bioRxivMolecular BiologyPreprint
Airborne Nanoplastics Perturb Mitochondrial Complex I via the ND6 Axis: Polymer-Specific Mitoepigenetic Remodeling Integrating Experimental, In Silico, and Machine Learning Analyses
Pradyumna Kumar MishraiD, Aneha K Rajan, Apoorva ChoukseyiD, Vikas GurjariD, Aniket Aglawe, Ashwani Pathak, Rajnarayan Ramshankar TiwariiD, Devojit Kumar SarmaiD, Ravi Prakash Tiwari, Rupesh K Srivasatava
Abstract
Airborne nanoplastics constitute an emerging class of environmental contaminants, but their mitoepigenetic effects on human immune cells have not been systematically investigated. Ex vivo human lymphocytes were used to investigate integrated mitochondrial, epigenetic, and inflammatory responses induced by polystyrene (PS), polypropylene (PP), and polyvinyl chloride (PVC) nanoplastics. Fluorescence microscopy at multiple exposure time points and flow cytometry confirmed efficient cellular internalization and progressive intracellular accumulation of nanoplastics. Exposure elicited coordinated transcriptional remodeling of genes regulating mitochondrial dynamics (DRP1, MFN1), mitochondrial DNA encoded oxidative phosphorylation components (MT-ATP6, MT-COX1, MT-ND6), DNA repair (OGG1, APE1), DNA methylation machinery (DNMT1, DNMT3a, DNMT3b), and mitochondrial-associated miRNAs (miR-21, miR-34a, miR-155). Functional analyses revealed polymer and time-dependent disruption of mitochondrial membrane potential and respiratory chain activities, with Complex I identified as the primary site of vulnerability. Correlation analysis showed strong positive associations among DRP1, OMA1, DELE1, and ND6 (r > 0.9, R{superscript 2} > 0.8, p 0.85), establishing a data-driven Complex I-ND6 axis. Collectively, these findings demonstrate that airborne nanoplastics induce integrated mitoepigenetic and immunometabolic dysregulation, underpinned by coordinated and antagonistic regulatory interactions in lymphocytes.
- organism: human
- assay: bulk RNA-seq
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: machine learning (classical)
- Citations
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