Limits...
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.

Show MeSH

Related in: MedlinePlus

A wide range of termination efficiencies can be measured, enabling monotonic control of transcription read-through and downstream gene expression. (A) Bar chart of termination efficiencies as quantified by flow cytometry for 61 terminator sequences using the RIIIG measurement device. Error bars represent the standard deviation of TE among single cells within a population. Terminators are colored according to their functional categories (inset legend). (B) Mapping of termination efficiencies to transcriptional read-through and expression levels. The chart serves as a quick visual reference to determine fold expression differences arising from the terminators characterized here. For example, swapping ‘amyA(L2)’ (TE ∼51%) with ‘trp[min]’ (TE ∼90%) results in a ∼5-fold decrease in downstream gene expression. As a second example, swapping ‘BBa_B1006 U10’ (TE ∼99.4%) with ‘M13 central + rrnD T1’ (TE ∼99.9%) also results in a ∼5-fold decrease in downstream gene expression.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC3643576&req=5

gkt163-F3: A wide range of termination efficiencies can be measured, enabling monotonic control of transcription read-through and downstream gene expression. (A) Bar chart of termination efficiencies as quantified by flow cytometry for 61 terminator sequences using the RIIIG measurement device. Error bars represent the standard deviation of TE among single cells within a population. Terminators are colored according to their functional categories (inset legend). (B) Mapping of termination efficiencies to transcriptional read-through and expression levels. The chart serves as a quick visual reference to determine fold expression differences arising from the terminators characterized here. For example, swapping ‘amyA(L2)’ (TE ∼51%) with ‘trp[min]’ (TE ∼90%) results in a ∼5-fold decrease in downstream gene expression. As a second example, swapping ‘BBa_B1006 U10’ (TE ∼99.4%) with ‘M13 central + rrnD T1’ (TE ∼99.9%) also results in a ∼5-fold decrease in downstream gene expression.

Mentions: We assembled and sub-cloned an expanded set of 61 putative terminator elements into the RIIIG measurement device (see ‘Materials and Methods’ section and Supplementary Figure S2). We characterized each terminator in bulk culture and among single cells by measuring expression levels of the two fluorescent reporters. We rank ordered the terminators based on calculated average TEs (see ‘Materials and Methods’ section, Figure 3A). Of the 61 sequences tested, 17 encoded TEs >95%. Overall, the set encoded terminators sufficient to control expressed protein levels across a ∼800-fold range (Figure 3B). Bulk and single-cell measurements of TEs were highly correlated (r = 0.99, n = 61, Supplementary Figure S6). We further observed that the mean and standard deviation of TEs within clonal populations were inversely correlated (Figure 3A and Supplementary Figure S7); highly active terminators exhibited little cell–cell variation, whereas the activities of weak terminators were highly dispersed among individual cells (Supplementary Figure S4).Figure 3.


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)

A wide range of termination efficiencies can be measured, enabling monotonic control of transcription read-through and downstream gene expression. (A) Bar chart of termination efficiencies as quantified by flow cytometry for 61 terminator sequences using the RIIIG measurement device. Error bars represent the standard deviation of TE among single cells within a population. Terminators are colored according to their functional categories (inset legend). (B) Mapping of termination efficiencies to transcriptional read-through and expression levels. The chart serves as a quick visual reference to determine fold expression differences arising from the terminators characterized here. For example, swapping ‘amyA(L2)’ (TE ∼51%) with ‘trp[min]’ (TE ∼90%) results in a ∼5-fold decrease in downstream gene expression. As a second example, swapping ‘BBa_B1006 U10’ (TE ∼99.4%) with ‘M13 central + rrnD T1’ (TE ∼99.9%) also results in a ∼5-fold decrease in downstream gene expression.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

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

gkt163-F3: A wide range of termination efficiencies can be measured, enabling monotonic control of transcription read-through and downstream gene expression. (A) Bar chart of termination efficiencies as quantified by flow cytometry for 61 terminator sequences using the RIIIG measurement device. Error bars represent the standard deviation of TE among single cells within a population. Terminators are colored according to their functional categories (inset legend). (B) Mapping of termination efficiencies to transcriptional read-through and expression levels. The chart serves as a quick visual reference to determine fold expression differences arising from the terminators characterized here. For example, swapping ‘amyA(L2)’ (TE ∼51%) with ‘trp[min]’ (TE ∼90%) results in a ∼5-fold decrease in downstream gene expression. As a second example, swapping ‘BBa_B1006 U10’ (TE ∼99.4%) with ‘M13 central + rrnD T1’ (TE ∼99.9%) also results in a ∼5-fold decrease in downstream gene expression.
Mentions: We assembled and sub-cloned an expanded set of 61 putative terminator elements into the RIIIG measurement device (see ‘Materials and Methods’ section and Supplementary Figure S2). We characterized each terminator in bulk culture and among single cells by measuring expression levels of the two fluorescent reporters. We rank ordered the terminators based on calculated average TEs (see ‘Materials and Methods’ section, Figure 3A). Of the 61 sequences tested, 17 encoded TEs >95%. Overall, the set encoded terminators sufficient to control expressed protein levels across a ∼800-fold range (Figure 3B). Bulk and single-cell measurements of TEs were highly correlated (r = 0.99, n = 61, Supplementary Figure S6). We further observed that the mean and standard deviation of TEs within clonal populations were inversely correlated (Figure 3A and Supplementary Figure S7); highly active terminators exhibited little cell–cell variation, whereas the activities of weak terminators were highly dispersed among individual cells (Supplementary Figure S4).Figure 3.

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