addgene 46169  (Addgene inc)

Journal: Cell systems

Article Title: A continuous epistasis model for predicting growth rate given combinatorial variation in gene expression and environment.

doi: 10.1016/j.cels.2024.01.003

Figure Lengend Snippet: Figure 1. Mismatch CRISPRi enables characterization of gene expression-growth rate relationships (A) A linear interpolation from experimental data exploring the growth rate effects of complete knockdown of one or two genes, akin to a classic genetic interaction measurement. Growth rate of wild-type cells (x = y = 0, red) linearly decreases with one or both knockdowns of genes 1 and 2 (dotted lines). Growth rate units here (and in all figures) are arbitrary units (AU), as growth rates are linearly rescaled such that wild-type growth is equal to 1 and no growth is equal to 0. (B) A computationally modeled, continuous, pairwise expression-growth rate model, fit from intermediate experimental data (gray dots). Growth rate no longer scales linearly with knockdown and is far more robust to expression perturbation than a linear model would suggest. (C) Tuning the expression of E. coli genes (dials) and thus relative protein abundances (gradients) alters cellular growth rate. (D) The concentration of mRNA in log-phase E. coli for each titrating sgRNA was quantified by RT-qPCR. A repression efficiency of 1 corresponds to complete knockdown, and a repression efficiency of 0 corresponds to the expression level following treatment with a nontargeting sgRNA control that does not perturb mRNA levels. Individual colored bars represent different titrating sgRNAs targeting thyA, with error bars describing the standard error of the mean (SEM) across n R 3 replicates. Individual measurements are shown as gray dots. (E) Growth rate was quantified by next-generation sequencing. We measured the log2 relative (Rel.) frequency of each sgRNA relative to a nontargeting control (gray dots along y = 0) over eight time points spanning 14 h. Color coding of sgRNAs is identical to (D). The slope of the line of best fit represents the growth rate of each knockdown relative to the nontargeting control for a single replicate. (F) Measurements of growth rate and repression efficiency show a sigmoidal relationship (blue line), with serious growth rate deficits emerging at severe repression levels. Color coding of sgRNAs is identical to (D); error bars represent SEM of growth rate measurements (vertical, n R 4) and RT-qPCR measurements (horizontal, n R 3).

Article Snippet: REAGENT or RESOURCE SOURCE IDENTIFIER Bacterial and virus strains Escherichia coli XL1-Blue (recA1 endA1 gyrA96 thi-1 hsdR17 supE44 relA1 lac [F0 proAB lacIq ZDM15 Tn10 (Tetr)]) Agilent #200236 Escherichia coli K12 MG1655 + dCas9 (F- l- ilvG- rfb-50 rph-1 HK022 attB:dCas9) AddGene #118727 Chemicals, peptides, and recombinant proteins Anhydrotetracycline Cayman Chemical Company #10009542 Thymidine Sigma-Aldrich #T1895 Methionine Sigma-Aldrich #M5308 Kanamycin Sigma-Aldrich #K1377 Critical commercial assays Luna Universal One-Step RT-qPCR Kit New England Biolabs #E3005 RNeasy Protect Bacteria Mini Kit QIAGEN #74524 RNase-Free DNase Set QIAGEN #79254 Picogreen assay Thermo Fisher Scientific #P7581 Qubit assay Thermo Fisher Scientific #Q32851 Deposited data Next-generation sequencing reads This paper SRA: PRJNA877364 All custom code, qPCR data, growth rate data, and model fits This paper Zenodo: 10.5281/zenodo.10278952 Oligonucleotides See Table S1 for all sgRNA sequences used in this study This paper Table S1 See Table S7 for all primer sequences used in this study This paper Table S7 Recombinant DNA Barcoded pCRISPR3 plasmid backbones This paper AddGene: #191856-191861 CRISPRi Libraries This paper Available upon request Software and algorithms Python version 3.9.12 Python Software Foundation RRID:SCR_008394 Jupyter Notebook Project Jupyter RRID:SCR_018315 CFX Maestro Software Bio-Rad https://www.bio-rad.com/en-us/ category/qpcr-analysis-software All custom code, qPCR data, growth rate data, and model fits This paper https://doi.org/10.5281/ zenodo.10278952 Other CFX Opus 384 Real-Time PCR System Bio-Rad #12011452 Synergy Neo2 Hybrid Multi Mode Reader Agilent #BTNEO2

Techniques: Gene Expression, Knockdown, Expressing, Concentration Assay, Quantitative RT-PCR, Control, Next-Generation Sequencing

Journal: Cell systems

Article Title: A continuous epistasis model for predicting growth rate given combinatorial variation in gene expression and environment.

doi: 10.1016/j.cels.2024.01.003

Figure Lengend Snippet: Figure 2. Mismatch CRISPRi for a diverse set of metabolic enzymes (A–D) sgRNAs were designed to target 9 E. coli genes from (A) diaminopimelate biosynthesis, (B) purine, (C) folate, and (D) glutamate metabolism. Gene names are listed in black italic text underneath the corresponding metabolic enzyme (bold black text). Metabolite names are in blue. (E–M) Repression efficiency of each CRISPRi sgRNA, as measured by RT-qPCR, correlated with growth rate, as measured by next-generation sequencing. A repression efficiency of 1 corre- sponds to complete knockdown, and a repression efficiency of 0 corresponds to the expression level following treatment with a nontargeting sgRNA control. Negative repression efficiency corresponds to an increase in relative mRNA abundance, which can be observed for sgRNAs with low homology to the gene of interest. Error bars represent SEM of growth rate measurements between n R 4 repli- cates (vertical) and RT-qPCR measurements across n R 3 replicates (horizontal). Blue lines represent the two-parameter logistic fit to each dataset. Blue shaded areas represent a pointwise 95% confi- dence interval of the logistic fit estimated by n = 100 bootstrapping iterations. The genes gdhA and gltB are nonessential in our experimental conditions and show no growth rate defect following repression.

Article Snippet: REAGENT or RESOURCE SOURCE IDENTIFIER Bacterial and virus strains Escherichia coli XL1-Blue (recA1 endA1 gyrA96 thi-1 hsdR17 supE44 relA1 lac [F0 proAB lacIq ZDM15 Tn10 (Tetr)]) Agilent #200236 Escherichia coli K12 MG1655 + dCas9 (F- l- ilvG- rfb-50 rph-1 HK022 attB:dCas9) AddGene #118727 Chemicals, peptides, and recombinant proteins Anhydrotetracycline Cayman Chemical Company #10009542 Thymidine Sigma-Aldrich #T1895 Methionine Sigma-Aldrich #M5308 Kanamycin Sigma-Aldrich #K1377 Critical commercial assays Luna Universal One-Step RT-qPCR Kit New England Biolabs #E3005 RNeasy Protect Bacteria Mini Kit QIAGEN #74524 RNase-Free DNase Set QIAGEN #79254 Picogreen assay Thermo Fisher Scientific #P7581 Qubit assay Thermo Fisher Scientific #Q32851 Deposited data Next-generation sequencing reads This paper SRA: PRJNA877364 All custom code, qPCR data, growth rate data, and model fits This paper Zenodo: 10.5281/zenodo.10278952 Oligonucleotides See Table S1 for all sgRNA sequences used in this study This paper Table S1 See Table S7 for all primer sequences used in this study This paper Table S7 Recombinant DNA Barcoded pCRISPR3 plasmid backbones This paper AddGene: #191856-191861 CRISPRi Libraries This paper Available upon request Software and algorithms Python version 3.9.12 Python Software Foundation RRID:SCR_008394 Jupyter Notebook Project Jupyter RRID:SCR_018315 CFX Maestro Software Bio-Rad https://www.bio-rad.com/en-us/ category/qpcr-analysis-software All custom code, qPCR data, growth rate data, and model fits This paper https://doi.org/10.5281/ zenodo.10278952 Other CFX Opus 384 Real-Time PCR System Bio-Rad #12011452 Synergy Neo2 Hybrid Multi Mode Reader Agilent #BTNEO2

Techniques: Quantitative RT-PCR, Next-Generation Sequencing, Knockdown, Expressing, Control

Journal: Cell systems

Article Title: A continuous epistasis model for predicting growth rate given combinatorial variation in gene expression and environment.

doi: 10.1016/j.cels.2024.01.003

Figure Lengend Snippet: Figure 3. Pairwise CRISPRi growth rate and epistasis measurements Each column and row represents a unique sgRNA perturbation. Gene names denote groups of sgRNAs targeting a given gene, and sgRNAs are sorted within each group by increasing CRISPRi repression strength (top-to-bottom and left-to- right). Nont indicates the nontargeting control sgRNA. The lower triangle of the matrix describes pairwise growth rate measurements calculated relative to the nontargeting control across 14 h, averaged across n R 4 experimental replicates, where wild-type-like (WT) growth is equal to 1. The upper triangle of the matrix describes pairwise growth rate epistasis. Epistasis was calculated as the difference between the observed growth rate and the multiplicative (Bliss) growth rate expectation of pairwise sgRNA perturbations (n R 4).

Article Snippet: REAGENT or RESOURCE SOURCE IDENTIFIER Bacterial and virus strains Escherichia coli XL1-Blue (recA1 endA1 gyrA96 thi-1 hsdR17 supE44 relA1 lac [F0 proAB lacIq ZDM15 Tn10 (Tetr)]) Agilent #200236 Escherichia coli K12 MG1655 + dCas9 (F- l- ilvG- rfb-50 rph-1 HK022 attB:dCas9) AddGene #118727 Chemicals, peptides, and recombinant proteins Anhydrotetracycline Cayman Chemical Company #10009542 Thymidine Sigma-Aldrich #T1895 Methionine Sigma-Aldrich #M5308 Kanamycin Sigma-Aldrich #K1377 Critical commercial assays Luna Universal One-Step RT-qPCR Kit New England Biolabs #E3005 RNeasy Protect Bacteria Mini Kit QIAGEN #74524 RNase-Free DNase Set QIAGEN #79254 Picogreen assay Thermo Fisher Scientific #P7581 Qubit assay Thermo Fisher Scientific #Q32851 Deposited data Next-generation sequencing reads This paper SRA: PRJNA877364 All custom code, qPCR data, growth rate data, and model fits This paper Zenodo: 10.5281/zenodo.10278952 Oligonucleotides See Table S1 for all sgRNA sequences used in this study This paper Table S1 See Table S7 for all primer sequences used in this study This paper Table S7 Recombinant DNA Barcoded pCRISPR3 plasmid backbones This paper AddGene: #191856-191861 CRISPRi Libraries This paper Available upon request Software and algorithms Python version 3.9.12 Python Software Foundation RRID:SCR_008394 Jupyter Notebook Project Jupyter RRID:SCR_018315 CFX Maestro Software Bio-Rad https://www.bio-rad.com/en-us/ category/qpcr-analysis-software All custom code, qPCR data, growth rate data, and model fits This paper https://doi.org/10.5281/ zenodo.10278952 Other CFX Opus 384 Real-Time PCR System Bio-Rad #12011452 Synergy Neo2 Hybrid Multi Mode Reader Agilent #BTNEO2

Techniques: Control

Journal: Cell systems

Article Title: A continuous epistasis model for predicting growth rate given combinatorial variation in gene expression and environment.

doi: 10.1016/j.cels.2024.01.003

Figure Lengend Snippet: Figure 4. The continuous epistasis model captures gene-gene coupling at all expres- sion levels (A) Pairwise expression-growth rate data following CRISPRi knockdown of both dapB and purN, which show positive coupling. Each row and column rep- resents a unique sgRNA, and pixels represent the average growth rate effect of a given sgRNA pair (n R 4). Rows and columns are sorted by knockdown intensity, ranging from wild-type-like expression (top left) to maximal double knockdown (bottom right). (B) Predicted expression-growth rate data utilizing the coupling-sensitive continuous epistasis model. (C) Predicted expression-growth rate data utilizing the coupling-insensitive Null model. (D–F) Same as (A)–(C), but for the gdhA/gltB gene pair, which shows negative coupling and synthetic lethality. (G) The performance of the coupling-sensitive model compared with the Null model. Each data point represents model RMSD across all pairwise sgRNA combinations for a given gene pair. Full expression- growth rate data are shown for white; annotated dots in this figure and Figure S3 (e.g., the white dot labeled 4A-C represents dapB/purN). Error bars are standard deviations of models fit on n = 100 boot- strapped single perturbation-growth curves. The dotted gray line is y = x. (H) Coupling constants (aij and aji values) fit across all pairwise growth rate data, as calculated from the continuous epistasis model. (I) Bliss epistasis of growth rate following the stron- gest possible pairwise knockdown of each gene pair, calculated as the difference between the experimentally determined growth rate of the double knockdown and the product of each single-gene knockdown growth rate (n R 4).

Article Snippet: REAGENT or RESOURCE SOURCE IDENTIFIER Bacterial and virus strains Escherichia coli XL1-Blue (recA1 endA1 gyrA96 thi-1 hsdR17 supE44 relA1 lac [F0 proAB lacIq ZDM15 Tn10 (Tetr)]) Agilent #200236 Escherichia coli K12 MG1655 + dCas9 (F- l- ilvG- rfb-50 rph-1 HK022 attB:dCas9) AddGene #118727 Chemicals, peptides, and recombinant proteins Anhydrotetracycline Cayman Chemical Company #10009542 Thymidine Sigma-Aldrich #T1895 Methionine Sigma-Aldrich #M5308 Kanamycin Sigma-Aldrich #K1377 Critical commercial assays Luna Universal One-Step RT-qPCR Kit New England Biolabs #E3005 RNeasy Protect Bacteria Mini Kit QIAGEN #74524 RNase-Free DNase Set QIAGEN #79254 Picogreen assay Thermo Fisher Scientific #P7581 Qubit assay Thermo Fisher Scientific #Q32851 Deposited data Next-generation sequencing reads This paper SRA: PRJNA877364 All custom code, qPCR data, growth rate data, and model fits This paper Zenodo: 10.5281/zenodo.10278952 Oligonucleotides See Table S1 for all sgRNA sequences used in this study This paper Table S1 See Table S7 for all primer sequences used in this study This paper Table S7 Recombinant DNA Barcoded pCRISPR3 plasmid backbones This paper AddGene: #191856-191861 CRISPRi Libraries This paper Available upon request Software and algorithms Python version 3.9.12 Python Software Foundation RRID:SCR_008394 Jupyter Notebook Project Jupyter RRID:SCR_018315 CFX Maestro Software Bio-Rad https://www.bio-rad.com/en-us/ category/qpcr-analysis-software All custom code, qPCR data, growth rate data, and model fits This paper https://doi.org/10.5281/ zenodo.10278952 Other CFX Opus 384 Real-Time PCR System Bio-Rad #12011452 Synergy Neo2 Hybrid Multi Mode Reader Agilent #BTNEO2

Techniques: Expressing, Knockdown, Labeling