From big data and experimental models to clinical trials: Iterative strategies in microbiome research

Abstract

Microbiome research has expanded significantly in the last two decades, yet translating findings into clinical applications remains challenging. This perspective discusses the persistent issue of correlational studies in microbiome research and proposes an iterative method leveraging in silico, in vitro, ex vivo, and in vivo studies toward successful preclinical and clinical trials. The evolution of research methodologies, including the shift from small cohort studies to large-scale, multi-cohort, and even “meta-cohort” analyses, has been facilitated by advancements in sequencing technologies, providing researchers with tools to examine multiple health phenotypes within a single study. The integration of multi-omics approaches—such as metagenomics, metatranscriptomics, metaproteomics, and metabolomics—provides a comprehensive understanding of host-microbe interactions and serves as a robust hypothesis generator for downstream in vitro and in vivo research. These hypotheses must then be rigorously tested, first with proof-of-concept experiments to clarify the causative effects of the microbiota, and then with the goal of deep mechanistic understanding. Only following these two phases can preclinical studies be conducted with the goal of translation into the clinic. We highlight the importance of combining traditional microbiological techniques with big-data approaches, underscoring the necessity of iterative experiments in diverse model systems to enhance the translational potential of microbiome research.