Network-medicine approach for the identification of genetic association of parathyroid adenoma with cardiovascular disease and type-2 diabetes.

Primary hyperparathyroidism is caused by solitary parathyroid adenomas (PTAs) in most cases (⁓85%), and it has been previously reported that PTAs are associated with cardiovascular disease (CVD) and type-2 diabetes (T2D). To understand the molecular basis of PTAs, we have investigated the genetic association amongst PTAs, CVD and T2D through an integrative network-based approach and observed a remarkable resemblance. The current study proposed to compare the PTAs-associated proteins with the overlapping proteins of CVD and T2D to determine the disease relationship. We constructed the protein-protein interaction network by integrating curated and experimentally validated interactions in humans. We found the $11$ highly clustered modules in the network, which contain a total of $13$ hub proteins (TP53, ESR1, EGFR, POTEF, MEN1, FLNA, CDKN2B, ACTB, CTNNB1, CAV1, MAPK1, G6PD and CCND1) that commonly co-exist in PTAs, CDV and T2D and reached to network's hierarchically modular organization. Additionally, we implemented a gene-set over-representation analysis over biological processes and pathways that helped to identify disease-associated pathways and prioritize target disease proteins. Moreover, we identified the respective drugs of these hub proteins. We built a bipartite network that helps decipher the drug-target interaction, highlighting the influential roles of these drugs on apparently unrelated targets and pathways. Targeting these hub proteins by using drug combinations or drug-repurposing approaches will improve the clinical conditions in comorbidity, enhance the potency of a few drugs and give a synergistic effect with better outcomes. This network-based analysis opens a new horizon for more personalized treatment and drug-repurposing opportunities to investigate new targets and multi-drug treatment and may be helpful in further analysis of the mechanisms underlying PTA and associated diseases.

An Integrative Network Approach to Identify Common Genes for the Therapeutics in Tuberculosis and Its Overlapping Non-Communicable Diseases.

Tuberculosis (TB) is the leading cause of death from a single infectious agent. The estimated total global TB deaths in 2019 were 1.4 million. The decline in TB incidence rate is very slow, while the burden of noncommunicable diseases (NCDs) is exponentially increasing in low- and middle-income countries, where the prevention and treatment of TB disease remains a great burden, and there is enough empirical evidence (scientific evidence) to justify a greater research emphasis on the syndemic interaction between TB and NCDs. The current study was proposed to build a disease-gene network based on overlapping TB with NCDs (overlapping means genes involved in TB and other/s NCDs), such as Parkinson's disease, cardiovascular disease, diabetes mellitus, rheumatoid arthritis, and lung cancer. We compared the TB-associated genes with genes of its overlapping NCDs to determine the gene-disease relationship. Next, we constructed the gene interaction network of disease-genes by integrating curated and experimentally validated interactions in humans and find the 13 highly clustered modules in the network, which contains a total of 86 hub genes that are commonly associated with TB and its overlapping NCDs, which are largely involved in the Inflammatory response, cellular response to cytokine stimulus, response to cytokine, cytokine-mediated signaling pathway, defense response, response to stress and immune system process. Moreover, the identified hub genes and their respective drugs were exploited to build a bipartite network that assists in deciphering the drug-target interaction, highlighting the influential roles of these drugs on apparently unrelated targets and pathways. Targeting these hub proteins by using drugs combination or drug repurposing approaches will improve the clinical conditions in comorbidity, enhance the potency of a few drugs, and give a synergistic effect with better outcomes. Thus, understanding the Mycobacterium tuberculosis (Mtb) infection and associated NCDs is a high priority to contain its short and long-term effects on human health. Our network-based analysis opens a new horizon for more personalized treatment, drug-repurposing opportunities, investigates new targets, multidrug treatment, and can uncover several side effects of unrelated drugs for TB and its overlapping NCDs.