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West Bengal, India

Pasquel D.,Yeshiva University | Doricakova A.,Palacky University | Li H.,Yeshiva University | Kortagere S.,Drexel University | And 6 more authors.
Biochimica et Biophysica Acta - Gene Regulatory Mechanisms | Year: 2016

Pregnane X receptor (PXR) is a major transcriptional regulator of xenobiotic metabolism and transport pathways in the liver and intestines, which are critical for protecting organisms against potentially harmful xenobiotic and endobiotic compounds. Inadvertent activation of drug metabolism pathways through PXR is known to contribute to drug resistance, adverse drug-drug interactions, and drug toxicity in humans. In both humans and rodents, PXR has been implicated in non-alcoholic fatty liver disease, diabetes, obesity, inflammatory bowel disease, and cancer. Because of PXR's important functions, it has been a therapeutic target of interest for a long time. More recent mechanistic studies have shown that PXR is modulated by multiple PTMs. Herein we provide the first investigation of the role of acetylation in modulating PXR activity. Through LC-MS/MS analysis, we identified lysine 109 (K109) in the hinge as PXR's major acetylation site. Using various biochemical and cell-based assays, we show that PXR's acetylation status and transcriptional activity are modulated by E1A binding protein (p300) and sirtuin 1 (SIRT1). Based on analysis of acetylation site mutants, we found that acetylation at K109 represses PXR transcriptional activity. The mechanism involves loss of RXRα dimerization and reduced binding to cognate DNA response elements. This mechanism may represent a promising therapeutic target using modulators of PXR acetylation levels. © 2016 Elsevier B.V. Source

Das S.,Uluberia College | Roymondal U.,Raidighi College | Chottopadhyay B.,Jadavpur University | Sahoo S.,Raidighi College
Gene | Year: 2012

The expression of functional proteins plays a crucial role in modern biotechnology. The free-living cynobacterium Synechocystis PCC 6803 is an interesting model organism to study oxygenic photosynthesis as well as other metabolic processes. Here we analyze a gene expression profiling methodology, RCBS (the scores of relative codon usage bias) to elucidate expression patterns of genes in the Synechocystis genome. To assess the predictive performance of the methodology, we propose a simple algorithm to calculate the threshold score to identify the highly expressed genes in a genome. Analysis of differential expression of the genes of this genome reveals that most of the genes in photosynthesis and respiration belong to the highly expressed category. The other genes with the higher predicted expression level include ribosomal proteins, translation processing factors and many hypothetical proteins. Only 9.5% genes are identified as highly expressed genes and we observe that highly expressed genes in Synechocystis genome often have strong compositional bias in terms of codon usage. An important application concerns the automatic detection of a set of impact codons and genes that are highly expressed tend to use this narrow set of preferred codons and display high codon bias .We further observe a strong correlation between RCBS and protein length indicating natural selection in favor of shorter genes to be expressed at higher level. The better correlations of RCBS with 2D electrophoresis and microarray data for heat shock proteins compared to the expression measure based on codon usage difference, E(g) and codon adaptive index, CAI indicate that the genomic expression profile available in our method can be applied in a meaningful way to study the mRNA expression patterns, which are by themselves necessary for the quantitative description of the biological states. © 2012 Elsevier B.V. Source

Sahoo S.,Raidighi College | Das S.,Uluberia College
Current Bioinformatics | Year: 2014

Synonymous codon usage has long been known as a factor that affects the average expression level of proteins in microorganisms. A systematic approach to study the role of codon usage bias underlying gene expression has been described. Facts and ideas presented in this short review are to derive biological information from genome sequences by means of various statistical analyses and appropriate design of algorithms. Using codon usage bias as a numerical estimator of gene expression, a comparative analysis of predicted highly expressed (PHE) genes was performed in bacteria, cyanobacteria, archaea, lower eukaryotes and higher eukaryotes. Here, it is suggested that both codon usage and as well as base compositions at three codon sites regulate the individual gene expression. Any correlation between gene length and expression level, however, remains unexplained. Relationship between gene expression levels and synonymous codon usage provides an important line of evidence for translational selection and suggests some general mechanism underlying protein evolution. © 2014 Bentham Science Publishers. Source

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