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Record W4408564583 · doi:10.1016/j.isci.2025.112243

Neonatal gut microbiota succession in mice mapped over time, site, injury and single immunoglobulin interleukin-1 related receptor genotype

2025· article· en· W4408564583 on OpenAlexaff
Shahid Umar, Wei Yu, Hao Xuan, Ishfaq Ahmed, Cuncong Zhong, Michael J. Morowitz, Venkatesh Sampath

Bibliographic record

VenueiScience · 2025
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmune Response and Inflammation
Canadian institutionsUniversity of Saskatchewan
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesNational Foundation for Science and Technology DevelopmentNational Institutes of HealthNational Science Foundation
KeywordsGenotypeEcological successionGut floraAntibodyBiologyReceptorImmunologyGeneticsGeneEcology

Abstract

fetched live from OpenAlex

<h2>Summary</h2> Microbial succession during postnatal gut development in mice is likely impacted by site of sampling, time, intestinal injury, and host genetics. We investigated this in wild-type and <i>Sigirr</i> transgenic mice that encode the p.Y168X mutation identified in a neonate with necrotizing enterocolitis (NEC). Temporal profiling of the ileal and colonic microbiome after birth to weaning revealed a clear pattern of progression from a less diverse, Proteobacteria/Escherichia_Shigella dominant community to a more diverse, Firmicutes/Bacteroidetes dominant community. Formula milk feeding, a risk factor for necrotizing enterocolitis, decreased Firmicutes and increased Proteobacteria leading to enrichment of bacterial genes denoting exaggerated glycolysis and increased production of acetate and lactate. <i>Sigirr</i> transgenic mice exhibited modest baseline differences in microbiota composition but exaggerated formula feeding-induced dysbiosis, mucosal inflammation, and villus injury. Postnatal intestinal microbiota succession in mice resembles human neonates and is shaped by developmental maturity, ileal vs. colonic sampling, formula feeding, and <i>Sigirr</i> genotype.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.131
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.004
GPT teacher head0.225
Teacher spread0.222 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2025
Admission routes1
Has abstractyes

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