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Record W3166371874 · doi:10.1101/2021.06.03.446997

The nasopharyngeal, ruminal, and vaginal microbiota and the core taxa shared across these microbiomes in virgin yearling heifers exposed to divergent in utero nutrition during their first trimester of gestation and in pregnant beef heifers in response to mineral supplementation

2021· preprint· en· W3166371874 on OpenAlexaff
Samat Amat, Devin B. Holman, Kaycie N. Schmidt, Ana Clara B Menezes, Friederike Baumgaertner, Thomas Winders, James D Kirsch, Tingting Liu, Timothy Schwinghamer, Kevin K. Sedivec, Carl R Dahlen

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2021
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGut microbiota and health
Canadian institutionsAgriculture and Agri-Food Canada
FundersNorth Dakota Corn Utilization CouncilNorth Dakota State University
KeywordsMicrobiomeBiologyGestationOffspringPregnancyVaginaAnimal sciencePhysiologyBioinformaticsGenetics

Abstract

fetched live from OpenAlex

ABSTRACT Emerging evidence has indicated that microbial transmission from the bovine dam to her fetus may take place before birth, and that the maternal microbiota during pregnancy modulates programming of fetal metabolic and nervous system development, highlighting the potential and extended role of the maternal microbiome in calf health and development. In the present study, we characterized the nasopharyngeal, ruminal and vaginal microbiota from two cohorts of beef heifers managed at the same location: 1) virgin yearling heifers (9 months old) born from dams received gestational diets which resulted in low (LG, n = 22) or medium (MG, n = 23) weight gain during the first 84 days of gestation; and 2) pregnant replacement heifers that received a vitamin and mineral supplement (VTM, n = 17) or not (Control, n = 15) during the first 6 months of gestation. Nasopharyngeal and vaginal swabs as well as ruminal fluid were collected from both cohorts and the microbiota of each sample was assessed using 16S rRNA gene sequencing. In addition to the comparison between treatment groups within each cohort, the similarity of the microbiota of the three sample types were evaluated, and shared taxa amongst these communities were identified. The bacterial genera present in the rumen and vagina that can influence methanogenic archaeal genera were predicted using a stepwise-selected generalized linear mixed model. No significant difference was observed in the alpha and beta diversity in any of the nasopharyngeal, ruminal and vaginal microbiota between LG and MG offspring virgin heifers, or between the control and VTM pregnant heifers ( p > 0.05). Subtle compositional changes in the vaginal microbiota in yearling heifers, and in the nasopharyngeal and ruminal microbiota of pregnant heifers were detected in response to treatments. Forty-one archaeal and bacterial OTUs were shared by over 60% of all samples from both virgin and pregnant heifers. Two taxa within the Methanobrevibacter genus were identified as core taxa and this genus was more relatively abundant in pregnant heifers compared to virgin heifers. Among the 25 top genera, Prevotella and Prevotella UCG-003 (negative) and Christensenellaceae R-7 group (positive) were predicted to have a significant effect on ruminal Methanobrevibacter spp. The results of this study indicate that there is little impact of divergent gestational nutrition during the first trimester on the calf microbiome at 9 months postnatal, and that VTM supplementation during pregnancy may not alter the maternal microbiome. This study provides evidence that there are several microbial taxa, including methanogenic archaea, that are shared across the respiratory, gastrointestinal, and reproductive tracts, suggesting the need for a holistic evaluation of the bovine microbiota when considering potential maternal sources for seeding calves with pioneer microbiota.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.012
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.013
GPT teacher head0.252
Teacher spread0.239 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations16
Published2021
Admission routes1
Has abstractyes

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