miRNA-mRNA network analysis identifies PAX5 as a potential regulator of adaptive immune response in COPD
Bibliographic record
Abstract
Abstract Micro-ribonucleic acids (miRNAs) are key post-transcriptional regulators of the immune system and may play a role in Chronic Obstructive Pulmonary Disease (COPD). In this paper, we constructed subject-specific miRNA-mRNA regulatory networks using bulk and deconvoluted whole blood RNA-sequencing, whole blood miRNA-sequencing, and B-cell receptor-sequencing data from up to 570 miRNAs, 11,859 mRNAs, and 3,190 participants in the COPDGene study. Analysis of whole blood networks revealed two subnetworks of miRNA-mRNA interactions significantly (FDR<0.05) associated with changes in FEV 1 /FVC. We found that miRNAs (and mRNAs) in the network-identified groups had distinct expression patterns, with miRNAs (and mRNAs) in one group having overall higher expression in COPD (decreasing FEV 1 /FVC) and miRNAs (and mRNAs) in the other group having overall higher expression in controls (increasing FEV 1 /FVC). In addition, miRNAs (and mRNAs) within the same group were positively correlated, while those in different groups were negatively correlated, indicating distinct functional roles for these miRNAs (and mRNAs) as a function of increased COPD severity. Network analysis also identified PAX5 , a transcription factor master regulator of B-cell development, as the main mRNA network hub. Using ChIP-seq data in lymphoblastoid cells, we identified a PAX5 binding site overlapping with a COPD genome-wide association signal in the promoter region of ADAM19 . We also found a loss of co-expression between PAX5 and ADAM19 in COPD subjects. Furthermore, in B-cell deconvoluted data, PAX5 was differentially co-expressed with genes associated with B-cell activation and differentiation, revealing a possible mechanism for the regulation of the immune response in COPD. Finally, in B-cell receptor sequencing data, PAX5 and the identified mRNA subnetworks were negatively associated (FDR<0.05) with immunoglobulin class switching, and positively associated with IgM and IgD counts. In conclusion, PAX5 is a known regulator of B-cell identity. B cells are recognized as key players in chronic inflammation and immune dysregulation in COPD. Our work suggests that PAX5 plays a mediating role both in ADAM19 regulation and in miRNA regulation of early B cells in 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 | 0.001 | 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 | 0.000 |
| 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".