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Low Dietary Vitamin D since Pre‐mating Does not Modify Fecal <i>Bacteroides</i> Counts of Mouse Dams at the End of Pregnancy

2017· article· en· W4389019100 on OpenAlexafffundabout
Christopher Villa, Amel Taïbi, Shivani Kasee, Jianmin Chen, Wendy E. Ward, Elena M. Comelli

Bibliographic record

VenueThe FASEB Journal · 2017
Typearticle
Languageen
FieldMedicine
TopicMaternal Mental Health During Pregnancy and Postpartum
Canadian institutionsBrock UniversityUniversity of Toronto
FundersUniversity of Toronto
KeywordsBiologyFecesOffspringPhysiologyPregnancyMatingLactationWeaningVitaminBacteroidesGut floraMicronutrientAnimal scienceEndocrinologyImmunologyMicrobiologyZoologyMedicineBacteriaGenetics

Abstract

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Rationale Pioneering data suggested that micronutrients, including vitamin D, affect gut microbiota composition. This may be particularly relevant during pregnancy, since vitamin D supplementation is a common strategy worldwide to support health of the mother and her fetus. Alteration of the gut microbiota during pregnancy may affect gut microbial colonization of the newborn via vertical transfer (mother to offspring) of mother‐derived microorganisms and hence have long‐lasting effects to offspring health. The objective of this study was to determine if altering the level of dietary vitamin D affects the counts of Bacteroides , representing a main Gram‐negative genus in the gut microbiota, in the feces of dams prior to delivery. Study Design & Methods 3‐week old female C57BL/6J mice were fed AIN‐93G diet containing either 25 (low, LD) or 5000 (high, HD) IU vitamin D/kg diet (significantly below and above the 1000 IU vitamin D/kg of the reference AIN‐93G diet) for 4 weeks before mating until the end of lactation (age 13 weeks). Body weight was assessed at mating and weaning. Freshly passed fecal samples were collected just before mating (n=3–6), 1 week post‐mating (n=3–6), and before birth (n=19–20), and used for DNA extraction and quantification of Bacteroides using genus‐specific primers by quantitative PCR. Counts were normalized to total bacteria. Results There was no difference in body weight at mating ( p = 0.771) nor in body weight gain throughout pregnancy and lactation between the groups ( p = 0.238). Percent of fecal Bacteroides normalized to total bacteria increased over time and peaked before birth, independent of the vitamin D intervention (Diet: 0.402, Time: 0.003, Interaction: 0.508). There were no differences in percent Bacteroides normalized to total bacteria between dams receiving high or low vitamin D at the end of pregnancy (LD: 9.10 ± 2.01%, HD: 14.15 ± 2.85%; p = 0.153). Conclusion Bacteroides belong to the core microbiome and are largely saccharolytic. Their increase throughout pregnancy may modify the relationship between microbiota and energy metabolism in the mother and thus her offspring, besides affecting colonization of the offspring. While vitamin D impacts murine microbiota in health and colitis models, here, two substantially different levels of dietary vitamin D did not affect fecal Bacteroides variation during pregnancy. This suggests that Bacteroides are not susceptible to dietary vitamin D or that its effects are masked by other factors in this context. Studies of the whole microbiome at various intestinal regions will help understand this relationship. Support or Funding Information ‐Centrum Foundation Pfizer Consumer Healthcare Research Innovation Fund and the Department of Nutritional Sciences at the University of Toronto to WEW and EMC. ‐NSERC Discovery Grant to EMC. ‐Christopher R. Villa was partially funded by the Banting and Best Diabetes Centre‐Novo Nordisk Studentship and ‐Tamarack Graduate Award in Diabetes Research and by an Ontario Graduate Scholarship. ‐Wendy Ward holds a Canada Research Chair in Bone and Muscle Development. ‐Elena Comelli holds the Lawson Family Chair in Microbiome Nutrition Research at the University of Toronto.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.026
GPT teacher head0.300
Teacher spread0.274 · 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 designObservational
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

Citations1
Published2017
Admission routes3
Has abstractyes

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