Metaprotein expression modeling for label-free quantitative proteomics

BMC Bioinformatics

M Arthur Moseley²  John McHutchison⁴  Keyur Patel⁴  Kathleen Schwarz¹  Phyllis Goldman³  Francisco Dieguez³  Ron Hendrickson³  Norah Shire³  Alexander Thompson⁴  Hans Tillmann⁴  Jeanette McCarthy²  Laura G Dubois²  J Will Thompson²  Joseph E Lucas² 

[1]Johns Hopkins Children’s Center, Baltimore, MD, USA

[2]Institute for Genome Sciences and Policy, Duke University, Durham, NC, USA

[3]Merck, Sharpe, and Dohme, Corp, Whitehouse Station, NJ, USA

[4]Gasteroenterology, Duke University School of Medicine, Durham, NC, USA

关键词: Mrm;    Srm;    Statistical model;    Statistics;    Open platform;    Hepatitis;    Factor;    Proteomics;   

Others  :  1088296
DOI  :  10.1186/1471-2105-13-74

 received in 2011-12-12, accepted in 2012-05-04,  发布年份 2012

【 摘 要 】

Background

Label-free quantitative proteomics holds a great deal of promise for the future study of both medicine and biology. However, the data generated is extremely intricate in its correlation structure, and its proper analysis is complex. There are issues with missing identifications. There are high levels of correlation between many, but not all, of the peptides derived from the same protein. Additionally, there may be systematic shifts in the sensitivity of the machine between experiments or even through time within the duration of a single experiment.

Results

We describe a hierarchical model for analyzing unbiased, label-free proteomics data which utilizes the covariance of peptide expression across samples as well as MS/MS-based identifications to group peptides—a strategy we call metaprotein expression modeling. Our metaprotein model acknowledges the possibility of misidentifications, post-translational modifications and systematic differences between samples due to changes in instrument sensitivity or differences in total protein concentration. In addition, our approach allows us to validate findings from unbiased, label-free proteomics experiments with further unbiased, label-free proteomics experiments. Finally, we demonstrate the clinical/translational utility of the model for building predictors capable of differentiating biological phenotypes as well as for validating those findings in the context of three novel cohorts of patients with Hepatitis C.

Conclusions

Mass-spectrometry proteomics is quickly becoming a powerful tool for studying biological and translational questions. Making use of all of the information contained in a particular set of data will be critical to the success of those endeavors. Our proposed model represents an advance in the ability of statistical models of proteomic data to identify and utilize correlation between features. This allows validation of predictors without translation to targeted assays in addition to informing the choice of targets when it is appropriate to generate those assays.

【 授权许可】

   
2012 Lucas et al.; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

Figure 2 .

Figure 3 .

Figure 4 .

Figure 5 .

Figure 6 .

Figure 7 .

Figure 8 .

【 参考文献 】

• [1]Ong SE, Blagoev B, Kratchmarova I, Kristensen D, Steen H, Pandey A, Mann M: Stable isotope labeling by amino acids in cell culture, SILAC, as a simple accurate approach to expression proteomics. Mol Cell Proteomics 2002, 1(5):376-386.
• [2]Wiese S, Reidegeld K, Meyer H, Warscheid B: Protein labeling by iTRAQ: a new tool for quantitative mass spectrometry in proteom research. Proteomics 2007, 7(3):340-350.
• [3]Gygi S, Rist B, Gerber S, Turecek F, Gelb M, Aebersold R: Quantitative analysis of comples protein mixtures using isotope-coded affinity tags. Nat Biotechnol 1999, 17:994-999.
• [4]Wang W, Zhou H, Lin H, Roy S, Shaler T, Hill L, Norton S, Kumar P, Anderle M, Becker C: Auantification of proteins and metabolites by mass spectrometry without isotopic labeling or spiked standards. Anal Chem 2003, 75(18):4818-4826.
• [5]Kito K, Ito T: Mass spectrometry-based paproaches toward absolute quantitave proteomics. Curr Genomics 2008, 9(4):263-274.
• [6]Wang G, Wu W, Zeng W, Chou CL, Shen RF: Label-free protein quantification using LC-coupled ion trap or FT mass spectrometry: Reproducibility, linearity, and application with complex proteomes. J Proteome Res 2006, 5(5):1214-1243.
• [7]Nesvizhskii AI, Keller A, Kolker E, Aebersold R: A statistical model for identifying proteins by tandem mass spectrometry. Anal Chem 2003, 75(17):4646-4658.
• [8]Silva JC, Denny R, Dorschel C, Gorenstein MV, Li GZ, Richardson K, Wall D, Geromanos SJ: Simultaneous qualitative and quantitative analysis of the Escherichia coli proteome: a sweet tale. Mol Cell Proteomics 2006, 5(4):598-607.
• [9]Silva JC, Gorenstein MV, Li GZ, Vissers JPC, Geromanos SJ: Absolute quantification of proteins by LCMSE: a virtue of parallel MS acquisition. Mol Cell Proteomics 2006, 5:144-156.
• [10]Polpitiya A, Qian WJ, Jaitly N, Petyuk V, Adkins J, Camp D, Gordon A, Smith R: DAnTE: a statistical tool for quantitative analysis of -omics data. Bioinformatics 2008, 24(13):1556-1558.
• [11]Clough T, Key M, Ott I, Ragg S, Schadow G, Vitek O: Protein Quantitation in Label-Free LC-MS Experiments. J Proteome Res 2009, 8:5275-5284.
• [12]Daly DS, Anderson KK, Panisko EA, Purvine SO, Fang R, Monroe ME, Baker SE: Mixed-Effects Statistical Model for Comparative LC-MS Proteomics Studies. J Proteome Res 2008, 7:1209-1217.
• [13]Karpievitch Y, Stanley J, Taverner T, Huang J, Adkins JN, Ansong C, Heffron F, Metz TO, Qian WJ, Yoon H, Smith RD, Dabney AR: A statistical framework for protein quantitation in bottom-up MS-based proteomics. Bioinformatics 2009, 25(16):2028-2034.
• [14]Roy S, Josephson SA, Fridlyand J, Karch J, Kadoch C, Karrim J, Damon L, Treseler P, Kunwar S, Shuman MA, Jones T, Becker CH, Schulman H, Rubenstein JL: Protein Biomarker Identification in the CSF of Patients With CNS Lymphoma. [http://jco.ascopubs.org/content/26/1/96.abstract] webciteJ Clin Oncol 2008, 26:96-105.
• [15]Carvalho C, Chang J, Lucas J, Nevins J, Wang QL, West M: High-Dimensional sparse factor modeling: Applications in gene expression genomics. J Am Stat Assoc 2008, 103:1438-1456.
• [16]Lucas J, Carvalho C, Wang Q, Bild A, Nevins J, West M: Sparse statistical modelling in gene expression genomics. In Bayesian Inference for Gene Expression and Proteomics. Edited by Vannucci M, Do KA, Müller P. Cambridge University Press, Cambridge; 2006:155-176.
• [17]Lucas J, Carvalho C, Chen JL-Y, Chi J-T, West M: Cross-study projections of genomic biomarkers: An evaluation in cancer genomics. [http:/ / dx.plos.org/ 10.1371/ journal.pone.0004523, PMCID:PMC2638 006, e4523. doi: 10.1371/ journal.pone.0004523] webcitePLoS One 2009.
• [18]Lucas J, Carvalho C, West M: A Bayesian Analysis Strategy for Cross-Study Translaton of Gene Expression Biomarkers. Statistical Applications in Genetics and Molecular Biology 18.
• [19]Mueller L, Rinner O, Schmidt A, Letarte S, Bodenmiller B, Brusniak M, Vitek O, Aebersold R, Muller M: SuperHirn—a novel tool for high resolution LC-MS-based peptide/protein profiling. Proteomics 2007, 7(19):3470-3480.
• [20]Patel K, Lucas JE, Thompson J, Dubois L, Tillman H, Thompson A, Uzarski D, Califf R, Moseley M, Ginsburg G, McHutchison J, McCarthy J: High Predictive accuracy of an unbiased proteomicd profile for sustained virologic response in chronic hepatitis C patients. Hepatology 2011, 53(6):1809-1818.
• [21]Raftery A, Madigan D, Hoeting J: Bayesian model averaging for linear regression models. J Am Stat Assoc 1997, 92:191-197.
• [22]Hans C, Dobra A, West M: Shotgun stochastic search in regression with many predictors. J Am Stat Assoc 2007, 102:507-516.
• [23]Schwarz KB, Gonzalez-Peralta RP, Murray KF, Molleston JP, Haber BA, Jonas MM, Rosentha P, Mohan P, Balistreri WF, Narkewicz MR, Smith L, Lobritto SJ, Rossi S, Valsamakis A, Goodman Z, Robuck PR, Barton BA: The combination of ribavirin and peginterferon is superior to peginterferon and placebo for children and adolescents with chronic hepatitis C. Gastroenterology 2011, 140(2):450-458.
• [24]Zhang H, Liu Q, Zimmerman LJ, Ham AJL, Slebos RJ, Rahaman J, Kikuchi T, Massion PP, Carbone DP, Billheimer D, Liebler DC: Methods for peptide and protein quantitation by liquid chromatography-multiple reaction monitoring mass spectrometry. Molecular and cellular proteomics 2011., 10(6)
• [25]Tressel T, McCarthy JB, Calaycay J, Lee TD, Legesse K, Shively JE, Pande H: Human plasma fibronectin. Demonstration of structural differences between the A0 and B0chains in the III CS region. Biochemical J 1991, 274(Pt. 3):731-738.
• [26]Paweletz C, Wiener M, Bondarenko A, Yates N, Song Q, Liaw A, Lee A, Hunt B, Henle E, Meng F, Sleph H, Holahan M, Sankaranarayanan S, Simon A, Settlage R, Sachs J, Shearman M, Sachs A, Cook J, Hendrickson R: Application of an end-to-end biomarker discovery platform to identify target engagement markers in cerebrospinal fluid by high resolution differential mass spectrometry. J Proteome Res 2010, 9(3):1392-1401.
• [27]Sietsema K, Meng F, Yates N, Hendrickson R, Liaw A, Song Q, Brass E, Ulrich R: Potential biomarkers of muscle injury after eccentric exercise. Biomarkers 2010, 15(3):249-258.
• [28]Neubert H, Bonnert T, Rumpel K, Hunt B, Henle E, James I: Label-free detection of differential protein expression by LC/MALDI mass spectrometry. J Proteome Res 2008, 7(6):2270-2279.
• [29]Meng F, Wiener M, Sachs J, Burns C, Verma P, Paweletz C, Mazur M, Keyanova E, Yates N, Hendrickson R: Quantitative analysis of complex peptide mixtures using FTMS and differential mass spectrometry. J Am Soc Mass Spectrom 2007, 18(2):226-233.
• [30]Nittis T, Guittat L, LeDuc R, Dao B, Duxin J, Rohrs H, Townsend R, Stewart S: Revealing novel telomere proteins using in vivo cross-linking, tandem affinity purification, and label free quantitative LC-FTICR-MS. Mol Cell Proteomics 2010, 9(6):1144-1156.
• [31]Keller A, Nesvizhskii A, Kolker E, Aebersold R: Empirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database search. Anal Chem 2002, 75:4646-4658.