Search

Introduction Zika virus (ZIKV) is an endemic, neurovirulent arbovirus whose primary vectors are Aedes aegypti and Aedes albopictus mosquitoes. Its primary transmission mode is the horizontal transfer of mosquito-infectious saliva [...], Zika virus (ZIKV) infection remains a worldwide concern, and currently no effective treatments or vaccines are available. Novel therapeutics are an avenue of interest that could probe viral RNA-human protein communication to stop viral replication. One specific RNA structure, G-quadruplexes (G4s), possess various roles in viruses and all domains of life, including transcription and translation regulation and genome stability, and serves as nucleation points for RNA liquid-liquid phase separation. Previous G4 studies on ZIKV using a quadruplex forming G-rich sequences Mapper located a potential G-quadruplex sequence in the 3' terminal region (TR) and was validated structurally using a 25-mer oligo. It is currently unknown if this structure is conserved and maintained in a large ZIKV RNA transcript and its specific roles in viral replication. Using bioin-formatic analysis and biochemical assays, we demonstrate that the ZIKV 3' TR G4 is conserved across all ZIKV isolates and maintains its structure in a 3' TR full-length transcript. We further established the G4 formation using pyridostatin and the BG4 G4-recognizing antibody binding assays. Our study also demonstrates that the human DEAD-box helicases, [DDX3X.sub.132-607] and [DDX17.sub.135-555], bind to the 3' TR and that [DDX17.sub.135-555] unfolds the G4 present in the 3' TR. These findings provide a path forward in potential therapeutic targeting of DDX3X or DDX17's binding to the 3' TR G4 region for novel treatments against ZIKV. Key words: Zika virus, 3' terminal region, G-quadruplex, BG4 antibody, DDX17, helicase assay

Author(s): Julia Mergner [sup.1] , Martin Frejno [sup.1] , Markus List [sup.2] , Michael Papacek [sup.3] , Xia Chen [sup.4] , Ajeet Chaudhary [sup.4] , Patroklos Samaras [sup.1] , Sandra [...], Plants are essential for life and are extremely diverse organisms with unique molecular capabilities.sup.1. Here we present a quantitative atlas of the transcriptomes, proteomes and phosphoproteomes of 30 tissues of the model plant Arabidopsis thaliana. Our analysis provides initial answers to how many genes exist as proteins (more than 18,000), where they are expressed, in which approximate quantities (a dynamic range of more than six orders of magnitude) and to what extent they are phosphorylated (over 43,000 sites). We present examples of how the data may be used, such as to discover proteins that are translated from short open-reading frames, to uncover sequence motifs that are involved in the regulation of protein production, and to identify tissue-specific protein complexes or phosphorylation-mediated signalling events. Interactive access to this resource for the plant community is provided by the ProteomicsDB and ATHENA databases, which include powerful bioinformatics tools to explore and characterize Arabidopsis proteins, their modifications and interactions. A quantitative atlas of the transcriptomes, proteomes and phosphoproteomes of 30 tissues of the model plant Arabidopsis thaliana provides a valuable resource for plant research.

Although bioinformatics, the use of computational science to study biology, has become imperative in many areas of the biological sciences and related career paths, introductory biochemistry courses may disregard practical knowledge on bioinformatics. For this reason, we merged a hands-on activity module into an undergraduate biochemistry course in two ways. First, we incorporated bioinformatics modules for building phylogenetic trees by aligning the active sites of 10 chosen related α-amylase enzymes using freely available data. Secondly, we chose three of those 10 α-amylase enzymes to compare the 3D structure of their active sites. This module gives the students an opportunity to understand how to access biological information from public databases such as GenBank, and analyze the information using software like MEGA, ClustalW, and RasMol. Overall, our module should provide instructors with ideas on how to develop similar modules and encourage students to develop further independence in the use of bioinformatics tools. Keywords: bioinformatics module, MEGA, phylogeny, molecular data, RasMol, 3D structure of α-amylase, INTRODUCTION Across the various branches of the life sciences over the latter part of the last century, easy accessibility of high performance computers and effective data management algorithms began to [...]

Author(s): Radwa N. Morgan[sup.1] , Sarra E. Saleh[sup.2] , Hala A. Farrag[sup.1] and Mohammad M. Aboulwafa[sup.2] Background Cyclomodulins are cell cycle modulating toxins that contribute to tumorgenesis. These toxins are [...], Background The colibactin and cytotoxic necrotizing factor 1 (Cnf 1) are toxins with cell cycle modulating effects that contribute to tumorgenesis and hyperproliferation. This study aimed to investigate the prevalence and pathologic effects of Cnf 1 and colibactin among hemolytic uropathogenic Escherichia coli (UPEC). The bioinformatics approach incorporated in this study aimed to expand the domain of the in vitro study and explore the prevalence of both toxins among other bacterial species. A total of 125 E. coli isolates were recovered from UTIs patients. The isolates were tested for their hemolytic activity, subjected to tissue culture and PCR assays to detect the phenotypic and genotypic features of both toxins. A rat ascending UTI in vivo model was conducted using isolates expressing or non-expressing Cnf 1 and colibactin (ClbA and ClbQ). The bioinformatics analyses were inferred by Maximum likelihood method and the evolutionary relatedness was deduced by MEGA X. Results Only 21 (16.8%) out of 125 isolates were hemolytic and 10 of these (47.62%) harbored the toxins encoding genes (cnf 1.sup.+, clbA.sup.+ and clbQ.sup.+). The phenotypic features of both toxins were exhibited by only 7 of the (cnf 1.sup.+clbA.sup.+clbQ.sup.+) harboring isolates. The severest infections, hyperplastic and genotoxic changes in kidneys and bladders were observed in rats infected with the cnf 1.sup.+clbA.sup.+clbQ.sup.+ isolates. Conclusion Only 33.3% of the hemolytic UPEC isolates exhibited the phenotypic and genotypic features of Cnf 1 and Colibactin. The in vivo animal model results gives an evidence of active Cnf 1 and Colibactin expression and indicates the risks associated with recurrent and chronic UTIs caused by UPEC. The bioinformatics analyses confirmed the predominance of colibactin pks island among Enterobacteriaceae family (92.86%), with the highest occurrence among Escherichia species (53.57%), followed by Klebsiella (28.57%), Citrobacter (7.14%), and Enterobacter species (3.57%). The Cnf 1 is predominant among Escherichia coli (94.05%) and sporadically found among Shigella species (1.08%), Salmonella enterica (0.54%), Yersinia pseudotuberculosis (1.08%), Photobacterium (1.08%), Moritella viscosa (0.54%), and Carnobacterium maltaromaticum (0.54%). A close relatedness was observed between the 54-kb pks island of Escherichia coli, the probiotic Escherichia coli Nissle 1917, Klebsiella aerogenes, Klebsiella pneumoniae and Citrobacter koseri. Keywords: Uropathogenic E. coli (UPEC), Cytotoxic necrotizing factor-1 (Cnf 1), Colibactin (pks island), Rat ascending UTI model, Bioinformatics

BACKGROUND: Computer-based modeling is part of a new approach to predictive toxicology. OBJECTIVES: We investigated the usefulness 01 an integrated computational systems biology approach in a case study involving the [...]

Introduction As increasingly more mammalian genomes are sequenced, the ensuing challenge in mammalian genomics is to understand how gene expression is regulated. For many years, mutation accumulation in protein sequences [...], cis-Elements CArG bound by serum response factor (SRF) are presently being intensively studied, but little is known about the substitution pattern of functional CArG elements. Here, we have performed the first evolutionary analysis of CArGome in the human and mouse genome through bioinformatic methods and statistical tests. We calculated the substitution rate at each site of the functional CArG elements. The results showed that the core sites of the functional CArG elements evolved faster than did the background DNA, indicating that these sites were likely to evolve under positive selection. Moreover, a strong TATA 'motif' was evident in the core region within the functional CArG elements in both human and mouse promoters. This motif could probably be a major contribution to the formation of the spatial structure, which was important for CArG-SRF recognition. Thus, the study further revealed the sequence character and substitution pattern of CArG elements and provided useful information for the study of the SRF-binding efficiencies of CArG promoters in functional assays. Key words: CArG elements, substitution pattern, evolutionary analysis, bioinformatics, mouse genome, human genome. Les elements cis CArG lies par le facteur de reponse au serum (SRF) font presentement l'objet de nombreux travaux, mais peu de choses sont connues au sujet des substitutions entre elements CArG fonctionnels. Dans ce travail, les auteurs ont realise la premiere analyse de revolution du CArGome au sein des genomes de l'humain et de la souris via une approche bioinformatique et des tests statistiques. Les auteurs ont calcule; le taux de substitution a chacun des sites chez les elements CArG fonctionnels. Les resultats montrent que les sites au centre des elements CArG fonctionnels ont evolue plus rapidement que le reste de l'ADN, ce qui suggere que ces sites auraient evoluee sous l'impulsion d'une selection positive. De plus, un fort << motif >> TATA etait evident au sein de la region centrale chez les elements CArG fonctionnels presents dans les promoteurs humains et murins. Ce << motif >> pourrait apporter une contribution majeure a la formation de la structure spatiale qui est importante pour la reconnaissance CArG-SRF. Cette etude a ainsi revele les proprietes de ces sequences et le type de substitutions qui caracterisent les elements CArG en plus de fournir des informations utiles pour l'etude de l'intensite de liaison des facteurs SRF sur les promoteurs CArG lors d'analyses fonctionnelles. Mots-cles: elements CArG, mode de substitution, analyse evolutive, bioinformatique, genome murin, genome humain. [Traduit par la Redaction]

Comprehensive bioinformatic analysis and filtering techniques are employed to identify a single-nucleotide coding variant in the feline leukemia virus subgroup C cellular receptor 1 (FLVCR1) which causes posterior column ataxia and retinitis pigmentosa (PCARP). The findings for aberrant FLVCR1 which causes a selective degeneration of a subpopulation of neurons in the retina and the posterior columns of the spinal cord via dysregulation of heme or iron homeostasis provide better insight into the molecular basis of sensory neural signaling to include common mechanisms that involve proprioception and vision.

Proteins evolve slowly because amino-acid substitutions are often deleterious owing to their effects on protein structure, expression or function (1-4). The selective constraint imposed on evolving proteins by these factors [...], The need to maintain the structural and functional integrity of an evolving protein severely restricts the repertoire of acceptable amino-acid substitutions (1-4). However, it is not known whether these restrictions impose a global limit on how far homologous protein sequences can diverge from each other. Here we explore the limits of protein evolution using sequence divergence data. We formulate a computational approach to study the rate of divergence of distant protein sequences and measure this rate for ancient proteins, those that were present in the last universal common ancestor. We show that ancient proteins are still diverging from each other, indicating an ongoing expansion of the protein sequence universe. The slow rate of this divergence is imposed by the sparseness of functional protein sequences in sequence space and the ruggedness of the protein fitness landscape: ~98 per cent of sites cannot accept an amino-acid substitution at any given moment but a vast majority of all sites may eventually be permitted to evolve when other, compensatory, changes occur. Thus, ~3.5 X [10.sup.9] yr has not been enough to reach the limit of divergent evolution of proteins, and for most proteins the limit of sequence similarity imposed by common function may not exceed that of random sequences.

Correct termination of protein synthesis is essential for life and must be achieved with high fidelity. Premature termination of translation at sense codons, leading to dysfunctional proteins, is rare and [...], In termination of protein synthesis, the bacterial release factors RF1 and RF2 bind to the ribosome through specific recognition of messenger RNA stop codons and trigger hydrolysis of the bond between the nascent polypeptide and the transfer RNA at the peptidyl-tRNA site, thereby releasing the newly synthesized protein. The release factors are highly specific for a U in the first stop-codon position (1) and recognize different combinations of purines in the second and third positions, with RF1 reading UAA and UAG and RF2 reading UAA and UGA. With recently determined crystal structures of termination complexes (2-4), it has become possible to decipher the energetics of stop-codon reading by computational analysis and to clarify the origin of the high release-factor binding accuracy. Here we report molecular dynamics free-energy calculations on different cognate and non-cognate termination complexes. The simulations quantitatively explain the basic principles of decoding in all three codon positions and reveal the key elements responsible for specificity of the release factors. The overall reading mechanism involves hitherto unidentified interactions and recognition switches that cannot be described in terms of a tripeptide anticodon model. Further simulations of complexes with [tRNA.sup.Trp], the tRNA recognizing the triplet codon for Trp, explain the observation of a 'leaky' stop codon (5) and highlight the fundamentally different third position reading by RF2, which leads to a high stop-codon specificity with strong discrimination against the Trp codon. The simulations clearly illustrate the versatility of codon reading by protein, which goes far beyond tRNA mimicry.

Several mutation analyses are conducted to explain the ways in which the haplosufficiency of TAB2 leads to the generation of several different congenital heart defects (CHDs) in humans. TAB2 is shown to play an extremely significant role in the development of human cardiac.

2,3-Dihydroxypropane-1-sulfonate (DHPS) is a widespread intermediate in plant and algal transformations of sulfoquinovose (SO,) from the plant sulfolipid sulfoquinovosyl diacylglycerol. Further, DHPS is recovered quantitatively during bacterial degradation of SO, by Klebsiella sp. strain ABR11. DHPS is also a putative precursor of sulfolactate in e.g. Ruegeria pomeroyi DSS-3. A bioinformatic approach indicated that some 28 organisms with sequenced genomes might degrade DHPS inducibly via sulfolactate, with three different desulfonative enzymes involved in its degradation in different organisms. The hypothesis for Cupriavidus pinatubonensis JMP134 (formerly Ralstonia eutropha) involved a seven-gene cluster (Reut_C6093-C6087) comprising a Lacl-type transcriptional regulator, HpsR, a major facilitator superfamily uptake system, HpsU, three [NAD(P).sup.+]-coupled DHPS dehydrogenases, HpsNOP, and (R)-sulfolactate sulfo-lyase (SuyAB) [EC 4.4.1.24]. HpsOP effected a DHPS-racemase activity, and HpsN oxidized (R)-DHPS to (R)-sulfolactate. The hypothesis for Roseovarius nubinhibens ISM was similar, but involved a tripartite ATP-independent transport system for DHPS, HpsKLM, and two different desulfonative enzymes, (S)-cysteate sulfo-lyase [EC 4.4.1.25] and sulfoacetaldehyde acetyltransferase (Xsc) [EC 2.3.3.15]. Representative organisms were found to grow with DHPS and release sulfate. C. pinatubonensis JMP134 was found to express at least one [NAD(P).sup.+]-coupled DHPS dehydrogenase inducibly, and three different peaks of activity were separated by anion-exchange chromatography. Protein bands (SDS-PAGE) were subjected to peptide-mass fingerprinting, which identified the corresponding genes (hpsNOP). Purified HpsN converted DHPS to sulfolactate. Reverse-transcription PCR confirmed that hpsNOUP were transcribed inducibly in strain JMP134, and that hpsKLM and hpsNOP were transcribed in strain ISM. DHPS degradation is widespread and diverse, implying that DHPS is common in marine and terrestrial environments. DOI 10.1099/mic.0.037580-0

To many biomedical researchers, effective tumor classification methods such as the support vector machine often appear like a black box not only because the procedures are complex but also because the required specifications, such as the choice of a kernel function, suffer from a clear guidance either mathematically or biologically. As commonly observed, samples within the same tumor class tend to be more similar in gene expression than samples from different tumor classes. But can this well-received observation lead to a useful procedure of classification and prediction? To address this issue, we first conceived a statistical framework and derived general conditions to serve as the theoretical foundation that supported the aforementioned empirical observation. Then we constructed a classification procedure that fully utilized the information obtained by comparing the distributions of within-class correlations with between-class correlations via Kullback--Leibler divergence. We compared our approach with many machine-learning techniques by applying to 22 binary- and multiclass gene-expression datasets involving human cancers. The results showed that our method performed as efficiently as support vector machine and Naive Bayesian and outperformed other learning methods (decision trees, linear discriminate analysis, and k-nearest neighbor). In addition, we conducted a simulation study and showed that our method would be more effective if the arriving new samples are subject to the often-encountered baseline shift or increased noise level problems. Our method can be extended for general classification problems when only the similarity scores between samples are available. cancer research | gene expression doi/ 10.1073/pnas.0910140107

It is generally known that bacterial genes working in the same biological pathways tend to group into operons, possibly to facilitate cotranscription and to provide stoichiometry. However, very little is understood about what may determine the global arrangement of bacterial genes in a genome beyond the operon level. Here we present evidence that the global arrangement of operons in a bacterial genome is largely influenced by the tendency that a bacterium keeps its operons encoding the same biological pathway in nearby genomic locations, and by the tendency to keep operons involved in multiple pathways in locations close to the other members of their participating pathways. We also observed that the activation frequencies of pathways also influence the genomic locations of their encoding operons, tending to have operons of the more frequently activated pathways more tightly clustered together. We have quantitatively assessed the influences on the global genomic arrangement of operons by different factors. We found that the current arrangements of operons in most of the bacterial genomes we studied tend to minimize the overall distance between consecutive operons of a same pathway across all pathways encoded in the genome. bacterial genome | bioinformatics | genome organization | nucleoid | neighboring genes doi/10.1073/pnas.0911237107

Numerous methods have been developed for inferring gene regulatory networks from expression data, however, both their absolute and comparative performance remain poorly understood. In this paper, we introduce a framework for critical performance assessment of methods for gene network inference. We present an in silico benchmark suite that we provided as a blinded, community-wide challenge within the context of the DREAM (Dialogue on Reverse Engineering Assessment and Methods) project. We assess the performance of 29 gene-network-inference methods, which have been applied independently by participating teams. Performance profiling reveals that current inference methods are affected, to various degrees, by different types of systematic prediction errors. In particular, all but the best-performing method failed to accurately infer multiple regulatory inputs (combinatorial regulation) of genes. The results of this community-wide experiment show that reliable network inference from gene expression data remains an unsolved problem, and they indicate potential ways of network reconstruction improvements. DREAM challenge | community experiment | reverse engineering transcriptional regulatory networks | performance assessment doi/10.1073/pnas.0913357107

To identify genetic factors that interact with social environments to impact human health, we used a bioinformatic strategy that couples expression array--based detection of environmentally responsive transcription factors with in silico discovery of regulatory polymorphisms to predict genetic loci that modulate transcriptional responses to stressful environments. Tests of one predicted interaction locus in the human IL6 promoter (SNP rs1800795) verified that it modulates transcriptional response to [beta]-adrenergic activation of the GATA1 transcription factor in vitro. In vivo validation studies confirmed links between adverse social conditions and increased transcription of GATA1 target genes in primary neural, immune, and cancer cells. Epidemiologic analyses verified the health significance of those molecular interactions by documenting increased 10-year mortality risk associated with late-life depressive symptoms that occurred solely for homozygous carriers of the GATA1-sensitive G allele of rs1800795. Gating of depression-related mortality risk by IL.6 genotype pertained only to inflammation-related causes of death and was associated with increased chronic inflammation as indexed by plasma C-reactive protein. Computational modeling of molecular interactions, in vitro biochemical analyses, in vivo animal modeling, and human molecular epidemiologic analyses thus converge in identifying [beta]-adrenergic activation of GATA1 as a molecular pathway by which social adversity can alter human health risk selectively depending on individual genetic status at the IL6 locus. gene--environment interaction | inflammation | social epidemiology | stress I transcription www.pnas.org/cgi/doi/10.1073/pnas.0911515107