SEX-DEPENDENT GUT MICROBIOME DIFFERENCES IN A TRANSGENIC MOUSE MODEL OF AUTISM
Bibliographic record
Abstract
ABSTRACT INTRODUCTION: A number of studies provide compelling evidence of a relationship between the overexpression of sAPPα, the non-amyloidogenic cleavage product of Amyloid Precursor Peptide (APP), and the abnormal neurological development observed in individuals with Autism Spectrum Disorder (ASD). Changes in the diversity of the gut flora, and co-occurring gastrointestinal pathologies, have been consistently noted in studies of individuals with ASD. A number of mechanisms exist through which sAPPα overexpression may exert an influence on the initial development of the gut microbi-ome, or contribute to proinflammatory conditions within the gastrointestinal tract. METHODS: In order to examine whether a relationship exists between sAPPα expression and gut microbiome composition, we performed cpn60 amplicon sequencing on fecal samples taken from transgenic mice expressing human sAPPα(NtgsAPPα = 10; Nwt = 18). RESULTS: We found no evidence of a strong effect on alpha or beta diversity, but did find significant reductions in the proportional abundance of Akkermansia muciniphila within sAPPα-overexpressing mice; suggesting that the sAPPα fragment may mediate differences in the growth medium provided by the intestinal mucosa. Though male and female controls differed in terms of the abundance of Akkermansia muciniphila detected at baseline, TgsAPPα males and females did not, suggesting that sex may influence the nature and extent of an sAPPα-mediated effect. CONCLUSION: We argue that these observations serve as evidence of a physiological relationship between overexpression of the sAPPα fragment and reduced proportional abundance of Akkermansia muciniphila, adding to existing regarding the co-incident enteric pathology frequently seen in the context of ASD.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.005 | 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".