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Results for “Kinase-substrate enrichment analysis”

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PubMed2026

Opposing kinase signaling may underlie the inverse relationship between cancer and Alzheimer's disease.

Cancer and Alzheimer's disease (AD) are leading causes of mortality and exhibit an inverse relationship, where AD patients have reduced cancer risk and vice versa. However, the molecular basis of this relationship remains poorly understood. We reanalyzed published proteomic and phosphoproteomic datasets to investigate this relationship. Differentially abundant proteins were identified in lung adenocarcinoma and glioblastoma samples relative to controls and compared with proteins altered in AD brains, revealing 37 proteins with opposing abundance patterns. Protein-protein interaction and pathway analyses revealed enrichment in kinase signaling and phosphorylation pathways. Phosphoproteomic analysis identified 52 differentially phosphorylated sites with opposing patterns, while kinase-substrate enrichment analysis identified 44 kinases with opposing inferred activity profiles. Integration of kinase activity and phosphosite data identified 29 kinase-phosphosite pairs, including 4 prioritized pairs with opposing patterns relevant to both diseases. Across seven independent cancer cohorts, 17 of 20 statistically significant phosphosite-cohort comparisons (85%) were concordant with the discovery findings, supporting reproducibility of the prioritized phosphosites. Together, these findings highlight opposing kinase signaling as a prominent feature of the inverse relationship and suggest potential biomarkers and therapeutic targets. This study provides a novel systems-level framework for investigating inverse relationships, supported by an R Shiny application for data exploration (https://advscancer.shinyapps.io/advscancer/). SIGNIFICANCE: This study presents an integrated proteomic and phosphoproteomic framework for investigating the inverse relationship between cancer and Alzheimer's disease (AD). By integrating differential protein abundance, phosphosite phosphorylation, inferred kinase activity, and curated kinase-substrate relationships, we identified opposing signaling patterns and prioritized four kinase-phosphosite pairs. Independent evaluation across seven CPTAC cancer cohorts supported the reproducibility of the prioritized phosphosite patterns. These findings provide insight into molecular processes potentially associated with the inverse relationship between cancer and AD, identify candidate biomarkers and therapeutic targets, and demonstrate the value of systems-level, data-driven approaches for investigating shared and opposing disease processes.

Humans↗

PubMed2026

Integrated multi-omics profiling identifies aging-related molecular signatures and convergent interferon signaling in systemic lupus erythematosus.

BACKGROUND: Systemic lupus erythematosus (SLE) is characterized by chronic immune activation and molecular alterations that overlap with aging-related biological processes. However, how these alterations are organized across molecular layers and whether they converge on shared regulatory networks remain incompletely understood. METHODS: We performed an integrative multi-omics analysis combining in-house proteomic and phosphoproteomic data from 130 patients with SLE and 90 healthy controls (HCs) and publicly available transcriptomic datasets comprising 1,461 SLE patients. Proteins and phosphorylation sites were annotated using established aging-related gene resources. Differential protein abundance and phosphorylation changes were analyzed across disease-status and disease-activity comparisons. Nominal P-value thresholds were used for exploratory feature selection, whereas FDR-adjusted P values were used to assess robustness after multiple-testing correction. Kinase-substrate enrichment, transcription factor annotation, and cell-type-resolved transcriptomic comparison were used to explore potential regulatory programs. RESULTS: We identified 128 nominally altered proteins annotated to aging-related biological processes, including genomic instability, mitochondrial dysfunction, and epigenetic alterations. Phosphoproteomic analysis revealed 36 nominally altered phosphorylation sites, including previously unreported sites in IFI16 (S153, S780) and PKCδ (S507, S664). Clustering analysis demonstrated heterogeneous protein co-regulation patterns across disease states. Kinase activity inference suggested altered activity of TBK1 and IKKβ. TF analysis further highlighted STAT1, RELA, and PML as potential central nodes within the inferred regulatory network. Notably, these multi-omic alterations were not randomly distributed but showed convergence toward shared signaling pathways, particularly those related to interferon responses. CONCLUSIONS: This integrative multi-omics study identifies inflammatory and interferon-dominated molecular alterations in SLE PBMCs that overlap with aging-related biological processes and converge on shared regulatory networks. These findings provide a hypothesis-generating framework for investigating the intersection between chronic immune activation and aging-related molecular remodeling in SLE.

Humans↗

PubMed2026

Proteomic and phosphoproteomic profiles of time-dependent dynamic changes in LPS-induced macrophage polarization.

The temporal proteomic and phosphoproteomic reprogramming during early M1 macrophage polarization (0-6 h) remains poorly understood. We performed time-resolved proteomic and phosphoproteomic analyses of LPS-stimulated RAW264.7 macrophages at seven time points within 6 h. Time-clustering of differentially expressed molecules revealed two patterns: initial change with partial recovery, and sustained dysregulation. Upregulated proteins and phosphorylation sites were enriched in the Rho GTPase signaling pathway, T-cell receptor signaling pathway, NF-κB cascade, osteoclast differentiation pathway, and antiviral immune pathway. Downregulated pathways were associated with cell cycle regulation, chromatin remodeling, RNA metabolism, and mRNA processing, indicating resource reallocation to prioritize acute inflammatory responses. Kinase-substrate network analysis confirmed the mitogen-activated protein kinase (MAPK), cyclin-dependent kinase (CDK), protein kinase B (AKT), and ribosomal S6 kinase (RSK) families as core upstream phosphorylation regulators. Integrated analysis revealed synergistic and antagonistic relationships between proteomic and phosphoproteomic changes. This study provides a temporal molecular atlas of M1 polarization, delineating inflammatory signaling dynamics and offering a basis for therapeutic target discovery in inflammatory diseases. SIGNIFICANCE: Macrophage M1 polarization is a central event in innate immune defense against pathogenic invasion, yet its dysregulation is a pivotal driver of the onset and progression of a broad spectrum of inflammation-associated disorders, spanning autoimmune diseases, infectious conditions and inflammatory bone diseases, making the dissection of its molecular regulatory mechanisms an urgent research priority in immunology and translational medicine. Dynamic molecular events within 0-6 h after LPS stimulation are critical for initiating and shaping M1 inflammatory activation, yet systematic time-resolved proteomic and phosphoproteomic profiling remains insufficient.In this study, we comprehensively characterized temporal proteome and phosphoproteome changes at seven consecutive time points during macrophage polarization, clarified two distinct dynamic molecular patterns, identified core signaling pathways and key kinase regulators involved in inflammatory reprogramming, and uncovered the leading role of post-translational phosphorylation modifications in initiating polarization. This work delineates the time-series molecular atlas of early macrophage activation, provides novel insights into the temporal regulatory mechanism of inflammatory signaling networks, and lays a solid experimental foundation for exploring new intervention targets and regulatory nodes in clinical translational research.

Lipopolysaccharides↗

PubMed2025

KINAID: an orthology-based kinase-substrate prediction and analysis tool for phosphoproteomics.

SUMMARY: Proteome-wide datasets of phosphorylated peptides, either measured in a condition of interest or in response to perturbations, are increasingly becoming available for model organisms across the evolutionary spectrum. We introduce KINAID (KINase Activity and Inference Dashboard), an interactive and extensible tool written in Dash/Plotly, that predicts kinase-substrate interactions, uncovers and displays kinases whose substrates are enriched amongst phosphorylated peptides, interactively illustrates kinase-substrate interactions, and clusters phosphopeptides targeted by similar kinases. KINAID is the first tool of its kind that can analyze data from not only Homo sapiens but also 10 additional model organisms (including Mus musculus, Danio rerio, Drosophila melanogaster, Caenorhabditis elegans, and Saccharomyces cerevisiae). We demonstrate KINAID's utility by applying it to recently published S. cerevisiae phosphoproteomics data. AVAILABILITY AND IMPLEMENTATION: Webserver is available at https://kinaid.princeton.edu; open-source python library is available at https://github.com/Singh-Lab/kinaid; archive is available at https://doi.org/10.24433/CO.8460107.v1.

Proteomics↗