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There are numerous genetic factors like MC4R (Melanocortin-4 receptor), POMC (Pro-opiomelanocortin), SIM1 (Single Minded Gene) etc. important in obesity, which can be used as biomarker. But more reliable diagnostic markers are the need for today, along with new therapeutic strategies that target specific molecules in the disease pathways. As in mouse and human genes, where mutations in one or both species are associated with some phenotypic characteristics as observed in human disease. In molecular mechanisms of development, differentiation, and disease gene expression data provide crucial insights. Up-regulation and down-regulation of selective genes can have major effects on diet-induced obesity, but there is little or no effect when animals are fed a low-fat diet. In present study we have studied the gene expression data of mouse at different theiler stages using GXD BioMart. The interacting partners and pathway of the genes that are already used as biomarker in mouse as well as in humans have been studied. A gene NPY1R (Neuropeptide Y1 receptor) was taken as common after STRING and KEGG results on the basis of biochemical pathways and interactions similar to MC4R. Our present work focuses on comparative genomics and proteomics analysis of NPY1R, which has led to identification of biomarker by comparing it with already known MC4R human and mouse biomarker. It has been concluded that both the proteins are structurally and functionally similar.
Biomedical research needs to leverage and exploit large amount of information reported in scientific publication. Literature data collected from publications has to be managed to extract information, transforms into an understandable structure using text mining approaches. Text mining refers to the process of deriving high-quality information from text by finding relationships between entities which do not show direct associations. Therefore, as an example of this approach, we present the link between two diseases i.e. breast cancer and obesity.Obesity is known to be associated with cancer mortality, but little is known about the link between lifetime changes in BMI of obese person and cancer mortality in both males and females. In this article, literature data for obesity and breast cancer was obtained using PubMed database and then methodologies which employs groups of common genes and keywords with their frequency of occurrence in the data were used, aimed to establish relation between obesity and breast cancer visualized using Pi-charts and bar graphs. From the data analysis, we obtained 1 gene which showed the link between both the diseases and validated using statistical analysis and disease-connect web server. We also proposed 8 common higher frequency keywords which could be used for indexing while searching the literature for obesity and breast cancer in combination.
Text mining is the process of extracting relevant information from the unstructured data obtained from the published corpora in biomedical literature. A vast amount of literature is available in context of biofilms in PubMed database. It is a very daunting task to extract useful information in the form of genes and proteins from text manually; therefore, text mining approaches are used for this purpose. The extracted relevant information can be appropriately visualized using network biology approaches. Therefore, here we present a framework that would help in discovering novel biomarkers (genes) and gene-drug associations, involved in Staphylococcus biofilm using the available published corpora. We initially utilized COREMINE tool to select biological processes (i.e. biofilm formation, growth, quorum sensing and pathogenesis) and extract genes from PubMed literature database for biofilms. We selected the co-occurring genes for four biofilm processes through network biology approach and validated them by using GO enrichment analysis. AgrB gene was validated as the gene involved in two biological processes i.e. biofilm quorum sensing and pathogenesis. Therefore, structure of AgrB encoded protein (AgrB) was predicted using bioinformatics tools and analysis of ligand binding was done using molecular docking which was further used in blocking quorum sensing and pathogenesis processes. The validation of results obtained from text mining suggests that this approach can be extended to reveal interesting trends and associations among different biological entities related to biofilms and other diseases.
Rotavirus G1 strains are the predominant cause of diarrhoea in children. Universally common rotavirus vaccines (Rotarix and RotaTeq) include G1 as the immunological component. India has recently introduced rotavirus vaccine in Universal Immunization Programme. Therefore, in the present study, VP7 gene of rotavirus G1 strains circulating in Himachal Pradesh, India is analysed to study their phylogenetic characteristics, and further comparative analysis was performed for assessment of their divergence from the vaccine strains. The rotavirus strains (JU-SOL-5, JU-SOL-58, JU-SOL-77, JU-SOL-173 and JU-SHI-14) analysed in the study were isolated from the faeces of diarrhoeic children during active surveillance for rotaviruses. The Himachal strains clustered together in G1-Lineage 1 in the phylogenetic analysis. All five isolates showed 96.4-98.8 % similarity with the other G1-Lineage 1 strains at amino acid level. However, none of them clustered in the pre-defined sublineages within lineage 1. Interestingly, all the strains were distantly related to the vaccine strains having 93.9-94.5 and 91.9-92.6 % similarities at amino acid level with Rotarix and RotaTeq strains, respectively. The comparative sequence and structural analysis of the Himachal strains with vaccine strains revealed differences in amino acids in epitope region of the protein especially at the antibody neutralization sites. The study highlights variations between the G1 strains from Himachal Pradesh, India and Rotarix and RotaTeq vaccine strains. These differences might have an impact on the neutralization efficiency of vaccine and subsequently on vaccine efficacy. This underscores further investigation to study intragenotype antigenic variability and also impact of viral evolution on vaccine effectiveness.
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