Time-course data analysis of gene expression profiles reveals purR regulon concerns in organic solvent tolerance in Escherichia coli

2005 ◽  
Vol 99 (1) ◽  
pp. 72-74 ◽  
Author(s):  
Kazunori Shimizu ◽  
Shuhei Hayashi ◽  
Noriyuki Doukyu ◽  
Takeshi Kobayashi ◽  
Hiroyuki Honda
Keyword(s):  
Gene Expression ◽  
Escherichia Coli ◽  
Data Analysis ◽  
Organic Solvent ◽  
Time Course ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Solvent Tolerance ◽  
Organic Solvent Tolerance ◽  
Time Course Data

scholarly journals Discovery of glpC, an Organic Solvent Tolerance-Related Gene in Escherichia coli, Using Gene Expression Profiles from DNA Microarrays

2005 ◽  
Vol 71 (2) ◽  
pp. 1093-1096 ◽  
Author(s):  
Kazunori Shimizu ◽  
Shuhei Hayashi ◽  
Takeshi Kako ◽  
Maiko Suzuki ◽  
Norihiko Tsukagoshi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Escherichia Coli ◽  
Organic Solvent ◽  
Dna Microarrays ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Solvent Tolerance ◽  
Organic Solvent Tolerance ◽  
Before And After ◽  
Glycerol 3 Phosphate Dehydrogenase

ABSTRACT Gene expression profiles were collected from Escherichia coli strains (OST3410, TK33, and TK31) before and after exposure to organic solvents, and the six genes that showed higher gene expression were selected. Among these genes, glpC encoding the anaerobic glycerol-3-phosphate dehydrogenase subunit C remarkably increased the organic solvent tolerance.

scholarly journals Bayesian approach for predicting responses to therapy from high-dimensional time-course gene expression profiles

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Arika Fukushima ◽  
Masahiro Sugimoto ◽  
Satoru Hiwa ◽  
Tomoyuki Hiroyasu
Keyword(s):  
Gene Expression ◽  
Time Course ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Response To Therapy ◽  
Hcv Infection ◽  
Entire Treatment Period ◽  
Time Points ◽  
Time Course Data ◽  
Conventional Methods

Abstract Background Historical and updated information provided by time-course data collected during an entire treatment period proves to be more useful than information provided by single-point data. Accurate predictions made using time-course data on multiple biomarkers that indicate a patient’s response to therapy contribute positively to the decision-making process associated with designing effective treatment programs for various diseases. Therefore, the development of prediction methods incorporating time-course data on multiple markers is necessary. Results We proposed new methods that may be used for prediction and gene selection via time-course gene expression profiles. Our prediction method consolidated multiple probabilities calculated using gene expression profiles collected over a series of time points to predict therapy response. Using two data sets collected from patients with hepatitis C virus (HCV) infection and multiple sclerosis (MS), we performed numerical experiments that predicted response to therapy and evaluated their accuracies. Our methods were more accurate than conventional methods and successfully selected genes, the functions of which were associated with the pathology of HCV infection and MS. Conclusions The proposed method accurately predicted response to therapy using data at multiple time points. It showed higher accuracies at early time points compared to those of conventional methods. Furthermore, this method successfully selected genes that were directly associated with diseases.

Effect of enrofloxacin on gene expression profiles of Escherichia coli

2010 ◽  
Vol 60 (4) ◽  
pp. 653-660 ◽  
Author(s):  
Hua Bai ◽  
Wen-zheng Su ◽  
Xiao-ling Zhu ◽  
Ming Hu ◽  
Yu-qing Liu
Keyword(s):  
Gene Expression ◽  
Escherichia Coli ◽  
Expression Profiles ◽  
Gene Expression Profiles

scholarly journals In Vivo mRNA Profiling of Uropathogenic Escherichia coli from Diverse Phylogroups Reveals Common and Group-Specific Gene Expression Profiles

mBio ◽  
2014 ◽  
Vol 5 (4) ◽  
Author(s):  
Piotr Bielecki ◽  
Uthayakumar Muthukumarasamy ◽  
Denitsa Eckweiler ◽  
Agata Bielecka ◽  
Sarah Pohl ◽  
...  
Keyword(s):  
Gene Expression ◽  
Escherichia Coli ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Specific Gene ◽  
Content Type ◽  
E Coli ◽  
Human Host ◽  
Tract Infections

ABSTRACTmRNA profiling of pathogens during the course of human infections gives detailed information on the expression levels of relevant genes that drive pathogenicity and adaptation and at the same time allows for the delineation of phylogenetic relatedness of pathogens that cause specific diseases. In this study, we used mRNA sequencing to acquire information on the expression ofEscherichia colipathogenicity genes during urinary tract infections (UTI) in humans and to assign the UTI-associatedE. coliisolates to different phylogenetic groups. Whereas thein vivogene expression profiles of the majority of genes were conserved among 21E. colistrains in the urine of elderly patients suffering from an acute UTI, the specific gene expression profiles of the flexible genomes was diverse and reflected phylogenetic relationships. Furthermore, genes transcribedin vivorelative to laboratory media included well-described virulence factors, small regulatory RNAs, as well as genes not previously linked to bacterial virulence. Knowledge on relevant transcriptional responses that drive pathogenicity and adaptation of isolates to the human host might lead to the introduction of a virulence typing strategy into clinical microbiology, potentially facilitating management and prevention of the disease.IMPORTANCEUrinary tract infections (UTI) are very common; at least half of all women experience UTI, most of which are caused by pathogenicEscherichia colistrains. In this study, we applied massive parallel cDNA sequencing (RNA-seq) to provide unbiased, deep, and accurate insight into the nature and the dimension of the uropathogenicE. coligene expression profile during an acute UTI within the human host. This work was undertaken to identify key players in physiological adaptation processes and, hence, potential targets for new infection prevention and therapy interventions specifically aimed at sabotaging bacterial adaptation to the human host.

scholarly journals Prediction of Pharmacological and Xenobiotic Responses to Drugs Based on Time Course Gene Expression Profiles

PLoS ONE ◽  
2009 ◽  
Vol 4 (12) ◽  
pp. e8126 ◽  
Author(s):  
Tao Huang ◽  
WeiRen Cui ◽  
LeLe Hu ◽  
KaiYan Feng ◽  
Yi-Xue Li ◽  
...  
Keyword(s):  
Gene Expression ◽  
Time Course ◽  
Expression Profiles ◽  
Gene Expression Profiles
Sign in / Sign up

Export Citation Format

Share Document