High-throughput fitness screening and transcriptomics identify a role for a type IV secretion system in the pathogenesis of Crohn’s disease-associated Escherichia coli
Bibliographic record
Abstract
Adherent-invasive Escherichia coli (AIEC) are pathogenic bacteria frequently isolated from patients who have Crohn's disease (CD). Despite the phenotypic differences between AIEC and commensal E. coli, comparative genomic approaches have been unable to differentiate these two groups, making the identification of key virulence factors a challenge. Here, we conduct a high-resolution, in vivo genetic screen to map AIEC genes required for intestinal colonization of mice. In addition, we use in vivo RNA-sequencing to define the host-associated AIEC transcriptome. We identify diverse metabolic pathways required for efficient gut colonization by AIEC and show that a type IV secretion system (T4SS) is required to form biofilms on the surface of epithelial cells, thereby promoting AIEC persistence in the gut. E. coli isolated from CD patients are enriched for a T4SS, suggesting a possible connection to disease activity. Our findings establish the T4SS as a principal AIEC colonization factor and highlight the use of genome-wide screens in decoding the infection biology of CD-associated bacteria that otherwise lack a defined genetic signature.
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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".