Shotgun proteomics aids discovery of novel protein-coding genes, alternative splicing, and “resurrected” pseudogenes in the mouse genome

Abstract

Recent advances in proteomic mass spectrometry (MS) offer the chance to marry high-throughput peptide sequencing to transcript models, allowing the validation, refinement, and identification of new protein-coding loci. We present a novel pipeline that integrates highly sensitive and statistically robust peptide spectrum matching with genome-wide protein-coding predictions to perform large-scale gene validation and discovery in the mouse genome for the first time. In searching an excess of 10 million spectra, we have been able to validate 32%, 17%, and 7% of all protein-coding genes, exons, and splice boundaries, respectively. Moreover, we present strong evidence for the identification of multiple alternatively spliced translations from 53 genes and have uncovered 10 entirely novel protein-coding genes, which are not covered in any mouse annotation data sources. One such novel protein-coding gene is a fusion protein that spans the Ins2 and Igf2 loci to produce a transcript encoding the insulin II and the insulin-like growth factor 2–derived peptides. We also report nine processed pseudogenes that have unique peptide hits, demonstrating, for the first time, that they are not just transcribed but are translated and are therefore resurrected into new coding loci. This work not only highlights an important utility for MS data in genome annotation but also provides unique insights into the gene structure and propagation in the mouse genome. All these data have been subsequently used to improve the publicly available mouse annotation available in both the Vega and Ensembl genome browsers (http://vega.sanger.ac.uk).

CovRS-Regulated Transcriptome Analysis of a Hypervirulent M23 Strain of Group A Streptococcus pyogenes Provides New Insights into Virulence Determinants

“…Recent studies suggested that pseudogenes may be transcribed and even translated to fragmented peptides but may lack properly folded structures (30,31), thus unnecessarily consuming energy from the bacteria. We did not find a pattern of growth-dependent regulation of the phage-carried virulence genes.…”

Exploring the potential of public proteomics data

“…One example is the reprocessing of proteomics datasets to improve genome annotation in so‐called proteogenomics approaches. For example, Brosch et al reprocessed shotgun proteomics data from PeptideAtlas to discover novel protein‐coding genes and to improve gene annotation in the mouse genome 86. At the time, they found alternatively spliced translations from 53 genes along with ten entirely novel protein‐coding genes.…”

Functional transcriptomics in the post-ENCODE era

“…Pseudogenes are classified according to their mode of formation: either by mRNA retroinsertion (processed pseudogenes), gene duplication (unprocessed pseudogenes), or by the inactivation of functional genes (unitary pseudogenes). While pseudogenes do not contain an intact or translated CDS, at least 9% of human pseudogenes are transcribed (Pei et al 2012), and there is evidence that pseudogene loci can gain new functionality via ''resurrection'' (Brosch et al 2011;Pei et al 2012;Johnsson et al 2013). Finally, GENCODE contains numerous categories of small RNA, which are beyond the scope of this article.…”

“…Second, both the processing of the spectra and the subsequent genomic mapping are complex, computationally intensive techniques. In particular, peptide to genome mappings may suffer from a high false positive rate unless rigorous methods are used (a situation confounded by the occurrence of AS) (Tanner et al 2007;Brosch et al 2011;Ezkurdia et al 2012).…”

“…Lineage specific protein-coding loci are commonly referred to as ''orphans.'' Their true prevalence in the human genome remains a source of uncertainty (Khalturin et al 2009;Brosch et al 2011;Tautz and Domazet-Loso 2011;Neme and Tautz 2013), particularly given that genuine CDSs can be under 300 bp in length, and yet random ORFs of a similar size are commonly found on transcripts due to chance (Clamp et al 2007). The functionality of lineage specific CDSs must therefore be judged through the integration of proteomics data.…”