Gut-immune reactivity underlies sex differences in the maternal immune activation preclinical model of autism
Bibliographic record
Abstract
The gut microbiome plays a vital role in health and disease, including neurodevelopmental disorders like autism spectrum disorder (ASD). ASD affects 4:1 males-to-females, and sex differences are apparent in gut microbiota composition among ASD individuals and in animal models of this condition, such as the maternal immune activation (MIA) mouse model. However, few studies have included sex as a biological variable when assessing the role of gut microbiota in mediating ASD symptoms. Using the MIA model of ASD, we assessed whether gut microbiota contributes to the sex differences in the presentation of ASD-like behaviors. Gut microbiota transplantation from MIA or vehicle/control male and female mice into healthy, otherwise unmanipulated, 4-week-old C57Bl/6 mice was performed for 6 treatments over 12 days. Colonization with male, but not female, MIA microbiota was sufficient to reduce sociability, increase repetitive burying behavior, decrease microbiota diversity and increase neuroinflammation with more pronounced deficits in male recipients. Colonization with both male and female donor microbiota altered juvenile ultrasonic vocalizations and anxiety-like behavior in recipients of both sexes, and there was an accompanied change in the gut microbiota and serum cytokine IL-4 and IL-7 levels of all recipients of MIA gut microbiota. In addition to the increases in gut microbes associated with pathological states, the female donor microbiota profile also had increases in gut microbes with known neural protective effects (e.g., Lactobacillus and Rikenella). These results suggest that gut reactivity to environmental insults, such as in the MIA model, plays a pivotal role in shaping the sex disparity observed in ASD 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".