Gene control of tyrosine kinase <i>TIE2</i> and vascular manifestations of infections
Bibliographic record
Abstract
Ligands of the endothelial-enriched tunica interna endothelial cell kinase 2 (Tie2) are markedly imbalanced in severe infections associated with vascular leakage, yet regulation of the receptor itself has been understudied in this context. Here, we show that TIE2 gene expression may constitute a novel vascular barrier control mechanism in diverse infections. Tie2 expression declined rapidly in wide-ranging models of leak-associated infections, including anthrax, influenza, malaria, and sepsis. Forced Tie2 suppression sufficed to attenuate barrier function and sensitize endothelium to permeability mediators. Rapid reduction of pulmonary Tie2 in otherwise healthy animals attenuated downstream kinase signaling to the barrier effector vascular endothelial (VE)-cadherin and induced vascular leakage. Compared with wild-type littermates, mice possessing one allele of Tie2 suffered more severe vascular leakage and higher mortality in two different sepsis models. Common genetic variants that influence TIE2 expression were then sought in the HapMap3 cohort. Remarkably, each of the three strongest predicted cis-acting SNPs in HapMap3 was also associated with the risk of acute respiratory distress syndrome (ARDS) in an intensive care unit cohort of 1,614 subjects. The haplotype associated with the highest TIE2 expression conferred a 28% reduction in the risk of ARDS independent of other major clinical variables, including disease severity. In contrast, the most common haplotype was associated with both the lowest TIE2 expression and 31% higher ARDS risk. Together, the results implicate common genetic variation at the TIE2 locus as a determinant of vascular leak-related clinical outcomes from common infections, suggesting new tools to identify individuals at unusual risk for deleterious complications of infection.
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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 | 0.000 |
| 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".