MAP2K2 Delays Recovery in Murine Models of Acute Lung Injury and Associates with Acute Respiratory Distress Syndrome Outcome
Bibliographic record
Abstract
Abstract Acute respiratory distress syndrome (ARDS) remains a significant problem in need of new pharmaceutical approaches to improve its resolution. Studies comparing gene expression signatures in rodents and humans with lung injury reveal conserved pathways, including MAPK (mitogen-activated protein kinase)/ERK (extracellular signal-related protein kinase) activation. In preclinical acute lung injury (ALI) models, inhibition of MAP2K1 (MAPK kinase 1)/MAP2K2 (MAPK kinase 2) improves measures of ALI. Myeloid cell deletion of MAP2K1 results in sustained MAP2K2 activation and nonresolving ALI, suggesting that MAP2K2 deactivation may be a key driver of ALI resolution. We used human genomic data from the iSPAAR (Identification of SNPs Predisposing to Altered Acute Lung Injury Risk) Consortium to assess genetic variants in MAP2K1 and MAP2K2 for association with mortality from ARDS. To determine the role of MAP2K2 in ALI recovery, we studied mice deficient in Map2k2 (Mek2 −/−) and wild-type control mice in ALI models. We identified a MAP2K2 variant that was associated with death in ARDS and MAP2K2 expression. In Pseudomonas aeruginosa ALI, Mek2 −/− mice had similar early alveolar neutrophilic recruitment but faster resolution of alveolar neutrophilia and vascular leak. Gene expression analysis revealed a role for MAP2K2 in promoting and sustaining select proinflammatory pathway activation in ALI. Bone marrow chimera studies indicate that leukocyte MAP2K2 is the key regulator of ALI duration. These studies implicate a role for MAP2K2 in ALI duration via transcriptional regulation of inflammatory programming with potential relevance to ARDS. Targeting leukocyte MAP2K2 may be an effective strategy to promote ALI resolution.
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) | 0.001 | 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 | 0.000 | 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".