Interferon gamma (IFN-γ), is a cytokine, which is an important regulator of host defense system by mediating both innate and adaptive immune responses. IFN-γ signaling is primarily associated with inflammation and cell-mediated immune responses. IFN-γ is also represented as antitumor cytokine which facilitates immunosurveillance in tumor cells. In addition, IFN-γ mediated signaling also elicits pro-tumorigenic transformations and promotes tumor progression. Impact of IFN-γ signaling in mammalian cells has been widely studied which indicate that IFN-γ orchestrates distinct cellular functions including immunomodulation, leukocyte trafficking, apoptosis, anti-microbial, and both anti- and pro-tumorigenic role. However, a detailed network of IFN-γ signaling pathway is currently lacking. Therefore, we systematically curated the literature information pertaining to IFN-γ signaling and develop a comprehensive signaling network to facilitate better understanding of IFN-γ mediated signaling. A total of 124 proteins were catalogued that were experimentally proven to be involved in IFN-γ signaling cascade. These 124 proteins were found to participate in 81 protein-protein interactions, 94 post-translational modifications, 20 translocation events, 54 activation/inhibiton reactions. Further, 236 differential expressed genes were also documented in IFN-γ mediated signaling. IFN-γ signaling pathway is made freely available to scientific audience through NetPath at ( http://www.netpath.org/pathways?path_id=NetPath_32 ). We believe that documentation of reactions pertaining to IFN-γ signaling and development of pathway map will facilitate further research in IFN-γ associated human diseases including cancer.

Purpose: Covalent inhibitors of KRASG12C specifically target tumors driven by this form of mutant KRAS, yet early studies show that bypass signaling drives adaptive resistance. Although several combination strategies have been shown to improve efficacy of KRASG12C inhibitors (KRASi), underlying mechanisms and predictive strategies for patient enrichment are less clear. Experimental Design: We performed mass spectrometry–based phosphoproteomics analysis in KRASG12C cell lines after short-term treatment with ARS-1620. To understand signaling diversity and cell type–specific markers, we compared proteome and phosphoproteomes of KRASG12C cells. Gene expression patterns of KRASG12C cell lines and lung tumor tissues were examined. Results: Our analysis suggests cell type–specific perturbation to ERBB2/3 signaling compensates for repressed ERK and AKT signaling following ARS-1620 treatment in epithelial cell type, and this subtype was also more responsive to coinhibition of SHP2 and SOS1. Conversely, both high basal and feedback activation of FGFR or AXL signaling were identified in mesenchymal cells. Inhibition of FGFR signaling suppressed feedback activation of ERK and mTOR, while AXL inhibition suppressed PI3K pathway. In both cell lines and human lung cancer tissues with KRASG12C, we observed high basal ERBB2/3 associated with epithelial gene signatures, while higher basal FGFR1 and AXL were observed in cells/tumors with mesenchymal gene signatures. Conclusions: Our phosphoproteomic study identified cell type–adaptive responses to KRASi. Markers and targets associated with ERBB2/3 signaling in epithelial subtype and with FGFR1/AXL signaling in mesenchymal subtype should be considered in patient enrichment schemes with KRASi.

Complementing genome sequence with deep transcriptome and proteome data could enable more accurate assembly and annotation of newly sequenced genomes. Here, we provide a proof-of-concept of an integrated approach for analysis of the genome and proteome of Anopheles stephensi, which is one of the most important vectors of the malaria parasite. To achieve broad coverage of genes, we carried out transcriptome sequencing and deep proteome profiling of multiple anatomically distinct sites. Based on transcriptomic data alone, we identified and corrected 535 events of incomplete genome assembly involving 1196 scaffolds and 868 protein-coding gene models. This proteogenomic approach enabled us to add 365 genes that were missed during genome annotation and identify 917 gene correction events through discovery of 151 novel exons, 297 protein extensions, 231 exon extensions, 192 novel protein start sites, 19 novel translational frames, 28 events of joining of exons, and 76 events of joining of adjacent genes as a single gene. Incorporation of proteomic evidence allowed us to change the designation of more than 87 predicted “noncoding RNAs” to conventional mRNAs coded by protein-coding genes. Importantly, extension of the newly corrected genome assemblies and gene models to 15 other newly assembled Anopheline genomes led to the discovery of a large number of apparent discrepancies in assembly and annotation of these genomes. Our data provide a framework for how future genome sequencing efforts should incorporate transcriptomic and proteomic analysis in combination with simultaneous manual curation to achieve near complete assembly and accurate annotation of genomes.