We envision a future when omics data-informed, computational model-based medical care is available to all. There are many ways to accelerate the pace of making this vision into a reality. For example, our group focuses on creating novel algorithms and computational tools for advancing precision medicine, as well as for better understanding how the human microbiome triggers, prolongs, or protects against disease. We elect to focus on the following three broad aims:

I. To design multi-omic computational biomarkers for precision medicine

Large-scale, data-driven approaches to healthcare have great potential to address some of the most critical clinical challenges, such as early disease diagnostics, assessment of therapeutic regimen efficacy, and real-time monitoring of health and disease.  Additionally, comprehensive biomolecular and cellular profiling technologies are now recognized as important tools for personal data metrics and analytics. We are currently using an extensive, multi-omic profiling approach (e.g., transcriptomic, metagenomic, metabolomic, and immunophenotyping) to deconvolute the biocomplexity underlying human diseases. With newly identified disease signatures, we aim to design robust algorithm-based, computational biomarkers that can serve as clinically actionable information.

  • Selected publications:

    • Hur et al., “Global Transcriptomic Profiling Identifies Differential Gene Expression Signatures between Inflammatory and Non-inflammatory Aortic Aneurysms”. Arthritis & Rheumatology (2022)

    • Gupta et al., “Gut Microbial Determinants of Clinically Important Improvement in Patients with Rheumatoid Arthritis”. Genome Medicine (2021)

    • Gupta et al., “A Predictive Index for Health Status Using Species-level Gut Microbiome Profiling”. Nature Communications (2020)

    • Hur et al., “Plasma Metabolomic Profiling in Patients with Rheumatoid Arthritis Identifies Biochemical Features Predictive of Quantitative Disease Activity”. Arthritis Research & Therapy (2021)

    • Sung et al., “Multi-study Integration of Brain Cancer Transcriptomes Reveals Organ-Level Molecular Signatures”. PLoS Computational Biology (2013)

II. To link statistical and mechanistic properties of gut microbiome ecology to clinical phenotype

The association between chronic disease and gut microbes is intriguingly complex, with no single microbe or pathogen, or microbial function, appearing to be causal. Instead, pathologies have been repeatedly linked to the overall gut ecology.

Compared to its early days, gut microbiome analyses have now evolved beyond descriptive, profiling investigations towards more hypothesis-driven, mechanism-focused studies. However, true progress in this direction will be contingent upon the maturation of our understanding of the systems-level, inner workings of a microbial community. Especially, we need quantitative models of how biological communities in our gut self-organize and interact, and how these relationships are relevant to a clinical phenotype. Moreover, to realize the full potential of microbiome-based therapy via either targeted intervention or whole microbiome transplantation, a comprehensive computational framework is required for directing such manipulations and predicting treatment outcomes. Therefore, we aim to develop computational tools that elucidate community-level interactions within the gut microbiome; and design ecological models that integrate statistical associations with metabolic information.

  • Selected publications:

    • Gupta et al., “TaxiBGC: a Taxonomy-guided Approach for Profiling Experimentally Characterized Microbial Biosynthetic Gene Clusters in Metagenomes”. mSystems (2022)

    • Cobo-López et al., “Stochastic Block Models Reveal a Robust Nested Pattern in Healthy Human Gut Microbiomes”. PNAS Nexus (2022)

    • Kim et al., “Resource-allocation Constraint Governs Structure and Function of Microbial Communities in Metabolic Modeling”. Metabolic Engineering (2022)

    • Sung et al., “Global Metabolic Interaction Network of the Human Gut Microbiota for Context-specific Community-scale Analysis”. Nature Communications (2017)

III. To characterize how prebiotics, probiotic foods, and pharmaceutical drugs influence gut microbiome taxonomy and function

Embarking on a relatively new but active research direction in our lab, we pose the following questions: Do prebiotics and probiotics actually work, and what does “work” even mean? Do gut microbes influence drug efficacy? How can we dissect the complex interactions between the gut microbiome and exogenous compounds? With collaborators who are dieticians, food scientists, and metabolic engineers, we are evaluating the efficacy of prebiotic compounds and probiotic foods in improving gut health.

Relevant to the clinical realm, we are performing longitudinal studies that monitor the influence of pharmaceutical drugs on the gut microbiome over time. Such studies are crucial for capturing the dynamic nature of drug-microbiome interactions and understanding the temporal stability of these relationships. These works may, in the short term, help predict drug response based on pre-treatment (baseline) gut microbiome compositions. Ultimately, in the long run, we aim to leverage the intricate interplay between the human gut microbiome and pharmacological agents to personalize therapeutic strategies and enhance drug safety.

  • Selected publications:

    • Gupta et al., “Safety, Feasibility, and Impact on the Gut Microbiome of Kefir Administration in Critically Ill Adults”. BMC Medicine (2024)

    • Lee et al., “Evaluating the Prebiotic Effect of Oligosaccharides on Gut Microbiome Wellness Using in vitro Fecal Fermentation”. NPJ Science of Food (2023)

    • Gupta et al., Jaeyun Sung# and Erin E. Longbrake#. “Alterations in Gut Microbiome-Host Relationships Induced after Immune Perturbation in Patients with Multiple Sclerosis”. submitted.