Abstract
Exposure to airborne fine particulate matter (PM2.5) has been linked to increased risk of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, yet the underlying mechanisms remain unclear. Here, by leveraging a fine-tuned foundation model of single-cell transcriptomics, we uncover shared transcriptional signatures between PM2.5 exposure and SARS-CoV-2 infection. We further validate this association using population-level epidemiological analyses and perform genome-wide association studies (GWAS) to identify genetic variants that modulate infection risk under PM2.5 exposure. In addition, we identify NPC1 as a key modulator involved in SARS-CoV-2 infection efficiency under virus-laden PM2.5 exposure through integrative functional genomic analyses and in vitro experiments. Our findings suggest that PM2.5 facilitates viral entry through an NPC1-modulated endo-lysosomal pathway, providing a mechanistic explanation for observed pollution-related susceptibility. By integrating artificial intelligence (AI)-guided transcriptomics, epidemiology, GWAS, functional genomics, and in vitro verification, our study elucidates how environmental and genetic factors jointly influence SARS-CoV-2 susceptibility. This work highlights how AI-assisted multi-omics integration systematically decodes the health impacts of environmental exposures from molecular to population levels and informs air quality policy and infectious disease preparedness.
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Data availability
The PM2.5 exposure transcriptomic data generated in this study have been deposited in the Gene Expression Omnibus (GEO) under accession number GSE296297. The single-cell RNA-seq data and bulk RNA-seq data were tokenized and hosted as datasets on Hugging Face (https://huggingface.co/datasets/keegan111/geneformer_PM2.5_classification_tokenized). Data from UK Biobank are available from the UK Biobank Access Management System (https://bbams.ndph.ox.ac.uk/ams/). Access is subject to approval by the UK Biobank and compliance with its data use policies. Genome-wide association study summary statistics generated in this study have been deposited in Figshare (https://doi.org/10.6084/m9.figshare.31386580). DNase-seq, Histone ChIP-seq, and micro-C datasets were obtained from the ENCODE portal (https://www.encodeproject.org/). High-throughput chromosome conformation capture (Hi-C) data of IMR-90 and A549 were collected from https://3dgenome.fsm.northwestern.edu/. The multi-tissue gene expression level data and eQTL data were obtained from the GTEx Portal V8 (https://gtexportal.org). The eQTL database used for Mendelian randomization analysis was obtained from eQTLGen Consortium (https://www.eqtlgen.org/cis-eqtls.html). Single-cell RNA-seq data of normal lung tissue and COVID-19 patients were obtained from GEO under accession number GSE171524. Source data are provided with this paper.
Code availability
All Python and R codes for conducting the main analyses are available on GitHub (https://github.com/Keegan17123/PM2.5_SARS-COV-2_infection). Model checkpoints are available on Hugging Face (https://huggingface.co/keegan111/geneformer_PM2.5_classification).
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Acknowledgements
We acknowledge the research support from the National Natural Science Foundation of China (No. 42277419 to Q.D.), the National Key Research and Development Program of China (No. 2024YFC3607002 to Q.D.), the National Natural Science Foundation of China (No. 42077396 to H.C., 81773394 to M.H.), the Major Project of Guangzhou National Laboratory (No. GZNL2024A01028 to S.L.), and the Research Fund of Vanke School of Public Health in Tsinghua University. The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. We also thank Dr. Andrew P. Lieberman, Azaria Ruth, and their colleagues from the Department of Pathology, University of Michigan Medical School, Ann Arbor, MI, USA, for their valuable technical guidance.
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G.F., W.Z., H.C., M.H., and K.H. contributed single-cell RNA-seq data collection and preliminary analysis. G.F., Y.T., W.X., Y.L., and S.Y. contributed AI model training and evaluation. G.F., W.X., and L.K. contributed in silico perturbation and gene set enrichment analysis. G.F., Q.H., L.L., Q.D., and B.Y. contributed epidemiological analysis and genome-wide association analysis. G.F., L.K., Z.D., Q.D., and Y.T. contributed post-GWAS functional genomic analysis. Z.D., G.F., X.L., J.L., X.M., and S.L. contributed genome editing and in vitro exposure experiments. G.F. drafted the manuscript. Z.D., S.L., and Q.D. supervised the study and revised the manuscript. All authors reviewed and approved the final manuscript.
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Feng, G., Dong, Z., Ke, L. et al. AI-guided multi-omics analysis identifies NPC1-modulated susceptibility to SARS-CoV-2 infection under PM2.5 exposure. Nat Commun (2026). https://doi.org/10.1038/s41467-026-71196-3
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DOI: https://doi.org/10.1038/s41467-026-71196-3
