Identification of novel fructo-oligosaccharide bacterial consumers by pulse metatranscriptomics in a human stool sample
Bibliographic record
Abstract
ABSTRACT Dietary fibers influence the composition of the human gut microbiota and directly contribute to its downstream effects on host health. As more research supports the use of glycans as prebiotics for therapeutic applications, the need to identify the gut bacteria that metabolize glycans of interest increases. Fructo-oligosaccharide (FOS) is a common diet-derived glycan that is fermented by the gut microbiota and has been used as a prebiotic. Despite being well studied, we do not yet have a complete picture of all FOS-consuming gut bacterial taxa. To identify new bacterial consumers, we used a short exposure of microbial communities in a stool sample to FOS or galactomannan as the sole carbon source to induce glycan metabolism genes. We then performed metatranscriptomics, paired with whole metagenomic sequencing, and 16S amplicon sequencing. The short incubation was sufficient to cause induction of genes involved in carbohydrate metabolism, like carbohydrate-active enzymes (CAZymes), including glycoside hydrolase family 32 genes, which hydrolyze fructan polysaccharides like FOS and inulin. Interestingly, FOS metabolism transcripts were notably overexpressed in Blautia species not previously reported to be fructan consumers. We therefore validated the ability of different Blautia species to ferment fructans by monitoring their growth and fermentation in defined media. This pulse metatranscriptomics approach is a useful method to find novel consumers of prebiotics and increase our understanding of prebiotic metabolism by CAZymes in the gut microbiota. IMPORTANCE Complex carbohydrates are key contributors to the composition of the human gut microbiota and play an essential role in the microbiota’s effects on host health. Understanding which bacteria consume complex carbohydrates, or glycans, provides a mechanistic link between dietary prebiotics and their beneficial health effects, an essential step for their therapeutic application. Here, we used a pulse metatranscriptomics pipeline to identify bacterial consumers based on glycan metabolism induction in a human stool sample. We identified novel consumers of fructo-oligosaccharide among Blautia species, expanding our understanding of this well-known glycan. Our approach can be applied to identify consumers of understudied glycans and expand our prebiotic repertoire. It can also be used to study prebiotic glycans directly in stool samples in distinct patient populations to help delineate the prebiotic mechanism.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 teacher head, 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".