The Clinical Proteomic Tumor Analysis Consortium (CPTAC) has provided some of the most in-depth analyses of the phenotypes of human tumors ever constructed. Today, the majority of proteomic data analysis is still performed using software housed on desktop computers which limits the number of sequence variants and post-translational modifications that can be considered. The original CPTAC studies limited the search for PTMs to only samples that were chemically enriched for those modified peptides. Similarly, the only sequence variants considered were those with strong evidence at the exon or transcript level. In this multi-institutional collaborative reanalysis, we utilized unbiased protein databases containing millions of human sequence variants in conjunction with hundreds of common post-translational modifications. Using these tools, we identified tens of thousands of high-confidence PTMs and sequence variants. We identified 4132 phosphorylated peptides in nonenriched samples, 93% of which were confirmed in the samples which were chemically enriched for phosphopeptides. In addition, our results also cover 90% of the high-confidence variants reported by the original proteogenomics study, without the need for sample specific next-generation sequencing. Finally, we report fivefold more somatic and germline variants that have an independent evidence at the peptide level, including mutations in ERRB2 and BCAS1. In this reanalysis of CPTAC proteomic data with cloud computing, we present an openly available and searchable web resource of the highest-coverage proteomic profiling of human tumors described to date.

Despite recent advancements, the high mortality rate remains a concern in colon cancer (CAC). Identification of therapeutic markers could prove to be a great asset in CAC management. Multiple studies have reported hyperactivation of de novo lipogenesis (DNL), but its association with the pathology is unclear. This study aims to establish the importance as well as the prognostic and therapeutic potential of DNL in CAC. The key lipogenic enzymes fatty acid synthase along with ATP citrate lyase were quantified using an LC–MS/MS-based targeted proteomics approach in the samples along with the matched controls. The potential capacity of the proteins to distinguish between the tumor and controls was demonstrated using random forest-based class prediction analysis using the peptide intensities. Furthermore, in-depth proteomics of DNL inhibition in the CAC cell line revealed the significance of the pathway in proliferation and metastasis. DNL inhibition affected the major signaling pathways, including DNA repair, PI3K–AKT–mTOR pathway, membrane trafficking, proteasome, etc. The study revealed the upregulation of 26S proteasome machinery as a result of the treatment with subsequent induction of apoptosis. Again, in silico molecular docking-based drug repurposing was performed to find potential drug candidates. Furthermore, we have demonstrated that blocking DNL could be explored as a therapeutic option in CAC treatment.

Introduction. COVID-19 has become a global impediment by bringing everything to a halt starting from January 2020. India underwent the lockdown starting from 22nd March 2020 with the sudden spike in the number of COVID-19 patients in major cities and states. This study focused on how metabolites play a crucial role in SARSCoV-2 prognosis.Materials and methods. Metabolome profiling of 106 plasma samples and 24 swab samples from symptomatic patients in the Indian population of the Mumbai region was done. COVID-19 positive samples were further segregated under the non-severe COVID-19 and severe COVID-19 patient cohort for both plasma and swab.Results. After analyzing the raw files, total 7,949 and 12,871 metabolites in plasma and swab were found. 11 and 35 significantly altered metabolites were found in COVID-19 positive compared to COVID-19 negative plasma and swab samples, respectively. Also, 9 and 23 significantly altered metabolites were found in severe COVID-19 positive to non-severe COVID-19 positive plasma and swab samples, respectively. The majorly affected pathways in COVID-19 patients were found to be the amino acid metabolism pathway, sphingosine metabolism pathway, and bile salt metabolism pathway.Conclusion. This study facilitates identification of potential metabolite-based biomarker candidates for rapid diagnosis and prognosis for clinical applications.

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