Interleukin-33 (IL-33), a member of the IL-1 superfamily cytokines, is an endogenous danger signal and a nuclear-associated cytokine. It is one of the essential mediators of both innate and adaptive immune responses. Aberrant IL-33 signaling has been demonstrated to play a defensive role against various infectious and inflammatory diseases. Although the signaling responses mediated by IL-33 have been previously reported, the temporal signaling dynamics are yet to be explored. To this end, we applied quantitative temporal phosphoproteomics analysis to elucidate pathways and proteins induced by IL-33 in THP-1 monocytes. Employing a TMT labeling-based quantitation and titanium dioxide (TiO₂)-based phosphopeptide enrichment strategy followed by mass spectrometry analysis, we identified and quantified 9448 unique phosphopeptides corresponding to 3392 proteins that showed differential regulation. Of these, 171 protein kinases, 60 phosphatases and 178 transcription factors were regulated at different phases of IL-33 signaling. In addition to the confirmed activation of canonical signaling modules including MAPK, NFκB, PI3K/AKT modules, pathway analysis of the time-dependent phosphorylation dynamics revealed enrichment of several cellular processes, including leukocyte adhesion, response to reactive oxygen species, cell cycle checkpoints, DNA damage and repair pathways. The detailed quantitative phosphoproteomic map of IL-33 signaling will serve as a potentially useful resource to study its function in the context of inflammatory and pathological conditions.

Mucin 1 (MUC1) is a type-I transmembrane glycoprotein, ranked as the second most promising cancer antigen for therapeutic development by the National Cancer Institute. Recently, MUC1 has gained significant attention as a therapeutic target in cancer immunotherapy. Thus, understanding MUC1-regulated signaling is essential for advancing therapeutic strategies. Since existing pathway repositories lack a comprehensive MUC1 signaling map, we constructed one by manually annotating experimentally validated signaling events from 115 articles following NetPath annotation criteria. Considering the topology of annotated molecules, a pathway map was developed using the PathVisio software. This pathway map is a valuable resource cataloguing 209 molecules and 690 signaling events, of which 395 events are unique. The catalogued reactions include 118 enzyme catalysis events, 133 molecular associations, 132 gene regulation events, 216 protein expressions, 49 activation/inhibition and 42 protein translocation events. MUC1 signaling pathway showed significant cross-talk with Wnt/β-catenin, NF-κB, PI3K-AKT, and MAPK signaling. The aberrant expression of MUC1 is associated with proliferation, metastasis, angiogenesis and invasion in various cancers, such as breast, lung, pancreatic, colon, oral, prostate, ovarian, and gastric cancers. Moreover, it also regulates the proliferation and function of immune cells, including myeloid-derived suppressor cells, tumour-associated macrophages, dendritic cells, CD4 + T-cells and CD8 + T-cells. This pathway map may serve as a valuable reference for understanding MUC1-driven oncogenic and immunomodulatory mechanisms, providing insights for developing novel therapeutic strategies for cancer treatment.

Drug resistance poses a significant obstacle to the success of anti-cancer therapy in head and neck cancers (HNCs). We aim to develop a platform for visualizing and analyzing molecular expression alterations associated with HNC drug resistance. Through data mining, we convened differentially expressed molecules and context-specific signaling events involved in drug resistance. The driver genes, interaction networks and transcriptional regulations were explored using bioinformatics approaches. A total of 2364 differentially expressed molecules were identified in 78 distinct drug-resistant cells against 14 anti-cancer drugs, comprising 1131 mRNAs, 746 proteins, 62 lncRNAs, 257 miRNAs, 1 circRNA, and 166 post-translational modifications. Among these, 255 molecules were considerably, the signature driver genes of HNC drug resistance. Further, we also developed a landscape of signaling pathways and their cross-talk with diverse signaling modules involved in drug resistance. Additionally, a publicly-accessible database named “HNCDrugResDb” was designed with browse, query, and pathway explorer options to fetch and enrich molecular alterations and signaling pathways altered in drug resistance. HNCDrugResDb is also enabled with a Drug Resistance Analysis tool as an initial platform to infer the likelihood of resistance based on the expression pattern of driver genes. HNCDrugResDb is anticipated to have substantial implications for future advancements in drug discovery and optimization of personalized medicine approaches.