Modulation of mitochondrial function by the microbiome metabolite propionic acid in autism and control cell lines
Bibliographic record
Abstract
Propionic acid (PPA) is a ubiquitous short-chain fatty acid, which is a major fermentation product of the enteric microbiome. PPA is a normal intermediate of metabolism and is found in foods, either naturally or as a preservative. PPA and its derivatives have been implicated in both health and disease. Whereas PPA is an energy substrate and has many proposed beneficial effects, it is also associated with human disorders involving mitochondrial dysfunction, including propionic acidemia and autism spectrum disorders (ASDs). We aimed to investigate the dichotomy between the health and disease effects of PPA by measuring mitochondrial function in ASD and age- and gender-matched control lymphoblastoid cell lines (LCLs) following incubation with PPA at several concentrations and durations both with and without an in vitro increase in reactive oxygen species (ROS). Mitochondrial function was optimally increased at particular exposure durations and concentrations of PPA with ASD LCLs, demonstrating a greater enhancement. In contrast, increasing ROS negated the positive PPA effect with the ASD LCLs, showing a greater detriment. These data demonstrate that enteric microbiome metabolites such as PPA can have both beneficial and toxic effects on mitochondrial function, depending on concentration, exposure duration and microenvironment redox state with these effects amplified in LCLs derived from individuals with ASD. As PPA, as well as enteric bacteria, which produce PPA, have been implicated in a wide variety of diseases, including ASD, diabetes, obesity and inflammatory diseases, insight into this metabolic modulator from the host microbiome may have wide applications for both health and disease.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".