A System Biology Approach to Explore Host-Pathogen Interaction Under Phytochemical Cross Linkages

Phyto-signalling molecules are minute, but tangible that has rigorous roles in any plant-pathogen interaction. Certainly, most of the pathogen alters their biosynthesis, transport, degradation and cellular signalling responses to pave their virulence. Therefore, the gene expressions of such molecules with their correlated defense mechanisms were analysed in Arabidopsis thaliana against Erysiphe orontii (a potential biotroph), Botrytis cinerea (a potential necrotroph), Pseudomonas syringae (a bacterial hemibiotroph), and Phytophthora infestans (a fungal hemibiotroph) using molecular biology/ system biology techniques. The findings strongly suggested that each pathogen has its own unique infection strategy based on up-regulation and down-regulation of host phyto-signalling genes. Our studies also explored four basic pathogenic infection maps based on cross linking phyto-signalling molecules.

A system biology approach identifies candidate drugs to reduce mortality in severely ill COVID-19 patients

Despite the availability of highly efficacious vaccines, Coronavirus Disease 2019 (COVID-19) caused by severe acute respiratory syndrome-related coronavirus-2 (SARS-CoV-2) lacks effective drug treatment which results in a high rate of mortality. To address this therapeutic shortcoming, we applied a system biology approach to the study of patients hospitalized with severe COVID. We show that, at the time of hospital admission, patients who were equivalent on the clinical ordinal scale displayed significant differential monocyte epigenetic and transcriptomic attributes between those who would survive and those who would succumb to COVID-19. We identified mRNA metabolism, RNA splicing, and interferon signaling pathways as key host responses overactivated by patients who would not survive. Those pathways are prime drug targets to reduce mortality of critically ill COVID-19 patients leading us to identify Tacrolimus, Zotatifin, and Nintedanib as three strong candidates for treatment of severely ill patients at the time of hospital admission.

Identification of Key Genes and Pathways for Childhood Obesity Using System Biology Approach Based on Comprehensive Gene Information

Introduction: Childhood obesity is one of the most important public health issues of the 21st century. Epidemiological studies have suggested that obesity during childhood increases the risk of developing comorbidities, such as type 2 diabetes, later in life. Childhood obesity is a complex disease whose molecular mechanisms are not completely elucidated. In this context, a system biology approach could contribute to the scientific knowledge regarding genetic factors related to childhood obesity onset. Aim: To identify molecular mechanisms involved in childhood obesity by implementing a system biology approach. Methods: Experimentally validated and computationally predicted genes related to Pediatric Obesity (C2362324) were downloaded from the DisGeNET v7.0 database. The protein-protein interaction (PPI) network was constructed using the STRING v11.0 database and analyzed using NetworkAnalyst v3.0 and Cytoscape v3.8.1. The relevance of each node for the network structure and functionality was assessed using the degree method to define hub genes. Functional and pathway enrichment analyses were performed based on Gene Ontology (GO) terms and KEGG Pathways. Results: The search on the DisGeNET database retrieved 191 childhood obesity-related genes. The PPI network of these genes showed 19 hub genes (STAT3, SIRT1, BCL2, IRS1, PPARG, SOCS3, TGFB1, HDAC4, DNMT1, ADCY3, PPARA, NEDD4L, ACACB, NR0B2, VEGFA, APOA1, GHR, CALR, and MKKS). These hub genes were involved in biological processes of lipid storage / kinase activity, regulation of fatty-acid metabolic processes, regulation of pri-miRNA transcription by RNA polymerase II, and negative regulation of small molecules and carbohydrate metabolic processes. In terms of molecular functions, repressing of transcription factors biding was found enriched. Regarding KEGG Pathways, the hub genes are involved with adipocytokine signaling, insulin resistance, longevity regulation, and cytokine signaling pathways. Conclusion: Our approach identified 19 hub genes, which are highly connected and probably have a key role in childhood obesity. Moreover, functional enrichment analyses demonstrated they are enriched in several biological processes and pathways related to the underlying molecular mechanisms of obesity. These findings provide a more comprehensive information regarding genetic and molecular factors behind childhood obesity pathogenesis. Further experimental investigation of our findings may shed light on the pathophysiology of this disease and contribute to the identification of new therapeutic targets.

Investigating the Relationship between Diabetes and Alzheimer’s Disease:

Ageing is associated with a number of diseases. Alzheimer’s disease (AD) and diabetes are among such most common diseases. These two diseases are considered to be fundamentally similar disorders because they share some common elements, though they differ in the time of onset, tissues affected as well as the magnitudes of their specific traits. The present study was undertaken to prospect the association between the genes involved in Diabetes and AD; and their common pathophysiology. Using a network system biology approach, the genes common between Diabetes and AD were retrieved from DisGeNET database. The common genes were analysed using in silico tool, Cyctoscape’s various plug-ins, ClusterONE, CytoHubba, ClueGO and CluePedia. Eleven genes which can act as potential marker for both Diabetes and AD namely IL4, ICAM1, ALB, INS, CSF2, IL6, TNF, IL10, GAPDH, TLR4, and AKT have been identified in the present study. This is the first study of its kind in which relationship between Diabetes and AD has been investigated to identify their common genes, which can help in better understanding of pathophysiology of these age-related diseases.