Brain microbiota disruption within inflammatory demyelinating lesions in multiple sclerosis
Bibliographic record
Abstract
Abstract Microbial communities reside in healthy tissues but are often disrupted during disease. Bacterial genomes and proteins are detected in brains from humans, nonhuman primates, rodents and other species in the absence of neurological disease. We investigated the composition and abundance of microbiota in frozen and fixed autopsied brain samples from patients with multiple sclerosis (MS) and age- and sex-matched nonMS patients as controls, using neuropathological, molecular and bioinformatics tools. 16s rRNA sequencing revealed Proteobacteria to be the dominant phylum with restricted diversity in cerebral white matter (WM) from MS compared to nonMS patients. Both clinical groups displayed 1,200–1,400 bacterial genomes/cm 3 and low bacterial rRNA:rDNA ratios in WM. RNAseq analyses showed a predominance of Proteobacteria in progressive MS patients’ WM, associated with increased inflammatory gene expression, relative to a broader range of bacterial phyla in relapsing-remitting MS patients’ WM. Although bacterial peptidoglycan (PGN) and RNA polymerase beta subunit immunoreactivities were observed in all patients, PGN immunodetection was correlated with demyelination and neuroinflammation in MS brains. Principal component analysis revealed that demyelination, PGN and inflammatory gene expression accounted for 86% of the observed variance. Thus, inflammatory demyelination is linked to an organ-specific dysbiosis in MS that could contribute to underlying disease mechanisms.
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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) | 0.001 | 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".