In one paragraphArticle in bioRxiv : the preprint server for biology, 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
1 · What the graph read from itWhat 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
29 authors.
Jiwoon ParkDepartment of Systems and Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA.ORCID 0000-0003-0045-1429 Elif OzcelikDepartment of Systems and Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA.
Onur EskiocakCold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA; Graduate Program in Genetics, Stony Brook University, Stony Brook, NY, USA.
Kadir A OzlerCold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.
Katie PapciakCell Biology Program, Sloan Kettering Institute, New York, NY, USA.
Santhilal SubhashDepartment of Biosciences and Bioengineering, Indian Institute of Technology Jammu, Jammu, India.ORCID 0000-0002-0077-4597 Aybuke AliciCold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.
Ceyda DurmazDepartment of Physiology and Biophysics and the WorldQuant Initiative for Quantitative Prediction, Weill Cornell Medicine, NY, USA.ORCID 0000-0002-7050-3653 Christopher MozsaryDepartment of Physiology and Biophysics and the WorldQuant Initiative for Quantitative Prediction, Weill Cornell Medicine, NY, USA.ORCID 0000-0001-5116-4767 Namita DamleDepartment of Physiology and Biophysics and the WorldQuant Initiative for Quantitative Prediction, Weill Cornell Medicine, NY, USA.
Deena NajjarDepartment of Physiology and Biophysics and the WorldQuant Initiative for Quantitative Prediction, Weill Cornell Medicine, NY, USA.ORCID 0009-0009-7950-2866 Theodore M NelsonDepartment of Systems and Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA.ORCID 0000-0002-8600-0444 Krista A RyonDepartment of Physiology and Biophysics and the WorldQuant Initiative for Quantitative Prediction, Weill Cornell Medicine, NY, USA.ORCID 0000-0001-9440-2729 Daniel J ButlerDepartment of Physiology and Biophysics and the WorldQuant Initiative for Quantitative Prediction, Weill Cornell Medicine, NY, USA.ORCID 0000-0002-3687-8419 Kivanc BirsoyLaboratory of Metabolic Regulation and Genetics, The Rockefeller University, New York, NY, USA.ORCID 0000-0002-7579-9895 Christopher E MasonDepartment of Physiology and Biophysics and the WorldQuant Initiative for Quantitative Prediction, Weill Cornell Medicine, NY, USA.ORCID 0000-0002-1850-1642 Funding
Single-Cell Biology Shared ResourceP30CA045508 · NCI · COLD SPRING HARBOR LABORATORY · PI David A Tuveson · 1987 to 2026
$118.9MTranslational Science Laboratory Shared ResourceP30CA138313 · NCI · MEDICAL UNIVERSITY OF SOUTH CAROLINA · PI John J Lemasters · 2009 to 2026
$42.7MTraining Program in Bioinformatics and Integrative GenomicsT32HG002295 · NHGRI · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI Peter J Park · 2001 to 2026
$15.8MWeill Cornell/Rockefeller/Sloan Kettering MST ProgramT32GM152349 · NIGMS · WEILL MEDICAL COLL OF CORNELL UNIV · PI KATHARINE C HSU · 2024 to 2026
$6.6MRESEARCH PILOT PROJECTS PROGRAMP30GM103339 · NIGMS · MEDICAL UNIVERSITY OF SOUTH CAROLINA · PI OGRETMEN, BESIM · 2012 to 2016
$5.4MData science tools to identify robust exposure-phenotype associations for precision medicineR01ES032470 · NIEHS · HARVARD MEDICAL SCHOOL · PI MANRAI, ARJUN KUMAR, PATEL, CHIRAG J. · 2021 to 2025
$3.5MCataloging multi-ancestry 'omic readouts of the environmental and genetic determinants of type 2 diabetesR01DK137993 · NIDDK · HARVARD MEDICAL SCHOOL · PI ARJUN KUMAR MANRAI, Josep Maria Mercader · 2024 to 2026
$2.0MMUSC FACIL EXPANSION &RENOV: SLE C06RR015455 · NCRR · MEDICAL UNIVERSITY OF SOUTH CAROLINA · PI CROUCH, ROSALIE K · 2002 to 2002
$2.0MMetabolic reprogramming to boost the fitness of anti-tumor immunity against metastatic colon cancerR37CA292807 · NCI · COLD SPRING HARBOR LABORATORY · PI Semir Beyaz · 2025 to 2026
$1.3MNCI NIH HHS P30 CA045508NCI NIH HHS P30 CA138313NCI NIH HHS R37 CA292807NCRR NIH HHS C06 RR015455NHGRI NIH HHS T32 HG002295NIDDK NIH HHS R01 DK137993NIEHS NIH HHS R01 ES032470NIGMS NIH HHS P30 GM103339NIGMS NIH HHS T32 GM152349
6 · The paper itselfAbstract
Dietary fat composition modulates host physiology and the gut microbiome, but the long-term effects of specific fat sources and the extent to which these changes resolve after dietary reversal remain incompletely defined. Here, we present a longitudinal multi-omic resource of mice maintained for one year on a purified control diet, seven high-fat diets differing in predominant fat source, or reversal regimens in which animals were switched from high-fat to control diet after 4 or 9 months. We further incorporated two cohorts with distinct pre-existing microbiome configurations to determine how baseline community structure shapes diet-induced remodeling of the gut microbiome ecosystem. By integrating longitudinal phenotyping, fecal metagenomics, fecal metabolomics, plasma metabolomics and lipidomics, and intestinal single-cell RNA sequencing, we defined the shared and dietary fat-specific responses across host and microbiome compartments. Baseline microbiome composition strongly influenced microbial responses to diet, indicating that pre-existing community structure is a major determinant of dietary ecosystem remodeling. Although many altered features shifted toward baseline after dietary reversal, only approximately half of diet-associated microbial changes recovered within the study window. A subset of taxa exhibited persistent alterations, including sustained depletion of
Identifiers
PMID41889866
PMCPMC13015288
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