Tight coupling of transcription and translation is considered a defining feature of bacterial gene expression1,2. The pioneering ribosome can both physically associate and kinetically coordinate with RNA polymerase (RNAP)3,4,5,6,7,8,9,10,11, forming a signal-integration hub for co-transcriptional regulation that includes translation-based attenuation12,13 and RNA quality control2. However, it remains unclear whether transcription–translation coupling—together with its broad functional consequences—is indeed a fundamental characteristic of bacteria other than Escherichia coli. Here we show that RNAPs outpace pioneering ribosomes in the Gram-positive model bacterium Bacillus subtilis, and that this ‘runaway transcription’ creates alternative rules for both global RNA surveillance and translational control of nascent RNA. In particular, uncoupled RNAPs in B. subtilis explain the diminished role of Rho-dependent transcription termination, as well as the prevalence of mRNA leaders that use riboswitches and RNA-binding proteins. More broadly, we identified widespread genomic signatures of runaway transcription in distinct phyla across the bacterial domain. Our results show that coupled RNAP–ribosome movement is not a general hallmark of bacteria. Instead, translation-coupled transcription and runaway transcription constitute two principal modes of gene expression that determine genome-specific regulatory mechanisms in prokaryotes.
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All data generated and analysed during this study are included in this published article (and its Supplementary Information). The high-throughput sequencing datasets analysed during the current study are available from the Gene Expression Omnibus repository with accession numbers: GSE53767, GSE95211 and GSE108295 (see ‘High-throughput expression datasets used’ for details). Uncropped gel source data for northern blots can be found in Supplementary Fig. 1. Source data are provided with this paper.
Scripts for terminator identification have been deposited to GitHub (https://github.com/jblalanne/intrinsic_trx_terminator_identifier). Core Rend-seq analysis scripts used can be found on Github (https://github.com/jblalanne/Rend_seq_core_scripts). Other custom scripts used for data analysis are available upon request.
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We thank J. Taggart, M. Tien, and A. Grossman’s laboratory for providing plasmids; D. McCormick for help with strain generation; L. Herzel, J. Taggart, and M. Tien for help collecting cultures for measurements of induction kinetics; members of the G.-W.L. and A. Grossman laboratories for discussions; L. Herzel, D. J. Parker, A. Grossman, J. Peters and V. Siegel for comments on the manuscript; and members of the BioMicroCenter at MIT for help in performing DNA sequencing. This research was supported by NIH grant R35GM124732, the Pew Biomedical Scholars Program, a Sloan Research Fellowship, the Searle Scholars Program, the Smith Family Award for Excellence in Biomedical Research, an NSF graduate research fellowship (to G.E.J.), an NIH Pre-Doctoral Training Grant (T32 GM007287, to G.E.J. and M.L.P.), an NSERC graduate fellowship (to J.-B.L.), and an HHMI International Student Fellowship (to J.-B.L.).
The authors declare no competing interests.
Peer review information Nature thanks Joseph Puglisi and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended data figures and tables
Induction time course of lacZ mRNA (top) and protein (bottom) as in Fig. 1b, d for WT B. subtilis grown in MOPS minimal media + 0.4% maltose (growth rate 0.65 h−1). Lines indicate linear fits after signals rise. Uncertainties are standard error of the mean (s.e.m.) among biological replicates (2).
a, Measurement of linear range of microplate reader. Fluorescence relative to input of dilutions of an induced culture of GLB503 (full-length lacZ) at steady-state (Methods). b, Effect of different stop solutions on stopping translation. Induction time courses of pycA-lacZα protein collected into a stop solution containing chloramphenicol and erythromycin (grey, all plots, from Fig. 1d) or with either flash freezing in liquid nitrogen (top), 15 μl toluene added to the stop solution (middle), or 50 μl 12.5 mg/ml lincomycin added to the stop solution (bottom), shown in red in each plot (as described in Methods). Lines indicate linear fits after signals rise and τTL is indicated. c, Induction time course of truncated pycA-lacZα mRNA (top) and protein (bottom) as in Fig. 1b, d. Lines indicate linear fits after signals rise. Uncertainties are standard error of the mean (s.e.m.) among biological replicates (2).
Extended Data Fig. 3 Contribution of non-essential RNAP subunits and transcription factors to fast transcription.
Induction time course of pycA-lacZα mRNA in various mutant backgrounds as in Fig. 1b, d. Time course of the same construct in WT from Fig. 1d also shown for reference. Lines indicate linear fits after signals rise. Uncertainties are standard error of the mean (s.e.m) among biological replicates (1 for ∆ykzG and 2 for all others). Time of appearance of full-length mRNA in mutants is not substantially different than that measured in WT (Supplementary Discussion SN4).
Multiple sequence alignments (Methods) for NusA (602 columns), NusG (325 columns), and the β subunit of the RNAP RpoB (1732 columns) for species shown in Fig. 4. The alignments are visualized in a binary fashion to highlight presence/absence of certain domains: white indicates presence of an amino acid in the alignment, and black indicates presence of a gap. The alignments were trimmed by removing columns with >95% gaps. Species with no homologues, partial or pseudogene homologues, or multiple homologues are shown as grey lines. Phylogenetic tree and fraction of terminators with stop-to-stem distances within 12 nt from Fig. 4 are reproduced in linearized form. The position of domains from the E. coli protein are identified by bars above the alignments. For RpoB, conserved bacterial regions identified by70 (βb1 to βb16) are shown. The NusA C-terminal domain11,71 (orange box) is missing in a large fraction of Firmicutes (partly present in Mollicutes, which include Mycoplasma and Spiroplasma; red brace), Campylobacterota, Thermotogota, Fusobacteria, and Actinobacteria. NusG has a largely conserved domain architecture, with Actinobacteria showing N-terminal extension. As previously noted in detail70, the β subunit of the RNAP has multiple insertion domains in diverse bacteria. Insertion domain βSI2, recently implicated10 (green box) in transcription-translation coupling is lineage-specific and absent in many clades of Gram-positive bacteria, as noted in10. Dashed box in tree highlights clade containing Mycoplasma. b. Close-up view of our analysis of the clade containing Mycoplasma (indicated by black dots). Sub-tree includes species with n ≤ 20 identified terminators (marked in light red). Grayscale representation of stop-to-stem distributions and fraction of terminators with d ≤ 12 nt are the same as Fig. 4. M. pneumoniae is highlighted in cyan, and has no identified terminator (0/14) with d ≤ 12 nt. c. Cumulative distribution of stop-to-stem distance for bioinformatically identified terminators in M. pneumoniae.
Extended Data Fig. 5 Details of ORF extension constructs and transcription terminator readthrough vs. stop-to-stem distances.
a, Sequence for terminators T1 and T2 for three variants (T1+: pupG original terminator, T1-: disrupted pupG terminator, ORF extension: original pupG with upstream ORF extended inside the loop of the terminator). For T1 and T2, blue and grey shading, respectively, marks the position of the terminator hairpin stems, with free energy of folding ∆G indicated. Black stars indicate introduced mutations. Downward carets (▼) indicate the position of the 3′ ends associated with intrinsic terminators as determined by Rend-seq. Red dashed line indicates the complementary region of the northern blot probe to the readthrough product. b, Terminator readthrough fraction (defined as the Rend-seq read density after terminator divided by read density upstream of terminator, see ref.35 for details) as a function of stop-to-stem distance for E. coli intrinsic terminators from Fig. 2 for which readthrough could be reliably estimated (n = 392). Terminators with stop-to-stem distance d ≤ 12 nt are highlighted in red. c, Cumulative distribution function of terminator readthrough for terminators far (black, d>12 nt) from and close (red, d ≤ 12 nt) to stop codons. Terminator close to genes have significantly more readthrough (less termination), P < 10−3 (q30d>12 and q30d ≤ 12 indicate the 30th percentile in the readthrough distribution for the two categories of terminators, with fold-change F30: = q30d ≤ 12/ q30d>12, P-value determined as the fraction of bootstrap random sub-samplings of the readthrough distributions with q30d>12 > q30d ≤ 12, Methods) d, Terminator readthrough as a function of ∆GU, the U-tract DNA/RNA hybrid free energy (measure of U-tract quality, with larger ∆GU corresponding to U-rich U-tract). Grey shading indicates cutoff (∆GU>-5 kcal/mol) to select good U-tract terminators. e, Same as c, but restricting to good U-tract terminators, still showing significantly less termination for terminators near ORF, P < 10−3 (same as above, Methods). f-i, same as b–e, but with terminators from B. subtilis. Terminators close to ORF do not show less readthrough than their gene-distal counterparts (P > 0.3, p-value determined with same strategy as above, Methods).
B. subtilis shows a number (n = 35, see Methods for selection criteria) of mRNAs with long untranslated regions fully encompassing genes in the antisense directions, which we call nested antisense RNAs (also termed non-contiguous operons53 or excludons54). The majority (n = 29/35) of these have a fold-change in mRNA level less than twofold upon rho deletion (Fig. 3b). a, Schematic of a nested antisense RNA with corresponding Rend-seq signal, with orange peaks and blue peaks marking 5′ and 3′ boundaries of the transcript. b, Representative examples of nested antisense RNAs with mRNA level fold change upon rho deletion less than 2. Rend-seq data (peak shadows removed, see35 for details on data processing) is shown. Orange and blue signal correspond to summed 5′-mapped reads and 3′-mapped reads, respectively (rpm: reads per million). Top trace corresponds to wild type, and bottom trace to ∆rho. Horizontal size marker provides positional scale (200 bp) on each subpanel. Sense and antisense genes are shown in dark and light grey, respectively. Double line breaks (//) indicate truncated Rend-seq signal at peaks. Dashed lines mark regions for which fold-change in read density for ∆rho/WT was estimated. The fold-change for each instance is indicated on the graph. c, Same as b, with representative examples of nested antisense RNAs with increased expression upon rho deletion (Fig. 3b). Three nested antisense RNAs were found in E. coli with identical criteria. See Supplementary Data 3 for a list of nested antisense RNAs identified.
Extended Data Fig. 7 Expressed pseudogenes with interrupted translation in B. subtilis show no polarity.
Expressed pseudogenes endogenously present in the extant genome were used as additional independent experiments to assess the prevalence of Rho-mediated nonsense polarity in B. subtilis in situations of obligately uncoupled transcription and translation. Concomitant Rend-seq (mapping operon architecture) and ribosome profiling (measurement of translation) provides stringent data to determine translational status and transcript integrity of mRNAs. a, Schematic of analysis: for expressed pseudogenes (see Methods for selection criteria) with translation disruption, polarity was assessed by (1) comparing the mRNA read density at start and end of transcription unit, with large changes (start/end ≫ 1) indicative of polarity, and (2) fold change of pseudogene transcript upon rho deletion. Position of translation disrupting mutation is shown by ▲ and X. Dark and pale grey indicates region prior and after translation disruption mutation. b, Rend-seq and ribosome profiling data for the 8 identified expressed pseudogenes. Each subpanel corresponds to a pseudogene region. Top traces show Rend-seq data (orange and blue signal correspond to summed 5′-mapped reads and 3′-mapped reads, peak shadows removed, see ref.35 for details on data processing). Orange peaks and blue peaks mark 5′ and 3′ boundaries of transcripts. Double line breaks (//) indicate truncated Rend-seq signal at peaks. Bottom traces show ribosome profiling data. Translation efficiency (ribosome profiling rpkm/Rend-seq rpkm) percentiles for each pseudogene sub-region (before and after translation disruption) are shown. Horizontal size marker provides positional scale (200 bp) on each subpanel. Nearby intact genes are shown in light blue. rpm: reads per million. Regions used to assess start to end decrease in RNA levels are marked by dashed lines. mRNA levels fold-changes (start/end, and ∆rho/WT) are shown. The ydzW region showed a second translation disruption the secondary frame, shown as a pale ▲ and X. See Methods, Fig. 3b and Supplementary Data 3 for details.
Extended Data Fig. 8 Most expressed pseudogenes with interrupted translation in E. coli show polarity.
Similar to Extended Data Fig. 7. Expressed pseudogenes endogenously present in the extant genome were used as additional independent experiments to assess the prevalence of Rho-mediated nonsense polarity in E. coli in situations of obligately uncoupled transcription and translation. Concomitant Rend-seq (mapping operon architecture) and ribosome profiling (monitoring translation) provides stringent data to determine translational status and transcript integrity on mRNAs. a, Schematic of analysis: for expressed pseudogenes (see Methods for selection criteria) with translation disruption, polarity was assessed by comparing the mRNA read density at start and end of transcription unit, with large changes (start/end≫1) indicative of polarity. b, Rend-seq and ribosome profiling data for the identified expressed pseudogene with evidence of polarity. Each subpanel corresponds to a pseudogene region. Top traces correspond to Rend-seq data (orange and blue signal correspond to summed 5′-mapped reads and 3′-mapped reads, peak shadows removed, see ref.35 for details on data processing). Orange peaks and blue peaks mark 5′ and 3′ boundaries of transcripts. Double line breaks (//) indicate truncated Rend-seq signal at peaks. Bottom traces show ribosome profiling data. Translation efficiency (ribosome profiling rpkm/Rend-seq rpkm) percentiles for each pseudogene sub-region (before and after translation disruption) are shown. Horizontal size marker provides positional scale (200 bp) on each subpanel. Light blue arrows correspond to nearby intact genes. rpm: reads per million. Regions used to assess start to end decrease in RNA levels are marked by dashed lines. mRNA levels fold-changes (start/end) are shown. The gapC region showed sequential translation disruptions secondary frames, shown as a pale ▲ and X. c, same as b, but for the two cases with no evidence of polarity. The translation disruptions mutation in ykiA and cybC are deletion of the beginning of ORFs. See Methods, Fig. 3b and Supplementary Data 3 for details.
a, Cumulative distributions of maximum C-to-G ratio (“Max C:G”) of 100 nt sliding windows within non Rho-terminated coding sequences (CDSs, blue, n = 2625) and Rho-terminated CDSs (magenta, n = 10). Median of Max C:G is higher for Rho-terminated CDSs (magenta) than non Rho-terminated CDSs (blue) (P < 10−5, less than one in 105 random sub-samplings (n = 10) of non Rho-terminated distribution had higher median maximum C-to-G ratio). b, Cumulative distributions as in a for asRNAs that are not terminated by Rho (blue, n = 112) and asRNAs that are terminated by Rho (magenta, n = 91). Median of Max C:G is higher for Rho-terminated asRNAs than non Rho-terminated asRNAs (P < 10−3, less than one in 103 random sub-samplings (n = 10) of non Rho-terminated distribution had higher median maximum C to G ratio compared to sub-sampling (n = 10) of Rho-terminated distribution) (Methods).
Extended Data Fig. 10 Illustration of terminator identification pipeline and analysis of stem-to-stop distribution stratified by phyla.
The terminator identification pipeline selects for strong hairpins immediately upstream of long U-tract found downstream of genes. Thresholds on hairpin folding free energy are determined on a species-by-species basis based on properties of randomly selected regions in respective genomes. The case of V. cholerae is illustrated in a-c. a, Results of folding 104 regions of 40 nt chosen at random positions in the genome. Left panel shows the 2D distribution as a heatmap (dark positions corresponding to more density) of hairpin geometrical parameters (number of base pairs in stem Nbp, length of loop). Geometric thresholds are highlighted with blue dashes (5 bp ≤ Nbp ≤ 15 bp, 3 nt ≤ Loop ≤ 8 nt) and retained region by blue shading. Right panel shows the 2D distribution as a heatmap (dark positions correspond to more density) of hairpin free energy of folding ∆Ghairpin and fraction of bases paired in stem f. Thresholds ∆G1 and ∆G2 on ∆Ghairpin are chosen such the total fraction of hairpin from random regions meeting geometrical (blue shading in left panel) and thermodynamic thresholds are 1% (orange, ∆Ghairpin ≤ ∆G1 and f ≥ 0.95) and 1.5% (red, ∆Ghairpin ≤ ∆G2 and f ≥ 0.9). b, Similar as for a, but for regions seeded by U-tracts (stretch of 5 or more consecutive T’s in the genome downstream of genes). Note the excess density of hairpins with strong energy of folding and large fraction of bases paired, corresponding to putative intrinsic terminators. c, Distribution of stop-to-stem distances for terminators passing thresholds shown in b. See Supplementary Data 2, Supplementary Data 3, and Methods for details of computational pipeline. d and e, Phylum stratified analysis on the stop-to-stem distribution. d, Each subpanel shows as a 2D greyscale the fraction of species within each phylum (shown in Fig. 4) for which more than fraction F (y-axis) of terminators have stop-to-stem distances less than or equal to D (x-axis). Black regions correspond to no species in the phylum, white all species. The contour line in the (D,F) space marks points where 50% of species in the phylum have fraction ≥ F of their terminators with stop-to-stem distance ≤ D. The yellow stars mark the thresholds used in Fig. 4 (D = 12 nt, F = 30%). For example, about 50% of species analysed in the Firmicutes have more than 30% of their terminators within 12 nt of upstream ORF (red contour line intersecting yellow star). e, The 50% species contour lines from d reported to the same panel, showing clear separation between phyla.
Contains Supplementary Discussion (on previous approaches to bioinformatically identify intrinsic terminators, on the possibility of translation initiation on B. subtilis nascent mRNAs, on transcription elongation rates in mutant backgrounds, and on the phylogeny of domain architecture in NusA, NusG, and RpoB). Supplementary Figure 1: raw Northern blot data.
Supplementary Data Supplementary Data 2: high-confidence list of intrinsic terminators. List of terminators with strong experimental evidence in B. subtilis and E. coli. Restricted subset from the list in Ref. 35, with additional quality criteria (Methods). See Fig. 2 and Extended Data Fig. 5.
Supplementary Data Supplementary Data 3: details for nested antisense RNAs and expressed pseudogenes. List of nested antisense RNAs and pseudogenes (nested antisense RNAs, final list of pseudogenes) and related properties. See Fig. 3 and Extended Data Fig. 6-8.
Supplementary Data Supplementary Data 4: species-by-species summary of identified intrinsic terminators. Summary of identified putative intrinsic terminators for the 1648 RefSeq species considered. See Fig. 4, Extended Data Fig. 10.
Supplementary Data Supplementary Data 5: list of all identified terminators. Complete list of all 301817 identified putative terminators for the 1648 RefSeq species. See Fig. 4 and Extended Data Fig. 10. We recommend parsing this file computationally.
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Johnson, G.E., Lalanne, JB., Peters, M.L. et al. Functionally uncoupled transcription–translation in Bacillus subtilis. Nature 585, 124–128 (2020). https://doi.org/10.1038/s41586-020-2638-5
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