Lung disease network reveals impact of comorbidity on SARS-CoV-2 infection and opportunities of drug repurposing

Current approaches in identification of a novel drug targets for drug repurposing

Currently, millions of drugs and their licence have been expired or will be expiring in near future. Therefore, existing USFDA approved drug can be used for treating another disease. The above-mentioned approach falls under the category of drug repurposing. Drug repurposing is an alternative strategy for finding new applications of existing USFDA approved drugs. Identification of a novel drug target is one of the go to way for drug repurposing so that new therapeutic applications of USFDA approved drugs could be determined. Recent advances in computational biology and bioinformatics can help to accelerate the same. Drug repurposing can save time and resource as compared to discovery of an entirely new drug molecule. In this chapter, we explore different strategies for discovery of a novel drug target and its uses for drug repurposing to treat disease.

Topology Driven Analysis of Protein - Protein Interactome for Prioritizing Key Comorbid Genes via Sub Graph Based Average Path Length Centrality

In gene-based therapies, local perturbations associated with one disease can lead to comorbidity as it influences the pathways involved with the other diseases. The key genes orchestrating the common biological mechanisms are need to be prioritized for addressing the challenges introduced by the cross talks between disease modules. Here, a local centrality measure named Sub graph based Average Path length Double Specific Betweenness centrality (SAPDSB) for prioritizing the comorbid genes via Protein-Protein Interaction Network (PPIN) analysis is presented. This approach can be used to identify putative biomarkers which can be repurposed for the management of comorbidity. Proposed network based topological measure is designed specifically to prioritize the comorbid genes that are most likely to be present in the overlap of disease modules. In order to attain this, the estimated average path length of the seed network which holds Protein-Protein Interactions (PPIs) of the disease genes is exploited. Prioritized comorbid genes are further pruned using centrality-based cut-off values and specificity scores. The biological significance of the resultant genes is corroborated with connectivity analysis using leave-one-out method, pathway enrichment analysis and a comparative analysis using single disease-based gene prioritization tools. For performance analysis, proposed approach is tested using case studies involving common diseases and rare neurodegenerative diseases. For case study1, diseases such as Diabetes, Carcinoma and Alzheimer's are considered in a pairwise manner while for case study2, Amyotrophic Lateral Sclerosis (ALS) and Spinal Muscular Atrophy (SMA) are considered. As outcome, prioritized candidate genes and biological pathways associated with respective disease pairs have been found. The associations from top 10 candidate genes in different disease pair combinations of Diabetes-Carcinoma-Alzheimer's revealed common genes like CREBBP, TP53, HSP90AA1 and the common pathway namely p53 pathway feedback loops 2. Out of the pathways retrieved from the top 10 genes associated with ALS-SMA disease pair, 60% of unique pathways are found to be leading to both diseases and its comorbidities. Comparative analysis of the proposed method with recent similar approach also reported a clear degree of benefits in performance.

Smoking status and SARS-CoV-2 infection severity among Lebanese adults: a cross-sectional study

Background

A paradoxical hypothesis about the effect of smoking on patients infected with severe acute respiratory syndrom 2 (SARS-CoV-2) infection still exists. Furthermore, gender-discrepancy in the impact of smoking on COVID-19 severity was given little attention. Thus, the aims of the present study were to evaluate the prevalence of smoking and the COVID-19 infection severity in a sample of adult patients diagnosed with COVID-19 and to explore the relationship between smoking status and SARS-CoV-2 infection severity in the overall sample and stratified by gender.

Methods

A retrospective analytical study was conducted on patients diagnosed with COVID-19 cases between December, 2020 and April, 2021 from three leading laboratories in Lebanon. Sociodemographic characteristics, smoking status and clinical symptoms were collected. Multinomial logistic regression analysis was used to explore the relationship between smoking status and SARS-CoV-2 infection severity.

Results

A total of 901 confirmed COVID-19 cases participated in the study, 50.8% were females. The mean age of patients was 38.4 years (SD = 15.3). Of the total sample, 521(57.8%) were current smokers. Regarding infection severity, 14.8% were asymptomatic, 69.9% had mild symptoms, while 15.3% had severe infection. In the overall sample, smoking status, smoking types and dose–response were not significantly associated with infection severity. Upon stratifying the entire sample by gender, no association was found between all the considered variables with infection severity among females. However, a significant association was found among male with mild infection compared to their asymptomatic counterparts (OR = 1.78 95% CI (1.01–3.13)). Waterpipe smoking was found to be associated with infection severity among male with mild infection (OR 2.64 (95% CI 1.32–5.27)) and severe infection 2.79, 95% CI (1.19–6.53) compared to their asymptomatic counterparts.

Conclusion

Our fundings highlight sex differences in the association between tobacco smoking and COVID-19 severity. Current tobacco smoking was not associated with SARS-CoV-2 infection severity among female patients, however, tobacco smoking, particularly waterpipe, was found to be associated with infection severity among male. Thus, the battle against smoking should continue by assisting smokers to successfully and permanently quit.

Differential Co-Expression Network Analysis Reveals Key Hub-High Traffic Genes as Potential Therapeutic Targets for COVID-19 Pandemic

Background

The recent emergence of COVID-19, rapid worldwide spread, and incomplete knowledge of molecular mechanisms underlying SARS-CoV-2 infection have limited development of therapeutic strategies. Our objective was to systematically investigate molecular regulatory mechanisms of COVID-19, using a combination of high throughput RNA-sequencing-based transcriptomics and systems biology approaches.

Methods

RNA-Seq data from peripheral blood mononuclear cells (PBMCs) of healthy persons, mild and severe 17 COVID-19 patients were analyzed to generate a gene expression matrix. Weighted gene co-expression network analysis (WGCNA) was used to identify co-expression modules in healthy samples as a reference set. For differential co-expression network analysis, module preservation and module-trait relationships approaches were used to identify key modules. Then, protein-protein interaction (PPI) networks, based on co-expressed hub genes, were constructed to identify hub genes/TFs with the highest information transfer (hub-high traffic genes) within candidate modules.

Results

Based on differential co-expression network analysis, connectivity patterns and network density, 72% (15 of 21) of modules identified in healthy samples were altered by SARS-CoV-2 infection. Therefore, SARS-CoV-2 caused systemic perturbations in host biological gene networks. In functional enrichment analysis, among 15 non-preserved modules and two significant highly-correlated modules (identified by MTRs), 9 modules were directly related to the host immune response and COVID-19 immunopathogenesis. Intriguingly, systemic investigation of SARS-CoV-2 infection identified signaling pathways and key genes/proteins associated with COVID-19's main hallmarks, e.g., cytokine storm, respiratory distress syndrome (ARDS), acute lung injury (ALI), lymphopenia, coagulation disorders, thrombosis, and pregnancy complications, as well as comorbidities associated with COVID-19, e.g., asthma, diabetic complications, cardiovascular diseases (CVDs), liver disorders and acute kidney injury (AKI). Topological analysis with betweenness centrality (BC) identified 290 hub-high traffic genes, central in both co-expression and PPI networks. We also identified several transcriptional regulatory factors, including NFKB1, HIF1A, AHR, and TP53, with important immunoregulatory roles in SARS-CoV-2 infection. Moreover, several hub-high traffic genes, including IL6, IL1B, IL10, TNF, SOCS1, SOCS3, ICAM1, PTEN, RHOA, GDI2, SUMO1, CASP1, IRAK3, HSPA5, ADRB2, PRF1, GZMB, OASL, CCL5, HSP90AA1, HSPD1, IFNG, MAPK1, RAB5A, and TNFRSF1A had the highest rates of information transfer in 9 candidate modules and central roles in COVID-19 immunopathogenesis.

Conclusion

This study provides comprehensive information on molecular mechanisms of SARS-CoV-2-host interactions and identifies several hub-high traffic genes as promising therapeutic targets for the COVID-19 pandemic.

Network Medicine-Based Analysis of Association Between Gynecological Cancers and Metabolic and Hormonal Disorders

Different metabolic and hormonal disorders like type 2 diabetes mellitus (T2DM), obesity, and polycystic ovary syndrome (PCOS) have tangible socio-economic impact. Prevalence of these metabolic and hormonal disorders is steadily increasing among women. There are clinical evidences that these physiological conditions are related to the manifestation of different gynecological cancers and their poor prognosis. The relationship between metabolic and hormonal disorders with gynecological cancers is quite complex. The need for gene level association study is extremely important to find markers and predicting risk factors. In the current work, we have selected metabolic disorders like T2DM and obesity, hormonal disorder PCOS, and 4 different gynecological cancers like endometrial, uterine, cervical, and triple negative breast cancer (TNBC). The gene list was downloaded from DisGeNET database (v 6.0). The protein interaction network was constructed using HIPPIE (v 2.2) and shared proteins were identified. Molecular comorbidity index and Jaccard coefficient (degree of similarity) between the diseases were determined. Pathway enrichment analysis was done using ReactomePA and significant modules (clusters in a network) of the constructed network was analyzed by MCODE plugin of Cytoscape. The comorbid conditions like PCOS-obesity found to increase the risk factor of ovarian and triple negative breast cancers whereas PCOS alone has highest contribution to the endometrial cancer. Different gynecological cancers were found to be differentially related to the metabolic/hormonal disorders and comorbid condition.