Browsing by Subject "Ulcerative Colitis"

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    Systems Modeling of Gut Microbiome Regulation of Estrogen Receptor Beta Signaling in Ulcerative Colitis
    (2023-04-28) Trinh, Alan; Munoz, Javier; Cross, Tzu-Wen; Brubaker, Doug
    Introduction: The pathogenesis of ulcerative colitis (UC), a chronic inflammatory disorder, involves interactions between gut microbiome dysbiosis, epithelial cell barrier disruption, and immune hyperactivity. Men are 20% more likely to develop UC and 60% more likely to progress to colitis-associated cancer than women. A possible explanation for this may be the anti-inflammatory and epithelial-protective role of estrogen via estrogen receptor beta (ESR2) in the gut. However, extracting insights into how microbiomes regulate host cell signaling is challenged by high-dimensional data integrations across kingdoms and the need to extract interpretable biological information from complex models. To address these challenges and understand microbiome regulation of ESR2 signaling, we developed a partial least squares path modeling (PLS-PM)-inspired microbiome multi-omic modeling framework. Materials and Methods: Gut metabolomic, colorectal transcriptomic, and stool 16S rRNA-seq data from unique UC or non-IBD controls subjects (n=35) were obtained from the Inflammatory Bowel Disease Multi-Omics Database. Single sample gene set enrichment analysis was used to calculate pathway scores for genes up or down-regulated by ESR2 (ESR2UP/ESR2DN respectively).Latent variables (LV) obtained via regularized sparse partial least square regression (sPLSR) mdoels were extracted and used as predictors in two linear regression meta-models with dependent variables of ESR2UP or ESR2DN scores, and independent variables in each model consisting of patient LV scores on metabolites and 16S LVs along with sex and UC status. Significance testing on regression coefficients identified LV interactions synergistically predictive of ER Beta pathway activity. Results and Discussion: The first two LVs from each single-omic sPLSR models were extracted to create terms in the multi-omic meta-model accounting for sex and disease status. The meta-model was predictive of ESR2UP pathway score, implicating UC status (p=0.046), microbiota LV1 (p=0.0006), metabolites LV2 (p=0.045), and interactions of metabolite LV1:microbiota LV1 (p=0.003), microbiota LV1:UC (p=0.0008), and microbiota LV2:sex (p=0.019) in predicting ESR2UP pathway status. For ESR2DN, the 16S model clustered by ESR2DN activity while the metabolomic model clustering was best illustrated by disease status. The ESR2DN meta-model was predictive of ESR2DN pathway activity, implicating main effects of microbiota LV1 (p =0.004), metabolites LV2 (p=0.004), and diagnosis and the interaction effects of metabolites LV1:microbiota LV1 (p=0.005), microbiota LV1:UC (p=0.014), microbiota LV2:sex (p=0.017), and metabolites LV2:UC (p=0.035) in predicting ESR2DN pathway status. Acesulfame, an artificial sweetener, and oxymetazoline, a nasal decongestant, were some of the metabolites predicted by our model to have a differential effect on ESR2 activity based on patient sex. The metabolites predicted in our models are tested in cancer cell lines to understand estrogen regulatory effects on inflammation observed in UC. Method developed in this study can be applied to gain insight regarding regulation of signaling pathways in pathologies not limited to UC. Conclusions: We demonstrate the effectiveness of a PLS-PM based method for modeling relationships between host signaling and microbiome multi-omics data via this investigation of ER Beta activity in UC patients. We quantified significant multi-omic microbiome interactions with disease status and sex that impact ER Beta signaling which may aid in identifying new microbiome-targeted UC therapeutics stratified by sex-specific disease characteristics.
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    Trans-omic knowledge transfer modeling infers gut microbiome biomarkers of anti-TNF resistance in ulcerative colitis
    (World Scientific, 2023) Trinh, Alan; Ran, Ran; Brubaker, Douglas K
    A critical challenge in analyzing multi-omics data from clinical cohorts is the re-use of these valuable datasets to answer biological questions beyond the scope of the original study. Transfer Learning and Knowledge Transfer approaches are machine learning methods that leverage knowledge gained in one domain to solve a problem in another. Here, we address the challenge of developing Knowledge Transfer approaches to map trans-omic information from a multi-omic clinical cohort to another cohort in which a novel phenotype is measured. Our test case is that of predicting gut microbiome and gut metabolite biomarkers of resistance to anti-TNF therapy in Ulcerative Colitis patients. Three approaches are proposed for Trans-omic Knowledge Transfer, and the resulting performance and downstream inferred biomarkers are compared to identify efficacious methods. We find that multiple approaches reveal similar metabolite and microbial biomarkers of anti-TNF resistance and that these commonly implicated biomarkers can be validated in literature analysis. Overall, we demonstrate a promising approach to maximize the value of the investment in large clinical multi-omics studies by re-using these data to answer biological and clinical questions not posed in the original study.