Exploration of the potentially common pathogenic mechanisms in COVID-19 and silicosis using bioinformatics and system biology

doi:10.21203/rs.3.rs-2467274/v1

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Research Article

Exploration of the potentially common pathogenic mechanisms in COVID-19 and silicosis using bioinformatics and system biology

https://doi.org/10.21203/rs.3.rs-2467274/v1

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published 06 Jun, 2023

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Silicosis is an occupational lung disease that is common worldwide. In recent years, coronavirus disease 2019 (COVID-19) has presented daunting challenges to public healthcare systems globally. However, inter-relational mechanisms between COVID-19 and silicosis remain unclear. Therefore, this study aimed to explore the shared molecular mechanisms and drug targets of COVID-19 and silicosis. Gene expression profiling identified four modules that were most closely associated with both diseases. Furthermore, we performed functional analysis and constructed a protein-protein interaction network. Seven hub genes (BUB1, PRC1, KIFC1, RPM2, CDKN3, CCNB2, and MCM6) were involved in the interaction between COVID-19 and silicosis. Thereafter, we investigated how these seven genes are regulated by diverse microRNAs and transcription factors. Subsequently, the correlation between the hub genes and infiltrating immune cells was explored. Further in-depth analyses were performed based on single-cell transcriptomic data of COVID-19, and the expression of hub-shared genes was characterized and located in multiple cell clusters. Finally, the results of the molecular docking analysis suggested that resveratrol might be a promising drug for treating both COVID-19 and silicosis. Taken together, the current study reveals the common pathogenesis of COVID-19 and silicosis, which may provide a novel reference for further research.

COVID-19

Silicosis

WGCNA

ScRNA-seq

Resveratrol

Coronavirus disease 2019 (COVID-19) is a highly infectious pneumonia caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and has been regarded as a global pandemic¹. As of 5:43 p.m. CEST on October 28, 2022, there have been 626,337,158 confirmed cases of COVID-19 globally, including 6,566,610 deaths, reported to the World Health Organization ^[^]. After a COVID-19 infection, some cases may progress to interstitial pneumonia, which often leads to pulmonary fibrosis^{2, 3}. With the ravaging effects of COVID-19, the comorbidities between COVID-19 and other respiratory diseases remain a major concern.

Silicosis is generally caused by the long-term exposure to dust containing excessive amounts of silica or crystalline silica⁴. Silica-induced silicosis is an occupational disease that is characterized by progressive lung fibrosis^{5, 6}. The incidence of silicosis remains high worldwide, especially in under-privileged communities⁶. A previous study reported that more than 2.3 million workers worldwide are exposed to hazardous levels of silica nanoparticles⁷. Both COVID-19 and silicosis present the risk of developing pulmonary fibrosis, and patients with both diseases generally tend to be more seriously ill than those with either disease⁸. Some occupations, such as mining or natural gas mining, are more likely to expose workers to silica⁸. Although more cases of COVID-19 have been reported among patients with silicosis, the shared biological pathways involved in the two diseases are not fully understood. This has attracted the attention of the medical community.

With the development of transcriptome sequencing technology, researchers can rapidly detect the expression levels of tens of thousands of genes in tissues, which is beneficial for a deeper understanding of disease pathogenesis. In our study, weighted correlation network analysis (WGCNA) was performed based on COVID-19-related datasets (GSE157103) and silicosis-related datasets (GSE49144 and GSE32147) to identify shared gene clusters between the two diseases. This approach is routinely used to identify the biological processes shared by genes in two disease phenotypes^9–11. The co-expression modules in COVID-19 and silicosis were identified using bulk RNA sequencing (bulk RNA-seq). Thereafter, gene set enrichment analysis was performed to explore the biological processes that may occur simultaneously in these two diseases. Subsequently, the hub-shared genes were screened in the protein-protein interaction (PPI) networks. Furthermore, transcription factor (TF)-gene interactions and gene-miRNA regulatory networks were constructed based on known hub-shared genes. In addition, based on the single-cell RNA sequencing (scRNA-seq) dataset GSE182123, the expression of hub-shared genes was characterized and located in multiple cell clusters. Finally, we performed a molecular docking study of potential drugs for hub-shared genes. To the best of our knowledge, this is the first study to identify shared genomic and molecular mechanisms between COVID-19 and silicosis.

2.1 Dataset and Source

The overall flowchart of this study is shown in Fig. 1. The Gene Expression Omnibus ^[^] database was used to download the gene expression profiles analyzed in this study. In the bulk RNA sequencing (bulk RNA-seq) of silicosis, we extracted rat lung tissue sequencing data from GSE32147 and GSE49144^{12, 13}. Subsequently, 22 rats treated with 15 mg/m³ of crystalline silica for 2–4 weeks and 14 control rats were summarized into the analysis matrix for silicosis. The GEO accession ID of COVID-19 was GSE157103, which included the bulk RNA-seq expression matrix from 128 human peripheral blood samples (102 COVID-19 samples and 26 healthy samples)¹⁴. Finally, we obtained peripheral blood scRNA-seq data from GSE182123 with 4 COVID-19 patients and 4 healthy people¹⁵. The sample sizes of the datasets included in the study are shown in Supplementary Table S1.

2.2 Weighted gene co-expression network analysis

The R software (v4.1.2; R Foundation, Vienna, Austria) was used for all analyses and visualizations performed in this study. All raw matrices of bulk RNA-seq were combined with the RNA probes after log normalization to form the subsequent analysis profiling matrix. Batch effects were removed from the gene expression profiles by merging GSE32147 and GSE49144 using the sva package in R. Furthermore, the “WGCNA” package was used to perform module analysis. After screening genes with a variance > 25%, the suitable power was determined using the pickSoftThreshold function. Through average linkage hierarchical clustering, the genes with similar expression profiles were categorized into gene modules using the TOM-based dissimilarity measure. Finally, the correlation coefficient between each module and the sample trait in COVID-19 and silicosis was assessed. Four module genes with the strongest positive and negative correlations in COVID-19 and silicosis were selected for subsequent analysis.

2.3 Protein interaction network and pathway enrichment analysis

Evenn^{16 [}^], a free online website, was used to screen for shared genes involved in COVID-19 and silicosis. Metascape ^[^] and Bioinformatics ^[^] websites were used to find gene ontology (GO) enrichment pathways involved in shared genes. The protein-protein interaction network was exported from the STRING database ^[^] and optimized using Cytoscape software. Moreover, the MCODE plugin was used to identify highly interconnected gene clusters in shared genes.

2.4 Immune Infiltration Analysis

We used the CIBERSORT algorithm ^[^] to compute the samples’ immune cell abundance. The pheatmap package was used to describe the contents of the 22 types of immune cells in COVID-19. Spearman’s correlation analysis was then performed to detect correlations between immune cells and gene expression levels. Finally, a lollipop chart, created using the ggplot2 package, was made to illustrate the relationship between hub-shared genes and immune cells.

2.5 Transcription factors, miRNAs, and the disease regulatory network

In the Enrichr database ^[^], we used the JASPAR and TargetScan modules to predict the TFs and miRNAs that may be combined with hub-shared genes. Subsequently, the DisGeNET ^[^] database was used to predict the diseases related to hub-shared genes. Ultimately, the TFs, miRNAs, and disease regulatory network of hub-shared genes were drawn using the Cytoscape software.

2.6 ScRNA-seq data processing

After sorting the scRNA-seq data, the Seurat package was used to convert the gene expression matrix into Seurat objects and standardize the data. The inclusion criteria for the cells analyzed were as follows: cells with 400–6,000 unique molecular identifiers, < 15% of mitochondrial genes, and nCount_RNA > 1000. The data were then integrated using a mutual principal component analysis function. The first 20 principal components were selected for the visualization of dimensionality reduction using t-distributed stochastic neighbor embedding (t-SNE). Moreover, the Single R package and CellMarker database ^[^] were used for cell annotation.

2.7 Drug screening and molecular docking

Based on the hub-shared genes in COVID-19 and silicosis, our study predicted the FDA-approved drugs and experimental compounds included in the DSigDB database ^[^]. The screening criterion for potential therapeutic drugs was an adj.P value < 0.05. To analyze the binding affinities and modes of interaction between the drug candidates and their targets, AutodockVina 1.2.2, an in-silico protein-ligand docking software, was used. The molecular structures of potential therapeutic drugs were retrieved from the PubChem database ^[^]. The 3D coordinates of proteins corresponding to hub-shared genes were downloaded from the PDB database ^[^]. For docking analysis, all protein and molecular files were converted into PDBQT format, while excluding all water molecules, and polar hydrogen atoms were added. Finally, PyMol software was used to present the molecular docking results.

3.1 Identification of module genes in silicosis and COVID-19

The quality control box plots of GSE32147 and GSE49144 datasets after being merged was shown in Figure S1. For these datasets, after removing the outlier sample with h > 18 (Fig. 2A), the optimal soft threshold, β = 9, was determined according to a scale-free fitting index of 0.80 (Fig. 2B). The silicosis gene set was then divided into eight gene modules, of which the turquoise module had the strongest negative correlation with silicosis (r = -0.90), and the blue module had the strongest positive correlation with silicosis (r = 0.41) (Fig. 2C, D). Moreover, the correlation between turquoise and blue module membership and the gene significance for silicosis was 0.92 and 0.25, respectively, which is statistically significant (Fig. 2E, F).

For the GSE157103 dataset, after removing the outlier sample with h > 100 (Fig. 2G), the optimal soft threshold, β = 12, was determined according to a scale-free fitting index of 0.80 (Fig. 2H). The COVID-19 gene set was then divided into eight gene modules, of which the magenta module had the strongest negative correlation with COVID-19 (r = -0.54), and the brown module had the strongest positive correlation with COVID-19 (r = 0.41) (Fig. 2I, J). Moreover, the correlation between magenta and brown module membership and the gene significance for COVID-19 was 0.82 and 0.40, respectively, which is statistically significant (Fig. 2K, L).

3.2 GO pathway enrichment analysis

For GO enrichment analysis, the top ten significant terms showed that the blue module of silicosis was mainly involved in ameboidal-type cell migration, epithelial cell migration, epithelial migration, muscle system processes, ossification, the regulation of protein binding, the regulation of vasculature development, response to mechanical stimulus, tissue migration, and vascular processes in the circulatory system (Fig. 3A). Concerning the GO enrichment analysis in the turquoise module of silicosis, the top ten significant terms were ameboidal type cell migration, cell chemotaxis, ERK1 and ERK2 cascade, leukocyte mediated immunity, leukocyte migration, myeloid leukocyte migration, neutrophil migration, regulation of angiogenesis, regulation of ERK1 and ERK2 cascade, and regulation of vasculature development (Fig. 3B).

Meanwhile, the top ten GO enrichment terms of the brown module in COVID-19 were lysosome organization, lytic vacuole organization, mitochondrial translational elongation, mitochondrial translational termination, mitochondrial transport, neutrophil activation involved in the immune response, neutrophil degranulation, the positive regulation of proteolysis, the proteasomal protein catabolic process, and the proteasome-mediated ubiquitin-dependent protein catabolic process (Fig. 3C). The top ten GO enrichment terms of the magenta module in COVID-19 were mainly involved in chromosome segregation, the defense response to bacteria, mitotic nuclear division, mitotic sister chromatid segregation, neutrophil activation involved in the immune response, neutrophil degranulation, nuclear chromosome segregation, nuclear division, organelle fission, and sister chromatid segregation (Fig. 3D).

3.3 Identification of shared genes in silicosis and COVID-19

According to the Venn diagram, the positive correlation module of silicosis and COVID-19 shared 20 genes, and the negative correlation module shared 23 genes (Fig. 4A). The PPI network of shared genes included 43 nodes and 33 edges, for which the PPI enrichment P-value was 1.65e-05. After removing the isolated genes, 21 genes were visualized using Cytoscape software (Fig. 4B). Based on the results of the enrichment analysis, these genes were mainly involved in nucleobase – containing small molecule metabolic pro, glial cell activation, mitotic cell cycle process, response to hydrogen peroxide, extrinsic apoptotic signaling pathway, regulation cell cycle process, regulation of cyclin-dependent protein serine/threonir, mitochondrial gene expression, neutrophil degranulation (Fig. 4C). Finally, submodule analysis showed that PRC1, KIFC1, BUB1, MCM6, CCNB2, CDKN3, and RRM2, which were hub shared genes of silicosis and COVID-19, possessed the highest MCODE scores (Fig. 4D).

3.4 Transcription factors, miRNAs, and the disease regulatory network

To reveal the regulatory mechanisms of shared genes at the transcriptome level, we utilized a network-based approach to determine the regulatory relationships between TFs and miRNAs. In the regulatory network of miRNAs, these seven shared genes were linked to 67 miRNAs, with RRM2 having the highest number of miRNA linkages (29) (Fig. 5A). Simultaneously, in the TF regulatory network, these shared genes were linked to 11 TFs, including TAF1, FOXP3, PAX3, ELF1, UBTF, AIRE, CCNE1, CCND1, HSF1, CEBPA, and MYC (Fig. 5B). The fact that there are genes that are shared between different diseases provides a direction for identifying common mechanisms for these diseases. According to the gene-disease association, 25 diseases may share these seven hub genes with silicosis and COVID-19 (Fig. 6). Interestingly, we also found that some lung diseases also share these hub genes; these include non-small cell lung carcinoma and adenocarcinoma of the lung.

3.5 Immune Infiltration Analysis of COVID-19

According to the CIBERSORT algorithm, the immune infiltration abundance of 22 immune cells in 128 samples of GSE157103 were evaluated (Fig. 7A). In the correlation analysis of immune cells, resting CD4 + T cells showed a strong positive correlation (r = 0.54) with plasma cells, whereas resting CD4 + T cells showed a strong negative correlation (r =-0.51) with M2 macrophages (Fig. 7B). The differences in immune cell abundance between patients with COVID-19 and healthy individuals were then explored. As shown in Fig. 7C, the number of naïve B cells, regulatory T cells, activated NK cells, and monocytes was higher in the COVID-19 group than in the normal group. Moreover, the numbers of plasma cells, naïve CD4 T cells, CD4 memory activated T cells, follicular helper T cells, gamma delta T cells, resting dendritic cells, and activated dendritic cells were higher in the normal group than in the COVID-19 group.

Subsequently, we analyzed the correlation between the seven hub-shared genes and 22 immune cells (Fig. 7D-J). PRC1 had the strongest positive correlation with naïve CD4 T cells and the strongest negative correlation with activated NK cells. KIFC1, MCM6, CDKN3, and RRM2 showed the strongest positive correlation with plasma cells and the strongest negative correlation with regulatory T cells. BUB1 showed the strongest positive correlation with plasma cells and the strongest negative correlation with activated NK cells, while CCNB2 had the strongest positive correlation with plasma cells and the strongest positive correlation with monocytes.

3.6 Analysis of COVID-19 single-cell data

Based on the screening criteria, 9,115 and 16,316 cells were retained in the COVID-19 and normal groups, respectively (Fig. 8A). After performing t-SNE dimension reduction, 11 cell clusters were identified (Fig. 8B). Combined with manual and single R package annotations, six cell clusters were annotated: B cells, CD4 + T cells, megakaryocytes, monocytes, and NK cells (Fig. 8C). A heatmap of the top 10 genes expressed in each cell cluster is shown in Fig. 8D. Subsequently, we visualized the cell expression of the seven shared genes on the t-SNE map (Fig. 8E). PRC1 and MCM6 had high expression levels in the various cell subsets, except in megakaryocytes (Fig. 8F). Meanwhile, KIFC1, CCNB2, CKKN3, and RRM2 were all expressed at relatively high levels in NK cells. Lastly, we also visualized the expression levels of the seven genes in the COVID-19 and normal groups using the t-SNE map (Fig. 8G).

3.7 Drug prediction and molecular docking

We predicted small-molecule compounds that might be bound by the seven shared genes in the DSigDB database. As shown in Table 1, the top 10 drugs that may bind to shared genes in the order of their binding score are testosterone, calcitriol, estradiol, cyclosporin A, lucanthone, cryptolepine, dasatinib, piroxicam, troglitazone, and resveratrol. Resveratrol is a natural polyphenol with important antiviral and antioxidant effects, which is beneficial for improving the prognosis of patients with COVID-19^{17, 18}. Therefore, we docked resveratrol with BUB1, KIFC1, and PRC1 proteins and it showed suitable binding energies (≤ -5.0 kcal/mol) (Fig. 9A-C).

Table 1

The top 10 drugs predicted by DSigDB database that may bind to shared genes
Drug	Adj.P value	Combined Score	Binding Genes
testosterone	< 0.0001	2551157.00	CCNB2; RRM2; PRC1; KIFC1; MCM6; BUB1; CDKN3
calcitriol	< 0.0001	2055055.00	CCNB2; RRM2; PRC1; KIFC1; MCM6; BUB1; CDKN3
estradiol	0.0003	1173564.00	CCNB2; RRM2; PRC1; KIFC1; MCM6; BUB1; CDKN3
cyclosporin A	0.0005	1057437.00	CCNB2; RRM2; PRC1; KIFC1; MCM6; BUB1; CDKN3
lucanthone	< 0.0001	14473.00	CCNB2; RRM2; PRC1; KIFC1; BUB1; CDKN3
cryptolepine	< 0.0001	11319.00	MCM6; BUB1; CDKN3
dasatinib	< 0.0001	4755.00	RRM2; PRC1; KIFC1; MCM6; BUB1; CDKN3
piroxicam	< 0.0001	4219.00	CCNB2; RRM2; PRC1; MCM6; BUB1; CDKN3
troglitazone	< 0.0001	3350.00	CCNB2; RRM2; PRC1; MCM6; BUB1; CDKN3
resveratrol	< 0.0001	1811.00	PRC1; KIFC1; BUB1

Since the start of the COVID-19 pandemic, there have been warnings that patients with preexisting lung conditions are more susceptible to COVID-19 infections. This may be attributed to the immune system being damaged and their state of health¹⁹. The co-occurrence of COVID-19 and silicosis has been sporadically reported⁸, but their shared gene signatures and related biological processes are still unclear. Therefore, this study aimed to identify the common mechanisms of COVID-19 and silicosis using integrated bioinformatics-based methods. Notably, the proposed method predicted that resveratrol, a traditional Chinese medicine reported to have anti-fibrosis properties, may have a therapeutic effect on the comorbidities of COVID-19 and silicosis.

We first performed WGCNA to identify co-expression gene modules that were significantly positively or negatively correlated with COVID-19 and silicosis, respectively. We systematically explored GO and KEGG functional enrichment analyses for four co-expressed gene modules. Thereafter, a Venn diagram was drawn based on the lists of genes significantly positively or negatively correlated with the two diseases. Furthermore, we constructed PPI networks and explored the biological pathways of the 43 intersecting genes. Seven hub-shared genes representing the intersection genes of the two diseases were identified from the PPI network, and used to further construct TF-gene interactions and gene-miRNA regulatory networks. In addition, bulk RNA-seq analysis of COVID-19 was performed to explore the correlation between shared hub genes and immune cells. Based on a single-cell sequencing dataset of COVID-19, the expression of seven key genes was identified in multiple cell clusters. Finally, it was predicted that the antioxidant resveratrol could accurately and directly bind to the three proteins corresponding to the key genes BUB1, KIFC1, and PRC1.

Enrichment analysis revealed that the co-expression gene module was significantly related to silicosis, and was mainly enriched in pathways associated with cell migration and inflammation, such as ameboidal-type cell migration, epithelial cell migration, leukocyte migration, and neutrophil migration. These results are consistent with silicosis, which is characterized by inflammatory cell infiltration and fibroblast migration. Similar findings were observed in the co-expressed gene modules that were significantly associated with COVID-19. The results showed that significant module genes were mainly enriched in pathways related to inflammation and the cell cycle, such as neutrophil activation involved in the immune response, mitochondrial transport, and mitotic nuclear division. Mounting evidence has linked the progression of COVID-19 to oxidative stress. Therefore, it is worth noting that mitochondria play an important role in the oxidative homeostasis of cells^20–22. Moreover, mitochondrial dysfunction is attributed to a heightened inflammatory or oxidative state²³. Our results are in accordance with those of previous studies.

In our study, seven genes (BUB1, PRC1, KIFC1, RPM2, CDKN3, CCNB2, and MCM6) were found to be involved in the interaction between COVID-19 and silicosis. The biological processes behind the role of key genes were also clarified. Notably, Budding Uninhibited by Benzimidazoles 1 (BUB1) performs important functions during mitosis^24–26. Some studies have demonstrated that BUB1 critically influences the pathophysiological processes of COVID-19^{27, 28}. In our study, BUB1 was negatively correlated with COVID-19 and silicosis, indicating a better prognosis. In addition to this, the function of cyclin B2 (CCNB2) in the cell cycle’s regulatory machinery is crucial²⁹. CCNB2 is not only a vital hub-shared gene in both silicosis and COVID-19, but it is also a critical hub-shared gene of COVID-19 and lung adenocarcinoma³⁰.

Resveratrol is a natural polyphenol that has antioxidant, anti-inflammatory, heart-protective, and anti-cancer properties. Angiotensin-converting enzyme-2 (ACE2), mainly expressed in endothelial cells, is a cell surface receptor that allows SARS CoV-2 to enter cells. Multiple studies have revealed that resveratrol exerts its anti-COVID-19 and curative effect by reducing the expression of the ACE-2 receptor and alleviating oxidative stress^31–34. It is well known that silica-induced silicosis is characterized by progressive lung fibrosis, and previous studies have reported that resveratrol can inhibit pulmonary fibrosis^35–37. Interestingly, our molecular docking results showed that resveratrol has the potential to treat COVID-19 and silicosis, which is consistent with the abovementioned literature.

It is important to note that this study has some inherent limitations. The datasets we adopted are not from the same species, especially silicosis-related datasets. This is because silicosis patients are rarely treated surgically, and it is difficult to obtain human specimens for testing. Therefore, most of these data are derived from models of animal experiments. Meanwhile, the mechanism by which hub-shared genes regulate the biological processes of silicosis and COVID-19 should be verified experimentally. In addition, although molecular docking is a feasible method to predict drugs, it has not yet been verified if resveratrol is effective in the comorbidity of silicosis and COVID-19.

Taken together, using bulk RNA-seq, we identified shared gene clusters that might be involved in the progression of silicosis and COVID-19. The prediction of resveratrol and the construction of regulatory networks for hub-shared genes may provide new insights into the management and treatment of patients with COVID-19 and silicosis.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Author Contributions

YT, BY and YZ: manuscript preparation, data analysis, and the research conception. SG and JL: manuscript revision. YT, BY, BL, SZ and YZ: data analysis.

Funding

This work was supported by the National Natural Science Foundation of China (Grant No. 81903268).

Acknowledgments

The authors would like to acknowledge scholars’ contribution to the GEO database for the availability of the data. We are very grateful to the editors and reviewers for their helpful comments to this paper. Besides, we acknowledge the efforts of all researchers in developing R package tools. The authors are very grateful to these researchers for their selfless dedication to GEO database. Financial support via the National Natural Science Foundation of China is also gratefully acknowledged. The authors cherish these precious public database resources very much.

Data Availability

The datasets for this study can be found in the Gene Expression Omnibus database [http://www.ncbi.nlm.nih.gov/geo/].

Sharma, A.; Ahmad Farouk, I.; Lal, S. K., COVID-19: A Review on the Novel Coronavirus Disease Evolution, Transmission, Detection, Control and Prevention. Viruses 2021, 13 (2).
George, P. M.; Wells, A. U.; Jenkins, R. G., Pulmonary fibrosis and COVID-19: the potential role for antifibrotic therapy. The Lancet. Respiratory medicine 2020, 8 (8), 807–815.
McDonald, L. T., Healing after COVID-19: are survivors at risk for pulmonary fibrosis? American journal of physiology. Lung cellular and molecular physiology 2021, 320 (2), L257-l265.
Steenland, K.; Ward, E., Silica: a lung carcinogen. CA: a cancer journal for clinicians 2014, 64 (1), 63–9.
García-Núñez, A.; Jiménez-Gómez, G.; Hidalgo-Molina, A.; Córdoba-Doña, J. A.; León-Jiménez, A.; Campos-Caro, A., Inflammatory indices obtained from routine blood tests show an inflammatory state associated with disease progression in engineered stone silicosis patients. Scientific reports 2022, 12 (1), 8211.
Leung, C. C.; Yu, I. T.; Chen, W., Silicosis. Lancet (London, England) 2012, 379 (9830), 2008–18.
Mazurek, J. M.; Wood, J. M.; Schleiff, P. L.; Weissman, D. N., Surveillance for Silicosis Deaths Among Persons Aged 15–44 Years - United States, 1999–2015. MMWR. Morbidity and mortality weekly report 2017, 66 (28), 747–752.
Naidoo, R. N.; Jeebhay, M. F., COVID-19: a new burden of respiratory disease among South African miners? Current opinion in pulmonary medicine 2021, 27 (2), 79–87.
Sezin, T.; Vorobyev, A.; Sadik, C. D.; Zillikens, D.; Gupta, Y.; Ludwig, R. J., Gene Expression Analysis Reveals Novel Shared Gene Signatures and Candidate Molecular Mechanisms between Pemphigus and Systemic Lupus Erythematosus in CD4(+) T Cells. Frontiers in immunology 2017, 8, 1992.
Yao, M.; Zhang, C.; Gao, C.; Wang, Q.; Dai, M.; Yue, R.; Sun, W.; Liang, W.; Zheng, Z., Exploration of the Shared Gene Signatures and Molecular Mechanisms Between Systemic Lupus Erythematosus and Pulmonary Arterial Hypertension: Evidence From Transcriptome Data. Frontiers in immunology 2021, 12, 658341.
Zhu, Y.; Ding, X.; She, Z.; Bai, X.; Nie, Z.; Wang, F.; Wang, F.; Geng, X., Exploring Shared Pathogenesis of Alzheimer's Disease and Type 2 Diabetes Mellitus via Co-expression Networks Analysis. Current Alzheimer research 2020, 17 (6), 566–575.
Sellamuthu, R.; Umbright, C.; Roberts, J. R.; Cumpston, A.; McKinney, W.; Chen, B. T.; Frazer, D.; Li, S.; Kashon, M.; Joseph, P., Molecular insights into the progression of crystalline silica-induced pulmonary toxicity in rats. Journal of applied toxicology: JAT 2013, 33 (4), 301–12.
Umbright, C.; Sellamuthu, R.; Roberts, J. R.; Young, S. H.; Richardson, D.; Schwegler-Berry, D.; McKinney, W.; Chen, B.; Gu, J. K.; Kashon, M.; Joseph, P., Pulmonary toxicity and global gene expression changes in response to sub-chronic inhalation exposure to crystalline silica in rats. Journal of toxicology and environmental health. Part A 2017, 80 (23–24), 1349–1368.
Overmyer, K. A.; Shishkova, E.; Miller, I. J.; Balnis, J.; Bernstein, M. N.; Peters-Clarke, T. M.; Meyer, J. G.; Quan, Q.; Muehlbauer, L. K.; Trujillo, E. A.; He, Y.; Chopra, A.; Chieng, H. C.; Tiwari, A.; Judson, M. A.; Paulson, B.; Brademan, D. R.; Zhu, Y.; Serrano, L. R.; Linke, V.; Drake, L. A.; Adam, A. P.; Schwartz, B. S.; Singer, H. A.; Swanson, S.; Mosher, D. F.; Stewart, R.; Coon, J. J.; Jaitovich, A., Large-Scale Multi-omic Analysis of COVID-19 Severity. Cell systems 2021, 12 (1), 23–40.e7.
Choi, B.; Kang, C. K.; Park, S.; Lee, D.; Lee, A. J.; Ko, Y.; Kang, S. J.; Kang, K.; Kim, S.; Koh, Y.; Jung, I., Single-cell transcriptome analyses reveal distinct gene expression signatures of severe COVID-19 in the presence of clonal hematopoiesis. Experimental & molecular medicine 2022, 54 (10), 1756–1765.
Chen, T.; Zhang, H.; Liu, Y.; Liu, Y. X.; Huang, L., EVenn: Easy to create repeatable and editable Venn diagrams and Venn networks online. Journal of genetics and genomics = Yi chuan xue bao 2021, 48 (9), 863–866.
Ahmadian, R.; Biganeh, H.; Panahi, Y.; Guest, P. C.; Jamialahmadi, T.; Sahebkar, A., Resveratrol as a Probable Multiheaded Treatment Approach for COVID-19. Advances in experimental medicine and biology 2021, 1328, 441–446.
Xiao, Z.; Ye, Q.; Duan, X.; Xiang, T., Network Pharmacology Reveals That Resveratrol Can Alleviate COVID-19-Related Hyperinflammation. Disease markers 2021, 2021, 4129993.
Dhikav, V., Are Silicosis Patients at Risk of Developing COVID-19? Indian journal of occupational and environmental medicine 2022, 26 (2), 133–134.
Saleh, J.; Peyssonnaux, C.; Singh, K. K.; Edeas, M., Mitochondria and microbiota dysfunction in COVID-19 pathogenesis. Mitochondrion 2020, 54, 1–7.
Singh, K. K.; Chaubey, G.; Chen, J. Y.; Suravajhala, P., Decoding SARS-CoV-2 hijacking of host mitochondria in COVID-19 pathogenesis. American journal of physiology. Cell physiology 2020, 319 (2), C258-c267.
Valdés-Aguayo, J. J.; Garza-Veloz, I.; Badillo-Almaráz, J. I.; Bernal-Silva, S.; Martínez-Vázquez, M. C.; Juárez-Alcalá, V.; Vargas-Rodríguez, J. R.; Gaeta-Velasco, M. L.; González-Fuentes, C.; Ávila-Carrasco, L.; Martinez-Fierro, M. L., Mitochondria and Mitochondrial DNA: Key Elements in the Pathogenesis and Exacerbation of the Inflammatory State Caused by COVID-19. Medicina (Kaunas, Lithuania) 2021, 57 (9).
Kloc, M.; Ghobrial, R. M.; Kubiak, J. Z., The Role of Genetic Sex and Mitochondria in Response to COVID-19 Infection. International archives of allergy and immunology 2020, 181 (8), 629–634.
Bolanos-Garcia, V. M.; Blundell, T. L., BUB1 and BUBR1: multifaceted kinases of the cell cycle. Trends in biochemical sciences 2011, 36 (3), 141–50.
Kim, T.; Gartner, A., Bub1 kinase in the regulation of mitosis. Animal cells and systems 2021, 25 (1), 1–10.
Singh, P.; Pesenti, M. E.; Maffini, S.; Carmignani, S.; Hedtfeld, M.; Petrovic, A.; Srinivasamani, A.; Bange, T.; Musacchio, A., BUB1 and CENP-U, Primed by CDK1, Are the Main PLK1 Kinetochore Receptors in Mitosis. Molecular cell 2021, 81 (1), 67–87.e9.
Agrawal, P.; Sambaturu, N.; Olgun, G.; Hannenhalli, S., A Path-Based Analysis of Infected Cell Line and COVID-19 Patient Transcriptome Reveals Novel Potential Targets and Drugs Against SARS-CoV-2. Frontiers in immunology 2022, 13, 918817.
Jin, Q.; Li, W.; Yu, W.; Zeng, M.; Liu, J.; Xu, P., Analysis and identification of potential type II helper T cell (Th2)-Related key genes and therapeutic agents for COVID-19. Computers in biology and medicine 2022, 150, 106134.
Gao, C. L.; Wang, G. W.; Yang, G. Q.; Yang, H.; Zhuang, L., Karyopherin subunit-α 2 expression accelerates cell cycle progression by upregulating CCNB2 and CDK1 in hepatocellular carcinoma. Oncology letters 2018, 15 (3), 2815–2820.
Yang, L.; Xiong, H.; Li, X.; Li, Y.; Zhou, H.; Lin, X.; Chan, T. F.; Li, R.; Lai, K. P.; Chen, X., Network Pharmacology and Comparative Transcriptome Reveals Biotargets and Mechanisms of Curcumol Treating Lung Adenocarcinoma Patients With COVID-19. Frontiers in nutrition 2022, 9, 870370.
de Souza Andrade, M. M.; Leal, V. N. C.; Fernandes, I. G.; Gozzi-Silva, S. C.; Beserra, D. R.; Oliveira, E. A.; Teixeira, F. M. E.; Yendo, T. M.; Sousa, M.; Teodoro, W. R.; Oliveira, L. M.; Alberca, R. W.; Aoki, V.; Duarte, A. J. S.; Sato, M. N., Resveratrol Downmodulates Neutrophil Extracellular Trap (NET) Generation by Neutrophils in Patients with Severe COVID-19. Antioxidants (Basel, Switzerland) 2022, 11 (9).
Domi, E.; Hoxha, M.; Kolovani, E.; Tricarico, D.; Zappacosta, B., The Importance of Nutraceuticals in COVID-19: What's the Role of Resveratrol? Molecules (Basel, Switzerland) 2022, 27 (8).
Horne, J. R.; Vohl, M. C., Biological plausibility for interactions between dietary fat, resveratrol, ACE2, and SARS-CoV illness severity. American journal of physiology. Endocrinology and metabolism 2020, 318 (5), E830-e833.
van Brummelen, R.; van Brummelen, A. C., The potential role of resveratrol as supportive antiviral in treating conditions such as COVID-19 - A formulator's perspective. Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie 2022, 148, 112767.
Chelladurai, P.; Boucherat, O.; Stenmark, K.; Kracht, M.; Seeger, W.; Bauer, U. M.; Bonnet, S.; Pullamsetti, S. S., Targeting histone acetylation in pulmonary hypertension and right ventricular hypertrophy. British journal of pharmacology 2021, 178 (1), 54–71.
Wang, J.; He, F.; Chen, L.; Li, Q.; Jin, S.; Zheng, H.; Lin, J.; Zhang, H.; Ma, S.; Mei, J.; Yu, J., Resveratrol inhibits pulmonary fibrosis by regulating miR-21 through MAPK/AP-1 pathways. Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie 2018, 105, 37–44.
Wang, Z.; Li, X.; Chen, H.; Han, L.; Ji, X.; Wang, Q.; Wei, L.; Miu, Y.; Wang, J.; Mao, J.; Zhang, Z., Resveratrol alleviates bleomycin-induced pulmonary fibrosis via suppressing HIF-1α and NF-κB expression. Aging 2021, 13 (3), 4605–4616.

No competing interests reported.

FigureS1.tif
Figure S1: The quality control box plots of GSE32147 and GSE49144 datasets after being merged.
TableS1.xlsx
Supplementary Table S1: Sample size of the dataset included in the study.

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published 06 Jun, 2023

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Editorial decision: Major revision
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Exploration of the potentially common pathogenic mechanisms in COVID-19 and silicosis using bioinformatics and system biology

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Journal Publication

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Abstract

Figures

1 Introduction

2 Materials And Methods

2.1 Dataset and Source

2.2 Weighted gene co-expression network analysis

2.3 Protein interaction network and pathway enrichment analysis

2.4 Immune Infiltration Analysis

2.5 Transcription factors, miRNAs, and the disease regulatory network

2.6 ScRNA-seq data processing

2.7 Drug screening and molecular docking

3 Results

3.1 Identification of module genes in silicosis and COVID-19

3.2 GO pathway enrichment analysis

3.3 Identification of shared genes in silicosis and COVID-19

3.4 Transcription factors, miRNAs, and the disease regulatory network

3.5 Immune Infiltration Analysis of COVID-19

3.6 Analysis of COVID-19 single-cell data

3.7 Drug prediction and molecular docking

4 Discussion

5 Conclusion

Declarations

References

Additional Declarations

Supplementary Files

Status:

Journal Publication

Version 1