Autoimmune susceptibility gene <i>PTPN2</i> is required for clearance of adherent-invasive <i>Escherichia coli</i> by integrating bacterial uptake and lysosomal defence
Bibliographic record
Abstract
Objectives Alterations in the intestinal microbiota are linked with a wide range of autoimmune and inflammatory conditions, including inflammatory bowel diseases (IBD), where pathobionts penetrate the intestinal barrier and promote inflammatory reactions. In patients with IBD, the ability of intestinal macrophages to efficiently clear invading pathogens is compromised resulting in increased bacterial translocation and excessive immune reactions. Here, we investigated how an IBD-associated loss-of-function variant in the protein tyrosine phosphatase non-receptor type 2 ( PTPN2 ) gene, or loss of PTPN2 expression affected the ability of macrophages to respond to invading bacteria. Design IBD patient-derived macrophages with wild-type (WT) PTPN2 or carrying the IBD-associated PTPN2 SNP, peritoneal macrophages from WT and constitutive PTPN2-knockout mice, as well as mice specifically lacking PTPN2 in macrophages were infected with non-invasive K12 Escherichia coli , the human adherent-invasive E. coli (AIEC) LF82 , or a novel mouse AIEC ( m AIEC) strain. Results Loss of PTPN2 severely compromises the ability of macrophages to clear invading bacteria. Specifically, loss of functional PTPN2 promoted pathobiont invasion/uptake into macrophages and intracellular survival/proliferation by three distinct mechanisms: Increased bacterial uptake was mediated by enhanced expression of carcinoembryonic antigen cellular adhesion molecule (CEACAM)1 and CEACAM6 in PTPN2 -deficient cells, while reduced bacterial clearance resulted from defects in autophagy coupled with compromised lysosomal acidification. In vivo, mice lacking PTPN2 in macrophages were more susceptible to m AIEC infection and m AIEC-induced disease. Conclusions Our findings reveal a tripartite regulatory mechanism by which PTPN2 preserves macrophage antibacterial function, thus crucially contributing to host defence against invading bacteria.
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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) | 0.002 | 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".