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Record W4410144372 · doi:10.1101/2025.05.06.651484

miRNA-mRNA network analysis identifies PAX5 as a potential regulator of adaptive immune response in COPD

2025· preprint· en· W4410144372 on OpenAlexaff
Michele Gentili, Margherita De Marzio, Brian D. Hobbs, Craig P. Hersh, Min Hyung Ryu, Marieke L. Kuijjer, Michael H. Cho, Matthew Moll, Courtney Tern, Peter J. Castaldi, Kimberly Glass

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2025
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicroRNA in disease regulation
Canadian institutionsUniversity of British Columbia
FundersNational Heart, Lung, and Blood InstituteNational Institutes of HealthNorges ForskningsrådUniversitetet i Oslo
KeywordsRegulatormicroRNAImmune systemAcquired immune systemMessenger RNABiologyComputational biologyImmunologyGeneGenetics

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.007
GPT teacher head0.225
Teacher spread0.219 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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