Emergent Behavior of IBD-Associated<i>Escherichia coli</i>During Disease
Bibliographic record
Abstract
Inflammatory bowel diseases are becoming increasingly common throughout the world, both in developed countries and increasingly in rapidly developing countries. Multiple lines of evidence point to a role for the microbial composition of the gastrointestinal tract in the etiology of IBD, but to date, attempts to define a specific microbial cause for IBD have proved unsuccessful. Microbial 16S rRNA profiling shows that IBD patients have elevated levels of Enterobacteriaceae, in particular Escherichia coli, and reduced levels of Faecalibacterium prausnitzii. The observed E. coli have been assigned to a specific pathovar, adherent-invasive E. coli (AIEC). Adherent-invasive E. coli are a genomically heterogenous group, and whereas many groups have attempted to identify specific genetic markers that differentiate AIEC from non-AIEC strains, very few concrete genetic associations have been uncovered. Here, we highlight the advantages of applying a phenotyping approach to the study of these organisms, rather than solely depending on a sequencing or genomic-based screening strategy because virulence-associated phenotypes exhibit behaviors of emergent systems. In this respect, attempts at genetic reductionism are prone to failure because there are numerous metabolic, regulatory or genetic paths that can underlie these virulence-associated behaviors. Here, we review these IBD-associated phenotypes in E. coli and make recommendations for experimental approaches to advance our understanding of IBD-associated bacteria more generally. With advances in high-throughput screening and nongenetically based metabolomic characterization of IBD-associated bacteria, we anticipate a fuller understanding of how altered microbial communities contribute to the development of IBD.
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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 | 0.001 | 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 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".