The Autoimmune Susceptibility Gene, <i>PTPN2</i> , Restricts Expansion of a Novel Mouse Adherent-Invasive <i>E. coli</i>
Bibliographic record
Abstract
Abstract Inflammatory bowel diseases (IBD) involve genetic and environmental factors that play major roles in disease pathogenesis. Loss-of-function single-nucleotide polymorphisms (SNPs) in the protein tyrosine phosphatase non-receptor type 2 ( PTPN2 ) gene increase the risk of IBD and are associated with altered microbiome population dynamics in IBD. Moreover, expansion of intestinal pathobionts, such as adherent-invasive E. coli (AIEC), is strongly implicated in the pathogenesis of IBD as AIEC increases pro-inflammatory cytokine production and alters tight junction protein regulation suggesting a potential mechanism of pathogen-induced barrier dysfunction and inflammation. The aim of this study was to identify if PTPN2 deficiency disturbs the composition of the intestinal microbiome to promote expansion of specific bacteria with pathogenic properties. In mice constitutively lacking Ptpn2 we identified increased abundance of a novel adherent-invasive E. coli (AIEC) that showed similar adherence and invasion of intestinal epithelial cells, but greater survival in macrophages to the IBD associated AIEC, LF82. Furthermore, we confirmed this novel mouse AIEC ( m AIEC) caused disease when administered to germ-free and mice lacking segmented-filamentous bacteria (SFB). Moreover, m AIEC infection increased severity of and prevented recovery from dextran-sodium sulfate (DSS)-induced colitis. m AIEC genome sequence analysis showed >90% similarity to LF82. Interestingly, m AIEC contained distinct attachment genes not found in LF82 thereby also demonstrating the novelty of this AIEC. We show here for the first time that an IBD susceptibility gene, PTPN2 , plays a key role in modulating the gut microbiome to protect against a novel pathobiont. This study generates new insights into gene-environment-microbiome interactions in 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".