wild type e coli k12 mg1655  (Addgene inc)

Journal: Microorganisms

Article Title: Accelerated Adaptive Laboratory Evolution by Automated Repeated Batch Processes in Parallelized Bioreactors

doi: 10.3390/microorganisms11020275

Figure Lengend Snippet: The concentrations of O 2 and C O 2 in the off-gas of an automated ALE experiment (GM2) in an L scale stirred-tank bioreactor are shown over the course of 25 consecutive batch experiments totaling a process duration of 200 h. A culture of E. coli K12 MG1655 was grown with RB medium at 37 °C, 600–1400 rpm, 40 vvm, and an initial glycerol concentration of 12 g L − 1 as sole carbon source. The ALE process was performed in an automated system in a repeated batch mode with a bioreactor volume of 575 mL. Vertical lines indicate the start and end of the medium exchange procedure between batches (grey). The concentrations of O 2 = 20.91 % and C O 2 = 0.04 % in the pressurized air in the inflow are depicted by the horizontal, dashed lines (black). Batch numbers are indicated with B4–B24. The shown data were used as input for the black box model to calculate OUR and CER, as well as derived state variables, such as the estimated biomass and substrate concentrations and the specific growth rate according to Equation –.

Article Snippet: The experiments were carried out using fresh cultures of the wild-type strain E. coli K12 MG1655 from the German Collection of Microorganisms and Cell Cultures (#DSM 18039, DSMZ GmbH, Braunschweig, Germany).

Techniques: Concentration Assay, Derivative Assay

Journal: Microorganisms

Article Title: Accelerated Adaptive Laboratory Evolution by Automated Repeated Batch Processes in Parallelized Bioreactors

doi: 10.3390/microorganisms11020275

Figure Lengend Snippet: The relative fitness of replicate ALE experiments with E. coli K12 MG1655 growing with the non-native carbon source glycerol. Relative fitness is defined as the stable specific growth rate divided by the average stable specific growth rate of the control group of WT E. coli without NTG. The cumulative number of cell divisions (CCD) is used as time scale to measure adaptation progress. The specific growth rate is considered stable if the moving average of three consecutive batches has an absolute standard deviation < 0.01 h − 1 and no further upwards trend. This definition of a stable phenotype is specific to this set of experiments. A decisive criterion was required to make near-real-time decisions while the experiment was running; hence, a variability based approach was chosen that focuses on the change in optimization metric: the specific growth rate. The specific growth rate of the stable phenotype of the E. coli wild-type cultures without NTG is 0.61 ± 0.03 h − 1 at a l o g 10 ( C C D ) = 14.09 ± 0.09 and is used to compare both groups and calculate the relative fitness (grey shaded area, relative fitness of the WT = 1 ± 0.05 ). The observed average specific growth rate of the WT strain with NTG is 0.70 ± 0.05 h − 1 and was reached at a l o g 10 ( C C D ) = 14.39 ± 0.04 (orange shaded area, relative fitness of the WT with NTG = 1.15 ± 0.08 ).

Article Snippet: The experiments were carried out using fresh cultures of the wild-type strain E. coli K12 MG1655 from the German Collection of Microorganisms and Cell Cultures (#DSM 18039, DSMZ GmbH, Braunschweig, Germany).

Techniques: Control, Standard Deviation

Journal: Nucleic Acids Research

Article Title: Improved sgRNA design in bacteria via genome-wide activity profiling

doi: 10.1093/nar/gky572

Figure Lengend Snippet: General framework combining experimental and computational approaches to depict a genome-wide sgRNA activity map in this work. ( A ) Schematic illustration of the workflow for the sgRNA activity screening experiments. The variable regions of a genome-wide sgRNA library are synthesized as oligomers on a microarray. The oligomers are subsequently amplified and cloned into an sgRNA expression vector by Golden Gate assembly. The constructed sgRNA library is transformed into E. coli host cells expressing Cas9 (selective condition) or dCas9 (control condition) protein. After cultivation in LB medium, the extracted sgRNA plasmids are amplified by PCR, and the abundance of each sgRNA is determined by NGS. The sgRNA activity is defined as the log 2 change in abundance between the selective (A i ) and control (A i,NC ) conditions. ( B ) The obtained genome-wide sgRNA activity map can be used directly in sgRNA selection for a genome-editing project in E. coli (the best sgRNA for every gene, promoter and RBS encoded by E. coli genome). ( C ) A machine learning approach is used to shed light on the sequence–activity relationship (sgRNA activity = f (sgRNA sequence)) of sgRNAs to provide more biophysical insight into CRISPR/Cas9-based genome editing as well as to extend the sgRNA activity prediction capacity to other prokaryotic organisms.

Article Snippet: MCm ( ) was constructed by integrating a chloramphenicol expression cassette cloned from pKM154 (Addgene plasmid #13036) into the smf locus of wild-type E. coli K12 MG1655.

Techniques: Genome Wide, Activity Assay, Synthesized, Microarray, Amplification, Clone Assay, Expressing, Plasmid Preparation, Construct, Transformation Assay, Control, Selection, Sequencing, CRISPR

Journal: Nucleic Acids Research

Article Title: Improved sgRNA design in bacteria via genome-wide activity profiling

doi: 10.1093/nar/gky572

Figure Lengend Snippet: Diversity of activity among sgRNAs. ( A ) The distribution of sgRNA activity in conjunction with Cas9, eSpCas9 and Cas9 in the Δ recA genetic background (Cas9 (Δ recA )). The activity distributions of negative control sgRNAs under these three conditions are also presented as references. ( B ) Distribution of the sgRNA with the strongest activity among all sgRNAs targeting each gene (under the three conditions as in (A)). Only genes with at least three sgRNAs were included (4,020 genes). ( C ) Activity comparisons between sgRNAs targeting the template or nontemplate strand in the gene-coding regions. A two-tailed MW U-test was used to test for significant differences. Cas9 dataset: 2,180 vs. 2,163 (template versus nontemplate sgRNAs, respectively, here and below), P = 0.794; Cas9 (Δ recA ) dataset: 2180 versus 2163, P = 0.316; eSpCas9 dataset: 2220 versus 2265, P = 10 −11.2 . ( D ) Activity comparisons between sgRNAs targeting the leading or lagging strand during replication across the E. coli chromosome. A two-tailed MW U-test was used to test for significant differences. Cas9 dataset: 27 356 versus 25 180 (leading strand versus lagging strand sgRNAs, respectively), P = 0.006; Cas9 (Δ recA ) dataset: 27 356 versus 25 180, P = 0.003; eSpCas9 dataset: 29 168 versus 26 213, P = 0.398.

Article Snippet: MCm ( ) was constructed by integrating a chloramphenicol expression cassette cloned from pKM154 (Addgene plasmid #13036) into the smf locus of wild-type E. coli K12 MG1655.

Techniques: Activity Assay, Negative Control, Two Tailed Test

Journal: Nucleic Acids Research

Article Title: Improved sgRNA design in bacteria via genome-wide activity profiling

doi: 10.1093/nar/gky572

Figure Lengend Snippet: E. coli genome-wide landscape of resistance to CRISPR/Cas9-induced lethal DNA DSBs. ( A ) Distribution of median sgRNA activity among all sgRNAs within each gene (for each of the three conditions, Cas9, eSpCas9 and Cas9 (Δ recA )). The median sgRNA activity of negative control sgRNAs is zero because of the normalization step in the data processing (see Materials and Methods). ( B ) Genes with significant resistance to CRISPR/Cas9-induced DSBs were extracted ((median sgRNA activity ≥ 0 or (median sgRNA activity ≥ −1 and FPR ≥ 0.01)) and (≥5 sgRNAs)), giving rise to 192, 188 and 70 genes in the Cas9, eSpCas9 and Cas9 (Δ recA ) datasets, respectively. Shown is a distribution of deviations in the activities of sgRNAs targeting individual genes of these three sets. The experimental noise as quantified by negative control sgRNA deviations is shown as dotted lines. ( C ) Median activity among all sgRNAs belonging to each gene is plotted as a bar plot (zoom in is shown on the upper left) within each circle (Cas9, red; eSpCas9, blue; Cas9 (Δ recA ), yellow). Genes with notable resistance to genome editing (the same threshold as in (B)) in each dataset (Cas9, eSpCas9 and Cas9 (Δ recA )) are highlighted with gene names. Essential genes in rich medium are tagged with ‘(e)’. The heatmap (black to white) below the relevant bar plot of each circle indicates the standard deviation of the within-gene sgRNA activity for each highlighted gene. The color bar is shown on the right. A high-resolution version of this genome-wide map is accessible ( https://figshare.com/s/127cecee6f9ea4e814e2 ) for downloading.

Article Snippet: MCm ( ) was constructed by integrating a chloramphenicol expression cassette cloned from pKM154 (Addgene plasmid #13036) into the smf locus of wild-type E. coli K12 MG1655.

Techniques: Genome Wide, CRISPR, Activity Assay, Negative Control, Standard Deviation