MicroRNAs have been implicated for the regulation of gene expression. These miRNA are a class of single stranded non coding RNAs, formed from endogenous transcripts and measure typically about 19-25 nucleotides in length. They are important regulators of the various biological and metabolic functions taking place in humans. Many miRNAs show tissue specific expression. Human heart is a complex organ which during various diseased and developed conditions shows differential expression of miRNA. Here, we overview the recent findings on miRNA in cardiac diseases and report the presence of high AU content in differentially expressed miRNA in developed and diseased condition of heart as compared to all the miRNA present in the human. A total of 905 human miRNA sequences taken from miRBase were computationally analyzed. Trend analysis was performed to study the influence of positional frequency of the nucleotides. This study will help us in understanding the significance of AU rich elements in miRNA during the development of cardiac diseases.

Obesity is a global epidemic affecting over 1.5 billion people and is one of the risk factors for several diseases such as type 2 diabetes mellitus and hypertension. We have constructed a comprehensive map of the molecules reported to be implicated in obesity. A deep curation strategy was complemented by a novel semi-automated text mining system in order to screen 1,000 full-length research articles and over 90,000 abstracts that are relevant to obesity. We obtain a scale free network of 804 nodes and 971 edges, composed of 510 proteins, 115 genes, 62 complexes, 23 RNA molecules, 83 simple molecules, 3 phenotype and 3 drugs in "bow-tie" architecture. We classify this network into 5 modules and identify new links between the recently discovered fat mass and obesity associated FTO gene with well studied examples such as insulin and leptin. We further built an automated docking pipeline to dock orlistat as well as other drugs against the 24,000 proteins in the human structural proteome to explain the therapeutics and side effects at a network level. Based upon our experiments, we propose that therapeutic effect comes through the binding of one drug with several molecules in target network, and the binding propensity is both statistically significant and different in comparison with any other part of human structural proteome.

The genome of the human pathogen Entamoeba histolytica contains non-long terminal repeat (LTR) retrotransposons, the EhLINEs and EhSINEs, which lack targeted insertion. We investigated the importance of local DNA structure, and sequence preference of the element-encoded endonuclease (EN) in selecting target sites for retrotransposon insertion. Pre-insertion loci were tested computationally to detect unique features based on DNA structure, thermodynamic considerations and protein interaction measures. Target sites could readily be distinguished from other genomic sites based on these criteria. The contribution of the EhLINE1-encoded EN in target site selection was investigated biochemically. The sequence-specificity of the EN was tested in vitro with a variety of mutated substrates. It was possible to assign a consensus sequence, 5'-GCATT-3', which was efficiently nicked between A-T and T-T. The upstream G residue enhanced EN activity, possibly serving to limit retrotransposition in the A+T-rich E.histolytica genome. Mutated substrates with poor EN activity showed structural differences compared with normal substrates. Analysis of retrotransposon insertion sites from a variety of organisms showed that, in general, regions of favorable DNA structure were recognized for retrotransposition. A combination of favorable DNA structure and preferred EN nicking sequence in the vicinity of this structure may determine the genomic hotspots for retrotransposition.

MicroRNAs (miRNAs) are small, conserved RNAs known to regulate several biological processes by influencing gene expression in eukaryotes. The implication of miRNAs as another player of regulatory layers during heart development and diseases has recently been explored. However, there is no study which elucidates the profiling of miRNAs during development of heart till date. Very limited miRNAs have been reported to date in cardiac context. In addition, integration of large scale experimental data with computational and comparative approaches remains an unsolved challenge.The present study was designed to identify the microRNAs implicated in heart development using next generation sequencing, bioinformatics and experimental approaches. We sequenced six small RNA libraries prepared from different developmental stages of the heart using chicken as a model system to produce millions of short sequence reads. We detected 353 known and 703 novel miRNAs involved in heart development. Out of total 1056 microRNAs identified, 32.7% of total dataset of known microRNAs displayed differential expression whereas seven well studied microRNAs namely let-7, miR-140, miR-181, miR-30, miR-205, miR-103 and miR-22 were found to be conserved throughout the heart development. The 3'UTR sequences of genes were screened from Gallus gallus genome for potential microRNA targets. The target mRNAs were appeared to be enriched with genes related to cell cycle, apoptosis, signaling pathways, extracellular remodeling, metabolism, chromatin remodeling and transcriptional regulators. Our study presents the first comprehensive overview of microRNA profiling during heart development and prediction of possible cardiac specific targets and has a big potential in future to develop microRNA based therapeutics against cardiac pathologies where fetal gene re-expression is witnessed in adult heart.

Mobile genetic elements (MGEs) are fragments of DNA that can move around within the genome through retrotransposition. These are responsible for various important events such as gene inactivation, transduction, regulation of gene expression and genome expansion. The present work involves the identification and study of the distribution of Alu and L1 retrotransposons in the genome of Macaca mulatta, an extensively used organism in biomedical studies. We also make comparisons with MGE distributions in other primate genomes and study the physicochemical properties of the local DNA structure around the transposon insertion site using ELAN. The present work also includes computational testing of the pre-insertion loci in order to detect unique features based on DNA structure, thermodynamic considerations and protein interaction measures. Although there is significant sequence divergence between the elements of M. mulatta and H. sapiens, their genome wide distribution is very similar; comparing the distribution of L1's in all available X chromosome sequences suggests a common mechanism behind the spread of MGE's in primate genomes.

Antigen identification is an important step in the vaccine development process. Computational approaches including deep learning systems can play an important role in the identification of vaccine targets using genomic and proteomic information. Here, we present a new computational system to discover and analyse novel vaccine targets leading to the design of a multi-epitope subunit vaccine candidate. The system incorporates reverse vaccinology and immuno-informatics tools to screen genomic and proteomic datasets of several pathogens such as Trypanosoma cruzi, Plasmodium falciparum, and Vibrio cholerae to identify potential vaccine candidates (PVC). Further, as a case study, we performed a detailed analysis of the genomic and proteomic dataset of T. cruzi (CL Brenner and Y strain) to shortlist eight proteins as possible vaccine antigen candidates using properties such as secretory/surface-exposed nature, low transmembrane helix (< 2), essentiality, virulence, antigenic, and non-homology with host/gut flora proteins. Subsequently, highly antigenic and immunogenic MHC class I, MHC class II and B cell epitopes were extracted from top-ranking vaccine targets. The designed vaccine construct containing 24 epitopes, 3 adjuvants, and 4 linkers was analysed for its physicochemical properties using different tools, including docking analysis. Immunological simulation studies suggested significant levels of T-helper, T-cytotoxic cells, and IgG1 will be elicited upon administration of such a putative multi-epitope vaccine construct. The vaccine construct is predicted to be soluble, stable, non-allergenic, non-toxic, and to offer cross-protection against related Trypanosoma species and strains. Further, studies are required to validate safety and immunogenicity of the vaccine.

Mobile genetic elements (MGEs) account for a significant fraction of eukaryotic genomes and are implicated in altered gene expression and disease. We present an efficient computational protocol for MGE insertion site analysis. ELAN, the suite of tools described here uses standard techniques to identify different MGEs and their distribution on the genome. One component, DNASCANNER analyses known insertion sites of MGEs for the presence of signals that are based on a combination of local physical and chemical properties. ISF (insertion site finder) is a machine-learning tool that incorporates information derived from DNASCANNER. ISF permits classification of a given DNA sequence as a potential insertion site or not, using a support vector machine. We have studied the genomes of Homo sapiens, Mus musculus, Drosophila melanogaster and Entamoeba histolytica via a protocol whereby DNASCANNER is used to identify a common set of statistically important signals flanking the insertion sites in the various genomes. These are used in ISF for insertion site prediction, and the current accuracy of the tool is over 65%. We find similar signals at gene boundaries and splice sites. Together, these data are suggestive of a common insertion mechanism that operates in a variety of eukaryotes.

Recently published gorilla genome has offered an opportunity to study human evolution through variety of approaches. Mobile genetic elements (MGEs) insert non randomly in genome through mechanisms such as retrotransposition and may cause gene inactivation, transduction, regulation of gene expression and genome expansion. Here we report that majority of gorilla genome is occupied with MGEs (> 36%) with presence of LTRs and Non-LTRs such as Alus and L1s. Other types of MGEs such as MIRs, retrovirus like elements ERVs and DNA transposons are also found using repeatmasker and ELAN pipeline. The distribution is similar to Humans and Macaca genome. Using DNA Scanner we also scanned preinsertion loci for number of different properties such as DNA denaturation, energy measures, potential for protein interactions and sequence based features. We also predicted preinsertion loci with > 70% accuracy using a machine learning tool called insertion site finder (ISF) based upon support vector machines.