Mechanisms and Therapeutic Strategies for Pulmonary Fibrosis Post-COVID-19 ARDS: Insights from Comprehensive Bioinformatics

Mechanisms and Therapeutic Strategies for Pulmonary Fibrosis Post-COVID-19 ARDS: Insights from Comprehensive Bioinformatics | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Mechanisms and Therapeutic Strategies for Pulmonary Fibrosis Post-COVID-19 ARDS: Insights from Comprehensive Bioinformatics Daixi Li, Naishan Zheng, Huiqing Zeng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4858965/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Coronavirus disease 2019 (COVID-19) pandemic has led to numerous cases of acute respiratory distress syndrome (ARDS), with a significant number of survivors developing pulmonary fibrosis as a chronic sequela. This condition poses severe long-term health challenges, significantly burdening public health systems. Despite significant research on the acute phase of COVID-19, the mechanisms underlying pulmonary fibrosis following COVID-19 associated ARDS remain poorly understood, and effective therapies are yet to be established. This study aims to elucidate the molecular mechanisms, identify potential biomarkers, and explore therapeutic options for pulmonary fibrosis post-COVID-19-related ARDS through comprehensive transcriptomic and bioinformatic analyses. Methods We collected datasets from Gene Expression Omnibus (GEO) database, including transcriptional profiles of COVID-19, ARDS, and pulmonary fibrosis. Differentially expressed genes (DEGs) common to these conditions were identified, reflecting the transcriptional landscape of pulmonary fibrosis post-COVID-19 ARDS. Functional and pathway enrichment analyses was conducted. Protein-protein interaction (PPI) network was constructed to determine the hub genes and their regulatory networks. Drugs that interact with hub genes were explored and gene-disease associations were analyzed to identify potential therapeutic strategies. Results We identified 116 common DEGs among COVID-19, ARDS, and pulmonary fibrosis datasets. Functional enrichment highlighted critical processes including inflammatory response, apoptosis, transcription regulation, and MAPK cascade. PPI network revealed hub genes which may play crucial roles in the pathogenesis of pulmonary fibrosis post-COVID-19-related ARDS. Notably, FCER1A, associated with immune response and inflammation, GATA2, involved in macrophage function and erythropoiesis, and CLC, indicative of eosinophil activity, emerged as central players. Regulatory network analysis highlighted significant transcription factors (TFs) and microRNAs (miRNAs) associated with hub genes. We found FDA-approved drugs that could interact with these hub genes, including omalizumab, mizolastine, desloratadine, epoetin alfa, and moxidectin. Gene-disease interaction analysis revealed that diseases caused by GATA2 deficiency and immunodeficiency were associated with hub genes. Conclusion Our findings provide valuable insights into the molecular underpinnings of pulmonary fibrosis post-COVID-19 ARDS and highlight potential biomarkers and therapeutic targets. The repurpose of drugs offers a promising avenue for rapid clinical application, potentially improving outcomes. This study provides ideas for improved treatment for pulmonary fibrosis post-COVID-19 ARDS. COVID-19 ARDS pulmonary fibrosis transcriptomics hub genes drug repurposing Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), coronavirus disease 2019 (COVID-19) could lead to acute respiratory distress syndrome (ARDS), which is the most common early complication in severe cases of COVID-19 [ 1 , 2 ]. Since pulmonary fibrosis is a well-known sequela of ARDS [ 3 ], concern has been raised regarding this possible chronic consequence following SARS-CoV-2 infection [ 4 ]. Indeed, growing evidence suggests that a considerable proportion of individuals recovering from COVID-19 do progress to pulmonary fibrosis [ 5 ]. Patients with pulmonary fibrosis resulting from post-COVID-19 ARDS often suffer from respiratory failure, leading to poor prognosis, including increased mortality and decreased life quality, thus creating an enormous burden to public health [ 6 ]. Past and current preclinical and clinical efforts have primarily centered on the mechanisms and manifestations of the acute phase of COVID-19; by comparison, pulmonary fibrosis, a critical aftermath in the persistent post-COVID-19 syndrome, has received little attention. As the COVID-19 pandemic gradually comes to an end and as a surge of treated patients are discharged from hospital, it is urgent to shift our focus to this long-term complication of pulmonary fibrosis. Evidences suggest that the invade of SARS-CoV-2 into alveolar epithelial cells stimulates immune disorder, including the production of various inflammatory cells and cytokines, and causes tissue injury, which manifests as ARDS [ 7 ]. Both the infection itself and the cytokine storm trigger the release of profibrotic cytokines like transforming growth factor-β (TGF-β), with increased circulating levels stimulating and enhancing the proliferation of lung fibroblasts. These activated lung fibroblasts are responsible for the production of an excess amount of extracellular matrix to promote pulmonary fibrosis [ 8 ]. However, the mechanism is ill-defined for pulmonary fibrosis post-COVID-19 associated ARDS. Although FDA-approved antifibrotic agents such as nintedanib and pirfenidone have shown some initial promise in alleviating pulmonary fibrosis, the exact effectiveness of antifibrotic therapy in COVID-19 is still waiting to be evidenced. Herein, determining the precise mechanism and potential biomarkers and searching for novel therapeutics is in highly desired. Idiopathic pulmonary fibrosis (IPF) is the most prevalent type of pulmonary fibrosis, with a median survival time of only two to four years [ 9 ]. Evidences illustrated that although IPF and pulmonary fibrosis following COVID-19 associated ARDS are distinct entities, the underlying pathological process and probable fibrogenic gene signatures of two diseases certainly share similarities [ 10 ]. As a well-studied model of fibrotic disease, IPF data can provide valuable insights into understanding of pulmonary fibrosis following COVID-19 associated ARDS. The goal of the current research is to explore the potential molecular mechanisms, identify probable biomarkers and examine available agents of pulmonary fibrosis resulting from post-COVID-19 associated ARDS using transcriptomics and comprehensive bioinformatic analyses. Firstly, datasets of COVID-19, ARDS, and pulmonary fibrosis were collected from the Gene Expression Omnibus (GEO) database. Subsequently, we identified differentially expressed gene (DEGs) common to above datasets, which reflect the condition of pulmonary fibrosis post-COVID-19 associated ARDS at transcriptional level. Using these common DEGs, we performed functional and pathway enrichment analysis and constructed protein-protein interaction (PPI) network, followed by determining the hub genes. Furthermore, regulation networks at transcriptional and post-transcriptional level were constructed. Drug repurposing enables the rapid deployment of existing drug candidates for emerging diseases. In view of this point, we predicted FDA-approved agents that could interact with the hub genes to explore the potential therapies. The workflow of this research is presented in Fig. 1 . Materials and Methods Datasets Collection We obtained three microarray datasets of COVID-19, ARDS, pulmonary fibrosis from the GEO database of the National Center for Biotechnology Information (NCBI) [ 11 ]. Dataset GSE213313 contains transcriptional profiles of blood samples in 83 COVID-19 and 11 normal subjects [ 12 ]. GSE76293 contains transcriptional profiles of blood samples in 12 ARDS and 12 normal subjects [ 13 ]. GSE28042 contains transcriptional profiles of blood samples of 75 IPF and 19 normal subjects [ 14 ]. An overview of the datasets is provided in Table 1 . Table 1 Overview of microarray datasets with their basic information from GEO database. Disease Datasets Platform Gene Chip COVID-19 GSE213313 GPL21185 Agilent-072363 SurePrint G3 Human GE v3 8x60K Microarray 039494 ARDS GSE76293 GLP570 Affymetrix Human Genome U133 Plus 2.0 Array IPF GSE28042 GLP6480 Agilent-014850 Whole Human Genome Microarray 4x44K G4112F Identification of DEGs and common DEGs among COVID-19, ARDS and pulmonary fibrosis The online tool GEO2R [ 15 ] was utilized to acquire the DEGs of three datasets respectively. The p value was set as less than 0.05. The logFC 0.5 represents upregulated genes. Volcano plots were drawn to show the differential genes in the three datasets. The common DEGs among the three datasets were identified using Venny 2.1 ( https://bioinfogp.cnb.csic.es/tools/venny/index.html ). Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analysis To understand the function and pathways of the common DEGs, DAVID database was used for GO and KEGG pathway analysis [ 16 ]. The p value below 0.05 was considered statistically significant. Bubble graphs were drawn to show the results of enrichment analysis. PPI network analysis for the identification of hub genes The STRING database was used to characterize the interactions among common DEGs [ 17 ]. Cytoscape software (v3.7.1) was employed to visualize the PPI network. To search for the key genetic elements and potential therapeutic targets against pulmonary fibrosis post-COVID-19 associated ARDS, the top 10 nodes were acquired as hub genes via ranking by EPC method in cytoHubba plugin, and a sub-network based on hub genes was constructed. Identification of transcription factors (TFs) and microRNAs (miRNAs) interacting with hub genes NetworkAnalyst platform was used to pinpoint topologically credible TFs from JASPAR database that tend to bind to hub genes [ 18 ]. Using mirTarbase database, potential miRNAs interacting with hub genes were searched out [ 19 ]. Interaction networks of both TFs-genes and miRNAs-genes were visualized in Cytoscape. Prediction of candidate drugs for hub genes Using the DGIDB database, FDA-approved drugs potentially targeting hub genes were identified [ 20 ]. Gene-disease association analysis DisGeNET database was used to examine human diseases or disorders associated to above identified hub genes and to construct the gene-disease interaction network [ 21 ]. Score of less than 0.1 was excluded. The gene-disease interaction network was displayed using Cytoscape. Results Identification of DEGs and common DEGs among COVID-19, ARDS and pulmonary fibrosis For dataset GSE213313 of COVID-19, 2259 upregulated DEGs and 2450 downregulated DEGs were identified (Figure S1 A and Table S1 ). For dataset GSE76293 of ARDS, 1065 upregulated DEGs and 1396 downregulated DEGs were identified (Figure S1 B and Table S2 ). For dataset GSE28042 of IPF, 905 upregulated DEGs and 1022 downregulated DEGs were identified (Figure S1 C and Table S3). Further comparison of the three datasets revealed an overlap of 116 common DEGs, of which 75 upregulated and 41 downregulated (Fig. 2 and Table S4). GO and KEGG pathway enrichment analyses of common DEGs In Biological Process (BP) catalogue of GO database, the top enriched terms are “regulation of transcription from RNA polymerase II promoter”, “inflammatory response”, “apoptotic process”, “response to lipopolysaccharide”, “positive regulation of apoptotic process”, “positive regulation of mitogen-activated protein kinase (MAPK) cascade”, “positive regulation of protein kinase B signaling”, and “regulation of apoptotic process”. In Cellular Component (CC) catalogue, it is enriched mainly in the terms of “plasma membrane”, “extracellular region”, “external side of plasma membrane”, “specific granule membrane”, “ficolin-1-rich granule lumen”, and “lysosome”. In Molecular Function (MF) catalogue, the terms of “protein binding”, “RNA polymerase II core promoter proximal region sequence-specific DNA binding”, “carbohydrate binding”, and “transcription cofactor binding” are enriched. For KEGG database, it is enriched in the terms of “Herpes simplex virus 1 infection”, “Hematopoietic cell lineage”, and “Asthma” (Fig. 3 ). Construction of PPI network and identification of hub genes The PPI network of common DEGs consists of 63 nodes and 152 edges depicted in Fig. 4 A. From among the PPI network, we identified the top 10 hub genes, namely FCER1A, GATA2, CCR7, IL7R, LEF1, CPA3, IKZF2, HDC, CCR3, and CLC. These hub genes could serve as potential key molecules explaining the mechanism of pulmonary fibrosis following COVID-19 associated ARDS, and may also direct to new therapeutic strategies. A sub-network describing the connectivity and proximity of hub genes is shown in Fig. 4 B. Construction of regulatory networks of hub genes To detect significant changes happening at transcriptional or post-transcriptional level and to gain insights into the regulatory molecules, a network-based approach was employed to decode the miRNAs and TFs of hub proteins. We identified 43 TFs that interact with above hub genes, constituting 91 interactions (Fig. 5 A). Among these, FOXC1, HINFP, YY1, FOXL1, CREB1, JUND, JUN, GATA3, TEAD1, E2F1, and TFAP2A have the highest degree of interaction with hub genes. Moreover, we identified 92 miRNAs that interact with six of hub genes (CCR7, LEF1, CPA3, GATA2, IL7R, and IKZF2), constituting 93 interactions (Fig. 5 B). Prediction of candidate drugs targeting hub genes Eight candidate drugs approved by FDA that target hub genes were predicted. Omalizumab, mizolastine, and desloratadine tended to target to FCER1A. Epoetin alfa, azacitidine, and bortezomib were predicted for targeting GATA2. Moxidectin was a candidate drug for CLC, and meanwhile omeprazole for HDC (Table 2 ). Identification of gene-disease association In addition to pulmonary fibrosis post-COVID-19 ARDS, other diseases or disorders were examined to be associated with the hub genes, of which acute myelocytic leukemia, GATA2 deficiency, myelodysplastic syndrome, Emberger syndrome, severe combined immunodeficiency (autosomal recessive, T cell negative, B cell positive, and NK cell positive), Gilles de la Tourette syndrome, precursor T-cell lymphoblastic leukemia-lymphoma and Omenn syndrome were the most correlated (Fig. 6 ). Discussion It has been reported that SARS-CoV-2 infection associated ARDS is linked with the development of pulmonary fibrotic changes after recovery [ 22 ], manifesting as respiratory failure and leading to a poor prognosis and an enormous burden to public health [ 6 ]. However, the molecular mechanism and effective therapies is still ill-defined. This study was to explore the key molecular elements and probable biomarkers and to identify potential targeted drugs for pulmonary fibrosis post-COVID-19 associated ARDS. We analyzed three datasets from GEO database and identified 116 common DEGs. These DEGs reflect the expression characteristics in pulmonary fibrosis following COVID-19 associated ARDS compared with healthy subjects. The functional analysis reveals that the common DEGs are predominantly associated with response to components of pathogens, inflammatory response, apoptosis, MAPK signaling pathway, transcription regulation, DNA binding and protein binding. In most patients, severe infection has likely led to ARDS, a major cause of early-stage edema and later-stage fibrosis in the lungs, thereby worsening the lung condition. The secondary lung injury and cytokine storm are linked to a significant increase in immune and inflammatory responses, leading to the activation and uncontrolled release of various mediators, especially TGF-β [ 23 ]. Meanwhile, SARS-CoV-2 induces apoptosis in alveolar cells, pneumocytes, bronchial cells, and T-lymphocytes, subsequently resulting in neutrophils death. Macrophages then migrate into the lungs to clear the debris by engulfing and digesting the dead cells, releasing increased amounts of TGF-β [ 8 ]. This is consistent with our observation that the functional enrichment of apoptosis and inflammatory response is evidenced in pulmonary fibrosis following COVID-19 associated ARDS. Research has shown that a variety of transcription factors are regulated by MAPK signaling, leading to the upregulation of pro-apoptotic proteins and the downregulation of anti-apoptotic proteins [ 24 ]. Therefore, we suppose that once the lung is infected with SARS-CoV-2 and progresses to ARDS, the MAPK cascade is activated, subsequently triggering downstream transcription factors. These transcription factors regulate gene expression, initiating a series of cellular responses, including apoptosis and immunoreaction, which gradually progress to pulmonary fibrosis. Our finding provides important evidence supporting the fibrogenesis mechanism and suggests that antifibrotic therapy for COVID-19 patients could be achieved by mediating MAPK and apoptosis. Based on PPI network construction and hub genes identification, we explored potential biomarkers, mechanisms and drug targets for pulmonary fibrosis post-COVID-19 ARDS patients. Developing new drugs rapidly for emerging diseases is often unrealistic, so repurposing existing drugs can be highly beneficial. Drug repurposing seeks new indications for known drugs, thus narrowing down candidates, avoiding safety issues, and shortening development time [ 25 ]. In view of this, we predicted candidate drugs targeting hub genes to explore promising treatments for this deadly complication of pulmonary fibrosis following COVID-19 associated ARDS. Our study found that FCER1A has the strongest interactions with other genes and is likely the most critical gene in pulmonary fibrosis following COVID-19 associated ARDS. FCER1A encodes the α-subunit of immunoglobulin E (IgE) receptor FcεRI, is known to participate in immune reactions and mediate the release of inflammatory mediators such as histamine, and is important in allergic disease especially asthma [ 26 ]. Chen et al. [ 27 ] found that the IgE concentration in serum was significantly elevated in patients of pneumoconiosis and mice exposed to silica, and FcεRI deficiency provide substantial protection against pulmonary inflammation and fibrosis induced by silica in a mouse model. Kim et al. [ 28 ] found that inhibition of sirtuin2 reduces mast cell-mediated allergic airway inflammation and fibrosis via blocking FcεRI/TGF-β signaling pathway. However, the role of FCER1A in pulmonary fibrosis following infection remains unclear. In our study, FCER1A was commonly downregulated in three datasets, thus we suppose that FcεRI might play a detrimental role in case of pulmonary fibrosis post-COVID-19 ARDS. Three FDA-approved drugs associated with FCER1A were identified: omalizumab, mizolastine and desloratadine. Binding to IgE, omalizumab is approved for treating allergic asthma in individuals sensitive to perennial allergens with elevated IgE levels [ 29 ]. Cao et al. [ 30 ] found that blocking IgE-FcεR1 signaling in mice by administrating the omalizumab suppressed cardiac hypertrophy and cardiac interstitial fibrosis significantly. Meanwhile, as antihistamine for allergic rhinitis and chronic urticaria, mizolastine and desloratadine block histamine H1 receptors of FcεR1-activated mast cells and basophils to alleviate symptoms [ 31 ]. Our study suggests that omalizumab, mizolastine, and desloratadine, interfering with IgE-FcεR1 signaling, may offer potential therapeutic benefits for managing pulmonary fibrosis post-COVID-19 ARDS patients. GATA2 is a member of the family of zinc finger transcription factors known to regulate alveolar macrophage phagocytosis and erythropoiesis [ 32 ]. It has been reported that GATA2 deficiency is associated with pulmonary fibrosis related to pulmonary alveolar proteinosis [ 33 , 34 ]. However, the effect of GATA2 expression on pulmonary fibrosis post-COVID-19 is unclear. Herein, our study revealed that the expression of GATA2 was commonly inhibited, pointing out the potential role of GATA2 in pulmonary fibrosis post-COVID-19 associated ARDS. In addition, we identified epoetin alfa associated with GATA2 as a potential remedy. Epoetin alfa stimulates erythropoiesis by activating EPO receptor and downstream pathways, while GATA2 regulates the gene expression of EPO receptor, making it more sensitive to epoetin alfa [ 35 ]. However, the effect of epoetin alfa to GATA and pulmonary fibrosis post-COVID-19 ARDS is unclear, waiting for further experimental exploration. CLC, referring to Charcot-Leyden crystal galectin or galectin-10 (Gal-10), belongs to the galectin superfamily that share highly conserved amino acid sequences constituting the carbohydrate recognition domain. CLC is considered as a hallmark of eosinophil involvement in allergic reactions and associated immune responses [ 36 ]. Meanwhile, evidences suggest that other member, like Gal-3, of the galectin superfamily could play a crucial role in COVID-19 [ 37 ] and pulmonary fibrosis [ 38 ]. However, the effect of CLC in pulmonary fibrosis post-COVID-19 has not been explored. Our study found that CLC is downregulated commonly in three datasets, indicating it the promising diagnostic biomarker and probable role in pulmonary fibrosis following COVID-19 associated ARDS. TD139, an inhaled small-molecule inhibitor of Gal-3, was found to be safe, well tolerated, and effective in engaging its target and reducing plasma biomarkers linked to IPF progression [ 38 ]. Herein, we identified moxidectin, which is a CLC-targeted anti-parasitic agent approved for the prevention of Onchocerca volvulus caused river blindness [ 39 ], as a potential drug for pulmonary fibrosis following COVID-19 associated ARDS. The effectiveness of moxidectin needs to be further confirmed. In addition, our study explores the transcriptional and post-transcriptional regulators of hub genes by revealing the relationships between TFs and hub genes as well as miRNAs and hub genes. Among the identified TFs, FOXL1, CREB1, YY1, GATA3, and E2F1 were reported to participate in the regulation of pulmonary fibrosis [ 40 – 44 ], and FOXC1 was reported to promote FGFR1 isoform switching following induction of TGF-β-mediated epithelial-to-mesenchymal transition, which is considered to contribute to pathogenesis of fibrosis [ 45 ]. Besides, CREB1 regulates SARS-CoV-2 proliferation by viral helicase nsp13 association [ 46 ], and the overexpression of GATA3 was associated with the severity and fatal outcome of COVID-19 [ 47 ]. Moreover, CREB1 participates in the regulation of acute lung injury [ 48 ]. Bioinformatics analyses suggests that hsa-miR-26b-5p [ 49 ] and hsa-miR-524-5p [ 50 ] may develop as diagnostic biomarkers and potential therapeutic targets for IPF, while hsa-miR-27a-3p [ 51 ] could be a potential regulator for patients with combined pulmonary fibrosis and emphysema. Also, downregulation of miR-1290 may be helpful in the treatment of pulmonary fibrosis and viral infections such as influenza A [ 52 ]. Our study suggests that these TFs and miRNAs might play a role in pulmonary fibrosis following COVID-19 associated ARDS, but further investigation is needed to confirm this. Finally, we conducted a gene-disease analysis to predict the relationships between hub genes and other diseases. These findings may inspire the development of potential therapies of pulmonary fibrosis post-COVID-19 ARDS, based on insights from the onset, progression and management of these diseases. Disorders or diseases, including deficiency of combined immune cells like B and natural killer lymphoid, acute myeloid leukemia, myelodysplastic syndrome, and Emberger syndrome (primary lymphedema with myelodysplasia), have been reported to result from germline mutations in GATA2 gene and to represent various manifestations of a same condition, subsequently termed GATA2 deficiency [ 53 , 54 ]. Meanwhile, GATA2 deficiency is associated with pulmonary fibrosis related to pulmonary alveolar proteinosis [ 33 , 34 ] and probably pulmonary fibrosis post-COVID-19 ARDS as mentioned above. It suggests that these diseases might share the same pathogenesis, and their treatment could provide insights for the treatment of pulmonary fibrosis following COVID-19 associated ARDS. On the other hand, mutations in the telomerase lead to dyskeratosis congenita, a bone marrow failure syndrome marked by mucocutaneous abnormalities, pulmonary fibrosis, and increased susceptibility to acute myeloid leukemia and myelodysplastic syndrome [ 55 ], indicating the future directions of treatment of pulmonary fibrosis post-COVID-19 ARDS. Omenn syndrome manifests as severe combined immune deficiency characterized by enlarged lymphoid tissue, elevated IgE levels, and eosinophilia, presenting as a distinct inflammatory process. The inflammation in these patients is initiated by clonally expanded T cells, which secrete a host of cytokines that drive autoimmune as well as allergic inflammation due to inadequate regulation by other components of the immune system [ 56 ]. The gene-disease interaction in our study suggests similar molecular mechanisms in the progression of these diseases, which could inform the development of new therapeutic strategies for pulmonary fibrosis post-COVID-19 ARDS. Conclusion Our study highlights the key genes and functional pathways involved in pulmonary fibrosis following COVID-19 associated ARDS. Through analyzing common DEGs and hub genes, we have uncovered potential therapeutic targets such as FCER1A, GATA2 and CLC. TFs and miRNAs interacting with hub genes were identified to explore the regulators at transcriptional and post-transcriptional level. What is more, the prediction of FDA-approved drugs targeting hub genes provides a promising avenue for developing effective treatments. This study underscores the importance of focusing on the long-term complication of COVID-19, paving the way for improved management and therapeutic strategies for pulmonary fibrosis. Further experimental validation is crucial for translating these findings into clinical applications, potentially improving outcomes for patients suffering from pulmonary fibrosis following COVID-19-related ARDS. Abbreviations COVID-19 Coronavirus disease 2019 ARDS Acute Respiratory Distress Syndrome GEO Gene Expression Omnibus DEGs Differentially expressed genes PPI Protein-protein interaction TFs transcription factors miRNAs microRNAs SARS-CoV-2 acute respiratory syndrome coronavirus 2 TGF-β transforming growth factor-β IPF Idiopathic pulmonary fibrosis NCBI National Center for Biotechnology Information BP Biological Process MAPK mitogen-activated protein kinase CC Cellular Component MF Molecular Function IgE immunoglobulin E Declarations Supplementary Information The Supplementary Material for this article can be found online at XXX. Ethics approval and consent to participate Not applicable. Consent for publication All authors gave their consent for publication. Availability of data and materials Publicly available datasets were analyzed in this study. The data could be downloaded from the GEO database of the NCBI (https://www.ncbi.nlm.nih.gov/geo/), accession numbers GSE213313, GSE76293, and GSE28042. Competing interests The authors declare no competing interests. Funding This work was supported by Fujian Province Natural Science Foundation (2021J05280) and Fujian Province Talent Introduction Program Foundation (01102801). Authors' contributions DL and HZ conceived and supervised the study. DL and NZ collected the data, conducted the data analysis and prepared the figures. DL and HZ prepared the manuscript. DL, NZ, and HZ contributed to the research revisions and reviewed the manuscript. Acknowledgements The authors thank the Zheng Wan from Zhongshan Hospital of Xiamen University for providing technical support. References Wang D, Hu B, Hu C, Zhu F, Liu X, Zhang J, et al. 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Ballerie A, Nimubona S, Meunier C, Gutierrez FL, Desrues B, Delaval P, et al. Association of pulmonary alveolar proteinosis and fibrosis: patient with GATA2 deficiency. Eur Respir J. 2016;48(5):1510–4. Jelkmann W. Regulation of erythropoietin production. J Physiol. 2011;589(Pt 6):1251–8. Grozdanovic MM, Doyle CB, Liu L, Maybruck BT, Kwatia MA, Thiyagarajan N, et al. Charcot-Leyden crystal protein/galectin-10 interacts with cationic ribonucleases and is required for eosinophil granulogenesis. J Allergy Clin Immunol. 2020;146(2):377–e389310. Gaughan EE, Quinn TM, Mills A, Bruce AM, Antonelli J, MacKinnon AC, et al. An Inhaled Galectin-3 Inhibitor in COVID-19 Pneumonitis: A Phase Ib/IIa Randomized Controlled Clinical Trial (DEFINE). Am J Respir Crit Care Med. 2023;207(2):138–49. Hirani N, MacKinnon AC, Nicol L, Ford P, Schambye H, Pedersen A et al. Target inhibition of galectin-3 by inhaled TD139 in patients with idiopathic pulmonary fibrosis. Eur Respir J. 2021; 57(5). Milton P, Hamley JID, Walker M, Basanez MG. Moxidectin: an oral treatment for human onchocerciasis. Expert Rev Anti Infect Ther. 2020;18(11):1067–81. Miyashita N, Horie M, Suzuki HI, Saito M, Mikami Y, Okuda K, et al. FOXL1 Regulates Lung Fibroblast Function via Multiple Mechanisms. Am J Respir Cell Mol Biol. 2020;63(6):831–42. Guan S, Wu Y, Zhang Q, Zhou J. TGF–β1 induces CREB1–mediated miR–1290 upregulation to antagonize lung fibrosis via Napsin A. Int J Mol Med. 2020;46(1):141–8. Lin X, Sime PJ, Xu H, Williams MA, LaRussa L, Georas SN, et al. Yin yang 1 is a novel regulator of pulmonary fibrosis. Am J Respir Crit Care Med. 2011;183(12):1689–97. Yi J, Gao H, Wei X, Wang M, Xu W, Yu D, et al. The transcription factor GATA3 positively regulates NRP1 to promote radiation-induced pulmonary fibrosis. Int J Biol Macromol. 2024;262(Pt 2):130052. Huang J, Cao Y, Li X, Yu F, Han X. E2F1 regulates miR-215-5p to aggravate paraquat-induced pulmonary fibrosis via repressing BMPR2 expression. Toxicol Res (Camb). 2022;11(6):940–50. Hopkins A, Coatham ML, Berry FB. FOXC1 Regulates FGFR1 Isoform Switching to Promote Invasion Following TGFbeta-Induced EMT. Mol Cancer Res. 2017;15(10):1341–53. Zheng T, Shen B, Bai Y, Li E, Zhang X, Hu Y, et al. The PKA-CREB1 axis regulates coronavirus proliferation by viral helicase nsp13 association. J Virol. 2024;98(4):e0156523. Abbasi-Dokht T, Vafaeinezhad A, Khalesi N, Malek F, Haghmorad D, Baharlou R. T-Cell Immune Responses and Immunological Factors Associated with Coronavirus Disease 2019 Progression as Predictors for the Severity of the Disease in Hospitalized Patients. Int Arch Allergy Immunol. 2023;184(6):557–66. Li Z, Jin T, Yang R, Guo J, Niu Z, Gao H, et al. Long non-coding RNA PFI inhibits apoptosis of alveolar epithelial cells to alleviate lung injury via miR-328-3p/Creb1 axis. Exp Cell Res. 2023;430(1):113685. Yu DH, Ruan XL, Huang JY, Liu XP, Ma HL, Chen C, et al. Analysis of the Interaction Network of Hub miRNAs-Hub Genes, Being Involved in Idiopathic Pulmonary Fibers and Its Emerging Role in Non-small Cell Lung Cancer. Front Genet. 2020;11:302. Li Q, Li M, Zheng K, Li H, Yang H, Ma S, et al. Detection of microRNA expression levels based on microarray analysis for classification of idiopathic pulmonary fibrosis. Exp Ther Med. 2020;20(4):3096–103. Tanwar N, Hasija Y. Explicate molecular landscape of combined pulmonary fibrosis and emphysema through explainable artificial intelligence: a comprehensive analysis of ILD and COPD interactions using RNA from whole lung homogenates. Med Biol Eng Comput. 2024. Kalhori MR, Soleimani M, Yari K, Moradi M, Kalhori AA. MiR-1290: a potential therapeutic target for regenerative medicine or diagnosis and treatment of non-malignant diseases. Clin Exp Med. 2023;23(3):737–50. Hahn CN, Chong CE, Carmichael CL, Wilkins EJ, Brautigan PJ, Li XC, et al. Heritable GATA2 mutations associated with familial myelodysplastic syndrome and acute myeloid leukemia. Nat Genet. 2011;43(10):1012–7. Ostergaard P, Simpson MA, Connell FC, Steward CG, Brice G, Woollard WJ, et al. Mutations in GATA2 cause primary lymphedema associated with a predisposition to acute myeloid leukemia (Emberger syndrome). Nat Genet. 2011;43(10):929–31. Calado RT. Telomeres and marrow failure. Hematol Am Soc Hematol Educ Program. 2009:338–43. Villa A, Notarangelo LD, Roifman CM. Omenn syndrome: inflammation in leaky severe combined immunodeficiency. J Allergy Clin Immunol. 2008;122(6):1082–6. Table 2 Table 2 is available in the Supplementary Files section. Supplementary Files 2024.8.5SupplementaryInformation.docx Table2.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4858965","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":339195665,"identity":"d2f4dbe1-985b-4657-bdf8-b99097e18976","order_by":0,"name":"Daixi Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzUlEQVRIiWNgGAWjYBACPmYGA4YEBgkGhgPMBw58+EGEFjaEFrbEgzN7iNHCANQCBgd4jA9zsBGjhZ1544eHbRYMfDdyPhxm4GGQ5xc7QMhhbMUSCWckGCRv5G44XGDBYDhzdgIhLTwGEgkVEgwGIC0zeBgSDG4T1mL8I8EApCXnwWEeNuK0mEFtyWEgVgtbmQXYL2eeGQADWYKwX/j5D2+++bOtjoHvePLjDx9+2MjzSxPQAgP1DQJglRLEKYfad4AU1aNgFIyCUTCSAAAGBUBou+EINQAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0001-7293-2008","institution":"Zhongshan Hospital Xiamen University","correspondingAuthor":true,"prefix":"","firstName":"Daixi","middleName":"","lastName":"Li","suffix":""},{"id":339195666,"identity":"38c1d7c1-6cdf-470c-90e3-e156c19294d9","order_by":1,"name":"Naishan Zheng","email":"","orcid":"","institution":"Zhongshan Hospital Xiamen University","correspondingAuthor":false,"prefix":"","firstName":"Naishan","middleName":"","lastName":"Zheng","suffix":""},{"id":339195667,"identity":"37de5d3b-1de0-4ff2-936c-f38188428696","order_by":2,"name":"Huiqing Zeng","email":"","orcid":"","institution":"Zhongshan Hospital Xiamen University","correspondingAuthor":false,"prefix":"","firstName":"Huiqing","middleName":"","lastName":"Zeng","suffix":""}],"badges":[],"createdAt":"2024-08-05 03:32:56","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4858965/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4858965/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":66123857,"identity":"d950a5bb-2cab-4315-8868-b848419fb928","added_by":"auto","created_at":"2024-10-08 02:31:52","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":737359,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic illustration of the overall workflow of this study.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4858965/v1/8aa61c332d76c986685710a6.jpg"},{"id":66123861,"identity":"5c7dc1d1-16ee-4a6e-af20-eb29f22f50bb","added_by":"auto","created_at":"2024-10-08 02:31:52","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":188546,"visible":true,"origin":"","legend":"\u003cp\u003eDEGs comparison among the datasets of GSE213313, GSE76293, and GSE28042 revealed an overlap of 116 common DEGs, of which 75 upregulated (A) and 41 downregulated (B).\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4858965/v1/9b43983d17d76974de1fefc4.jpg"},{"id":66125579,"identity":"b267a5d2-1935-4975-8d33-dce9e902e1b6","added_by":"auto","created_at":"2024-10-08 02:39:52","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":601904,"visible":true,"origin":"","legend":"\u003cp\u003eBubble graphs indicate the results of GO analysis in terms of biological process (BP) (A), cellular component (CC) (B), and molecular function (MF) (C) and the results of KEGG pathway enrichment analysis (D).\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4858965/v1/f1b5438c3d7e9a5291dfd9cf.jpg"},{"id":66123862,"identity":"638c7cfa-5443-4c70-92c0-ca7531fb225e","added_by":"auto","created_at":"2024-10-08 02:31:52","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":399199,"visible":true,"origin":"","legend":"\u003cp\u003eThe PPI network of common DEGs consists of 63 nodes and 152 edges (A). Top ten hub genes were identified from among the PPI network (B). Red hexagons stand for upregulated genes, while blue hexagonsfor downregulated genes.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4858965/v1/834eb363bf45288869405912.jpg"},{"id":66125580,"identity":"034cbaa2-7685-441d-a777-d2ff8b332f62","added_by":"auto","created_at":"2024-10-08 02:39:52","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":677365,"visible":true,"origin":"","legend":"\u003cp\u003e(A) illustrates the interaction network of TFs with hub genes. Yellow triangles represent TFs, while blue hexagons represent hub genes. (B) illustrates the interaction network of miRNAs with hub genes. Orange ellipses represent miRNAs, and blue hexagons represent hub genes.\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4858965/v1/e120cdc2508eaa56d8b585e4.jpg"},{"id":66123863,"identity":"22d564c4-054e-4547-9c29-57026e11f0a0","added_by":"auto","created_at":"2024-10-08 02:31:53","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":177592,"visible":true,"origin":"","legend":"\u003cp\u003eThe gene-disease interaction network represents disorders or diseases associated with hub genes. Pink square nodes depict the diseases or disorders, and blue hexagons stand for hub genes.\u003c/p\u003e","description":"","filename":"Figure61.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4858965/v1/c9fb15668795948c4b44fdb1.jpg"},{"id":66126902,"identity":"198d7485-01cd-4b30-8c38-2929dc364f69","added_by":"auto","created_at":"2024-10-08 03:03:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3149040,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4858965/v1/aa1213fe-7947-42d4-a1f3-6af4d29073d7.pdf"},{"id":66126085,"identity":"ba5df82a-ae48-4441-ac0a-84d7ca183714","added_by":"auto","created_at":"2024-10-08 02:47:52","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":236032,"visible":true,"origin":"","legend":"","description":"","filename":"2024.8.5SupplementaryInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-4858965/v1/bc9e0911fa7e20a51e9713b5.docx"},{"id":66123859,"identity":"d8a11a38-b585-48a6-a7ce-f14a6de56d90","added_by":"auto","created_at":"2024-10-08 02:31:52","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":116422,"visible":true,"origin":"","legend":"","description":"","filename":"Table2.docx","url":"https://assets-eu.researchsquare.com/files/rs-4858965/v1/b1c740d65774a6fbcd1160fd.docx"}],"financialInterests":"","formattedTitle":"Mechanisms and Therapeutic Strategies for Pulmonary Fibrosis Post-COVID-19 ARDS: Insights from Comprehensive Bioinformatics","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCaused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), coronavirus disease 2019 (COVID-19) could lead to acute respiratory distress syndrome (ARDS), which is the most common early complication in severe cases of COVID-19 [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Since pulmonary fibrosis is a well-known sequela of ARDS [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], concern has been raised regarding this possible chronic consequence following SARS-CoV-2 infection [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Indeed, growing evidence suggests that a considerable proportion of individuals recovering from COVID-19 do progress to pulmonary fibrosis [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Patients with pulmonary fibrosis resulting from post-COVID-19 ARDS often suffer from respiratory failure, leading to poor prognosis, including increased mortality and decreased life quality, thus creating an enormous burden to public health [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Past and current preclinical and clinical efforts have primarily centered on the mechanisms and manifestations of the acute phase of COVID-19; by comparison, pulmonary fibrosis, a critical aftermath in the persistent post-COVID-19 syndrome, has received little attention. As the COVID-19 pandemic gradually comes to an end and as a surge of treated patients are discharged from hospital, it is urgent to shift our focus to this long-term complication of pulmonary fibrosis.\u003c/p\u003e \u003cp\u003eEvidences suggest that the invade of SARS-CoV-2 into alveolar epithelial cells stimulates immune disorder, including the production of various inflammatory cells and cytokines, and causes tissue injury, which manifests as ARDS [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Both the infection itself and the cytokine storm trigger the release of profibrotic cytokines like transforming growth factor-β (TGF-β), with increased circulating levels stimulating and enhancing the proliferation of lung fibroblasts. These activated lung fibroblasts are responsible for the production of an excess amount of extracellular matrix to promote pulmonary fibrosis [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. However, the mechanism is ill-defined for pulmonary fibrosis post-COVID-19 associated ARDS. Although FDA-approved antifibrotic agents such as nintedanib and pirfenidone have shown some initial promise in alleviating pulmonary fibrosis, the exact effectiveness of antifibrotic therapy in COVID-19 is still waiting to be evidenced. Herein, determining the precise mechanism and potential biomarkers and searching for novel therapeutics is in highly desired.\u003c/p\u003e \u003cp\u003eIdiopathic pulmonary fibrosis (IPF) is the most prevalent type of pulmonary fibrosis, with a median survival time of only two to four years [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Evidences illustrated that although IPF and pulmonary fibrosis following COVID-19 associated ARDS are distinct entities, the underlying pathological process and probable fibrogenic gene signatures of two diseases certainly share similarities [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. As a well-studied model of fibrotic disease, IPF data can provide valuable insights into understanding of pulmonary fibrosis following COVID-19 associated ARDS.\u003c/p\u003e \u003cp\u003eThe goal of the current research is to explore the potential molecular mechanisms, identify probable biomarkers and examine available agents of pulmonary fibrosis resulting from post-COVID-19 associated ARDS using transcriptomics and comprehensive bioinformatic analyses. Firstly, datasets of COVID-19, ARDS, and pulmonary fibrosis were collected from the Gene Expression Omnibus (GEO) database. Subsequently, we identified differentially expressed gene (DEGs) common to above datasets, which reflect the condition of pulmonary fibrosis post-COVID-19 associated ARDS at transcriptional level. Using these common DEGs, we performed functional and pathway enrichment analysis and constructed protein-protein interaction (PPI) network, followed by determining the hub genes. Furthermore, regulation networks at transcriptional and post-transcriptional level were constructed. Drug repurposing enables the rapid deployment of existing drug candidates for emerging diseases. In view of this point, we predicted FDA-approved agents that could interact with the hub genes to explore the potential therapies. The workflow of this research is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003eDatasets Collection\u003c/p\u003e\u003cp\u003eWe obtained three microarray datasets of COVID-19, ARDS, pulmonary fibrosis from the GEO database of the National Center for Biotechnology Information (NCBI) [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Dataset GSE213313 contains transcriptional profiles of blood samples in 83 COVID-19 and 11 normal subjects [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. GSE76293 contains transcriptional profiles of blood samples in 12 ARDS and 12 normal subjects [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. GSE28042 contains transcriptional profiles of blood samples of 75 IPF and 19 normal subjects [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. An overview of the datasets is provided in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOverview of microarray datasets with their basic information from GEO database.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisease\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDatasets\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePlatform\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGene Chip\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOVID-19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGSE213313\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGPL21185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAgilent-072363 SurePrint G3 Human GE v3 8x60K Microarray 039494\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eARDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGSE76293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGLP570\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAffymetrix Human Genome U133 Plus 2.0 Array\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIPF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGSE28042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGLP6480\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAgilent-014850 Whole Human Genome Microarray 4x44K G4112F\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIdentification of DEGs and common DEGs among COVID-19, ARDS and pulmonary fibrosis\u003c/p\u003e \u003cp\u003eThe online tool GEO2R [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] was utilized to acquire the DEGs of three datasets respectively. The p value was set as less than 0.05. The logFC\u0026thinsp;\u0026lt;\u0026thinsp;0.5 represents downregulated genes, while the logFC\u0026thinsp;\u0026gt;\u0026thinsp;0.5 represents upregulated genes. Volcano plots were drawn to show the differential genes in the three datasets. The common DEGs among the three datasets were identified using Venny 2.1 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://bioinfogp.cnb.csic.es/tools/venny/index.html\u003c/span\u003e\u003cspan address=\"https://bioinfogp.cnb.csic.es/tools/venny/index.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analysis\u003c/p\u003e \u003cp\u003eTo understand the function and pathways of the common DEGs, DAVID database was used for GO and KEGG pathway analysis [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The p value below 0.05 was considered statistically significant. Bubble graphs were drawn to show the results of enrichment analysis.\u003c/p\u003e \u003cp\u003ePPI network analysis for the identification of hub genes\u003c/p\u003e \u003cp\u003eThe STRING database was used to characterize the interactions among common DEGs [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Cytoscape software (v3.7.1) was employed to visualize the PPI network. To search for the key genetic elements and potential therapeutic targets against pulmonary fibrosis post-COVID-19 associated ARDS, the top 10 nodes were acquired as hub genes via ranking by EPC method in cytoHubba plugin, and a sub-network based on hub genes was constructed.\u003c/p\u003e \u003cp\u003eIdentification of transcription factors (TFs) and microRNAs (miRNAs) interacting with hub genes\u003c/p\u003e \u003cp\u003eNetworkAnalyst platform was used to pinpoint topologically credible TFs from JASPAR database that tend to bind to hub genes [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Using mirTarbase database, potential miRNAs interacting with hub genes were searched out [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Interaction networks of both TFs-genes and miRNAs-genes were visualized in Cytoscape.\u003c/p\u003e \u003cp\u003ePrediction of candidate drugs for hub genes\u003c/p\u003e \u003cp\u003eUsing the DGIDB database, FDA-approved drugs potentially targeting hub genes were identified [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGene-disease association analysis\u003c/p\u003e \u003cp\u003eDisGeNET database was used to examine human diseases or disorders associated to above identified hub genes and to construct the gene-disease interaction network [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Score of less than 0.1 was excluded. The gene-disease interaction network was displayed using Cytoscape.\u003c/p\u003e"},{"header":"Results","content":" \u003cp\u003eIdentification of DEGs and common DEGs among COVID-19, ARDS and pulmonary fibrosis\u003c/p\u003e \u003cp\u003eFor dataset GSE213313 of COVID-19, 2259 upregulated DEGs and 2450 downregulated DEGs were identified (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA and Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). For dataset GSE76293 of ARDS, 1065 upregulated DEGs and 1396 downregulated DEGs were identified (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eB and Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). For dataset GSE28042 of IPF, 905 upregulated DEGs and 1022 downregulated DEGs were identified (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eC and Table S3). Further comparison of the three datasets revealed an overlap of 116 common DEGs, of which 75 upregulated and 41 downregulated (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Table S4).\u003c/p\u003e \u003cp\u003eGO and KEGG pathway enrichment analyses of common DEGs\u003c/p\u003e \u003cp\u003eIn Biological Process (BP) catalogue of GO database, the top enriched terms are \u0026ldquo;regulation of transcription from RNA polymerase II promoter\u0026rdquo;, \u0026ldquo;inflammatory response\u0026rdquo;, \u0026ldquo;apoptotic process\u0026rdquo;, \u0026ldquo;response to lipopolysaccharide\u0026rdquo;, \u0026ldquo;positive regulation of apoptotic process\u0026rdquo;, \u0026ldquo;positive regulation of mitogen-activated protein kinase (MAPK) cascade\u0026rdquo;, \u0026ldquo;positive regulation of protein kinase B signaling\u0026rdquo;, and \u0026ldquo;regulation of apoptotic process\u0026rdquo;. In Cellular Component (CC) catalogue, it is enriched mainly in the terms of \u0026ldquo;plasma membrane\u0026rdquo;, \u0026ldquo;extracellular region\u0026rdquo;, \u0026ldquo;external side of plasma membrane\u0026rdquo;, \u0026ldquo;specific granule membrane\u0026rdquo;, \u0026ldquo;ficolin-1-rich granule lumen\u0026rdquo;, and \u0026ldquo;lysosome\u0026rdquo;. In Molecular Function (MF) catalogue, the terms of \u0026ldquo;protein binding\u0026rdquo;, \u0026ldquo;RNA polymerase II core promoter proximal region sequence-specific DNA binding\u0026rdquo;, \u0026ldquo;carbohydrate binding\u0026rdquo;, and \u0026ldquo;transcription cofactor binding\u0026rdquo; are enriched. For KEGG database, it is enriched in the terms of \u0026ldquo;Herpes simplex virus 1 infection\u0026rdquo;, \u0026ldquo;Hematopoietic cell lineage\u0026rdquo;, and \u0026ldquo;Asthma\u0026rdquo; (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eConstruction of PPI network and identification of hub genes\u003c/p\u003e \u003cp\u003eThe PPI network of common DEGs consists of 63 nodes and 152 edges depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA. From among the PPI network, we identified the top 10 hub genes, namely FCER1A, GATA2, CCR7, IL7R, LEF1, CPA3, IKZF2, HDC, CCR3, and CLC. These hub genes could serve as potential key molecules explaining the mechanism of pulmonary fibrosis following COVID-19 associated ARDS, and may also direct to new therapeutic strategies. A sub-network describing the connectivity and proximity of hub genes is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB.\u003c/p\u003e \u003cp\u003eConstruction of regulatory networks of hub genes\u003c/p\u003e \u003cp\u003eTo detect significant changes happening at transcriptional or post-transcriptional level and to gain insights into the regulatory molecules, a network-based approach was employed to decode the miRNAs and TFs of hub proteins. We identified 43 TFs that interact with above hub genes, constituting 91 interactions (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Among these, FOXC1, HINFP, YY1, FOXL1, CREB1, JUND, JUN, GATA3, TEAD1, E2F1, and TFAP2A have the highest degree of interaction with hub genes. Moreover, we identified 92 miRNAs that interact with six of hub genes (CCR7, LEF1, CPA3, GATA2, IL7R, and IKZF2), constituting 93 interactions (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003ePrediction of candidate drugs targeting hub genes\u003c/p\u003e \u003cp\u003eEight candidate drugs approved by FDA that target hub genes were predicted. Omalizumab, mizolastine, and desloratadine tended to target to FCER1A. Epoetin alfa, azacitidine, and bortezomib were predicted for targeting GATA2. Moxidectin was a candidate drug for CLC, and meanwhile omeprazole for HDC (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIdentification of gene-disease association\u003c/p\u003e \u003cp\u003eIn addition to pulmonary fibrosis post-COVID-19 ARDS, other diseases or disorders were examined to be associated with the hub genes, of which acute myelocytic leukemia, GATA2 deficiency, myelodysplastic syndrome, Emberger syndrome, severe combined immunodeficiency (autosomal recessive, T cell negative, B cell positive, and NK cell positive), Gilles de la Tourette syndrome, precursor T-cell lymphoblastic leukemia-lymphoma and Omenn syndrome were the most correlated (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e "},{"header":"Discussion","content":"\u003cp\u003eIt has been reported that SARS-CoV-2 infection associated ARDS is linked with the development of pulmonary fibrotic changes after recovery [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], manifesting as respiratory failure and leading to a poor prognosis and an enormous burden to public health [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. However, the molecular mechanism and effective therapies is still ill-defined. This study was to explore the key molecular elements and probable biomarkers and to identify potential targeted drugs for pulmonary fibrosis post-COVID-19 associated ARDS.\u003c/p\u003e \u003cp\u003eWe analyzed three datasets from GEO database and identified 116 common DEGs. These DEGs reflect the expression characteristics in pulmonary fibrosis following COVID-19 associated ARDS compared with healthy subjects. The functional analysis reveals that the common DEGs are predominantly associated with response to components of pathogens, inflammatory response, apoptosis, MAPK signaling pathway, transcription regulation, DNA binding and protein binding. In most patients, severe infection has likely led to ARDS, a major cause of early-stage edema and later-stage fibrosis in the lungs, thereby worsening the lung condition. The secondary lung injury and cytokine storm are linked to a significant increase in immune and inflammatory responses, leading to the activation and uncontrolled release of various mediators, especially TGF-β [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Meanwhile, SARS-CoV-2 induces apoptosis in alveolar cells, pneumocytes, bronchial cells, and T-lymphocytes, subsequently resulting in neutrophils death. Macrophages then migrate into the lungs to clear the debris by engulfing and digesting the dead cells, releasing increased amounts of TGF-β [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. This is consistent with our observation that the functional enrichment of apoptosis and inflammatory response is evidenced in pulmonary fibrosis following COVID-19 associated ARDS. Research has shown that a variety of transcription factors are regulated by MAPK signaling, leading to the upregulation of pro-apoptotic proteins and the downregulation of anti-apoptotic proteins [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Therefore, we suppose that once the lung is infected with SARS-CoV-2 and progresses to ARDS, the MAPK cascade is activated, subsequently triggering downstream transcription factors. These transcription factors regulate gene expression, initiating a series of cellular responses, including apoptosis and immunoreaction, which gradually progress to pulmonary fibrosis. Our finding provides important evidence supporting the fibrogenesis mechanism and suggests that antifibrotic therapy for COVID-19 patients could be achieved by mediating MAPK and apoptosis.\u003c/p\u003e \u003cp\u003eBased on PPI network construction and hub genes identification, we explored potential biomarkers, mechanisms and drug targets for pulmonary fibrosis post-COVID-19 ARDS patients. Developing new drugs rapidly for emerging diseases is often unrealistic, so repurposing existing drugs can be highly beneficial. Drug repurposing seeks new indications for known drugs, thus narrowing down candidates, avoiding safety issues, and shortening development time [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. In view of this, we predicted candidate drugs targeting hub genes to explore promising treatments for this deadly complication of pulmonary fibrosis following COVID-19 associated ARDS.\u003c/p\u003e \u003cp\u003eOur study found that FCER1A has the strongest interactions with other genes and is likely the most critical gene in pulmonary fibrosis following COVID-19 associated ARDS. FCER1A encodes the α-subunit of immunoglobulin E (IgE) receptor FcεRI, is known to participate in immune reactions and mediate the release of inflammatory mediators such as histamine, and is important in allergic disease especially asthma [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Chen et al. [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] found that the IgE concentration in serum was significantly elevated in patients of pneumoconiosis and mice exposed to silica, and FcεRI deficiency provide substantial protection against pulmonary inflammation and fibrosis induced by silica in a mouse model. Kim et al. [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] found that inhibition of sirtuin2 reduces mast cell-mediated allergic airway inflammation and fibrosis via blocking FcεRI/TGF-β signaling pathway. However, the role of FCER1A in pulmonary fibrosis following infection remains unclear. In our study, FCER1A was commonly downregulated in three datasets, thus we suppose that FcεRI might play a detrimental role in case of pulmonary fibrosis post-COVID-19 ARDS. Three FDA-approved drugs associated with FCER1A were identified: omalizumab, mizolastine and desloratadine. Binding to IgE, omalizumab is approved for treating allergic asthma in individuals sensitive to perennial allergens with elevated IgE levels [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Cao et al. [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] found that blocking IgE-FcεR1 signaling in mice by administrating the omalizumab suppressed cardiac hypertrophy and cardiac interstitial fibrosis significantly. Meanwhile, as antihistamine for allergic rhinitis and chronic urticaria, mizolastine and desloratadine block histamine H1 receptors of FcεR1-activated mast cells and basophils to alleviate symptoms [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Our study suggests that omalizumab, mizolastine, and desloratadine, interfering with IgE-FcεR1 signaling, may offer potential therapeutic benefits for managing pulmonary fibrosis post-COVID-19 ARDS patients.\u003c/p\u003e \u003cp\u003eGATA2 is a member of the family of zinc finger transcription factors known to regulate alveolar macrophage phagocytosis and erythropoiesis [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. It has been reported that GATA2 deficiency is associated with pulmonary fibrosis related to pulmonary alveolar proteinosis [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. However, the effect of GATA2 expression on pulmonary fibrosis post-COVID-19 is unclear. Herein, our study revealed that the expression of GATA2 was commonly inhibited, pointing out the potential role of GATA2 in pulmonary fibrosis post-COVID-19 associated ARDS. In addition, we identified epoetin alfa associated with GATA2 as a potential remedy. Epoetin alfa stimulates erythropoiesis by activating EPO receptor and downstream pathways, while GATA2 regulates the gene expression of EPO receptor, making it more sensitive to epoetin alfa [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. However, the effect of epoetin alfa to GATA and pulmonary fibrosis post-COVID-19 ARDS is unclear, waiting for further experimental exploration.\u003c/p\u003e \u003cp\u003eCLC, referring to Charcot-Leyden crystal galectin or galectin-10 (Gal-10), belongs to the galectin superfamily that share highly conserved amino acid sequences constituting the carbohydrate recognition domain. CLC is considered as a hallmark of eosinophil involvement in allergic reactions and associated immune responses [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Meanwhile, evidences suggest that other member, like Gal-3, of the galectin superfamily could play a crucial role in COVID-19 [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] and pulmonary fibrosis [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. However, the effect of CLC in pulmonary fibrosis post-COVID-19 has not been explored. Our study found that CLC is downregulated commonly in three datasets, indicating it the promising diagnostic biomarker and probable role in pulmonary fibrosis following COVID-19 associated ARDS. TD139, an inhaled small-molecule inhibitor of Gal-3, was found to be safe, well tolerated, and effective in engaging its target and reducing plasma biomarkers linked to IPF progression [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Herein, we identified moxidectin, which is a CLC-targeted anti-parasitic agent approved for the prevention of Onchocerca volvulus caused river blindness [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], as a potential drug for pulmonary fibrosis following COVID-19 associated ARDS. The effectiveness of moxidectin needs to be further confirmed.\u003c/p\u003e \u003cp\u003eIn addition, our study explores the transcriptional and post-transcriptional regulators of hub genes by revealing the relationships between TFs and hub genes as well as miRNAs and hub genes. Among the identified TFs, FOXL1, CREB1, YY1, GATA3, and E2F1 were reported to participate in the regulation of pulmonary fibrosis [\u003cspan additionalcitationids=\"CR41 CR42 CR43\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], and FOXC1 was reported to promote FGFR1 isoform switching following induction of TGF-β-mediated epithelial-to-mesenchymal transition, which is considered to contribute to pathogenesis of fibrosis [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Besides, CREB1 regulates SARS-CoV-2 proliferation by viral helicase nsp13 association [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], and the overexpression of GATA3 was associated with the severity and fatal outcome of COVID-19 [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Moreover, CREB1 participates in the regulation of acute lung injury [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Bioinformatics analyses suggests that hsa-miR-26b-5p [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e] and hsa-miR-524-5p [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e] may develop as diagnostic biomarkers and potential therapeutic targets for IPF, while hsa-miR-27a-3p [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e] could be a potential regulator for patients with combined pulmonary fibrosis and emphysema. Also, downregulation of miR-1290 may be helpful in the treatment of pulmonary fibrosis and viral infections such as influenza A [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Our study suggests that these TFs and miRNAs might play a role in pulmonary fibrosis following COVID-19 associated ARDS, but further investigation is needed to confirm this.\u003c/p\u003e \u003cp\u003eFinally, we conducted a gene-disease analysis to predict the relationships between hub genes and other diseases. These findings may inspire the development of potential therapies of pulmonary fibrosis post-COVID-19 ARDS, based on insights from the onset, progression and management of these diseases. Disorders or diseases, including deficiency of combined immune cells like B and natural killer lymphoid, acute myeloid leukemia, myelodysplastic syndrome, and Emberger syndrome (primary lymphedema with myelodysplasia), have been reported to result from germline mutations in GATA2 gene and to represent various manifestations of a same condition, subsequently termed GATA2 deficiency [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. Meanwhile, GATA2 deficiency is associated with pulmonary fibrosis related to pulmonary alveolar proteinosis [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] and probably pulmonary fibrosis post-COVID-19 ARDS as mentioned above. It suggests that these diseases might share the same pathogenesis, and their treatment could provide insights for the treatment of pulmonary fibrosis following COVID-19 associated ARDS. On the other hand, mutations in the telomerase lead to dyskeratosis congenita, a bone marrow failure syndrome marked by mucocutaneous abnormalities, pulmonary fibrosis, and increased susceptibility to acute myeloid leukemia and myelodysplastic syndrome [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e], indicating the future directions of treatment of pulmonary fibrosis post-COVID-19 ARDS. Omenn syndrome manifests as severe combined immune deficiency characterized by enlarged lymphoid tissue, elevated IgE levels, and eosinophilia, presenting as a distinct inflammatory process. The inflammation in these patients is initiated by clonally expanded T cells, which secrete a host of cytokines that drive autoimmune as well as allergic inflammation due to inadequate regulation by other components of the immune system [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. The gene-disease interaction in our study suggests similar molecular mechanisms in the progression of these diseases, which could inform the development of new therapeutic strategies for pulmonary fibrosis post-COVID-19 ARDS.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur study highlights the key genes and functional pathways involved in pulmonary fibrosis following COVID-19 associated ARDS. Through analyzing common DEGs and hub genes, we have uncovered potential therapeutic targets such as FCER1A, GATA2 and CLC. TFs and miRNAs interacting with hub genes were identified to explore the regulators at transcriptional and post-transcriptional level. What is more, the prediction of FDA-approved drugs targeting hub genes provides a promising avenue for developing effective treatments. This study underscores the importance of focusing on the long-term complication of COVID-19, paving the way for improved management and therapeutic strategies for pulmonary fibrosis. Further experimental validation is crucial for translating these findings into clinical applications, potentially improving outcomes for patients suffering from pulmonary fibrosis following COVID-19-related ARDS.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.127035830618894%\" valign=\"top\"\u003e\n \u003cp\u003eCOVID-19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"76.8729641693811%\" valign=\"top\"\u003e\n \u003cp\u003eCoronavirus disease 2019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.127035830618894%\" valign=\"top\"\u003e\n \u003cp\u003eARDS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"76.8729641693811%\" valign=\"top\"\u003e\n \u003cp\u003eAcute Respiratory Distress Syndrome\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.127035830618894%\" valign=\"top\"\u003e\n \u003cp\u003eGEO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"76.8729641693811%\" valign=\"top\"\u003e\n \u003cp\u003eGene Expression Omnibus\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.127035830618894%\" valign=\"top\"\u003e\n \u003cp\u003eDEGs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"76.8729641693811%\" valign=\"top\"\u003e\n \u003cp\u003eDifferentially expressed genes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.127035830618894%\" valign=\"top\"\u003e\n \u003cp\u003ePPI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"76.8729641693811%\" valign=\"top\"\u003e\n \u003cp\u003eProtein-protein interaction\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.127035830618894%\" valign=\"top\"\u003e\n \u003cp\u003eTFs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"76.8729641693811%\" valign=\"top\"\u003e\n \u003cp\u003etranscription factors\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.127035830618894%\" valign=\"top\"\u003e\n \u003cp\u003emiRNAs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"76.8729641693811%\" valign=\"top\"\u003e\n \u003cp\u003emicroRNAs\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.127035830618894%\" valign=\"top\"\u003e\n \u003cp\u003eSARS-CoV-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"76.8729641693811%\" valign=\"top\"\u003e\n \u003cp\u003eacute respiratory syndrome coronavirus 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.127035830618894%\" valign=\"top\"\u003e\n \u003cp\u003eTGF-\u0026beta;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"76.8729641693811%\" valign=\"top\"\u003e\n \u003cp\u003etransforming growth factor-\u0026beta;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.127035830618894%\" valign=\"top\"\u003e\n \u003cp\u003eIPF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"76.8729641693811%\" valign=\"top\"\u003e\n \u003cp\u003eIdiopathic pulmonary fibrosis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.127035830618894%\" valign=\"top\"\u003e\n \u003cp\u003eNCBI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"76.8729641693811%\" valign=\"top\"\u003e\n \u003cp\u003eNational Center for Biotechnology Information\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.127035830618894%\" valign=\"top\"\u003e\n \u003cp\u003eBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"76.8729641693811%\" valign=\"top\"\u003e\n \u003cp\u003eBiological Process\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.127035830618894%\" valign=\"top\"\u003e\n \u003cp\u003eMAPK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"76.8729641693811%\" valign=\"top\"\u003e\n \u003cp\u003emitogen-activated protein kinase\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.127035830618894%\" valign=\"top\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"76.8729641693811%\" valign=\"top\"\u003e\n \u003cp\u003eCellular Component\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.127035830618894%\" valign=\"top\"\u003e\n \u003cp\u003eMF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"76.8729641693811%\" valign=\"top\"\u003e\n \u003cp\u003eMolecular Function\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.127035830618894%\" valign=\"top\"\u003e\n \u003cp\u003eIgE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"76.8729641693811%\" valign=\"top\"\u003e\n \u003cp\u003eimmunoglobulin E\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003eSupplementary Information\u003c/p\u003e\n\u003cp\u003eThe Supplementary Material for this article can be found online at XXX.\u003c/p\u003e\n\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eAll authors gave their consent for publication.\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials\u003c/p\u003e\n\u003cp\u003ePublicly available datasets were analyzed in this study. The data could be downloaded from the GEO database of the NCBI (https://www.ncbi.nlm.nih.gov/geo/), accession numbers\u0026nbsp;GSE213313,\u0026nbsp;GSE76293, and\u0026nbsp;GSE28042.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis work was supported by Fujian Province Natural Science Foundation (2021J05280) and Fujian Province Talent Introduction Program Foundation (01102801).\u003c/p\u003e\n\u003cp\u003eAuthors\u0026apos; contributions\u003c/p\u003e\n\u003cp\u003eDL and HZ conceived and supervised the study. DL and NZ collected the data, conducted the data analysis and prepared the figures. DL and HZ prepared the manuscript. DL, NZ, and HZ contributed to the research revisions and reviewed the manuscript.\u003c/p\u003e\n\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eThe authors thank the Zheng Wan from Zhongshan Hospital of Xiamen University for providing technical support.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWang D, Hu B, Hu C, Zhu F, Liu X, Zhang J, et al. Clinical Characteristics of 138 Hospitalized Patients With 2019 Novel Coronavirus-Infected Pneumonia in Wuhan, China. JAMA. 2020;323(11):1061\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang J, Guo Y, Mak M, Tao Z. Translational medicine for acute lung injury. J Transl Med. 2024;22(1):25.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBurnham EL, Janssen WJ, Riches DW, Moss M, Downey GP. The fibroproliferative response in acute respiratory distress syndrome: mechanisms and clinical significance. 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E2F1 regulates miR-215-5p to aggravate paraquat-induced pulmonary fibrosis via repressing BMPR2 expression. Toxicol Res (Camb). 2022;11(6):940\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHopkins A, Coatham ML, Berry FB. FOXC1 Regulates FGFR1 Isoform Switching to Promote Invasion Following TGFbeta-Induced EMT. Mol Cancer Res. 2017;15(10):1341\u0026ndash;53.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZheng T, Shen B, Bai Y, Li E, Zhang X, Hu Y, et al. The PKA-CREB1 axis regulates coronavirus proliferation by viral helicase nsp13 association. J Virol. 2024;98(4):e0156523.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbbasi-Dokht T, Vafaeinezhad A, Khalesi N, Malek F, Haghmorad D, Baharlou R. T-Cell Immune Responses and Immunological Factors Associated with Coronavirus Disease 2019 Progression as Predictors for the Severity of the Disease in Hospitalized Patients. Int Arch Allergy Immunol. 2023;184(6):557\u0026ndash;66.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi Z, Jin T, Yang R, Guo J, Niu Z, Gao H, et al. Long non-coding RNA PFI inhibits apoptosis of alveolar epithelial cells to alleviate lung injury via miR-328-3p/Creb1 axis. Exp Cell Res. 2023;430(1):113685.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYu DH, Ruan XL, Huang JY, Liu XP, Ma HL, Chen C, et al. Analysis of the Interaction Network of Hub miRNAs-Hub Genes, Being Involved in Idiopathic Pulmonary Fibers and Its Emerging Role in Non-small Cell Lung Cancer. Front Genet. 2020;11:302.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi Q, Li M, Zheng K, Li H, Yang H, Ma S, et al. Detection of microRNA expression levels based on microarray analysis for classification of idiopathic pulmonary fibrosis. Exp Ther Med. 2020;20(4):3096\u0026ndash;103.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTanwar N, Hasija Y. Explicate molecular landscape of combined pulmonary fibrosis and emphysema through explainable artificial intelligence: a comprehensive analysis of ILD and COPD interactions using RNA from whole lung homogenates. Med Biol Eng Comput. 2024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKalhori MR, Soleimani M, Yari K, Moradi M, Kalhori AA. MiR-1290: a potential therapeutic target for regenerative medicine or diagnosis and treatment of non-malignant diseases. Clin Exp Med. 2023;23(3):737\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHahn CN, Chong CE, Carmichael CL, Wilkins EJ, Brautigan PJ, Li XC, et al. Heritable GATA2 mutations associated with familial myelodysplastic syndrome and acute myeloid leukemia. Nat Genet. 2011;43(10):1012\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOstergaard P, Simpson MA, Connell FC, Steward CG, Brice G, Woollard WJ, et al. Mutations in GATA2 cause primary lymphedema associated with a predisposition to acute myeloid leukemia (Emberger syndrome). Nat Genet. 2011;43(10):929\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCalado RT. Telomeres and marrow failure. Hematol Am Soc Hematol Educ Program. 2009:338\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVilla A, Notarangelo LD, Roifman CM. Omenn syndrome: inflammation in leaky severe combined immunodeficiency. J Allergy Clin Immunol. 2008;122(6):1082\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Table 2","content":"\u003cp\u003eTable 2 is available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"COVID-19, ARDS, pulmonary fibrosis, transcriptomics, hub genes, drug repurposing","lastPublishedDoi":"10.21203/rs.3.rs-4858965/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4858965/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eCoronavirus disease 2019 (COVID-19) pandemic has led to numerous cases of acute respiratory distress syndrome (ARDS), with a significant number of survivors developing pulmonary fibrosis as a chronic sequela. This condition poses severe long-term health challenges, significantly burdening public health systems. Despite significant research on the acute phase of COVID-19, the mechanisms underlying pulmonary fibrosis following COVID-19 associated ARDS remain poorly understood, and effective therapies are yet to be established. This study aims to elucidate the molecular mechanisms, identify potential biomarkers, and explore therapeutic options for pulmonary fibrosis post-COVID-19-related ARDS through comprehensive transcriptomic and bioinformatic analyses.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe collected datasets from Gene Expression Omnibus (GEO) database, including transcriptional profiles of COVID-19, ARDS, and pulmonary fibrosis. Differentially expressed genes (DEGs) common to these conditions were identified, reflecting the transcriptional landscape of pulmonary fibrosis post-COVID-19 ARDS. Functional and pathway enrichment analyses was conducted. Protein-protein interaction (PPI) network was constructed to determine the hub genes and their regulatory networks. Drugs that interact with hub genes were explored and gene-disease associations were analyzed to identify potential therapeutic strategies.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eWe identified 116 common DEGs among COVID-19, ARDS, and pulmonary fibrosis datasets. Functional enrichment highlighted critical processes including inflammatory response, apoptosis, transcription regulation, and MAPK cascade. PPI network revealed hub genes which may play crucial roles in the pathogenesis of pulmonary fibrosis post-COVID-19-related ARDS. Notably, FCER1A, associated with immune response and inflammation, GATA2, involved in macrophage function and erythropoiesis, and CLC, indicative of eosinophil activity, emerged as central players. Regulatory network analysis highlighted significant transcription factors (TFs) and microRNAs (miRNAs) associated with hub genes. We found FDA-approved drugs that could interact with these hub genes, including omalizumab, mizolastine, desloratadine, epoetin alfa, and moxidectin. Gene-disease interaction analysis revealed that diseases caused by GATA2 deficiency and immunodeficiency were associated with hub genes.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eOur findings provide valuable insights into the molecular underpinnings of pulmonary fibrosis post-COVID-19 ARDS and highlight potential biomarkers and therapeutic targets. The repurpose of drugs offers a promising avenue for rapid clinical application, potentially improving outcomes. This study provides ideas for improved treatment for pulmonary fibrosis post-COVID-19 ARDS.\u003c/p\u003e","manuscriptTitle":"Mechanisms and Therapeutic Strategies for Pulmonary Fibrosis Post-COVID-19 ARDS: Insights from Comprehensive Bioinformatics","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-08 02:31:47","doi":"10.21203/rs.3.rs-4858965/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1e97c023-17d0-4d7d-b108-f28b0400a7e5","owner":[],"postedDate":"October 8th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-10-08T02:31:49+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-08 02:31:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4858965","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4858965","identity":"rs-4858965","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}