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Measurement and modeling of intrinsic transcription terminators.

Cambray G, Guimaraes JC, Mutalik VK, Lam C, Mai QA, Thimmaiah T, Carothers JM, Arkin AP, Endy D - Nucleic Acids Res. (2013)

Bottom Line: We found that structures extending beyond the core terminator stem are likely to increase terminator activity.By excluding terminators encoding such context-confounding elements, we were able to develop a linear sequence-function model that can be used to estimate termination efficiencies (r = 0.9, n = 31) better than models trained on all terminators (r = 0.67, n = 54).The resulting systematically measured collection of terminators should improve the engineering of synthetic genetic systems and also advance quantitative modeling of transcription termination.

View Article: PubMed Central - PubMed

Affiliation: BIOFAB International Open Facility Advancing Biotechnology (BIOFAB), 5885 Hollis Street, Emeryville, CA 94608, USA.

ABSTRACT
The reliable forward engineering of genetic systems remains limited by the ad hoc reuse of many types of basic genetic elements. Although a few intrinsic prokaryotic transcription terminators are used routinely, termination efficiencies have not been studied systematically. Here, we developed and validated a genetic architecture that enables reliable measurement of termination efficiencies. We then assembled a collection of 61 natural and synthetic terminators that collectively encode termination efficiencies across an ∼800-fold dynamic range within Escherichia coli. We simulated co-transcriptional RNA folding dynamics to identify competing secondary structures that might interfere with terminator folding kinetics or impact termination activity. We found that structures extending beyond the core terminator stem are likely to increase terminator activity. By excluding terminators encoding such context-confounding elements, we were able to develop a linear sequence-function model that can be used to estimate termination efficiencies (r = 0.9, n = 31) better than models trained on all terminators (r = 0.67, n = 54). The resulting systematically measured collection of terminators should improve the engineering of synthetic genetic systems and also advance quantitative modeling of transcription termination.

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Quantitative sequence activity modeling of transcription termination. (A) Scatter plot of observed versus predicted termination efficiencies for a non-curated model that enables poor predictions compared with a model based on curated data set. (B) Scatter plot of observed versus predicted termination efficiencies for the 31 curated terminators used to train the model. Pearson correlation coefficient r = 0.9 and cross-validated (CV) r = 0.85 (‘Materials and Methods’ section). (C) Residual error distributions for each terminator category predicted via the curated model.
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gkt163-F5: Quantitative sequence activity modeling of transcription termination. (A) Scatter plot of observed versus predicted termination efficiencies for a non-curated model that enables poor predictions compared with a model based on curated data set. (B) Scatter plot of observed versus predicted termination efficiencies for the 31 curated terminators used to train the model. Pearson correlation coefficient r = 0.9 and cross-validated (CV) r = 0.85 (‘Materials and Methods’ section). (C) Residual error distributions for each terminator category predicted via the curated model.

Mentions: We defined 12 sequence features potentially involved in modulating terminator activity by reviewing the published literature and considering the roles of sequence context as noted earlier in the text (Supplementary Table S6). We developed a generic linear model for TE to select sequence features that might best account for observed TEs [Equation (4); ‘Materials and Methods’ section]. We found that increasing the number of predictors increased accuracy up to five predictors (Supplementary Figure S9). Overall, correlations between observed and computed TEs were modest (r = 0.67, cross-validated r = 0.61, n = 54; Figure 5A). The two features representing sequence context effects (‘folding frequency’ and ‘ability to form extended terminators’) were selected as the second and third most important variables. Additionally, we noted that terminators with very low TEs were poorly predicted. By systematically varying a TE cut-off, we found that improved correlations could be achieved by excluding seven terminators with TEs <35% (low-efficiency terminators, LET; Supplementary Figures S8B and S9). Likewise, we determined that terminators with simulated folding frequencies <90% reduced prediction quality, presumably because their TEs are not entirely encoded by core sequence features (low folding frequency terminators, LFFT; Supplementary Figures S8D and S9). We also suspected that the complex organization of extended terminators (ET) might confound model feature identification and excluded such terminators from a redacted modeling data set (Supplementary Figure S8C).Figure 5.


Measurement and modeling of intrinsic transcription terminators.

Cambray G, Guimaraes JC, Mutalik VK, Lam C, Mai QA, Thimmaiah T, Carothers JM, Arkin AP, Endy D - Nucleic Acids Res. (2013)

Quantitative sequence activity modeling of transcription termination. (A) Scatter plot of observed versus predicted termination efficiencies for a non-curated model that enables poor predictions compared with a model based on curated data set. (B) Scatter plot of observed versus predicted termination efficiencies for the 31 curated terminators used to train the model. Pearson correlation coefficient r = 0.9 and cross-validated (CV) r = 0.85 (‘Materials and Methods’ section). (C) Residual error distributions for each terminator category predicted via the curated model.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

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Show All Figures
getmorefigures.php?uid=PMC3643576&req=5

gkt163-F5: Quantitative sequence activity modeling of transcription termination. (A) Scatter plot of observed versus predicted termination efficiencies for a non-curated model that enables poor predictions compared with a model based on curated data set. (B) Scatter plot of observed versus predicted termination efficiencies for the 31 curated terminators used to train the model. Pearson correlation coefficient r = 0.9 and cross-validated (CV) r = 0.85 (‘Materials and Methods’ section). (C) Residual error distributions for each terminator category predicted via the curated model.
Mentions: We defined 12 sequence features potentially involved in modulating terminator activity by reviewing the published literature and considering the roles of sequence context as noted earlier in the text (Supplementary Table S6). We developed a generic linear model for TE to select sequence features that might best account for observed TEs [Equation (4); ‘Materials and Methods’ section]. We found that increasing the number of predictors increased accuracy up to five predictors (Supplementary Figure S9). Overall, correlations between observed and computed TEs were modest (r = 0.67, cross-validated r = 0.61, n = 54; Figure 5A). The two features representing sequence context effects (‘folding frequency’ and ‘ability to form extended terminators’) were selected as the second and third most important variables. Additionally, we noted that terminators with very low TEs were poorly predicted. By systematically varying a TE cut-off, we found that improved correlations could be achieved by excluding seven terminators with TEs <35% (low-efficiency terminators, LET; Supplementary Figures S8B and S9). Likewise, we determined that terminators with simulated folding frequencies <90% reduced prediction quality, presumably because their TEs are not entirely encoded by core sequence features (low folding frequency terminators, LFFT; Supplementary Figures S8D and S9). We also suspected that the complex organization of extended terminators (ET) might confound model feature identification and excluded such terminators from a redacted modeling data set (Supplementary Figure S8C).Figure 5.

Bottom Line: We found that structures extending beyond the core terminator stem are likely to increase terminator activity.By excluding terminators encoding such context-confounding elements, we were able to develop a linear sequence-function model that can be used to estimate termination efficiencies (r = 0.9, n = 31) better than models trained on all terminators (r = 0.67, n = 54).The resulting systematically measured collection of terminators should improve the engineering of synthetic genetic systems and also advance quantitative modeling of transcription termination.

View Article: PubMed Central - PubMed

Affiliation: BIOFAB International Open Facility Advancing Biotechnology (BIOFAB), 5885 Hollis Street, Emeryville, CA 94608, USA.

ABSTRACT
The reliable forward engineering of genetic systems remains limited by the ad hoc reuse of many types of basic genetic elements. Although a few intrinsic prokaryotic transcription terminators are used routinely, termination efficiencies have not been studied systematically. Here, we developed and validated a genetic architecture that enables reliable measurement of termination efficiencies. We then assembled a collection of 61 natural and synthetic terminators that collectively encode termination efficiencies across an ∼800-fold dynamic range within Escherichia coli. We simulated co-transcriptional RNA folding dynamics to identify competing secondary structures that might interfere with terminator folding kinetics or impact termination activity. We found that structures extending beyond the core terminator stem are likely to increase terminator activity. By excluding terminators encoding such context-confounding elements, we were able to develop a linear sequence-function model that can be used to estimate termination efficiencies (r = 0.9, n = 31) better than models trained on all terminators (r = 0.67, n = 54). The resulting systematically measured collection of terminators should improve the engineering of synthetic genetic systems and also advance quantitative modeling of transcription termination.

Show MeSH
Related in: MedlinePlus