Identification of single amino acid polymorphisms in MS/MS spectra of peptides

Quantitative analysis of single amino acid variant peptides associated with pancreatic cancer in serum by an isobaric labeling quantitative method

Single amino acid variations are highly associated with many human diseases. The direct detection of peptides containing single amino acid variants (SAAVs) derived from nonsynonymous single nucleotide polymorphisms (SNPs) in serum can provide unique opportunities for SAAV associated biomarker discovery. In the present study, an isobaric labeling quantitative strategy was applied to identify and quantify variant peptides in serum samples of pancreatic cancer patients and other benign controls. The largest number of SAAV peptides to date in serum including 96 unique variant peptides were quantified in this quantitative analysis, of which five variant peptides showed a statistically significant difference between pancreatic cancer and other controls (p-value < 0.05). Significant differences in the variant peptide SDNCEDTPEAGYFAVAVVK from serotransferrin were detected between pancreatic cancer and controls, which was further validated by selected reaction monitoring (SRM) analysis. The novel biomarker panel obtained by combining α-1-antichymotrypsin (AACT), Thrombospondin-1 (THBS1) and this variant peptide showed an excellent diagnostic performance in discriminating pancreatic cancer from healthy controls (AUC = 0.98) and chronic pancreatitis (AUC = 0.90). These results suggest that large-scale analysis of SAAV peptides in serum may provide a new direction for biomarker discovery research.

Large-scale mass spectrometric detection of variant peptides resulting from nonsynonymous nucleotide differences

Each individual carries thousands of nonsynonymous single nucleotide variants (nsSNVs) in their genome, each corresponding to a single amino acid polymorphism (SAP) in the encoded proteins. It is important to be able to directly detect and quantify these variations at the protein level to study post-transcriptional regulation, differential allelic expression, and other important biological processes. However, such variant peptides are not generally detected in standard proteomic analyses due to their absence from the generic databases that are employed for mass spectrometry searching. Here we extend previous work that demonstrated the use of customized SAP databases constructed from sample-matched RNA-Seq data. We collected deep-coverage RNA-Seq data from the Jurkat cell line, compiled the set of nsSNVs that are expressed, used this information to construct a customized SAP database, and searched it against deep-coverage shotgun MS data obtained from the same sample. This approach enabled the detection of 421 SAP peptides mapping to 395 nsSNVs. We compared these peptides to peptides identified from a large generic search database containing all known nsSNVs (dbSNP) and found that more than 70% of the SAP peptides from this dbSNP-derived search were not supported by the RNA-Seq data and thus are likely false positives. Next, we increased the SAP coverage from the RNA-Seq derived database by utilizing multiple protease digestions, thereby increasing variant detection to 695 SAP peptides mapping to 504 nsSNV sites. These detected SAP peptides corresponded to moderate to high abundance transcripts (30+ transcripts per million, TPM). The SAP peptides included 192 allelic pairs; the relative expression levels of the two alleles were evaluated for 51 of those pairs and were found to be comparable in all cases.