An integrated gene network analysis to decode the multi-drug resistance mechanism in Klebsiella pneumoniae.

Antimicrobial resistance (AMR) among microorganisms has become one of the worldwide concerns of this century and continues to challenge us. To properly understand this problem, it is essential to know the genes that cause AMR and their resistance mechanisms. Our present study focused on Klebsiella pneumoniae, which possesses AMR genes conferring resistance against multiple antibiotics. A gene interaction network of 42 functional partners was constructed and analyzed to broaden our understanding. Three closely related clusters (C1-C3) having an association with multi-drug resistance mechanisms were identified by clustering analysis. The enrichment analysis illustrated 30 genes in biological processes, 24 genes in molecular function, and 25 genes in cellular components having a significant role. The analysis of the gene interaction network revealed genes birA2, folP, pabC, folA, gyrB, glmM, gyrA, thyA_2 had maximum no. of interactions with their functional partners viz. 26, 25, 25, 24, 23, 23, 23, 23 respectively and can be considered as hub genes. Analyzing the enriched pathways and Gene Ontologies provides insight into AMR's molecular basis. In addition, the proposed study could aid the researchers in developing new treatment options to combat multi-drug resistant K. pneumoniae.

Unraveling the molecular landscape of Ataxia Telangiectasia: Insights into Neuroinflammation, immune dysfunction, and potential therapeutic target.

BACKGROUND: Ataxia Telangiectasia (AT) is a genetic disorder characterized by compromised DNA repair, cerebellar degeneration, and immune dysfunction. Understanding the molecular mechanisms driving AT pathology is crucial for developing targeted therapies. METHODS: In this study, we conducted a comprehensive analysis to elucidate the molecular mechanisms underlying AT pathology. Using publicly available RNA-seq datasets comparing control and AT samples, we employed in silico transcriptomics to identify potential genes and pathways. We performed differential gene expression analysis with DESeq2 to reveal dysregulated genes associated with AT. Additionally, we constructed a Protein-Protein Interaction (PPI) network to explore the interactions between proteins implicated in AT. RESULTS: The network analysis identified hub genes, including TYROBP and PCP2, crucial in immune regulation and cerebellar function, respectively. Furthermore, pathway enrichment analysis unveiled dysregulated pathways linked to AT pathology, providing insights into disease progression. CONCLUSION: Our integrated approach offers a holistic understanding of the complex molecular landscape of AT and identifies potential targets for therapeutic intervention. By combining transcriptomic analysis with network-based methods, we provide valuable insights into the underlying mechanisms of AT pathogenesis.

Advancements and hurdles in the development of a vaccine for triple-negative breast cancer: A comprehensive review of multi-omics and immunomics strategies.

Triple-Negative Breast Cancer (TNBC) presents a significant challenge in oncology due to its aggressive behavior and limited therapeutic options. This review explores the potential of immunotherapy, particularly vaccine-based approaches, in addressing TNBC. It delves into the role of immunoinformatics in creating effective vaccines against TNBC. The review first underscores the distinct attributes of TNBC and the importance of tumor antigens in vaccine development. It then elaborates on antigen detection techniques such as exome sequencing, HLA typing, and RNA sequencing, which are instrumental in identifying TNBC-specific antigens and selecting vaccine candidates. The discussion then shifts to the in-silico vaccine development process, encompassing antigen selection, epitope prediction, and rational vaccine design. This process merges computational simulations with immunological insights. The role of Artificial Intelligence (AI) in expediting the prediction of antigens and epitopes is also emphasized. The review concludes by encapsulating how Immunoinformatics can augment the design of TNBC vaccines, integrating tumor antigens, advanced detection methods, in-silico strategies, and AI-driven insights to advance TNBC immunotherapy. This could potentially pave the way for more targeted and efficacious treatments.

Identification of key biomarkers and associated pathways of pancreatic cancer using integrated transcriptomic and gene network analysis.

Pancreatic cancer shows malignancy around the world standing in 4th position for causing death globally. This cancer is majorly divided into exocrine and neuroendocrine where exocrine pancreatic ductal adenocarcinoma is observed to be nearly 85% of cases. The lack of diagnosis of pancreatic cancer is considered to be one of the major drawbacks to the prognosis and treatment of pancreatic cancer patients. The survival rate after diagnosis is very low, due to the higher incidence of drug resistance to cancer which leads to an increase in the mortality rate. The transcriptome analysis for pancreatic cancer involves dataset collection from the ENA database, incorporating them into quality control analysis to the quantification process to get the summarized read counts present in collected samples and used for further differential gene expression analysis using the DESeq2 package. Additionally, explore the enriched pathways using GSEA software and represented them by utilizing the enrichment map finally, the gene network has been constructed by Cytoscape software. Furthermore, explored the hub genes that are present in the particular pathways and how they are interconnected from one pathway to another has been analyzed. Finally, we identified the CDKN1A, IL6, and MYC genes and their associated pathways can be better biomarker for the clinical processes to increase the survival rate of of pancreatic cancer.

Molecular docking and dynamic simulation to identify potential phytocompound inhibitors for EGFR and HER2 as anti-breast cancer agents.

Breast cancer is the most prevalent cancer in women worldwide. To treat human breast cancer by inhibiting EGFR and HER2 targets is an important therapeutic option. Phytochemicals are found to have beneficial health effects in treating various diseases. An effort has been made to virtually screen phytochemical inhibitor by molecular docking and dynamic simulation in the current studies. The docking scores analysis resulted in a common hit Panaxadiol ligand with a low dock score for EGFR and HER2 targets. The inhibitory action of the phytocompounds was also validated by comparing it with the reference compounds Erlotinib for EGFR and Neratinib for HER2. Molecular dynamic simulation of EGFR and HER2 lead complexes ensure the ligand's appropriate refinement in the dynamic system. The target and ligand complex interaction motif established a high affinity of lead candidates in a dynamic system similar to molecular docking results. This study reveals that Panaxadiol hit molecule can be developed as a novel multi-target EGFR and HER2 target inhibitor with greater potential and low toxicity.Communicated by Ramaswamy H. Sarma.

Exploration and evaluation of bioactive phytocompounds against BRCA proteins by in silico approach.

The proteins encoded by the two major breast cancer genes (BRCA1 and BRCA2), ensure the stability of DNA and prevent uncontrolled cell growth; mutation of these genes is linked to the development of hereditary breast cancers. Exploration of human breast cancer inhibitors plays a vital role in the drug discovery process. In the current work, in silico studies were performed which involves a computational approach for the identification of active phytocompounds from the diverse set of medicinal plant products against the BRCA receptor. The in silico study through pharmacokinetics and pharmacodynamics properties shown promising outcomes for these phytocompounds data set as breast cancer inhibitors. It was observed that the compounds conformed to the Lipinski's rule of five and had good bioavailability. The drug-likeness model score and ADMET profile of the designed ligands also established their potential as a drug candidate. The docking study provided useful insights on potential target-lead interactions and indicated that the newly designed leads had a good binding affinity for BRCA targets. A pharmacophore model was built to explore the scaffolds for BRCA inhibitory activity. An effort is made to screen an inhibitor against BRCA targets by combining the use of ADMET, docking score, and pharmacophore model.Communicated by Ramaswamy H. Sarma.