Genomics meets proteomics: identifying the culprits in disease

Abstract

Genome-wide association studies (GWAS) revealed genomic risk loci that potentially have an impact on disease and phenotypic traits. This extensive resource holds great promise in providing novel directions for personalized medicine, including disease risk prediction, prevention and targeted medication. One of the major challenges that researchers face on the path between the initial identification of an association and precision treatment of patients is the comprehension of the biological mechanisms that underlie these associations. Currently, the focus to solve these questions lies on the integrative analysis of system-wide data on global genome variation, gene expression, transcription factor binding, epigenetic profiles and chromatin conformation. The generation of this data mainly relies on next-generation sequencing. However, due to multiple recent developments, mass spectrometry-based proteomics now offers additional, by the GWAS field so far hardly recognized possibilities for the identification of functional genome variants and, in particular, for the identification and characterization of (differentially) bound protein complexes as well as physiological target genes. In this review, we introduce these proteomics advances and suggest how they might be integrated in post-GWAS workflows. We argue that the combination of highly complementary techniques is powerful and can provide an unbiased, detailed picture of GWAS loci and their mechanistic involvement in disease.

Fig. 1

Flow-chart representing the integration of genomics and proteomics technologies for the functional characterization of common disease or phenotypic trait-associated genome variations

Fig. 2

Illustration of different DNA-pulldown workflows. General workflows for the identification of SNP-dependent, dynamic DNA–protein interactions using DNA-pulldowns based on metabolic isotope labeling (a), chemical labeling (b) or label-free protein quantification (c). a A cell line of interest is cultured in medium containing light (C12N14) or heavy (C13N15) arginine and lysine. After full incorporation of amino acids into the proteome, nuclear extracts are prepared. Biotinylated oligonucleotides containing either variant of the SNP are immobilized on streptavidin beads and incubated with the light and heavy nuclear extract. Unbound proteins are removed by several wash steps. Subsequently, proteins are eluted and differentially labeled eluates are mixed prior to tryptic digestion. Peptides are separated and identified using reversed phase liquid chromatography coupled online to a mass spectrometer (LC–MS/MS). SILAC ratios from two replicate experiments are plotted against each other. Dynamic SNP interacting proteins (large or small ratio) can thus be distinguished from unspecific background binders or proteins that bind to other parts of the oligonucleotides (log2(ratio) = 0) (Mittler et al. 2009). b In contrast to the workflow described in a, a normal, unlabeled nuclear extract from cells or tissue can be used. After the pulldown, proteins are eluted and digested separately. Subsequently, peptides from both pulldowns are differentially labeled by chemically introducing isotopes at the N-termini and the arginine and lysine side chains (Ranish et al. 2003). c In the label-free approach, all steps, including the LC–MS/MS acquisition, are carried out separately. Peptide intensities between all runs are compared using advanced label-free quantification algorithms. Dynamic SNP interacting proteins can be identified by their differential peptide intensities and their p value in a t test based on triplicates (Hubner et al. 2010)