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AbstractVibrio vulnificus an opportunistic human pathogen native to marine/estuarine environment, is one of the leading causes of death due to seafood consumption and exposure of wounds to seawater worldwide. The present study involves the whole genome sequence analysis of an environmental strain of V. vulnificus (clinical genotype) isolated from seafood along the Mangaluru coast of India. The sequenced genome data was subjected to in-silico analysis of phylogeny, virulence genes, antimicrobial resistance determinants, and secretary proteins using suitable bioinformatics tools. The sequenced isolate had an overall genome length of 4.8 Mb and GC content of 46% with 4400 coding DNA sequences. The sequenced strain belongs to a new sequence type (Multilocus sequence typing) and was also found to branch with a phylogenetic lineage that groups the most infectious strains of V. vulnificus. The seafood isolate had complete genes involved in conferring serum resistance yet showed limited serum resistance. The study identified several genes against the antibiotics that are commonly used in their treatment, highlighting the need for alternative treatments. Also, the secretory protein analysis revealed genes associated with major pathways like ABC transporters, two-component systems, quorum sensing, biofilm formation, cationic antimicrobial peptide (CAMP) resistance, and others that play a critical role in the pathogenesis of the V. vulnificus. To the best of our knowledge, this is the first report of a detailed analysis of the genomic information of a V. vulnificus isolated from the Indian subcontinent and provides evidence that raises public health concerns about the safety of seafood.

Abstract Purpose It is important to comprehend how the molecular mechanisms shift when gastric cancer in its early stages (GC). We employed integrative bioinformatics approaches to locate various biological signalling pathways and molecular fingerprints to comprehend the pathophysiology of the GC. To facilitate the discovery of their possible biomarkers, a rapid diagnostic may be made, which leads to an improved diagnosis and improves the patient’s prognosis. Methods Through protein–protein interaction networks, functional differentially expressed genes (DEGs), and pathway enrichment studies, we examined the gene expression profiles of individuals with chronic atrophic gastritis and GC. Results A total of 17 DEGs comprising 8 upregulated and 9 down-regulated genes were identified from the microarray dataset from biopsies with chronic atrophic gastritis and GC. These DEGs were primarily enriched for CDK regulation of DNA replication and mitotic M-M/G1 phase pathways, according to KEGG analysis (p > 0.05). We discovered two hub genes, MCM7 and CDC6, in the protein–protein interaction network we obtained for the 17 DEGs (expanded with increased maximum interaction with 110 nodes and 2103 edges). MCM7 was discovered to be up-regulated in GC tissues following confirmation using the GEPIA and Human Protein Atlas databases. Conclusion The elevated expression of MCM7 in both chronic atrophic gastritis and GC, as shown by our comprehensive investigation, suggests that this protein may serve as a promising biomarker for the early detection of GC.

Background: Glioblastoma (GBM) is the most malignant and accounts for 60% of brain tumors in adults. Current therapy for GBM involves surgical removal of the tumor followed by radiotherapy with concomitant adjuvant therapy temozolomide. Despite improvements in therapy, patient survival remains low. The exact etiology of a brain tumor is uncertain, and numerous unknown genes are involved in the progression of GBM. The aim of the present study was to evaluate various genes involved in GBM through bioinformatic approach. Methods: In the present study, gene expression profile of GSE68424 was retrieved from the GEO database to explore the genes in GBM. Results: Analysis of expression profile data revealed that 33 genes were upregulated and 1189 genes were downregulated based on the log2 fold change cut-off criteria. The genes S-Phase kinase-associated protein 1 (SKP1) and Catenin beta-1 (CTNNB1) have been linked to GBM prognosis. Conclusion: SKP1 and CTNNB1 were identified as a candidate gene for GBM study as a result of these findings. Catenin beta-1 was the protein with the highest closeness centrality value and is the key component of canonical Wnt signaling downstream pathway. More study is needed to establish the molecular function of SKP1 and CTNNB1 in GBM development, as well as the biomarker's specificity and sensitivity.

Abstract Background Comorbid type 2 diabetes mellitus (T2DM) increases the risk for tuberculosis (TB) and its associated complications, although the pathological connections between T2DM and TB are unknown. The current research aims to identify shared molecular gene signatures and pathways that affirm the epidemiological association of T2DM and TB and afford clues on mechanistic basis of their association through integrative systems biology and bioinformatics approaches. Earlier research has found specific molecular markers linked to T2DM and TB, but, despite their importance, only offered a limited understanding of the genesis of this comorbidity. Our investigation used a network medicine method to find possible T2DM-TB molecular mediators. Results Functional annotation clustering, interaction networks, network cluster analysis, and network topology were part of our systematic investigation of T2DM-TB linked with 1603 differentially expressed genes (DEGs). The functional enrichment and gene interaction network analysis emphasized the importance of cytokine/chemokine signalling, T cell receptor signalling route, NF-kappa B signalling pathway and Jak-STAT signalling system. Furthermore, network analysis revealed significant DEGs such as ITGAM and STAT1, which may be necessary for T2DM-TB immune responses. Furthermore, these two genes are modulators in clusters C4 and C5, abundant in cytokine/chemokine signalling and Jak-STAT signalling pathways. Conclusions Our analyses highlight the role of ITGAM and STAT1 in T2DM-TB-associated pathways and advances our knowledge of the genetic processes driving this comorbidity.

Abstract Background One of the most common hormonal disorders in women of reproductive age is polycystic ovary syndrome (PCOS). In recent years, it has been found that insulin resistance is a common metabolic abnormality in women with PCOS and leads to an elevated risk of type 2 diabetes mellitus. To explore the differentially expressed genes (DEGs) that regulate these kinds of metabolic risks in PCOS women, we chose the gene expression profile of GSE8157 from the gene expression omnibus (GEO) database. Results Using the GEO2R tool, we identified a total of 339 DEGs between the case and the control sample groups. Gene ontology and Kyoto encyclopedia of gene and genome pathway enrichment analysis were subsequently conducted. High connectivity, betweenness centrality, bottleneck centrality, closeness centrality, and radiality measures were used to rank the ten hub genes. Furthermore, the overlap of these genes resulted in the development of two key genes, AR and STK11. The AMPK and adipocytokine signaling pathways are the two main pathways that these DEGs are involved. Conclusions The backbone genes, hub genes and pathways identified would assist us in further exploring the molecular basis of developing risk of type 2 diabetes mellitus in PCOS women and thus provide diagnostic or therapeutic clues.

Fatty acid transport protein 1 (FATP1) is an integral transmembrane protein that is involved in facilitating the translocation of long-chain fatty acids (LCFA) across the plasma membrane, thereby orchestrating the importation of LCFA into the cell. FATP1 also functions as an acyl-CoA ligase, catalyzing the ATP-dependent formation of fatty acyl-CoA using LCFA and VLCFA (very-long-chain fatty acids) as substrates. It is expressed in various types of tissues and is involved in the regulation of crucial signalling pathways, thus playing a vital role in numerous physiological and pathological conditions. Structural insight about FATP1 is, thus, extremely important for understanding the mechanism of action of this protein and developing efficient treatments against its anomalous expression and dysregulation, which are often associated with pathological conditions such as breast cancer. As of now, there has been no prior prediction or evaluation of the 3D configuration of the human FATP1 protein, hindering a comprehensive understanding of the distinct functional roles of its individual domains. In our pursuit to unravel the structure of the most commonly expressed isoforms of FATP1, we employed the cutting-edge ALPHAFOLD 2 model for an initial prediction of the entire protein's structure. This prediction was complemented by molecular dynamics simulations, focusing on the most promising model. We predicted the structure of FATP1 in silico and thoroughly refined and validated it using coarse and molecular dynamics in the absence of the complete crystal structure. Their relative dynamics revealed the different properties of the characteristic FATP1. [ABSTRACT FROM AUTHOR]