ObjectivesAbrus precatorius (AP) is a medicinal plant seldomly studied for its beneficial effects and as such significant gap exists in understanding the basis by which AP leaf bioactives (APLBs) elicit beneficial effects against diabetes mellitus (DM) type-1. MethodsWe estimated DM type-1 related parameters - total protein (TP), direct bilirubin (DB), urea, creatinine, alanine transaminase (ALT), aspartate transaminase (AST), alkaline phosphatase (ALP) and serum glucose (SG) after treatment with methanol extracts of AP leaves (APMLE) for 21 days followed by histopathological analysis of kidney and liver sections. APLBs were collected from GCMS fractions, database and literature searches and common targets were cross-intersected with annotated DM type-1 genes from experimental GSE14503 microarray dataset and genecard database. Overlapping differentially expressed genes were collected, their protein-protein interaction network was constructed and analyzed using various bioinformatics tools: Enrichr, SRplot, GSEA, Cytoscape, PyRx, and Discovery Studio to provide insight into the potential molecular basis of APLBs in DM-type-1.Results 15 compounds were identified from GCMS analysis of APMLE. Antidiabetic potential of APMLE was observed with significant (p <0.05) normalization of SG, TP, DB, ALT, AST, ALP, urea & creatinine while hepatorenal photomicrographs indicated moderate safety of use. Erucic acid, oleic acid, phytol and stigmasterol interact with 25 type-1 DM biomarkers revealed to be enriched in lipid and prostaglandin metabolic processes, neuroactive ligand receptor interaction, PPAR signaling pathway, diabetic cardiomyopathy, and cAMP signaling pathway. Furthermore, PPARalpha (peroxisome proliferator-activated alpha) and SCD (stearoyl-coenzyme A desaturase) were revealed as core biotargets interacting with APLBs via hydrogen bond, hydrophobic interaction and van der Waals forces from docking study. ConclusionThis study demonstrated the potency of APLBs in vivo against type-1 DM. The potential basis of action has been studied from computational perspective. Future interests may provide additional experimental data into mechanisms by which APLBs elicit this remarkable ability.

Cocaine addiction is a global health problem that causes substantial damage to the health of addicted individuals around the world. Dopamine synthesizing neurons in the brain play a vital role in the addiction to cocaine. But the underlying molecular mechanisms that help cocaine exert its addictive effect have not been very well understood. Bioinformatics can be a useful tool in the attempt to broaden our understanding in this area. In the present study, Gene Set Enrichment Analysis (GSEA) was carried out on the upregulated genes from a dataset of Dopamine synthesizing neurons of post-mortem human brain of cocaine addicts. As a result of this analysis, 3 miRNAs have been identified as having significant influence on transcription of the upregulated genes. These 3 miRNAs hold therapeutic potential for the treatment of cocaine addiction.

Hashimoto’s thyroiditis (HT) is a common autoimmune disease, with its prevalence rapidly increasing. Both genetic and environmental risk factors contribute to the development of HT. Recently, viral infection has been suggested to act as a trigger of HT by eliciting the host immune response and subsequent autoreactivity. We analyzed features of HT through bioinformatics analysis so as to identify markers of HT development. We accessed public microarray data of HT patients from the Gene Expression Omnibus (GEO) and obtained differentially expressed genes (DEGs) under HT. Gene Ontology (GO) and KEGG pathway enrichment analyses were performed for functional clustering of our protein-protein interaction (PPI) network. Utilizing ranked gene lists, we performed Gene Set Enrichment Analysis (GSEA) using the clusterprofiler R package. By comparing the expression signatures of the huge perturbation database with the queried rank-ordered gene list, connectivity map (CMap) analysis was performed to screen potential therapeutic targets and agents. The gene expression profile of the HT group was in line with general characteristics of HT. Biological processes related to the immune response and viral infection pathways were obtained for the upregulated DEGs. GSEA results revealed activation of autoimmune disease-related pathways and several viral infection pathways. Autoimmune disease and viral infection pathways were highly interconnected by common genes, while the HLA genes which are shared by both were significantly upregulated. CMap analysis suggested that perturbagens, including SRRM1, NLK, and CCDC92, have the potential to reverse the HT expression profile. Several lines of evidence suggested that viral infection and the host immune response are activated during HT. Viral infection is suspected to act as a key trigger of HT by causing autoimmunity. SRRM1, an alternative splicing factor, which responds to viral activity, might serve as potential marker of HT.

Traditional gene set enrichment analysis falters when applied to large genomic domains, where neighboring genes often share functions. This spatial dependency creates misleading enrichments, mistaking mere physical proximity for genuine biological connections. Here we present Spatial Adjusted Gene Ontology (SAGO), a novel cyclic permutation-based approach, to tackle this challenge. SAGO separates enrichments due to spatial proximity from genuine biological links by incorporating the genes' spatial arrangement into the analysis. We applied SAGO to various datasets in which the identified genomic intervals are large, including replication timing domains, H3K9me3 and H3K27me3 large domains, HiC compartments and lamina associated domains (LADs). Intriguingly, applying SAGO to prostate cancer samples with large copy number alteration (CNAs) domains eliminated most of the enriched GO terms, thus helping to accurately identify biologically relevant gene sets linked to oncogenic processes, free from spatial bias.