Proteomics in non-human primates: utilizing RNA-Seq data to improve protein identification by mass spectrometry in vervet monkeys

Abstract

BackgroundShotgun proteomics utilizes a database search strategy to compare detected mass spectra to a library of theoretical spectra derived from reference genome information. As such, the robustness of proteomics results is contingent upon the completeness and accuracy of the gene annotation in the reference genome. For animal models of disease where genomic annotation is incomplete, such as non-human primates, proteogenomic methods can improve the detection of proteins by incorporating transcriptional data from RNA-Seq to improve proteomics search databases used for peptide spectral matching. Customized search databases derived from RNA-Seq data are capable of identifying unannotated genetic and splice variants while simultaneously reducing the number of comparisons to only those transcripts actively expressed in the tissue.ResultsWe collected RNA-Seq and proteomic data from 10 vervet monkey liver samples and used the RNA-Seq data to curate sample-specific search databases which were analyzed in the program Morpheus. We compared these results against those from a search database generated from the reference vervet genome. A total of 284 previously unannotated splice junctions were predicted by the RNA-Seq data, 92 of which were confirmed by peptide spectral matches. More than half (53/92) of these unannotated splice variants had orthologs in other non-human primates, suggesting that failure to match these peptides in the reference analyses likely arose from incomplete gene model information. The sample-specific databases also identified 101 unique peptides containing single amino acid substitutions which were missed by the reference database. Because the sample-specific searches were restricted to actively expressed transcripts, the search databases were smaller, more computationally efficient, and identified more peptides at the empirically derived 1 % false discovery rate.ConclusionProteogenomic approaches are ideally suited to facilitate the discovery and annotation of proteins in less widely studies animal models such as non-human primates. We expect that these approaches will help to improve existing genome annotations of non-human primate species such as vervet.Electronic supplementary materialThe online version of this article (doi: 10.1186/s12864-017-4279-0) contains supplementary material, which is available to authorized users.

Integrated omics: tools, advances and future approaches

“…The field has moved forward from 2D-PAGE-based (dye/fluorescence labeling) protein spot extraction followed by LC-MS or matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) MS characterization to more system-wide screening approaches with quantitative steps that take advantage of label-based approaches such as Isotope-Coded Affinity Tagging, Stable Isotope Labeling with Amino Acids in Cell Culture (SILAC), 18 O Stable Isotope Labeling, Isobaric Tagging for Relative and Absolute Quantitation (iTRAQ) and Tandem Mass Tags (TMT) (Bakalarski & Kirkpatrick 2016) or are label-free (Bantscheff et al 2012, Anand et al 2017. Both label-free (Proffitt et al 2017) and label-based efforts such as TMT proteomics from diverse biological matrices have yielded favorable results. The community has not yet built a consensus in terms of data formatting, cleaning and normalization, for example, the use of ion intensity vs peptide-to-spectrum matches, despite the ongoing efforts through the Proteomics Standards Initiative (Deutsch et al 2017).…”

“…For example, use of an iterative approach to annotate transcripts for non-standard model species, where the species genome is first used for annotation and unannotated transcripts are aligned against multiple other genomes, significantly improves the number of annotated transcripts (Cox et al 2012). In addition, creating peptide reference libraries using speciesand individual-specific RNA-Seq transcript sequence data, significantly improves peptide annotation; a study of the baboon liver proteome by Proffitt et al (2017) identified novel unannotated splice variants and 101 unique peptides missed by standard reference databases. In case of metabolomics data, not only the relative metabolite abundance, but also the chemical repertoire of an organism is often unknown, and annotation of molecules is even more challenging without the knowledge of their transcriptomes and proteomes.…”

Proteomic variations of esophageal squamous cell carcinoma revealed by combining RNA-seq proteogenomics and G-PTM search strategy

“…Proteomics variations such as SAV peptides (single amino acid variations), NSJ peptides (Novel splice junctions) and, PTM (Post-translational Modifications) are widely uncovered by utilization of high-throughput RNA-seq data [ 27 ]. Galaxy server is a multi-omics interface that allows users to utilize tools and programs to analyze genomics and proteomics data ( Figure 1 ) [ 28 ].…”

Use of short-read RNA-Seq data to identify transcripts that can translate novel ORFs

“…In contrast, a proteogenomic approach, where the MS is searched against a custom database generated from a 3-frame or 6-frame translated version of either genomic or transcript sequences obtained from the same sample, is the only credential way to identify nORF encoded peptides 4,20,28 . Additionally, using proteogenomics to identify peptide spectra and validate unannotated splice junctions and translations from regions currently annotated as noncoding, could aid in the refinement of existing genome annotations 28,29 .…”