No abstract

Folate metabolism genes are pivotal to critical biological processes and are related to several conditions, including developmental, cognitive, and cardiovascular anomalies. A systematic catalog of genetic polymorphisms in protein coding regions, regulatory transcription factor binding sites, and miRNA binding sites associated with folate pathway genes may contribute to personalized medicine. We performed a comprehensive computational survey of single nucleotide polymorphisms (SNPs) of folate pathway genes to highlight functional polymorphisms in the coding region, transcription factor binding sites, and miRNAs binding sites. Folate pathway genes were searched through PubMed and Kyoto Encyclopedia of Genes and Genomes pathway databases. SNPs were identified and characterized using the University of California, Santa Cruz genome browser and SNPnexus tool. Functional characterization of nonsynonymous SNPs (nsSNPS) was performed using bioinformatics tools, and common deleterious nsSNPs were identified. We identified 48 genes of folate pathway containing 287 SNPs in the coding regions. Out of these SNPs, rs5742905, rs45511401, and rs1801133 were predicted to be deleterious through four different bioinformatics tools. Three-dimensional structures of two proteins with and without deleterious nsSNPs were predicted by SWISSPDB viewer and SuperPose. Besides, a total of 237 SNPs was identified in transcription factor binding sites using the Genomatix software suite and six miRNA target site SNPs using miRNASNP. This systematic and extensive in silico analysis of functional SNPs of folate pathway may provide a foundation for future targeted mechanistic, structure-function, and genetic epidemiological studies.

Staphylococcus aureus is a major pathogen associated with diabetic foot ulcer infections. To gain insight into their pathogenicity and virulence potential, we report draft genome sequences of four strains of Staphylococcus aureus, isolated from diabetic foot ulcers, showing profiles with various degrees of resistance to common antibiotics.

Histone deacetylase 2 (HDAC 2) of class I HDACs plays a major role in embryonic and neural developments. However, HDAC 2 overexpression triggers cell proliferation by diverse mechanisms in cancer. Over the decades, many pan and class-specific inhibitors of HDAC were discovered. Limitations such as toxicity and differential cell localization of each isoform led researchers to hypothesize that isoform selective inhibitors may be relevant to bring about desired effects. In this study, we have employed the PHASE module to develop an e-pharmacophore model and virtually screened four focused libraries of around 300,000 compounds to identify isoform selective HDAC 2 inhibitors. The compounds with phase fitness score greater than or equal to 2.4 were subjected to structure-based virtual screening with HDAC 2. Ten molecules with docking score greater than  -12 kcal/mol were chosen for selectivity study, QikProp module (ADME prediction) and dG/bind energy identification. Compound 1A with the best dock score of  -13.3 kcal/mol and compound 1I with highest free binding energy,  -70.93 kcal/mol, were selected for molecular dynamic simulation studies (40 ns simulation). The results indicated that compound 1I may be a potent and selective HDAC 2 inhibitor. Further, in vitro and in vivo studies are necessary to validate the potency of selected lead molecule and its derivatives. Graphical abstract

Background: The one-carbon metabolism pathway is vital in maintaining tissue homeostasis by driving the critical reactions of folate and methionine cycles. A myriad of genetic and epigenetic events mark the rate of reactions in a tissue-specific manner. Integration of these to predict and provide personalized health management requires robust computational tools that can process multiomics data. The DNA sequences that may determine the chain of biological events and the endpoint reactions within one-carbon metabolism genes remain to be comprehensively recorded. Hence, we designed the one-carbon metabolism database (1-CMDb) as a platform to interrogate its association with a host of human disorders. Methods: DNA sequence and network information of a total of 48 genes were extracted from a literature survey and KEGG pathway that are involved in the one-carbon folate-mediated pathway. The information generated, collected, and compiled for all these genes from the UCSC genome browser included the single nucleotide polymorphisms (SNPs), CpGs, copy number variations (CNVs), and miRNAs, and a comprehensive database was created. Furthermore, a significant correlation analysis was performed for SNPs in the pathway genes. Results: Detailed data of SNPs, CNVs, CpG islands, and miRNAs for 48 folate pathway genes were compiled. The SNPs in CNVs (9670), CpGs (984), and miRNAs (14) were also compiled for all pathway genes. The SIFT score, the prediction and PolyPhen score, as well as the prediction for each of the SNPs were tabulated and represented for folate pathway genes. Also included in the database for folate pathway genes were the links to 124 various phenotypes and disease associations as reported in the literature and from publicly available information. Conclusion: A comprehensive database was generated consisting of genomic elements within and among SNPs, CNVs, CpGs, and miRNAs of one-carbon metabolism pathways to facilitate (a) single source of information and (b) integration into large-genome scale network analysis to be developed in the future by the scientific community. The database can be accessed at http://slsdb.manipal.edu/ocm/.