Search

TIS11d tandem zinc finger (TZF) domain can bind the class II AU-rich element of target mRNA and regulate mRNA turnover by promoting or inhibiting degradation. NMR spectra show that TIS11dTZF undergoes a transition from disordered to well folded upon binding to Zn and mRNA. To gain an insight into the mechanism, we have performed explicit-solvent molecular dynamics simulations (MD) for both bound and apo-TIS11dTZF to study the interdependence of binding and folding in the mRNA-TIS11dTZF complex. These results are in qualitative agreement with NMR experiment. Furthermore, this method could be used to other study about protein folding upon ligand binding. [ABSTRACT FROM AUTHOR]

Background: Protein-protein interface holds important information of protein-protein interactions which play key roles in most biological processes. In the past few years, a lot of efforts have been made to improve interface residue recognition by characterizing protein-protein interfaces and extracting relevant features. However, most previous studies were carried out in a qualitative level, and there are also some inconsistencies between them. Results: In the present work, to improve interface residue recognition, we built a novel quantitative residue protein-protein interface propensity index (QIPI) and gained a comprehensive picture of protein-protein interface through analyzing protein-protein interfaces on our comprehensive protein-protein interfaces dataset (Astral2.05-40- 4506). Furthermore, in order to assess the effect of QIPI in improving the protein-protein interface prediction, we developed an interface residue recognition method SPR (Single domain based Patch Recognition) based on the QIPI. The evaluation results proved that our novel QIPI is able to improve the interface residue recognition. Conclusions: Through a comprehensive quantitative analysis of protein-protein interface, we constructed a novel quantitative protein-protein interface propensity index (QIPI), which could be easily applied to improve the interface residue recognition and helpful in understanding the protein-protein interface. [ABSTRACT FROM AUTHOR]

Background: Glioma is the most common brain tumor and it has very high mortality rate due to its infiltration and heterogeneity. Precise classification of glioma subtype is essential for proper therapeutic treatment and better clinical prognosis. However, the molecular mechanism of glioma is far from clear and the classical classification methods based on traditional morphologic and histopathologic knowledge are subjective and inconsistent. Recently, classification methods based on molecular characteristics are developed with rapid progress of high throughput technology. Methods: In the present study, we designed a novel integrated gene coexpression analysis approach, which involves differential coexpression and differential regulation analysis (DCEA and DRA), to investigate glioma prognostic biomarkers and molecular subtypes based on six glioma transcriptome data sets. Results: We revealed a novel three-transcription-factor signature including AHR, NFIL3 and ZNF423 for glioma molecular subtypes. This three-TF signature clusters glioma patients into three major subtypes (ZG, NG and IG subtypes) which are significantly different in patient survival as well as transcriptomic patterns. Notably, ZG subtype is featured with higher expression of ZNF423 and has better prognosis with younger age at diagnosis. NG subtype is associated with higher expression of NFIL3 and AHR, and has worse prognosis with elder age at diagnosis. According to our inferred differential networking information and previously reported signalling knowledge, we suggested testable hypotheses on the roles of AHR and NFIL3 in glioma carcinogenesis. Conclusions: With so far the least biomarkers, our approach not only provides a novel glioma prognostic molecular classification scheme, but also helps to explore its dysregulation mechanisms. Our work is extendable to prognosisrelated classification and signature identification in other cancer researches. [ABSTRACT FROM AUTHOR]

Disease similarity study provides new insights into disease taxonomy, pathogenesis, which plays a guiding role in diagnosis and treatment. The early studies were limited to estimate disease similarities based on clinical manifestations, disease-related genes, medical vocabulary concepts or registry data, which were inevitably biased to well-studied diseases and offered small chance of discovering novel findings in disease relationships. In other words, genome-scale expression data give us another angle to address this problem since simultaneous measurement of the expression of thousands of genes allows for the exploration of gene transcriptional regulation, which is believed to be crucial to biological functions. Although differential expression analysis based methods have the potential to explore new disease relationships, it is difficult to unravel the upstream dysregulation mechanisms of diseases. We therefore estimated disease similarities based on gene expression data by using differential coexpression analysis, a recently emerging method, which has been proved to be more potential to capture dysfunctional regulation mechanisms than differential expression analysis. A total of 1,326 disease relationships among 108 diseases were identified, and the relevant information constituted the human disease network database (DNetDB). Benefiting from the use of differential coexpression analysis, the potential common dysfunctional regulation mechanisms shared by disease pairs (i.e. disease relationships) were extracted and presented. Statistical indicators, common disease-related genes and drugs shared by disease pairs were also included in DNetDB. In total, 1,326 disease relationships among 108 diseases, 5,598 pathways, 7,357 disease-related genes and 342 disease drugs are recorded in DNetDB, among which 3,762 genes and 148 drugs are shared by at least two diseases. DNetDB is the first database focusing on disease similarity from the viewpoint of gene regulation mechanism. It provides an easy-to-use web interface to search and browse the disease relationships and thus helps to systematically investigate etiology and pathogenesis, perform drug repositioning, and design novel therapeutic interventions. [ABSTRACT FROM AUTHOR]

Background: Elucidation of human disease similarities has emerged as an active research area, which is highly relevant to etiology, disease classification, and drug repositioning. In pioneer studies, disease similarity was commonly estimated according to clinical manifestation. Subsequently, scientists started to investigate disease similarity based on gene-phenotype knowledge, which were inevitably biased to well-studied diseases. In recent years, estimating disease similarity according to transcriptomic behavior significantly enhances the probability of finding novel disease relationships, while the currently available studies usually mine expression data through differential expression analysis that has been considered to have little chance of unraveling dysfunctional regulatory relationships, the causal pathogenesis of diseases. Methods: We developed a computational approach to measure human disease similarity based on expression data. Differential coexpression analysis, instead of differential expression analysis, was employed to calculate differential coexpression level of every gene for each disease, which was then summarized to the pathway level. Disease similarity was eventually calculated as the partial correlation coefficients of pathways' differential coexpression values between any two diseases. The significance of disease relationships were evaluated by permutation test. Results: Based on mRNA expression data and a differential coexpression analysis based method, we built a human disease network involving 1326 significant Disease-Disease links among 108 diseases. Compared with disease relationships captured by differential expression analysis based method, our disease links shared known disease genes and drugs more significantly. Some novel disease relationships were discovered, for example, Obesity and cancer, Obesity and Psoriasis, lung adenocarcinoma and S. pneumonia, which had been commonly regarded as unrelated to each other, but recently found to share similar molecular mechanisms. Additionally, it was found that both the type of disease and the type of affected tissue influenced the degree of disease similarity. A sub-network including Allergic asthma, Type 2 diabetes and Chronic kidney disease was extracted to demonstrate the exploration of their common pathogenesis. Conclusion: The present study produces a global view of human diseasome for the first time from the viewpoint of regulation mechanisms, which therefore could provide insightful clues to etiology and pathogenesis, and help to perform drug repositioning and design novel therapeutic interventions. [ABSTRACT FROM AUTHOR]

Background: Astragalus membranaceus Bge. var. mongolicus (Bge.) Hsiao (A. mongolicus, family Leguminosae) is one of the most important traditional Chinese herbs. Among many secondary metabolites it produces, the effective bioactive constituents include isoflavonoids and triterpene saponins. The genomic resources regarding the biosynthesis of these metabolites in A. mongolicus are limited. Although roots are the primary material harvested for medical use, the biosynthesis of the bioactive compounds and its regulation in A. mongolicus are not well understood. Therefore, a global transcriptome analysis on A. mongolicus tissues was performed to identify the genes essential for the metabolism and to profile their expression patterns in greater details. Results: RNA-sequencing was performed for three different A. mongolicus tissues: leaf, stem, and root, using the Illumina Hiseq2000 platform. A total of 159.5 million raw sequence reads were generated, and assembled into 186,324 unigenes with an N50 of 1,524bp. Among them, 129,966 unigenes (~69.7%) were annotated using four public databases (Swiss-Prot, TrEMBL, CDD, Pfam), and 90,202, 63,946, and 78,326 unigenes were found to express in leaves, roots, and stems, respectively. A total of 8,025 transcription factors (TFs) were identified, in which the four largest families, bHLH, MYB, C3H, and WRKY, were implicated in regulation of tissue development, metabolisms, stress response, etc. Unigenes associated with secondary metabolism, especially those with isolavonoids and triterpene saponins biosynthesis were characterized and profiled. Most genes involved in the isoflavonoids biosynthesis had the lowest expression in the leaves, and the highest in the stems. For triterpene saponin biosynthesis, we found the genes in MVA and non-MVA pathways were differentially expressed among three examined tissues, indicating the parallel but compartmentally separated biosynthesis pathways of IPP and DMAPP in A. mongolicus. The first committed enzyme in triterpene saponin biosynthesis from A. mongolicus, cycloartenol synthase (AmCAS), which belongs to the oxidosqualene cyclase family, was cloned by us to study the astragalosides biosynthesis. Further co-expression analysis indicated the candidate CYP450s and glycosyltransferases (GTs) in the cascade of triterpene saponins biosynthesis. The presence of the large CYP450 families in A. mongolicus was further compared with those from Medicago truncatula and Arabidopsis thaliana, and the diversity and phylegenetic relationships of the CYP450 families were established. Conclusion: A transcriptome study was performed for A. mongolicus tissues to construct and profile their metabolic pathways, especially for the important bioactive molecules. The results revealed a comprehensive profile for metabolic activities among tissues, pointing to the equal importance of leaf, stem, and root in A. mongolicus for the production of bioactive compounds. This work provides valuable resources for bioengineering and in vitro synthesis of the natural compounds for medical research and for potential drug development. [ABSTRACT FROM AUTHOR]

Background: We are witnessing rapid progress in the development of methodologies for building the combinatorial gene regulatory networks involving both TFs (Transcription Factors) and miRNAs (microRNAs). There are a few tools available to do these jobs but most of them are not easy to use and not accessible online. A web server is especially needed in order to allow users to upload experimental expression datasets and build combinatorial regulatory networks corresponding to their particular contexts. Methods: In this work, we compiled putative TF-gene, miRNA-gene and TF-miRNA regulatory relationships from forward-engineering pipelines and curated them as built-in data libraries. We streamlined the R codes of our two separate forward-and-reverse engineering algorithms for combinatorial gene regulatory network construction and formalized them as two major functional modules. As a result, we released the cGRNB (combinatorial Gene Regulatory Networks Builder): a web server for constructing combinatorial gene regulatory networks through integrated engineering of seed-matching sequence information and gene expression datasets. The cGRNB enables two major network-building modules, one for MPGE (miRNA-perturbed gene expression) datasets and the other for parallel miRNA/mRNA expression datasets. A miRNA-centered two-layer combinatorial regulatory cascade is the output of the first module and a comprehensive genome-wide network involving all three types of combinatorial regulations (TF-gene, TF-miRNA, and miRNA-gene) are the output of the second module. Conclusions: In this article we propose cGRNB, a web server for building combinatorial gene regulatory networks through integrated engineering of seed-matching sequence information and gene expression datasets. Since parallel miRNA/mRNA expression datasets are rapidly accumulated by the advance of next-generation sequencing techniques, cGRNB will be very useful tool for researchers to build combinatorial gene regulatory networks based on expression datasets. [ABSTRACT FROM AUTHOR]

Background: S. erythraea is a Gram-positive filamentous bacterium used for the industrial-scale production of erythromycin A which is of high clinical importance. In this work, we sequenced the whole genome of a highproducing strain (E3) obtained by random mutagenesis and screening from the wild-type strain NRRL23338, and examined time-series expression profiles of both E3 and NRRL23338. Based on the genomic data and transcriptpmic data of these two strains, we carried out comparative analysis of high-producing strain and wild-type strain at both the genomic level and the transcriptomic level. Results: We observed a large number of genetic variants including 60 insertions, 46 deletions and 584 single nucleotide variations (SNV) in E3 in comparison with NRRL23338, and the analysis of time series transcriptomic data indicated that the genes involved in erythromycin biosynthesis and feeder pathways were significantly up-regulated during the 60 hours time-course. According to our data, BldD, a previously identified ery cluster regulator, did not show any positive correlations with the expression of ery cluster, suggesting the existence of alternative regulation mechanisms of erythromycin synthesis in S. erythraea. Several potential regulators were then proposed by integration analysis of genomic and transcriptomic data. Conclusion: This is a demonstration of the functional comparative genomics between an industrial S. erythraea strain and the wild-type strain. These findings help to understand the global regulation mechanisms of erythromycin biosynthesis in S. erythraea, providing useful clues for genetic and metabolic engineering in the future. [ABSTRACT FROM AUTHOR]

Retinoic acid inducible gene I (RIG-I) senses viral RNA5 and triggers innate antiviral responses through induction of type I IFN5 and inflammatory cytokines. However, whether RIG-I interacts with host cellular RNA remains undetermined. Here we report that Rig-I interacts with multiple cellular mRNAs, especially Nf-κBl. Rig-I is required for NF-κBactivity via regulating F4f-icbl expression at posttranscriptional levels. It interacts with the multiple binding sites within 3'-UTR of Nf-icbl mRNA. Further analyses reveal that three distinct tandem motifs enriched in the 3'-UTR fragments can be recognized by Rig-I. The 3'-UTR binding with Rig-I plays a critical role in normal translation of Nf-κBl by recruiting the ribosomal proteins [ribosomal protein L13 (Rp113) and Rpl8~ and rRNAS (18S and 285). Down-regulation of Rig-I or Rpll3 significantly reduces Nf-icbl and 3'-UTR-mediated luciferase expression levels. These findings indicate that Rig-I functions as a positive regulator for NF-κBsignaling and is involved in multiple biological processes in addition to host antivirus immunity. [ABSTRACT FROM AUTHOR]

Escherichia coli adenylate kinase (ADK) is a monomeric phosphotransferase enzyme that catalyzes reversible transfer of phosphoryl group from ATP to AMP with a large-scale domain motion. The detailed mechanism for this conformational transition remains unknown. In the current study, we performed long time-scale molecular dynamics simulations on both open and closed states of ADK. Based on the structural analyses of the simulation trajectories, we detected over 20 times conformational transitions between the open and closed states of ADK and identified two novel conformations as intermediate states in the catalytic processes. With these findings, we proposed a possible mechanism for the large-scale domain motion of Escherichia coli ADK and its catalytic process: (1) the substrate free ADK adopted an open conformation; (2) ATP bound with LID domain closure; (3) AMP bound with NMP domain closure; (4) phosphoryl transfer occurred with ATP, and AMP converted into two ADPs, and no conformational transition was detected in the enzyme; (5) LID domain opened with one ADP released; (6) another ADP released with NMP domain open. As both open and closed states sampled a wide range of conformation transitions, our simulation strongly supported the conformational selection mechanism for Escherichia coli ADK. [ABSTRACT FROM AUTHOR]

Influenza virus, which spreads around the world in seasonal epidemics and leads to large numbers of deaths every year, has several ribonucleoproteins in the central core of the viral particle. These viral ribonucleoproteins can specifically bind the conserved 3' and 5' caps of the viral RNAs with responsibility for replication and transcription of the viral RNA in the nucleus of infected cells. A fundamental question of most importance is that how the cap-binding proteins in the influenza virus discriminates between capped RNAs and non-capped ones. To get an answer, we performed molecular dynamics simulations and free energy calculations on the influenza A virus PB2 subunit, an important component of the RNP complexes, with a cap analog m7GTP. Our calculations showed that some key residues in the active site, such as Arg355, His357, Glu361 as well as Gln406, could offer significant hydrogen bonding and hydrophobic interactions with the guanine ring of the cap analog m7GTP to form an aromatic sandwich mechanism for the cap recognition and positioning in the active site. Subsequently, we applied this idea to a virtual screening procedure and identified 5 potential candidates that might be inhibitors against the PB2 subunit. Interestingly, 2 candidates Cpd1 and Cpd2 have been already reported to have inhibitory activities to the influenza virus cap-binding proteins. Further calculation also showed that they had comparatively higher binding affinities to the PB2 subunit than that of m7GTP. We believed that our findings could give an atomic insight into the deeper understanding of the cap recognition and binding mechanism, providing useful information for searching or designing novel drugs against influenza viruses. [ABSTRACT FROM AUTHOR]

Bacterial pathogens continue to threaten public health worldwide today. Identification of bacterial virulence factors can help to find novel drug/vaccine targets against pathogenicity. It can also help to reveal the mechanisms of the related diseases at the molecular level. With the explosive growth in protein sequences generated in the postgenomic age, it is highly desired to develop computational methods for rapidly and effectively identifying virulence factors according to their sequence information alone. In this study, based on the protein-protein interaction networks from the STRING database, a novel network-based method was proposed for identifying the virulence factors in the proteomes of UPEC 536, UPEC CFT073, P. aeruginosa PAO1, L. pneumophila Philadelphia 1, C. jejuni NCTC 11168 and M. tuberculosis H37Rv. Evaluated on the same benchmark datasets derived from the aforementioned species, the identification accuracies achieved by the network-based method were around 0.9, significantly higher than those by the sequence-based methods such as BLAST, feature selection and VirulentPred. Further analysis showed that the functional associations such as the gene neighborhood and co-occurrence were the primary associations between these virulence factors in the STRING database. The high success rates indicate that the network-based method is quite promising. The novel approach holds high potential for identifying virulence factors in many other various organisms as well because it can be easily extended to identify the virulence factors in many other bacterial species, as long as the relevant significant statistical data are available for them. [ABSTRACT FROM AUTHOR]

Background: Both transcriptional control and microRNA (miRNA) control are critical regulatory mechanisms for cells to direct their destinies. At present, the combinatorial regulatory network composed of transcriptional regulations and post-transcriptional regulations is often constructed through a forward engineering strategy that is based solely on searching of transcriptional factor binding sites or miRNA seed regions in the putative target sequences. If the reverse engineering strategy is integrated with the forward engineering strategy, a more accurate and more specific combinatorial regulatory network will be obtained. Results: In this work, utilizing both sequence-matching information and parallel expression datasets of miRNAs and mRNAs, we integrated forward engineering with reverse engineering strategies and as a result built a hypothetical combinatorial gene regulatory network in human cancer. The credibility of the regulatory relationships in the network was validated by random permutation procedures and supported by authoritative experimental evidence-based databases. The global and local architecture properties of the combinatorial regulatory network were explored, and the most important tumor-regulating miRNAs and TFs were highlighted from a topological point of view. Conclusions: By integrating the forward engineering and reverse engineering strategies, we manage to sketch a genome-scale combinatorial gene regulatory network in human cancer, which includes transcriptional regulations and miRNA regulations, allowing systematic study of cancer gene regulation. Our work establishes a pipeline that can be extended to reveal conditional combinatorial regulatory landscapes correlating to specific cellular contexts. [ABSTRACT FROM AUTHOR]

Background: Comprehensive kinetic models of microbial metabolism can enhance the understanding of system dynamics and regulatory mechanisms, which is helpful in optimizing microbial production of industrial chemicals. Clostridium acetobutylicum produces solvents (acetone-butanol-ethanol, ABE) through the ABE pathway. To systematically assess the potential of increased production of solvents, kinetic modeling has been applied to analyze the dynamics of this pathway and make predictive simulations. Up to date, only one kinetic model for C. acetobutylicum supported by experiment has been reported as far as we know. But this model did not integrate the metabolic regulatory effects of transcriptional control and other complex factors. It also left out the information of some key intermediates (e.g. butyryl-phosphate). Results: We have developed an improved kinetic model featured with the incorporation of butyryl-phosphate, inclusion of net effects of complex metabolic regulations, and quantification of endogenous enzyme activity variations caused by these regulations. The simulation results of our model are more consistent with published experimental data than the previous model, especially in terms of reflecting the kinetics of butyryl-phosphate and butyrate. Through parameter perturbation analysis, it was found that butyrate kinase has large and positive influence on butanol production while CoA transferase has negative effect on butanol production, suggesting that butyrate kinase has more efficiency in converting butyrate to butanol than CoA transferase. Conclusions: Our improved kinetic model of the ABE process has more capacity in approaching real circumstances, providing much more insight in the regulatory mechanisms and potential key points for optimization of solvent productions. Moreover, the modeling strategy can be extended to other biological processes. [ABSTRACT FROM AUTHOR]

Large efforts have been taken to search for genes responsible for type 2 diabetes (T2D), but have resulted in only about 20 in humans due to its complexity and heterogeneity. The GK rat, a spontanous T2D model, offers us a superior opportunity to search for more diabetic genes. Utilizing array comparative genome hybridization (aCGH) technology, we identifed 137 non-redundant copy number variation (CNV) regions from the GK rats when using normal Wistar rats as control. These CNV regions (CNVRs) covered approximately 36 Mb nucleotides, accounting for about 1% of the whole genome. By integrating information from gene annotations and disease knowledge, we investigated the CNVRs comprehensively for mining new T2D genes. As a result, we prioritized 16 putative protein-coding genes and two microRNA genes (rno-mir-30b and rno-mir-30d) as good candidates. The catalogue of CNVRs between GK and Wistar rats identified in this work served as a repository for mining genes that might play roles in the pathogenesis of T2D. Moreover, our efforts in utilizing bioinformatics methods to prioritize good candidate genes provided a more specific set of putative candidates. These findings would contribute to the research into the genetic basis of T2D, and thus shed light on its pathogenesis. [ABSTRACT FROM AUTHOR]

Rapamycin analogs temsirolimus and everolimus have been approved for the treatment of advanced renal cancer and are being tested in a wide spectrum of human tumors. However, objective response rates with rapalogs in clinical trials were modest and variable. Identification of biomarkers capable of predicting response to rapalogs is of increasing interest. We analyzed pairwise Pearson correlation coefficients ( r) between rapalogs activity and gene expression profile for each NCI-60 cell line. p27 showed the highest positive correlation among 9,706 gene probes tested. At cellular levels, breast cancer MCF-7, T47D, and BT-474 cells, expressing high levels of p27, were sensitive to rapalogs, whereas the cells expressed low levels of p27, such as MDA-MB-231, MDA-MB-468, and MDA-MB-435 cells, exhibited resistance to rapalogs. Mechanistic study indicated that this correlation is likely determined by the basal level of p27 regardless of the phosphorylation or redistribution of p27 upon rapalogs treatment, which may provide a putative threshold to block G1/S transition. Consistently, down-regulation of p27 by siRNA conferred MCF-7 and BT-474 cells insensitive to rapalogs. Moreover, a significant positive correlation between p27 gene expression and rapamycin anti-tumor activity was also observed in mice bearing different human cancer cell xenografts. In conclusion, p27 expression level is positively correlated with the anticancer activity of rapalogs in vitro and in vivo. We propose p27 expression level may be also a candidate predictive biomarker for patient selection for rapalogs-based therapy, which requires clinical validation in a series of patients treated with rapalogs. [ABSTRACT FROM AUTHOR]

Background: The Saccharopolyspora erythraea genome sequence was released in 2007. In order to look at the gene regulations at whole transcriptome level, an expression microarray was specifically designed on the S. erythraea strain NRRL 2338 genome sequence. Based on these data, we set out to investigate the potential transcriptional regulatory networks and their organization. Methodology/Principal Findings: In view of the hierarchical structure of bacterial transcriptional regulation, we constructed a hierarchical coexpression network at whole transcriptome level. A total of 27 modules were identified from 1255 differentially expressed transcript units (TUs) across time course, which were further classified in to four groups. Functional enrichment analysis indicated the biological significance of our hierarchical network. It was indicated that primary metabolism is activated in the first rapid growth phase (phase A), and secondary metabolism is induced when the growth is slowed down (phase B). Among the 27 modules, two are highly correlated to erythromycin production. One contains all genes in the erythromycin-biosynthetic (ery) gene cluster and the other seems to be associated with erythromycin production by sharing common intermediate metabolites. Non-concomitant correlation between production and expression regulation was observed. Especially, by calculating the partial correlation coefficients and building the network based on Gaussian graphical model, intrinsic associations between modules were found, and the association between those two erythromycin production-correlated modules was included as expected. Conclusions: This work created a hierarchical model clustering transcriptome data into coordinated modules, and modules into groups across the time course, giving insight into the concerted transcriptional regulations especially the regulation corresponding to erythromycin production of S. erythraea. This strategy may be extendable to studies on other prokaryotic microorganisms. [ABSTRACT FROM AUTHOR]

The metabolic stability is a very important idiosyncracy of proteins that is related to their global flexibility, intramolecular fluctuations, various internal dynamic processes, as well as many marvelous biological functions. Determination of protein's metabolic stability would provide us with useful information for in-depth understanding of the dynamic action mechanisms of proteins. Although several experimental methods have been developed to measure protein's metabolic stability, they are time-consuming and more expensive. Reported in this paper is a computational method, which is featured by (1) integrating various properties of proteins, such as biochemical and physicochemical properties, subcellular locations, network properties and protein complex property, (2) using the mRMR (Maximum Relevance & Minimum Redundancy) principle and the IFS (Incremental Feature Selection) procedure to optimize the prediction engine, and (3) being able to identify proteins among the four types: ''short'', ''medium'', ''long'', and ''extra-long'' half-life spans. It was revealed through our analysis that the following seven characters played major roles in determining the stability of proteins: (1) KEGG enrichment scores of the protein and its neighbors in network, (2) subcellular locations, (3) polarity, (4) amino acids composition, (5) hydrophobicity, (6) secondary structure propensity, and (7) the number of protein complexes the protein involved. It was observed that there was an intriguing correlation between the predicted metabolic stability of some proteins and the real half-life of the drugs designed to target them. These findings might provide useful insights for designing protein-stability-relevant drugs. The computational method can also be used as a large-scale tool for annotating the metabolic stability for the avalanche of protein sequences generated in the post-genomic age. [ABSTRACT FROM AUTHOR]

Leptospirosis is a widespread zoonotic disease caused by pathogenic spirochetes of the genus Leptospira that infects humans and a wide range of animals. By combining computational prediction and high-accuracy tandem mass spectra, we revised the genome annotation of Leptospira interrogans serovar Lai, a free-living pathogenic spirochete responsible for leptospirosis, providing substantial peptide evidence for novel genes and new gene boundaries. Subsequently, we presented a high-coverage proteome analysis of protein expression and multiple posttranslational modifications (PTMs). Approximately 64.3% of the predicted L. interrogans proteins were cataloged by detecting 2 540 proteins. Meanwhile, a profile of multiple PTMs was concurrently established, containing in total 32 phosphorylated, 46 acetylated and 155 methylated proteins. The PTM systems in the serovar Lai show unique features. Unique eukaryotic-like features of L. interrogans protein modifications were demonstrated in both phosphorylation and arginine methylation. This systematic analysis provides not only comprehensive information of high-coverage protein expression and multiple modifications in prokaryotes but also a view suggesting that the evolutionarily primitive L. interrogans shares significant similarities in protein modification systems with eukaryotes. [ABSTRACT FROM AUTHOR]

Background: Clostridium beijerinckii is a valuable bacteria species which has the ability of ABE (acetone, butanol and ethanol) production. It has been shown that Phosphotransferase (PTS) is an important and common system for both carbohydrate uptake and phosphorylation in bacteria, but detailed study of the system, especially its fructose/mannose/sorbose family is scant. Results: In the genome of Clostridium beijerinckii NCIMB 8052, a model strain recently sequenced, there are large number of PTS genes, among them 9 complete sets belong to the fructose/mannose/sorbose family of its enzyme II complex. Our study, based on evidences provided by phylogenetic relationship, analyses of gene contents and clusters, as well as synteny examination, indicates that it is possible to further classify this PTS family into three sub-groups, which are corresponding to the three sugar substrates. Furthermore, we proposed a model how these PTS systems are evolved in bacteria. Conclusion: This work may explain the experimental result that Clostridium beijerinckii NCIMB 8052 can better utilize fructose as substrate, thus could lead to a better understanding of the ABE-producing mechanism in Clostridium beijerinckii and other microbial species. It may help to illustrate a higher butanol-productivity future. [ABSTRACT FROM AUTHOR]

Background: A 'head-to-head' (h2h) gene pair is defined as a genomic locus in which two adjacent genes are divergently transcribed from opposite strands of DNA. In our previous work, this gene organization was found to be ancient and conserved, which subjects functionally related genes to transcriptional co-regulation. However, some of the biological features of h2h pairs still need further clarification. Results: In this work, we assorted human h2h pairs into four sequentially inclusive sets of gradually incremental conservation, and examined whether those previously asserted features were conserved or sharpened in the more conserved h2h pair sets in order to identify the inherent features of the h2h gene organization. The features of TSS distance, expression correlation within h2h pairs and among h2h genes, transcription factor association and functional similarities of h2h genes were examined. Our conservation-based analyses found that the bi-directional promoters of h2h gene pairs are most likely shorter than 100 bp; h2h gene pairs generally have only significant positive expression correlation but not negative correlation, and remarkably high positive expression correlations exist among h2h genes, as well as between h2h pairs observed in our previous study; h2h paired genes tend to share transcription factors. In addition, expression correlation of h2h pairs is positively related with the TF-sharing and functional coordination, while not related with TSS distance. Conclusions: Our findings remove the uncertainties of h2h genes about TSS distance, expression correlation and functional coordination, which provide insights into the study on the molecular mechanisms and functional consequences of the transcriptional regulation based on this special gene organization. [ABSTRACT FROM AUTHOR]

More and more people are concerned by the risk of unexpected side effects observed in the later steps of the development of new drugs, either in late clinical development or after marketing approval. In order to reduce the risk of the side effects, it is important to look out for the possible xenobiotic responses at an early stage. We attempt such an effort through a prediction by assuming that similarities in microarray profiles indicate shared mechanisms of action and/or toxicological responses among the chemicals being compared. A large time course microarray database derived from livers of compound-treated rats with thirty-four distinct pharmacological and toxicological responses were studied. The mRMR (Minimum-Redundancy-Maximum-Relevance) method and IFS (Incremental Feature Selection) were used to select a compact feature set (141 features) for the reduction of feature dimension and improvement of prediction performance. With these 141 features, the Leave-one-out cross-validation prediction accuracy of first order response using NNA (Nearest Neighbor Algorithm) was 63.9%. Our method can be used for pharmacological and xenobiotic responses prediction of new compounds and accelerate drug development. [ABSTRACT FROM AUTHOR]

A major problem when xylose is used for ethanol production is the intercellular redox imbalance arising from different coenzyme specificities of xylose reductase (XR) and xylitol dehydrogenase. The residue Lys21 in XR from Pichia stipitis was subjected to site-directed mutagenesis to alter its coenzyme specificity. The N272D mutant exhibited improved catalytic efficiency when NADH was the coenzyme. Both K21A and K21A/N272D preferred NADH to NADPH, their catalytic efficiencies for NADPH were almost zero. The catalytic efficiency of K21A/N272D for NADH was almost 9-fold and 2-fold that of K21A and the wild-type enzyme, respectively. Complete reversal of coenzyme specificity toward NADH and improved catalytic efficiency were achieved. [ABSTRACT FROM AUTHOR]

Coordination of cell differentiation and proliferation is a key issue in the development process of multi-cellular organisms and stem cells. Here we provide evidence that the establishment of adipocyte differentiation of 3T3-L1 cells requires two processes: the licensing of an adipogenesis gene-expression program within a particular growth-arrest stage, i.e., the contact-inhibition stage, and then the execution of this program in a cell-cycle-independent manner, by which the licensed progenitors are differentiated into adipocytes in the presence of inducing factors. Our results showed that differentiation licensing of 3T3-L1 cells during the contact-inhibition stage involved epigenetic modifications such as DNA methylation and histone modifications, whereas disturbing these epigenetic modifications by DNA methylation inhibitors or RNAi during the contact-inhibition stage significantly reduced adipogenesis efficiency. More importantly, when these licensed 3T3-L1 cells were re-cultured under non-differentiating conditions or treated only with insulin, this adipogenesis commitment could be maintained from one cell generation to the next, whereby the licensed program could be activated in a cell-cycle-independent manner once these cells were subjected to adipogenesis-inducing conditions. This result suggests that differentiation licensing and differentiation execution can be uncoupled and disparately linked to cell proliferation. Our findings deliver a new concept that cell-fate decision can be subdivided into at least two stages, licensing and execution, which might have different regulatory relationships with cell proliferation. In addition, this new concept may provide a clue for developing new strategies against obesity.Cell Research (2009) 19:216–223. doi: 10.1038/cr.2008.319; published online 9 December 2008 [ABSTRACT FROM AUTHOR]

Background: MicroRNAs (miRNAs) are a large group of RNAs that play important roles in regulating gene expression and protein translation. Several studies have indicated that some miRNAs are specifically expressed in human, mouse and zebrafish tissues. For example, miR-1 and miR-133 are specifically expressed in muscles. Tissue-specific miRNAs may have particular functions. Although previous studies have reported the presence of human, mouse and zebrafish tissue-specific miRNAs, there have been no detailed reports of rat tissue-specific miRNAs. In this study, Home-made rat miRNA microarrays which established in our previous study were used to investigate rat neural tissue-specific miRNAs, and mapped their target genes in rat tissues. This study will provide information for the functional analysis of these miRNAs. Results: In order to obtain as complete a picture of specific miRNA expression in rat neural tissues as possible, customized miRNA microarrays with 152 selected miRNAs from miRBase were used to detect miRNA expression in 14 rat tissues. After a general clustering analysis, 14 rat tissues could be clearly classified into neural and non-neural tissues based on the obtained expression profiles with p values < 0.05. The results indicated that the miRNA profiles were different in neural and non-neural tissues. In total, we found 30 miRNAs that were specifically expressed in neural tissues. For example, miR-199a was specifically expressed in neural tissues. Of these, the expression patterns of four miRNAs were comparable with those of Landgraf et al., Bak et al., and Kapsimani et al. Thirty neural tissue-specific miRNAs were chosen to predict target genes. A total of 1,475 target mRNA were predicted based on the intersection of three public databases, and target mRNA's pathway, function, and regulatory network analysis were performed. We focused on target enrichments of the dorsal root ganglion (DRG) and olfactory bulb. There were four Gene Ontology (GO) functions and five KEGG pathways significantly enriched in DRG. Only one GO function was significantly enriched in the olfactory bulb. These targets are all predictions and have not been experimentally validated. Conclusion: Our work provides a global view of rat neural tissue-specific miRNA profiles and a target map of miRNAs, which is expected to contribute to future investigations of miRNA regulatory mechanisms in neural systems. [ABSTRACT FROM AUTHOR]

Chloroplast is a typical plant cell organelle where photosynthesis takes place. In this study, a total of 1 808 chloroplast core proteins in Arabidopsis thaliana were reliably identified by combining the results of previously published studies and our own predictions. We then constructed a chloroplast protein interaction network primarily based on these core protein interactions. The network had 22 925 protein interaction pairs which involved 2 214 proteins. A total of 160 previously uncharacterized proteins were annotated in this network. The subunits of the photosynthetic complexes were modularized, and the functional relationships among photosystem I (PSI), photosystem II (PSII), light harvesting complex of photosystem I (LHC I) and light harvesting complex of photosystem I (LHC II) could be deduced from the predicted protein interactions in this network. We further confirmed an interaction between an unknown protein AT1G52220 and a photosynthetic subunit PSI-D2 by yeast two-hybrid analysis. Our chloroplast protein interaction network should be useful for functional mining of photosynthetic proteins and investigation of chloroplast-related functions at the systems biology level in Arabidopsis.Cell Research (2008) 18:1007–1019. doi: 10.1038/cr.2008.286; published online 23 September 2008 [ABSTRACT FROM AUTHOR]

Background: Recent advances in proteomics have shed light to discover serum proteins or peptides as biomarkers for tracking the progression of diabetes as well as understanding molecular mechanisms of the disease. Results: In this work, human serum of non-diabetic and diabetic cohorts was analyzed by proteomic approach. To analyze total 1377 high-confident serum-proteins, we developed a computing strategy called localized statistics of protein abundance distribution (LSPAD) to calculate a significant bias of a particular protein-abundance between these two cohorts. As a result, 68 proteins were found significantly over-represented in the diabetic serum (p,0.01). In addition, a pathway-associated analysis was developed to obtain the overall pathway bias associated with type 2 diabetes, from which the significant over-representation of complement system associated with type 2 diabetes was uncovered. Moreover, an upstream activator of complement pathway, ficolin-3, was observed over-represented in the serum of type 2 diabetic patients, which was further validated with statistic significance (p = 0.012) with more clinical samples. Conclusions: The developed LSPAD approach is well fit for analyzing proteomic data derived from biological complex systems such as plasma proteome. With LSPAD, we disclosed the comprehensive distribution of the proteins associated with diabetes in different abundance levels and the involvement of ficolin-related complement activation in diabetes. [ABSTRACT FROM AUTHOR]

Circadian clocks are endogenous time-keeping systems that temporally organize biological processes. Gating of cell cycle events by a circadian clock is a universal observation that is currently considered a mechanism serving to protect DNA from diurnal exposure to ultraviolet radiation or other mutagens. In this study, we put forward another possibility: that such gating helps to insulate the circadian clock from perturbations induced by transcriptional inhibition during the M phase of the cell cycle. We introduced a periodic pulse of transcriptional inhibition into a previously published mammalian circadian model and simulated the behavior of the modified model under both constant darkness and light-dark cycle conditions. The simulation results under constant darkness indicated that periodic transcriptional inhibition could entrain/lock the circadian clock just as a light-dark cycle does. At equilibrium states, a transcriptional inhibition pulse of certain periods was always locked close to certain circadian phases where inhibition on Per and Bmal1 mRNA synthesis was most balanced. In a light-dark cycle condition, inhibitions imposed at different parts of a circadian period induced different degrees of perturbation to the circadian clock. When imposed at the middle- or late-night phase, the transcriptional inhibition cycle induced the least perturbations to the circadian clock. The late-night time window of least perturbation overlapped with the experimentally observed time window, where mitosis is most frequent. This supports our hypothesis that the circadian clock gates the cell cycle M phase to certain circadian phases to minimize perturbations induced by the latter. This study reveals the hidden effects of the cell division cycle on the circadian clock and, together with the current picture of genome stability maintenance by circadian gating of cell cycle, provides a more comprehensive understanding of the phenomenon of circading gating of cell cycle. [ABSTRACT FROM AUTHOR]

Background: The status of a disease can be reflected by specific transcriptional profiles resulting from the induction or repression activity of a number of genes. Here, we proposed a timedependent diagnostic model to predict the treatment effects of interferon and ribavirin to HCV infected patients by using time series microarray gene expression profiles of a published study. Methods: In the published study, 33 African-American (AA) and 36 Caucasian American (CA) patients with chronic HCV genotype 1 infection received pegylated interferon and ribavirin therapy for 28 days. HG-U133A GeneChip containing 22283 probes was used to analyze the global gene expression in peripheral blood mononuclear cells (PBMC) of all the patients on day 0 (pretreatment), 1, 2, 7, 14, and 28. According to the decrease of HCV RNA levels on day 28, two categories of responses were defined: good and poor. A voting method based on Student's t test, Wilcoxon test, empirical Bayes test and significance analysis of microarray was used to identify differentially expressed genes. A time-dependent diagnostic model based on C4.5 decision tree was constructed to predict the treatment outcome. This model not only utilized the gene expression profiles before the treatment, but also during the treatment. Leave-one-out cross validation was used to evaluate the performance of the model. Results: The model could correctly predict all Caucasian American patients' treatment effects at very early time point. The prediction accuracy of African-American patients achieved 85.7%. In addition, thirty potential biomarkers which may play important roles in response to interferon and ribavirin were identified. Conclusion: Our method provides a way of using time series gene expression profiling to predict the treatment effect of pegylated interferon and ribavirin therapy on HCV infected patients. Similar experimental and bioinformatical strategies may be used to improve treatment decisions for other chronic diseases. [ABSTRACT FROM AUTHOR]

The reactions between nitrile oxides and alkenes are of considerable interest in organic synthesis as the resulting heterocycles are versatile intermediates for the synthesis of natural products and biologically active compounds. In this paper, we design a series of reactions of phosphonyl nitrile oxides with acrylonnitrile, which can give 2:1 cycloaddition products with no crystal structure released so far, and present a detailed theoretical study on the mechanism of the 2:1 1, 3-dipolar cycloaddition reaction, which has been explored with density functional theory calculations at B3LYP/6-31G* level. The results reveal that the following mechanism is quite possible. Firstly, it starts as a normal 1,3-dipolar cycloaddition reaction to produce a regiospecific 1:1 product. Subsequently, highly reactive diisopropanyl phosphonyl nitrile oxide sequentially reacts with the aforementioned regiospecific 1:1 product and gives the corresponding cycloadduct. Further study is underway to expand the scope of this methodology, as well as to ascertain mechanistic details of the cycloaddition process. [ABSTRACT FROM AUTHOR]

Background: The wide use of Affymetrix microarray in broadened fields of biological research has made the probeset annotation an important issue. Standard Affymetrix probeset annotation is at gene level, i.e. a probeset is precisely linked to a gene, and probeset intensity is interpreted as gene expression. The increased knowledge that one gene may have multiple transcript variants clearly brings up the necessity of updating this gene-level annotation to a refined transcript-level. Results: Through performing rigorous alignments of the Affymetrix probe sequences against a comprehensive pool of currently available transcript sequences, and further linking the probesets to the International Protein Index, we generated transcript-level or protein-level annotation tables for two popular Affymetrix expression arrays, Mouse Genome 430A 2.0 Array and Human Genome U133A Array. Application of our new annotations in re-examining existing expression data sets shows increased expression consistency among synonymous probesets and strengthened expression correlation between interacting proteins. Conclusion: By refining the standard Affymetrix annotation of microarray probesets from the gene level to the transcript level and protein level, one can achieve a more reliable interpretation of their experimental data, which may lead to discovery of more profound regulatory mechanism. [ABSTRACT FROM AUTHOR]

Several "head-to-head" (or "bidirectional") gene pairs have been studied in individual experiments, but genome-wide analysis of this gene organization, especially in terms of transcriptional correlation and functional association, is still insufficient. We conducted a systematic investigation of head-to-head gene organization focusing on structural features, evolutionary conservation, expression correlation and functional association. Of the present 1,262, 1,071, and 491 head-to-head pairs identified in human, mouse, and rat genomes, respectively, pairs with 1- to 400-base pair distance between transcription start sites form the majority (62.36%, 64.15%, and 55.19% for human, mouse, and rat, respectively) of each dataset, and the largest group is always the one with a transcription start site distance of 101 to 200 base pairs. The phylogenetic analysis among Fugu, chicken, and human indicates a negative selection on the separation of head-to-head genes across vertebrate evolution, and thus the ancestral existence of this gene organization. The expression analysis shows that most of the human head-to-head genes are significantly correlated, and the correlation could be positive, negative, or alternative depending on the experimental conditions. Finally, head-to-head genes statistically tend to perform similar functions, and gene pairs associated with the significant cofunctions seem to have stronger expression correlations. The findings indicate that the head-to-head gene organization is ancient and conserved, which subjects functionally related genes to correlated transcriptional regulation and thus provides an exquisite mechanism of transcriptional regulation based on gene organization. These results have significantly expanded the knowledge about head-to-head gene organization. Supplementary materials for this study are available at http://www.scbit.org/h2h. [ABSTRACT FROM AUTHOR]

Although many numerical clustering algorithms have been applied to gene expression data analysis, the essential step is still biological interpretation by manual inspection. The correlation between genetic co-regulation and affiliation to a common biological process is what biologists expect. Here, we introduce some clustering algorithms that are based on graph structure constituted by biological knowledge. After applying a widely used dataset, we compared the result clusters of two of these algorithms in terms of the homogeneity of clusters and coherence of annotation and matching ratio. The results show that the clusters of knowledge-guided analysis are the kernel parts of the clusters of Gene Ontology (GO)-Cluster software, which contains the genes that are most expression correlative and most consistent with biological functions. Moreover, knowledge-guided analysis seems much more applicable than GO-Cluster in a larger dataset. Edited by Shigeto SENO [ABSTRACT FROM AUTHOR]

Background: Several high-throughput searches for ppotential natural antisense transcripts (NATs) have been performed recently, but most of the reports were focused on cis type. A thorough in silico analysis of human transcripts will help expand our knowledge of NATs. Results: We have identified 568 NATs from human RefSeq RNA sequences. Among them, 403 NATs are reported for the first time, and at least 157 novel NATs are trans type. According to the pairing region of a sense and antisense RNA pair, hNATs are divided into 6 classes, of which about 87% involve 5′ or 3′ UTR sequences, supporting the regulatory role of UTRs. Among a total of 535 NAT pairs related with splice variants, 77.4% (414/535) have their pairing regions affected or completely eliminated by alternative splicing, suggesting significant relationship of alternative splicing and antisense-directed regulation. The extensive occurrence of splice variants in hNATs and other multiple pairing patterns results in a one-to-many relationship, allowing the formation of complex regulation networks. Based on microarray data from Stanford Microarray Database, two hNAT pairs were found to display significant inverse expression patterns before and after insulin injection. Conclusion: NATs might carry out more extensive and complex functions than previously thought. Combined with endogenous micro RNAs, hNATs could be regarded as a special group of transcripts contributing to the complex regulation networks. [ABSTRACT FROM AUTHOR]

Elucidation of human disease similarities has provided new insights into etiology, disease classification and drug repositioning. Since dysfunctional regulation would be manifested as the decoupling of expression correlation, disease similarity (DS) in terms of dysfunctional regulation mechanism (DRM) could be estimated by using a differential coexpression based approach, which is described in a companion paper. Due to the lack of tools for estimating DS from the viewpoint of DRM in public domain, we implemented an R package 'DSviaDRM' to identify significant DS via DRM based on transcriptomic data. DSviaDRM contains five easy-to-use functions, DCEA, DCpathway, DS, comDCGL and comDCGLplot, for identifying disease relationships and showing common differential regulation information shared by similar diseases. [ABSTRACT FROM AUTHOR]

A letter to the editor in response to an article that compared a proteomic and a microarray-based array approach to detect exons in the mouse genome is presented.

Protein tyrosine phosphatases 1B (PTP1B) is a major negative regulator of both insulin and leptin signaling pathways. In view of this, it becomes an important target for drug development against cancers, diabetes and obesity. The aim of the current study is to use the long time-scale molecular dynamics (MD) simulations to investigate the structural and dynamic factors that cause its inhibition by INTA and INTB, the two most potent and highly selective PTP1B inhibitors known so far. In order to investigate the mode of collective motions that is vitally important to the biological function, the covariance matrix of Cα atoms was introduced for performing the dynamic analysis of the inhibition systems. It has been observed that the conformational and dynamic features of WPD-Loop, R-Loop and S-Loop play a key role in providing a smooth entrance for the inhibitors moving into the binding pocket as well as a favorable microenvironment to stabilize them. Furthermore, the hydrogen bonding networks formed around the active site with INTA and INTB may be the main reason of why the inhibition of PTP1B by the two ligands is so potent and selective. All these findings might provide useful insights for developing novel and effective drugs to treat cancer, diabetes and obesity. [ABSTRACT FROM AUTHOR]

DBH2H collects head-to-head (h2h) gene pairs identified from human, mouse, rat, chicken and fugu genomes, and distinguishes the ortholog mapping relationship among them. The gene pairs in DBH2H are annotated with sequential features including single nucleotide polymorphisms, CpG islands and transcription factor binding sites, as well as functional terms and genetic disorders. In addition, the expression correlation information based on 117 microarray datasets is included. By providing user-friendly access to these data, DBH2H represents a valuable resource for further analyses of this important gene arrangement in terms of transcriptional regulation mechanisms, evolutionary conservation, disease relevance, etc. [ABSTRACT FROM PUBLISHER]