Molecular Modelling of nsSNPs in GABRA2 Gene in Epilepsy and Study of Their Impact On Structure and Stability of GABRA2 Protein

Epilepsy is a neurological condition characterized by abrupt, unprovoked, and recurrent seizures that are unpredictable in frequency. The objective of this analysis was to identify novel non-synonymous polymorphisms in the GABRA2 gene and determine their effect on protein structure and stability. Most pathogenic/deleterious nsSNPs were predicted using six different bioinformatic tools. Mutpred2, Mupro was used to check the impact of identified nsSNPS on protein structure and stability. The pathogenic score of SNPs was predicted using the FATHMM tool. The CONSURF webserver was used for conservation analysis of pathogenic SNPs.3-D structure of the protein was realized using SWISS-MODEL and residue position in protein visualized by Lite Mol. GeneMANIA and STRING databases were used to predict the function of interlinked gene interactions. Out of 228 nsSNPs retrieved from the dbSNP database, we identified a novel missense variant, F285S (rs41310789), as most deleterious and its possible association with epilepsy syndrome focal epilepsy. Stability and conservation analysis results interpret rs41310789 present in evolutionarily conserved regions of a gene and affect its structure. This analysis provides information regarding the impact of nsSNPs that might affect the structure and activity of GABRA2 protein. Thus, these coding variants should be taken into scrutiny while genetic screening of epileptic patients.

Analysis of Potential Hub Genes and Related Transcription Factors in Rosacea Patients using Bioinformatics Analysis

Objective The aim of this study is to discover the adipocyte genes and pathways involved in rosacea using bioinformatics analysis.Methods The GSE65914 gene expression profile was obtained. The GEO2R tool was used to screen out differentially expressed genes (DEGs). It was further analyzed with Gene Ontology (GO) to explore functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) to explore cell signaling pathways. Protein-protein interaction (PPI) networks among the DEGs were found by STRING databases and visualized in Cytoscape software. The related transcription factors regulatory network of the DEGs were also constructed.Results A total of 254 DEGs, including 72 up-regulated genes and 182 down-regulated genes, were obtained in rosacea samples. The biological functions of DEGs are mainly involved in the inflammatory response and chemokine activity. A PPI network consisting of 217 nodes and 710 edges was constructed using STRING, and ten hub genes were identified with Cytoscape software. Some transcriptional factors were also found to interact with these hub DEGs.Conclusion In this study, we obtained ten hub genes, including CXCL8, CCR5, CXCR4, CXCL10, MMP9, CD2, CCL19, CXCL9, CCL5, CD3D, which play an essential role in the pathology of rosacea, and these genes may provide a basis for the screening of treatment biomarkers for rosacea in the future.

Molecular Human Targets of Bioactive Alkaloid-Type Compounds from Tabernaemontana cymose Jacq.

Alkaloids are a group of secondary metabolites that have been widely studied for the discovery of new drugs due to their properties on the central nervous system and their anti-inflammatory, antioxidant and anti-cancer activities. Molecular docking was performed for 10 indole alkaloids identified in the ethanol extract of Tabernaemontana cymosa Jacq. with 951 human targets involved in different diseases. The results were analyzed through the KEGG and STRING databases, finding the most relevant physiological associations for alkaloids. The molecule 5-oxocoronaridine proved to be the most active molecule against human proteins (binding energy affinity average = −9.2 kcal/mol) and the analysis of the interactions between the affected proteins pointed to the PI3K/ Akt/mTOR signaling pathway as the main target. The above indicates that indole alkaloids from T. cymosa constitute a promising source for the search and development of new treatments against different types of cancer.

Multi-factor mediated functional modules identify novel classification of ulcerative colitis and functional gene panel

Ulcerative colitis is a chronic, idiopathic, and inflammatory disease of the rectal and colonic mucosa, the behavior of which is of heterogeneity in individuals. Here, we explored the multifactor-mediated functional modules associated with ulcerative colitis classification in the whole genome. Datasets downloaded from the GEO database were used to identify differentially expressed genes between ulcerative colitis patients and healthy individuals initially, followed by acquisition of the remaining ulcerative colitis -related genes from the OMIM and STRING databases. The results identified 914 ulcerative colitis-related genes, of which 60 were differentially expressed genes obtained from GEO datasets. Through weighted co-expression network analysis of ulcerative colitis-related genes, four modules were obtained, three of which were related to ulcerative colitis. Following interactions between microRNA, long noncoding RNA, transcription factors, and module hub genes were predicted and used to construct ulcerative colitis multifactor networks. Additionally, we performed consensus clustering of the ulcerative colitis samples. The results revealed that ulcerative colitis could be divided into four subtypes, with six hub genes identified as potential biomarkers for classification. These findings offer novel insights into ulcerative colitis and a basis for disease classification of ulcerative colitis.

Chemoreactomic analysis of inositol stereoisomers: different profiles of pharmacological activity of myo-inositol and D-chiro-inositol in females with reproductive system disorders

Inositol-containing drugs (stereoisomers of cyclohexane-1,2,3,4,5,6-hexol) are used to compensate insulin resistance, particularly in patients with menstrual and ovarian disorders. Inositols are effective in preventing folate-resistant fetal malformations. Objective. To analyze pharmacological differences between the four main biologically active stereoisomers of inositol: myoinositol (MI), D-chiro-inositol (DCI), neo-inositol (NI), and scylloinositol (SCI) using the comparative chemoreactomic method. Materials and methods. Chemoreactom analysis, PubChem / PHARMGKBb HMDB, STRING databases Results. DCI is more sufficient than MI for (1) processing of branched-chain amino acids, which promotes normalization of glucose metabolism, as well as (2) metabolism of folates, vitamin PP, vitamin B5, and magnesium, (3) activation of insulin-like growth factor 1 (IGF-1) receptor, whose activity is important for the prevention of sarcopenia, (4) antitumor effects (by inhibiting hyperproliferative effects, including those associated with excess glucose and an imbalance of androgens and estrogens), (5) inhibition of proinflammatory proteins (matrix metalloproteinase 15, ICAM1, and IRAK4 that mediate effects of interleukin-1). Conclusion. The profiles of pharmacological activity of DCI and SCI differ significantly from those of MI and NI. Therefore, combinations of MI and DCI are a more promising option to increase the sensitivity of cells to insulin than separate use of MI or DCI. Key words: insulin resistance, hyperandrogenism, polycystic ovary syndrome, post-genomic pharmacology, myo-inositol, D-chiro-inositol, neo-inositol, scylloinositol, Dikirogen

In silico characterization of the citrate synthase family in Mycobacterium tuberculosis / Mycobacterium tuberculosis’te sitrat sentaz ailesinin in silico karakterizasyonu

Objective: Mycobacterium tuberculosis (MTB) is an obligate aerobe bacterial pathogen. Here, the citrate synthase (CS) family, an important component of aerobic respiration, was investigated in MTB.Methods: MTB genome was analyzed in silico to reveal the members of CS family. The nucleotide and amino acid sequences were retrieved from the NCBI database, and searched for the similarity using the NCBI BLAST tool. Sequence alignment and phylogenetic analysis were performed using MEGA6. The physicochemical parameters, cellular localization, HMM profiles, motif structure, 3D modeling, and the interactions of the proteins were analyzed using GPMAW, PSORTb, Pfam and SMART, MEME, Phyre2, and STRING databases, respectively.Results: The members of CS family in MTB were identified as CitA, GltA2, and PrpC. The CitA and PrpC were found to be closer in phylogeny than GltA2, and the trees of three proteins were shown to be similar to that constructed based on 16S rRNA in mycobacteria. The CitA contains two CS domains while a single CS domain is found in GltA2 and PrpC. Besides, LHGGA and MGFGHRVY motifs are conserved in MTB and various bacteria. The molecular weight and pI values of CitA, GltA2, and PrpC were calculated as 40.1, 47.9, and 42.9 kDa, and 5.41, 5.35, and 9.31, respectively. Cellular localization of the proteins was predicted as cytoplasm. The highest expression ratio was found to be for gltA2 followed by prpC and citA, respectively, in the retrieved RNA-seq datasets obtained from the aerobic log phase of MTB H37Rv.Conclusion: This comprehensive bioinformatics analysis of CS family in MTB has a contribution to the knowledge of the genetics and physiology of this pathogen.

Mining Statistically Significant Substrings based on the Chi-Square Measure

With the tremendous expansion of reservoirs of sequence data stored worldwide, efficient mining of large string databases in various domains including intrusion detection systems, player statistics, texts, and proteins, has emerged as a practical challenge. Searching for an unusual pattern within long strings of data is one of the foremost requirements for many diverse applications. Given a string, the problem is to identify the substrings that differ the most from the expected or normal behavior, i.e., the substrings that are statistically significant (or, in other words, less likely to occur due to chance alone). We first survey and analyze the different statistical measures available to meet this end. Next, we argue that the most appropriate metric is the chi-square measure. Finally, we discuss different approaches and algorithms proposed for retrieving the top-k substrings with the largest chi-square measure.

Mining Statistically Significant Substrings Based on the Chi-Square Measure

With the tremendous expansion of reservoirs of sequence data stored worldwide, efficient mining of large string databases in various domains including intrusion detection systems, player statistics, texts, and proteins, has emerged as a practical challenge. Searching for an unusual pattern within long strings of data is one of the foremost requirements for many diverse applications. Given a string, the problem is to identify the substrings that differ the most from the expected or normal behavior, i.e., the substrings that are statistically significant (or, in other words, less likely to occur due to chance alone). We first survey and analyze the different statistical measures available to meet this end. Next, we argue that the most appropriate metric is the chi-square measure. Finally, we discuss different approaches and algorithms proposed for retrieving the top-k substrings with the largest chi-square measure.

An Optimized LCP Table Based Algorithm for Frequent String Mining

Given m databases D1,...,Dm of strings, the purpose of the frequent string mining is to find all strings that fulfill certain constraints of all string databases. In this paper, a useful data structure is proposed to construct suffix and LCP table which can reduce the total space consumption of string mining efficiently. We demonstrate the use of this data structure by optimizing the algorithm proposed by A.Kügel et al [7] and present the improved algorithm. It is achieved that the space consumption in our algorithm is proportional to the length of the largest string of all databases. A set of comprehensive performance experiments shows that the processing rate is enhanced because amount of items are reduced in new data structure.