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author:("Dam, phuong")
1.  Systems Biology Analysis of Zymomonas mobilis ZM4 Ethanol Stress Responses 
PLoS ONE  2013;8(7):e68886.
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.
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.
PMCID: PMC3712917  PMID: 23874800
2.  Regulation of gene expression in ovarian cancer cells by luteinizing hormone receptor expression and activation 
BMC Cancer  2011;11:280.
Since 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.
The 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.
Through 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.
Overall, 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.
PMCID: PMC3141782  PMID: 21711548
Ovarian cancer; gonadotropin; luteinizing hormone; luteinizing hormone receptor; SKOV3 cells; microarray
3.  Insights into plant biomass conversion from the genome of the anaerobic thermophilic bacterium Caldicellulosiruptor bescii DSM 6725 
Nucleic Acids Research  2011;39(8):3240-3254.
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.
PMCID: PMC3082886  PMID: 21227922
4.  Genome Sequence of the Anaerobic, Thermophilic, and Cellulolytic Bacterium “Anaerocellum thermophilum” DSM 6725▿  
Journal of Bacteriology  2009;191(11):3760-3761.
“Anaerocellum thermophilum” DSM 6725 is a strictly anaerobic bacterium that grows optimally at 75°C. It uses a variety of polysaccharides, including crystalline cellulose and untreated plant biomass, and has potential utility in biomass conversion. Here we report its complete genome sequence of 2.97 Mb, which is contained within one chromosome and two plasmids (of 8.3 and 3.6 kb). The genome encodes a broad set of cellulolytic enzymes, transporters, and pathways for sugar utilization and compared to those of other saccharolytic, anaerobic thermophiles is most similar to that of Caldicellulosiruptor saccharolyticus DSM 8903.
PMCID: PMC2681903  PMID: 19346307
5.  DOOR: a database for prokaryotic operons 
Nucleic Acids Research  2008;37(Database issue):D459-D463.
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
PMCID: PMC2686520  PMID: 18988623
6.  Operon prediction using both genome-specific and general genomic information 
Nucleic Acids Research  2006;35(1):288-298.
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.
PMCID: PMC1802555  PMID: 17170009
7.  Operon prediction in Pyrococcus furiosus 
Nucleic Acids Research  2006;35(1):11-20.
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.
PMCID: PMC1761436  PMID: 17148478
8.  Computational inference and experimental validation of the nitrogen assimilation regulatory network in cyanobacterium Synechococcus sp. WH 8102 
Nucleic Acids Research  2006;34(3):1050-1065.
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.
PMCID: PMC1363776  PMID: 16473855

Results 1-8 (8)