Distinct respiratory tract biological pathways characterizing ARDS molecular phenotypes
Bibliographic record
Abstract
Abstract Background Two molecular phenotypes of the acute respiratory distress syndrome (ARDS) with divergent clinical trajectories and responses to therapy have been identified. Classification as “hyperinflammatory” or “hypoinflammatory” depends on plasma biomarker profiling. Differences in pulmonary biology underlying these phenotypes are unknown. Methods We analyzed tracheal aspirate (TA) RNA sequencing (RNASeq) data from 41 ARDS patients and 5 mechanically ventilated controls to assess differences in lung inflammation and repair between ARDS phenotypes. In a subset of subjects, we also analyzed plasma proteomic data. We performed single-cell RNA sequencing (scRNASeq) on TA samples from 9 ARDS patients. We conducted differential gene expression and gene set enrichment analyses, in silico prediction of pharmacologic treatments, and compared results to experimental models of acute lung injury. Findings In bulk RNASeq data, 1334 genes were differentially expressed between ARDS phenotypes (false detection rate < 0.1). Hyperinflammatory ARDS was characterized by an exaggerated innate immune response, increased activation of the integrated stress response, interferon signaling, apoptosis, and T-cell activation. Gene sets from experimental models of lipopolysaccharide lung injury overlapped more strongly with hyperinflammatory than hypoinflammatory ARDS, though overlap in gene expression between experimental and clinical samples was variable. ScRNASeq demonstrated a central role for T-cells in the hyperinflammatory phenotype. Plasma proteomics confirmed a role for innate immune activation, interferon signaling, and T-cell activation in the hyperinflammatory phenotype. Predicted candidate therapeutics for the hyperinflammatory phenotype included imatinib and dexamethasone. Interpretation Hyperinflammatory and hypoinflammatory ARDS phenotypes have distinct respiratory tract biology, which could inform targeted therapeutic development. Funding National Institutes of Health; University of California San Francisco ImmunoX CoLabs; Chan Zuckerberg Foundation; Genentech
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".