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ABSTRACT:
PROVIDER: PRJNA477384 | ENA |
REPOSITORIES: ENA
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Project description:Raghunathan2009 - Genome-scale metabolic
network of Salmonella typhimurium (iRR1083)
This model is described in the article:
Constraint-based analysis of
metabolic capacity of Salmonella typhimurium during
host-pathogen interaction.
Raghunathan A, Reed J, Shin S,
Palsson B, Daefler S.
BMC Syst Biol 2009; 3: 38
Abstract:
BACKGROUND: Infections with Salmonella cause significant
morbidity and mortality worldwide. Replication of Salmonella
typhimurium inside its host cell is a model system for studying
the pathogenesis of intracellular bacterial infections.
Genome-scale modeling of bacterial metabolic networks provides
a powerful tool to identify and analyze pathways required for
successful intracellular replication during host-pathogen
interaction. RESULTS: We have developed and validated a
genome-scale metabolic network of Salmonella typhimurium LT2
(iRR1083). This model accounts for 1,083 genes that encode
proteins catalyzing 1,087 unique metabolic and transport
reactions in the bacterium. We employed flux balance analysis
and in silico gene essentiality analysis to investigate growth
under a wide range of conditions that mimic in vitro and host
cell environments. Gene expression profiling of S. typhimurium
isolated from macrophage cell lines was used to constrain the
model to predict metabolic pathways that are likely to be
operational during infection. CONCLUSION: Our analysis suggests
that there is a robust minimal set of metabolic pathways that
is required for successful replication of Salmonella inside the
host cell. This model also serves as platform for the
integration of high-throughput data. Its computational power
allows identification of networked metabolic pathways and
generation of hypotheses about metabolism during infection,
which might be used for the rational design of novel
antibiotics or vaccine strains.
This model is hosted on
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MODEL1507180058.
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2015-07-30 | MODEL1507180058 | BioModels
Project description:Examination of intestinal colonization of enteric pathogen Salmonella typhimurium
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Project description:Many non-typhoidal serovars of Salmonella such as Salmonella enterica serovar Typhimurium (S. Typhimurium) are the leading cause of food-borne gastroenteritis, resulting in millions of infections each year and sometimes death. Salmonella enterica serovar Typhimurium is the most common non-typhoidal Salmonella strain isolated from patients around the world and is used as a mouse model to study bacterial pathogenesis and host-microbe interactions. Furthermore, S. Typhimurium is an important pathogen in livestock animals including chickens and cattle. S. Typhimurium utilises a multitude of virulence factors to reach and invade host cells and for its intracellular survival. However, little is known about the mechanism of protein synthesis of these virulence factors at the codon level. Here, we performed RNA-seq and ribosome profiling. Ribosome profiling allows the global mapping of translating ribosomes on the transcriptome and therefore provides direct measure of protein synthesis.
2023-04-01 | E-MTAB-8808 | biostudies-arrayexpress
Project description:RNA from in vitro grown Salmonella typhimurium is compared with RNA extracted from Salmonella typhimurium from infected chick caecums using a common DNA reference. Keywords: Disease state analysis, infected versus uninfected, common reference
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Project description:In this study, we have defined the NsrR regulon in Salmonella enterica sv. Typhimurium 14028s using a transcriptional microarray. Wild-type and nsrR mutant S. Typhimurium were grown aerobically to early log-phase (OD600~0.5) at 37C in LB medium. Total RNA was isolated from three independent cultures of both strains and interrogated on a PCR product array representing almost all ORFs.
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2013-09-19 | E-MTAB-1848 | biostudies-arrayexpress
Project description:ChIP-on-chip analysis of RNAP and RpoD binding to the Salmonella enterica serovar Typhimurium chromosome demonstrated a high degree of overlap between RNAP and RpoD binding and provided us with important insights into the global distribution of these factors. Furthermore this data was correlated with information on the location of 1873 transcription start sites identified by RNA-Seq technology, thereby providing a detailed transcriptional map of Salmonella Typhimurium.
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