From Microbial Signatures to Clinical Tools: Analytical Pipelines and Multi-Omics Prediction of Treatment Response in IBD

Predicting and personalizing treatment response in inflammatory bowel disease (IBD) using microbiome and multi-omics data is a major step toward precision gastroenterology—but its impact depends on rigorous, reproducible analytics that generalize across cohorts and care settings. Emerging evidence suggests that the composition, function, and dynamics of the gut microbiome can forecast therapeutic efficacy and adverse outcomes across biologics, small molecules, dietary interventions, and microbiome-modulating strategies. This Research Topic invites studies that identify, validate, and implement microbial biomarkers—taxonomic, functional (e.g., metabolic pathways, enzymes), and ecological (e.g., network structure, stability)—and that advance the analytical pipelines needed to connect these signals to clinical endpoints such as endoscopic remission, steroid-free remission, mucosal healing, and biomarker normalization. Multimodal approaches integrating metagenomics, metatranscriptomics, metabolomics, host genomics, and immune profiling are especially encouraged to disentangle causal from correlative signals and to enable robust, transportable prediction models.Equally critical is translating microbiome-derived signatures into clinically actionable tools. We seek contributions that address real-world implementation, including standardized sampling and preprocessing protocols, temporal monitoring frameworks, batch-effect and heterogeneity mitigation, model interpretability, and prospective validation across diverse populations and care environments. Methodological innovations—such as causal inference, domain adaptation, and privacy-preserving or federated learning—are particularly welcome to improve reproducibility and external validity. Studies on microbiome-informed treatment selection, sequencing, adaptive dosing, and combination strategies (e.g., biologics plus microbiome-modulating therapies) can illuminate how microbial and host-response data reshape clinical decision-making. Ultimately, this topic aims to bridge discovery and deployment by delivering practical algorithms, benchmarks, reference datasets, and open resources that accelerate equitable precision therapeutics in IBD.We welcome articles addressing, but not limited to, the following themes:-Define and validate microbial signatures predictive of response across drug classes, diets, and microbiome-modulating interventions.-Leverage multi-omic integration (metagenomics, metabolomics, host–immune data) for mechanistic insight and improved model performance.-Develop reproducible, standardized pipelines for sampling, sequencing, preprocessing, quality control, and analysis.-Employ causal inference and longitudinal designs to distinguish drivers from bystanders and capture treatment dynamics over time.-Build interpretable, calibrated, externally validated prediction models that are ready for clinical and real-world workflows.-Address confounders and sources of heterogeneity, including antibiotics, diet, prior therapies, disease phenotype, geography, and technical batch effects.-Explore microbiome-guided treatment selection, sequencing, adaptive dosing, and combination strategies.-Investigate microbial metabolism (e.g., SCFAs, bile acids) and host–microbe immune crosstalk as links between biology and outcomes.-Enable equitable translation via diverse cohorts and privacy-preserving or federated analytics across institutions.-Share open datasets, benchmarks, and reference standards to foster reproducibility and field-wide comparison. Topic editors: Laura Ramos López, Ralf Weiskirchen, Gratiela Gradisteanu Pircalabioru Source: https://www.frontiersin.org/research-topics/78117/from-microbial-signatures-to-clinical-tools-analytical-pipelines-and-multi-omics-prediction-of-treatment-response-in-ibd