 5 μm particles) (New Objectives, Wubrun, MA). The separation was performed at a constant flow rate of 20 μl min−1, with a splitter to get an effective flow rate of 0.2 μl min−1. The mass spectrometer data acquired in the positive ion mode, with a selected mass range of 300–2000 m/z. Peptides with +2 to +4 charge states were selected for MS/MS. The three most abundantly charged peptides above a count threshold were selected for MS/MS and dynamically excluded for 30 s with ±30 mDa mass tolerance. Smart information-dependent acquisition (IDA) was activated with automatic collision energy and automatic MS/MS accumulation. The fragment intensity multiplier was set to 20 and maximum accumulation time was 2 s. The peak areas of the iTRAQ reporter ions reflect the relative abundance of the proteins in the samples. For peptide identification, Triple TOF 5600 mass spectrometer used in this study has high mass accuracy (less than 2 ppm). Other identification parameters used included: Fragment mass tolerance: ± 0.1 Da; Mass values: monoisotopic; Variable modifications: Gln->pyro-Glu (N-term Q), Oxidation (M), iTRAQ8plex (Y); Peptide mass tolerance: 0.05 Da; Max missed cleavages: 1; Fixed modifications: Carbamidomethyl (C), iTRAQ8plex (N-term), iTRAQ8plex (K); Other parameters: default. 4) Proteomic data analysis: the MS data were processed using Proteome Discoverer software (Version 1.2.0.208) (Thermo Scientific, Marietta, OH) to generating peak list. The default parameters of Proteome Discoverer software (Version 1.2.0.208) were used. The data acquisition was performed with Analyst QS 2.0 software (Applied Biosystems/MDS SCIEX). Protein identification and quantification were performed using Mascot 2.3.02 (Matrix Science, London, United Kingdom) (23Qiao J. Wang J. Chen L. Tian X. Huang S. Ren X. Zhang W. Quantitative iTRAQ LC-MS/MS proteomics reveals metabolic responses to biofuel ethanol in cyanobacterial Synechocystis sp. PCC 6803.J. Proteome Res. 2012; 11: 5286-5300Crossref PubMed Scopus (116) Google Scholar). For iTRAQ quantification, the peptide for quantification was automatically selected by the algorithm to calculate the reporter peak area, error factor (EF) and p value (default parameters in Mascot Software package). The resulting data set was auto bias-corrected to get rid of any variations imparted because of the unequal mixing during combining different labeled samples. Genome sequence and annotation information of Synechocystis sp. PCC 6803 were downloaded from NCBI, the Comprehensive Microbial Resource (CMR) of TIGR (http://www.tigr.org/CMR) and CyanoBase (http://genome.microbedb.jp/cyanobase/) (27Kaneko T. Nakamura Y. Sasamoto S. Watanabe A. Kohara M. Matsumoto M. Shimpo S. Yamada M. Tabata S. Structural analysis of four large plasmids harboring in a unicellular cyanobacterium, Synechocystis sp. PCC 6803.DNA Res. 2003; 10: 221-228Crossref PubMed Scopus (102) Google Scholar). The Synechocystis sp. PCC 6803 genome contains 3569 predicted protein (27Kaneko T. Nakamura Y. Sasamoto S. Watanabe A. Kohara M. Matsumoto M. Shimpo S. Yamada M. Tabata S. Structural analysis of four large plasmids harboring in a unicellular cyanobacterium, Synechocystis sp. PCC 6803.DNA Res. 2003; 10: 221-228Crossref PubMed Scopus (102) Google Scholar). The ratio between the mutant and the wild type was obtained directly based on the protein abundance for any given protein. Proteins with 1.5-fold or more change between ethanol-treated and control samples and p value of statistical evaluation less than 0.05 were determined as differentially expressed proteins. The quantitation was performed at the peptide level by following the procedures described in http://www.matrixscience.com/help/quant_statistics_help.html. The student's t test was performed using the Mascot 2.3.02 software. Briefly, a protein ratio is reported in bold face if it is significantly different from unity. The comparison test is:|X¯−μ|≤t*sNEq. 1 If this inequality is true, then there is no significant difference at the stated confidence level. (N is the number of peptide ratios, s is the standard deviation and x the mean of the peptide ratios, both numbers calculated in log space. The true value of the ratio, μ, is 0 in log space. t is student's t for N-1 degrees of freedom and a two-sided confidence level of 95%). To verify the proteomic results, the identical cell samples used for protein isolation as described above were also used f
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