Dynamics of transcription–translation coordination tune bacterial indole signaling


Indole signaling is an important cross-species communication pathway in the mammalian gut. In bacteria, upon induction by tryptophan, the molecular sensor (tnaC) controls indole biosynthesis by precisely coordinating dynamics of the corresponding macromolecular machineries during its transcription and translation. Our understanding of this regulatory program is still limited owing to its rapid dynamic nature. To address this shortcoming, we adopted a massively parallel profiling method to quantify the responses of 1,450 synthetic tnaC variants in the presence of three concentrations of tryptophan in living bacterial cells. The resultant dataset enabled us to comprehensively probe the key intermediate states of macromolecular machineries during the transcription and translation of tnaC. We also used modeling to provide a systems-level understanding of how these critical states collectively shape the output of this regulatory program quantitatively. A similar methodology will likely apply to other poorly understood dynamics-dependent cis-regulatory elements.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Fig. 1: FACS–seq produces a high-quality dataset.
Fig. 2: Overview of residues regulating ligand induction and the basal response of the tnaC sensor.
Fig. 3: RNAP–ribosome synchronization during transcription–translation of tnaC.
Fig. 4: Key intermediate states during transcription–translation of tnaC and dynamic modeling of macromolecule coordination.
Fig. 5: Dynamic modeling provides insight about the molecular mechanism of tnaC.
Fig. 6: The two subsections of tnaC are functionally independent and face divergent selection pressures.

Data availability

NGS raw data from FACS–seq have been deposited in the NCBI Sequence Read Archive under BioProject PRJNA503322. Any other data or materials related to this work are available from the corresponding author upon request.

Code availability

The Python script used to process raw FACS–seq data and perform reliability test simulation can be accessed at https://github.com/wtmbiohacker/FACS_NGS.git. The custom Python script used to perform the modeling of macromolecular coordination during the tnaC sensor response can be accessed at https://github.com/wtmbiohacker/tnaC_kinetics_modeling. We also provide a comprehensive user guide to enable reuse of this code.


  1. 1.

    Ronen, M., Rosenberg, R., Shraiman, B. I. & Alon, U. Assigning numbers to the arrows: parameterizing a gene regulation network by using accurate expression kinetics. Proc. Natl Acad. Sci. USA 99, 10555–10560 (2002).

  2. 2.

    Zong, Y. et al. Insulated transcriptional elements enable precise design of genetic circuits. Nat. Commun. 8, 52 (2017).

  3. 3.

    Widom, J. R. et al. Ligand modulates cross-coupling between riboswitch folding and transcriptional pausing. Mol. Cell 72, 541–552 (2018).

  4. 4.

    Winkler, W., Nahvi, A. & Breaker, R. R. Thiamine derivatives bind messenger RNAs directly to regulate bacterial gene expression. Nature 419, 952–956 (2002).

  5. 5.

    Serganov, A. & Patel, D. J. Ribozymes, riboswitches and beyond: regulation of gene expression without proteins. Nat. Rev. Genet. 8, 776–790 (2007).

  6. 6.

    Gong, F. & Yanofsky, C. Instruction of translating ribosome by nascent peptide. Science 297, 1864–1867 (2002).

  7. 7.

    Seidelt, B. et al. Structural Insight into nascent polypeptide chain-mediated translational stalling. Science 326, 1412–1415 (2009).

  8. 8.

    Vazquez-Laslop, N., Thum, C. & Mankin, A. S. Molecular mechanism of drug-dependent ribosome stalling. Mol. Cell 30, 190–202 (2008).

  9. 9.

    Calvo, S. E., Pagliarini, D. J. & Mootha, V. K. Upstream open reading frames cause widespread reduction of protein expression and are polymorphic among humans. Proc. Natl Acad. Sci. USA 106, 7507–7512 (2009).

  10. 10.

    Rolland, F., Moore, B., Sheen, J. & Smeekens, S. Sugar sensing and signaling in plants. Plant Cell 14, S185–S205 (2002).

  11. 11.

    Tran, M. K., Schultz, C. J. & Baumann, U. Conserved upstream open reading frames in higher plants. BMC Genomics 9, 361 (2008).

  12. 12.

    Vega, N. M., Allison, K. R., Khalil, A. S. & Collins, J. J. Signaling-mediated bacterial persister formation. Nat. Chem. Biol. 8, 431–433 (2012).

  13. 13.

    Dar, D. et al. Term-seq reveals abundant ribo-regulation of antibiotics resistance in bacteria. Science 352, aad9822 (2016).

  14. 14.

    Tsai, A., Kornberg, G., Johansson, M., Chen, J. & Puglisi, J. D. The dynamics of SecM-induced translational stalling. Cell Rep. 7, 1521–1533 (2014).

  15. 15.

    Fowler, D. M. & Fields, S. Deep mutational scanning: a new style of protein science. Nat. Methods 11, 801–807 (2014).

  16. 16.

    Romero, P. A., Tran, T. M. & Abate, A. R. Dissecting enzyme function with microfluidic-based deep mutational scanning. Proc. Natl Acad. Sci. USA 112, 7159–7164 (2015).

  17. 17.

    Reynolds, Ka, McLaughlin, R. N. & Ranganathan, R. Hot spots for allosteric regulation on protein surfaces. Cell 147, 1564–1575 (2011).

  18. 18.

    McLaughlin, R. N. Jr, Poelwijk, F. J., Raman, A., Gosal, W. S. & Ranganathan, R. The spatial architecture of protein function and adaptation. Nature 491, 138–142 (2012).

  19. 19.

    Adams, R. M., Kinney, J. B., Mora, T. & Walczak, A. M. Measuring the sequence-affinity landscape of antibodies with massively parallel titration curves. Elife 5, e23156 (2016).

  20. 20.

    Whitehead, T. A. et al. Optimization of affinity, specificity and function of designed influenza inhibitors using deep sequencing. Nat. Biotechnol. 30, 543–548 (2012).

  21. 21.

    Stewart, V. & Yanofsky, C. Evidence for transcription antitermination control of tryptophanase operon expression in Escherichia coli K-12. J. Bacteriol. 164, 731–740 (1985).

  22. 22.

    Roager, H. M. & Licht, T. R. Microbial tryptophan catabolites in health and disease. Nat. Commun. 9, 3294 (2018).

  23. 23.

    Lee, H. H., Molla, M. N., Cantor, C. R. & Collins, J. J. Bacterial charity work leads to population-wide resistance. Nature 467, 82–85 (2010).

  24. 24.

    Gong, F., Ito, K., Nakamura, Y. & Yanofsky, C. The mechanism of tryptophan induction of tryptophanase operon expression: tryptophan inhibits release factor-mediated cleavage of TnaC–peptidyl-tRNAPro. Proc. Natl Acad. Sci. USA 98, 8997–9001 (2001).

  25. 25.

    Cruz-Vera, L. R., Rajagopal, S., Squires, C. & Yanofsky, C. Features of ribosome–peptidyl-tRNA interactions essential for tryptophan induction of tna operon expression. Mol. Cell 19, 333–343 (2005).

  26. 26.

    Townshend, B., Kennedy, A. B., Xiang, J. S. & Smolke, C. D. High-throughput cellular RNA device engineering. Nat. Methods 12, 989–994 (2015).

  27. 27.

    Binder, S. et al. A high-throughput approach to identify genomic variants of bacterial metabolite producers at the single-cell level. Genome Biol. 13, R40 (2012).

  28. 28.

    Fang, M. et al. Intermediate-sensor assisted push–pull strategy and its application in heterologous deoxyviolacein production in Escherichia coli. Metab. Eng. 33, 41–51 (2016).

  29. 29.

    Cruz-Vera, L. R. & Yanofsky, C. Conserved residues Asp16 and Pro24 of TnaC–tRNAPro participate in tryptophan induction of tna operon expression. J. Bacteriol. 190, 4791–4797 (2008).

  30. 30.

    Stewart, V. & Yanofsky, C. Role of leader peptide synthesis in tryptophanase operon expression in Escherichia coli K-12. J. Bacteriol. 167, 383–386 (1986).

  31. 31.

    Schmidt, A. et al. The quantitative and condition-dependent Escherichia coli proteome. Nat. Biotechnol. 34, 104–110 (2015).

  32. 32.

    Martinez, A. K. et al. Interactions of the TnaC nascent peptide with rRNA in the exit tunnel enable the ribosome to respond to free tryptophan. Nucleic Acids Res. 42, 1245–1256 (2014).

  33. 33.

    Seip, B., Sacheau, G., Dupuy, D. & Innis, C. A. Ribosomal stalling landscapes revealed by high-throughput inverse toeprinting of mRNA libraries. Life Sci. Alliance 1, e201800148 (2018).

  34. 34.

    Melnikov, S. et al. Molecular insights into protein synthesis with proline residues. EMBO Rep. 17, 1776–1784 (2016).

  35. 35.

    Proshkin, S., Rahmouni, R., Mironov, A. & Nudler, E. Cooperation between translating ribosomes and RNA polymerase in transcription elongation. Science 328, 504–508 (2010).

  36. 36.

    Shaham, G. & Tuller, T. Genome scale analysis of Escherichia coli with a comprehensive prokaryotic sequence-based biophysical model of translation initiation and elongation. DNA Res. 25, 195–205 (2018).

  37. 37.

    Salis, H. M., Mirsky, E. A. & Voigt, C. A. Automated design of synthetic ribosome binding sites to control protein expression. Nat. Biotechnol. 27, 946–950 (2009).

  38. 38.

    Artsimovitch, I. & Landick, R. Interaction of a nascent RNA structure with RNA polymerase is required for hairpin-dependent transcriptional pausing but not for transcript release. Genes Dev. 12, 3110–3122 (1998).

  39. 39.

    Gong, F. & Yanofsky, C. A transcriptional pause synchronizes translation with transcription in the tryptophanase operon leader region. J. Bacteriol. 185, 6472–6476 (2003).

  40. 40.

    Bonde, M. T. et al. Predictable tuning of protein expression in bacteria. Nat. Methods 13, 233–236 (2016).

  41. 41.

    Bischoff, L., Berninghausen, O. & Beckmann, R. Molecular basis for the ribosome functioning as an l-tryptophan sensor. Cell Rep. 9, 469–475 (2014).

  42. 42.

    Seip, B. & Innis, C. A. How widespread is metabolite sensing by ribosome-arresting nascent peptides? J. Mol. Biol. 428, 2217–2227 (2016).

  43. 43.

    Zhang, H. et al. Genome editing of upstream open reading frames enables translational control in plants. Nat. Biotechnol. 36, 894–898 (2018).

  44. 44.

    Elowitz, M. B., Levine, A. J., Siggia, E. D. & Swain, P. S. Stochastic gene expression in a single cell. Science 297, 1183–1186 (2002).

  45. 45.

    Chu, D. Limited by sensing—a minimal stochastic model of the lag-phase during diauxic growth. J. Theor. Biol. 414, 137–146 (2017).

  46. 46.

    Lycus, P et al. A bet-hedging strategy for denitrifying bacteria curtails their release of N2O. Proc. Natl Acad. Sci. USA 115, 11820–11825 (2018).

  47. 47.

    Yano, J. M. et al. Indigenous bacteria from the gut microbiota regulate host serotonin biosynthesis. Cell 161, 264–276 (2015).

  48. 48.

    Lu, J. et al. Combinatorial modulation of galP and glk gene expression for improved alternative glucose utilization. Appl. Microbiol. Biotechnol. 93, 2455–2462 (2012).

  49. 49.

    Gohl, D. M et al. Systematic improvement of amplicon marker gene methods for increased accuracy in microbiome studies. Nat. Biotechnol. 34, 942–949 (2016).

  50. 50.

    Monk, J. M. et al. Genome-scale metabolic reconstructions of multiple Escherichia coli strains highlight strain-specific adaptations to nutritional environments. Proc. Natl Acad. Sci. USA 110, 20338–20343 (2013).

  51. 51.

    Kumar, S., Stecher, G. & Tamura, K. MEGA7: molecular evolutionary genetics analysis version 7.0 for bigger datasets. Mol. Biol. Evol. 33, 1870–1874 (2016).

  52. 52.

    Gruber, A. R., Lorenz, R., Bernhart, S. H., Neubock, R. & Hofacker, I. L. The Vienna RNA websuite. Nucleic Acids Res. 36, W70–W74 (2008).

Download references


We would like to thank C. Pan for support in FACS experiment. We thank C. Guan and H. Feng from the C. Zhang and H. Xing laboratories for their assistance in treatment of raw NGS data and phylogenetic analysis. We also would like to thank J. Zhang for critical discussion about the dynamic process, Q. Hu for critical discussion of structural biology and J. Liu for comments on the manuscript. This work was supported by the National Natural Science Foundation of China (NSFC21676156, to C.Z.), Tsinghua University Initiative Scientific Research Program (20161080108, to C.Z.), the National Natural Science Foundation of China (NSFC21627812, to X.-H.X.), a postdoctoral innovation support plan from the China Postdoctoral Science Foundation (to T.W.) and a postdoctoral fellowship from the Tsinghua–Peking Joint Center for Life Sciences (to T.W.).

Author information

T.W. conceived the project and contributed to all computational analysis, validation experiments, data analysis, model design and preparation of the manuscript. X.Z. conceived the project, performed FACS–seq, validation experiments and data analysis, and helped write the manuscript. H.J. helped in performing validation experiments. T.L.W. helped in analysis of the TnaC–nascent peptide structure. C.Z. conceived and supervised the project, and edited the manuscript. X.-H.X. supervised the project.

Correspondence to Tianmin Wang or Chong Zhang.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Tables 1–9, Supplementary Figs. 1–29, Supplementary Note

Reporting Summary

Supplementary Dataset 1

The result of FACS-seq about the response of each sensor variant (mean value, u)

Supplementary Dataset 2

The result of FACS-seq about the response of each sensor variant (noise, σ)

Supplementary Dataset 3

The nucleotide sequences of the 1,450 synthetic tnaC variants used in this work

Supplementary Dataset 4

The nucleotide sequences of all collected tnaC genes from gut bacteria

Supplementary Dataset 5

The mRNA folding energy values of tnaC variants with mutations covering codon 2~9 (n = 444)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Wang, T., Zheng, X., Ji, H. et al. Dynamics of transcription–translation coordination tune bacterial indole signaling. Nat Chem Biol 16, 440–449 (2020). https://doi.org/10.1038/s41589-019-0430-3

Download citation