Neutrophil-mediated innate immune resistance to bacterial pneumonia is dependent on Tet2 function
Bibliographic record
Abstract
Individuals with clonal hematopoiesis of indeterminate potential (CHIP) are at increased risk of aging related health conditions and all-cause mortality, but whether CHIP affects risk of infection is much less clear. Using UK Biobank data, we revealed a positive association between CHIP and incident pneumonia in 438,421 individuals. We show that inflammation enhanced pneumonia risk, as CHIP carriers with a hypomorphic IL6 receptor polymorphism were protected. To better characterize the pathways of susceptibility, we challenged hematopoietic Tet Methylcytosine Dioxygenase 2-knockout (Tet2-/-) and floxed control mice (Tet2fl/fl) with Streptococcus pneumoniae. As with human CHIP carriers, Tet2-/- mice had hematopoietic abnormalities resulting in the expansion of inflammatory monocytes and neutrophils in peripheral blood. Yet, these cells were insufficient in defending against S. pneumoniae and resulted in increased pathology, impaired bacterial clearance, and higher mortality in Tet2-/- mice. We delineated the transcriptional landscape of Tet2-/- neutrophils and found that, while inflammation-related pathways were upregulated in Tet2-/- neutrophils, migration and motility pathways were compromised. Using live-imaging techniques, we demonstrated impairments in motility, pathogen uptake, and neutrophil extracellular trap (NET) formation by Tet2-/- neutrophils. Collectively, we show that CHIP is a risk factor for bacterial pneumonia related to innate immune impairments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".