Mining for disease-associated microbial metabolites in an age-dependent model of multiple sclerosis
Bibliographic record
Abstract
Abstract Age is a risk factor for the neurological decline and physical disability that characterize progressive multiple sclerosis (MS). The intestinal microbiota and the bioactive compounds it produces can influence aging, immunity, and the central nervous system (CNS). Here, we use an experimental autoimmune encephalomyelitis (EAE) model that mimics features of progressive MS in aged, but not young, mice to address the intersection of age and the microbiota on EAE outcomes. Although the microbiota of SJL/J mice aged under controlled laboratory conditions does not promote an ‘aged’ non-remitting EAE phenotype, young mice harboring heterochronic human fecal microbiota transplants (hFMT) developed a range of EAE phenotypes. Metabolomic profiling of mice colonized with an aged hFMT that promoted non-remitting EAE indicated a severe reduction in circulating levels of the microbiota-derived tryptophan metabolite indole 3-propionic acid (IPA). IPA-supplementation enforced remission in mice colonized with the non-remitting hFMT, demonstrating the utility of this in vivo pipeline for discovering metabolites associated with progressive MS-like disease. Summary The microbiota is a critical determinant of disease susceptibility in mouse models of MS. Here, Pu & Fettig et al . demonstrate that disease outcomes (remitting or non-remitting) are microbiota-responsive, and describe an in vivo pipeline that can be mined for microbial metabolites with therapeutic potential for progressive MS.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".