A234 IN VITRO GUT MICROBIOME AND METABOLITE RESPONSES TO RESISTANT STARCH ARE INDIVIDUALIZED
Bibliographic record
Abstract
Abstract Background Gut microbes degrade and ferment resistant starch (RS) into metabolites that help maintain gut homeostasis. Clinical trials have evaluated RS for various health conditions, but individuals respond to RS with profound variability. The reason for this variability is unclear. Aims Using in vitro culturing methods and multi-omic analyses, we hypothesize that individuals will elicit variable responses to RS with respect to overall fermentation, bacterial composition, short chain acid production, and metabolite flux. Methods As part of an ongoing clinical trial, we have selected 4 pediatric patients with inflammatory bowel disease to better understand microbiome-RS interactions. We cultured stools anaerobically using a high-throughput platform (“RapidAIM”) with 9 different pre-digested RS. After 18-hour incubations, media supernatants were used to measure pH and perform targeted and semi-targeted metabolomic analyses with a panel of 116 compounds. Bacterial pellets were isolated for 16S rRNA gene sequencing analyses to evaluate changes in microbiome compositions. Data were analyzed with generalized linear mixed models, principal component analysis (PCA), random forest (RF) classification with feature selection, and network construction with graphical lasso. Results Changes in several microbiome parameters were different across individuals, including the magnitude of pH changes, metabolite signatures, and relative abundances of important bacterial taxa. Bacterial species known to degrade RS were more abundant in individuals showing stronger RS fermentation. Inter-individual discrimination was accomplished with PCA and RF, from which we could identify metabolite signatures. The robustness of microbiome networks corresponded to RS fermentation and butyrate production. Conclusions We report a novel perspective on how individuals respond to RS’ differently. Butyrate remains an important hub of the microbiome architecture with respect to RS fermentation. Future work will interrogate the roles of individualized metabolomic responses on host physiology. In vivo responses to RS are being evaluated in an ongoing clinical trial. Funding Agencies CCC, CIHRGenome Ontario, Genome Canada
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".