001     299585
005     20250316015800.0
024 7 _ |a 10.1016/j.cell.2025.01.038
|2 doi
024 7 _ |a pmid:40054445
|2 pmid
024 7 _ |a 0092-8674
|2 ISSN
024 7 _ |a 1097-4172
|2 ISSN
024 7 _ |a altmetric:174947127
|2 altmetric
037 _ _ |a DKFZ-2025-00526
041 _ _ |a English
082 _ _ |a 610
100 1 _ |a Turjeman, Sondra
|b 0
245 _ _ |a From big data and experimental models to clinical trials: Iterative strategies in microbiome research.
260 _ _ |a [Cambridge, Mass.]
|c 2025
|b Cell Press
336 7 _ |a article
|2 DRIVER
336 7 _ |a Output Types/Journal article
|2 DataCite
336 7 _ |a Journal Article
|b journal
|m journal
|0 PUB:(DE-HGF)16
|s 1741690353_25289
|2 PUB:(DE-HGF)
|x Review Article
336 7 _ |a ARTICLE
|2 BibTeX
336 7 _ |a JOURNAL_ARTICLE
|2 ORCID
336 7 _ |a Journal Article
|0 0
|2 EndNote
520 _ _ |a 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.
536 _ _ |a 314 - Immunologie und Krebs (POF4-314)
|0 G:(DE-HGF)POF4-314
|c POF4-314
|f POF IV
|x 0
588 _ _ |a Dataset connected to CrossRef, PubMed, , Journals: inrepo02.dkfz.de
650 _ 7 |a ex vivo studies
|2 Other
650 _ 7 |a human clinical trials
|2 Other
650 _ 7 |a in vitro studies
|2 Other
650 _ 7 |a in vivo studies
|2 Other
650 _ 7 |a iterative research approaches
|2 Other
650 _ 7 |a meta-cohorts
|2 Other
650 _ 7 |a microbiome
|2 Other
650 _ 7 |a preclinical studies
|2 Other
650 _ 2 |a Microbiota
|2 MeSH
650 _ 2 |a Humans
|2 MeSH
650 _ 2 |a Big Data
|2 MeSH
650 _ 2 |a Animals
|2 MeSH
650 _ 2 |a Clinical Trials as Topic
|2 MeSH
650 _ 2 |a Metagenomics: methods
|2 MeSH
650 _ 2 |a Metabolomics: methods
|2 MeSH
700 1 _ |a Rozera, Tommaso
|b 1
700 1 _ |a Elinav, Eran
|0 P:(DE-He78)725ad944da4e1ea60389fe9dbbed2c7c
|b 2
|u dkfz
700 1 _ |a Ianiro, Gianluca
|b 3
700 1 _ |a Koren, Omry
|b 4
773 _ _ |a 10.1016/j.cell.2025.01.038
|g Vol. 188, no. 5, p. 1178 - 1197
|0 PERI:(DE-600)2001951-8
|n 5
|p 1178 - 1197
|t Cell
|v 188
|y 2025
|x 0092-8674
909 C O |o oai:inrepo02.dkfz.de:299585
|p VDB
910 1 _ |a Deutsches Krebsforschungszentrum
|0 I:(DE-588b)2036810-0
|k DKFZ
|b 2
|6 P:(DE-He78)725ad944da4e1ea60389fe9dbbed2c7c
913 1 _ |a DE-HGF
|b Gesundheit
|l Krebsforschung
|1 G:(DE-HGF)POF4-310
|0 G:(DE-HGF)POF4-314
|3 G:(DE-HGF)POF4
|2 G:(DE-HGF)POF4-300
|4 G:(DE-HGF)POF
|v Immunologie und Krebs
|x 0
914 1 _ |y 2025
915 _ _ |a Nationallizenz
|0 StatID:(DE-HGF)0420
|2 StatID
|d 2025-01-01
|w ger
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0200
|2 StatID
|b SCOPUS
|d 2025-01-01
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0300
|2 StatID
|b Medline
|d 2025-01-01
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0199
|2 StatID
|b Clarivate Analytics Master Journal List
|d 2025-01-01
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1050
|2 StatID
|b BIOSIS Previews
|d 2025-01-01
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0160
|2 StatID
|b Essential Science Indicators
|d 2025-01-01
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1030
|2 StatID
|b Current Contents - Life Sciences
|d 2025-01-01
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1190
|2 StatID
|b Biological Abstracts
|d 2025-01-01
915 _ _ |a WoS
|0 StatID:(DE-HGF)0113
|2 StatID
|b Science Citation Index Expanded
|d 2025-01-01
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0150
|2 StatID
|b Web of Science Core Collection
|d 2025-01-01
915 _ _ |a JCR
|0 StatID:(DE-HGF)0100
|2 StatID
|b CELL : 2022
|d 2025-01-01
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0600
|2 StatID
|b Ebsco Academic Search
|d 2025-01-01
915 _ _ |a Peer Review
|0 StatID:(DE-HGF)0030
|2 StatID
|b ASC
|d 2025-01-01
915 _ _ |a IF >= 60
|0 StatID:(DE-HGF)9960
|2 StatID
|b CELL : 2022
|d 2025-01-01
920 1 _ |0 I:(DE-He78)D480-20160331
|k D480
|l Mikrobiom und Krebs
|x 0
980 _ _ |a journal
980 _ _ |a VDB
980 _ _ |a I:(DE-He78)D480-20160331
980 _ _ |a UNRESTRICTED


LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21