Ontology highlight
ABSTRACT:
PROVIDER: PRJNA595472 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR10687555_1.fastq.gz | Fastqsanger.gz | |||
| SRR10687555_2.fastq.gz | Fastqsanger.gz | |||
| SRR10687556.fastq.gz | Fastqsanger.gz | |||
| SRR10687556_1.fastq.gz | Fastqsanger.gz | |||
| SRR10687556_2.fastq.gz | Fastqsanger.gz |
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Project description:Understanding constraints which shape antibiotic resistance is key for predicting and controlling drug resistance. Here, we performed high-throughput laboratory evolution of Escherichia coli. The transcriptome, resistance, and genomic profiles for the evolved strains in 48 environments were quantitatively analyzed. By analyzing the quantitative datasets through interpretable machine learning techniques, the emergence of low dimensional phenotypic states within the 192 strains was observed. Further analysis revealed the underlying biological processes responsible for the distinct states. We also report a novel constraint which leads to decelerated evolution. These findings bridge the genotypic, gene expression, and drug resistance space, and lead to a comprehensive understanding of constraints for antibiotic resistance.
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Project description:BACKGROUND: Microbial safety has become a research hotspot with the development of manned space technology. Escherichia coli is a conditional pathogen that can cause infectious diseases. Therefore, it is necessary to study the influence of space environment on Escherichia coli. METHODS: Phenotypic experiment including growth curves, morphology, and environmental resistance experiment were used to study the phenotypic changes of E. coli after exposure to space environment for 12 days carried by “SJ-10” satellite. TMT was used to assess the proteome change of E. coli. RESULTS: The survival rate of E. coli in spaceflight group was decreased when cultivated in acidic and high salt environments. Proteomic analysis identified 72 down-regulated proteins involved in chemotaxis, intracellular pH elevation, glycolate catabolic process and glutamate metabolic process in the spaceflight group. Meanwhile, only one protein mtr which involved in the uptake of tryptophan in E. coli was up-regulated in spaceflight group. CONCLUSION: Our research shows that proteomics results can explain phenotypic results, demonstrated the successful application of proteomics in mechanism research. And our data provide a comprehensive resource for understanding the effect of the space environment on E. coli.
2020-12-16 | PXD023158 |
Project description:Background Compelling evidence indicates that Shigella species, the etiologic agents of bacillary dysentery, as well as enteroinvasive Escherichia coli, are derived from multiple origins of Escherichia coli and form a single pathovar. To further understand the genome diversity and virulence evolution of Shigella, comparative genomic hybridization microarray analysis was employed to compare the gene content of E. coli K-12 with those of 43 Shigella strains from all serotypes. Results For the 43 strains subjected to CGH microarray analyses, the common backbone of the Shigella genome was estimated to contain more than 1,900 open reading frames, with a mean number of 729 undetectable ORFs. The mosaic distribution of absent regions indicated that insertions and/or deletions have led to the highly diversified genomes of pathogenic strains. Conclusion These results support the hypothesis that by gain and loss of functions, Shigella species became successful human pathogens through convergent evolution from diverse genomic backgrounds. Moreover, we also found many specific differences between different lineages, providing a window into understanding bacterial speciation and taxonomic relationships. Keywords: comparative genomic hybridization
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Project description:The rate and direction of phenotypic evolution depends on the availability of phenotypic variants induced either genetically or environmentally for selection act upon. Theoretical suggest that genetic interactions between genes governing phenotypes could explain how the bias in phenotypic variability arises. However, it remains unknown whether the phenotypic variability is explained by real known genetic interactions as expected. To address this question, we analyzed transcriptional variability in a model microorganism, Escherichia coli, in response to different environmental and genetic perturbations. Thanks to the rich knowledge of the genetic interactions of E. coli, we identified common genetic properties that potentially affect transcriptional variability in both environmental and genetic causes. These empirical evidences support the relevance of the genetic interactions to shape phenotypic variability that is shared by different perturbations. Our results provide a mechanistic insight into how evolution learns from past environments to generalize to new environments.
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Project description:Reed2003 - Genome-scale metabolic network of
Escherichia coli (iJR904)
This model is described in the article:
An expanded genome-scale
model of Escherichia coli K-12 (iJR904 GSM/GPR).
Reed JL, Vo TD, Schilling CH,
Palsson BO.
Genome Biol. 2003; 4(9): R54
Abstract:
BACKGROUND: Diverse datasets, including genomic,
transcriptomic, proteomic and metabolomic data, are becoming
readily available for specific organisms. There is currently a
need to integrate these datasets within an in silico modeling
framework. Constraint-based models of Escherichia coli K-12
MG1655 have been developed and used to study the bacterium's
metabolism and phenotypic behavior. The most comprehensive E.
coli model to date (E. coli iJE660a GSM) accounts for 660 genes
and includes 627 unique biochemical reactions. RESULTS: An
expanded genome-scale metabolic model of E. coli (iJR904
GSM/GPR) has been reconstructed which includes 904 genes and
931 unique biochemical reactions. The reactions in the expanded
model are both elementally and charge balanced. Network gap
analysis led to putative assignments for 55 open reading frames
(ORFs). Gene to protein to reaction associations (GPR) are now
directly included in the model. Comparisons between predictions
made by iJR904 and iJE660a models show that they are generally
similar but differ under certain circumstances. Analysis of
genome-scale proton balancing shows how the flux of protons
into and out of the medium is important for maximizing cellular
growth. CONCLUSIONS: E. coli iJR904 has improved capabilities
over iJE660a. iJR904 is a more complete and chemically accurate
description of E. coli metabolism than iJE660a. Perhaps most
importantly, iJR904 can be used for analyzing and integrating
the diverse datasets. iJR904 will help to outline the
genotype-phenotype relationship for E. coli K-12, as it can
account for genomic, transcriptomic, proteomic and fluxomic
data simultaneously.
This model is hosted on
BioModels Database
and identified by:
MODEL1507180060.
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BioModels Database:
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To the extent possible under law, all copyright and related or
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the public domain worldwide. Please refer to
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2015-07-30 | MODEL1507180060 | BioModels
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