Bioinformatics and system biology approach to identify potential common pathogenesis for COVID-19 infection and osteoarthritis.

A growing of evidence has showed that patients with osteoarthritis (OA) had a higher coronavirus 2019 (COVID-19) infection rate and a poorer prognosis after infected it. Additionally, scientists have also discovered that COVID-19 infection might cause pathological changes in the musculoskeletal system. However, its mechanism is still not fully elucidated. This study aims to further explore the sharing pathogenesis of patients with both OA and COVID-19 infection and find candidate drugs. Gene expression profiles of OA (GSE51588) and COVID-19 (GSE147507) were obtained from the Gene Expression Omnibus (GEO) database. The common differentially expressed genes (DEGs) for both OA and COVID-19 were identified and several hub genes were extracted from them. Then gene and pathway enrichment analysis of the DEGs were performed; protein-protein interaction (PPI) network, transcription factor (TF)-gene regulatory network, TF-miRNA regulatory network and gene-disease association network were constructed based on the DEGs and hub genes. Finally, we predicted several candidate molecular drugs related to hub genes using DSigDB database. The receiver operating characteristic curve (ROC) was applied to evaluate the accuracy of hub genes in the diagnosis of both OA and COVID-19. In total, 83 overlapping DEGs were identified and selected for subsequent analyses. CXCR4, EGR2, ENO1, FASN, GATA6, HIST1H3H, HIST1H4H, HIST1H4I, HIST1H4K, MTHFD2, PDK1, TUBA4A, TUBB1 and TUBB3 were screened out as hub genes, and some showed preferable values as diagnostic markers for both OA and COVID-19. Several candidate molecular drugs, which are related to the hug genes, were identified. These sharing pathways and hub genes may provide new ideas for further mechanistic studies and guide more individual-based effective treatments for OA patients with COVID-19 infection.

Integrated interactome and transcriptome analysis reveals key host factors critical for SARS-CoV-2 infection.

The coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has seriously threatened global public health and caused huge economic losses. Omics studies of SARS-CoV-2 can help understand the interaction between the virus and host, thereby providing a new perspective in guiding the intervention and treatment of the SARS-CoV-2 infection. Since large amount of SARS-CoV-2 omics data have been accumulated in public databases, this study aimed to identify key host factors involved in SARS-CoV-2 infection through systematic integration of transcriptome and interactome data. By manually curating published studies, we obtained a comprehensive SARS-CoV-2-human protein-protein interactions (PPIs) network, comprising 3591 human proteins interacting with 31 SARS-CoV-2 viral proteins. Using the RobustRankAggregation method, we identified 123 multiple cell line common genes (CLCGs), of which 115 up-regulated CLCGs showed host enhanced innate immunity and chemotactic response signatures. Combined with network analysis, co-expression and functional enrichment analysis, we discovered four key host factors involved in SARS-CoV-2 infection: IFITM1, SERPINE1, DDX60, and TNFAIP2. Furthermore, SERPINE1 was found to facilitate SARS-CoV-2 replication, and can alleviate the endoplasmic reticulum (ER) stress induced by ORF8 protein through interaction with ORF8. Our findings highlight the importance of systematic integration analysis in understanding SARS-CoV-2-human interactions and provide valuable insights for future research on potential therapeutic targets against SARS-CoV-2 infection.

covSampler: A subsampling method with balanced genetic diversity for large-scale SARS-CoV-2 genome data sets.

Phylogenetic analysis has been widely used to describe, display, and infer the evolutionary patterns of viruses. The unprecedented accumulation of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genomes has provided valuable materials for the real-time study of SARS-CoV-2 evolution. However, the large number of SARS-CoV-2 genome sequences also poses great challenges for data analysis. Several methods for subsampling these large data sets have been introduced. However, current methods mainly focus on the spatiotemporal distribution of genomes without considering their genetic diversity, which might lead to post-subsampling bias. In this study, a subsampling method named covSampler was developed for the subsampling of SARS-CoV-2 genomes with consideration of both their spatiotemporal distribution and their genetic diversity. First, covSampler clusters all genomes according to their spatiotemporal distribution and genetic variation into groups that we call divergent pathways. Then, based on these divergent pathways, two kinds of subsampling strategies, representative subsampling and comprehensive subsampling, were provided with adjustable parameters to meet different users' requirements. Our performance and validation tests indicate that covSampler is efficient and stable, with an abundance of options for user customization. Overall, our work has developed an easy-to-use tool and a webserver (https://www.covsampler.net) for the subsampling of SARS-CoV-2 genome sequences.

Effects of the COVID-19 Pandemic on the Success of Traditional Small and Medium Enterprises (SMEs): An Investigation of the Footprints of Economic Crisis Attributable to COVID-19

The COVID-19 pandemic created a significant economic decline and altered market behavior, forcing buyers and dealers online. The traditional local market merchants are not fully equipped with e-commerce business techniques and strategies, which is a barrier to their e-commerce behavior and success. The study aims to help small-medium firms adapt to an uncertain economic environment instead of reducing or shutting down business-like in Pakistan. From health to education, economy to domestic and social protection, various researches have been done since 2020. The researcher used primary data sources and did a Quantitative study after collecting the 240 samples size of data from the successful e-commerce players of Pakistan. The results confirm that customer satisfaction is essential for entrepreneurs to succeed, as customers were not satisfied with online shopping during COVID-19. Customer knowledge management (CKM) and job satisfaction (JS) are studied as potential and realized capacity variables. CKM act as a strategic asset to collect and assimilate the external customer knowledge. In contrast, satisfied employees act as a valuable asset that dynamically responds to changing customer needs and business environment by efficiently utilizing their knowledge and skills and reaching business success which is mirrored in customer satisfaction. Conclusive results enable practitioners to perceive the business success during economic crises in the organization’s absorptive capacity.

covSampler: A Subsampling Method with Balanced Genetic Diversity for Large-Scale SARS-CoV-2 Genome Data Sets

Phylogenetic analysis has been widely used to describe, display and infer the evolutionary patterns of viruses. The unprecedented accumulation of SARS-CoV-2 genomes has provided valuable materials for the real-time study of SARS-CoV-2 evolution. However, the large number of SARS-CoV-2 genome sequences also poses great challenges for data analysis. Several methods for subsampling these large data sets have been introduced. However, current methods mainly focus on the spatiotemporal distribution of genomes without considering their genetic diversity, which might lead to postsubsampling bias. In this study, a subsampling method named covSampler was developed for the subsampling of SARS-CoV-2 genomes with consideration of both their spatiotemporal distribution and their genetic diversity. First, covSampler clusters all genomes according to their spatiotemporal distribution and genetic variation into groups that we call divergent pathways. Then, based on these divergent pathways, two kinds of subsampling strategies, representative subsampling and comprehensive subsampling, were provided with adjustable parameters to meet different users’ requirements. Our performance and validation tests indicate that covSampler is efficient and stable, with an abundance of options for user customization. Overall, our work has developed an easy-to-use tool and a webserver (https://www.covsampler.net) for the subsampling of SARS-CoV-2 genome sequences.

Incidence of pulmonary tuberculosis under the regular COVID-19 epidemic prevention and control in China.

BACKGROUND: The COVID-19 pandemic has driven public health intervention strategies, including keeping social distance, wearing masks in crowded places, and having good health habits, to prevent the transmission of the novel coronavirus (SARS-CoV-2). However, it is unknown whether the use of these intervention strategies influences morbidity in other human infectious diseases, such as tuberculosis. METHODS: In this study, three prediction models were constructed to compare variations in PTB incidences after January 2020 without or with intervention includes strict and regular interventions, when the COVID-19 outbreak began in China. The non-interventional model was developed with an autoregressive integrated moving average (ARIMA) model that was trained with the monthly incidence of PTB in China from January 2005 to December 2019. The interventional model was established using an ARIMA model with a continuing intervention function that was trained with the monthly PTB incidence in China from January 2020 to December 2020. RESULTS: Starting with the assumption that no COVID-19 outbreak had occurred in China, PTB incidence was predicted, and then the actual incidence was compared with the predicted incidence. A remarkable overall decline in PTB incidence from January 2020 to December 2020 was observed, which was likely due to the potential influence of intervention policies for COVID-19. If the same intervention strategy is applied for the next 2 years, the monthly PTB incidence would reduce on average by about 1.03 per 100,000 people each month compared with the incidence predicted by the non-interventional model. The annual incidence estimated 59.15 under regular intervention per 100,000 in 2021, and the value would decline to 50.65 with strict interventions. CONCLUSIONS: Our models quantified the potential knock-on effect on PTB incidence of the intervention strategy used to control the transmission of COVID-19 in China. Combined with the feasibility of the strategies, these results suggested that continuous regular interventions would play important roles in the future prevention and control of PTB.

Network analysis for elucidating the mechanisms of Shenfu injection in preventing and treating COVID-19 combined with heart failure.

BACKGROUND: The emergence of the novel coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to millions of infections and is exerting an unprecedented impact on society and economies worldwide. The evidence showed that heart failure (HF) is a clinical syndrome that could be encountered at different stages during the progression of COVID-19. Shenfu injection (SFI), a traditional Chinese medicine (TCM) formula has been widely used for heart failure therapy in China and was suggested to treat critical COVID-19 cases based on the guideline for diagnosis and treatment of COVID-19 (the 7th version) issued by National Health Commission of the People's Republic of China. However, the active components, potential targets, related pathways, and underlying pharmacology mechanism of SFI against COVID-19 combined with HF remain vague. OBJECTIVE: To investigate the effectiveness and possible pharmacological mechanism of SFI for the prevention and treatment of COVID-19 combined with HF. METHODS: In the current study, a network analysis approach integrating active compound screening (drug-likeness, lipophilicity, and aqueous solubility models), target fishing (Traditional Chinese Medicine Systems Pharmacology, fingerprint-based Similarity Ensemble Approach, and PharmMapper databases), compound-target-disease network construction (Cytoscape software), protein-protein interaction network construction (STRING and Cytoscape software), biological process analysis (STRING and Cytoscape plug-in Clue GO) and pathway analysis (Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis) was developed to decipher the active ingredients, potential targets, relevant pathways, and the therapeutic mechanisms of SFI for preventing and treating COVID-19 combined with HF. RESULTS: Finally, 20 active compounds (DL ≥ 0.18, 1≤Alog P ≤ 5, and -5≤LogS ≤ -1) and 164 relevant targets of SFI were identified related to the development of COVID-19 combined with HF, which were mainly involved in three biological processes including metabolic, hemostasis, and cytokine signaling in immune system. The C-T-D network and reactome pathway analysis indicated that SFI probably regulated the pathological processes of heart failure, respiratory failure, lung injury, and inflammatory response in patients with COVID-19 combined with HF through acting on several targets and pathways. Moreover, the venn diagram was used to identify 54 overlapped targets of SFI, COVID-19, and HF. KEGG pathway enrichment analysis showed that 54 overlapped targets were highly enriched to several COVID-19 and HF related pathways, such as IL-17 signaling pathway, Th17 cell differentiation, and NF-kappa B signaling pathway. CONCLUSIONS: A comprehensive network analysis approach framework was developed to systematically elucidate the potential pharmacological mechanism of SFI for the prevention and treatment of SFI against COVID-19 combined with HF. The current study may not only provide in-depth understanding of the pharmacological mechanisms of SFI, but also a scientific basis for the application of SFI against COVID-19 combined with HF.

Compositional diversity and evolutionary pattern of coronavirus accessory proteins.

Accessory proteins play important roles in the interaction between coronaviruses and their hosts. Accordingly, a comprehensive study of the compositional diversity and evolutionary patterns of accessory proteins is critical to understanding the host adaptation and epidemic variation of coronaviruses. Here, we developed a standardized genome annotation tool for coronavirus (CoroAnnoter) by combining open reading frame prediction, transcription regulatory sequence recognition and homologous alignment. Using CoroAnnoter, we annotated 39 representative coronavirus strains to form a compositional profile for all of the accessary proteins. Large variations were observed in the number of accessory proteins of 1-10 for different coronaviruses, with SARS-CoV-2 and SARS-CoV having the most (9 and 10, respectively). The variation between SARS-CoV and SARS-CoV-2 accessory proteins could be traced back to related coronaviruses in other hosts. The genomic distribution of accessory proteins had significant intra-genus conservation and inter-genus diversity and could be grouped into 1, 4, 2 and 1 types for alpha-, beta-, gamma-, and delta-coronaviruses, respectively. Evolutionary analysis suggested that accessory proteins are more conservative locating before the N-terminal of proteins E and M (E-M), while they are more diverse after these proteins. Furthermore, comparison of virus-host interaction networks of SARS-CoV-2 and SARS-CoV accessory proteins showed that they share multiple antiviral signaling pathways, those involved in the apoptotic process, viral life cycle and response to oxidative stress. In summary, our study provides a tool for coronavirus genome annotation and builds a comprehensive profile for coronavirus accessory proteins covering their composition, classification, evolutionary pattern and host interaction.

Application of Traditional Chinese Medicine and Systems Pharmacology in Drug Prevention and Treatment against COVID-19.

A novel coronavirus named SARS-CoV-2 is causing the severe acute pneumonia (COVID-19) and rapid spread nationally and internationally, resulting in a major global health emergency. Chinese governments and scientists have implemented a series of rigorous measures and scientific research to prevent and control the SARS-CoV-2 infection. However, there is still no specific antiviral drug or vaccine against SARS-CoV-2. It has been proven that traditional Chinese medicine (TCM) exerts an important role in the prevention and treatment of the COVID-19 caused by SARS-CoV-2 during the outbreak. Although the therapeutic effects of these TCM formulas are attractive, the molecular mechanism of action has not been fully elucidated. An emerging strategy of systems pharmacology has been proposed to be a promising method to interpret drug action in complex biological systems and quickly screen out the bioactive compounds from TCM to treat treatment of COVID-19 caused by SARS-CoV-2. Therefore, in this study, the epidemiology, TCM therapy, and the systems pharmacology-based method for TCM are reviewed for COVID-19 to provide a perspective for the prevention and treatment of SARS-CoV-2 infection. Further efforts should be made to reduce disease burden and improve the ability to design antiviral drugs and vaccines, which will benefit the health care system, economic development and even social stability.

Magnesium supplementation alleviates corticosteroid-associated muscle atrophy in rats.

PURPOSE: Corticosteroid (CS) therapy for infectious and rheumatological diseases showed to decrease serum magnesium (Mg++) level and induce muscle atrophy in patients. The present study investigated the effects of Mg++ supplementation on preventing CS-induced muscle atrophy in an animal model, which provided experimental data for potential clinical translation. METHODS: Twelve 24-week-old male Sprague-Dawley rats were treated with lipopolysaccharide (LPS) and CS methylprednisolone (MPS) to induce muscle atrophy, with half of the rats also given daily 50 mg/kg Mg++ oral supplementation. Additional six rats without LPS + CS treatments were used as normal controls. After treatment for 6 weeks, serum was collected for Mg++ quantification, animal dual-energy X-ray absorptiometry (DXA) was performed for tissue composition, and the extensor digitorum longus (EDL) was collected for muscle functional test and histology including muscle fiber size, intramuscular fat infiltration and fiber typing. In vitro myotube atrophy model was used to study the in vitro effect associated with in vivo muscle atrophy. RESULTS: LPS + CS treatments induced hypomagnesemia while the serum Mg++ level was in normal range after Mg++ supplementation. DXA showed 53.0% lower fat percent and 29.7% higher lean mass in LPS + CS + Mg group when compared to LPS + CS group. Muscle functional test showed 22.2% higher specific twitch force and 40.3% higher specific tetanic force in LPS + CS + Mg group when compared to LPS + CS group. Histological analysis showed 4.1% higher proportion of muscle fibers area to total area and 63.6% lower intramuscular fat infiltration in EDL sections in LPS + CS + Mg group when compared to LPS + CS group. LPS + CS + Mg group had 33.0% higher area proportion and 29.4% higher cross-sectional area (CSA) of type IIb muscle fiber. Myoblast culture results showed that Mg++ supplementation group had larger myotube diameter. The mRNA expressions of the muscle atrophy marker genes MuRF1 and MAFbx were lower in Mg++ supplementation group both in vitro and in vivo. CONCLUSION: The current study demonstrated that Mg++ supplementation successfully alleviated CS-associated muscle atrophy in rats at both functional and morphology levels, indicating a translational potential for patients undergoing CS therapy. This study provided the evidence for the first time that Mg++ supplementation could prevent muscle atrophy-an adverse effect of CS therapy, currently also adopted for treating coronavirus disease 2019 (COVID-19).