Colonic transcriptomics and microbiota analysis of female and male mice under CNS inflammatory demyelination 2140
Bibliographic record
Abstract
Abstract Description Multiple Sclerosis (MS) is a chronic autoimmune disease that targets the myelin sheath of the central nervous system (CNS), with a higher prevalence in female patients. Our previous works demonstrated a bidirectional link between the gut microbiota and experimental autoimmune encephalomyelitis (EAE), a rodent model of MS. In this study, we aimed to assess sex as a biological variable in the host and microbiota colonic microenvironment at early and late stages of neuroinflammation. A combination of two independent experiments showed no significant differences between male and female C57BL/6J mice in the EAE incidence, disease onset, and disease severity. We performed colonic transcriptomics and microbiota analysis in colonic fecal content of EAE mice and controls on the day of disease induction, pre-onset, and peak disease (seven mice per group/times). Our transcriptomics analysis revealed significant sex-specific differences in colonic gene expression during EAE. Notably, at peak disease, these pathways involved a small number of crucial genes. Similarly, significant effects on the gut microbiota of female and male mice were observed by 16S rRNA sequencing. Our findings suggest the involvement of critical inflammatory pathways during the early stages of the disease, with remarkable differences between males and females associated with microbiota alterations triggered by disease induction and progression. Funding Sources Supported by Institutional Development Awards (IDeA) from the National Institute of General Medical Sciences of NIH under Grants #P20GM103408, P20GM109095, and 1C06RR020533, the Biomolecular Research Center at Boise State funded by the National Science Foundation, Grants #0619793 and #0923535, the M. J. Murdock Charitable Trust, Lori and Duane Stueckle, and the Idaho State Board of Education. Topic Categories Neuroimmunology (NEUR)
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".