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Cross-species transcriptomic signatures predict response to MK2 inhibition in mouse models of chronic inflammation.

ABSTRACT: Inflammatory bowel diseases (IBDs) are genetically complex and exhibit significant inter-patient heterogeneity in disease presentation and therapeutic response. Here, we show that mouse models of IBD exhibit variable responses to inhibition of MK2, a pro-inflammatory serine/threonine kinase, and that MK2 inhibition suppresses inflammation by targeting inflammatory monocytes and neutrophils in murine models. Using a computational approach (TransComp-R) that allows for cross-species comparison of transcriptomic features, we identified an IBD patient subgroup that is predicted to respond to MK2 inhibition, and an independent preclinical model of chronic intestinal inflammation predicted to be non-responsive, which we validated experimentally. Thus, cross-species mouse-human translation approaches can help to identify patient subpopulations in which to deploy new therapies.

SUBMITTER: Suarez-Lopez L

PROVIDER: S-EPMC8609096 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature

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Cross-species transcriptomic signatures predict response to MK2 inhibition in mouse models of chronic inflammation.

Suarez-Lopez Lucia L Shui Bing B Brubaker Douglas K DK Hill Marza M Bergendorf Alexander A Changelian Paul S PS Laguna Aisha A Starchenko Alina A Lauffenburger Douglas A DA Haigis Kevin M KM

iScience 20211107 12

Inflammatory bowel diseases (IBDs) are genetically complex and exhibit significant inter-patient heterogeneity in disease presentation and therapeutic response. Here, we show that mouse models of IBD exhibit variable responses to inhibition of MK2, a pro-inflammatory serine/threonine kinase, and that MK2 inhibition suppresses inflammation by targeting inflammatory monocytes and neutrophils in murine models. Using a computational approach (TransComp-R) that allows for cross-species comparison of ...[more]

PMID: 34849469

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Project description:Prostate cancer (PCa) remains a significant global health challenge, with approximately 1.6 million new cases and 366,000 deaths annually. Despite high survival rates for localized prostate cancer, recurrence poses a substantial risk due to inherent biological factors and residual disease. Early detection and intervention are essential for enhancing patient outcomes and reducing mortality. However, traditional diagnostics such as PSA tests, digital rectal examinations, and biopsies often lack specificity resulting in overdiagnosis. There is a pressing need for novel biomarkers to enhance precision medicine approaches for PCa. This study employs a machine learning approach to identify DNA methylation and RNA expression biomarkers predictive of PCa recurrence using datasets from The Cancer Genome Atlas (TCGA). We analyzed 49,133 genes, identifying 684 differentially methylated genes (DMGs) and 691 differentially expressed genes (DEGs) between recurrence and non-recurrence groups. Ten genes (TNNI2, SPIN2, COL5A3, RNF169, CCND1, FGFR1, SLC17A2, FAMM71F2, RREB1, AOX1) were found to have significant correlations between methylation and expression, forming the basis for our predictive model. A support vector machine (SVM) model was developed using these ten genes, achieving an area under the curve (AUC) of 0.773, demonstrating robust predictive capability. Multivariate regression analysis confirmed the SVM score as an independent predictor of recurrence (HR = 0.45; 95% CI 0.28-0.69, P < 0.001). The analysis of recurrence-free survival suggested that patients with low-risk scores experienced significantly better outcomes compared to those with high-risk scores. Functional enrichment analyses of DMGs revealed significant involvement in biological processes such as transcription regulation, signal transduction, and immune response, highlighting the potential mechanistic pathways of these biomarkers. Validation using real-time PCR confirmed differential expression and methylation patterns of the identified genes in prostate cancer (PC3) and non-cancerous cell lines (PNT2). In conclusion, our study hihglights the DNA methylation biomarkers linked to PCa recurrence and introduces a promising SVM model for early prediction, potentially improving treatment outcomes. Further research is needed to explore the biological roles of these genes in PCa aiming to refine therapeutic approaches.

Dataset Information

Cross-species transcriptomic signatures predict response to MK2 inhibition in mouse models of chronic inflammation.

Publications

Cross-species transcriptomic signatures predict response to MK2 inhibition in mouse models of chronic inflammation.

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