Project description:Development of an updated genome-scale metabolic model of Clostridium thermocellum and its application for integration of multi-omics datasets
Project description:Purpose: The purpose of this study is to clarify the response of Clostridium perfringens ATCC 13124 to host polysaccharide. Methods: Clostridium perfringens ATCC 13124 cells were cultured anaerobically in a medium containing Minimal medium-like condition Poor + medium, medium in which hyaluronic acid or mucin was added to Poor + medium. Total RNA was extracted from bacterial cells by the Hot-Phenol method. Samples for RNA-seq were prepared according to the Illmina protocol available from the manufacturer. Array leads passed through quality filters were analyzed at the transcript isoform level using bowtie v 1.1.2. Results: Using the optimized data analysis workflow, we mapped about 50 million sequence leads per sample to the whole genome of Clostridium perfringens ATCC 13124. In addition, 2735 transcripts in C. perfringens ATCC 13124 were identified using a Bowtie aligner. Lead counts per genome were extracted from known gene annotations using the HTSeq program.
Project description:Bacteria-host interactions are dynamic processes, and understanding transcriptional responses that directly or indirectly regulate the expression of genes involved in initial infection stages would illuminate the molecular events that result in host colonization. We used oligonucleotide microarrays to monitor (in vitro) differential gene expression in group A streptococci during pharyngeal cell adherence, the first overt infection stage. We present neighbor clustering, a new computational method for further analyzing bacterial microarray data that combines two informative characteristics of bacterial genes that share common function or regulation: (1) similar gene expression profiles (i.e., co-expression); and (2) physical proximity of genes on the chromosome. This method identifies statistically significant clusters of co-expressed gene neighbors that potentially share common function or regulation by coupling statistically analyzed gene expression profiles with the chromosomal position of genes. We applied this method to our own data and to those of others, and we show that it identified a greater number of differentially expressed genes, facilitating the reconstruction of more multimeric proteins and complete metabolic pathways than would have been possible without its application. We assessed the biological significance of two identified genes by assaying deletion mutants for adherence in vitro and show that neighbor clustering indeed provides biologically relevant data. Neighbor clustering provides a more comprehensive view of the molecular responses of streptococci during pharyngeal cell adherence.
Project description:BackgroundThe discovery of genetic mutations in children with inherited syndromes of intrahepatic cholestasis allows for diagnostic specificity despite similar clinical phenotypes. Here, we aimed to determine whether mutation screening of target genes could assign a molecular diagnosis in children with idiopathic cholestasis.Patients and methodsDNA samples were obtained from 51 subjects with cholestasis of undefined etiology and surveyed for mutations in the genes SERPINA1, JAG1, ATP8B1, ABCB11, and ABCB4 by a high-throughput gene chip. Then, the sequence readouts for all 5 genes were analyzed for mutations and correlated with clinical phenotypes. Healthy subjects served as controls.ResultsSequence analysis of the genes identified 14 (or 27%) subjects with missense, nonsense, deletion, and splice site variants associated with disease phenotypes based on the type of mutation and/or biallelic involvement in the JAG1, ATP8B1, ABCB11, or ABCB4 genes. These patients had no syndromic features and could not be differentiated by biochemical markers or histopathology. Among the remaining subjects, 10 (or ∼20%) had sequence variants in ATP8B1 or ABCB11 that involved only 1 allele, 8 had variants not likely to be associated with disease phenotypes, and 19 had no variants that changed amino acid composition.ConclusionsGene sequence analysis assigned a molecular diagnosis in 27% of subjects with idiopathic cholestasis based on the presence of variants likely to cause disease phenotypes.
Project description:Lee2008 - Genome-scale metabolic network of
Clostridium acetobutylicum (iJL432)
This model is described in the article:
Genome-scale reconstruction
and in silico analysis of the Clostridium acetobutylicum ATCC
824 metabolic network.
Lee J, Yun H, Feist AM, Palsson
BØ, Lee SY.
Appl. Microbiol. Biotechnol. 2008 Oct;
80(5): 849-862
Abstract:
To understand the metabolic characteristics of Clostridium
acetobutylicum and to examine the potential for enhanced
butanol production, we reconstructed the genome-scale metabolic
network from its annotated genomic sequence and analyzed
strategies to improve its butanol production. The generated
reconstructed network consists of 502 reactions and 479
metabolites and was used as the basis for an in silico model
that could compute metabolic and growth performance for
comparison with fermentation data. The in silico model
successfully predicted metabolic fluxes during the acidogenic
phase using classical flux balance analysis. Nonlinear
programming was used to predict metabolic fluxes during the
solventogenic phase. In addition, essential genes were
predicted via single gene deletion studies. This genome-scale
in silico metabolic model of C. acetobutylicum should be useful
for genome-wide metabolic analysis as well as strain
development for improving production of biochemicals, including
butanol.
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MODEL1507180030.
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