Host–gut microbiota interactions during pregnancy
Bibliographic record
Abstract
Mammalian pregnancy is characterized by a well-known suite of physiological changes that support fetal growth and development, thereby positively affecting both maternal and offspring fitness. However, mothers also experience trade-offs between current and future maternal reproductive success, and maternal responses to these trade-offs can result in mother-offspring fitness conflicts. Knowledge of the mechanisms through which these trade-offs operate, as well as the contexts in which they operate, is critical for understanding the evolution of reproduction. Historically, hormonal changes during pregnancy have been thought to play a pivotal role in these conflicts since they directly and indirectly influence maternal metabolism, immunity, fetal growth and other aspects of offspring development. However, recent research suggests that gut microbiota may also play an important role. Here, we create a foundation for exploring this role by constructing a mechanistic model linking changes in maternal hormones, immunity and metabolism during pregnancy to changes in the gut microbiota. We posit that marked changes in hormones alter maternal gut microbiome composition and function both directly and indirectly via impacts on the immune system. The gut microbiota then feeds back to influence maternal immunity and metabolism. We posit that these dynamics are likely to be involved in mediating maternal and offspring fitness as well as trade-offs in different aspects of maternal and offspring health and fitness during pregnancy. We also predict that the interactions we describe are likely to vary across populations in response to maternal environments. Moving forward, empirical studies that combine microbial functional data and maternal physiological data with health and fitness outcomes for both mothers and infants will allow us to test the evolutionary and fitness implications of the gestational microbiota, enriching our understanding of the ecology and evolution of reproductive physiology.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".