Quantitative Proteogenomic Characterization of Inflamed Murine Colon Tissue Using an Integrated Discovery, Verification, and Validation Proteogenomic Workflow

Andrew T Rajczewski, Qiyuan Han, Subina P Mehta, Praveen Kumar, Pratik D. Jagtap, Charles G. Knutson, James G. Fox, Natalia Y. Tretyakova, Timothy J Griffin

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Chronic inflammation of the colon causes genomic and/or transcriptomic events, which can lead to expression of non-canonical protein sequences contributing to oncogenesis. To better understand these mechanisms, Rag2-/-Il10-/- mice were infected with Helicobacter hepaticus to induce chronic inflammation of the cecum and the colon. Transcriptomic data from harvested proximal colon samples were used to generate a customized FASTA database containing non-canonical protein sequences. Using a proteogenomic approach, mass spectrometry data for proximal colon proteins were searched against this custom FASTA database using the Galaxy for Proteomics (Galaxy-P) platform. In addition to the increased abundance in inflammatory response proteins, we also discovered several non-canonical peptide sequences derived from unique proteoforms. We confirmed the veracity of these novel sequences using an automated bioinformatics verification workflow with targeted MS-based assays for peptide validation. Our bioinformatics discovery workflow identified 235 putative non-canonical peptide sequences, of which 58 were verified with high confidence and 39 were validated in targeted proteomics assays. This study provides insights into challenges faced when identifying non-canonical peptides using a proteogenomics approach and demonstrates an integrated workflow addressing these challenges. Our bioinformatic discovery and verification workflow is publicly available and accessible via the Galaxy platform and should be valuable in non-canonical peptide identification using proteogenomics.

Original languageEnglish (US)
Article number11
Issue number2
StatePublished - Jun 2022

Bibliographical note

Funding Information:
Funding: We acknowledge funding for this work from the National Cancer Institute—Informatics Technology for Cancer Research (NCI-ITCR) grant 1U24CA199347 to T.J.G., as well as the National Institutes of Health (NIH) grants R01-CA100670, R01-CA095039 to N.Y.T. A.T.R. was supported by the National Institutes of Health Biotechnology Training Grant: NIH T32GM008347.

Publisher Copyright:
© 2022 by the authors. Licensee MDPI, Basel, Switzerland.


  • Bioinformatics
  • Colon cancer
  • Inflammation
  • Proteogenomics

PubMed: MeSH publication types

  • Journal Article


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