Novel carbohydrate binding modules in the surface anchored α‐amylase of <i>Eubacterium rectale</i> provide a molecular rationale for the range of starches used by this organism in the human gut
Bibliographic record
Abstract
Gut bacteria recognize accessible glycan substrates within a complex environment. Carbohydrate binding modules (CBMs) of cell surface glycoside hydrolases often drive binding to the target substrate. Eubacterium rectale, an important butyrate-producing organism in the gut, consumes a limited range of substrates, including starch. Host consumption of resistant starch increases the abundance of E. rectale in the intestine, likely because it successfully captures the products of resistant starch degradation by other bacteria. Here, we demonstrate that the cell wall anchored starch-degrading α-amylase, Amy13K of E. rectale harbors five CBMs that all target starch with differing specificities. Intriguingly these CBMs efficiently bind to both regular and high amylose corn starch (a type of resistant starch), but have almost no affinity for potato starch (another type of resistant starch). Removal of these CBMs from Amy13K reduces the activity level of the enzyme toward corn starches by ∼40-fold, down to the level of activity toward potato starch, suggesting that the CBMs facilitate activity on corn starch and allow its utilization in vivo. The specificity of the Amy13K CBMs provides a molecular rationale for why E. rectale is able to only use certain starch types without the aid of other organisms.
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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 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".