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1.  Identification and Expression Analysis of Ribosome Biogenesis Factor Co-orthologs in Solanum lycopersicum 
Ribosome biogenesis involves a large inventory of proteinaceous and RNA cofactors. More than 250 ribosome biogenesis factors (RBFs) have been described in yeast. These factors are involved in multiple aspects like rRNA processing, folding, and modification as well as in ribosomal protein (RP) assembly. Considering the importance of RBFs for particular developmental processes, we examined the complexity of RBF and RP (co-)orthologs by bioinformatic assignment in 14 different plant species and expression profiling in the model crop Solanum lycopersicum. Assigning (co-)orthologs to each RBF revealed that at least 25% of all predicted RBFs are encoded by more than one gene. At first we realized that the occurrence of multiple RBF co-orthologs is not globally correlated to the existence of multiple RP co-orthologs. The transcript abundance of genes coding for predicted RBFs and RPs in leaves and anthers of S. lycopersicum was determined by next generation sequencing (NGS). In combination with existing expression profiles, we can conclude that co-orthologs of RBFs by large account for a preferential function in different tissue or at distinct developmental stages. This notion is supported by the differential expression of selected RBFs during male gametophyte development. In addition, co-regulated clusters of RBF and RP coding genes have been observed. The relevance of these results is discussed.
PMCID: PMC4325683
next generation sequencing; orthologous prediction; ribosome biogenesis; MACE; qRT-PCR; tomato
2.  POEAS: Automated Plant Phenomic Analysis Using Plant Ontology 
Biological enrichment analysis using gene ontology (GO) provides a global overview of the functional role of genes or proteins identified from large-scale genomic or proteomic experiments. Phenomic enrichment analysis of gene lists can provide an important layer of information as well as cellular components, molecular functions, and biological processes associated with gene lists. Plant phenomic enrichment analysis will be useful for performing new experiments to better understand plant systems and for the interpretation of gene or proteins identified from high-throughput experiments. Plant ontology (PO) is a compendium of terms to define the diverse phenotypic characteristics of plant species, including plant anatomy, morphology, and development stages. Adoption of this highly useful ontology is limited, when compared to GO, because of the lack of user-friendly tools that enable the use of PO for statistical enrichment analysis. To address this challenge, we introduce Plant Ontology Enrichment Analysis Server (POEAS) in the public domain. POEAS uses a simple list of genes as input data and performs enrichment analysis using Ontologizer 2.0 to provide results in two levels, enrichment results and visualization utilities, to generate ontological graphs that are of publication quality. POEAS also offers interactive options to identify user-defined background population sets, various multiple-testing correction methods, different enrichment calculation methods, and resampling tests to improve statistical significance. The availability of such a tool to perform phenomic enrichment analyses using plant genes as a complementary resource will permit the adoption of PO-based phenomic analysis as part of analytical workflows. POEAS can be accessed using the URL
PMCID: PMC4274039  PMID: 25574136
phenomics; plant ontology; phenotype enrichment; plant genomics; Arabidopsis thaliana
3.  Computational Methods for CLIP-seq Data Processing 
RNA-binding proteins (RBPs) are at the core of post-transcriptional regulation and thus of gene expression control at the RNA level. One of the principal challenges in the field of gene expression regulation is to understand RBPs mechanism of action. As a result of recent evolution of experimental techniques, it is now possible to obtain the RNA regions recognized by RBPs on a transcriptome-wide scale. In fact, CLIP-seq protocols use the joint action of CLIP, crosslinking immunoprecipitation, and high-throughput sequencing to recover the transcriptome-wide set of interaction regions for a particular protein. Nevertheless, computational methods are necessary to process CLIP-seq experimental data and are a key to advancement in the understanding of gene regulatory mechanisms. Considering the importance of computational methods in this area, we present a review of the current status of computational approaches used and proposed for CLIP-seq data.
PMCID: PMC4196881  PMID: 25336930
RNA-binding proteins; RBP; CLIP-based; CLIP-seq; HITS-CLIP; PAR-CLIP; RBPome; RNA–Protein; post transcriptional regulation
4.  Interactions Among Plant Transcription Factors Regulating Expression of Stress-responsive Genes 
Plants are simultaneously subjected to a variety of stress conditions in the field and are known to combat the hostile conditions by up/down-regulating number of genes. There exists a significant level of cross-talk between different stress responses in plants. In this study, we predict the interacting pairs of transcription factors that regulate the multiple abiotic stress-responsive genes in the plant Arabidopsis thaliana. We identified the interacting pair(s) of transcription factors (TFs) based on the spatial proximity of their binding sites. We also examined the interactions between the predicted pairs of TFs using molecular docking. Subsequent to docking, the best interaction pose was selected using our scoring scheme DockScore, which ranks the docked solutions based on several interface parameters and aims to find optimal interactions between proteins. We analyzed the selected docked pose for the interface residues and their conservation.
PMCID: PMC4167486  PMID: 25249757
transcription factors; protein–protein interactions; Arabidopsis thaliana; docking; stress conditions
5.  Integrated Analysis of Dysregulated miRNA-gene Expression in HMGA2-silenced Retinoblastoma Cells 
Retinoblastoma (RB) is a primary childhood eye cancer. HMGA2 shows promise as a molecule for targeted therapy. The involvement of miRNAs in genome-level molecular dys-regulation in HMGA2-silenced RB cells is poorly understood. Through miRNA expression microarray profiling, and an integrated array analysis of the HMGA2-silenced RB cells, the dysregulated miRNAs and the miRNA-target relationships were modelled. Loop network analysis revealed a regulatory association between the transcription factor (SOX5) and the deregulated miRNAs (miR-29a, miR-9*, miR-9-3). Silencing of HMGA2 deregulated the vital oncomirs (miR-7, miR-331, miR-26a, miR-221, miR-17~92 and miR-106b∼25) in RB cells. From this list, the role of the miR-106b∼25 cluster was examined further for its expression in primary RB tumor tissues (n = 20). The regulatory targets of miR-106b∼25 cluster namely p21 (cyclin-dependent kinase inhibitor) and BIM (pro-apoptotic gene) were elevated, and apoptotic cell death was observed, in RB tumor cells treated with the specific antagomirs of the miR-106b∼25 cluster. Thus, suppression of miR-106b∼25 cluster controls RB tumor growth. Taken together, HMGA2 mediated anti-tumor effect present in RB is, in part, mediated through the miR-106b∼25 cluster.
PMCID: PMC4159370  PMID: 25232279
Retinoblastoma; High mobility group proteins (HMG)A2; miR-106b-25 cluster; Integrated mRNA-miRNA analysis; Antagomirs
6.  Computational Identification of Dengue Virus MicroRNA-Like Structures and their Cellular Targets 
MicroRNAs (miRNAs) are small, noncoding RNA molecules that regulate transcriptional and posttranscriptional gene regulation of the cell. Experimental evidence shows that miRNAs have a direct role in different cellular processes, such as immune function, apoptosis, and tumorigenesis. In a viral infection context, miRNAs have been connected with the interplay between host and pathogen, occupying a major role in pathogenesis. While numerous viral miRNAs from DNA viruses have been identified, characterization of functional RNA virus-encoded miRNAs and their potential targets is still ongoing. Here, we used an in silico approach to analyze dengue Virus genome sequences. Pre-miRNAs were extracted through VMir software, and the identification of putative pre-miRNAs and mature miRNAs was accessed using Support Vector Machine web tools. The targets were scanned using miRanda software and functionally annotated using ClueGo. Via computational tools, eight putative miRNAs were found to hybridize with numerous targets of morphogenesis, differentiation, migration, and growth pathways that may play a major role in the interaction of the virus and its host. Future approaches will focus on experimental validation of their presence and target messenger RNA genes to further elucidate their biological functions in human and mosquito cells.
PMCID: PMC4149395  PMID: 25210446
flavivirus; dengue virus; in silico screening; microRNA precursor; target prediction; functional annotation
7.  In Silico Studies of Medicinal Compounds Against Hepatitis C Capsid Protein from North India 
Hepatitis viral infection is a leading cause of chronic hepatitis, cirrhosis, and hepatocellular carcinoma (HCC). Over one million people are estimated to be persistently infected with hepatitis C virus (HCV) worldwide. As capsid core protein is the key element in spreading HCV; hence, it is considered to be the superlative target of antiviral compounds. Novel drug inhibitors of HCV are in need to complement or replace the current treatments such as pegylated interferon’s and ribavirin as they are partially booming and beset with various side effects. Our study was conducted to predict 3D structure of capsid core protein of HCV from northern part of India. Core, the capsid protein of HCV, handles the assembly and packaging of HCV RNA genome and is the least variable of all the ten HCV proteins among the six HCV genotypes. Therefore, we screened four phytochemicals inhibitors that are known to disrupt the interactions of core and other HCV proteins such as (a) epigallocatechin gallate (EGCG), (b) ladanein, (c) naringenin, and (d) silybin extracted from medicinal plants; targeted against active site of residues of HCV-genotype 3 (G3) (Q68867) and its subtypes 3b (Q68861) and 3g (Q68865) from north India. To study the inhibitory activity of the recruited flavonoids, we conducted a quantitative structure–activity relationship (QSAR). Furthermore, docking interaction suggests that EGCG showed a maximum number of hydrogen bond (H-bond) interactions with all the three modeled capsid proteins with high interaction energy followed by naringenin and silybin. Thus, our results strongly correlate the inhibitory activity of the selected bioflavonoid. Finally, the dynamic predicted capsid protein molecule of HCV virion provides a general avenue to target structure-based antiviral compounds that support the hypothesis that the screened inhibitors for viral capsid might constitute new class of potent agents but further confirmation is necessary using in vitro and in vivo studies.
PMCID: PMC4076477  PMID: 25002815
hepatitis C virus; hepatocellular carcinoma; capsid protein; docking; inhibitors
8.  DOR – a Database of Olfactory Receptors – Integrated Repository for Sequence and Secondary Structural Information of Olfactory Receptors in Selected Eukaryotic Genomes 
Olfaction is the response to odors and is mediated by a class of membrane-bound proteins called olfactory receptors (ORs). An understanding of these receptors serves as a good model for basic signal transduction mechanisms and also provides important clues for the strategies adopted by organisms for their ultimate survival using chemosensory perception in search of food or defense against predators. Prior research on cross-genome phylogenetic analyses from our group motivated the addressal of conserved evolutionary trends, clustering, and ortholog prediction of ORs. The database of olfactory receptors (DOR) is a repository that provides sequence and structural information on ORs of selected organisms (such as Saccharomyces cerevisiae, Drosophila melanogaster, Caenorhabditis elegans, Mus musculus, and Homo sapiens). Users can download OR sequences, study predicted membrane topology, and obtain cross-genome sequence alignments and phylogeny, including three-dimensional (3D) structural models of 100 selected ORs and their predicted dimer interfaces. The database can be accessed from Such a database should be helpful in designing experiments on point mutations to probe into the possible dimerization modes of ORs and to even understand the evolutionary changes between different receptors.
PMCID: PMC4069036  PMID: 25002814
olfaction; insect olfactory system; membrane proteins; odor perception
9.  Identification of Chicken Pulmonary miRNAs Targeting PB1, PB1-F2, and N40 Genes of Highly Pathogenic Avian Influenza Virus H5N1 In Silico 
Highly pathogenic Avian influenza (HPAI) is a notifiable viral disease caused by avian influenza type A viruses of the Orthomyxoviridae family. Type A influenza genome consists of eight segments of negative-sense RNA. RNA segment 2 encodes three proteins, PB1, PB1-F2, and N40, which are translated from the same mRNA by ribosomal leaky scanning and reinitiation. Since these proteins are critical for viral replication and pathogenesis, targeting their expression can be one of the approaches to control and resist HPAI. MicroRNAs are short noncoding RNAs that regulate a variety of biological processes such as cell growth, tissue differentiation, apoptosis, and viral infection. In this study, a set of 300 miRNAs expressed in chicken lungs were screened against the HPAI virus (H5N1) segment 2 with different screening parameter like thermodynamic stability of heteroduplex, seed sequence complementarity, conserved target sequence, and target-site accessibility for identifying miRNAs that can potentially target the transcript of segment 2 of H5N1. Chicken miRNAs gga-mir-133c, gga-mir-1710, and gga-mir-146c* are predicted to target the expression of PB1, PB1-F2, and N40 proteins. This indicates that chicken has genetic potential to resist/tolerate H5N1 infection and these can be suitably exploited in designing strategies for control of avian influenza in chicken.
PMCID: PMC4069037  PMID: 25002813
chicken; miRNA; H5N1; PB1; PB1-F2; N40
10.  A Protocol for mtGenome Analysis on Large Sample Numbers 
The mitochondrial genome is widely studied in a variety of fields, such as population, forensic, and human and medical genetics. Most studies have been limited to a small portion of the sequence that, although highly diverse, does not describe the total variability. The arrival of modern high-throughput sequencing technologies has made it possible to investigate larger sequences in a shorter amount of time as well as in a more affordable fashion. This work aims to describe a protocol for sequencing and analyzing the complete mitochondrial genome with the Ion PGM™ platform. To evaluate the protocol, the mitochondrial genome was sequenced to approximately 210 Mbp, with high-quality sequences distributed between 12 samples that had an average coverage of 1023× per sample. Several variant callers were compared to improve the protocol outcome. The results suggest that it is possible to run up to 120 samples per run without any loss of any significant quality. Therefore, this protocol is an efficient and accurate tool for full mitochondrial genome analysis.
PMCID: PMC4069038  PMID: 25002812
next-generation sequencing; mitochondrial DNA; analysis protocol; polymorphism; population genetics
11.  High-throughput Methods Redefine the Rumen Microbiome and Its Relationship with Nutrition and Metabolism 
Diversity in the forestomach microbiome is one of the key features of ruminant animals. The diverse microbial community adapts to a wide array of dietary feedstuffs and management strategies. Understanding rumen microbiome composition, adaptation, and function has global implications ranging from climatology to applied animal production. Classical knowledge of rumen microbiology was based on anaerobic, culture-dependent methods. Next-generation sequencing and other molecular techniques have uncovered novel features of the rumen microbiome. For instance, pyrosequencing of the 16S ribosomal RNA gene has revealed the taxonomic identity of bacteria and archaea to the genus level, and when complemented with barcoding adds multiple samples to a single run. Whole genome shotgun sequencing generates true metagenomic sequences to predict the functional capability of a microbiome, and can also be used to construct genomes of isolated organisms. Integration of high-throughput data describing the rumen microbiome with classic fermentation and animal performance parameters has produced meaningful advances and opened additional areas for study. In this review, we highlight recent studies of the rumen microbiome in the context of cattle production focusing on nutrition, rumen development, animal efficiency, and microbial function.
PMCID: PMC4055558  PMID: 24940050
cattle; metabolism; microbiome; nutrition; rumen
12.  The Purine Bias of Coding Sequences is Determined by Physicochemical Constraints on Proteins 
For this report, we analyzed protein secondary structures in relation to the statistics of three nucleotide codon positions. The purpose of this investigation was to find which properties of the ribosome, tRNA or protein level, could explain the purine bias (Rrr) as it is observed in coding DNA. We found that the Rrr pattern is the consequence of a regularity (the codon structure) resulting from physicochemical constraints on proteins and thermodynamic constraints on ribosomal machinery. The physicochemical constraints on proteins mainly come from the hydropathy and molecular weight (MW) of secondary structures as well as the energy cost of amino acid synthesis. These constraints appear through a network of statistical correlations, such as (i) the cost of amino acid synthesis, which is in favor of a higher level of guanine in the first codon position, (ii) the constructive contribution of hydropathy alternation in proteins, (iii) the spatial organization of secondary structure in proteins according to solvent accessibility, (iv) the spatial organization of secondary structure according to amino acid hydropathy, (v) the statistical correlation of MW with protein secondary structures and their overall hydropathy, (vi) the statistical correlation of thymine in the second codon position with hydropathy and the energy cost of amino acid synthesis, and (vii) the statistical correlation of adenine in the second codon position with amino acid complexity and the MW of secondary protein structures. Amino acid physicochemical properties and functional constraints on proteins constitute a code that is translated into a purine bias within the coding DNA via tRNAs. In that sense, the Rrr pattern within coding DNA is the effect of information transfer on nucleotide composition from protein to DNA by selection according to the codon positions. Thus, coding DNA structure and ribosomal machinery co-evolved to minimize the energy cost of protein coding given the functional constraints on proteins.
PMCID: PMC4039185  PMID: 24899802
genomics; ancestral codon; RNY; purine bias; secondary structure; helix; sheet; turn coil; ribosome; translation; energy cost
13.  Promise and Reality in the Expanding Field of Network Interaction Analysis: Metabolic Networks 
In the last few decades, metabolic networks revealed their capabilities as powerful tools to analyze the cellular metabolism. Many research fields (eg, metabolic engineering, diagnostic medicine, pharmacology, biochemistry, biology and physiology) improved the understanding of the cell combining experimental assays and metabolic network-based computations. This process led to the rise of the “systems biology” approach, where the theory meets experiments and where two complementary perspectives cooperate in the study of biological phenomena. Here, the reconstruction of metabolic networks is presented, along with established and new algorithms to improve the description of cellular metabolism. Then, advantages and limitations of modeling algorithms and network reconstruction are discussed.
PMCID: PMC3999820  PMID: 24812497
metabolic network; metabolic adjustments; enzymatic perturbations; metabolic impairments; genome-scale models; pathway simulation; -omics dataset integration
14.  Computational Insights of the Interaction among Sea Anemones Neurotoxins and Kv1.3 Channel 
Sea anemone neurotoxins are peptides that interact with Na+ and K+ channels, resulting in specific alterations on their functions. Some of these neurotoxins (1ROO, 1BGK, 2K9E, 1BEI) are important for the treatment of about 80 autoimmune disorders because of their specificity for Kv1.3 channel. The aim of this study was to identify the common residues among these neurotoxins by computational methods, and establish whether there is a pattern useful for the future generation of a treatment for autoimmune diseases. Our results showed eight new key common residues between the studied neurotoxins interacting with a histidine ring and the selectivity filter of the receptor, thus showing a possible pattern of interaction. This knowledge may serve as an input for the design of more promising drugs for autoimmune treatments.
PMCID: PMC3999815  PMID: 24812496
neurotoxins; potassium channel; Kv1.3; computational methods; autoimmune diseases
15.  An In Silico Approach for Characterization of an Aminoglycoside Antibiotic-Resistant Methyltransferase Protein from Pyrococcus furiosus (DSM 3638) 
Pyrococcus furiosus is a hyperthermophilic archaea. A hypothetical protein of this archaea, PF0847, was selected for computational analysis. Basic local alignment search tool and multiple sequence alignment (MSA) tool were employed to search for related proteins. Both the secondary and tertiary structure prediction were obtained for further analysis. Three-dimensional model was assessed by PROCHECK and QMEAN6 programs. To get insights about the physical and functional associations of the protein, STRING network analysis was performed. Binding of the SAM (S-adenosyl-l-methionine) ligand with our protein, fetched from an antibiotic-related methyltransferase (PDB code: 3P2K: D), showed high docking energy and suggested the function of the protein as methyltransferase. Finally, we tried to look for a specific function of the proposed methyltransferase, and binding of the geneticin bound to the eubacterial 16S rRNA A-site (PDB code: 1MWL) in the active site of the PF0847 gave us the indication to predict the protein responsible for aminoglycoside antibiotic resistance.
PMCID: PMC3965365  PMID: 24683305
methyltransferase; aminoglycoside antibiotic resistance; 16S rRNA A-site; molecular docking
16.  Postpartal Subclinical Endometritis Alters Transcriptome Profiles in Liver and Adipose Tissue of Dairy Cows 
Transcriptome alterations in liver and adipose tissue of cows with subclinical endometritis (SCE) at 29 d postpartum were evaluated. Bioinformatics analysis was performed using the Dynamic Impact Approach by means of KEGG and DAVID databases. Milk production, blood metabolites (non-esterified fatty acids, magnesium), and disease biomarkers (albumin, aspartate aminotransferase) did not differ greatly between healthy and SCE cows. In liver tissue of cows with SCE, alterations in gene expression revealed an activation of complement and coagulation cascade, steroid hormone biosynthesis, apoptosis, inflammation, oxidative stress, MAPK signaling, and the formation of fibrinogen complex. Bioinformatics analysis also revealed an inhibition of vitamin B3 and B6 metabolism with SCE. In adipose, the most activated pathways by SCE were nicotinate and nicotinamide metabolism, long-chain fatty acid transport, oxidative phosphorylation, inflammation, T cell and B cell receptor signaling, and mTOR signaling. Results indicate that SCE in dairy cattle during early lactation induces molecular alterations in liver and adipose tissue indicative of immune activation and cellular stress.
PMCID: PMC3934763  PMID: 24578603
uterine infection; liver; adipose; cow genomics
17.  Probing the Unknowns in Cytokinin-Mediated Immune Defense in Arabidopsis with Systems Biology Approaches 
Plant hormones involving salicylic acid (SA), jasmonic acid (JA), ethylene (Et), and auxin, gibberellins, and abscisic acid (ABA) are known to regulate host immune responses. However, plant hormone cytokinin has the potential to modulate defense signaling including SA and JA. It promotes plant pathogen and herbivore resistance; underlying mechanisms are still unknown. Using systems biology approaches, we unravel hub points of immune interaction mediated by cytokinin signaling in Arabidopsis. High-confidence Arabidopsis protein–protein interactions (PPI) are coupled to changes in cytokinin-mediated gene expression. Nodes of the cellular interactome that are enriched in immune functions also reconstitute sub-networks. Topological analyses and their specific immunological relevance lead to the identification of functional hubs in cellular interactome. We discuss our identified immune hubs in light of an emerging model of cytokinin-mediated immune defense against pathogen infection in plants.
PMCID: PMC3929428  PMID: 24558299
systems biology; plant hormones; interaction networks; gene expression; cytokinin
18.  Longitudinal Analysis of Whole Blood Transcriptomes to Explore Molecular Signatures Associated With Acute Renal Allograft Rejection 
In this study, we explored a time course of peripheral whole blood transcriptomes from kidney transplantation patients who either experienced an acute rejection episode or did not in order to better delineate the immunological and biological processes measureable in blood leukocytes that are associated with acute renal allograft rejection. Using microarrays, we generated gene expression data from 24 acute rejectors and 24 nonrejectors. We filtered the data to obtain the most unambiguous and robustly expressing probe sets and selected a subset of patients with the clearest phenotype. We then performed a data-driven exploratory analysis using data reduction and differential gene expression analysis tools in order to reveal gene expression signatures associated with acute allograft rejection. Using a template-matching algorithm, we then expanded our analysis to include time course data, identifying genes whose expression is modulated leading up to acute rejection. We have identified molecular phenotypes associated with acute renal allograft rejection, including a significantly upregulated signature of neutrophil activation and accumulation following transplant surgery that is common to both acute rejectors and nonrejectors. Our analysis shows that this expression signature appears to stabilize over time in nonrejectors but persists in patients who go on to reject the transplanted organ. In addition, we describe an expression signature characteristic of lymphocyte activity and proliferation. This lymphocyte signature is significantly downregulated in both acute rejectors and nonrejectors following surgery; however, patients who go on to reject the organ show a persistent downregulation of this signature relative to the neutrophil signature.
PMCID: PMC3921155  PMID: 24526836
blood transcriptomics; microarray; kidney transplant rejection; peripheral whole blood; neutrophil to lymphocyte ratio
19.  GeneSV – an Approach to Help Characterize Possible Variations in Genomic and Protein Sequences 
A computational approach for identification and assessment of genomic sequence variability (GeneSV) is described. For a given nucleotide sequence, GeneSV collects information about the permissible nucleotide variability (changes that potentially preserve function) observed in corresponding regions in genomic sequences, and combines it with conservation/variability results from protein sequence and structure-based analyses of evaluated protein coding regions. GeneSV was used to predict effects (functional vs. non-functional) of 37 amino acid substitutions on the NS5 polymerase (RdRp) of dengue virus type 2 (DENV-2), 36 of which are not observed in any publicly available DENV-2 sequence. 32 novel mutants with single amino acid substitutions in the RdRp were generated using a DENV-2 reverse genetics system. In 81% (26 of 32) of predictions tested, GeneSV correctly predicted viability of introduced mutations. In 4 of 5 (80%) mutants with double amino acid substitutions proximal in structure to one another GeneSV was also correct in its predictions. Predictive capabilities of the developed system were illustrated on dengue RNA virus, but described in the manuscript a general approach to characterize real or theoretically possible variations in genomic and protein sequences can be applied to any organism.
PMCID: PMC3893053  PMID: 24453480
dengue virus (DENV); quasispecies; genomic sequence variability; mutant viability; protein structure
20.  Three Dimensional Structure Prediction of Fatty Acid Binding Site on Human Transmembrane Receptor CD36 
CD36 is an integral membrane protein which is thought to have a hairpin-like structure with alpha-helices at the C and N terminals projecting through the membrane as well as a larger extracellular loop. This receptor interacts with a number of ligands including oxidized low density lipoprotein and long chain fatty acids (LCFAs). It is also implicated in lipid metabolism and heart diseases. It is therefore important to determine the 3D structure of the CD36 site involved in lipid binding. In this study, we predict the 3D structure of the fatty acid (FA) binding site [127–279 aa] of the CD36 receptor based on homology modeling with X-ray structure of Human Muscle Fatty Acid Binding Protein (PDB code: 1HMT). Qualitative and quantitative analysis of the resulting model suggests that this model was reliable and stable, taking in consideration over 97.8% of the residues in the most favored regions as well as the significant overall quality factor. Protein analysis, which relied on the secondary structure prediction of the target sequence and the comparison of 1HMT and CD36 [127–279 aa] secondary structures, led to the determination of the amino acid sequence consensus. These results also led to the identification of the functional sites on CD36 and revealed the presence of residues which may play a major role during ligand-protein interactions.
PMCID: PMC3859822  PMID: 24348024
CD36; fatty acids binding site; homology modeling; 3D model
21.  ChIP-Seq Data Mining: Remarkable Differences in NRSF/REST Target Genes between Human ESC and ESC-Derived Neurons 
The neuron-restrictive silencer factor (NRSF) is a zinc finger transcription factor that represses neuronal gene transcription in non-neuronal cells by binding to the consensus repressor element-1 (RE1) located in regulatory regions of target genes. NRSF silences the expression of a wide range of target genes involved in neuron-specific functions. Previous studies showed that aberrant regulation of NRSF plays a key role in the pathological process of human neurodegenerative diseases. However, a comprehensive set of NRSF target genes relevant to human neuronal functions has not yet been characterized. We performed genome-wide data mining from chromatin immunoprecipitation followed by deep sequencing (ChIP-Seq) datasets of NRSF binding sites in human embryonic stem cells (ESC) and the corresponding ESC-derived neurons, retrieved from the database of the ENCODE/HAIB project. Using bioinformatics tools such as Avadis NGS and MACS, we identified 2,172 NRSF target genes in ESC and 308 genes in ESC-derived neurons based on stringent criteria. Only 40 NRSF target genes overlapped between both data sets. According to motif analysis, binding regions showed an enrichment of the consensus RE1 sites in ESC, whereas they were mainly located in poorly defined non-RE1 sites in ESC-derived neurons. Molecular pathways of NRSF target genes were linked with various neuronal functions in ESC, such as neuroactive ligand-receptor interaction, CREB signaling, and axonal guidance signaling, while they were not directed to neuron-specific functions in ESC-derived neurons. Remarkable differences in ChIP-Seq-based NRSF target genes and pathways between ESC and ESC-derived neurons suggested that NRSF-mediated silencing of target genes is highly effective in human ESC but not in ESC-derived neurons.
PMCID: PMC3855043  PMID: 24324330
ChIP-seq; data mining; ESC; GenomeJack; Huntington’s disease; human neurons; NRSF; REST
22.  A Computational Assay to Design an Epitope-Based Peptide Vaccine Against Saint Louis Encephalitis Virus 
Saint Louis encephalitis virus, a member of the flaviviridae subgroup, is a culex mosquito-borne pathogen. Despite severe epidemic outbreaks on several occasions, not much progress has been made with regard to an epitope-based vaccine designed for Saint Louis encephalitis virus. The envelope proteins were collected from a protein database and analyzed with an in silico tool to identify the most immunogenic protein. The protein was then verified through several parameters to predict the T-cell and B-cell epitopes. Both T-cell and B-cell immunity were assessed to determine that the protein can induce humoral as well as cell-mediated immunity. The peptide sequence from 330–336 amino acids and the sequence REYCYEATL from the position 57 were found as the most potential B-cell and T-cell epitopes, respectively. Furthermore, as an RNA virus, one important thing was to establish the epitope as a conserved one; this was also done by in silico tools, showing 63.51% conservancy. The epitope was further tested for binding against the HLA molecule by computational docking techniques to verify the binding cleft epitope interaction. However, this is a preliminary study of designing an epitope-based peptide vaccine against Saint Louis encephalitis virus; the results awaits validation by in vitro and in vivo experiments.
PMCID: PMC3855041  PMID: 24324329
epitope; computational tools; humoral; cell-mediated immunity; conservancy
23.  On Crowd-verification of Biological Networks 
Biological networks with a structured syntax are a powerful way of representing biological information generated from high density data; however, they can become unwieldy to manage as their size and complexity increase. This article presents a crowd-verification approach for the visualization and expansion of biological networks.
Web-based graphical interfaces allow visualization of causal and correlative biological relationships represented using Biological Expression Language (BEL). Crowdsourcing principles enable participants to communally annotate these relationships based on literature evidences. Gamification principles are incorporated to further engage domain experts throughout biology to gather robust peer-reviewed information from which relationships can be identified and verified.
The resulting network models will represent the current status of biological knowledge within the defined boundaries, here processes related to human lung disease. These models are amenable to computational analysis. For some period following conclusion of the challenge, the published models will remain available for continuous use and expansion by the scientific community.
PMCID: PMC3798292  PMID: 24151423
community curation; biological network models; reputation system; Biological Expression Language
24.  Automatic Identification of Algal Community from Microscopic Images 
A good understanding of the population dynamics of algal communities is crucial in several ecological and pollution studies of freshwater and oceanic systems. This paper reviews the subsequent introduction to the automatic identification of the algal communities using image processing techniques from microscope images. The diverse techniques of image preprocessing, segmentation, feature extraction and recognition are considered one by one and their parameters are summarized. Automatic identification and classification of algal community are very difficult due to various factors such as change in size and shape with climatic changes, various growth periods, and the presence of other microbes. Therefore, the significance, uniqueness, and various approaches are discussed and the analyses in image processing methods are evaluated. Algal identification and associated problems in water organisms have been projected as challenges in image processing application. Various image processing approaches based on textures, shapes, and an object boundary, as well as some segmentation methods like, edge detection and color segmentations, are highlighted. Finally, artificial neural networks and some machine learning algorithms were used to classify and identifying the algae. Further, some of the benefits and drawbacks of schemes are examined.
PMCID: PMC3798295  PMID: 24151424
Algae identification; segmentation; neural network; feature extraction; identification
25.  Human Retrovirus Codon Usage from tRNA Point of View: Therapeutic Insights 
The purpose of this study was to investigate the balance between transfer ribonucleic acid (tRNA) supply and demand in retrovirus-infected cells, seeking the best targets for antiretroviral therapy based on the hypothetical tRNA Inhibition Therapy (TRIT). Codon usage and tRNA gene data were retrieved from public databases. Based on logistic principles, a therapeutic score (T-score) was calculated for all sense codons, in each retrovirus-host system. Codons that are critical for viral protein translation, but not as critical for the host, have the highest T-score values. Theoretically, inactivating the cognate tRNA species should imply a severe reduction of the elongation rate during viral mRNA translation. We developed a method to predict tRNA species critical for retroviral protein synthesis. Four of the best TRIT targets in HIV-1 and HIV-2 encode Large Hydrophobic Residues (LHR), which have a central role in protein folding. One of them, codon CUA, is also a TRIT target in both HTLV-1 and HTLV-2. Therefore, a drug designed for inactivating or reducing the cytoplasmatic concentration of tRNA species with anticodon TAG could attenuate significantly both HIV and HTLV protein synthesis rates. Inversely, replacing codons ending in UA by synonymous codons should increase the expression, which is relevant for DNA vaccine design.
PMCID: PMC3798314  PMID: 24151425
codon usage; tRNA; HIV; HTLV; therapy

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