Macrophages with reduced expressions of classical M1 and M2 surface markers in human bronchoalveolar lavage fluid exhibit pro-inflammatory gene signatures
Bibliographic record
Abstract
The classical M1/M2 polarity of macrophages may not be applicable to inflammatory lung diseases including chronic obstructive pulmonary disease (COPD) due to the complex microenvironment in lungs and the plasticity of macrophages. We examined macrophage sub-phenotypes in bronchoalveolar lavage (BAL) fluid in 25 participants with CD40 (a M1 marker) and CD163 (a M2 marker). Of these, we performed RNA-sequencing on each subtype in 10 patients using the Illumina NextSeq 500. Approximately 25% of the macrophages did not harbor classical M1 or M2 surface markers (double negative, DN), and these cells were significantly enriched in COPD patients compared with non-COPD patients (46.7% vs. 14.5%, p < 0.001). 1886 genes were differentially expressed in the DN subtype compared with all other subtypes at a 10% false discovery rate. The 602 up-regulated genes included 15 mitochondrial genes and were enriched in 86 gene ontology (GO) biological processes including inflammatory responses. Modules associated with cellular functions including oxidative phosphorylation were significantly down-regulated in the DN subtype. Macrophages in the human BAL fluid, which were negative for both M1/M2 surface markers, harbored a gene signature that was pro-inflammatory and suggested dysfunction in cellular homeostasis. These macrophages may contribute to the pathogenesis and manifestations of inflammatory lung diseases such as COPD.
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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".