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.

Structural immunoinformatics approach for rational design of a multi-epitope vaccine against triple negative breast cancer

TNBC is a highly malignant breast cancer known for its aggressive behavior affecting young female adults. The standard treatment for TNBC includes surgery, chemotherapy, and radiotherapy, which often have significant side effects. Therefore, novel preventive methods are required to combat TNBC effectively. In this study, we utilized immunoinformatics to construct an in-silico vaccine against TNBC using the TRIM25 molecule via the reverse vaccinology method. Four vaccines were designed by generating T and B-cell epitopes linked with four different linkers. The modeled vaccine was docked and the results showed that vaccine-3 exhibited the highest affinity with the immune receptors. The molecular dynamics results revealed that the binding affinity and stability of Vaccine-3 were greater than those of Vaccine 2 complexes. This study has great potential preventive measures for TNBC, and further research is warranted to evaluate its efficacy in preclinical settings. This study presents an innovative preventive strategy for triple-negative breast cancer (TNBC) through immunoinformatics and reverse vaccinology to develop an in-silico vaccine. Leveraging these innovative techniques offers a novel avenue for combating the complex challenges associated with TNBC. This approach demonstrates considerable potential as a significant breakthrough in preventive measures for this particularly aggressive and malignant form of breast cancer.

Palladium-Mediated Synthesis of 2-([Biphenyl]-4-yloxy)quinolin-3-carbaldehydes through Suzuki-Miyaura Cross-Coupling and Their in Silico Breast Cancer Studies on the 3ERT Protein

A series of novel quinoline appended biaryls have been synthesized (5a-5o) by reacting various substituted boronic acids (4e-4h) with various substituted 2-(4-bromophenoxy)quinolin-3-carbaldehydes (3a-3d) through carbon-carbon bond formation. Effects of various quinoline appended biaryls (5a-5o) on the breast cancer protein 3ERT are moderate to high, as found by in silico molecular docking studies. Comparatively, all quinoline appended biaryls (5a-5o) 5h show better efficacy with a binding energy of -9.39 kcal/mol, and hydrogen bonds are Thr347, Glu353, and Arg394 in the binding pocket. Conclusively, the final novel quinoline appended biaryls (5a-5o) have been confirmed with all the spectral studies, and their efficacy has been validated with in silico studies.

Screening approaches against claudin-4 focusing on therapeutics through molecular docking and the analysis of their relative dynamics: a theoretical approach

Claudin-4 (CLDN4) is a class of transmembrane protein in the family of tight junction (TJ) proteins. Overexpression of CLDN4 is reported in the case of ovarian cancer and epithelial malignancies. The current study is focused on the identification of lead compounds for CLDN4 adopting the structure-based drug design method. The Schrodinger glide is used as a molecular docking tool for the initial docking of CLDN4 with Asinex Database by performing high throughput virtual screening, top hits were identified. Then, compounds BDF 33196188 and BDE 30874918 were identified by molecular docking based on binding energy in the active site of CLDN4. Subsequently, critical residues were identified such as Asp146 and Arg158 with the least binding energy from Extra Precision method. Further, molecular dynamics simulations of claudin-4 protein were used for the optimization of best ligands with claudin-4 in a dynamic system. Molecular docking and molecular dynamics simulations predicted critically important residues ASP146 and ARG158 involved in claudin-4 binding. The hits retrieved from screening were docked into protein by relevant procedures including HTVS, SP, and XP. Finally, two molecules were identified as potential claudin-4 inhibitors. The two ligands BDF 33196188 and BDE 30874918 are suggested as potential inhibitors for CLDN4. In summary, our computational strategy established novel leads against CLDN4 from Asinex Database and recommended as anti-cancer agents.

Virtual screening of National Cancer Institute database for claudin-4 inhibitors: Synthesis, biological evaluation, and molecular dynamics studies

Claudin-4 (CLDN4) is a vital member of tight-junction proteins that is often overexpressed in cancer and other malignancies. The three-dimensional structure of human CLDN4 was constructed based on homology modeling approach. A total of 265 242 molecules from the National Cancer Institute (NCI) database has been utilized as a dataset for this study. In the present work, structure-based virtual screening is performed with the NCI database using Glide. By molecular docking, 10 candidate molecules with high scoring functions, which binds to the active site of CLDN4 were identified. Subsequently, molecular dynamics simulations of membrane protein were used for optimization of the top-three lead compounds (NCI110039, NCI344682, and NCI661251) with CLDN4 in a dynamic system. The lead molecule from NCI database NCI11039 (purpurogallin carboxylic acid) was synthesized and cytotoxic properties were evaluated with A549, MCF7 cell lines. Our docking and dynamics simulations predicted that ARG31, ASN142, ASP146, and ARG158 as critically important residues involved in the CLDN4 activity. Finally, three lead candidates from the NCI database were identified as potent CLDN4 inhibitors. Cytotoxicity assays had proved that purpurogallin carboxylic acid had an inhibitory effect towards breast (MCF7) and lung (A549) cancer cell lines. Computational insights and in vitro (cytotoxicity) studies reported in this study are expected to be helpful for the development of novel anticancer agents.

Pharmacophore feature prediction and molecular docking approach to identify novel anti-HCV protease inhibitors

Discovering a potential drug for HCV treatment is a challenging task in the field of drug research. This study initiates with computational screening and modeling of promising ligand molecules. The foremost modeling method involves the identification of novel compound and its molecular interaction based on pharmacophore features. A total of 197 HCV compounds for NS3/4A protein target were screened for our study. The pharmacophore models were generated using PHASE module implemented in Schrodinger suite. The pharmacophore features include one hydrogen bond acceptor, one hydrogen bond donor, and three hydrophobic sites. As a result, based on mentioned hypothesis the model ADHHH.159 corresponds to the CID 59533233. Furthermore, docking was performed using maestro for all the 197 compounds. Among these, the CID 59533313 and 59533233 possess the best binding energy of -11.75 and -10.40 kcal/mol, respectively. The interactions studies indicated that the CID complexed with the NS3/4A protein possess better binding affinity with the other compounds. Further the compounds were subjected to calculate the ADME properties. Therefore, it can be concluded that these two compounds could be a potential alternative drug for the development of HCV.

Synthesis of 1,7-bis(4-hydroxypenyl)-4-hydroxy-1,3-heptadiene-5-one, an antiplatelet diarylheptanoid from Alpinia blepharocalyx K. schum

A general and staight forward total synthesis of 1,7-bis(4-hydroxyphenyl)-3-hydroxy-1,3-heptadiene-5-one (5) starting from 4-methoxycinnamic acid (6) via ethyl 5-(4-methoxyphenyl)-3-hydroxy-2.4-pentadienoate (14) was accomplished in 19% overall yield.