bioRxivMolecular BiologyPreprint
Identification of smoking-enabled blood miRNA regulatory networks
Michele GentiliiD, Brian D. HobbsiD, Alla MalininaiD, Craig P. HershiD, Peter J. CastaldiiD, Heena Rijhwani, Justin Sui, Corrine R. KlimentiD, Michael H Cho, Kimberly GlassiD, Enid NeptuneiD
Abstract
Cigarette smoking induces complex signaling disruptions that contribute to diseases such as COPD and lung cancer, yet the molecular mechanisms underlying these effects remain incompletely understood. To address this gap, we analyzed peripheral blood from 3190 COPDGene participants using LIONESS and PUMA and constructed miRNA-mRNA regulatory networks associated with smoking status. Comparing networks for active versus former smokers uncovered a striking shift in regulatory architecture: active smokers exhibited elevated miRNA targeting of the mitochondrial complex I protein NDUFA12. This finding was validated in lung tissue expression data from the Lung Genomics Research Consortium (LGRC), where we observed that ever-smokers showed consistent dysregulation of Ndufa12-targeting miRNAs compared to never-smokers. This allowed us to identify a set of smoking-defined circulating and tissue-associated miRNAs. To investigate the specific cellular compartment, we analyzed cell-type deconvoluted expression data from COPDGene blood and LTRC (Lung Tissue Research Consortium) lung tissue, as well as lung transcriptomics data from cigarette smoke-exposed mice, and identified the monocyte/macrophage compartment as a principal site of NDUFA12/Ndufa12 expression. Human THP-1 macrophages treated with cigarette smoke extract demonstrated selective inhibition of NDUFA12 by network-defined miRNAs. These distinct, NDUFA12-targeting, smoking-associated miRNA signatures, revealed through network analysis, describe new smoking-mitochondrial interactions that may serve as novel targets for therapeutic intervention.
- organism: human
- assay: bulk RNA-seq
- research focus: gene regulatory network inference
- research focus: differential expression
- research focus: cell type annotation
- approach: statistical model
- approach: benchmark study
- Citations
- 0
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