In one paragraphArticle in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
2 · The registryThe trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
3 · Its place in the literatureWho cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
4 · The recordCorrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
5 · Who and what moneyAuthors and funding
20 authors.
Tingting ZhengTranslational Microbiome Data Integration, School of Life Sciences, Technical University of Munich, Freising, Germany.
Akshat JoshipuraInstitute of Molecular Immunology, School of Medicine and Health, Technical University of Munich, Munich, Germany.ORCID 0009-0003-5331-9007 Aurélie CenierTranslational Microbiome Data Integration, School of Life Sciences, Technical University of Munich, Freising, Germany.ORCID 0009-0000-3360-7181 Ezgi AtayDepartment of Microbiome Science, Max Planck Institute for Biology Tübingen, Tübingen, Germany.ORCID 0000-0003-0208-4095 Yi Han TanDepartment of Microbiome Science, Max Planck Institute for Biology Tübingen, Tübingen, Germany.
Daniela WetzelTranslational Microbiome Data Integration, School of Life Sciences, Technical University of Munich, Freising, Germany.
Ranko GacesaDepartment of Gastroenterology and Hepatology, University of Groningen and University Medical Center Groningen, Groningen, Netherlands.ORCID 0000-0003-2119-0539 Shijie ZhaoDepartment of Biological Engineering, MIT, Cambridge, MA, USA.
Claudia MengoniDepartment of Cellular, Computational and Integrative Biology, University of Trento, Trento, Italy.ORCID 0000-0001-9629-2930 Shen JinTranslational Microbiome Data Integration, School of Life Sciences, Technical University of Munich, Freising, Germany.ORCID 0000-0001-5159-1187 Khoa CoInstitute of Molecular Immunology, School of Medicine and Health, Technical University of Munich, Munich, Germany.
Nicola SegataDepartment of Cellular, Computational and Integrative Biology, University of Trento, Trento, Italy.ORCID 0000-0002-1583-5794 Ruth E LeyDepartment of Microbiome Science, Max Planck Institute for Biology Tübingen, Tübingen, Germany.ORCID 0000-0002-9087-1672 Monica YabalInstitute of Molecular Immunology, School of Medicine and Health, Technical University of Munich, Munich, Germany.
Rinse K WeersmaDepartment of Gastroenterology and Hepatology, University of Groningen and University Medical Center Groningen, Groningen, Netherlands.ORCID 0000-0001-7928-7371 Melanie SchirmerTranslational Microbiome Data Integration, School of Life Sciences, Technical University of Munich, Freising, Germany. melanie.schirmer@tum.de.ORCID 0000-0001-6456-3679 Funding
Deutsche Forschungsgemeinschaft (German Research Foundation) 395357507Deutsche Forschungsgemeinschaft (German Research Foundation) 426120468Deutsche Forschungsgemeinschaft (German Research Foundation) 471011418Deutsche Forschungsgemeinschaft (German Research Foundation) 504986538
6 · The paper itselfAbstract
Desulfovibrio spp. are associated with inflammatory diseases and human health, yet limited representative genomes and isolates hinder our understanding of their role in disease. Here, we assembled a comprehensive database of 2658 Desulfovibrio genomes across 90 diseases and 32 countries, including 24 human isolates. Genomic analyses showed extensive species diversity and revealed disease-associated functional traits, including flagellin and adaptation-associated genes (i.e., ureases). Flagellin-mediated Toll-like receptor 5 activation was species-specific and D. desulfuricans flagellin downregulated TGF-beta signalling in murine small intestinal organoids, suggesting impaired immune tolerance. Additionally, we investigated genomic capacity for hydrogen sulfide (H
Indexed as
DesulfovibrioDesulfovibrionaceae InfectionsGenetic VariationGenome, BacterialAnimalsFlagellinGenomicsHumansHydrogen SulfideInflammatory Bowel DiseasesMicePhylogenySpecies SpecificityTetrathionic AcidToll-Like Receptor 5FlagellinHydrogen SulfideTetrathionic AcidToll-Like Receptor 5
Identifiers
PMID42859157
PMCPMC13653922
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