Maternal humoral factors modulate offspring gut immune homeostasis to mitigate diabetes development
Bibliographic record
Abstract
Abstract Environmental risk factors possess the potential to modulate the pathogenesis of type I diabetes (T1D). Foremost among these factors are early life influences impacting the gastrointestinal (GI) tract. During infancy, both the microbiota and immune system are influenced by maternal factors contributing to key events in the neonatal GI tract. Despite the well-known importance of maternal factors on infant immune development, whether maternal immune dysregulation and dysbiosis can perpetuate the same in offspring remains largely unknown. To explore how these maternal factors impact offspring disease development, we used IgA-deficiency induced maternal dysbiosis in Non-Obese Diabetic (NOD) dams to study T1D development in their progeny. We found that maternal dysbiosis and absence of IgA led to changes in IgA-sufficient offspring immune development resulting in heightened GI immune activity. Maternal dysbiosis also contributed to altered microbiome establishment in progeny, such that pups exhibited reduced colonic abundance of Akkermansia muciniphila and Clostridoides difficile . In adulthood, these mice exhibited a lowered incidence of T1D. This protection was replicated by fostering high incidence offspring to dysbiotic dams, prompting us to propose that altered breast milk composition in dysbiotic dams can influence immune development and microbiome establishment in offspring, contributing to T1D resistance.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".