Cancer is a complex multifaceted illness that affects different patients in discrete ways. For a number of cancers the use of chemotherapy has become standard practice. Chemotherapy is a use of cytostatic drugs to cure cancer. Cytostatic agents not only affect cancer cells but also affect the growth of normal cells; leading to side effects. Because of this, radiotherapy gained importance in treating cancer. Slaughtering of cancerous cells by radiotherapy depends on the radiosensitivity of the tumor cells. Efforts to improve the therapeutic ratio have resulted in the development of compounds that increase the radiosensitivity of tumor cells or protect the normal cells from the effects of radiation. Amifostine is the only chemical radioprotector approved by the US Food and Drug Administration (FDA), but due to its side effect and toxicity, use of this compound was also failed. Hence the use of herbal radioprotectors bearing pharmacological properties is concentrated due to their low toxicity and efficacy. Notably, in silico methods can expedite drug discovery process, to lessen the compounds with unfavorable pharmacological properties at an early stage of drug development. Hence a detailed perspective of these properties, in accordance with their prediction and measurement, are pivotal for a successful identification of radioprotectors by drug discovery process.

The integration of large-scale gene data and their functional analysis needs the effective application of various computational tools. Here we attempted to unravel the biological processes and cellular pathways in response to ionizing radiation using a systems biology approach. Analysis of gene ontology shows that 80, 42, 25, and 35 genes have roles in the biological process, molecular function, the cellular process, and immune system pathways, respectively. Therefore, our study emphasizes gene/protein network analysis on various differentially expressed genes (DEGs) to reveal the interactions between those proteins and their functional contribution upon radiation exposure. A gene/protein interaction network was constructed, which comprises 79 interactors with 718 interactions and TP53, MAPK8, MAPK1, CASP3, MAPK14, ATM, NOTCH1, VEGFA, SIRT1, and PRKDC are the top 10 proteins in the network with high betweenness centrality values. Further, molecular complex detection was used to cluster these associated partners in the network, which produced three effective clusters based on the Molecular Complex Detection (MCODE) score. Interestingly, we found a high functional similarity from the associated genes/proteins in the network with known radiation response genes. This network-based approach on DEGs of human lymphocytes upon response to ionizing radiation provides clues for an opportunity to improve therapeutic efficacy.

Background: Comorbid type 2 diabetes mellitus (T2DM) increases the risk for tuberculosis (TB) and its associated complications, although the pathological connections between T2DM and TB are unknown. The current research aims to identify shared molecular gene signatures and pathways that affirm the epidemiological association of T2DM and TB and afford clues on mechanistic basis of their association through integrative systems biology and bioinformatics approaches. Earlier research has found specific molecular markers linked to T2DM and TB, but, despite their importance, only offered a limited understanding of the genesis of this comorbidity. Our investigation used a network medicine method to find possible T2DM-TB molecular mediators. Results: Functional annotation clustering, interaction networks, network cluster analysis, and network topology were part of our systematic investigation of T2DM-TB linked with 1603 differentially expressed genes (DEGs). The functional enrichment and gene interaction network analysis emphasized the importance of cytokine/chemokine signalling, T cell receptor signalling route, NF-kappa B signalling pathway and Jak-STAT signalling system. Furthermore, network analysis revealed significant DEGs such as ITGAM and STAT1, which may be necessary for T2DM-TB immune responses. Furthermore, these two genes are modulators in clusters C4 and C5, abundant in cytokine/chemokine signalling and Jak-STAT signalling pathways. Conclusions: Our analyses highlight the role of ITGAM and STAT1 in T2DM-TB-associated pathways and advances our knowledge of the genetic processes driving this comorbidity.

Severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) has infected >235 million people and killed over 4.8 million individuals worldwide. Although vaccines have been developed for prophylactic management, there are no clinically proven antivirals to treat the viral infection. Continuous efforts are being made all over the world to develop effective drugs but these are being delayed by periodic outbreak of mutated SARS-CoV-2 and a lack of knowledge of molecular mechanisms underlying viral pathogenesis and post-infection complications. In this regard, the involvement of Annexin A2 (AnxA2), a lipid-raft related phospholipid-binding protein, in SARS-CoV-2 attachment, internalization, and replication has been discussed. In addition to the evidence from published literature, we have performed in silico docking of viral spike glycoprotein and RNA-dependent RNA polymerase with human AnxA2 to find the molecular interactions. Overall, this review provides the molecular insights into a potential role of AnxA2 in the SARS-CoV-2 pathogenesis and post-infection complications, especially thrombosis, cytokine storm, and insulin resistance.

The main goal of treating any Helicobacter pylori (H. pylori)-related gastrointestinal disease is completely eradicating infection. Falling eradication efficiency, off-target effects, and patient noncompliance with prolonged and broad spectrums have sparked clinical interest in exploring other effective, safer therapeutic choices. As natural substances are risk-free and privileged with high levels of antibacterial activity, most of these natural chemical’s specific modes of action are unknown. With the aid of in silico molecular docking-based virtual screening studies and molecular dynamic simulations, the current study is intended to gather data on numerous such natural chemicals and assess their affinity for the S-ribosyl homocysteine lyase (LuxS) protein of H. pylori. The ligand with the highest binding energy with LuxS, glucoraphanin, catechin gallate and epigallocatechin gallate were rationally selected for further computational analysis. The solution stability of the three compounds’ optimal docking postures with LuxS was initially assessed using long-run molecular dynamics simulations. Using molecular dynamics simulation, the epigallocatechin gallate was found to be the most stable molecule with the highest binding free energy, indicating that it might compete with the natural ligand of the inhibitors. According to ADMET analysis, his phytochemical was a promising therapeutic candidate for an antibacterial action since it had a range of physicochemical, pharmacokinetic, and drug-like qualities and had no discernible adverse effects. Additional in vitro, in vivo, and clinical trials are needed to confirm the drug’s precise efficacy during H. pylori infection. Communicated by Ramaswamy H. Sarma