Prosenjit Paul

Interplay between miRNAs and human diseases

MicroRNAs (miRNAs) are endogenous, non‐coding RNAs, which have evoked a great deal of interest due to their importance in many aspects of homeostasis and diseases. MicroRNAs are stable and are essential components of gene regulatory networks. They play a crucial role in healthy individuals and their dysregulations have also been implicated in a wide range of diseases, including diabetes, cardiovascular disease, kidney disease, and cancer. This review summarized the current understanding of interactions between miRNAs and different diseases and their role in disease diagnosis and therapy.

Computational prediction of submergence responsive microRNA and their binding position within the genome of Oryza sativa.

Background: MicroRNAs (miRNAs) are small noncoding RNAs which play crucial role in response to the adverse biotic and abiotic stress conditions at the post transcriptional level. The functions of the miRNAs are generally based on complementarity to their target region. Results: We used the online tool psRNA Target for the identification of submergence responsive miRNA using the gene expression profile related to the submergence condition. We wrote a perl script for the prediction of miRNA target gene. The position based feature of the script increases the overall specificity of the program. Our perl script performed well on the genomic data of Oryza sativa and produced significant results with their positions. These results were analyzed on the basis of complementarity and the statistical scores are used to find out the most probable binding regions. These predicted binding regions are aligned with their respective miRNAs to find out the consensus sequence. We scored the alignment using a position dependent, mismatch penalty system. We also identified the rate of conservation of bases at a particular position for all the predicted binding regions and it was found that all the predicted binding regions maintain above 70% rate of conservation of bases. Conclusion: Our approach provides a novel framework for screening the genome of Oryza sativa. It can be broadly applied to identify complementarity specific miRNA targets computationally by doing a little modification of the script depending on the type of the miRNA.

Cytochrome P450 genes in coronary artery diseases: Codon usage analysis reveals genomic GC adaptation

Establishing codon usage biases are imperative for understanding the etiology of coronary artery diseases (CAD) as well as the genetic factors associated with these diseases. The aim of this study was to evaluate the contribution of 18 responsible cytochrome P450 (CYP) genes for the risk of CAD. Effective number of codon (Nc) showed a negative correlation with both GC3 and synonymous codon usage order (SCUO) suggesting an antagonistic relationship between codon usage and Nc of genes. The dinucleotide analysis revealed that CG and TA dinucleotides have the lowest odds ratio in these genes. Principal component analysis showed that GC composition has a profound effect in separating the genes along the first major axis. Our findings revealed that mutational pressure and natural selection could possibly be the major factors responsible for codon bias in these genes. The study not only offers an insight into the mechanisms of genomic GC adaptation, but also illustrates the complexity of CYP genes in CAD.

Recombination hotspots: Models and tools for detection

Recombination hotspots are the regions within the genome where the rate, and the frequency of recombination are optimum with a size varying from 1 to 2kb. The recombination event is mediated by the double-stranded break formation, guided by the combined enzymatic action of DNA topoisomerase and Spo 11 endonuclease. These regions are distributed non-uniformly throughout the human genome and cause distortions in the genetic map. Numerous lines of evidence suggest that the number of hotspots known in humans has increased manifold in recent years. A few facts about the hotspot evolutions were also put forward, indicating the differences in the hotspot position between chimpanzees and humans. In mice, recombination hot spots were found to be clustered within the major histocompatibility complex (MHC) region. Several models, that help explain meiotic recombination has been proposed. Moreover, scientists also developed some computational tools to locate the hotspot position and estimate their recombination rate in humans is of great interest to population and medical geneticists. Here we reviewed the molecular mechanisms, models and in silico prediction techniques of hot spot residues.

Guanine and Cytosine at the Second Codon Position Influence Gene Expression in Cereals

The compositional variation and the codon usage bias of the coding sequences of genes were analyzed in five species of grass family i.e. Oryza sativa, Triticum aestivum, Sorghum bicolor, Hordeum vulgare and Zea mays along with two outlier species belonging to family Solanaceae viz. Lycopersicon esculentum and Nicotiana tabacum. Pyrimidine-skew and keto-skew values varied across the cereals. These two parameters showed moderate positive correlation with codon adaptation index which was used as a measure of gene expression and with the tRNA adaptation index commonly used as a measure of translational efficiency. These were further elucidated by regression analysis and adaptive codon enrichment analysis. The present analysis revealed that the guanine and cytosine content at second codon position influence the gene expression in cereal crops. Although the codon usage pattern in five cereals was similar but differed significantly from the outliers L. esculentum and N. tabacum.

A cross talk between codon usage bias in human oncogenes.

Background: Oncogenes are the genes that have the potential to induce cancer. The extent and origin of codon usage bias is an important indicator of the forces shaping genome evolution in living organisms. Results: We observed moderate correlations between gene expression as measured by CAI and GC content at any codon site. The findings of our results showed that there is a significant positive correlation (Spearmans r= 0.45, P<0.01) between GC content at first and second codon position with that of third codon position. Further, striking negative correlation (r = -0.771, P < 0.01) between ENC with the GC3s values of each gene and positive correlation (r=0.644, P<0.01) in between CAI and ENC was also observed. Conclusions: The mutation pressure is the major determining factor in shaping the codon usage pattern of oncogenes rather than natural selection since its effects are present at all codon positions. The results revealed that codon usage bias determines the level of oncogene expression in human. Highly expressed oncogenes had rich GC contents with high degree of codon usage bias.

Codon usage and amino acid usage influence genes expression level

Highly expressed genes in any species differ in the usage frequency of synonymous codons. The relative recurrence of an event of the favored codon pair (amino acid pairs) varies between gene and genomes due to varying gene expression and different base composition. Here we propose a new measure for predicting the gene expression level, i.e., codon plus amino bias index (CABI). Our approach is based on the relative bias of the favored codon pair inclination among the genes, illustrated by analyzing the CABI score of the Medicago truncatula genes. CABI showed strong correlation with all other widely used measures (CAI, RCBS, SCUO) for gene expression analysis. Surprisingly, CABI outperforms all other measures by showing better correlation with the wet-lab data. This emphasizes the importance of the neighboring codons of the favored codon in a synonymous group while estimating the expression level of a gene.

Codon usage vis-a-vis start and stop codon context analysis of three dicot species

To understand the variation in genomic composition and its effect on codon usage, we performed the comparative analysis of codon usage and nucleotide usage in the genes of three dicots, Glycine max, Arabidopsis thaliana and Medicago truncatula. The dicot genes were found to be A/T rich and have predominantly A-ending and/or T-ending codons. GC3s directly mimic the usage pattern of global GC content. Relative synonymous codon usage analysis suggests that the high usage frequency of A/T over G/C mononucleotide containing codons in AT-rich dicot genome is due to compositional constraint as a factor of codon usage bias. Odds ratio analysis identified the dinucleotides TpG, TpC, GpA, CpA and CpT as over-represented, where, CpG and TpA as under-represented dinucleotides. The results of (NcExp−NcObs)/NcExp plot suggests that selection pressure other than mutation played a significant role in influencing the pattern of codon usage in these dicots. PR2 analysis revealed the significant role of selection pressure on codon usage. Analysis of varience on codon usage at start and stop site showed variation in codon selection in these sites. This study provides evidence that the dicot genes were subjected to compositional selection pressure.

Compositional bias coupled with selection and mutation pressure drives codon usage in Brassica campestris genes

The plant Brassica campestris includes the vegetables turnip and Chinese cabbage, important plants of economic importance. Here, we have analysed the codon usage bias of B. campestris for 116 protein coding genes. Neutrality analysis showed that B. campestris had a wide range of GC3s, and a significant correlation was observed between GC12 and GC3. Nc versus GC3s plot showed a few genes on or proximate to the expected curve, but the majority of points were found to be scattered distantly from the expected curve. Correspondence analysis on codon usage revealed that the position preference of codons on multidimensional space totally depends on the presence of A and T at synonymous third codon position. These results altogether suggest that composition bias along with selection (major) and mutation pressure (minor) affects the codon usage pattern of the protein coding genes in Brassica campestris.

Nasopharyngeal carcinoma: understanding its molecular biology at a fine scale.

Among all cancers, the incidence of nasopharyngeal carcinoma (NPC) is quite high in the endemic regions. NPC is a head and neck cancer with poor survival rate, and is rare throughout most of the world but common in certain geographic areas, like southern Asia and some regions of North East India (Nagaland, Manipur, and Mizoram). A clear understanding of its etiology is still lacking, but NPC is widely suspected to be the result of both genetic susceptibility and exposure to environmental factors or Epstein–Barr virus infection. Diagnosis in the early stages needs a high index of clinical acumen, and, although most cross-sectional imaging investigations show the tumor with precision, confirmation is dependent on histology. This article reviews all related research reports on NPC histopathological classifications worldwide that have been published within the past 20 years. Genome-wide association studies suggested that there might be common disease mechanisms between that disease and NPC. Personalized management rules, quality assessment of life in patients, and an understanding of the essential mechanisms of recurrence could be directed toward research into recurrent NPC. Hence, this literature would offer otolaryngologists a deeper insight into the etiological and management aspects of NPC.