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Image Search Results
Journal: bioRxiv
Article Title: SspA is a transcriptional regulator of CRISPR adaptation in E. coli
doi: 10.1101/2024.05.24.595836
Figure Lengend Snippet: a. Overview of the CRISPR adaptation process, highlighting key known host factors. b . Schematic of the CRISPRi adaptation host factor screen. c . Binned coverage plot of sgRNAs across the E. coli genome. sgRNA occupancy was calculated as the difference between the normalised (post/pre-screen) binned sgRNA counts per base of the experimental (+dCas9) and paired control (–dCas9) conditions. Regions of the genome with high (“enriched”) sgRNA coverage are interpreted to be genomic loci that positively regulate CRISPR adaptation; regions of the genome with low (or negative, i.e., “depleted”) sgRNA coverage are interpreted to be genomic loci that negatively regulate CRISPR adaptation. The highest-ranking regions with attributable genes are labelled; other labelled loci are the Ori and Ter regions, the murA gene, and the CRISPR-II array. n = 9 biological replicates. d . Volcano plot showing log2 fold change for each sgRNA versus adjusted –log10 p-values ( n = 9 biological replicates). The horizontal dashed line represents an adjusted p-value of 0.05; the vertical lines represent log2 fold changes of –0.75 and 0.75. Genes targeted by sgRNAs differentially enriched that were selected for individual validation are coloured in pink. e . Top: deep-sequencing based measurement of the rates of new spacer acquisition in Keio knockouts harbouring pSCL565, after growth for 48h in liquid culture without induction of Cas1-Cas2 expression. Acquisition rates are shown relative to the wild-type parental strain. Open circles represent biological replicates ( n ≥ 3), bars are the mean (one-way ANOVA effect of strain P <0.0001; Sidak’s corrected multiple comparisons for wild-type vs. knockouts, Δ pcnB P=0.00217, Δ sspA P=0.000102, polA ΔKlenow P<0.0001; others ns). Bottom: representative agarose gel for the data shown. Expansions of the CRISPR array can be seen as higher sized bands above the parental array length. Additional statistical details in Supplemental Table 1 .
Article Snippet:
Techniques: CRISPR, Control, Biomarker Discovery, Sequencing, Expressing, Agarose Gel Electrophoresis
Journal: bioRxiv
Article Title: SspA is a transcriptional regulator of CRISPR adaptation in E. coli
doi: 10.1101/2024.05.24.595836
Figure Lengend Snippet: a. Prespacer substrates for CRISPR adaptation arise from a variety of sources. b. Breakdown of normalised spacer count (total number of new spacers / number of CRISPR arrays sequenced) according to spacer origin ( E. coli or plasmid) and strain of interest. c. Breakdown of percent of spacer attributable to each spacer origin ( E. coli or plasmid) and strain of interest. d. Motifs in the 15bp up- and downstream of the newly acquired spacer in its source location. e-f: Binned coverage plot of newly acquired spacer across the E. coli genome (outer, purple) and pSCL565 plasmid (inner, tan) for the wild-type strain ( e ) and derivatives ( f-h ). See for the full set. i. qPCR-based measurement of the relative copy number of pSCL565 Ori and cas1 sequences in the wild-type and polA ΔKlenow mutant. Open circles represent biological replicates ( n ≥ 3), bars are the mean (one-way ANOVA effect of strain and target P <0.0001; Sidak’s corrected multiple comparisons for wild-type vs. Δ sspA , CDF ori copy number P<0.0001, cas1 copy number P<0.0001). Additional statistical details in Supplemental Table 1 .
Article Snippet:
Techniques: CRISPR, Plasmid Preparation, Mutagenesis
Journal: bioRxiv
Article Title: SspA is a transcriptional regulator of CRISPR adaptation in E. coli
doi: 10.1101/2024.05.24.595836
Figure Lengend Snippet: Binned coverage plot of newly acquired spacer across the E. coli genome (left) and pSCL565 plasmid (right) for strains selected for individual validation. a-i : wild-type, Δ pcnB , Δ sspA , Δ uraA , Δ omsF , polA ΔKlenow, Δ rclR , Δ yeaO and Δ ompC . Wild-type is E. coli BW25113, parental strain to the Keio collection; all other strains besides polA ΔKlenow are from the Keio collection. polA ΔKlenow was constructed as described previously .
Article Snippet:
Techniques: Plasmid Preparation, Biomarker Discovery, Construct
Journal: bioRxiv
Article Title: SspA is a transcriptional regulator of CRISPR adaptation in E. coli
doi: 10.1101/2024.05.24.595836
Figure Lengend Snippet: a . sspAB operon, proteins and function. Bottom left: crystal structure of an SspA dimer (blue) in complex with E. coli RNAP-promoter open complex, showing the conserved SspA PHP 84–86 residues (red) interacting with RNAP (pink) and α (purple) (PDB 7DY6 ). Top right: crystal structure of SspB escorting an SsrA-tagged substrate being delivered to the ClpXP protease complex (PDB 8ET3 65 ). b . Schematic of the sspAB operon of WT, Δ sspA :: kan R and Δ sspB :: kan R strains. kan R : kanamycin resistance cassette. c . Deep-sequencing based measurement of the rates of new spacer acquisition in strains harbouring pSCL565 and, in the case of the Δ sspA :: kan R , either an empty plasmid or a low (∼5) copy plasmid encoding the sspAB operon, after growth for 48h in liquid culture. Adaptation rates are shown relative to the wild-type parental strain. Open circles represent biological replicates ( n ≥ 3), bars are the mean. Horizontal dashed line represents the mean rate of spacer acquisition in the wild-type strain (one-way ANOVA effect of strain P <0.0001; Sidak’s corrected multiple comparisons for wild-type vs. knockouts, Δ sspA P<0.0001, Δ sspB P=0.109807; Δ sspA vs. Δ sspB P<0.0001). d . Schematic of the sspAB operon variant rescue plasmids. All plasmids are low (∼5) copy, and encode variants of the sspAB operon under its native regulation. Frameshift mutants of SspA (AN – >AQ – GCC|AAC>GC T | CAA |C) and SspB (PR – >PS – CCA|CGT>CCA| T CG |T) encode sequences with single base insertions to cause protein translation to terminate early. The SspA PHP 84–86 >AAA 84–86 mutant is RNAP-binding deficient and thus does not enable the shift in promoter use (α σ α S ) . A single sspA rescue plasmid yielded no transformants into the Δ sspA :: kan R strain over multiple attempts. e . Top: deep-sequencing based measurement of the rates of new spacer acquisition in strains harbouring pSCL565 and, in the case of the Δ sspA :: kan R , either an empty plasmid or a low (∼5) copy plasmid encoding variants of the sspAB operon as described in d ., after growth for 48h in liquid culture. Adaptation rates are shown relative to the wild-type parental strain. Open circles represent biological replicates ( n ≥ 3), bars are the mean. Horizontal dashed line represents the mean rate of spacer acquisition in the wild-type strain (one-way ANOVA effect of strain P <0.0001; Sidak’s corrected multiple comparisons for wild-type vs. knockouts, Δ sspA P<0.0001, Δ sspA + empty plasmid P<0.0001, Δ sspA + sspAB rescue P = 1, Δ sspA + sspA * (PHP84-86>AAA84-86) & sspB rescue P<0.0001; Δ sspA vs. rescues, Δ sspA + empty vector P=0.997758, Δ sspA + sspA * (PHP84-86>AAA84-86) & sspB P=0.334315, Δ sspA + sspAB P<0.0001, Δ sspA + sspB P=0.892991, Δ sspA + sspA * & sspB * (frameshifted) P=1). Bottom: representative agarose gel for the data shown. Expansions of the CRISPR array can be seen as higher sized bands above the parental array length. Additional statistical details in Supplemental Table 1.
Article Snippet:
Techniques: Sequencing, Plasmid Preparation, Variant Assay, Mutagenesis, Binding Assay, Agarose Gel Electrophoresis, CRISPR
Journal: bioRxiv
Article Title: SspA is a transcriptional regulator of CRISPR adaptation in E. coli
doi: 10.1101/2024.05.24.595836
Figure Lengend Snippet: a . Model for SspA-mediated regulation of CRISPR-Cas defence. Phage infection triggers upregulation of SspA , which in turn induces a global transcriptional shift towards 0 S -regulated promoters. This results in H-NS downregulation , , induction of CRISPR-Cas mediated defence through de-repression Cas gene expression , , leading to increased rates of CRISPR adaptation and interference. b . Schematic of the sspAB and hns operons of WT, Δ sspA :: FRT, Δ hns :: FRT and Δ sspA :: FRT Δ hns :: FRT strains. FRT : flippase recognition target, a scar left after the removal of resistance cassettes. c . Schematic of the CRISPR interference-mediated defence assays in pre-immunised E. coli strains. Top: schematic of the CRISPR-I immunisation (defence) plasmids. All plasmids are low (∼5) copy, and encode an E. coli CRISPR-I array with a first spacer encoding either a Target (complementary to the α genome , ), or a Non-Target (NT) spacer. Bottom: The experimental strains were electroporated with either the T or NT plasmid, and infected to varying titres of αvir. Note that the strains encode a complete endogenous E. coli Type I-E CRISPR-Cas system. d . Representative plaque assays of αvir on experimental strains (described above) pre-immunised with either T or NT defence plasmids. Strains were infected with αvir and grown on plates at 30°C for 16h. Full plaque assay plates for n = 3 biological replicates in . e . Efficiency of plating of αvir on experimental strains. Open circles represent biological replicates ( n ≥ 3) of individual plaque assays, bars are the mean (one-way ANOVA effect of strain P =0.033454; Sidak’s corrected multiple comparisons for wild-type vs. knockouts, Δ sspA P=0.181757, Δ hns P=0.043319, ΔsspA Δhns P = 0.043316; for Δ hns vs. Δ sspA Δhns P=1). f. Anti-phage defence and growth in overnight liquid culture of experimental strains, post αvir infection (MOI: 0.1). Hue around solid line (mean) represents the standard deviation across 3 biological replicates.
Article Snippet:
Techniques: CRISPR, Infection, Gene Expression, Plasmid Preparation, Plaque Assay, Standard Deviation
Journal: bioRxiv
Article Title: SspA is a transcriptional regulator of CRISPR adaptation in E. coli
doi: 10.1101/2024.05.24.595836
Figure Lengend Snippet: a . Deep-sequencing based measurement of the rates of new spacer acquisition in strains pre-immunised with either a T or NT defence plasmid, harvested 3h post λvir infection in liquid culture and growth at 30°C. Open circles represent biological replicates ( n ≥ 3), bars are the mean (one-way ANOVA effect of strain P < <0.0001; Sidak’s corrected multiple comparisons for wild-type +T vs. knockouts +T, Δ sspA P=082553, Δ hns P<0.0001, Δ sspA Δ hns P=0.999999; Δ sspA +T vs. knockouts + T, Δ hns P<0.0001, Δ sspA Δ hns P=0.154762; Δ hns + T vs. Δ hns +NT P<0.0001; Δ hns + T vs. Δ sspA Δ hns +T P<0.0001). b. Breakdown of normalised spacer count (total number of new spacers / number of CRISPR arrays sequenced) according to spacer origin ( E. coli , lambda or plasmid) and strain of interest. c. Binned coverage plot of Δ hns + T newly acquired spacers across the lambda genome (outer, purple). The location of the T immunisation spacer is shown on the lambda genome; “missing in /\vir” indicates a genomic region missing in our strain of /\vir. d . Percent of spacers acquired that are on the same strand as the T immunisation spacer, according to the spacer source ( E. coli or lambda). e . Schematic of the sspAB and hns operonic rescue plasmids. All plasmids are low (∼5) copy, and encode either 1. The sspAB operon, 2. The hns operon, or 3. both, under their native regulation. f . Schematic of the CRISPR adaptation assays in wild-type, sspA and/or hns mutant strains. Strains were electroporated with pSCL565 and rescue plasmids 1., 2., or 3. (see e .), and assessed for their ability to acquire new spacers into the endogenous CRISPR I array. g . PCR-based detection of new spacer acquisition into the CRISPR I array of wild-type, of WT, Δ sspA :: FRT, Δ hns :: FRT and Δ sspA :: FRT Δ hns :: FRT strains harbouring pSCL565 and rescue plasmids 1., 2., or 3. (see e .), after growth for 48h in liquid culture. Open circles represent biological replicates ( n ≥ 3), bars are the mean. Horizontal dashed line represents the mean rate of spacer acquisition in the wild-type strain (one-way ANOVA effect of strain P < <0.0001; Sidak’s corrected multiple comparisons for wild-type vs. knockouts, Δ sspA P<0.0001, Δ hns P<0.0001, Δ sspA Δ hns P<0.0001; Δ sspA vs. knockouts, Δ hns P=0.714182, Δ sspA Δ hns P=0.002269, Δ sspA + sspAB rescue P<0.0001; Δ hns vs. knockouts, Δ sspA Δ hns P<0.0001, Δ hns + hns rescue P<0.0001; Δ sspA Δ hns vs. Δ sspA Δ hns + sspA & hns rescues P<0.0001). h . PCR-based detection of new spacer acquisition into the CRISPR I array of WT, Δ sspA :: FRT, Δ hns :: FRT , Δ sspA :: FRT Δcas3-Cascade::Cm R or Δ hns :: FRT Δcas3-Cascade::Cm R strains harbouring pSCL565 after growth for 48h in liquid culture. Open circles represent biological replicates ( n ≥ 3), bars are the mean (one-way ANOVA effect of strain P <0.0001; Sidak’s corrected multiple comparisons for wild-type vs. knockouts, Δ sspA P<0.0001, Δ hns P<0.0001, Δ sspA Δ cas3-cascade P<0.0001, Δ hns Δ cas3-cascade P=0.125466; Δ sspA vs. Δ hns P=0.004161; Δ sspA vs. Δ sspA Δ cas3 - cascade P=0.310715; Δ hns vs. Δ hns Δ cas3-cascade P<0.0001; Δ sspA Δ cas3 - cascade vs. Δ hns Δ cas3 - cascade P<0.0001). Horizontal dashed line represents the mean rate of spacer acquisition in the wild-type strain. Additional statistical details in Supplemental Table 1 .
Article Snippet:
Techniques: Sequencing, Plasmid Preparation, Infection, CRISPR, Mutagenesis
Journal: bioRxiv
Article Title: SspA is a transcriptional regulator of CRISPR adaptation in E. coli
doi: 10.1101/2024.05.24.595836
Figure Lengend Snippet: Distribution of newly acquired spacers in Δ hns +T and Δ sspA Δ hns +T strains upon lambda infection. a . Binned coverage plot of Δ hns + T newly acquired spacers across the E. coli genome (outer, purple). b . Binned coverage plot of Δ sspA Δ hns + T newly acquired spacers across the lambda genome (outer, purple).
Article Snippet:
Techniques: Infection
Journal: Journal of the Royal Society Interface
Article Title: Multipad agarose plate: a rapid and high-throughput approach for antibiotic susceptibility testing
doi: 10.1098/rsif.2023.0730
Figure Lengend Snippet: The MAP platform can be used to measure bacteria growth automatically with high resolution and reproducibility. ( a ) Schematic of the MAP. The platform features 96 square pads of 4 mm size, arranged in a 12 × 8 grid with 9 mm pitch. The standard well-plate format facilitates compatibility with standard multichannel pipettes and stage holders. ( b ) A detailed cross-sectional view of the MAP illustrates the path of brightfield illumination. The light traverses through the base plate and agarose pad before intersecting with the imaging plane. Like most biology labs, we use inverted microscopy, where the objective is located below the sample. The acrylic base and well plates are kept together using the same design of adhesive sheet that glues the glass slide to the well plate. The bacteria sample grows in the interface between the agarose pad and the glass slide. ( c ) Cropped frames with time-series microcolony growth of E. coli on LB broth with 1% w/v agarose at 37°C. This sequence of images illustrates the expansion of a colony-forming unit (CFU) into a microcolony. The border marks the colony segmentation masks. Time is reported as time after imaging is started, which is typically 20 to 30 min after the bacteria are placed on the pads. After 2.5 h of growth, the microcolony is still growing in a single layer, but half an hour later, stacking has started to occur. ( d ) Varying the agarose concentration does not significantly affect the growth rate between 0.069 to 2% w/v. For low agarose concentrations, growth rates cannot be consistently tracked, leading to very large standard deviations. The data represent 12 replicate pads for two repeat experiments. The arrow indicates how 1% was chosen as optimal. Electronic supplementary material, figure S1. A shows how the MAP platform was set up. ( e ) Varying the optical density (OD600) of the liquid bacteria samples placed on each pad affects the density of CFUs. Each line represents the average colony area growth rate over time after imaging is started on the microscope. The lines terminate when there are less than four colonies tracked for a given condition. The data represent seven replicate pads per seeding density. The arrow shows that an OD of 0.02 was used for further experiments. Electronic supplementary material, figure S1.B shows how the MAP platform was set up. ( f ) Frame sections from the first 10 min of imaging illustrate how seeding densities typically look on the pads. Below OD 0.002, most FOVs do not have any bacteria present. ( g ) An assessment of cross-contamination between adjacent pads on the MAP shows individual pads are unaffected by their neighbours. Test pads containing 0 μg ml −1 of antibiotic were placed around pads containing 50 μg ml −1 of antibiotic, and vice versa. Each data point in the plot represents the growth rate of a single colony within the initial 3 h after imaging was started. Each repeat contains data from about 12 pads per condition. Electronic supplementary material, figure S1.C shows how the MAP platform was set up. ( h ) The E. coli exhibit little to no variation in growth rate for different wavelengths of brightfield illumination during time-lapse imaging. In total, 395, 450, 530 and 660 nm light was tested, corresponding to ultraviolet, blue, green and red, respectively. The arrow shows that 450 nm was chosen as optimal. The data represent 12 replicate pads per illumination wavelength for three repeat experiments. Electronic supplementary material, figure S1.D shows how the MAP platform was set up. ( i ) Images are included after 2 h of growth to illustrate how bacteria look when illuminated by the different wavelengths.
Article Snippet: In all experiments,
Techniques: Bacteria, Imaging, Inverted Microscopy, Adhesive, Sequencing, Concentration Assay, Microscopy
Journal: Journal of the Royal Society Interface
Article Title: Multipad agarose plate: a rapid and high-throughput approach for antibiotic susceptibility testing
doi: 10.1098/rsif.2023.0730
Figure Lengend Snippet: The MAP platform is used to perform AST on monocultures of E. coli using a test set of nine antibiotics. ( a ) Colony areas develop over time for varying concentrations of tetracycline. The areas in this plot represent standard deviation, and the lines terminate in points where most colonies grow to exceed the FOV and tracking for that pad is stopped. All experiments comprise data from four repeat experiments. ( b ) Colony growth rates develop over time for varying concentrations of tetracycline. Growth rates are calculated based on the time derivative of the colony area curve for each colony individually (see §2.5 for details). The dashed growth-rate region between 2 and 3 h indicates the time span used for evaluating AST. ( c ) Growth rates are assessed for different concentrations of tetracycline. Each point in this plot is computed as the average growth rate for that given concentration in the dashed region from b , with error bars corresponding to the standard deviation. In these plots, any negative growth rate is considered spurious and set to zero. Hill curves have been independently fitted for each of four repeats, and the associated IC 90 concentration is indicated with a vertical dashed line. For the four repeats, IC 90 is computed to be 2.5, 2.4, 2.8 and 2.5 μg ml −1 , respectively. ( d ) Considering ampicillin, we see how colony areas initially develop in a similar fashion regardless of antibiotic concentration. See ( a ) for details about the plot. ( e ) We see significant changes in growth rate over time for higher concentrations of ampicillin. ( f ) There is a high correspondence between the four repeats with ampicillin. For the four repeats, IC 90 is computed to be 71, 74, 85 and 110 μg ml −1 respectively.
Article Snippet: In all experiments,
Techniques: Standard Deviation, Concentration Assay
Journal: Journal of the Royal Society Interface
Article Title: Multipad agarose plate: a rapid and high-throughput approach for antibiotic susceptibility testing
doi: 10.1098/rsif.2023.0730
Figure Lengend Snippet: The AST results from the MAP platform correspond well with the results obtained from broth microdilution, ETest and EUCAST tabulated data. ( a ) Overview of all Hill fits, with each of the nine antibiotics we evaluated. The shaded area corresponds to the standard deviation between the Hill curve fits of the four independent repeats. ( b ) Comparing MIC obtained from MAP with that obtained from broth microdilution for the set of antibiotics. The dashed line is drawn for x = y . The closer to this line the data points fall, the better they correspond. ( c ) Comparing MIC measured by MAP with broth microdilution and ETest comparison assays using the same strain of E. coli and LB growth media. Error bars are computed based on the standard deviation between repeats and the factor of two concentration steps. The EUCAST data is based on the EUCAST database of MIC distributions for wild-type E. coli , where the error bars represent the first and third quartile . ( d ) Lower triangular matrix showing Pearson’s correlation coefficient for the four methods. ( e ) Lower triangular matrix showing Spearman’s rank correlation coefficient for the four methods.
Article Snippet: In all experiments,
Techniques: Standard Deviation, Comparison, Concentration Assay
Journal: Journal of the Royal Society Interface
Article Title: Multipad agarose plate: a rapid and high-throughput approach for antibiotic susceptibility testing
doi: 10.1098/rsif.2023.0730
Figure Lengend Snippet: Comparison of minimum inhibitory concentrations (MIC) for nine antibiotics: assessment by the MAP platform versus broth microdilution and ETest methods using E. coli K-12 MG1655. Data from EUCAST literature review for E. coli . Annotations: ‘none’ denotes an unobtainable value; ‘≥254’ indicates resistance to the maximum concentration in the ETest.
Article Snippet: In all experiments,
Techniques: Comparison, Concentration Assay
Journal: Microorganisms
Article Title: It Takes Two to Make a Thing Go Right: Epistasis, Two-Component Response Systems, and Bacterial Adaptation
doi: 10.3390/microorganisms12102000
Figure Lengend Snippet: Differential expression of cus genes across silver-adapted E. coli populations: This figure illustrates the expression levels of the cusS/R two-component response system (TCRS) genes and the cusCFBA efflux genes, which are essential for silver and copper ion efflux, across various silver-adapted E. coli populations in both the presence (checkered bars) and absence of silver nitrate (solid bars). All expression levels are normalized to the wild-type (WT) in the absence of silver nitrate, where WT is assigned a log fold change (logFC) of 1. LogFC values were plotted using GraphPad Prism. The upregulation observed in populations with the R15L mutation in cusS highlights its role in enhancing the efficiency of the efflux system, thereby increasing silver resistance. This figure also demonstrates the variation in gene expression across different populations, reflecting their differing capacities to manage metal ion toxicity. Notably, SAM populations that do not carry the R15L mutation exhibit no expression in the cusS/R genes and downregulate the efflux pump genes, indicating a distinct response mechanism.
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Techniques: Quantitative Proteomics, Expressing, Mutagenesis, Gene Expression
Journal: Microorganisms
Article Title: It Takes Two to Make a Thing Go Right: Epistasis, Two-Component Response Systems, and Bacterial Adaptation
doi: 10.3390/microorganisms12102000
Figure Lengend Snippet: Comparative gene expression across 10 biological categories in silver-adapted E. coli populations: Heatmaps illustrate differential expression of genes across 10 key biological categories in various silver-adapted E. coli populations, normalized to the wild-type (WT) in the absence of silver nitrate. Differentially expressed genes were categorized based on their biological functions, and the heatmaps display the averaged differential expression for all genes within each category. Warmer colors indicate higher expression levels, while cooler colors indicate lower expression levels. The heatmaps were generated using GraphPad Prism. Subfigure ( A ) shows gene expression in the absence of silver nitrate, serving as a baseline to display natural variations in gene regulation and adaptation strategies across different populations. Subfigure ( B ) highlights gene expression in the presence of silver nitrate. Subfigure ( C ) is a difference map illustrating the changes in gene expression between conditions with and without silver nitrate, providing a direct comparison of how silver exposure affects gene expression across different biological categories. These heatmaps collectively emphasize how specific genetic backgrounds modulate these adaptive responses to silver exposure.
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Techniques: Gene Expression, Quantitative Proteomics, Expressing, Generated, Comparison
Journal: Microorganisms
Article Title: It Takes Two to Make a Thing Go Right: Epistasis, Two-Component Response Systems, and Bacterial Adaptation
doi: 10.3390/microorganisms12102000
Figure Lengend Snippet: 24-hour final OD600 values from growth response assays of E. coli populations to increasing silver nitrate concentrations. The 24-hour time point from growth response assays of WT, R15L, and SAM1-6 populations in Davis Minimal Broth (DMB) is shown under increasing concentrations of silver nitrate (0–750 ng/mL). OD600 values were measured hourly over 24 h, matching the selection time point used in the original experimental evolution study where the SAM populations evolved. Data were collected in triplicate, with means and standard errors of the mean (SEMs) plotted using GraphPad Prism. To calculate statistical variation between each time point and the WT, we performed a two-way ANOVA with multiple comparisons. Detailed statistical results are provided in . These growth curves were also used to determine the minimum inhibitory concentration (MIC), defined as the lowest silver concentration at which no growth was observed for a population.
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Techniques: Selection, Concentration Assay
Journal: Microorganisms
Article Title: It Takes Two to Make a Thing Go Right: Epistasis, Two-Component Response Systems, and Bacterial Adaptation
doi: 10.3390/microorganisms12102000
Figure Lengend Snippet: Detailed growth metrics across silver nitrate concentrations. This figure presents detailed growth metrics for each E. coli population under varying silver nitrate concentrations, analyzed using the R package Growthcurver (v0.3.1). Growthcurver fits the growth data to a logistic model, providing key metrics that describe the dynamics of each population. These metrics include growth rate (r), reflecting how quickly the population grows; generation time (t_gen), indicating the time required for the population to double; midpoint time (t_mid), representing the time at which the population reaches half its carrying capacity; and carrying capacity (k), denoting the maximum population size supported under the given conditions. The data generated by Growthcurver were plotted using GraphPad Prism. ( A ) (k) carrying capacity, ( B ) (r) on growth rate, ( C ) on generation time, and ( D ) on time to mid-log phase. These results, derived from Growthcurver’s comprehensive analysis, highlight the impact of silver stress on growth dynamics and reveal the differing adaptive responses among the populations.
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Techniques: Generated, Derivative Assay
Journal: Microorganisms
Article Title: It Takes Two to Make a Thing Go Right: Epistasis, Two-Component Response Systems, and Bacterial Adaptation
doi: 10.3390/microorganisms12102000
Figure Lengend Snippet: Comparative fitness of E. coli populations under silver nitrate stress. Relative fitness (ω) of each E. coli population under varying concentrations of silver nitrate (0–750 ng/mL) is shown. To calculate relative fitness, the OD 600 of each population was divided by the maximum OD 600 observed among other genotypes in the population at the same time points. Prior to this calculation, all negative growth values were set to 0 to ensure accurate comparisons, and the data were plotted in GraphPad Prism. Statistical analyses were conducted using a one-way ANOVA with pairwise multiple comparisons in GraphPad Prism. These results underscore the impact of silver stress on the competitive dynamics among the populations.
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Techniques:
Journal: Microorganisms
Article Title: It Takes Two to Make a Thing Go Right: Epistasis, Two-Component Response Systems, and Bacterial Adaptation
doi: 10.3390/microorganisms12102000
Figure Lengend Snippet: Refined model of adaptive responses in silver-resistant E. coli Mutants: A refined three-step adaptive response mechanism in silver-resistant E. coli mutants is illustrated, emphasizing the roles of epistasis and genotype-by-environment (GxE) dynamics throughout the adaptive process. Dotted arrows indicate interactions that have not yet been characterized, while thick, bold arrows represent the most critical pathways in the response. The model begins with the Primary Response, where epistatic interactions among mutations in the cusS gene and other regulatory genes lead to the enhanced expression of the CusSR two-component regulatory system (TCRS) and its downstream efflux pump genes, cusCFBA . These interactions, influenced by specific environmental conditions, establish a baseline level of silver resistance that varies across SAM populations due to GxE dynamics. The combined effects of these mutations create a context-dependent expression of resistance traits.
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Techniques: Expressing
Journal: Journal of Bacteriology
Article Title: Identification of Functional LsrB-Like Autoinducer-2 Receptors
doi: 10.1128/jb.00976-09
Figure Lengend Snippet: FIG. 1. AI-2 removal profile for bacteria producing AI-2. Shown is extracellular AI-2 activity in cell-free culture fluids from LuxS strains E. coli MG1655 (triangles) and UPEC UTI89 (circles) (A) and B. cereus (diamonds) and B. anthracis (squares) (B) cultures. Aliquots were taken at the specified times. AI-2 activity is reported as the change from the induction of light produced by V. harveyi BB170.
Article Snippet: Bacterial strains used in this study Strain Source and/or
Techniques: Bacteria, Activity Assay, Produced
Journal: Cell
Article Title: A conserved family of immune effectors cleaves cellular ATP upon viral infection.
doi: 10.1016/j.cell.2023.07.020
Figure Lengend Snippet: Figure 2. A clade of prokaryotic nucleosidases is associated with anti-phage systems (A) Phylogenetic tree of prokaryotic PNP domains. Prokaryotic proteins were clustered based on sequence homology, and clusters with homology to the PNP_UDP_1 domain (pfam PF01048) were identified. A representative sequence from each cluster was used to build the tree. The tree is based on the PNP domain only (see STAR Methods). The total number of proteins represented by the representative sequences is shown for each clade. Ultrafast bootstrap values are shown for major branches.34 Known housekeeping proteins are shown in gray. The p value in the Cap17 clade represents the false discovery rate of a binomial test assessing the association of proteins from this clade with known anti-phage systems (see STAR Methods and Table S3). (B) A detailed phylogenetic tree of the Cap17 PNP clade (marked blue in A). The PNP domain of E. coli MtnN was used as an outgroup. The colored ring represents known or predicted anti-phage systems harboring a PNP domain, with example systems shown as gene cassettes. Abbreviations: pAgo, prokaryotic argonaute; CD-NTase, cGAS/DncV-like nucleotidyltransferase; SAVED, SMODS-associated and fused to various effector domains; NLR, nucleotide-binding leucine-rich repeat; FGE-S, sulfatase-modifying factor enzyme 1; TPR, tetratricopeptide repeat; DRT8, defensive-associated reverse transcriptase type 8.
Article Snippet: REAGENT or RESOURCE SOURCE IDENTIFIER Antibodies primary mouse anti-FLAG antibody Sigma Cat#F1804; RRID:AB_262044 HRP-conjugated goat anti-rabbit secondary antibody Thermo Scientific Cat#31460: RRID:AB_228341 Bacterial and
Techniques: Sequencing, Binding Assay, Reverse Transcription
Journal: Biophysical Journal
Article Title: Physical Properties of Escherichia coli Spheroplast Membranes
doi: 10.1016/j.bpj.2014.09.034
Figure Lengend Snippet: E. coli spheroplasts. (A) E. coli cells, filamentous cells, spheroplasts in 100% STOP solution, and in diluted STOP solutions. Scale bar = 5 μm. (B) Size distribution among a population in different concentrations of STOP solution. As the external concentration decreased below ∼ 50%, more spheroplasts appeared to have lost the interior phase contrast. The spheroplasts without interior phase contrast were not included in the radius survey. (C) The average radius of the population in different concentrations. (D) Correlations of size versus phase contrast (I0 − I)/I0 where I was the average phase contrast intensity of cytosol, I0 that of the outside solution. Spheroplasts without phase contrast had (I0 − I)/I0≲0.01. To see this figure in color, go online.
Article Snippet: Bacterial strains and
Techniques: Concentration Assay
Journal: Biophysical Journal
Article Title: Physical Properties of Escherichia coli Spheroplast Membranes
doi: 10.1016/j.bpj.2014.09.034
Figure Lengend Snippet: Very swollen E. coli spheroplasts were compared with GUVs of total E. coli lipid extract. (A1) A very swollen spheroplast aspirated by a micropipette in a series of increasing tension: left to right, τ = 1.00, 1.11, and 1.56 mN/m. Scale bar = 2.5 μm. The dotted circles are the fits to the protrusions into the micropipette that are used to calculate the area changes (see Fig. S1). (A2) Two τ versus ΔA/A measurements of very swollen spheroplasts. (B1) Aspiration measurement of a GUV of E. coli total lipid extract at, left to right, τ = 0.3, 1.02, and 1.92 mN/m. Scale bar = 10 μm. (B2) Two τ versus ΔA/A measurements of GUVs of E. coli total lipid extract. To see this figure in color, go online.
Article Snippet: Bacterial strains and
Techniques: