Adaptation of the gut holobiont to malnutrition during mouse pregnancy depends on the type of nutritional adversity
Bibliographic record
Abstract
Abstract Malnutrition can influence maternal physiology and programme offspring development. Yet, in pregnancy, little is known about how dietary challenges that influence maternal phenotype affect gut structure and function. Emerging evidence suggests that interactions between the environment, multidrug resistance (MDR) transporters and microbes may influence maternal adaptation to pregnancy and regulate fetoplacental development. We hypothesised that the gut holobiont (host and microbes) during pregnancy adapts differently to suboptimal maternal diets, evidenced by changes in the gut microenvironment, morphology, and expression of key protective MDR transporters during pregnancy. Mice were fed a control diet (CON) during pregnancy, or undernourished (UN) by 30% of control intake from gestational day (GD)5.5-18.5, or fed 60% high fat diet (HF) for eight weeks before and during pregnancy. At GD18.5, maternal small intestinal (SI) architecture (H&E), proliferation (Ki67), P-glycoprotein (P-gp - encoded by Abcb1a/b ) and breast cancer resistance protein (BCRP/ Abcg2 ) MDR transporter expression and levels of pro-inflammatory biomarkers were assessed. Circulating inflammatory biomarkers and maternal caecal microbiome composition (G3 PhyloChipTM) were measured. MDR transporter expression was also assessed in fetal gut. HF diet increased maternal SI crypt depth and proinflammatory load, and decreased SI expression of Abcb1a mRNA, whilst UN increased SI villi proliferation and Abcb1a , but decreased Abcg2 , mRNA expression. There were significant associations between Abcb1a and Abcg2 mRNA levels with relative abundance of specific microbial taxa. Using a systems physiology approach we report that common nutritional adversities provoke adaptations in the pregnancy holobiont in mice, and reveal new mechanisms that could influence reproductive outcomes and fetal development.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".