Phuongan Dam

DOOR: a database for prokaryotic operons

We present a database DOOR (Database for prOkaryotic OpeRons) containing computationally predicted operons of all the sequenced prokaryotic genomes. All the operons in DOOR are predicted using our own prediction program, which was ranked to be the best among 14 operon prediction programs by a recent independent review. Currently, the DOOR database contains operons for 675 prokaryotic genomes, and supports a number of search capabilities to facilitate easy access and utilization of the information stored in it. Querying the database: the database provides a search capability for a user to find desired operons and associated information through multiple querying methods. Searching for similar operons: the database provides a search capability for a user to find operons that have similar composition and structure to a query operon. Prediction of cis-regulatory motifs: the database provides a capability for motif identification in the promoter regions of a user-specified group of possibly coregulated operons, using motif-finding tools. Operons for RNA genes: the database includes operons for RNA genes. OperonWiki: the database provides a wiki page (OperonWiki) to facilitate interactions between users and the developer of the database. We believe that DOOR provides a useful resource to many biologists working on bacteria and archaea, which can be accessed at http://csbl1.bmb.uga.edu/OperonDB.

Operon prediction using both genome-specific and general genomic information

We have carried out a systematic analysis of the contribution of a set of selected features that include three new features to the accuracy of operon prediction. Our analyses have led to a number of new insights about operon prediction, including that (i) different features have different levels of discerning power when used on adjacent gene pairs with different ranges of intergenic distance, (ii) certain features are universally useful for operon prediction while others are more genome-specific and (iii) the prediction reliability of operons is dependent on intergenic distances. Based on these new insights, our newly developed operon-prediction program achieves more accurate operon prediction than the previous ones, and it uses features that are most readily available from genomic sequences. Our prediction results indicate that our (non-linear) decision tree-based classifier can predict operons in a prokaryotic genome very accurately when a substantial number of operons in the genome are already known. For example, the prediction accuracy of our program can reach 90.2 and 93.7% on Bacillus subtilis and Escherichia coli genomes, respectively. When no such information is available, our (linear) logistic function-based classifier can reach the prediction accuracy at 84.6 and 83.3% for E.coli and B.subtilis, respectively.

Insights into plant biomass conversion from the genome of the anaerobic thermophilic bacterium Caldicellulosiruptor bescii DSM 6725

Caldicellulosiruptor bescii DSM 6725 utilizes various polysaccharides and grows efficiently on untreated high-lignin grasses and hardwood at an optimum temperature of ∼80°C. It is a promising anaerobic bacterium for studying high-temperature biomass conversion. Its genome contains 2666 protein-coding sequences organized into 1209 operons. Expression of 2196 genes (83%) was confirmed experimentally. At least 322 genes appear to have been obtained by lateral gene transfer (LGT). Putative functions were assigned to 364 conserved/hypothetical protein (C/HP) genes. The genome contains 171 and 88 genes related to carbohydrate transport and utilization, respectively. Growth on cellulose led to the up-regulation of 32 carbohydrate-active (CAZy), 61 sugar transport, 25 transcription factor and 234 C/HP genes. Some C/HPs were overproduced on cellulose or xylan, suggesting their involvement in polysaccharide conversion. A unique feature of the genome is enrichment with genes encoding multi-modular, multi-functional CAZy proteins organized into one large cluster, the products of which are proposed to act synergistically on different components of plant cell walls and to aid the ability of C. bescii to convert plant biomass. The high duplication of CAZy domains coupled with the ability to acquire foreign genes by LGT may have allowed the bacterium to rapidly adapt to changing plant biomass-rich environments.

Computational inference and experimental validation of the nitrogen assimilation regulatory network in cyanobacterium Synechococcus sp. WH 8102

Deciphering the regulatory networks encoded in the genome of an organism represents one of the most interesting and challenging tasks in the post-genome sequencing era. As an example of this problem, we have predicted a detailed model for the nitrogen assimilation network in cyanobacterium Synechococcus sp. WH 8102 (WH8102) using a computational protocol based on comparative genomics analysis and mining experimental data from related organisms that are relatively well studied. This computational model is in excellent agreement with the microarray gene expression data collected under ammonium-rich versus nitrate-rich growth conditions, suggesting that our computational protocol is capable of predicting biological pathways/networks with high accuracy. We then refined the computational model using the microarray data, and proposed a new model for the nitrogen assimilation network in WH8102. An intriguing discovery from this study is that nitrogen assimilation affects the expression of many genes involved in photosynthesis, suggesting a tight coordination between nitrogen assimilation and photosynthesis processes. Moreover, for some of these genes, this coordination is probably mediated by NtcA through the canonical NtcA promoters in their regulatory regions.

Systems Biology Analysis of Zymomonas mobilis ZM4 Ethanol Stress Responses

Background Zymomonas mobilis ZM4 is a capable ethanologenic bacterium with high ethanol productivity and ethanol tolerance. Previous studies indicated that several stress-related proteins and changes in the ZM4 membrane lipid composition may contribute to ethanol tolerance. However, the molecular mechanisms of its ethanol stress response have not been elucidated fully. Methodology/Principal Findings In this study, ethanol stress responses were investigated using systems biology approaches. Medium supplementation with an initial 47 g/L (6% v/v) ethanol reduced Z. mobilis ZM4 glucose consumption, growth rate and ethanol productivity compared to that of untreated controls. A proteomic analysis of early exponential growth identified about one thousand proteins, or approximately 55% of the predicted ZM4 proteome. Proteins related to metabolism and stress response such as chaperones and key regulators were more abundant in the early ethanol stress condition. Transcriptomic studies indicated that the response of ZM4 to ethanol is dynamic, complex and involves many genes from all the different functional categories. Most down-regulated genes were related to translation and ribosome biogenesis, while the ethanol-upregulated genes were mostly related to cellular processes and metabolism. Transcriptomic data were used to update Z. mobilis ZM4 operon models. Furthermore, correlations among the transcriptomic, proteomic and metabolic data were examined. Among significantly expressed genes or proteins, we observe higher correlation coefficients when fold-change values are higher. Conclusions Our study has provided insights into the responses of Z. mobilis to ethanol stress through an integrated “omics” approach for the first time. This systems biology study elucidated key Z. mobilis ZM4 metabolites, genes and proteins that form the foundation of its distinctive physiology and its multifaceted response to ethanol stress.

Operon prediction in Pyrococcus furiosus

Identification of operons in the hyperthermophilic archaeon Pyrococcus furiosus represents an important step to understanding the regulatory mechanisms that enable the organism to adapt and thrive in extreme environments. We have predicted operons in P.furiosus by combining the results from three existing algorithms using a neural network (NN). These algorithms use intergenic distances, phylogenetic profiles, functional categories and gene-order conservation in their operon prediction. Our method takes as inputs the confidence scores of the three programs, and outputs a prediction of whether adjacent genes on the same strand belong to the same operon. In addition, we have applied Gene Ontology (GO) and KEGG pathway information to improve the accuracy of our algorithm. The parameters of this NN predictor are trained on a subset of all experimentally verified operon gene pairs of Bacillus subtilis. It subsequently achieved 86.5% prediction accuracy when applied to a subset of gene pairs for Escherichia coli, which is substantially better than any of the three prediction programs. Using this new algorithm, we predicted 470 operons in the P.furiosus genome. Of these, 349 were validated using DNA microarray data.

Regulation of gene expression in ovarian cancer cells by luteinizing hormone receptor expression and activation

BackgroundSince a substantial percentage of ovarian cancers express gonadotropin receptors and are responsive to the relatively high concentrations of pituitary gonadotropins during the postmenopausal years, it has been suggested that receptor activation may contribute to the etiology and/or progression of the neoplasm. The goal of the present study was to develop a cell model to determine the impact of luteinizing hormone (LH) receptor (LHR) expression and LH-mediated LHR activation on gene expression and thus obtain insights into the mechanism of gonadotropin action on ovarian surface epithelial (OSE) carcinoma cells.MethodsThe human ovarian cancer cell line, SKOV-3, was stably transfected to express functional LHR and incubated with LH for various periods of time (0-20 hours). Transcriptomic profiling was performed on these cells to identify LHR expression/activation-dependent changes in gene expression levels and pathways by microarray and qRT-PCR analyses.ResultsThrough comparative analysis on the LHR-transfected SKOV-3 cells exposed to LH, we observed the differential expression of 1,783 genes in response to LH treatment, among which five significant families were enriched, including those of growth factors, translation regulators, transporters, G-protein coupled receptors, and ligand-dependent nuclear receptors. The most highly induced early and intermediate responses were found to occupy a network impacting transcriptional regulation, cell growth, apoptosis, and multiple signaling transductions, giving indications of LH-induced apoptosis and cell growth inhibition through the significant changes in, for example, tumor necrosis factor, Jun and many others, supportive of the observed cell growth reduction in in vitro assays. However, other observations, e.g. the substantial up-regulation of the genes encoding the endothelin-1 subtype A receptor, stromal cell-derived factor 1, and insulin-like growth factor II, all of which are potential therapeutic targets, may reflect a positive mediation of ovarian cancer growth.ConclusionOverall, the present study elucidates the extensive transcriptomic changes of ovarian cancer cells in response to LH receptor activation, which provides a comprehensive and objective assessment for determining new cancer therapies and potential serum markers, of which over 100 are suggested.

Identification of Novel Proteins Involved in Plant Cell-Wall Synthesis Based on Protein−Protein Interaction Data

The plant cell wall is mainly composed of polysaccharides, representing the richest source of biomass for future biofuel production. Currently, the majority of the cell-wall synthesis-related (CWSR) proteins are unknown even for model plant Arabidopsis thaliana. We report a computational framework for predicting CWSR proteins based on protein-protein interaction (PPI) data and known CWSR proteins. We predict a protein to be a CWSR protein if it interacts with known CWSR proteins (seeds) with high statistical significance. Using this technique, we predicted 100 candidate CWSR proteins in Arabidopsis thaliana, 8 of which were experimentally confirmed by previous reports. Forty-two candidates have either independent supporting evidence or strong functional relevance to cell-wall synthesis and, hence, are considered as the most reliable predictions. For 33 of the predicted CWSR proteins, we have predicted their detailed functional roles in CWS, based on analyses of their domain architectures, phylogeny, and current functional annotation in conjunction with a literature search. We present the constructed PPIs covering all the known and predicted CWSR proteins at http://csbl.bmb.uga.edu/∼zhouchan/CellWallProtein/. The 42 most reliable candidates provide useful targets to experimentalists for further investigation, and the PPI data constructed in this work provides new information for cell-wall research.

Dynamic models of the complex microbial metapopulation of lake mendota

Like many other environments, Lake Mendota, WI, USA, is populated by many thousand microbial species. Only about 1,000 of these constitute between 80 and 99% of the total microbial community, depending on the season, whereas the remaining species are rare. The functioning and resilience of the lake ecosystem depend on these microorganisms, and it is therefore important to understand their dynamics throughout the year. We propose a two-layered set of dynamic mathematical models that capture and interpret the yearly abundance patterns of the species within the metapopulation. The first layer analyzes the interactions between 14 subcommunities (SCs) that peak at different times of the year and together contain all species whereas the second layer focuses on interactions between individual species and SCs. Each SC contains species from numerous families, genera, and phyla in strikingly different abundances. The dynamic models quantify the importance of environmental factors in shaping the dynamics of the lake’s metapopulation and reveal positive or negative interactions between species and SCs. Three environmental factors, namely temperature, ammonia/phosphorus, and nitrate+nitrite, positively affect almost all SCs, whereas by far the most interactions between SCs are inhibitory. As far as the interactions can be independently validated, they are supported by literature information. The models are quite robust and permit predictions of species abundances over many years both, under the assumption that conditions do not change drastically, or in response to environmental perturbations.

Characterization of Protein Structure and Function at Genome Scale with a Computational Prediction Pipeline

Recent advances in high-throughput production capabilities for biological data such as genomic sequence (1,2), large-scale gene expression data (3-5), genomescale protein-protein interactions (6,7), and protein structures (8), arc revolutionizing the biological sciences. Essential to this new revolution are capabilities to computationally interpret large quantities of biological data generated under various experimental conditions and build mathematical models that fit these data. The combination of on-line bioinformatics tools and easy access to the high-speed Internet has made it generally possible to facilitate such computational steps and make biological discoveriesin silicoin a highly efficient manner. By utilizing various bioinformatics prediction, analysis and modeling tools, one can quickly generate hypotheses and theoretical models, which could then guide the design of experiments for further validation. The paradigm that links and integrates systematic data generation, computational data interpretation, and experimental validation is clearly providing a new and powerful way for conducting biological research. The focus of this paper is on (a) development of new computational tools for interpretation of large quantity of genomic sequence data for structural and functional inference and (b) example applications of these tools to studies of microbial genomes, particularlycyanobacterialgenomes.