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@ARTICLE{Turjeman:299585,
      author       = {S. Turjeman and T. Rozera and E. Elinav$^*$ and G. Ianiro
                      and O. Koren},
      title        = {{F}rom big data and experimental models to clinical trials:
                      {I}terative strategies in microbiome research.},
      journal      = {Cell},
      volume       = {188},
      number       = {5},
      issn         = {0092-8674},
      address      = {[Cambridge, Mass.]},
      publisher    = {Cell Press},
      reportid     = {DKFZ-2025-00526},
      pages        = {1178 - 1197},
      year         = {2025},
      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.},
      subtyp        = {Review Article},
      keywords     = {Microbiota / Humans / Big Data / Animals / Clinical Trials
                      as Topic / Metagenomics: methods / Metabolomics: methods /
                      ex vivo studies (Other) / human clinical trials (Other) /
                      in vitro studies (Other) / in vivo studies (Other) /
                      iterative research approaches (Other) / meta-cohorts (Other)
                      / microbiome (Other) / preclinical studies (Other)},
      cin          = {D480},
      ddc          = {610},
      cid          = {I:(DE-He78)D480-20160331},
      pnm          = {314 - Immunologie und Krebs (POF4-314)},
      pid          = {G:(DE-HGF)POF4-314},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:40054445},
      doi          = {10.1016/j.cell.2025.01.038},
      url          = {https://inrepo02.dkfz.de/record/299585},
}