Proteomic Studies Related to Genetic Determinants of Variability in

Perspective pubs.acs.org/jpr

Proteomic Studies Related to Genetic Determinants of Variability in Protein Concentrations Péter Horvatovich,*,†,‡,§ Lude Franke,∥ and Rainer Bischoff†,‡,§ †

Analytical Biochemistry, Department of Pharmacy, University of Groningen, A. Deusinglaan 1, 9713 AV Groningen, The Netherlands ‡ Netherlands Bioinformatics Centre, Geert Grooteplein 28, 6525 GA Nijmegen, The Netherlands § Netherlands Proteomics Centre, Padualaan 8, 3584 CH Utrecht, The Netherlands ∥ Department of Genetics, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands ABSTRACT: Genetic variation has multiple effects on the proteome. It may influence the expression level of proteins, modify their sequences through single nucleotide polymorphisms, the occurrence of allelic variants, or alternative splicing (ASP) events. This perspective paper summarizes the major effects of genetic variability on protein expression and isoforms and provides an overview of proteomics techniques and methods that allow studying the effects of genetic variability at different levels of the proteome. The paper provides an overview of recent quantitative trait loci studies performed to explore the effect of genetic variation on protein expression (pQTL). Finally it gives a perspective view on advances in proteomics technology and the role of the Chromosome-Centric Human Proteome Project (C-HPP) by creating large-scale resources that may facilitate performing more comprehensive pQTL experiments in the future. KEYWORDS: quantitative trait loci, genetic variation, proteomics, transcriptomics, mass spectrometry, single nucleotide polymorphism, genome-wide association studies

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INTRODUCTION Current genomics technologies enable studying the genomewide effect of genetic variability on the transciptome by using expression quantitative trait locus (eQTL)1−5 mapping and associate single nucleotide polymorphisms (SNPs) with disease using genome-wide association studies (GWAS). Combining these two techniques allows for identifying genes that are involved in particular phenotypes and, more importantly, that may be in a causal relationship with disease (Figure 1) (Mendelian randomization and causal inference analysis can address this question in exact manner but require very large sample sizes of at least 20 000 samples).6 These genomic techniques are nowadays creating large numbers of hypotheses on the role of SNPs and genes in disease. However, the validity of these hypotheses must be tested at the protein level to get a comprehensive picture of the molecular mechanisms of biological events in relation to disease onset and progression. Genetic variation can have two types of effects at the proteome level: (1) it may cause different isoforms for a protein encoded in a gene, which require identification of protein isoform sequences, or (2) it may influence the concentration and activity of the translated protein, which can be assessed by protein quantitative trait loci (pQTL) mapping. © 2013 American Chemical Society

One should realize that SNPs do not necessarily solely affect total gene expression levels: they can also exert their effect by causing alternative splicing (ASP),1−3 they can be nonsynonymous (nsSNP) resulting in a different protein sequence, they can cause alternative polyadenylation of the 3′ UTR,7 or they can result in altered RNA decay.8 SNPs in noncoding regions may influence transcription and translation regulation and RNA editing mechanisms via ADAR and APOBEC proteins9,10 and are sources of transcriptome and protein diversity. Proteins are regulated by genomic and environmental factors via more complex mechanisms than transcripts alone. However, the main structure of protein interaction networks and pathways are reflected at both levels.11 This common framework can be used to integrate transciptomics and proteomics data and to validate the results of eQTL and GWAS studies at the proteome level. Post-translational modification (PTM) of proteins by enzymatic or nonenzymatic chemical reactions add another dimension of regulation creating an enormous Special Issue: Chromosome-centric Human Proteome Project Received: July 23, 2013 Published: November 17, 2013 5

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Figure 1. Summary of the most widely used analysis techniques in genomics studies. Genome-wide association studies (GWAS) identify SNPs (upper panel) that are associated with disease without studying which genes are involved with the association. Association is summarized in a Manhattan plot, which shows for each SNP (x axis) the strength of association as the negative logarithm of the probability of, e.g., the odds ratio based on a chi-square test (y axis). The threshold is calculated using multiple testing corrections. The Manhattan plot is taken from ref 78. Expression quantitative trait loci (eQTL) identify SNPs, which influence gene expression levels (lower panel). One SNP may influence the expression level of one or multiple genes, which can be shown with box plots (lower right plot taken from ref 35). In cis eQTL the associated SNP and gene transcript (trait) are close (80 000 subjects identifies multiple loci for C-reactive protein levels. Circulation 2011, 123 (7), 731−8. (52) Garge, N.; Pan, H.; Rowland, M. D.; Cargile, B. J.; Zhang, X.; Cooley, P. C.; Page, G. P.; Bunger, M. K. Identification of quantitative trait loci underlying proteome variation in human lymphoblastoid cells. Mol. Cell. Proteomics 2010, 9 (7), 1383−99. (53) Melzer, D.; Perry, J. R.; Hernandez, D.; Corsi, A. M.; Stevens, K.; Rafferty, I.; Lauretani, F.; Murray, A.; Gibbs, J. R.; Paolisso, G.; Rafiq, S.; Simon-Sanchez, J.; Lango, H.; Scholz, S.; Weedon, M. N.; Arepalli, S.; Rice, N.; Washecka, N.; Hurst, A.; Britton, A.; Henley, W.; van de Leemput, J.; Li, R.; Newman, A. B.; Tranah, G.; Harris, T.; Panicker, V.; Dayan, C.; Bennett, A.; McCarthy, M. I.; Ruokonen, A.; Jarvelin, M. R.; Guralnik, J.; Bandinelli, S.; Frayling, T. M.; Singleton, A.; Ferrucci, L. A genome-wide association study identifies protein quantitative trait loci (pQTLs). PLoS Genet. 2008, 4 (5), e1000072.

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