Measurement and modeling of intrinsic transcription terminators

被引:130
作者
Cambray, Guillaume [1 ,2 ]
Guimaraes, Joao C. [1 ,3 ,4 ]
Mutalik, Vivek K. [1 ,2 ,5 ]
Lam, Colin [1 ,2 ]
Quynh-Anh Mai [1 ,2 ]
Thimmaiah, Tim [3 ]
Carothers, James M. [5 ]
Arkin, Adam P. [1 ,2 ,3 ,5 ]
Endy, Drew [1 ,6 ]
机构
[1] BIOFAB Int Open Facil Adv Biotechnol BIOFAB, Emeryville, CA 94608 USA
[2] Univ Calif Berkeley, Calif Inst Quantitat Biosci, Berkeley, CA 94720 USA
[3] Univ Calif Berkeley, Dept Bioengn, Berkeley, CA 94720 USA
[4] Univ Minho, Dept Informat, Comp Sci & Technol Ctr, P-4700 Braga, Portugal
[5] Univ Calif Berkeley, Lawrence Berkeley Natl Lab, Phys Biosci Div, Berkeley, CA 94720 USA
[6] Stanford Univ, Dept Bioengn, Stanford, CA 94305 USA
基金
美国国家科学基金会;
关键词
ESCHERICHIA-COLI RNA; GENE-EXPRESSION; TRANSLATION INITIATION; MUTATIONAL ANALYSIS; BACTERIAL GENOMES; STEM-LOOP; POLYMERASE; PREDICTION; ELEMENTS; REGULATORS;
D O I
10.1093/nar/gkt163
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
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 similar to 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.
引用
收藏
页码:5139 / 5148
页数:10
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