Project description:Study of the mechanisms of RecB mutant terminus DNA loss in Escherichia coli. FX158: WT MG1655 FX35: recB- FX37: ruvAB- FX51: matP- MIC18: recB- sbcD- sbcC- MIC20: recB- ruvAB- MIC24: matP- recB- MIC25: recA- recB- MIC31: sbcB- sbcD- MIC34: recA- recD- MIC40: linear chromosome MIC41: linear chromosome recB- MIC42: matP- ftsKC- MIC43: matP- ftsKC- recB- MIC48: recA- Cells were grown in M9 minimal medium supplemented with 0.4 % glucose to exponential phase (0.2 OD 650 nm). Chromosomal DNA was extracted using the Sigma GenElute bacterial genomic DNA kit. 5 μg of DNA were used to generate a genomic library according to Illumina's protocol. The libraries and the sequencing were performed by the High-throughput Sequencing facility of the I2BC (http://www.i2bc.paris-saclay.fr/spip.php?article399&lang=en, CNRS, Gif-sur-Yvette, France). Genomic DNA libraries were made with the ‘Nextera DNA library preparation kit’ (Illumina) following the manufacturer’s recommendations. Library quality was assessed on an Agilent Bioanalyzer 2100, using an Agilent High Sensitivity DNA Kit (Agilent technologies). Libraries were pooled in equimolar proportions. 75 bp single reads were generated on an Illumina MiSeq instrument, using a MiSeq Reagent kit V2 (500 cycles) (Illumina), with an expected depth of 217X. An in-lab written MATLAB-based script was used to perform marker frequency analysis. Reads were aligned on the Escherichia coli K12 MG1655 genome using BWA software. Data were normalized by dividing uniquely mapping sequence reads by the total number of reads. Enrichment of uniquely mapping sequence reads in 1 kb non-overlapping windows were calculated and plotted against the chromosomal coordinates.
Project description:Protein expression by E. coli 26561 during the late-exponential phase of cultures under anaerobic conditions was examined. E. coli 26561 is a multidrug resistant (MDR) and shows an unusual hyper-mucoviscous phenotype. Resistance includes ESBL (CTX-M-14) and proteome was determined with and without exposure to sub-MIC concentrations of the 3rd generation cephalosporin ceftazidime. Ceftazidime exposure was at two sub-MIC levels, specifically 0.25x MIC (samples 5-7), 0.5x MIC (samples 8 - 10); samples 1-4 provided the unexposed Control. Both whole and phospho-enriched fractions for each sample were analysed. Quantification of peptides was assessed using 10-plex TMT labelling in conjunction with an Orbitrap Fusion Tribrid. Raw data produced by the Orbitrap were processed using Max Quant 126.96.36.199 using the included Andromeda search engine. Peptides were searched against our own database of E. coli 26561 proteins which was produced from a hybrid assembly of our reads obtained from MiSeq and PacBio sequencing platforms.
Project description:These E. coli strains were grown with various signaling molecules and the expression profiles were determined. Keywords: addition of quorum and host hormone signals Overall design: These E. coli strains were grown with various signaling molecules and the expression profiles were determined using the Affymetrix E. coli 2.0 Array. Comparisons of the profiles were used to guide further examination using RT-PCR.
Project description:Shimoni2009 - Escherichia Coli SOS
Simple model, involving only the basic components of the circuit, sufficient to explain the peaks in the promoter activities of recA and lexA.
This model is described in the article:
Stochastic analysis of the SOS response in Escherichia coli.
Shimoni Y, Altuvia S, Margalit H, Biham O
PloS one. 2009; 4(5):e5363
BACKGROUND: DNA damage in Escherichia coli evokes a response mechanism called the SOS response. The genetic circuit of this mechanism includes the genes recA and lexA, which regulate each other via a mixed feedback loop involving transcriptional regulation and protein-protein interaction. Under normal conditions, recA is transcriptionally repressed by LexA, which also functions as an auto-repressor. In presence of DNA damage, RecA proteins recognize stalled replication forks and participate in the DNA repair process. Under these conditions, RecA marks LexA for fast degradation. Generally, such mixed feedback loops are known to exhibit either bi-stability or a single steady state. However, when the dynamics of the SOS system following DNA damage was recently studied in single cells, ordered peaks were observed in the promoter activity of both genes (Friedman et al., 2005, PLoS Biol. 3(7):e238). This surprising phenomenon was masked in previous studies of cell populations. Previous attempts to explain these results harnessed additional genes to the system and deployed complex deterministic mathematical models that were only partially successful in explaining the results.
PRINCIPAL FINDINGS: Here we apply stochastic methods, which are better suited for dynamic simulations of single cells. We show that a simple model, involving only the basic components of the circuit, is sufficient to explain the peaks in the promoter activities of recA and lexA. Notably, deterministic simulations of the same model do not produce peaks in the promoter activities.
SIGNIFICANCE: We conclude that the double negative mixed feedback loop with auto-repression accounts for the experimentally observed peaks in the promoter activities. In addition to explaining the experimental results, this result shows that including additional regulations in a mixed feedback loop may dramatically change the dynamic functionality of this regulatory module. Furthermore, our results suggests that stochastic fluctuations strongly affect the qualitative behavior of important regulatory modules even under biologically relevant conditions, thus emphasizing the importance of stochastic analysis of regulatory circuits.
This model is hosted on BioModels Database and identified by: MODEL2937159804.
To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.
To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.
Project description:Archer2011 - Genome-scale metabolic model of
Escherichia coli (iCA1273)
This model is described in the article:
The genome sequence of E.
coli W (ATCC 9637): comparative genome analysis and an improved
genome-scale reconstruction of E. coli.
Archer CT, Kim JF, Jeong H, Park JH,
Vickers CE, Lee SY, Nielsen LK.
BMC Genomics 2011; 12: 9
BACKGROUND: Escherichia coli is a model prokaryote, an
important pathogen, and a key organism for industrial
biotechnology. E. coli W (ATCC 9637), one of four strains
designated as safe for laboratory purposes, has not been
sequenced. E. coli W is a fast-growing strain and is the only
safe strain that can utilize sucrose as a carbon source.
Lifecycle analysis has demonstrated that sucrose from sugarcane
is a preferred carbon source for industrial bioprocesses.
RESULTS: We have sequenced and annotated the genome of E. coli
W. The chromosome is 4,900,968 bp and encodes 4,764 ORFs. Two
plasmids, pRK1 (102,536 bp) and pRK2 (5,360 bp), are also
present. W has unique features relative to other sequenced
laboratory strains (K-12, B and Crooks): it has a larger genome
and belongs to phylogroup B1 rather than A. W also grows on a
much broader range of carbon sources than does K-12. A
genome-scale reconstruction was developed and validated in
order to interrogate metabolic properties. CONCLUSIONS: The
genome of W is more similar to commensal and pathogenic B1
strains than phylogroup A strains, and therefore has greater
utility for comparative analyses with these strains. W should
therefore be the strain of choice, or 'type strain' for group
B1 comparative analyses. The genome annotation and tools
created here are expected to allow further utilization and
development of E. coli W as an industrial organism for
sucrose-based bioprocesses. Refinements in our E. coli
metabolic reconstruction allow it to more accurately define E.
coli metabolism relative to previous models.
This model is hosted on
and identified by:
To cite BioModels Database, please use:
An enhanced, curated and annotated resource for published
quantitative kinetic models.
To the extent possible under law, all copyright and related or
neighbouring rights to this encoded model have been dedicated to
the public domain worldwide. Please refer to
Public Domain Dedication for more information.