Measurement and modeling of intrinsic transcription terminators

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.

Figure 1.

Architecture of a standardized genetic device for termination efficiency measurements. (A) Anatomy of an intrinsic terminator (purple) and generic architecture of processed mRNA originating from a terminator measurement device. RNase recognition sites (orange diamonds) are intended to standardize the 3′- or 5′-ends of processed mRNA encoding upstream (UP, red) and downstream (DW, green) reporter genes. The four features selected in our best quantitative model of termination efficiencies (main text), numbered by decreasing importance (grey regions: 1 = TTHP_utail_score; 2 = hp_norm_dg; 3 = closing_stackGC; 4 = dna_dna_pattern). (B) Six terminator measurement device variants tested here. Green (G, green box) and red (R, red box) fluorescent reporter coding sequences bracket a terminator (purple T) test site flanked by RNase E sites (E, blue diamonds), RNase III sites (3, orange diamonds) or non-functional RNase III sites (*, orange diamonds).

Figure 2.

Testing and selection of a validated terminator measurement device. (A) Upstream reporter gene fluorescence data from a test panel of 20 terminator sequences cloned within six candidate terminator measurement devices; fluorescence values are normalized by the mean value obtained with each candidate measurement device. Expression levels for each terminator are connected (dotted lines). One standard deviation (shaded grey range) and coefficients of variation for expression levels (bottom bar graph) across all terminators within a given test device, as noted. (B) As in (A) but for a downstream reporter gene, the expression of which is expected to further vary as a result of differential termination efficiencies among the test terminator sequences. (C) Correlation in estimated terminator read-through measurements as upstream and downstream reporter genes are swapped. Green before red fluorescent protein versus red before green with RNase E sites (left) and with RNase III sites (right). (D) Pearson correlation scores for read-through measurements of the 20 terminator test panel. Correlation scores arising from comparing single cell (upper right) and bulk (lower left) measurements across the six candidate terminator measurement devices, as noted. Single-cell versus bulk correlation scores for each measurement device as given (main diagonal). Best performing (i.e. most consistent) measurement devices are bracketed (thick white line).

Figure 3.

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.

Figure 4.

Immediate local sequence impacts on termination efficiencies. (A) Comparison of normalized transcription read-through (TR_NORM, 0.0–1.0) for terminators flanked by 30 nt of native upstream and downstream genomic sequence (blue) relative to minimal cognate terminators (red). Numbers above bars indicate the fold-increase in read-through for the minimal context. (B) Varying flanking contexts modify the predicted folding kinetics of some terminators. Each graph compares the folding frequency (0.0–1.0) for a core terminator stem over time (x-axes: 0, 0.5, 1, 10, 20 and 30 s) for expanded context (blue) and minimal terminators (red), as derived from co-transcriptional folding simulations (main text). (C) Outer terminators extending past core terminator motifs. Core terminator motifs (red bases) and native (blue, main panel) or minimal (black, insets) flanking sequences as indicated. For four terminators an extended terminator stem comprising part of the poly-U tail and closed by a GC pair could be identified in their expanded native context (main panel), but not within a minimal context (insets). Variable positions indicated at the base of the stems for paralogs rrnB and rrnD (stars).

Figure 5.

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.