Role of OAS gene family in COVID-19 induced heart failure

doi:10.21203/rs.3.rs-2142806/v1

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Research Article

Role of OAS gene family in COVID-19 induced heart failure

https://doi.org/10.21203/rs.3.rs-2142806/v1

This work is licensed under a CC BY 4.0 License

Journal Publication

published 22 Mar, 2023

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Background

COVID-19, the current global pandemic caused by SARS-CoV-2 infection, can damage the heart and lead to heart failure (HF) and even cardiac death. The 2',5'-oligoadenylate synthetase (OAS) gene family encode interferon (IFN)-induced antiviral proteins which is associated with the antiviral immune responses of COVID-19. While the potential association of OAS family with cardiac injury and failure in COVID-19 has not been determined.

Methods

The expression levels and biological functions of OAS gene family in SARS-CoV-2 infected cardiomyocytes dataset (GSE150392) and HF dataset (GSE120852) were determined by comprehensive bioinformatic analysis and experimental validation. The associated microRNAs (miRNAs) were explored from Targetscan and GSE104150 databases. The potential OAS gene family-regulatory chemicals or ingredients were predicted using Comparative Toxicogenomics Database (CTD) and SymMap database.

Results

The OAS genes were highly expressed in both SARS-CoV-2 infected cardiomyocytes and in the failing hearts. The differentially expression genes (DEGs) in the two datasets were enriched in cardiovascular disease and COVID-19 related pathways, respectively. The miRNAs-target analysis indicated that 9 miRNAs could increase the expression of OAS genes. A variety of chemicals or ingredients were predicted regulating the expression of OAS gene family especially estradiol.

Conclusion

OAS gene family is an important mediator of HF in COVID-19 and may serve as a potential therapeutic target for cardiac injury and HF in COVID-19.

OAS gene family

COVID-19

SARS-CoV-2

cardiomyocyte

heart failure

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection [1], is a highly transmissible and pathogenic coronavirus emerged in late 2019 and has greatly threatened human health and public safety worldwide [2]. So far, the epidemic of COVID-19 has spread all over the world, as of 19 June 2022, over 536 million confirmed cases and over 6.3 million deaths have been reported globally [3]. Patients with COVID-19 exhibit a variety of symptoms, such as cough, fever, chest discomfort, and even respiratory distress syndrome in severe cases [4]. Those with comorbidities, older age and male sex tend to suffer from severe COVID-19 after infection [5, 6]. In addition to causing severe respiratory illness, COVID-19 can also damage multiple organs especially the heart, causing myocardial injury and HF [7, 8]. HF is the fourth most frequent complication of COVID-19 [9], and is also the final outcome of most cardiovascular diseases. The mortality rate of HF is extremely high [10]. Therefore, it is necessary to investigate the regulatory mechanisms of HF in COVID‐19 in order to better control the worse cardiac consequences of this infectious disease.

In human genome, OAS gene family includes OAS1, OAS2, OAS3, and OASL [11, 12]. As an important immune regulator [13], OAS gene family participates in antiviral biological process and innate immune. Upon virus infection, OAS genes catalyze ATP into 2',5'-linked oligomers of adenosine in the presence of double-stranded (ds) RNA. These oligomers then activate RNaseL [14, 15]. OAS proteins act as a sensor of dsRNA and block viral replication by activating RNaseL [16, 17]. In this process, NOD-like proteins can interact with the OAS family to enhance the activity of RNase L. The degraded RNA activates retinoic acid-inducible gene-I (RIG-1) [18, 19]. RIG-1 amplify production of IFN-α/β and activate NF-κB to produce inflammatory factors, leading to cell apoptosis [20]. In addition, the JAK-STAT pathway and IFN regulatory factor (IRF) family are involved in the regulation of this process [21]. However, it is unknown whether the OAS gene family actually plays a beneficial or harmful role in SARS-CoV-2-infected cardiomyocytes and COVID-19-induced cardiac injury and failure, because activations of antiviral genes do not necessarily produce a beneficial effect on the viral diseases such as COVID-19.

In the present study, we speculated that OAS gene family is an effective mediator for COVID-19 to worsen the cardiac function and cause HF. Numerous studies have found that untimely or overreacted antiviral responses may cause imbalance of the immune system, leading to inflammatory response or cytokine storm [22]. SARS-CoV-2 infects the respiratory cells and other cells including the cardiomyocytes, causes host cell apoptotic death by inducing inflammation and even cytokine storm [23, 24], thus may worsen the cardiac function and cause HF, one of the most common outcomes of COVID-19. Here, using extensive bioinformatic analyses and experimental validation, we found that the expressions of OAS genes were significantly upregulated in both the SARS-CoV-2-infected cardiomyocytes and in the failing hearts of COVID-19-free cases. These findings support our speculation and suggest that OAS gene family promotes the development of HF in COVID-19. Targeting OAS genes may be a potential therapeutic approach in treating COVID-19 associated HF.

Data download and processing

The original gene expression profiles of GSE150392, GSE120852, GSE147507, GSE 179850 and GSE104150 were download from the National Center of Biotechnology Information - Gene Expression Omnibus (NCBI-GEO) database (https://www.ncbi.nlm.nih.gov/geo/), the largest, free and open accessing public gene expression database presently in the world.

GSE150392 is a dataset of mRNA expression in SARS-CoV-2 infected cardiomyocytes, including three groups of SARS-CoV-2 infected hiPSC-cardiomyocytes and three groups of hiPSC-cardiomyocytes with mock. The detailed information of GSE150392 dataset is shown in Table 1.

Table 1

Detail information of GSE150392
GEO accession	Organism	Platform	Contributor(s)	Samples
GSE150392	Homo sapiens (Cell)	GPL18573	Sharma A, et al	SARS-CoV-2 Infected hiPSC- cardiomyocytes	hiPSC-cardiomyocytes with mock
GSE150392	Homo sapiens (Cell)	GPL18573	Sharma A, et al	3	3

GSE120852 is a mRNA expression dataset of HF (COVID-19 free), including 5 non-failing (NF) left ventricles (LV), 5 NF right ventricles (RV), 5 LV tissues from subjects of LV failure, 5 RV tissues from LV failure, 5 LV tissues from biventricular failure (Bi-HF), and 5 RV tissues from Bi-HF. In the condition of LV failure, the mRNA expression profiles of LV and the RV were extracted and compared with the corresponding normal ventricular tissues (non-failing ventricles) to obtain the differential gene 1 (diff-1) and gene 2 (diff-2). In the condition of biventricular failure, the mRNA expression profiles of LV and the RV were derived and compared with that of the corresponding normal ventricular tissues to obtain the differential gene 3 (diff-3) and gene 4 (diff-4). The detailed information of GSE120852 dataset is shown in Table 2.

Table 2

Detail information of GSE120852
GEO accession	Organism	Platform	Contributor(s)
GSE120852	Homo sapiens (Heart tissue)	GPL11154	Luo X, et al		Diff-1		Diff-2		Diff-3		Diff-4
				Condition	Non Failing (NF)	Left Ventricle Heart Failure (LV-HF)	Non Failing (NF)	Left Ventricle Heart Failure (LV-HF)	Non Failing (NF)	BiVentricular Heart Failure (Bi-HF)	Non Failing (NF)	BiVentricular Heart Failure (Bi-HF)
				Tissue	Left Ventricle (LV)	Left Ventricle (LV)	Right Ventricle (RV)	Right Ventricle (RV)	Left Ventricle (LV)	Left Ventricle (LV)	Right Ventricle (RV)	Right Ventricle (RV)
					5	5	5	5	5	5	5	5

GSE147507 and GSE179850 are mRNA expression databsets related with COVID-19. In GSE147507, we chose three groups of human lung epithelial (NHBE) cells treated with mock and three groups of NHBE cells treated with SARS-CoV-2, the detailed information is showed in Table 3. In GSE179850, there are 16 groups of healthy controls and 31 groups of COVID-19 patients blood samples, details are displayed in Table 4.

Table 3

Detail information of GSE147507
GEO accession	Organism	Platform	Contributor(s)	Samples
GSE147507	Homo sapiens (Cell)	GPL18573	Sharma A, et al	Mock treated NHBE cells	NHBE infected with SARS-CoV-2
GSE147507	Homo sapiens (Cell)	GPL18573	Sharma A, et al	3	3

Table 4

Detail information of GSE179850
GEO accession	Organism	Platform	Contributor(s)	Samples
GSE179850	Homo sapiens (Blood)	GPL28038	Ebihara T, et al	Healthy control	COVID-19 patient
GSE179850	Homo sapiens (Blood)	GPL28038	Ebihara T, et al	16	31

GSE104150 is a microRNA (miRNA) expression dataset of HF, including seven groups of blood samples from healthy controls and nine groups of blood samples from HF subjects. The detailed information of GSE104150 dataset is shown in Table 5.

Table 5

Detail information of GSE104150 (microRNA expression dataset)
GEO accession	Organism	Platform	Contributor(s)	Samples
GSE104150	Homo sapiens (Blood)	GPL20712	Liu W, et al	Healthy control	Heart failure
GSE104150	Homo sapiens (Blood)	GPL20712	Liu W, et al	7	9

Analyses of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enrichment

GO and KEGG pathways of DEGs were analyzed using Metascape. Metascape [25] (http://metascape.org) is a public online database, it is designed to provide a comprehensive gene list annotation and analysis resource. GO is a gene function classification system to describe gene property. In the GO enrichment analysis, three aspects were analyzed, including biological processes (BP), cellular components (CC), and molecular functions (MF). KEGG pathway was used to analyze gene function, genomic information, and target relationship of pathways. P < 0.01 was chosen as the cut-off criteria.

Creation of mouse heart failure model

Eight-week-old male C57BL/6 mice (22−25 g) were purchased from Sibeifu Co., (Beijing). All mice were maintained under specific-pathogen-free (SPF) conditions. HF in the mice were induced by transverse aortic constriction (TAC) for 8 weeks. TAC surgery was conducted as reported by Tavakoli et al. [26] and Wu et al. [27]. Pressure gradients between the proximal and distal sites of TAC were determined by doppler echocardiography. Heart weight/body weight (HW/BW) ratio, ejection fraction (EF), fractional shortening (FS), left ventricular posterior wall thickness at end diastole (LVPWd), and left ventricular posterior wall thickness at systole (LVPWs) were calculated.

Cell culture and treatment

H9C2 cells (a rat myocardial cell line) were purchased from Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences (Shanghai, China), and cultured in 6-well plates using DMEM with 10% fetal bovine serum and 1% penicillin/streptomycin at 37 ℃ in a humidified environment containing 5% CO₂. Cells were cultured in serum-free DMEM medium for 12 h, then were challenged with angiotensin II (Ang Ⅱ) for 48 h to induce cardiomyocyte hypertrophic injury. Cells were harvested for quantitative real-time PCR (qPCR).

RNA isolation and qPCR

Total mRNA was extracted from mice failing heart tissues and H9C2 cells using TRIzol (Invitrogen, Carlsbad, CA), then was reverse-transcribed into cDNA according to the instructions of TaKaRa PrimeScript RT reagent Kit with gDNA Eraser (TaKaRa, Osaka, Japan), according to the manufacturer’s instruction. PCR amplifications were quantified according to the instructions of TaKaRa TB Green Premix Ex Taq II (TaKaRa, Osaka, Japan). The primer sequences for qPCR were designed by Sangon Biotech Co., Ltd (Shanghai, China). The expression data were normalized to the reference glyceraldehyde-3-phosphate dehydrogenase (GAPDH) and the mRNA levels were calculated using the 2 ^−∆∆Ct method. Primer sequences for qPCR are shown in Additional file 1: Table S1.

Analysis of differentially expressed genes (DEGs)

The “limma” package in the R language was used to analyze the data downloaded from NCBI-GEO. P < 0.05 and |logFC| > 1 were chosen as the cut-off criteria in the analyses of GSE150392 and GSE104150; P < 0.05 and |logFC| > 0.8 were set as the cut-off criteria in GSE120852. The common DEGs in GSE120852 were generated and visualized using FunRich software (version 3.1.3.).

Analysis of DEGs interactions and screening of hub genes

The protein-protein interaction (PPI) network of DEGs were determined using STRING [28] (version 11.5, https://string-db.org/). STRING is an online database for searching protein-protein interactions including direct physical interactions and indirect functional correlations between proteins. In the PPI analysis, the minimally required interaction score was set as medium confident 0.4. Then, results were used to screen hub genes using CytoHubba in Cytoscape (version 3.9.0.). CytoHubba [29] is a plugin in Cytoscape, it provides 12 topological analysis methods which can be used to explore important nodes in biological networks. We used Density of Maximum Neighborhood Component (DMNC), one of the 12 topological analysis methods, to search the top 30 hub genes.

Analysis of miRNAs targeting the OAS gene family

The microRNAs (miRNAs) are short non-coding RNA molecules with 19 to 25 nucleotides in size and have the functions of degrading or blocking target mRNAs at the post-transcriptional level [30]. Targetscan [31] (http://www.targetscan.org/vert_72/) was used to identify the upstream miRNAs of OAS gene family.

Prediction of chemicals and ingredients interacting with OAS gene family

Comparative Toxicogenomics Database (CTD) and SymMap database were used to analyze the chemicals or drugs which interact with the OAS gene family. CTD [32] (http://ctdbase.org/) is a powerful public database which integrates large amounts of data among chemicals, genes, functional phenotypes and diseases. It provides information on the associations of chemical-gene/protein, chemical-disease, and gene-disease, and thus helps to predict mechanistic hypotheses about the influence of environment on disease. In the CTD database, we chose chemicals with “interaction” > 3 as the cut-off criteria to perform further analysis. SymMap [33] database (http://www.symmap.org/) is an integrative database of Traditional Chinese Medicine (TCM) enhanced by symptom mapping. It contains six components, including herbs, TCM symptoms, modern medicine (MM) symptoms, ingredients, targets (or genes) and disease. Target function was used to analyze the ingredients that interact with the OAS gene family.

Docking analysis of affinity between chemicals/ingredients and OAS gene family

Molecular docking approach was used to identify the affinities of chemicals/ingredients with OAS1, OAS2, OAS3, and OASL. The structures of the small molecules from PubChem database (https://pubchem.ncbi.nlm.nih.gov/) were downloaded, and Chem3D software was used to minimize the ligand molecular energy. The 3D structures of OAS genes were obtained from PDB database (https://www1.rcsb.org/) or UniProt database (https://www.uniprot.org/). AutoDockTools 1.5.6 software was used to find out the active pockets. Vina script was run to calculate the molecular binding energy, Vina ≤ −7.0 kcal·mol^− 1 indicated strong binding of “ligand” with “receptor”. PyMOL software was used to display the results.

Statistical analysis

GraphPad Prism 5.0 were used to performed statistical analysis. Data were presented as mean ± standard deviation (SD). Two-tail t-test was used for comparison of two groups. Statistical significance was set at P < 0.05.

DEGs and hub genes derived from the GSE150392 dataset

In GSE150392 (mRNA expression dataset of SARS-CoV-2 infected cardiomyocytes), a total of 1,448 DEGs were screened out. Among them, 745 DEGs were up-expressed and 703 DEGs were down-expressed. Notably, OAS1, OAS2, OAS3, and OASL were all up-expressed in the cardiomyocytes with SARS-CoV-2 infection (Fig. 1A, B; Additional file 2), and their logFCs were 6.94, 7.13, 5.83, and 5.49, respectively (Fig. 1C).

The protein-protein interactions among the DEGs were analyzed using STRING online database and results were presented using cytoscape (Fig. 1D). In this analysis, the algorithms of DMNC in the plugin cytoHubba was used to calculate the top 30 hub genes. OAS1, OAS2, OAS3, and OASL were ranked 14, 13, 9, and 28 among the top 30 hub genes, respectively (Fig. 1E).

DEGs and hub genes derived from the GSE120852 dataset

In GSE120852 (mRNA expression dataset of HF), four groups were included and their intersection genes (common DEGs) were selected to perform further analysis. Diff-1 indicated the comparison result of LV heart failure (LV-HF) vs. non-failing LV (LV-NF). As a result, 849 DEGs were screened out, 507 were up-expressed and 342 DEGs were down-expressed. Diff-2 was obtained from RV which reflected the RV mRNA difference between LV-HF and LV-NF. In the Diff-2 analysis, 1,128 DEGs were screened out, and among them, 638 were up-expressed and 490 DEGs were down-expressed. Diff-3 was obtained from LV by comparing biventricular heart failure (Bi-HF) and non-failing (NF) hearts. Total 973 DEGs were screened out, in which 614 were up-expressed and 359 DEGs were down-expressed. Diff-4 was gained from RV by comparing Bi-HF and NF hearts, total 1,175 DEGs were screened out, 673 were up-expressed and 502 DEGs were down-expressed (Fig. 2A, B; Additional file 3). Total 239 common DEGs were found in the above four groups of DEGs, and 169 were up-expressed and 70 were down-expressed (Fig. 2C). Of note, OAS1, OAS2, OAS3 and OASL were all highly expressed in HF. In diff-1, the logFCs of OAS1, OAS2, OAS3, and OASL were 1.04, 0.81, 0.98, and 1.03, respectively. In diff-2, the logFCs of OAS1, OAS2, OAS3 and OASL were 1.23, 0.97, 1.22, and 1.53, respectively. In diff-3, the logFCs of OAS1, OAS2, OAS3 and OASL were 1.35, 1.03, 0.93, and 1.57, respectively. In diff-4, the logFCs of OAS1, OAS2, OAS3 and OASL were 1.26, 1.21, 1.37 and 1.94, respectively. (Fig. 2D).

SRTING online software was used to explore the relationships among the 239 common DEGs, and results were shown via cytoscape (Fig. 2E). The algorithm of DMNC in plugin cytoHubba was used to calculate the top 30 hub genes. To our speculation, OAS1, OAS2, OAS3, and OASL were all among the top 30 hub genes, and they were ranked 15, 6, 6, and 2, respectively (Fig. 2F).

Experimental validation of high mRNA expressions of OAS genes

To verify the expression of OAS genes shown in Figs. 1 and 2, we further analyzed the GSE147507 dataset (SARS-CoV-2 infected NHBE) and the GSE179850 dataset (blood sample of COVID-19 patients) to evaluate the expression of OAS gene family in alternative tissues of COVID-19 patients. Results demonstrated that OAS1, OAS2, OAS3, and OASL were all highly expressed in SARS-CoV-2 infected NHBE (Fig. 3A) (OAS 1−3, P < 0.01) and in the blood leucocytes of COVID-19 patients (Fig. 3B) (all P < 0.001). In addition, the qPCR results of 8-week TAC mice and Ang Ⅱ-stimulated H9C2 cells showed that the mRNA levels of atrial natriuretic peptide (ANP), brain natriuretic peptide (BNP), and myosin heavy chain β (β-MHC) were all significantly increased in the cardiac tissues of TAC mice (Fig. 4A, B) and Ang Ⅱ-challenged H9C2 cells (Fig. 4C, D), and the mRNA levels of OAS1, OAS2, OAS3, and OASL were also significantly elevated in the failing hearts of TAC mice (Fig. 4B) and Ang Ⅱ-treated H9C2 cells (Fig. 4D). These results indicate a consistence between the bioinformatic analysis and the experimental validation.

GO and KEGG pathway analyses revealing the intersecting signaling between COVID-19 and HF

The biological function enrichment analyses of DEGs in GSE150392 and GSE120852 datasets were performed using Metascape.

Results of GO analysis from GSE150392 are shown in Fig. 5A and Additional file 1: Table S4. The DEGs of GSE150392 were significantly enriched in GO:0048018 (MF: receptor ligand activity), GO:0030017 (CC: sarcomere), GO:0071345 (BP: cellular response to cytokine stimulus), GO:0003013 (BP: circulatory system process), GO:0044057 (BP: regulation of system process), GO:0030155(BP: regulation of cell adhesion), GO:0001819(BP: positive regulation of cytokine production), GO:0009615 (BP: response to virus), GO:0010817 (BP: regulation of hormone levels), GO:0044706 (BP: multi-multicellular organism process), GO:0043408 (BP: regulation of MAPK cascade), GO:0006936 (BP: muscle contraction), GO:0043269 (BP: regulation of ion transport), GO:0061061(BP: muscle structure development), GO:0007167 (BP: enzyme-linked receptor protein signaling pathway), GO:0050900 (BP: leukocyte migration), GO:0032649 (BP: regulation of interferon-gamma production), GO:0010942(BP: positive regulation of cell death), GO:0009725(BP: response to hormone), and GO:0048511(BP: rhythmic process). Results of KEGG pathway analysis from GSE150392 were displayed in Fig. 5B and Additional file 1: Table S5. The DEGs of GSE150392 were significantly enriched in hsa04668 (TNF signaling pathway), hsa04060 (cytokine-cytokine receptor interaction), hsa04260 (Cardiac muscle contraction), hsa05164 (influenza A), hsa05202 (transcriptional misregulation in cancer), hsa05143 (African trypanosomiasis), hsa04010 (MAPK signaling pathway), hsa04080 (neuroactive ligand-receptor interaction), hsa04020 (calcium signaling pathway), hsa05415 (Diabetic cardiomyopathy), hsa04913 (ovarian steroidogenesis), hsa04210 (apoptosis), hsa04750 (inflammatory mediator regulation of TRP channels), hsa05203 (viral carcinogenesis), hsa00350 (tyrosine metabolism), hsa04670 (leukocyte transendothelial migration), hsa04727 (GABAergic synapse), hsa04024 (cAMP signaling pathway), hsa04630 (JAK-STAT signaling pathway), and hsa04713 (circadian entrainment).

Results of GO analysis from GSE120852 were shown in Fig. 6A and Additional file 1: Table S6. The DEGs of this analysis included GO:0031012 (CC: extracellular matrix), GO:0030155 (BP: regulation of cell adhesion), GO:0006935 (BP: chemotaxis), GO:0098552 (CC: side of membrane), GO:0098609 (BP: cell-cell adhesion), GO:0050900 (BP: leukocyte migration), GO:0005539 (MF: glycosaminoglycan binding), GO:0045321 (BP: leukocyte activation), GO:0005518 (MF: collagen binding), GO:0002685 (BP: regulation of leukocyte migration), GO:0002252 (BP: immune effector process), GO:0005178 (MF: integrin binding), GO:0005604 (CC: basement membrane), GO:0002683 (BP: negative regulation of immune system process), GO:0050727 (BP: regulation of inflammatory response), GO:0048525 (BP: negative regulation of viral process), GO:0032103 (BP: positive regulation of response to external stimulus), GO:0019221 (BP: cytokine-mediated signaling pathway), GO:0030934 (CC: anchoring collagen complex), and GO:0046886 (BP: positive regulation of hormone biosynthetic process). Results of KEGG pathway analysis from GSE120852 were displayed in Fig. 6B and Additional file 1: Table S7. The DEGs of this analysis included hsa05164 (influenza A), hsa04060 (cytokine-cytokine receptor interaction), hsa04062 (chemokine signaling pathway), hsa04974 (protein digestion and absorption), hsa04670 (leukocyte transendothelial migration), hsa05171 (coronavirus disease-COVID-19), hsa05340 (primary immunodeficiency), hsa04210 (apoptosis), hsa04064 (NF-kappa B signaling pathway), hsa04929 (GnRH secretion), hsa00260 (glycine, serine and threonine metabolism), hsa04380 (osteoclast differentiation), hsa04926 (relaxin signaling pathway), hsa00480 (glutathione metabolism), hsa03250 (viral life cycle-HIV-1), hsa05221 (acute myeloid leukemia), hsa05120 (epithelial cell signaling in helicobacter pylori infection), hsa04512 (ECM-receptor interaction), and hsa04350 (TGF-beta signaling pathway).

Results of GSE150392 analysis, including GO:0003013 (circulatory system process) and hsa04260 (cardiac muscle contraction), were related to cardiac function in SARS-CoV-2 infected cardiomyocytes. Some results from GSE120852, such as GO:0050727 (regulation of inflammatory response) and hsa05171 (COVID-19), were also related to COVID-19. In addition, some results, including GO:0030155 (BP: regulation of cell adhesion), GO:0006935 (BP: chemotaxis), GO:0050900 (BP: leukocyte migration), hsa05164 (Influenza A), hsa04060 (Cytokine-cytokine receptor interaction), hsa04670 (leukocyte transendothelial migration), and hsa04210 (apoptosis), were common results of GSE150392 and GSE120852, suggesting that the processes of HF and COVID-19 employ the same biological pathways to some extents.

The miRNAs regulating the expression of OAS gene family

To explore the regulatory mechanism of OAS gene family expression, we analyzed the miRNAs derived from GSE104150 (miRNA expression dataset of HF) and took intersection with the predicting results from Targetscan. Total 88 different miRNAs were obtained from GSE104150, and among them, 22 miRNAs were upregulated and 44 miRNAs were downregulated (Fig. 7A, B; Additional file 1: Table S8). From the intersection of Targetscan and GSE104150, we found that 6 miRNAs regulated OAS1, including hsa-miR-1225-5p, hsa-miR-1229-5p, hsa-miR-15a-3p, hsa-miR-4270, hsa-miR-6751-3p, and hsa-miR-7106-5p. There were 33 miRNAs that regulated OAS2, including hsa-miR-1225-5p, hsa-miR-1229-5p, hsa-miR-1275, hsa-miR-181a-5p, hsa-miR-197-5p, hsa-miR-2392, hsa-miR-23a-5p, hsa-miR-26b-5p, hsa-miR-3149, hsa-miR-4433a-3p, hsa-miR-4459, hsa-miR-4484, hsa-miR-4499, hsa-miR-4632-5p, hsa-miR-4721, hsa-miR-4769-3p, hsa-miR-5196-5p, hsa-miR-548aq-5p, hsa-miR-548d-5p, hsa-miR-576-5p, hsa-miR-5787, hsa-miR-580-3p, hsa-miR-6124, hsa-miR-671-5p, hsa-miR-6751-3p, hsa-miR-6752-5p, hsa-miR-6756-5p, hsa-miR-6785-5p, hsa-miR-6812-5p, hsa-miR-6850-5p, hsa-miR-6893-5p, hsa-miR-7106-5p, hsa-miR-7641. There were 4 miRNAs that regulated OAS3, they were hsa-miR-1273g-3p, hsa-miR-197-3p, hsa-miR-23a-5p, and hsa-miR-3149. There were 5 miRNAs regulated OASL, including hsa-miR-6812-5p, hsa-miR-5787, hsa-miR-671-5p, hsa-miR-1273g-3p, hsa-miR-186-3p (Fig. 7C). These intersecting miRNAs were shown in Fig. 7D and Additional file 1: Table S9.

Among these intersecting miRNAs, 10 miRNAs, including hsa-miR-15a-3p, hsa-miR-23a-5p, hsa-miR-26b-5p, hsa-miR-186-3p, hsa-miR-4433a-3p, hsa-miR-548aq-5p, hsa-miR-548d-5p, hsa-miR-576-5p, hsa-miR-580-3p, and hsa-miR-6850-5p, were down-expressed, this may be the reason for the high expression of OAS gene family in HF.

Predicted chemicals and ingredients interacting with OAS gene family

CTD and SymMap databases were used to predict the chemicals and ingredients that may interact with the OAS genes. CTD results showed that 12, 14, 10, and 6 chemicals interacted with OAS1, OAS2, OAS3, and OASL, respectively. Notably, estradiol and tetrachlorodibenzodioxin were found the common chemicals regulating the four OAS genes (Fig. 8A). SymMap results showed that 4, 3, 5, and 4 ingredients acted on OAS1, OAS2, OAS3, and OASL, respectively. Among them, 17-β-estradiol, hydrargyrum and saccharose were the common chemicals or ingredients that regulated the OAS gene family (Fig. 8B). The 17-β-estradiol, also named estradiol [34], were commonly recommended by CTD and SymMap database.

Results of docking analysis

The 3D structure of OAS1 was obtained from PDB database (PDB ID: 4IG8), and the 3D structures of OAS2, OAS3, and OASL were downloaded from UniProt database. The binding energies of 17-β-estradiol with OAS1, OAS2, OAS3, and OASL were −7.1 kcal·mol⁻¹, −7.6 kcal·mol⁻¹, −8.4 kcal·mol⁻¹, and −8.7 kcal·mol⁻¹, respectively, suggesting that the bonding between 17-β-estradiol and the four “receptors” (OAS proteins) were strong. From the results of ligand-receptor protein interaction, we found that 17-β-estradiol could form hydrophilic binding with SER63, GLN229, and THR19, and had hydrophobic interactions with the amino acid residues ASP77, GLY62, GLN194, LEU150, and THR188 of OAS1 (Fig. 9A). In addition, 17-β-estradiol exhibited hydrophilic force with LEU340 and LYS556, while had hydrophobic force with PHE341, TRP663, GLU552, PRO339, LEU340, GLN235, GLU659, and MET266, of OAS2 (Fig. 9B). The 17-β-estradiol also had hydrophilic effects on the ARG65 of OAS3 while had hydrophobic effects on ALA182, TRP303, GLY61, ASP74, SER145, GLU76, VAL125, VAL147, and ALA128 of OAS3 (Fig. 9C). Furthermore, 17-β-estradiol showed hydrophilic bonding with ASN72, GLU237 and VAL132 but hydrophobic interaction with TYR234, GLY68, GLU83, VAL67, CYS188, SER192, GLN185 and VAL199 of OASL (Fig. 9D). These forces made 17-β-estradiol stably binding to the pockets of the four OAS proteins.

The highlight of this study was the discovery of an important role of OAS gene family in the process of COVID-19 induced HF. HF is one of the major adverse consequences of COVID-19 [35]. The fact that OAS genes were highly expressed in both SARS-CoV-2-infected cardiomyocytes and human HF tissues provides us a reason to believe that some similar signaling molecules may mediate the developments of COVID-19 and HF. Or in other words, there may be some similar or common molecular mechanisms in the two diseases, and OAS genes may be the common genetic factors. Based on the present bioinformatics analysis (shown in Fig. 1 and Fig. 3) and previous reports, OAS genes are highly expressed in SARS-CoV-2 infected cardiomyocytes and COVID-19 patients [36–39], and our qPCR experiments further verified the high expressions of OAS genes in the myocardium of COVID-19-free HF cases (shown in Fig. 2 and Fig. 4). Previous studies have reported that OAS cluster variants are associated with greater risk of severe COVID-19, and OAS gene family plays an important role in the innate antiviral mechanisms linking to SARS-CoV-2 infection [37, 39–41]. These findings may be associated with the immune dysregulation and cytokine storm leading to HF in COVID-19 patients [42, 43]. During the cytokine storm, a large number of inflammatory factors are produced, such as TNF-α, IL-1, IL-6, and IFN-γ, leading to severe inflammation, multi-organ failure and even death. Several previous studies have reported that cytokine storm can occur in COVID-19 patients, and the level of pro-inflammatory factors is positively correlated with disease severity [44, 45]. This may be a crucial reason for developing HF in COVID-19 cases.

To corelate OAS gene family with COVID-19 associated HF, it is worthy to mention the important roles of IFN and IFN-stimulated genes (ISGs) in the endogenous anti-virus processes. OAS gene family is closely related to the induction of IFN [21]. In COVID-19 patients, SARS-CoV-2 strongly triggers the expressions of many ISGs and activates immune cells. ISGs have immunopathogenic potentials, including overexpression of inflammatory genes. In some COVID-19 cases, the level of IFN-I is low at the early stage, leading to excessive viral proliferation; when the IFN-I reaches a high level at the advanced stage, it may be too late to be rescued. Massive viral replication and early immune escape result in hyperactivation of pro-inflammatory responses [46–48]. In severe COVID-19 cases, IFN-I response arouses an excessive inflammatory response by promoting TNF/IL-1β-driven inflammation leading to cytokine storm [49]. As the critical members of innate immunity, OAS genes play important roles in immune responses and even the cytokine storm, this may one reason why COVID-19 can develop to HF. Zhang et al. [50] found a highly preserved transcriptional profile of IFN-I dependent genes for COVID-19 complementary diagnosis, and OAS genes were included in the profile. These evidences suggest the important role of OAS gene family in COVID-19. Adeghate et al. [51] reported that SARS-CoV-2 invasion can damage the heart via the following mechanisms: 1) inflammatory cells infiltrate into the myocardium; 2) pro-inflammatory cytokines cause cardiomyocyte death; 3) viruses damage the endothelial cells coupled with micro-thrombosis; 4) hypoxia caused by respiratory failure indirectly contributes to HF. Among these mechanisms, OAS genes may take roles at least in some of them, for example, the immune responses.

COVID-19 is associated with many inflammation-related signaling pathways, such as interleukin-6/Janus kinase/STAT (IL-6/JAK/STAT) pathway, interferon (IFN) cell signaling pathway, tumor necrosis factor-α/nuclear factor-kappa (TNFα/NF-κB) pathway, toll-like receptor (TLR) pathway, T-cell receptor (TCR) pathway, etc. [52–54]. Our study on COVID-19 dataset reveals that inflammation-related pathways, such as hsa04668 (TNF signaling pathway), hsa04210 (Apoptosis), and hsa04630 (JAK-STAT signaling pathway), are consistent with the above viewpoint. These signaling pathways also have important biological functions in HF [55–57]. In addition, our results show that after cardiomyocytes being infected by SARS-CoV-2, some HF-associated biological pathways became prevalent, such as hsa04260 (cardiac muscle contraction) and hsa05415 (diabetic cardiomyopathy), which directly indicates the signaling by which COVID-19 causes HF. By analyzing the DEGs in HF dataset GSE120852, we found that some signaling pathways associated with the DEGs, such as GO:0002252 (BP: immune effector process), GO:0002683 (BP: negative regulation of immune system process), GO:0050727 (BP: regulation of inflammatory response), hsa05171 (coronavirus disease-COVID-19), hsa04210 (apoptosis), and hsa04064 (NF-kappa B signaling pathway), are COVID-19 or inflammation related pathways. Especially, the term hsa05171 (coronavirus disease-COVID-19) from the HF dataset fully illustrates the close relationship of COVID-19 with HF.

Currently, there is no specific and effective drug for COVID-19. Thus, development of this kind of drugs is in urgent need. Some antiviral drugs have the potential to treat COVID-19, such as remdesivir, lopinavir/ritonavir, interferon β-1a, and hydroxychloroquine/chloroquine [58]. In the present study, we predicted some potential chemicals or ingredients which regulate the expression of OAS genes by analyzing the CTD and SymMap databases (shown in Fig. 8). Notably, we found that estradiol is one of them. Except for regulating the sexual system, estradiol plays an important role in anti-inflammation and suppression of virus-induced innate immune inflammatory response. High physiological concentration of estradiol can reduce the production of pro-inflammatory cytokines, such as IL-6, IL-1β, TNF-α, and CCL2, and prevent migration of monocytes and neutrophils into inflamed tissues. Therefore, under the influence of estradiol, immune dysregulation caused by cytokine storm in COVID-19 is ameliorated [59, 60]. Women generally show better immune responses to viruses than men, men are more susceptible to severe COVID-19 [61, 62]. A study proposed that a combination of estradiol with vitamin D and quercetin can be used to relieve COVID-19 [63]. Estradiol also has a certain protective effect on cardiovascular diseases [64, 65]. Estradiol can rescue severe HF through the classical estrogen receptor beta (ERβ), which is present in the heart [66]. Frump et al. [67] reported that estradiol can protect the function of right ventricle in pulmonary hypertension via BMPR2 and apelin. These evidences suggest the protective effect of estradiol on the heart, including the heart damage in COVID-19.

The study has some limitations. We were unable to obtain cardiac tissues from COVID-19 and HF patients to verify the expression and mechanism of OAS gene family in these diseases. These issues warrant future studies.

In conclusion, we found that OAS genes are highly expressed in SARS-CoV-2 infected cardiomyocytes, tissues of COVID-19 patients, and COVID-19-free human failing hearts. OAS gene family strongly links COVID-19 with HF. OAS genes can interact with the invaded SARS-CoV-2 and activates the OAS/RNaseL antiviral system to degrade the virus. The degraded small RNA molecules activate NF-kB inflammation-related pathways, produce inflammatory factors or even cytokine storms, lead to cell death, and ultimately cause HF. The detailed flow is shown in Fig. 10.

HF: Heart Failure; COVID-19: Coronavirus disease 2019; SARS-CoV-2: severe acute respiratory syndrome coronavirus 2; OAS:2',5'-oligoadenylate synthetase; DEGs: differentially expression genes; miRNA: microRNAs; RIG-1: acid-inducible gene-I; IRF: IFN regulatory factor; NCBI-GEO: National Center of Biotechnology Information- Gene Expression Omnibus; NF: non-failing; LV: left ventricles; RV: right ventricles; Bi-HF: biventricular failure; NHBE: human lung epithelial; GO: Gene Ontology; KEGG: Kyoto Encyclopedia of Genes and Genomes; BP: biological processes; CC: cellular components; MF: molecular functions; SPF: specific-pathogen-free; TAC: transverse aortic constriction; HW/BW: Heart weight/body weight; EF: ejection fraction; FS: fractional shortening; LVPWd: left ventricular posterior wall thickness at end diastole; LVPWs: left ventricular posterior wall thickness at systole; Ang Ⅱ: angiotensin II; qPCR: quantitative real-time PCR; PPI: protein-protein interaction; DMNC: Density of Maximum Neighborhood Component; CTD: Comparative Toxicogenomics Database; TCM: Traditional Chinese Medicine; MM: modern medicine; ISGs: IFN-stimulated genes.

Ethics approval and consent to participate

Not applicable.

Consent for publication

All authors agree to publish.

Competing interests

The authors declare that they have no competing interests.

Funding

This study was supported by National Natural Science foundation of China (82170523), Key Medical Science and Technology Program of Shanxi Province (2020XM01), Shanxi “1331” Project Quality and Efficiency Improvement Plan (1331KFC), and Basic Research Program of Shanxi Province (202103021223238).

Author contributions

L.-J.G. and J.-M.C. conceived and designed the study. L.-J.G., Z.-M.H., Y.-Y.L., R.-R.Y., M.Y., X.S. performed experiments, analyzed data and prepared figures. L.-J.G., drafted manuscript. J.-M.C. revised manuscript. All authors reviewed the manuscript.

Acknowledgements

We thank the mentioned public databases for providing us the data and analytical tools.

Availability of data and material

All data in this study are available from the corresponding author upon reasonable request. All authors read and approved the final manuscript. The datasets (GSE150392, GSE120852, GSE147507, GSE 179850 and GSE104150) presented in this study can be found in NCBI Gene Expression Omnibus.

Declaration of interests

The authors declare no conflict of interest in this work.

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Role of OAS gene family in COVID-19 induced heart failure

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Abstract

Background

Methods

Results

Conclusion

Figures

Background

Materials And Methods

Data download and processing

Analyses of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enrichment

Creation of mouse heart failure model

Cell culture and treatment

RNA isolation and qPCR

Analysis of differentially expressed genes (DEGs)

Analysis of DEGs interactions and screening of hub genes

Analysis of miRNAs targeting the OAS gene family

Prediction of chemicals and ingredients interacting with OAS gene family

Docking analysis of affinity between chemicals/ingredients and OAS gene family

Statistical analysis

Results

DEGs and hub genes derived from the GSE150392 dataset

DEGs and hub genes derived from the GSE120852 dataset

Experimental validation of high mRNA expressions of OAS genes

GO and KEGG pathway analyses revealing the intersecting signaling between COVID-19 and HF

The miRNAs regulating the expression of OAS gene family

Predicted chemicals and ingredients interacting with OAS gene family

Results of docking analysis

Discussion

Conclusions

Abbreviations

Declarations

References

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