Short-chain fatty and carboxylic acid changes associated with fecal microbiota transplant communally influence microglial inflammation
Bibliographic record
Abstract
infection (rCDI) frequently report depressive symptoms, which are improved after fecal microbiota transplantation (FMT); however, mechanisms underlying this association are poorly understood. Short-chain fatty acids and carboxylic acids (SCCA) produced by the intestinal microbiota cross the blood brain barrier and have been proposed to contribute to gut-brain communication. We hypothesized that changes in serum SCCA measured before and after successful FMT for rCDI influences the inflammatory response of microglia, the resident immune cells of the central nervous system. Serum SCCA were quantified using gas chromatography-mass spectroscopy from 38 patients who participated in a randomized trial comparing oral capsule-vs colonoscopy-delivered FMT for rCDI, and quality of life was assessed by SF-36 at baseline, 4, and 12 weeks after FMT treatment. Successful FMT was associated with improvements in mental and physical health, as well as significant changes in a number of circulating SCCA, including increased butyrate, 2-methylbutyrate, valerate, and isovalerate, and decreased 2-hydroxybutyrate. Primary cultured microglia were treated with SCCA and the response to a pro-inflammatory stimulus was measured. Treatment with a combination of SCCA based on the post-FMT serum profile, but not single SCCA species, resulted in significantly reduced inflammatory response including reduced cytokine release, reduced nitric oxide release, and accumulation of intracellular lipid droplets. This suggests that both levels and diversity of SCCA may be an important contributor to gut-brain communication.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".