Determining the Effects of Pebiotics through Community-based Metabolic Modeling
Bibliographic record
Abstract
The human gut microbiome plays an important role where it performs functions such as promoting immune system development and protecting against enteric pathogens. Amongst many other functions, the microbiome produces metabolites from breakdown of prebiotics that can be beneficial to human health. These metabolites include SCFAs. A recent study found that gut microbiota derived SCFAs can play an important role in repair capacity in response to heart damage. It is hypothesized that this property of the gut microbiome can be utilized to improve the recovery of patients after surgery. The purpose of this study was to simulate the infant gut microbiome in silico and identify prebiotics that could increase the level of SCFAs. In this study, six infant stool samples were taken, and bacterial DNA was extracted and sequenced using 16S and shotgun metagenomics. The shotgun metagenomic sequences were utilized to construct metabolic models of the microbes present in the infant gut. On an average there were 20 metabolic models per sample. The infant gut microbiome was populated with the phylum Actinobacteria, specifically the Bifidobacteriaceae family. Simulations were performed using the metabolic models constructed from the metagenomic data. Upon addition of prebiotics to the simulation, the overall composition did not dramatically change. However, the proportion of SCFAs increased with the addition of prebiotics in the simulation. Metabolic modeling is an invaluable tool to make efficient predictions for the human gut microbiome.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".