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Biology subjects

Amin, M. F.

Publications and source records attributed to Amin, M. F..

2 recordsLinked to original sources

Unveiling Common Molecular Signatures and Pathways in Psychiatric Disorders and Alcohol Use Disorder through Integrated Transcriptome Analysis

Research studies have demonstrated that persons who have Alcohol Use Disorder (AUD) exhibit a more severe progression of psychiatric disorders, indicating potential causal connections between AUD and psychiatric disorders. Identifying underlying risk variables between AUD and psychiatric problems continues to be challenging. To address these issues, we created a bioinformatics pipeline and employed network-based methods to discover genes that exhibit improper expression in both AUD and psychiatric disorders. The objective of our study was to identify common molecular pathways that may elucidate the relationship between AUD and psychiatric disorders. We identified 49 genes that were expressed differently in tissue samples of patients with both AUD and psychiatric disorders. The DAVID online platform was used to discover the most significant Gene Ontology (GO) keywords and metabolic pathways. It detected the involvement of immune response, chemokine activity, TNF signaling, IL-17 signaling, and prostaglandin signaling pathways among the common DEGs. In addition, eleven topological algorithms identified a single hub protein, specifically TTR, from the protein-protein interaction (PPI) network. Through regulatory network analysis, we identified four crucial transcription factors (TFs)--YY1, FOXC1, JUND, and GATA2--and seven miRNAs (e.g., hsa-mir-146a-5p, hsa-mir-20a-5p) that play vital roles in regulating the development of AUD and psychiatric disorders. These miRNAs may serve as potential therapeutic targets. Validation of the hub gene using ROC analysis indicated acceptable predictive performance. Our approach revealed several potential biomarkers and signaling pathways linking AUD with psychiatric disorders, offering new insights for diagnosis and treatment. HighlightsO_LIIntegration of genome-scale transcriptomic datasets with biomolecular networks identified key hub genes (TTR, SOCS3, CXCL10, MMP9, and C4A). C_LIO_LICommon transcription factors, including YY1, FOXC1, JUND, and GATA2, were uncovered as potential regulatory elements. C_LIO_LICritical miRNAs (hsa-miR-146a-5p, hsa-miR-20a-5p, hsa-miR-107, hsa-miR-124-3p, hsa-miR-138-5p, and hsa-miR-330-3p) were identified as key post-transcriptional regulators. C_LIO_LIHistone modification profiling revealed multiple modification sites in hub genes and transcription factors, linking them to Intellectual Disability, Bipolar Disorder, Schizophrenia, and Alcohol Use Disorder. C_LIO_LIProtein-drug interaction analysis highlighted 10 candidate compounds with potential therapeutic relevance for the identified markers across ID, Bipolar Disorder, Schizophrenia, and AUD. C_LI

bioinformatics↗

Computational investigation unveils pathogenic LIG3 non-synonymous mutations and therapeutic targets in acute myeloid leukemia

Single nucleotide polymorphisms (SNPs) in DNA repair genes can impair protein structure and function, contributing to disease development, including cancer. Non-synonymous SNPs (nsSNPs) in the LIG3 gene are linked to genomic instability and increased cancer risk, particularly acute myeloid leukemia (AML). This study aims to identify the most deleterious nsSNPs in the LIG3 gene and potential therapeutic targets for DNA repair restoration in AML. We employed in-silico computational methods to analyze LIG3 nsSNPs, using PredictSNP and Mutation3D to assess pathogenicity. Subsequently, molecular docking and dynamics simulations were conducted to evaluate ligand-binding affinities and protein stability. Out of the 12,191 mapped SNPs, 132 were nsSNPs located in the coding region. Among these, 18 nsSNPs were identified as detrimental including 12 destabilizing and 6 stabilizing nsSNPs. Nine cancer-associated nsSNPs, including L381R and R528C, were predicted due to their structural and functional impacts. Further analysis revealed key phosphorylation and methylation sites, such as 529S and 224R. Molecular dynamics simulations highlighted stable interactions of compounds AHP-MPC and DM-BFC with wild-type and R528C mutant LIG3 proteins, while R671G and V781M mutants showed instability. Protein-protein interaction networks and functional enrichment linked LIG3 to DNA repair pathways. Kaplan-Meier analysis associated high LIG3 expression with improved survival in breast cancer and AML, suggesting its role as a prognostic biomarker. This study emphasizes the mutation-specific effects of LIG3 nsSNPs on protein stability and ligand interactions. We recommend identifying DM-BFC to advance personalized medicine approaches for targeting deleterious variants, following in vitro and in vivo validation for AML treatment.

cancer biology↗