Aedes is an important vector for various viruses that cause dengue, chikungunya and zika, which affect human health globally. Due to regular outbreaks of these diseases worldwide, there is a need to identify essential vector proteins that are critical for the survival of the vector, which may be targeted to control the spread of vector-borne disease (VBD). In silico computational methods involving comparative proteomics, analysis of orthologous proteins common amongst members of Aedes genus and protein-protein interaction (PPI) pathway were used to identify essential proteins that could act as novel therapeutic candidates. Twenty-three conserved proteins between A. aegypti and A. albopictus were identified from a BLASTP search with an e-value threshold of 0.005, and their PPI networks were constructed in the STRING database. The merged network was analyzed using various Cytoscape plugins viz. ClusterONE, Cytohubba and MCODE. Thirty-one hub proteins were identified from the system's network biology analysis, and detailed data and literature mining were carried out. Twelve novel vector-control target proteins of A. aegypti, having no human homologs, were determined in the present study that can effectively act as potential therapeutic candidates for drug design and vaccine development.

Complex diseases that occur by perturbations of molecular pathways and genetic factors result in pathophysiology of diseases. Network-centric systems biology approaches play an important role in understanding disease complexity. Diabetes, cardiovascular disease and depression are such complex diseases that have been reported to be comorbid in various epidemiological studies but there are no reports of the genetic and underlying factors which may be responsible for their reported co-occurrences. The present study was undertaken to investigate the molecular factors responsible for co-occurrence of diabetes, depression and cardiovascular disease using in-silico network systems biology approach. Genes common amongst these three diseases were retrieved from DisGeNET, a database of human diseases and their interactions were retrieved from STRING database. The resulting network containing 99 nodes (which represent genes) and 1252 edges (which represent various interactions between nodes) was analyzed using Cytoscape v: 3.7.2 and its various plug-ins i.e. ClusterONE, Cytohubba, ClueGO and Cluepedia. The hub genes identified in the present study namely IL1B, VEGFA, LEP, CAT, CXCL8, PLG, IL6, IL10, PTGS2, TLR4 and AKT1 were found to be enriched in various metabolic pathways and several mechanisms such as inflammation. These genes and their protein products may act as potential biomarkers for early detection of predisposition to diseases and potential therapeutic targets based on the common molecular underpinnings of co-occurrence of diabetes, depression and cardiovascular disease.