Integrative epigenome-transcriptome analysis unravels cancer-specific over-expressed genes potentially regulating immune microenvironment in clear cell renal cell carcinoma.

BACKGROUND: Clear cell Renal Cell Carcinoma (ccRCC) is the frequently diagnosed histological life-threatening tumor subtype in the urinary system. Integrating multi-omics data is emerging as a tool to provide a comprehensive view of biology and disease for better therapeutic interventions. METHOD: We have integrated freely available ccRCC data sets of genome-wide DNA methylome, transcriptome, and active histone modification marks, H3K27ac, H3K4me1, and H3K4me3 specific ChIP-seq data to screen genes with higher expression. Further, these genes were filtered based on their effect on survival upon alteration in expression. RESULTS: The six multi-omics-based identified genes, RUNX1, MSC, ADA, TREML1, TGFA, and VWF, showed higher expression with enrichment of active histone marks and hypomethylated CpG in ccRCC. In continuation, the identified genes were validated by an independent dataset and showed a correlation with nodal and metastatic status. Furthermore, gene ontology and pathway analysis revealed that immune-related pathways are activated in ccRCC patients. CONCLUSIONS: The network analysis of six overexpressed genes suggests their potential role in an immunosuppressive environment, leading to tumor progression and poor prognosis. Our study shows that the multi-omics approach helps unravel complex biology for patient subtyping and proposes combination strategies with epi-drugs for more precise immunotherapy in ccRCC.

Mechanistic influence of discreet conformation of human telomerase linker region.

Approximately 90% of malignancies have been shown to have human telomerase activity, establishing it as a viable therapeutic target. The crystal structure of telomerase was determined recently. However, the tertiary structure of the non-conserved flexible linker region remains unresolved. This study aims to predict the full-length tertiary structure of the human telomerase reverse transcriptase (hTERT). Two strategies were employed to determine the full-length structure of hTERT (1132 amino acids); iterative threading and a conjoined model generated from machine learning and energy functions. After energy minimization, Ramachandran Plot analysis, and simulation; the conjoined model was considered of better quality and stability. The linker region of the conjoined depicted two helices from approximately 275-284 and 201-211 amino acids respectively in contrast to the iterative threading model which has a single helix. Moreover, the region was observed to undergo major structural changes throughout the simulation. These changes signify its flexibility which might be due to the region having a significant number of glycine and proline and could enhance the clamping movement.Communicated by Ramaswamy H. Sarma.

Potential inhibitory activity of phytoconstituents against black fungus: In silico ADMET, molecular docking and MD simulation studies.

Mucormycosis or "black fungus" has been currently observed in India, as a secondary infection in COVID-19 infected patients in the post-COVID-stage. Fungus is an uncommon opportunistic infection that affects people who have a weak immune system. In this study, 158 antifungal phytochemicals were screened using molecular docking against glucoamylase enzyme of Rhizopus oryzae to identify potential inhibitors. The docking scores of the selected phytochemicals were compared with Isomaltotriose as a positive control. Most of the compounds showed lower binding energy values than Isomaltotriose (-6.4 kcal/mol). Computational studies also revealed the strongest binding affinity of the screened phytochemicals was Dioscin (-9.4 kcal/mol). Furthermore, the binding interactions of the top ten potential phytochemicals were elucidated and further analyzed. In-silico ADME and toxicity prediction were also evaluated using SwissADME and admetSAR online servers. Compounds Piscisoflavone C, 8-O-methylaverufin and Punicalagin exhibited positive results with the Lipinski filter and drug-likeness and showed mild to moderate of toxicity. Molecular dynamics (MD) simulation (at 300 K for 100 ns) was also employed to the docked ligand-target complex to explore the stability of ligand-target complex, improve docking results, and analyze the molecular mechanisms of protein-target interactions.

Characterization of Human-malarial Parasite Species based on DHFR and GST Targets Resulting in Changes in Anti-malarial Drug Binding Conformations.

BACKGROUND: In this study, we focused primarily on three anti-malarial drugs, namely chloroquine, mefloquine, and proguanil, and these were tested against two malarial targets DHFR and GST. The species Plasmodium falciparum, Plasmodium malariae, Plasmodium ovale, Plasmodium vivax were used for the study. OBJECTIVE: The purpose of this study was to determine the sequence and structural similarity of the proteins DHFR and GST among four Plasmodium species as well as to discover the in silico interactions with the aforementioned drug candidates. METHODS: Bioinformatics databases, such as PDB, UniProt, DrugBank, PubChem, and tools, and software like Phyre 2.0, Clustal O (1.2.4), AutoDock 4, AutoDock Vina, and Discovery Studio Visualizer were used to determine the evolutionary significance of the Plasmodium species. RESULT: The variations showed a difference in the binding patterns of drugs with our target proteins. Our finding reveals the Plasmodium spp divergence or convergence as well as the structural and sequential similarity or dissimilarity features. CONCLUSION: Our result suggests that due to the deviation in the sequences and structures, variations in protein-drug binding patterns have emerged.

Molecular modeling and structure-based drug discovery approach reveals protein kinases as off-targets for novel anticancer drug RH1.

Potential drug target identification and mechanism of action is an important step in drug discovery process, which can be achieved by biochemical methods, genetic interactions or computational conjectures. Sometimes more than one approach is implemented to mine out the potential drug target and characterize the on-target or off-target effects. A novel anticancer agent RH1 is designed as pro-drug to be activated by NQO1, an enzyme overexpressed in many types of tumors. However, increasing data show that RH1 can affect cells in NQO1-independent fashion. Here, we implemented the bioinformatics approach of modeling and molecular docking for search of RH1 targets among protein kinase species. We have examined 129 protein kinases in total where 96 protein kinases are in complexes with their inhibitor, 11 kinases were in the unbound state with any ligand and for 22 protein kinases 3D structure were modeled. Comparison of calculated free energy of binding of RH1 with indigenous kinase inhibitors binding efficiency as well as alignment of their pharmacophoric maps let us predict and ranked protein kinases such as KIT, CDK2, CDK6, MAPK1, NEK2 and others as the most prominent off-targets of RH1. Our finding opens new avenues in search of protein targets that might be responsible for curing cancer by new promising drug RH1 in NQO1-independent way.

Synthesis and molecular modeling studies of 3-chloro-4-substituted-1-(8-hydroxy-quinolin-5-yl)-azetidin-2-ones as novel anti-filarial agents.

A series of 3-chloro-4-substituted-1-(8-hydroxy-quinolin-5-yl)-azetidin-2-ones were synthesized and evaluated for their in vitro anti-filarial activity. To pre-assess the anti-filarial behavior of synthesized compounds (V(a-f)) on a structural basis, automated docking studies were carried out with Molecular Design Suite (MDS v 3.5) into the active site of glutathione-S-transferase (GST) enzyme; scoring functions of these compounds at the active site of the GST enzyme were used for correlation with observed activity. Compounds V(e) and V(f) have shown good affinity for receptor GST, as well as in vitro anti-filarial potency.