AI-guided multi-omics analysis identifies NPC1-modulated susceptibility to SARS-CoV-2 infection under PM_2.5 exposure

Domain: biology
Published: 2026-03-30
Editorial outcome: minor_revision

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 NPCR1-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.

Introduction
In the past few years, the coronavirus disease 2019 (COVID-19) pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) imposed a profound impact on human health1, such as high fatality rates2,3 , “long-COVID” symptoms4,5, and mental health problems6,7. Despite global interventions and extensive research efforts, the health burden posed by SARS-CoV-2 infection still persists, driven by factors such as recurrent infections8, the emergence of novel viral variants9, and other unresolved biological determinants10,11. Moreover, extensive studies suggested that environmental factors could interfere with SARS-CoV-2 infection, such as air pollution, temperature, and humidity12,13. However, the underlying mechanism is still elusive. Therefore, investigating the interaction between environmental factors and viral infections is crucial for preventing future pandemics.

Fine particulate matter, also named PM2.5, is particulate matter with an aerodynamic diameter less than 2.5 μm, bringing severe health burden to global public health14. Ambient PM2.5 is a complex mixture of substances—including hydrocarbons, salts, dust, and microorganisms—originating from natural sources (e.g., wildfires, dust storms), human activities (e.g., fuel combustion, traffic, residential energy use), and secondary formation through atmospheric chemical reactions15,16. According to the World Health Organization (WHO) Interim Targets 1 to 4 (S-IT1–S-IT4) and the Air Quality Guideline (S-AQG), unhealthy PM2.5 exposure affected 96% of the world’s populated regions for at least one day, 82% for at least one week, and 53% for at least one full month in 202217. Air pollution is now a leading contributor to premature mortality and disability, with a global disease burden that surpasses other major health risk factors, such as hypertension and smoking18. The associations between PM2.5 exposure and a wide range of health outcomes have been extensively studied, including mortality19, hospital admission20, cardiovascular disease21, neurological disorders22, and respiratory syndromes23. Moreover, recent studies have explored additional aspects of PM2.5’s impact on COVID-19-related outcomes, including its associations with COVID-19 hospitalization and mortality rates24-26. PM2.5 can function as a carrier of viruses and protect their viability and infectiousness27, and may even facilitate viral infection and replication28. However, few studies have revealed the molecular mechanism of the interaction between PM2.5 exposure and viral infection.

Recently, artificial intelligence (AI), particularly deep learning models leveraging attention mechanisms, has accelerated scientific discovery in the life sciences. Bioinformatics data of various modalities have been used for model training, such as proteins29 and nucleotides30. Given the large data size and cell-level resolution, a variety of AI models employing distinct encoding methods have been trained on single-cell transcriptomic data. The knowledge embedded in pre-trained networks enables models to adapt to diverse downstream tasks, including cell clustering and annotation31, gene and pathway prediction32, and the discovery of novel cell types33. Therefore, AI models utilizing single-cell transcriptomics have the potential to elucidate associations between PM2.5 exposure and SARS-CoV-2 infection at the level of gene expression network dynamics.

In this study, we aimed to elucidate the molecular mechanisms by which PM2.5 exposure may enhance host susceptibility to SARS-CoV-2 infection. To achieve this, we developed an AI-driven framework integrating single-cell transcriptomics, genome-wide association analysis, functional genomics analyses, and in vitro validation. This multidisciplinary approach identified the transcriptomic signatures and genetic variants underlying environmentally modulated viral susceptibility, highlighting key gene–environment interactions relevant to COVID-19.

Results
Single-cell transcriptomic AI model on PM2.5 exposure classification

Previous studies have sought to elucidate the health effect of PM2.5 exposure; however, they have largely converged on well-established mechanisms such as inflammation and oxidative stress34, which may stem from the omission of complex non-linear gene network interactions. To address this, we employed an rank-encoded single-cell RNA sequencing (scRNA-seq) transformer architecture (Geneformer)32 to model gene network characteristics under PM2.5 exposure (Fig. 1a, c). Single-cell transcriptomic profiles of mouse lung tissue under PM2.5 exposure and control conditions were collected (45340 cells), providing a representative distribution for model training (Fig. 1b). Subsequently, we fine-tuned the pre-trained Geneformer model (i.e., the fine-tuned model) by exposure labels classification (Fig. 1c) with different data curation and layer freezing strategies (Supplementary Fig. 1, described in Methods), which resulted in robust model performance. Moreover, to evaluate the impact of pretraining, we also trained a series of BERT (Bidirectional encoder representations from transformers) models from scratch, each with identical configuration but differing in model depth (the number of encoder layers, i.e., BERT-1L, BERT-2L, BERT-4L, BERT-6L). We found that as the training data size increased, the BERT models achieved performance comparable to that of the fine-tuned model, and all transformer-based models outperformed alternative machine learning approaches, including random forest and support vector machine (Fig. 1d, Supplementary Fig. 2). However, our results demonstrated that the fine-tuned model outperformed the BERT models when trained on a limited data size (Fig. 1e). Together, these findings highlight the value of transfer learning in capturing complex gene network dynamics under environmental exposure.

Next, we implemented two approaches to evaluate model generalization. First, we evaluated whether models trained exclusively on single-cell transcriptomic data could effectively classify bulk RNA sequencing (RNA-seq) samples. We curated 71 bulk RNA-seq samples from the Gene Expression Omnibus (GEO), comprising both PM2.5 exposure and control groups (Fig. 1f). We predicted exposure status on the bulk RNA-seq data and observed that the performance of BERT models improved with increasing model depth; however, it remained inferior to the fine-tuned model (Fig. 1g, Supplementary Fig. 3a). Notably, performance was slightly lower for samples derived from non-lung tissues, suggesting potential tissue-context awareness. Second, we partitioned our scRNA-seq dataset into multiple subsets based on cell type annotations, such that each test set contained a single cell type while the remaining cell types were used for training. Compared to BERT models, fine-tuned models demonstrated robust performance on cell types that were absent from the training data (Fig. 1h, i, Supplementary Fig. 3b-f). In summary, our results revealed that pre-trained weights facilitate the downstream adaptation, enabling a comprehensive understanding of the gene expression networks under PM2.5 exposure. Moreover, our results also implied that PM2.5 exposure might induce similar molecular response across cell types and organizational levels.

Associations between PM2.5 and COVID-19 revealed by pathway-level characteristics and epidemiological analyses

To identify critical genes relevant to PM2.5 exposure, we conducted in silico perturbation using our transformer-based models. We averaged the outputs from the final transformer block to generate cell embeddings, which were subsequently visualized using Uniform Manifold Approximation and Projection (UMAP). We found that the fine-tuned model exhibited a clear decision boundary (Fig. 2a, Supplementary Fig. 4), implying improved generalization enabled by pre-trained weights.

Building on this, we conducted in silico gene deletion in the input sequences and compared the embedding shifts of perturbed cells to PM2.5 exposure and control clusters (Fig. 2b). A substantial shift in exposure clusters following gene deletion suggests that the gene may contribute to cellular responses distinguishing PM2.5 exposure from control condition. Genes exhibiting embedding shift with statistical significance were selected for pathway enrichment analyses (Fig. 2c). Enrichment analyses revealed that genes associated with PM2.5 exposure were enriched in COVID-19 and ribosome function pathways (Fig. 2d). Additional enriched terms suggested mechanisms related to immune responses and viral genome translation following infection (Fig. 2e). Specifically, genes enriched in COVID-19 pathway were associated with cellular stress (e.g. FOS, JUN), inflammation (e.g. NFKBIA, CASP1), and antiviral immune response (e.g. TLR7, ISG15, CXCL10), consistent with previous reports34. Moreover, vesicle-related pathways (such as phagosome, lysosome) were also enriched, implying a mediating role (Fig. 2f). We also performed in silico overexpression and consistently found enriched pathways related to COVID-19 (Fig. 2g-i, Supplementary Fig. 5). In addition, perturbation analyses identified substantial overlapping PM2.5-related genes and enriched pathways across different cell type groups (Fig. 2j, Supplementary Fig. 6). Overall, our model not only captured well-established molecular alteration of PM2.5 exposure, but also pointed to a potential connection between PM2.5 and SARS-CoV-2 infection through shared pathway-level characteristics.

To validate these insights, we performed epidemiological analysis to investigate the association between PM2.5 exposure and SARS-CoV-2 infection in the UK Biobank participants, adjusting for multiple covariates (Supplementary Table 1). SARS-CoV-2 infection was defined by SARS-CoV-2 nucleocapsid antibody assay. We estimated individual PM2.5 exposure based on ambient PM2.5 level, residential address, and assay date. Significant positive correlations with SARS-CoV-2 infection were observed at both annual and monthly exposure levels, and findings remained consistent across sensitivity analyses (Supplementary Table 2). To account for potential lagged effects35, we further analyzed PM2.5 exposure across different time lags and found consistent positive associations (Fig. 2k, l). Subgroup analyses also demonstrated consistent associations across population strata (Supplementary Fig. 7). These findings align with previous studies24,26, supporting an association between PM2.5 exposure and SARS-CoV-2 infection risk.

Identification of genomic loci that modulate the association between PM2.5 exposure and SARS-CoV-2 infection

Despite the availability of large-scale datasets and advanced long-range modeling approaches, single-cell transcriptomic data remain inherently constrained to pinpoint core regulatory genes. This limitation arises from the complex cascade of downstream gene expression changes induced by PM2.5 exposure, which obscures the identification of upstream regulators contributing to increased susceptibility of SARS-CoV-2 infection. To elucidate the genetic mechanisms underlying the observed association between PM2.5 exposure and SARS-CoV-2 infection, we performed genome-wide association studies (GWAS) using data from UK Biobank participants. This analysis identified significant single nucleotide polymorphisms (SNPs) and potential effector genes (Fig. 3a, d).

First, we analyzed genetic variants associated with positive COVID-19 test results and identified significant variants at 3p21.31 with the lead SNP rs73062389, which is near SLC6A20 and LZTFL1 (Fig. 3b, c). Consistently, previous GWAS have identified SLC6A20 and LZTFL1 as effector genes associated with COVID-1936-38.

We next performed an additional GWAS to investigate gene–environment interaction between SNPs and PM2.5 exposure. This analysis identified marginally significant SNPs located within KCNJ3 on Chromosome 2 (Supplementary Fig. 8a). However, existing evidence does not support an association between KCNJ3 and either SARS-CoV-2 infection or PM2.5 exposure, and its expression in the lung is minimal (Supplementary Fig. 8b). To enhance statistical power to detect gene-level associations, we applied set-based association analysis using GCTA-fastBAT39. We identified a cluster of SNPs significantly enriched at chromosome 18q11.2 (chr18:23,470,036–23,589,830, GRCh38; p = 1.5 × 10-6), exceeding the Bonferroni-corrected significance threshold (0.05/23582). These SNPs are located near NPC1 and RMC1 (C18orf8), suggesting potential regulatory roles for these genes (Fig. 3e, f). Specifically, a significant positive gene–environment interaction was observed per risk allele (relative to the reference allele), such that the joint presence of higher PM2.5 exposure and risk alleles was associated with increased susceptibility to SARS-CoV-2 infection (Supplementary Table 3).

Identification of candidate causal variants and tissue-specific regulatory mechanism in lung

Causal SNPs with biological regulatory functions may not always reach genome-wide significance due to linkage disequilibrium. To prioritize potential causal variants, we focused on SNPs in strong linkage disequilibrium (r2 > 0.6) with the lead SNP rs1652376 (Supplementary Fig. 9a). According to previous GWAS reports, these SNPs were associated with obesity-related traits40, lipid metabolism41, socioeconomic status42, and cognition43, but not COVID-19 (Supplementary Fig. 9b). Although some variants are located within coding regions, they do not alter amino acid sequences. Furthermore, none are located within untranslated regions (UTRs) or conserved microRNA binding sites44, nor are they predicted to affect mRNA splicing by SpliceAI45 (Supplementary Table 4). To investigate the regulatory potential of these SNPs, we examined DNase I hypersensitivity profiles across 95 different cell types from Encyclopedia of DNA Elements (ENCODE) dataset to identify open chromatin regions. Four variants (rs891387, rs1652376, rs2960578, and rs1788783) were located within open chromatin peaks, exhibiting cell type specificity (fibroblasts, epithelial cells) as well as tissue specificity (lung tissue) relevant to COVID-19 (Fig. 4a, Supplementary Fig. 10). Among these, rs1788783 exhibited the highest relative signal intensity in lung-related cell lines. Histone H3 lysine 4 mono-methylation (H3K4me1) and chromosome conformation capture (3C) data further implied a potential cis-regulatory mechanism for these candidate variants (Fig. 4a, Supplementary Fig. 11).

Expression quantitative trait loci (eQTL) analysis using the Genotype-Tissue Expression (GTEx) portal revealed that risk alleles of four candidate variants were associated with increased expression of NPC1 and RMC1 in lung (Fig. 4c, d, Supplementary Fig. 12). Lung is the primary target organ of both PM2.5 exposure and SARS-CoV-2 infection. Bulk RNA-seq data from GTEx revealed that both RMC1 and NPC1 are highly expressed in the lung (Fig. 4b) compared to other organs and tissues (Supplementary Fig. 13a, b). Moreover, scRNA-seq data indicated that NPC1 and RMC1 are expressed across multiple lung cell types, particularly alveolar type 2 (AT2) cells, which play a key role in SARS-CoV-2 pathogenesis46 (Fig. 4e, Supplementary Fig. 13c-e, Supplementary Fig. 14). To assess potential causal effects of NPC1 and RMC1, we conducted a Mendelian randomization analysis based on summary statistics of GWAS and eQTL47 (Fig. 4f). The results suggested that higher expression levels of NPC1 and RMC1 were associated with increased susceptibility to SARS-CoV-2 infection (Supplementary Table 5).

To further explore functional roles, we conducted in silico gene deletions of NPC1 and RMC1 in the fine-tuned model and examined the resulting gene network perturbations. We found that single-gene perturbation of NPC1 or RMC1 identified enrichment of pathways related to endocytosis and autophagy, consistent with their biological functions, whereas the COVID-19 pathway showed lower enrichment significance (Fig. 4 g, h). In contrast, co-deletion of NPC1 and RMC1 increased the enrichment significance of the COVID-19 pathway, suggesting a potential synergistic role of these two genes in SARS-CoV-2 infection (Fig. 4i). Moreover, previous studies have linked NPC1, RMC1 to viral infection mechanisms. NPC1 plays a crucial role in intracellular cholesterol transport48,49 and mediates viral genome release into cytoplasm50-52. RMC1 is a scaffolding subunit of the MON1-CCZ1 complex, which serves as the guanine nucleotide exchange factor for RAB7A53,54. Notably, CCZ1 and RAB7A have been identified as regulators in viral infection and trafficking55-58. Collectively, these findings suggested a potential role of NPC1 and RMC1 in modulating SARS-CoV-2 infection in the context of PM2.5 exposure.

Regulatory role of rs1788783 and NPC1 in SARS-CoV-2 infection under virus-laden PM2.5 exposure

Through the aforementioned in silico analyses, we identified candidate causal variants along with the potential effector genes NPC1 and RMC1. To validate the inferred molecular mechanism linking genomic variants, effector genes, and phenotype, we performed in vitro experiments involving genome editing and virus exposure assays. We employed CRISPR knockout (CRISPR–KO) in A549 alveolar basal epithelial adenocarcinoma cells to delete genomic regions encompassing the candidate variants (Fig. 5a, Supplementary Fig. 15). Using western blotting, we found that among the four knockout sites, rs1788783 led to a marked reduction in NPC1 protein expression (Fig. 5b, c). In contrast, RMC1 protein levels did not show a clear difference between groups.

Previous studies have shown that viruses maintain infectivity when loaded onto PM particles, entering host cells via receptor-independent endocytosis28. As a key mediator of endo-lysosomal trafficking, NPC1 plays a critical role in post-endocytic proliferation process of the virus50,52,59,60. Therefore, we designed exposure experiments under solo viruses (the free virus control) and virus-laden PM (PM2.5-borne viruses) and evaluated viral replication by assessing the relative expression levels of open reading frame 1 (ORF1) and nucleocapsid (N) genes. First, we confirmed that exposure to virus-laden PM enhanced viral replication compared with solo virus exposure at 24 hours post infection (hpi) (Fig. 5d). Moreover, compared with wild-type A549 cells, rs1788783 knockout cells exhibited attenuated viral replication under both exposure conditions. Using the pseudovirus carrying green fluorescent protein (GFP) and luciferase reporters, we further examined infection kinetics over time (Fig. 5e). For both solo viruses and virus-laden PM exposure, no significant differences in viral replication were observed between wild-type and knockout cells at 12 and 24 hpi. However, wild-type cells exhibited markedly stronger luminescence intensity under virus-laden PM exposure at 48 hpi, compared to solo viruses. In contrast, knockout cells showed significantly reduced viral replication under both exposure conditions. These findings suggest a role for NPC1-modulated endo-lysosomal pathways in regulating late-stage viral entry kinetics60.

To further validate the role of NPC1 in SARS-CoV-2 infection directly, we performed NPC1 knockdown using small interfering RNA (siRNA). Three siRNA molecules led to varying degrees of NPC1 protein knockdown (Fig. 5f), and corresponding reductions in viral replication were observed. Notably, Lower NPC1 expression levels were associated with reduced viral replication (Fig. 5g).

To rule out the contribution of angiotensin-converting enzyme 2 (ACE2)-mediated viral entry, we used DX600, a selective ACE2 inhibitor, to block ACE2 enzymatic activity. As expected, ACE2 inhibition reduced viral infection under both SARS-CoV-2 pseudovirus and pseudovirus-laden PM exposures. However, viral replication remained consistently higher in the pseudovirus-laden PM group compared with pseudovirus alone (Fig. 6a–d, Supplementary Fig. 16a–c), supporting an ACE2-independent entry mechanism. We further validated these findings across multiple cell lines, where varying degrees of NPC1 knockdown were associated with dose-dependent reductions in viral infection (Fig. 6e–f, Supplementary Fig. 16d–f). Overall, these experiments provided evidence consistent with a regulatory role of NPC1 and its associated genomic variants in modulating SARS-CoV-2 infection under virus-laden PM2.5 exposure, offering mechanistic support linking omics findings with population-level associations.

Methods
Ethics
The collection of samples and data for this study conformed to all relevant ethical regulations. The research followed the ethical guidelines for laboratory animals of China Medical University (approval No. CMU2021230) and the Animal Care and Use Committee of Tianjin University Haihe Hospital (approval No. 2017-kt-04). All animal experiments were conducted in accordance with relevant institutional and national guidelines. The study was conducted using the UK Biobank Resource. All UK Biobank participants provided written informed consent. Ethical information (http://www.ukbiobank.ac.uk/ethics/) about UK Biobank is available online.

Animal and PM2.5 exposure
Male C57BL/6J mice (Mus musculus), aged 6–8 weeks, were obtained from Sipeifu Bioscience Co., Ltd. (Beijing, China). Animals were maintained on a C57BL/6 genetic background. Mice were housed and managed at the animal facility. Mice were housed in a specific pathogen-free (SPF) facility under controlled environmental conditions with a 12 h light/12 h dark cycle, ambient temperature of 22 ± 2 °C, and relative humidity of 50 ± 10%. Airborne PM2.5 was collected by air PM sampler and multi-nozzle cascade impactor (MCI) (Tokyo Dylec Co.). The collected airborne PM2.5 was exposed to mice via the respiratory tract. The duration of ambient PM2.5 exposure was one month to investigate the relatively long-term physiological effects. Subsequently, fresh lung tissue of mice was cleaned and placed in tissue preservation solution. C57BL/6 mice were randomly assigned to the PM2.5 exposure group and the control group to minimize batch effects in the single-cell experiment. More details can be found elsewhere86,87.

Single-cell library construction and quality control
Isolated total lung cells were pooled and processed using the Chromium Single Cell 3' Reagent Kits v3 (10x Genomics, Pleasanton, CA, USA). The pooled cells were then loaded onto the Chromium Single Cell Controller (10x Genomics) to generate single-cell gel beads in emulsion. The libraries were then pooled and sequenced on the NovaSeq platform (Illumina, San Diego, CA, USA) using a 150 bp paired-end configuration. Additionally, a subset of single-cell libraries was also prepared using the DNBelab C Series Single-Cell Library Prep Set and sequenced on a BGISEQ500 sequencer. The raw sequencing data were preprocessed and analyzed by the Cell Ranger Single-Cell Software Suite (v. 5.0.1) with default settings. The reads were aligned to the mouse reference genome (mm10). Seurat (version 5.1.0) was utilized for downstream analysis. The data underwent additional filtering based on the following criteria: (1) cells expressing fewer than 200 unique genes or more than 5,500 unique genes; (2) cells with mitochondrial gene expression exceeded 15%. We applied Harmony88 to eliminate batch effects.

Data preprocessing and encoding of single-cell transcriptomic data
Overall, we collected 45960 cells of the PM2.5 exposure experiments from mouse lung tissue for single-cell RNA-seq. We used loompy, scanpy89, anndata, scipy, numpy, and pandas to process the datasets into the .loom files, retaining key metadata in row and column attributes, such as quality control matrices, cell type annotation, and PM2.5 exposure labels. Then, we used the tokenizer from Geneformer to convert single-cell transcriptomic data from annotation data format to Hugging Face Datasets90, with features including tokenized gene sequences, cell types, organ labels, and PM2.5 exposure, etc. This encoding method generated token sequences based on the rank of the gene expression level for each cell. Considering the pre-trained model setup and the sequencing depth of the currently used single-cell transcriptomic dataset, we selected 2048 genes with the highest expression levels to generate input sequences. In addition, we padded each cell’s input sequence to ensure consistent input length. This encoding method reduces the high-dimensional, sparse expression matrix to ranked tokens, not only optimizing the space occupancy of transcriptomic data, but also potentially alleviating the batch effect of different samples32.

Model architecture and training
To make full use of context knowledge on gene network dynamics, we fine-tuned the Geneformer pre-trained model for PM2.5 exposure classification. We used the checkpoint with an input size of 2048, 256 embedding dimensions, 4 attention heads, feed forward size of 512, and 6 encoder layers32. We used “BertForSequenceClassification” from the Hugging Face Transformers library to fine-tune the pre-trained model. The number of labels for classification is set to 2 for distinguishing exposure label of “PM2.5” and “Control”. To compare performance with fine-tuned models, we trained BERT models from scratch with 1, 2, 4, 6 transformer encoder layers with randomly initiated weights. The activation function was ReLU (Rectified Linear Unit), with an attention dropout and hidden dropout rate of 0.02. The model used absolute positional embeddings with a maximum sequence length of 2048 tokens and a vocabulary size of 25426. Training was conducted using the Adam optimizer with weight decay fix, a maximum learning rate of 5 × 10⁻⁵, linear learning rate scheduling with 500 warmup steps, and a weight decay of 0.001. A batch size of 26 was used, and all models were trained and evaluated on 4 NVIDIA A100-40G GPUs. Gradient checkpointing was disabled, and training was performed using PyTorch with Transformers version 4.28.0. The full model configuration and training scripts are available via our public GitHub and Hugging Face repositories to ensure full reproducibility.

To comprehensively evaluate the influence of input sequence length and padding on model performance, we trained the fine-tuned models under three data curation strategies: (1) keep all transcriptomic data, (2) keep transcriptomic data that included more than 856 non-zero genes, (3) keep transcriptomic data that included more than 2048 non-zero genes. The choice of 856 as the mild filtering threshold is based on its identification as a turning point in the data distribution. Finally, the 3 datasets contain 45960, 36118, and 24231 cells, respectively. In addition, since exposure classification is quite different from the pre-trained task, we also fine-tuned models with different numbers of frozen layers (n = 0, 2, 4) to evaluate fine-tuning performance. Overall, we trained 9 groups of models to comprehensively evaluate the performance differences caused by different data curation and frozen layers. We used 5-fold cross-validation and reported the result as area under the curve (AUC) ± standard deviation.

To test the generalization of our models, we collected bulk RNA-seq raw counts of PM2.5 exposure from the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO)91, and accession numbers were listed as follows: GSE20833192, GSE22025293, GSE14370994, GSE220165, GSE18219995, GSE23779596, GSE15561697, GSE21362098, GSE21541199, GSE206862100, GSE244959101, GSE135262102. In these studies, we focused exclusively on tissues and cells from the PM2.5 exposure and control groups, resulting in a total of 71 included samples. We used these data to evaluate the fine-tuned model and BERT models. Finally, we evaluated model performance by comparing predicted and true labels, and reported the results as F1 score ± standard deviation.

In silico perturbation
To test the impact of gene deletion or expression on the gene expression network, we performed in silico perturbations using our fine-tuned model32,103. In this model, each gene token is embedded in a 256-dimensional space and continuously updated during the forward pass of the transformer blocks. These embeddings capture the regulatory context and network interactions of a cell. The resulting cell-level embedding is computed as the average of all gene embeddings from the final hidden layer, effectively summarizing the overall transcriptional state. Perturbing a specific gene in the input sequence—either by removing it (deletion) or artificially increasing its influence (overexpression via rank promotion)—alters the embeddings of other genes and consequently the overall cell state. By comparing the perturbed and unperturbed cell embeddings using cosine similarity, we can evaluate how much influence a given gene exerts on the cell state. In this study, we applied two types of perturbations: (1) in silico deletion, where a specific gene is removed from the token input sequence, and (2) in silico overexpression, where the gene is moved to the front of the token sequence, mimicking high expression or importance.

For the analysis of the perturbation results, we used two methods. (1) Cell embeddings were grouped based on classification labels (e.g., PM2.5 exposure vs. control), and centroids were computed to represent the average embedding of each group. Perturbation of each gene was performed, and the cosine similarity shift between perturbed and unperturbed embeddings was calculated to assess its influence on cell state alteration; (2) Gene embedding network analysis, in which we investigated how perturbing a target gene altered the embeddings of other genes, which were further interpreted using prior knowledge, such as Gene Ontology (GO) or Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database to understand affected molecular programs.

To define high-impact genes, we followed an approach validated in the original Geneformer framework. Specifically, we: (1) computed cosine similarity between original and perturbed cell embeddings for each gene; (2) compared the distribution of cosine shifts for each perturbed gene to a null distribution generated from random gene perturbations using the Wilcoxon rank-sum test; (3) applied Benjamini–Hochberg correction for multiple testing; (4) designated genes with FDR-adjusted P values < 0.05 as high-impact genes. This procedure allowed us to prioritize genes with significant regulatory influence on cell state, and these high-impact genes were used for downstream interpretation of exposure-related transcriptional mechanisms.

Study population and covariates
To investigate the association between SARS-CoV-2 infection and PM2.5 exposure, we used data from the UK Biobank and restricted the analysis to participants who agreed to take part in the coronavirus infection study. A total of 60274 participants were included in the present analysis (37268 females and 23006 males; age range 52–86 years). Sex was determined based on self-report as recorded in the UK Biobank dataset. Blood samples were collected using the Thriva COVID-19 antibody test kit and analyzed for SARS-CoV-2 nucleocapsid (N) antibodies using the Roche Elecsys Anti-SARS-CoV-2 assay. Results were integrated into the UK Biobank database. All UK Biobank participants provided written informed consent. No additional participant compensation was provided for this study, as analyses were conducted using existing UK Biobank data. Covariates were selected from the UK Biobank dataset, including demographic factors (age, sex, ethnicity, body mass index), socioeconomic factors (household income, education, urbanicity), dietary factors (vegetable intake, fruit intake), lifestyle variables (smoking, alcohol intake, physical activity, sleep duration), and vaccination history. Sex was included as a covariate in regression models, and sex-stratified analyses were conducted. Detailed population characteristics are provided in Supplementary Table 1.

Exposure
PM2.5 monitoring data were collected from 105 air quality stations managed by the UK Department for Environment, Food and Rural Affairs (Defra) (https://uk-air.defra.gov.uk). These monitoring data were used to create a 0.01º × 0.01º dataset using the Ordinary Kriging (OK) interpolation method, a widely used approach for estimating air pollutants104. The OK method predicts PM2.5 levels at specific locations by leveraging data from nearby monitoring stations and accounts for spatiotemporal correlations105. Previous studies have demonstrated that the OK method performs well in areas with dense sampling, achieving accuracy comparable to that of land-use regression models106. Ambient temperature was assessed using data from the European Centre for Medium-Range Weather Forecasts’ fifth-generation atmospheric reanalysis (ERA5) dataset (https://cds.climate.copernicus.eu/), which has a horizontal resolution of 0.1º×0.1º, even in remote areas with fewer monitoring sites. The exposure dataset provided comprehensive coverage for the entire UK, integrating observations from multiple sources with a high degree of accuracy. For all exposure variables used in our study, we generated lagged exposures for both annual and monthly levels. Environmental exposures were matched to each participant based on the longitude and latitude of their home address.

Regression analyses
We adopted a generalized additive model to analyze the associations between PM2.5 exposure and SARS-CoV-2 infection, adjusting for multiple covariates, as described above. To identify potentially vulnerable populations, we performed subgroup analyses based on factors such as sex, age group, body mass index category (categorized as underweight, healthy, overweight, obese using cut-off points of 18.5, 25, and 30), ethnicity, physical activity, smoking, sleep duration, alcohol consumption, household income, and educational attainment. Moreover, to identify critical exposure windows, we investigated the associations between different PM2.5 exposure lags and SARS-CoV-2 infection. Additionally, we performed sensitivity analyses to validate the robustness of our results, applying various covariate adjustments. All analyses were performed using R version 4.3.1.

Genome-wide association study
We performed logistic association analyses between genetic variants and SARS-CoV-2 infection. We applied quality control analysis based on the following criteria107: information score ≥ 0.4, Minor allele frequency (MAF) ≥ 0.001, Hardy-Weinberg equilibrium P value -log (P) ≤ 7, and inclusion of autosomal and biallelic variants. These inclusion criteria were consistent with previous studies on the same study population108. For the GWAS analysis, SARS-CoV-2 infection (categorical variable, 0 or 1) was used as the primary phenotype and dependent variable, while the independent variables included SNP term, genotyping array, age, genetic sex, and 15 principal components to control for population stratification. We applied genomic control to minimize the probability of false-positive results arising from population structure confounding. Our study not only examined the direct effect of SNPs on the phenotype, but also analyzed the interaction between SNP and PM2.5 exposure in relation to the phenotype.

The above genome-wide association analyses were implemented using Plink and Plink2 (www.cog-genomics.org/plink/2.0/)109. We used R packages such as ggplot2 to visualize GWAS summary results, including Q-Q and Manhattan plots. Moreover, we utilized LocusZoom110 to visualize association signals, linkage disequilibrium, and nearby genes in local genomic regions.

Post-GWAS analyses using public data resources
We used a series of datasets to refine SNPs of interest with potential biological functions. We utilized the GWAS Catalog (https://www.ebi.ac.uk/gwas/)111 to find out whether SNPs of interest were reported to be associated with specific phenotypes in other GWAS. We used LDassoc112 to identify the linkage disequilibrium patterns and SNPs in high linkage disequilibrium with the lead SNP rs1652376 (r2 > 0.6). Furthermore, we applied TargetScan44 and SpliceAI45 to determine possible conserved microRNA binding sites and mRNA splicing.

To further investigate the potential biological functions, cell type specificity, and downstream regulated genes, we comprehensively integrated the chromatin accessibility, histone modification, and chromosome conformation capture data by tracks analyses. We incorporated DNase-seq, Histone ChIP-seq, and micro-C datasets from the ENCODE portal (https://www.encodeproject.org/) 113. High-throughput chromosome conformation capture (Hi-C) data of IMR-90 and A549 were collected from https://3dgenome.fsm.northwestern.edu/. Liftover114 was used to convert the genome coordinates and annotations between different genome assemblies (GRCh38/hg38 and GRCh37/hg19). We applied pyGenomeTracks115 to visualize tracks from different datasets in specific genomic regions. In the preliminary analysis, we used the UCSC Genome Browser (http://genome.ucsc.edu)116 for quick visualization.

To study the expression of candidate genes at the organ and cell type levels, we used the GTEx portal (https://gtexportal.org) and multiple single-cell RNA-seq datasets. The multi-tissue gene expression level data were obtained from the GTEx Portal V8. Single-cell RNA-seq data of normal lung tissue and COVID-19 patients117 were downloaded from GEO (GSE171524). We used scanpy89 to exhibit gene expression among cell types of single-cell transcriptomic data. Moreover, to verify the association between candidate genomic variants and regulated gene expression, we also obtained lung-tissue eQTL data from GTEx (https://gtexportal.org/home/snp).

Mendelian randomization
To investigate the causal effect of gene expression on phenotype, we performed Mendelian randomization. In the framework of Mendelian randomization analysis, our outcome of interest (Y) is the infection of SARS-CoV-2. The gene expression (X) represents the exposure of interest, while the SNP/genetic variant (Z) acts as the instrumental variable. We use bxy to denote the effect size of gene expression (X) on the outcome of interest (Y). Similarly, bzy represents the effect size of the SNP (Z) on the outcome of interest (Y), and bzx is the effect size of the SNP (Z) on gene expression (X). Our primary objective is to ascertain the effect size of gene expression on SARS-CoV-2 infection, denoted by bxy, which can be calculated as 𝑏 = 𝑏𝑧𝑦 / 𝑏𝑧𝑥. Based on previous research, bxy can be estimated from summary-level data, despite the outcome of interest, gene expression, and SNP being obtained from different data samples; the statistical power of bxy estimation can be significantly increased if bzy and bzx are derived from two independent datasets with large sample sizes118. Our genome-wide association study (GWAS) analysis was based on nearly 60000 individuals, while eQTL data were procured from a separate large-scale sample from eQTLGen Consortium47. We calculated the test statistic of bxy according to a previously published study: 𝑇 ≈ 𝑍𝑧𝑦𝑍𝑧𝑥 / sqrt(𝑍𝑧𝑦^2 + 𝑍𝑧𝑥^2)118, where Zzy and Zzx were z-test statistics obtained from GWAS and eQTL dataset.

Cell culture and genome editing
To study the potential regulatory mechanism of specific SNPs, A549 wild-type cells and CRISPR-KO cells were obtained from Ubigene Biosciences (China, #YC-C016). Cells were cultured in F12K medium (Gibco, USA) supplemented with 10% fetal bovine serum (Gibco, USA) and 1% penicillin/streptomycin (Gibco, USA) at 37 °C, 5% CO2. Two guide RNAs (gRNAs) with appropriate Protospacer Adjacent Motifs (PAMs) were designed to target regions of the variants of interest, and plasmid DNA encoding CRISPR-Cas9 and gRNA was delivered into cells by electroporation. The sequences of gRNAs were provided in Supplementary Table 6. After antibiotic screening, we used the TIANGEN DNA extraction kit to perform PCR near the gRNA target site (primer sequences provided in Supplementary Table 7). Genome editing was validated by PCR amplification followed by Sanger sequencing.

Primary human bronchial epithelial cells (HBECs) were provided by Procell Life Science & Technology Co., Ltd. (China, #CP-H009). Cells were seeded on rat-tail collagen type I-coated dishes and maintained in the complete bronchial epithelial basal medium with growth factors. Human bronchial epithelial BEAS-2B cells (Beyotime, China, #C6106) and African green monkey kidney epithelial Vero E6 cells (Ubigene Biosciences, China, #YC-A003) were cultured in Dulbecco’s Modified Eagle’s medium (DMEM, Gibco, USA) supplemented with 10% fetal bovine serum (FBS, Gibco, USA) and 1% penicillin/streptomycin (Gibco, USA). Human lung adenocarcinoma epithelial Calu-3 cells (Ubigene Biosciences, China, #YC-A054) were maintained in minimum essential medium (Gibco, USA) supplemented with 10% FBS, 1% nonessential amino acids (Gibco, USA), and 1% penicillin/streptomycin. Mouse alveolar epithelial MLE-12 cells (SUNNCELL, China, #SNL-414) were cultured in DMEM/nutrient mixture F-12 medium (Corning, USA) supplemented with 10% FBS and 1% penicillin/streptomycin. All cells were maintained at 37 °C, 5% CO2 in humidified incubators.

Protein extraction and western blot analyses
Cells were collected and lysed by radioimmunoprecipitation assay (RIPA) buffer supplemented with protease inhibitor (Roche, Switzerland). After centrifugation at 12000 rpm (12500 × g) for 10 min to remove insoluble debris, the total protein contents were determined using a bicinchoninic acid (BCA) assay kit (Thermo Fisher Scientific Inc., USA). Equal amounts of proteins (30 μg) were mixed with 5× protein loading buffer and resolved by sodium dodecyl sulphate-polyacrylamide gel electrophoresis (SDS-PAGE). The separated protein was transferred to polyvinylidene fluoride (PVDF) membranes (Millipore, USA) and blocked with 5% milk in Tris-Buffered Saline (TBS) with 0.1% Tween 20 (TBST). Membranes were incubated with primary antibodies at 4 °C overnight, including Niemann Pick C1 Monoclonal antibody (ab134113, Abcam, 1:1000), RMC1 Polyclonal antibody (20111-1-AP, Proteintech, 1:1000), and Beta Actin Recombinant antibody (81115-1-RR, Proteintech, 1:5000). After incubation with horseradish peroxidase (HRP) conjugated Goat Anti-Rabbit IgG (H+L) (SA00001-2, Proteintech, 1:5000) for 1 h at room temperature, the immunoreactivity was detected with ImmobilonTM Western Chemiluminescent HRP Substrate (Millipore, USA) and ChemiDoc XRS+ System (Bio-Rad, USA). Uncropped blot images are provided in the Source Data.

ACE2 inhibition and receptor-blocking assays
To inhibit the enzymatic activity of ACE2, cells were treated with 10, 20, and 50 μM DX600 (MCE, USA) at 37 °C for 1 h. ACE2 activity of cell lysates was assessed with the Fluorometric ACE2 Activity Assay Kit (Beyotime, China) according to the manufacturer’s directions. Fluorescence kinetics were measured by a VarioskanTM Flash multimode reader (Thermo Fisher Scientific, USA) at an excitation wavelength of 325 nm and an emission wavelength of 393 nm. To further verify the ACE2-independent infection process of PM2.5-loaded viruses, cells were pre-treated with 50 μM DX600 at 37 °C for 1 h, followed by infection of solo pseudovirus or PM2.5-loaded pseudovirus for 24 h. Pseudovirus infection was evaluated by luciferase assay via the VarioskanTM Flash multimode reader.

SARS-CoV-2 pseudovirus infection
SARS-CoV-2 pseudovirus was procured from Yeasen Biotechnology Co., Ltd. (China). To prepare SARS-CoV-2 pseudovirus-laden PM exposure, 50 µg/mL PM2.5 was co-incubated with 1 × 105 copies of viruses for 1 h at room temperature. The mixtures were washed three times with cold phosphate buffer saline (PBS) by centrifugation at 2000 rpm (500 g) for 6 min to remove free pseudovirus. Afterwards, A549 cells and four KO cell lines were incubated with 50 µg/mL pseudovirus-laden PM2.5 at 37 °C for 1 h. After thoroughly washing with PBS, cells were cultured in the complete F12K medium for 12, 24, and 48 h. To test the pseudovirus infection, the luciferase luminescence was detected by a VarioskanTM Flash multimode reader (Thermo Fisher Scientific, USA).

RNA extraction and qRT-PCR analyses
After SARS-CoV-2 pseudovirus infection, cellular RNAs were extracted using TRIzol reagent (Invitrogen, USA) according to the manufacturer’s protocol. Total RNAs were quantified by a spectrophotometer (DeNovix, USA) and reverse transcribed into cDNAs using RevertAid First Strand cDNA Synthesis Kit (Thermo Fisher Scientific, USA). Quantitative reverse transcription-PCR (qRT-PCR) was performed through the SYBR Green Premix Pro Taq HS qPCR Kit (Accurate Biology, China) and CFX96 Real-Time System (Bio-Rad, USA). Relative gene expression was normalized to the GAPDH housekeeping gene. The primer sequences for this study are listed in Supplementary Table 8.

RNA interference (RNAi)
The small interfering RNA (siRNA) oligos of NPC1 and negative control siRNA (NC-siRNA) were synthesized by Genomeditech Co., Ltd. (Shanghai, China). Cells were plated and grown to 80% confluence in 6-well plates, followed by the transfection with 100 pmol siRNAs using lipofectamine 2000 (Invitrogen, USA). NPC1 protein expression levels in the transfected cells were examined by western blot analyses after 48 h. To evaluate the infection of SARS-CoV-2 pseudovirus, transfected cells were further infected with 50 µg/mL pseudovirus-laden PM2.5 or free pseudovirus at 37 °C for 24 h, and analyzed via SYBR Green-based qRT-PCR following the extraction of total cellular RNAs. The NPC1 siRNA sequences for this study are listed in Supplementary Tables 9-11.

Fluorescence imaging analyses
Cells were infected with 50 µg/mL pseudovirus-laden PM2.5 or free pseudovirus for 12, 24, and 48 h. Afterwards, cells were extensively washed with PBS and fixed with paraformaldehyde (4%) for 20 min. The fixed cells were stained with 100 nM rhodamine-conjugated phalloidin (Solarbio, China) for 30 min, followed by staining with 100 nM DAPI (Thermo Fisher Scientific, USA) for 15 min at room temperature. After thoroughly washing, fluorescent signals were visualized through an LSM900 Airyscan laser scanning confocal microscope (Zeiss, Germany).

Statistical analysis
Performance evaluations for selected AI models were conducted using 5-fold cross-validation to ensure the robustness of the results. All experiments were repeated at least 3 times independently (biological replicates). Statistical analysis was performed using two-sided Student’s t-tests.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Summary\nThis manuscript explores the molecular mechanisms underlying the observed epidemiological link between ambient PM2.5 exposure and increased susceptibility to SARS-CoV-2 infection. The authors employ a multidisciplinary and AI-driven approach combining fine-tuned single-cell transcriptomic models (Geneformer), genome-wide association studies (GWAS), population-level epidemiology (UK Biobank), and experimental validations (CRISPR and siRNA perturbations) to identify NPC1 as a key effector gene mediating this interaction. The study is ambitious, timely, and highly relevant to both biomedical research and public health.\n________________________________________\nMajor Comments\n1. Methodology: AI modeling and perturbation analysis\na) The use of Geneformer, a transformer model pre-trained on single-cell transcriptomic data, is particularly well-suited for the study’s objectives. Its fine-tuning on mouse lung cells exposed to PM2.5 is methodologically sound, and the comparison with BERT models of varying depth (1L, 2L, 4L, 6L) provides a valuable performance benchmark. While increasing the number of layers in BERT models trained from scratch does not substantially improve classification performance, it does appear to enhance the structure of latent representations, as visualized by UMAP. This suggests a better modeling of transcriptional complexity, although still inferior to the performance achieved by Geneformer. In practice, pre-training a BERT model on domain-specific biological data would require significant computational resources, without any guarantee of outperforming Geneformer. The methodological choice made here therefore represents a well-balanced and robust compromise between performance, interpretability, and feasibility. From this perspective, consider highlighting the value of the comparative analyses performed by addressing these identified performance advantages of Geneformer in the discussion section.\nb) The in silico perturbation method is well-designed and leverages the architecture of Geneformer in a biologically meaningful way. By systematically silencing each gene and observing the shift in latent space and classification output, the authors identify key transcriptional drivers of PM2.5 exposure signatures. This strategy, combined with cross-validation using virus–host interaction databases, provides robust biological plausibility. However, further clarification on how the threshold for “high-impact” genes was selected would be helpful. Specifically, the criteria used to define high-impact genes (e.g., a cutoff in cosine similarity or p-value) are not clearly described. Furthermore, analyses addressing potential redundancy among top-ranked genes or variability across different cell types are lacking.\n2. Epidemiological modeling and environmental confounding\nThe correlation between PM2.5 exposure and SARS-CoV-2 infection risk is potentially influenced by shared environmental and demographic factors, such as urban density, socioeconomic status, and behavior patterns. The authors appropriately address this issue by performing extensive multivariable adjustments and stratified sensitivity analyses. Supplementary Table 2 and Figure S6 show that the association between PM2.5 exposure and seropositivity remains consistent after controlling for key covariates (including deprivation, physical activity, smoking, BMI, and vaccination status) and across diverse subgroups. Additionally, the use of fine-scale exposure estimates based on kriging interpolation reinforces the individual-level resolution of the exposure data. Although some residual confounding cannot be fully excluded—as is often the case in environmental epidemiology—the authors’ approach aligns well with best practices and supports the robustness of their findings. For these reasons, there is no particular comment or change to suggest for this section.\n3. GWAS and gene–environment interaction analysis\nThe genome-wide association and G×E interaction analyses are well-integrated and follow current standards. The authors leverage UK Biobank data to examine how genetic variation modulates the effect of PM2.5 exposure on SARS-CoV-2 infection risk. Their models are adjusted for a broad range of potential confounders, including population structure, vaccination status, sociodemographic factors, and behavioral variables. The identification of the SNP rs1788783 in the 18q11.2 region, and its dual role as a cis-eQTL for NPC1 and as a PM2.5-sensitive infection risk locus, is compelling. The authors further support this association using eQTL data from GTEx and a Mendelian randomization framework. While the results are robust and biologically plausible, it would be useful to discuss the generalizability of the findings to non-European populations, and to consider the potential for false positives in G×E analyses, which are inherently prone to reduced statistical power. Nonetheless, the integration of genetic, environmental, and functional data significantly strengthens the conclusions.\n4. Functional validation: strengths and limitations\nThe authors provide convincing in vitro validation of NPC1’s role in PM2.5-facilitated SARS-CoV-2 infection using both CRISPR-mediated SNP deletions and siRNA knockdown in A549 lung epithelial cells. The targeting of rs1788783 is carefully validated through PCR, Sanger sequencing, and western blotting, and functional readouts (qPCR, luciferase assays, confocal microscopy) are clearly described and statistically supported. Additionally, the use of A549 cells, which have minimal ACE2 expression, supports the hypothesis of an alternative viral entry pathway, but the lack of direct testing (e.g., ACE2 complementation or receptor blocking) restricts mechanistic insight. Incorporating 3D or primary cell models in future studies could further strengthen the physiological relevance of these findings.\n5. Discussion and interpretation\nThe discussion section clearly highlights the novelty and relevance of the work, particularly in establishing a mechanistic link between PM2.5 exposure and SARS-CoV-2 infection via NPC1. The authors appropriately position their study as a multidisciplinary contribution combining AI-based omics analyses with functional genomics. However, while they acknowledge limitations regarding RMC1 knockdown, they do not fully address potential caveats related to the use of A549 cells (e.g., low ACE2 expression, tumor-derived origin). This could impact the generalizability of the receptor-independent entry mechanism. Nonetheless, the integration of multiple data types and levels of analysis strengthens the credibility of their conclusions. Future studies should consider extending this work to more physiologically relevant 3D models or animal systems to confirm the translational impact of these findings.\n6. Title and interpretation of NPC1’s role\nWhile the study presents a well-designed and multifaceted approach combining AI-based gene prioritization, genetic association analyses, and in vitro validation, the central role attributed to NPC1 may be somewhat overstated in the title and abstract. The evidence supports NPC1 as a promising candidate involved in the PM2.5-associated transcriptional response and potentially in viral entry, but its mechanistic involvement remains hypothetical. No direct functional assays demonstrate that NPC1 mediates SARS-CoV-2 entry, nor is the pathway shown to be independent of ACE2 in a conclusive way. The knockdown and knockout experiments confirm a modulatory role, but not necessarily a causal or specific mechanism. A more cautious phrasing in the main claims would better reflect the strength of the data and maintain scientific rigor.\n________________________________________\nMinor Comments\n• Abstract clarity: Simplify the opening sentence and clarify the specific contribution of the study.\n• Context: A deeper context on ambient PM2.5 pollution would strengthen the introduction and emphasize the importance of the study and its findings (e.g., main types and sources, abundance in the environment, more on its impact on human health…).\n• Objective(s): The last paragraph of the introduction is a summary of the methods, results, and conclusions, and reads as a second abstract. To facilitate reading, simplify it by providing a clear statement of the objectives instead of a summary of findings in this section.\n• In vitro confirmation: In Figure 5e, some images are not clear on the differences in luminescence intensity shown in the quantitative graphs. This is particularly notable for the WT pseudovirus-laden PM2.5 condition vs the W T pseudovirus condition at 24 and 48 hpi. Consider choosing images that match the results more closely.", "reviewer_2": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.", "reviewer_3": "Noteworthy Results\nThe study provides compelling evidence of the synergistic effect between PM2.5 exposure and SARS-CoV-2 infection through an integrative approach combining single-cell transcriptomics, AI models, genome-wide association studies (GWAS), and in vitro experiments. The identification of NPC1 as a key effector gene mediating viral infection under PM2.5 exposure is particularly significant. The authors demonstrated that PM2.5 can serve as a carrier for the virus, facilitating infection of relevant cell types in the alveoli, such as type II alveolar epithelial (AT2) cells. The findings offer a molecular explanation for the population-level associations observed between air pollution and SARS-CoV-2 infection.\nSignificance to the Field\nThis work represents a substantial contribution to the understanding of the interplay between environmental factors and viral infections. It advances the field by elucidating the molecular mechanisms underlying the enhanced susceptibility to SARS-CoV-2 infection in the context of PM2.5 exposure. The innovative use of AI-driven single-cell transcriptomic analysis provides a powerful tool for exploring complex gene expression networks and their dynamic responses to environmental exposures. The identification of specific genomic variants and effector genes (NPC1 and RMC1) that mediate this interaction has important implications for public health interventions and the development of targeted preventive strategies.\nComparison to Established Literature\nWhile previous studies have documented the adverse health effects of PM2.5 and its associations with COVID-19 outcomes, this study goes beyond existing literature by uncovering the specific molecular pathways and genetic factors involved. Unlike prior research that primarily focused on epidemiological associations or general inflammatory responses, this work delves into the mechanistic level, demonstrating how PM2.5 exposure can alter host cell gene expression profiles to facilitate viral entry and replication. The integration of multi-omics data with functional validation strengthens the credibility of the findings and sets a new standard for studies investigating the health impacts of environmental exposures.\nSupport for Conclusions\nThe conclusions drawn from this study are well-supported by the presented data. The authors conducted a comprehensive set of analyses, including single-cell transcriptomic profiling, in silico perturbation, epidemiological analysis, GWAS, and in vitro experiments. These multiple lines of evidence converge to support the claim that PM2.5 exposure enhances SARS-CoV-2 infection through NPC1-mediated mechanisms. However, some aspects could benefit from further clarification or additional evidence. For instance, the potential involvement of other cell types or alternative pathways in mediating the observed effects could be explored in more depth to provide a complete picture of the interaction between PM2.5 and SARS-CoV-2.\nData Analysis and Interpretation\nThe data analysis appears robust and methodologically sound. The authors employed advanced AI models and appropriate statistical methods to analyze the complex datasets. The use of in silico perturbation to identify critical genes and pathways is innovative and provides valuable insights into the gene regulatory networks affected by PM2.5 exposure. The interpretation of the results is generally balanced and cautious, acknowledging the limitations of the study while highlighting the key findings. However, in some cases, the interpretation could be enhanced by more extensive discussion of the biological significance of the identified genes and pathways in the context of viral infection.\nMethodology Soundness\nThe methodology employed in this study is comprehensive and well-designed to address the research questions. The integration of single-cell transcriptomics, AI models, GWAS, and functional experiments represents a multidisciplinary approach that leverages the strengths of each method. The experimental procedures, including the CRISPR-KO and siRNA experiments, are appropriately designed to validate the findings from the omics analyses. The use of both annual and monthly PM2.5 exposure levels in the epidemiological analysis helps to account for different exposure time frames and strengthens the observed associations.\nReproducibility of Methods\nThe methods are described in sufficient detail to allow for reproduction of the study by other researchers. The authors provide detailed descriptions of the experimental procedures, data collection methods, and analytical approaches. The availability of code and data on platforms such as GitHub and Hugging Face further enhances the transparency and reproducibility of the research. However, some additional details regarding the specific parameters and settings used in the AI models could be provided to facilitate exact reproduction of the analyses.\nSuggestion for Citation\nThe authors may consider citing the following related work to further strengthen the context of their study: Towards space-time modelling of PM2.5 inhalation volume with ST-exposure. This study presents a space-time modelling framework for estimating PM2.5 inhalation volume, which could provide complementary insights into the population-level exposure assessment and enhance the understanding of the health impacts of PM2.5."}, "author_response": "Point-to-point Response for Comments\nDear Reviewers,\nWe sincerely thank you for your thorough and constructive reviews of our manuscript,\nwhich has significantly guided enhancement of our manuscript. In response, we have\nrevised the manuscript substantially to address all major and minor concerns. We\nbelieve these revisions have substantially strengthened our study, and we are confident\nthat the manuscript will make a significant contribution to the research community.\nBelow, we provide a detailed point-by-point response to each comment. We appreciate\nthe opportunity to revise our work and look forward to your further evaluation.\nFor Reviewer 1\nSummary\nThis manuscript explores the molecular mechanisms underlying the observed\nepidemiological link between ambient PM2.5 exposure and increased susceptibility to\nSARS-CoV-2 infection. The authors employ a multidisciplinary and AI-driven\napproach combining fine-tuned single-cell transcriptomic models (Geneformer),\ngenome-wide association studies (GWAS), population-level epidemiology (UK\nBiobank), and experimental validations (CRISPR and siRNA perturbations) to identify\nNPC1 as a key effector gene mediating this interaction. The study is ambitious, timely,\nand highly relevant to both biomedical research and public health.\nResponse: We sincerely thank the reviewer for this generous and encouraging summary\nof our work. We are pleased that our integrative approach—spanning AI-driven single-\ncell modeling, genetic analysis, population epidemiology, and functional validation—\nwas seen as a timely and multidisciplinary contribution.\nMajor Comments\n1. Methodology: AI modeling and perturbation analysis\na) The use of Geneformer, a transformer model pre-trained on single-cell transcriptomic\ndata, is particularly well-suited for the study’s objectives. Its fine-tuning on mouse lung\ncells exposed to PM2.5 is methodologically sound, and the comparison with BERT\nmodels of varying depth (1L, 2L, 4L, 6L) provides a valuable performance benchmark.\nWhile increasing the number of layers in BERT models trained from scratch does not\nsubstantially improve classification performance, it does appear to enhance the\nstructure of latent representations, as visualized by UMAP. This suggests a better\nmodeling of transcriptional complexity, although still inferior to the performance\nachieved by Geneformer. In practice, pre-training a BERT model on domain-specific\nbiological data would require significant computational resources, without any\nguarantee of outperforming Geneformer. The methodological choice made here\ntherefore represents a well-balanced and robust compromise between performance,\ninterpretability, and feasibility. From this perspective, consider highlighting the value\nof the comparative analyses performed by addressing these identified performance\nadvantages of Geneformer in the discussion section.\nResponse:\nWe appreciate the reviewer’s insightful comment. In the revised manuscript, we have\nexpanded the Discussion section (Lines 365-376) to emphasize the comparative\nadvantages of Geneformer over BERT models trained from scratch. Additionally, we\ndiscuss the broader implications for domain-specific modeling under constrained data\nconditions. Pretraining large transformer models requires vast computational resources\nand substantial training data, which may be impractical in specialized biological\ncontexts. In contrast, pretrained Geneformer leverages extensive training on single-cell\ntranscriptomic data to provide efficient and biologically informed representations,\nwhich is particularly advantageous when fine-tuning datasets are limited.\nWe conclude that the use of Geneformer represents a pragmatic and robust trade-off\nbetween performance, interpretability, and feasibility for transcriptome-based\nclassification tasks. These points are now clearly articulated in the revised manuscript\nto highlight the methodological significance of our model comparison.\nb) The in silico perturbation method is well-designed and leverages the architecture of\nGeneformer in a biologically meaningful way. By systematically silencing each gene\nand observing the shift in latent space and classification output, the authors identify key\ntranscriptional drivers of PM2.5 exposure signatures. This strategy, combined with\ncross-validation using virus–host interaction databases, provides robust biological\nplausibility. However, further clarification on how the threshold for “high-impact”\ngenes was selected would be helpful. Specifically, the criteria used to define high-\nimpact genes (e.g., a cutoff in cosine similarity or p-value) are not clearly described.\nResponse:\nWe thank the reviewer for highlighting the need to clarify the definition and selection\ncriteria of “high-impact” genes. In our study, we define high-impact genes based on the\nshift in cell embeddings caused by in silico gene deletion. Specifically, we compute the\ncosine similarity between the original and perturbed cell embeddings for each gene,\nand use this as a measure of perturbation impact. To assess statistical significance, we\ncompare the distribution of cosine shifts for each target gene against a null distribution\ngenerated by random gene perturbations, using the Wilcoxon rank-sum test. Multiple\nhypothesis testing is corrected using the Benjamini-Hochberg procedure to control the\nfalse discovery rate. Genes with FDR-adjusted p-values below a defined threshold\n(<0.05) are designated as “high-impact”.\nThis approach has been previously validated and adopted as a downstream perturbation\ntask in the Geneformer framework, demonstrating its effectiveness in identifying\nfunctionally important genes in single-cell contexts. We have now added a detailed\ndescription of this procedure to the Methods section (Lines 504-512).\nFurthermore, analyses addressing potential redundancy among top-ranked genes or\nvariability across different cell types are lacking.\nTo address the concern regarding potential redundancies and cell-type-specific\nvariation among top-ranked genes, we performed additional in silico perturbation\nanalyses stratified by eight major cell-type groups selected based on abundance and\nfunctional representativeness: T cells, B cells, monocytes, neutrophils, macrophages,\nfibroblasts, epithelial cells, and endothelial cells. For each group, we identified\nstatistically significant “high-impact” genes (FDR < 0.05) and conducted KEGG\npathway enrichment analysis. Notably, the COVID-19 disease pathway remained\nconsistently and significantly enriched across all cell types (Response Fig. 1),\nsuggesting a shared transcriptional response module to PM exposure. At the same\n2.5\ntime, each cell-type group exhibited unique enrichment patterns—reflecting lineage-\nspecific biological processes.\nFrom another perspective, we identified subsets of overlapping genes that are shared\nacross multiple cell types, alongside distinct, non-overlapping high-impact gene sets\n(Fig. 2j). These findings highlight the coexistence of global and cell-specific regulatory\nprograms, supporting the biological plausibility of the AI-derived signatures. For\nexample, immune and stromal cells responded with gene sets distinct from those of\nepithelial cells, consistent with their divergent immunologic and structural roles. This\nobservation aligns well with our prior studies on environmental transcriptomic\nresponses in the lung1.\nWe believe this addition enhances the interpretability and credibility of our AI-guided\npathway discovery. Nevertheless, we acknowledge that such cell-type-aware\nperturbation analysis could be further improved in future work by incorporating explicit\ncell-type encoding or attention modules into the model framework. We have now\nincluded this stratified perturbation result in the revised manuscript (Supplementary\nFig. 6).\nSignificant genes across cell types\ns\nti\ni\no\nze\nn400\n200\nSupplementary Fig. 6). KEGG pathway enrichment of identified genes of major 8 cell\ntypes in lung.\n2. Epidemiological modeling and environmental confounding\nThe correlation between PM2.5 exposure and SARS-CoV-2 infection risk is potentially\ninfluenced by shared environmental and demographic factors, such as urban density,\nsocioeconomic status, and behavior patterns. The authors appropriately address this\nissue by performing extensive multivariable adjustments and stratified sensitivity\nanalyses. Supplementary Table 2 and Figure S6 show that the association between\nPM2.5 exposure and seropositivity remains consistent after controlling for key\ncovariates (including deprivation, physical activity, smoking, BMI, and vaccination\n0 0001 0002\na KEGG Pathway Enrichment of Epithelial cells b KEGG Pathway Enrichment of Macrophages\nCoronavirus disease − COVID−19 Coronavirus disease − COVID−19\nPhagosome PI3K−Akt signaling pathway\nEpithelial cells Tuberculosis Count ECM−receptor interaction Count\nB cells Hematopoie L tic e i c s e h l m l l a in n e ia a s g i e s 50 Ras sig F n o a c li a n l g a p d a h t e h s w io a n y 5 6 0 0\nN F e ib u r t o r b o l p a h s i t l s s Intestinal immune G n r e a t T f w t y − o p v r e e k r I f s o d u r i s a I − g b A h e o t e p s s r t o d m d i u s e e c ll t a i i t o u s n e s 2 3 4 0 0 0 Cy P t r o P im s P k a A e r l R y e t i s o m i n g m n in u a n l m in o u g d s e p c f a l i e c t i h e c w n e a c ll y s y 2 3 4 0 0 0\nMacrophages Epstein−Barr virus infection Hematopoietic cell lineage\nRheumatoid arthritis Axon guidance\nEndothelial cells 0 5 10 15 20 0 2 4 6 8\nT cells\n−log10(adjusted p−value) −log10(adjusted p−value)\nSet size c KEGG Pathway Enrichment of Neutrophils d KEGG Pathway Enrichment of B cells\nCoronavirus disease − COVID−19 Coronavirus disease − COVID−19\nCytoskeleton in muscle cells PI3K−Akt signaling pathway\nPI3K−Akt signaling pathway Count Leukocyte transendothelial migration Count\nFocal adhesion 70 Focal adhesion 80\nProstate cancer 60 Lipid and atherosclerosis 70\nECM−receptor interaction 50 Proteoglycans in cancer 60\nParathyroid hormone synthesis, secretion and action 40 Pathogenic Escherichia coli infection 50\n30 40\nRas signaling pathway Regulation of actin cytoskeleton\nAxon guidance Axon guidance\nRelaxin signaling pathway Relaxin signaling pathway\n0.0 2.5 5.0 7.5 0.0 2.5 5.0 7.5 10.0\n−log10(adjusted p−value) −log10(adjusted p−value)\ne KEGG Pathway Enrichment of Fibroblasts f KEGG Pathway Enrichment of T cells\nCoronavirus disease − COVID−19 Coronavirus disease − COVID−19\nHematopoietic cell lineage Proteoglycans in cancer\nFocal adhesion Count Leukocyte transendothelial migration Count\nPI3K−Akt signaling pathway 70 B cell receptor signaling pathway 80\nB cell receptor signaling pathway 60 Chemokine signaling pathway 70\nLeishmaniasis 50 PI3K−Akt signaling pathway 60\nProteoglycans in cancer 40 Prostate cancer 50\nRegulation of actin cytoskeleton 30 Parathyroid hormone synthesis, secretion and action 40\nLeukocyte transendothelial migration Cell adhesion molecules\nProstate cancer Regulation of actin cytoskeleton\n0 5 10 0 5 10\n−log10(adjusted p−value) −log10(adjusted p−value)\ng KEGG Pathway Enrichment of Monocytes h KEGG Pathway Enrichment of Endothelial cells\nCoronavirus disease − COVID−19 Coronavirus disease − COVID−19\nPI3K−Akt signaling pathway Hematopoietic cell lineage\nAntigen processing and presentation Count PI3K−Akt signaling pathway Count\nECM−receptor interaction B cell receptor signaling pathway 70\nFocal adhesion 50 Leishmaniasis 60\nNon−alcoholic fatty liver disease 40 Osteoclast differentiation 50\nPathways of neurodegeneration − multiple diseases 20 Phagosome 30\nHematopoietic cell lineage Viral myocarditis 20\nOxidative phosphorylation ECM−receptor interaction\nViral myocarditis IL−17 signaling pathway\n0 2 4 6 8 0 5 10\n−log10(adjusted p−value) −log10(adjusted p−value)\nstatus) and across diverse subgroups. Additionally, the use of fine-scale exposure\nestimates based on kriging interpolation reinforces the individual-level resolution of the\nexposure data. Although some residual confounding cannot be fully excluded—as is\noften the case in environmental epidemiology—the authors’ approach aligns well with\nbest practices and supports the robustness of their findings. For these reasons, there is\nno particular comment or change to suggest for this section.\nResponse:\nWe thank the reviewer for this positive evaluation. We are glad that our approach was\nfound to be appropriate and aligned with current best practices in environmental\nepidemiology. Although no specific revisions were requested, we have carefully\nreviewed the relevant sections and made minor clarifications in the Methods and\nResults to improve clarity.\n3. GWAS and gene–environment interaction analysis\nThe genome-wide association and G×E interaction analyses are well-integrated and\nfollow current standards. The authors leverage UK Biobank data to examine how\ngenetic variation modulates the effect of PM2.5 exposure on SARS-CoV-2 infection\nrisk. Their models are adjusted for a broad range of potential confounders, including\npopulation structure, vaccination status, sociodemographic factors, and behavioral\nvariables. The identification of the SNP rs1788783 in the 18q11.2 region, and its dual\nrole as a cis-eQTL for NPC1 and as a PM2.5-sensitive infection risk locus, is\ncompelling. The authors further support this association using eQTL data from GTEx\nand a Mendelian randomization framework. While the results are robust and\nbiologically plausible, it would be useful to discuss the generalizability of the findings\nto non-European populations, and to consider the potential for false positives in G×E\nanalyses, which are inherently prone to reduced statistical power. Nonetheless, the\nintegration of genetic, environmental, and functional data significantly strengthens the\nconclusions.\nResponse:\nWe thank the reviewer for the thoughtful evaluation and are pleased that our G×E\nanalysis, integration of UK Biobank data, and supporting evidence from eQTL and\nMendelian randomization were found to be robust.\nIn response to your constructive suggestions, we have added a paragraph to the\nDiscussion section (Lines 353–361) addressing two important issues:\n(1) Generalizability: We now explicitly acknowledge the limitation that the UK\nBiobank predominantly includes participants of European ancestry, and have\ndiscussed the importance of replicating these gene-environment interactions in\nmore ancestrally diverse cohorts.\n(2) False positives in G×E analyses: We agree that G×E analyses are susceptible to\nreduced power and increased false positives. To mitigate this, we used genomic\ncontrol, set-based association analysis and cross-validated the findings by multiple\nlayers of evidence (cis-eQTL, functional genomics, Mendelian randomization, and\nin vitro experiments).\nHowever, although our GWAS and G×E analyses were performed in individuals of\npredominantly European ancestry, the functional role of rs1788783 as a cis-eQTL for\nNPC1 is likely biologically conserved across populations, as supported by GTEx and\ncross-ancestry regulatory annotations. Therefore, what differs across populations could\nbe the allele frequency of rs1788783. For example, data from the ALFA project (dbGaP)\nshow that the risk allele (T) is more prevalent in East Asian (EAS) populations\ncompared to European (EUR) and African (AFR) groups (Response Fig. 2). These\nfrequency differences may help explain population-level variation in genetic\nsusceptibility to environmental modulation. Nonetheless, validation in non-European\npopulations will be pivotal to determine the generalizability of our findings.\nWe sincerely hope that our responses and the additional discussion have addressed the\nreviewer’s concerns and provided greater clarity and context to our G×E analyses. We\nonce again thank the reviewer for the valuable comments that helped strengthen this\naspect of our study.\nResponse Fig. 2. Allele frequencies of rs1788783 across populations. The ALFA\nproject provided aggregate allele frequency from dbGaP. Source data is available at\nhttps://www.ncbi.nlm.nih.gov/snp/rs1788783#frequency_tab.\n4. Functional validation: strengths and limitations\nThe authors provide convincing in vitro validation of NPC1’s role in PM2.5-facilitated\nSARS-CoV-2 infection using both CRISPR-mediated SNP deletions and siRNA\nknockdown in A549 lung epithelial cells. The targeting of rs1788783 is carefully\nvalidated through PCR, Sanger sequencing, and western blotting, and functional\nreadouts (qPCR, luciferase assays, confocal microscopy) are clearly described and\nstatistically supported. Additionally, the use of A549 cells, which have minimal ACE2\nexpression, supports the hypothesis of an alternative viral entry pathway, but the lack\nof direct testing (e.g., ACE2 complementation or receptor blocking) restricts\nmechanistic insight. Incorporating 3D or primary cell models in future studies could\nfurther strengthen the physiological relevance of these findings.\nResponse:\nWe sincerely thank the reviewer for the insightful comments regarding the functional\nvalidation experiments. In response, we have conducted additional receptor-blocking\nassays using DX600, a selective ACE2 inhibitor, to directly test the involvement of\nalternative viral entry pathways. PM exposure remained capable of enhancing viral\n2.5\ninfection even under ACE2 inhibition, as pseudovirus-laden PM led to significantly\n2.5\nhigher infection levels than pseudovirus alone, despite DX600 treatment (Response\nFig. 3). These results suggest that PM may facilitate an ACE2-independent viral entry\n2.5\npathway, such as endocytosis-mediated endo-lysosomal trafficking. This finding is\nconsistent with our hypothesis that NPC1-related endosomal trafficking serves as a\ncompensatory route for viral entry under polluted conditions, particularly when ACE2-\nTMPRSS2 pathways are inefficient or blocked. Nevertheless, we acknowledge that\nwhile these results support the modulatory role of NPC1 under ACE2‑blocking\nconditions, further biophysical assays will be necessary to conclusively establish\nwhether NPC1 directly interacts with virus‑laden PM complexes or functions strictly\n2.5\ndownstream of entry.\nFurthermore, recognizing the physiological limitations of A549 cells, we extended our\nin vitro validation to multiple complementary models (Response Fig. 3, Response Fig\n4), including BEAS-2B (human bronchial epithelial cells), primary human bronchial\nepithelial cells (HBECs), MLE-12 (mouse alveolar epithelial cells), Calu-3 (human\nlung adenocarcinoma cells), and Vero E6 (African green monkey kidney cells). These\nexperiments consistently supported our original findings.\nWe believe that these additional validations substantially strengthen the robustness and\nphysiological relevance of our conclusions. Nevertheless, in alignment with the\nreviewer’s suggestions, we have also acknowledged the limitations of in vitro systems\nand highlighted the need for future validation in 3D culture or organoid models in the\nrevised Discussion section (Lines 361–364).\nTime (min)\nResponse Fig. 3. Receptor-blocking assays in commonly used cell lines validated\nthat virus-laden PM enhances SARS-CoV-2 infection via an ACE2-independent\n2.5\npathway (also Fig. 6 and Supplementary Fig. 16). Verification of ACE2 inhibition\nusing a series of DX600 concentrations and the corresponding viral infection efficiency\nin wide-type A549 (alveolar basal epithelial adenocarcinoma cells) (a), rs1788783\nknockout (KO) A549 (b), primary human bronchial epithelial cells (HBECs) (c),\nBEAS-2B (human bronchial epithelial cells) (d), Calu-3 (human lung adenocarcinoma\ncells) (e), MLE-12 (mouse alveolar epithelial cells) (f), and Vero E6 (African green\nmonkey kidney cells) (g), respectively. Relative luminescence intensities are presented\nas mean ± SD (n = 10). Statistical significance was assessed using two-tailed t-tests.\n*p < 0.05, **p < 0.01, ***p < 0.001, ns (not significant, p > 0.05).\necnecseroulF evitaleR\necnecseroulF\nevitaleR\n)UFR( tinU\n)UFR(\ntinU\nTime (min)\nTime (min)\necnecseroulF\nevitaleR\necnecseroulF\nevitaleR\n)UFR(\ntinU\n)UFR(\ntinU\nTime (min)\nTime (min)\necnecseroulF evitaleR )UFR( tinU\nTime (min)\nTime (min)\nytisnetni\necnecsenimul evitaleR\nytisnetni\necnecsenimul\nevitaleR\nytisnetni\necnecsenimul\nevitaleR\nytisnetni\necnecsenimul\nevitaleR\necnecseroulF\nevitaleR\necnecseroulF\nevitaleR\n)UFR(\ntinU\n)UFR(\ntinU\nytisnetni\necnecsenimul evitaleR\nytisnetni\necnecsenimul\nevitaleR\nytisnetni\necnecsenimul\nevitaleR\na *** e\n6 4 0 0 D D D X X X 6 6 6 0 0 0 0 0 0 1 2 5 0 0 0 μ μ μ M M M 1.0 *** 2 1 0 5 0 0 D D D X X X 6 6 6 0 0 0 0 0 0 1 2 5 0 0 0 μ μ μ M M M 1 1 . . 0 5 *** *** 20 D D X X 6 6 0 0 0 0 0 5 μ 0μ M M 0.5 1 5 0 0 0 D D X X 6 6 0 0 0 0 5 0μ 0μ M M 0.5\nb 0 0 20 40 60 SARS-Co P V s - e 2 u p d s o e 0 v u ir S d u A o s v - R l i a r S u d - s e C n o P V P s - M e 2 u p d 2. s o 5 e v u ir d u o s v -l i a ru d s en PM2.5 f 0 0 20 40 60 SARS-Co P V s - e 2 u p d s o 0 e v u ir S d u A o s v - R l i a r S u d - s e C n o P V P s - M e 2 u p d 2. s o 5 e v u ir d u o s v * -l i a r * u d s e * n PM2.5\n***\nA549-rs1788783 *** MLE-12 *** 80 DX600 0μM 2.0 *** 150 DX600 0μM 1.5\nDX600 10μM *** *** DX600 10μM ***\n40 1.0\nDX600 0μM DX600 0μM 20 DX600 50μM 0.5 50 DX600 50μM 0.5\n0 0 20 40 60 SARS-Co P V s - e 2 u p d s o e 0 v u ir S d u A o s v - R l i a r S u d - s e C n o P V P s - M e 2 u p d 2. s o 5 e v u ir d u o s v -l i a ru d s en PM2.5 0 0 20 40 60 SARS-Co P V s - e 2 u p d s o 0 e v u ir S d u A o s v - R l i a r S u d - s e C n o P V P s - M e 2 u p d 2. s o 5 e v u ir d u o s v -l i a ru d s en PM2.5\nc g ***\n*** 20 D D X X 6 6 0 0 0 0 1 2 0 0 μ μ M M 1.5 *** 200 D D X X 6 6 0 0 0 0 1 2 0 0 μ μ M M 1.0 *** DX600 50μM 150 DX600 50μM\n1.0\nDX600 0μM 100 DX600 0μM 10 DX600 50μM 0.5 50 DX600 50μM 0.5\n0 0 0 0\n0 20 40 60 SARS-Co P V s - e 2 u p d s o e v u ir S d u A o s v - R l i a r S u d - s e C n o P V P s - M e 2 u p d 2. s o 5 e v u ir d u o s v -l i a ru d s en PM2.5 0 20 40 60 SARS-Co P V s - e 2 u p d s o e v u ir S d u A o s v - R l i a r S u d - s e C n o P V P s - M e 2 u p d 2. s o 5 e v u ir d u o s v -l i a ru d s en PM2.5\nd\nBEAS-2B ***\nDX600 50μM\n0 0\n0 20 40 60 SARS-Co P V s - e 2 u p d s o e v u ir S d u A o s v - R l i a r S u d - s e C n o P V P s - M e 2 u p d 2. s o 5 e v u ir d u o s v -l i a ru d s en PM2.5\na\nN C si\nR\nNA\nsi R\nNA-1\nsi R\nNA-2\nsi R\nNA-3\nkDa 4 ***\n*\n4 ***\nns\n** **\nNPC1 180 3 3\n*** ***\n** **\n1 1\nβ-actin\n40\n0 0\nNC\nsiRNA siRNA-1 siRNA-\ns\n2 iRNA-3\nNC\nsiRNA siRNA-\ns\n1 iRNA-2 siRNA-3\nNC\nsiRNA siRNA-1 siRNA-\ns\n2 iRNA-3\nNC\nsiRNA siRNA-\ns\n1 iRNA-2 siRNA-3\nb\nN C\nsi\nR\nNA\nsi R\nNA-1\nsi R\nNA-2\nsi R\nNA-3\nkDa 3 ns *\nns\n3\nns\nNPC1 * 180 * 130 2 2\nns ns RMC1 70 * * ns **\n1 1\nβ-actin\n40\n0 0\nc\nN C si\nR\nNA\nsi R\nNA-1\nsi R\nNA-2\nsi R\nNA-3\nkDa 2.\nN\nsiRNA siRNA-1 siRNA-\ns\n2 iRNA-3\nNC\nsiRNA siRN\nn\nA\ns\n-\ns\n1\n*\niRN\n*\nA-2 siRNA-3\n2.\nN\nsiRNA siRNA-1 siRNA-\ns\n2 iRNA-3\nNC\nsiRNA siRN\nn\nA\ns\n-\ns\n1\n*\niRN\n*\nA-2 siRNA-3\nNPC1 2.0 2.0 180\n130 ** * 1.5 *** 1.5 * RMC1 70 ns ns\n1.0 1.0\n0.5 0.5\nβ-actin\n40\n0 0\nd\nN C\nsi R NA\nsi R\nNA-1\nsi R\nNA-2\nsi R\nNA-3\nkDa 2.\nN\n*\nA- s 1\n*\niR * NA * -2 siRNA-3\n2.\nN\n*\nN\n*\nA-2 siRNA-3\n*\nNPC1 180 2.0 2.0 ns\n130 1.5 ** * 1.5 ** * RMC1 70 ns *\n1.0 1.0\n0.5 0.5\nβ-actin\n40\n0 0\nNC\nsiRNA siRNA-1 siRNA-\ns\n2 iRNA-3\nNC\nsiRNA siRNA-\ns\n1 iRNA-2 siRNA-3\nNC\nsiRNA siRNA-1 siRNA-\ns\n2 iRNA-3\nNC\nsiRNA siRNA-\ns\n1 iRNA-2 siRNA-3\ne\nN C\nsi R\nNA\nsi R\nNA-1\nsi R\nNA-2\nsi R\nNA-3\nkDa 2.0 ns ** * 2.0 ns * * ns * ns\nNPC1 180 1.5 * 1.5\n130 ns *\nRMC1 70 1.0 1.0\n0.5 0.5\nβ-actin\n40\n0 0\nNC\nsiRNA siRNA-1 siRNA-\ns\n2 iRNA-3\nNC\nsiRNA siRNA-\ns\n1 iRNA-2 siRNA-3\nNC\nsiRNA siRNA-1 siRNA-\ns\n2 iRNA-3\nNC\nsiRNA siRNA-\ns\n1 iRNA-2 siRNA-3\nResponse Fig. 4. In vitro experiments in commonly employed cell lines validated\nconfirmed the critical regulatory role of NPC1 (also Fig. 6 and Supplementary Fig.\n16). Reduced NPC1 expression levels using 3 distinct small interfering RNAs (siRNAs)\neneg\n1FRO\nfo egnahc\nevitaleR\neneg\n1FRO\nfo egnahc\nevitaleR\neneg\n1FRO\nfo egnahc\nevitaleR\neneg\n1FRO\nfo egnahc\nevitaleR\neneg\n1FRO\nfo\negnahc\nevitaleR\neneg\nN\nfo\negnahc\nevitaleR\neneg\nN\nfo egnahc\nevitaleR\neneg\nN fo egnahc\nevitaleR\neneg\nN fo egnahc\nevitaleR\neneg\nN fo\negnahc\nevitaleR\nSARS-CoV-2 pseudovirus\nPseudovirus-laden PM2.5\nand negative control siRNA (NC siRNA), and the corresponding viral infection\nefficiency in primary human bronchial epithelial cells (HBECs) (a), BEAS-2B (human\nbronchial epithelial cells) (b), Calu-3 (human lung adenocarcinoma cells) (c), MLE-12\n(mouse alveolar epithelial cells) (d), and Vero E6 (African green monkey kidney cells)\n(e), respectively. Viral replication was quantified by the expression levels of ORF1 and\nN genes. Data are presented as mean ± SD from 3 biological replicates. Statistical\nsignificance was assessed using two-tailed t-tests. *p < 0.05, **p < 0.01, ***p < 0.001,\nns (not significant, p > 0.05).\n5. Discussion and interpretation\nThe discussion section clearly highlights the novelty and relevance of the work,\nparticularly in establishing a mechanistic link between PM2.5 exposure and SARS-\nCoV-2 infection via NPC1. The authors appropriately position their study as a\nmultidisciplinary contribution combining AI-based omics analyses with functional\ngenomics. However, while they acknowledge limitations regarding RMC1 knockdown,\nthey do not fully address potential caveats related to the use of A549 cells (e.g., low\nACE2 expression, tumor-derived origin). This could impact the generalizability of the\nreceptor-independent entry mechanism. Nonetheless, the integration of multiple data\ntypes and levels of analysis strengthens the credibility of their conclusions. Future\nstudies should consider extending this work to more physiologically relevant 3D\nmodels or animal systems to confirm the translational impact of these findings.\nResponse:\nWe thank the reviewer for the thoughtful comments. As noted in our response to last\ncomment, we fully recognize the limitations of using A549 cells, particularly their low\nACE2 expression and tumor-derived origin, which may affect the generalizability of\nreceptor-independent entry mechanisms. In response, we have conducted additional\nvalidation experiments using a range of commonly employed cell lines in SARS-CoV-\n2 infection studies, as we mentioned and showed in Response Fig. 3 and Response Fig\n4. These experiments yielded consistent results, supporting the robustness of our\nfindings. Also, we appreciate the reviewer’s recommendation to further explore\nphysiologically relevant 3D models or animal systems. We have now included it as a\nlimitation and direction for future research (Lines 361–364).\n6. Title and interpretation of NPC1’s role\nWhile the study presents a well-designed and multifaceted approach combining AI-\nbased gene prioritization, genetic association analyses, and in vitro validation, the\ncentral role attributed to NPC1 may be somewhat overstated in the title and abstract.\nThe evidence supports NPC1 as a promising candidate involved in the PM2.5-\nassociated transcriptional response and potentially in viral entry, but its mechanistic\ninvolvement remains hypothetical. No direct functional assays demonstrate that NPC1\nmediates SARS-CoV-2 entry, nor is the pathway shown to be independent of ACE2 in\na conclusive way. The knockdown and knockout experiments confirm a modulatory\nrole, but not necessarily a causal or specific mechanism. A more cautious phrasing in\nthe main claims would better reflect the strength of the data and maintain scientific\nrigor.\nResponse:\nWe thank the reviewer for this important comment and fully agree that mechanistic\nclaims should be presented with appropriate caution. In response, we have revised the\nmanuscript title to adopt a more balanced and precise phrasing. However, we would\nalso like to clarify that SARS-CoV-2 enters host cells via two established pathways—\ncell surface entry and endo-lysosomal entry—and that NPC1 has been implicated in\nregulating the latter, as supported by multiple lines of prior evidence.\nExtensive prior research has established two major SARS-CoV-2 entry pathways: (1)\ncell surface fusion, which depends on ACE2 binding and subsequent cleavage by\nTMPRSS2, allowing direct release of viral RNA into the host cytoplasm; and (2) endo-\nlysosomal entry, in which the virus is internalized via endocytosis and activated by\ncathepsin-mediated cleavage within acidic endosomes before cytoplasmic release2.\nBuilding on this, Zhu et al. demonstrated that virions bearing intact spike proteins\npreferentially use plasma membrane fusion, whereas spike-deficient mutants use the\nendosomal route3. Notably, endosomal entry is generally less efficient and associated\nwith reduced infectivity and transmissibility compared to TMPRSS2-mediated entry.\nHowever, our study reveals that PM exposure enhances ACE2-independent viral\n2.5\nuptake via endocytosis, thus potentially compensating for the lower efficiency of the\nendosomal entry route. Moreover, NPC1 was initially identified as a key host factor for\nEbola virus infection4-7 and has since been implicated in SARS-CoV-2 infection by\nmultiple genome-wide CRISPR screens3,8,9. Khan et al. reported that inhibition of\nNPC1 reduced SARS-CoV-2 entry and found that NPC1 directly binds the SARS-CoV-\n2 spike protein at the receptor-binding domain (RBD)10. These findings collectively\nsupport a plausible role for NPC1 in facilitating SARS-CoV-2 infection through the\nendo-lysosomal pathway.\nIn our study, we build upon this prior knowledge by demonstrating that NPC1\nknockdown and CRISPR-mediated deletion reduce viral infection efficiency\nspecifically under PM exposure, underscoring the genetic basis and NPC1’s\n2.5\nimportance in modulating host susceptibility in polluted environments. While our data\nsupport a modulatory role for NPC1, we agree that a direct mechanistic link—such as\nphysical interaction between NPC1 and virus-laden PM components—remains to be\n2.5\nconfirmed, and we have revised the manuscript to reflect this accordingly. We now\ninclude this clarification and supporting literature in the revised Discussion (Lines 315–\n333), and explicitly highlight the need for further mechanistic studies.\nMinor Comments\n• Abstract clarity: Simplify the opening sentence and clarify the specific contribution\nof the study.\nResponse:\nIn the revised manuscript, we have simplified the opening sentence of the abstract and\nclarified the specific contribution of our study, particularly emphasizing the AI-guided\nmulti-omics framework and its application in decoding environmentally modulated\nviral susceptibility.\n• Context: A deeper context on ambient PM2.5 pollution would strengthen the\nintroduction and emphasize the importance of the study and its findings (e.g., main\ntypes and sources, abundance in the environment, more on its impact on human\nhealth…).\nResponse:\nIn the revised manuscript, we have expanded the introduction to provide deeper\nbackground on ambient PM pollution, including its main components and sources,\n2.5\nits global abundance and exposure burden, as well as its established associations with\na wide range of adverse health outcomes and disease risks (Lines 36–48). We believe\nthis additional context helps underscore the importance and relevance of our study.\n• Objective(s): The last paragraph of the introduction is a summary of the methods,\nresults, and conclusions, and reads as a second abstract. To facilitate reading, simplify\nit by providing a clear statement of the objectives instead of a summary of findings in\nthis section.\nResponse:\nIn the revised manuscript, we have revised the final paragraph of the introduction to\nclearly state the study objectives, rather than summarizing methods and key findings\n(Lines 65–71).\n• In vitro confirmation: In Figure 5e, some images are not clear on the differences in\nluminescence intensity shown in the quantitative graphs. This is particularly notable for\nthe WT pseudovirus-laden PM2.5 condition vs the WT pseudovirus condition at 24 and\n48 hpi. Consider choosing images that match the results more closely.\nResponse:\nIn the revised manuscript, we have replaced the original images with clearer\nrepresentative examples that better reflect the luminescence differences shown in the\ncorresponding quantitative analyses, particularly for the WT pseudovirus-laden PM\n2.5\nvs. WT pseudovirus conditions at 24 and 48 hpi. We believe this revision improves\nvisual clarity and consistency with the reported results (Fig. 5e).\nFor Reviewer 2\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports.\nThis is part of the Nature Communications initiative to facilitate training in peer review\nand to provide appropriate recognition for Early Career Researchers who co-review\nmanuscripts.\nResponse:\nWe sincerely thank the reviewer and the Early Career Researcher involved for their\ntime, effort, and constructive evaluation. Their thoughtful input has helped us\nstrengthen the clarity, rigor, and impact of our manuscript.\nFor Reviewer 3\nThe study provides compelling evidence of the synergistic effect between PM2.5\nexposure and SARS-CoV-2 infection through an integrative approach combining\nsingle-cell transcriptomics, AI models, genome-wide association studies (GWAS), and\nin vitro experiments. The identification of NPC1 as a key effector gene mediating viral\ninfection under PM2.5 exposure is particularly significant. The authors demonstrated\nthat PM2.5 can serve as a carrier for the virus, facilitating infection of relevant cell\ntypes in the alveoli, such as type II alveolar epithelial (AT2) cells. The findings offer a\nmolecular explanation for the population-level associations observed between air\npollution and SARS-CoV-2 infection.\nResponse:\nWe thank the reviewer for the encouraging assessment of our work. We are pleased that\nthe integrative design of our study—spanning AI-guided transcriptomic modeling,\nGWAS, and experimental validation—was found to provide compelling evidence for\nthe link between PM exposure and SARS-CoV-2 infection. The identification of\n2.5\nNPC1 as a key modulator gene and the translational relevance helps to elucidate the\nmolecular basis underlying population-level epidemiological associations. We have\naimed to maintain a multidisciplinary approach, in order to better understand complex\nenvironmental–viral interactions. We are grateful for the reviewer’s support and\nvalidation of this framework.\nSignificance to the Field\nThis work represents a substantial contribution to the understanding of the interplay\nbetween environmental factors and viral infections. It advances the field by elucidating\nthe molecular mechanisms underlying the enhanced susceptibility to SARS-CoV-2\ninfection in the context of PM2.5 exposure. The innovative use of AI-driven single-cell\ntranscriptomic analysis provides a powerful tool for exploring complex gene expression\nnetworks and their dynamic responses to environmental exposures. The identification\nof specific genomic variants and effector genes (NPC1 and RMC1) that mediate this\ninteraction has important implications for public health interventions and the\ndevelopment of targeted preventive strategies.\nResponse:\nWe are pleased that the integrative design and AI-driven single-cell analysis were\nrecognized as valuable contributions to understanding the molecular mechanisms\nlinking PM exposure and SARS-CoV-2 susceptibility. We appreciate the reviewer’s\n2.5\nrecognition of the potential public health implications of our findings.\nComparison to Established Literature\nWhile previous studies have documented the adverse health effects of PM2.5 and its\nassociations with COVID-19 outcomes, this study goes beyond existing literature by\nuncovering the specific molecular pathways and genetic factors involved. Unlike prior\nresearch that primarily focused on epidemiological associations or general\ninflammatory responses, this work delves into the mechanistic level, demonstrating\nhow PM2.5 exposure can alter host cell gene expression profiles to facilitate viral entry\nand replication. The integration of multi-omics data with functional validation\nstrengthens the credibility of the findings and sets a new standard for studies\ninvestigating the health impacts of environmental exposures.\nResponse:\nWe thank the reviewer for this generous and insightful evaluation. We are glad that our\nmechanistic focus and multi-omics integration were recognized as a meaningful\nadvancement beyond prior epidemiological studies.\nSupport for Conclusions\nThe conclusions drawn from this study are well-supported by the presented data. The\nauthors conducted a comprehensive set of analyses, including single-cell transcriptomic\nprofiling, in silico perturbation, epidemiological analysis, GWAS, and in vitro\nexperiments. These multiple lines of evidence converge to support the claim that PM2.5\nexposure enhances SARS-CoV-2 infection through NPC1-mediated mechanisms.\nHowever, some aspects could benefit from further clarification or additional evidence.\nFor instance, the potential involvement of other cell types or alternative pathways in\nmediating the observed effects could be explored in more depth to provide a complete\npicture of the interaction between PM2.5 and SARS-CoV-2.\nResponse:\nWe thank the reviewer for the positive evaluation and constructive suggestions. We\nhave revised and added more discussion regarding the potential involvement of\nadditional cell types and alternative pathways that may mediate the effects of PM on\n2.5\nSARS-CoV-2 infection. Specifically, we note that PM -induced inflammation and\n2.5\nlung injury may contribute to increased host susceptibility to viral infection. This is\nsupported by our transcriptomic data, which revealed activation of pathways related to\nviral infection, inflammation, and immune responses across multiple cell types. In\naddition, previous studies have suggested that PM exposure may increase the\n2.5\nexpression of angiotensin-converting enzyme II (ACE2), thereby facilitating viral entry\nand exacerbating SARS-CoV-2 infection11. We hope that this expanded discussion\n(Lines 294–305) helps address your concern and provides a more comprehensive\ninterpretation of the potential mechanisms underlying our findings.\nData Analysis and Interpretation\nThe data analysis appears robust and methodologically sound. The authors employed\nadvanced AI models and appropriate statistical methods to analyze the complex datasets.\nThe use of in silico perturbation to identify critical genes and pathways is innovative\nand provides valuable insights into the gene regulatory networks affected by PM2.5\nexposure. The interpretation of the results is generally balanced and cautious,\nacknowledging the limitations of the study while highlighting the key findings.\nHowever, in some cases, the interpretation could be enhanced by more extensive\ndiscussion of the biological significance of the identified genes and pathways in the\ncontext of viral infection.\nResponse:\nWe thank the reviewer for the encouraging comments and valuable suggestion. In the\nrevised manuscript, we have expanded the discussion on the biological relevance of\nkey genes and pathways identified in our analysis, including identified pathways and\ngenes uncovered through our AI-based approach (Lines 296-302), and the roles of\nNPC1, RMC1 in viral entry and intracellular transport, supported by previous studies\non SARS-CoV-2 and other viruses (Lines 315-349). These additions are intended to\nstrengthen the biological interpretation of our findings and underscore the plausibility\nof the inferred gene–environment interactions.\nMethodology Soundness\nThe methodology employed in this study is comprehensive and well-designed to\naddress the research questions. The integration of single-cell transcriptomics, AI\nmodels, GWAS, and functional experiments represents a multidisciplinary approach\nthat leverages the strengths of each method. The experimental procedures, including\nthe CRISPR-KO and siRNA experiments, are appropriately designed to validate the\nfindings from the omics analyses. The use of both annual and monthly PM2.5 exposure\nlevels in the epidemiological analysis helps to account for different exposure time\nframes and strengthens the observed associations.\nResponse:\nWe greatly appreciate the reviewer’s positive assessment of our multidisciplinary\nmethodology.\nReproducibility of Methods\nThe methods are described in sufficient detail to allow for reproduction of the study by\nother researchers. The authors provide detailed descriptions of the experimental\nprocedures, data collection methods, and analytical approaches. The availability of\ncode and data on platforms such as GitHub and Hugging Face further enhances the\ntransparency and reproducibility of the research. However, some additional details\nregarding the specific parameters and settings used in the AI models could be provided\nto facilitate exact reproduction of the analyses.\nResponse:\nWe thank the reviewer for highlighting the importance of reproducibility. In the revised\nmanuscript, we have added more details regarding the AI model architecture, training\nprocedures, and selected hyperparameters in the Methods section (Lines 436–455). In\naddition, the model configuration files and training scripts have been made publicly\navailable on our GitHub and Hugging Face repositories.\nSuggestion for Citation\nThe authors may consider citing the following related work to further strengthen the\ncontext of their study: Towards space-time modelling of PM2.5 inhalation volume with\nST-exposure. This study presents a space-time modelling framework for estimating\nPM2.5 inhalation volume, which could provide complementary insights into the\npopulation-level exposure assessment and enhance the understanding of the health\nimpacts of PM2.5.\nResponse:\nWe thank the reviewer for this helpful suggestion. We have reviewed the recommended\nwork and agree that its space-time modeling approach offers valuable complementary\ninsights into PM exposure assessment. We have now cited this reference in the\n2.5\nrevised manuscript (Lines 353) to strengthen the contextual background of our study.\nReferences\nHazardous Materials 459, 132227 (2023).\nhttps://doi.org:https://doi.org/10.1016/j.jhazmat.2023.132227\nNature Reviews Molecular Cell Biology 23, 3-20 (2022). https://doi.org:10.1038/s41580-021-\n00418-x\nentry. Nature Communications 12, 961 (2021). https://doi.org:10.1038/s41467-021-21213-4\nNature 477, 340-343 (2011). https://doi.org:10.1038/nature10348\ninfection. Nature 477, 344-348 (2011). https://doi.org:10.1038/nature10380\nLate Cell Entry Kinetics: Evidence that Transport to NPC1+ Endolysosomes Is a\nRate-Defining Step. Journal of Virology 89, 2931-2943 (2015).\nhttps://doi.org:doi:10.1128/jvi.03398-14\nVirus. Journal of Virology 89, 6481-6493 (2015). https://doi.org:10.1128/jvi.03156-14\nCoronaviruses. Cell 184, 106-119.e114 (2021).\nhttps://doi.org:https://doi.org/10.1016/j.cell.2020.12.004\nCells. Cell 184, 92-105.e116 (2021). https://doi.org:https://doi.org/10.1016/j.cell.2020.10.030\nfusion protein binding to Niemann-Pick C1. Nature Communications 15, 162 (2024).\nhttps://doi.org:10.1038/s41467-023-44504-4\nthe expression of angiotensin converting enzyme II. Journal of Hazardous Materials 485,\n136887 (2025). https://doi.org:https://doi.org/10.1016/j.jhazmat.2024.136887"}, {"round": 2, "reviewer_comments": {"reviewer_4": "I would like to thank the authors for their completeness in their responses to the comments and suggestions of reviewer 1.\n(Remarks on code availability)\nThe code looks well documented."}, "author_response": ""}]

Decision letter