A proteomic approach towards understanding the cross talk between <i>Bacteroides fragilis</i> and <i>Bifidobacterium longum</i> in coculture
Bibliographic record
Abstract
A better understanding of the interactions among intestinal microbes is needed to decipher the complex cross talk that takes place within the human gut. Bacteroides and Bifidobacterium genera are among the most relevant intestinal bacteria, and it has been previously reported that coculturing of these 2 microorganisms affects their survival. Therefore, coculturing of Bifidobacterium longum NB667 and Bacteroides fragilis DSMZ2151 was performed with the aim of unravelling the mechanisms involved in their interaction. To this end, we applied proteomic (2D-DIGE) analyses, and by chromatographic techniques we quantified the bacterial metabolites produced during coincubation. Coculture stimulated the growth of B. longum, retarding that of B. fragilis, with concomitant changes in the production of some proteins and metabolites of both bacteria. The combined culture promoted upregulation of the bifidobacterial pyruvate kinase and downregulation of the Bacteroides phosphoenolpyruvate carboxykinase - 2 enzymes involved in the catabolism of carbohydrates. Moreover, B. fragilis FKBP-type peptidyl-prolyl cis-trans isomerase, a protein with chaperone-like activity, was found to be overproduced in coculture, suggesting the induction of a stress response in this microorganism. This study provides mechanistic data to deepen our understanding of the interaction between Bacteroides and Bifidobacterium intestinal populations.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".