Burden-driven feedback control of gene expression

Cells use feedback regulation to ensure robust growth despite fluctuating demands on resources and different environmental conditions. Yet the expression of foreign proteins from engineered constructs is an unnatural burden on resources that cells are not adapted for. Here we combined multiplex RNAseq with an in vivo assay to reveal the major transcriptional changes in two E. coli strains when a set of inducible synthetic constructs are expressed. We identified that native promoters related to the heat-shock response activate expression rapidly in response to synthetic expression, regardless of the construct. Using these promoters, we built a CRISPR/dCas9-based feedback regulation system that automatically adjusts synthetic construct expression in response to burden. Cells equipped with this general-use controller maintain capacity for native gene expression to ensure robust growth and as such outperform unregulated cells at protein yields in batch production. This engineered feedback is the first example of a universal, burden-based biomolecular control system and is modular, tuneable and portable.

[1]  John Doyle,et al.  Module-Based Analysis of Robustness Tradeoffs in the Heat Shock Response System , 2006, PLoS Comput. Biol..

[2]  Peter D. Karp,et al.  The EcoCyc database: reflecting new knowledge about Escherichia coli K-12 , 2016, Nucleic Acids Res..

[3]  Howard M. Salis,et al.  A Biophysical Model of CRISPR/Cas9 Activity for Rational Design of Genome Editing and Gene Regulation , 2016, PLoS Comput. Biol..

[4]  Jacob Beal,et al.  A standard-enabled workflow for synthetic biology. , 2017, Biochemical Society transactions.

[5]  A. Arkin,et al.  Contextualizing context for synthetic biology – identifying causes of failure of synthetic biological systems , 2012, Biotechnology journal.

[6]  Christopher A. Voigt,et al.  Genetic circuit performance under conditions relevant for industrial bioreactors. , 2012, ACS synthetic biology.

[7]  Schuyler F. Baldwin,et al.  The Complete Genome Sequence of Escherichia coli DH10B: Insights into the Biology of a Laboratory Workhorse , 2008, Journal of bacteriology.

[8]  G. Stan,et al.  Overloaded and stressed: whole-cell considerations for bacterial synthetic biology. , 2016, Current opinion in microbiology.

[9]  G. Stan,et al.  Quantifying cellular capacity identifies gene expression designs with reduced burden , 2015, Nature Methods.

[10]  Thomas E Gorochowski,et al.  A Minimal Model of Ribosome Allocation Dynamics Captures Trade-offs in Expression between Endogenous and Synthetic Genes. , 2016, ACS synthetic biology.

[11]  M. Salit,et al.  Synthetic Spike-in Standards for Rna-seq Experiments Material Supplemental Open Access License Commons Creative , 2022 .

[12]  S. Tringe,et al.  Validation of two ribosomal RNA removal methods for microbial metatranscriptomics , 2010, Nature Methods.

[13]  P. Dennis,et al.  Modulation of Chemical Composition and Other Parameters of the Cell at Different Exponential Growth Rates , 2008, EcoSal Plus.

[14]  M. Lynch,et al.  The bioenergetic costs of a gene , 2015, Proceedings of the National Academy of Sciences.

[15]  Michael P. Snyder,et al.  RNA‐Seq: A Method for Comprehensive Transcriptome Analysis , 2010, Current protocols in molecular biology.

[16]  Claus O. Wilke,et al.  Controlled Measurement and Comparative Analysis of Cellular Components in E. coli Reveals Broad Regulatory Changes in Response to Glucose Starvation , 2015, PLoS Comput. Biol..

[17]  John C. Doyle,et al.  Surviving heat shock: control strategies for robustness and performance. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[18]  Adam J. Meyer,et al.  A ‘resource allocator’ for transcription based on a highly fragmented T7 RNA polymerase , 2014, Molecular systems biology.

[19]  A. Jaramillo,et al.  Using promoter libraries to reduce metabolic burden due to plasmid-encoded proteins in recombinant Escherichia coli. , 2016, New biotechnology.

[20]  Herbert M Sauro,et al.  Visualization of evolutionary stability dynamics and competitive fitness of Escherichia coli engineered with randomized multigene circuits. , 2013, ACS synthetic biology.

[21]  Ilias Tagkopoulos,et al.  A synthetic biology approach to self-regulatory recombinant protein production in Escherichia coli , 2012, Journal of biological engineering.

[22]  P. Swain,et al.  Mechanistic links between cellular trade-offs, gene expression, and growth , 2015, Proceedings of the National Academy of Sciences.

[23]  Tom Ellis,et al.  R2oDNA designer: computational design of biologically neutral synthetic DNA sequences. , 2014, ACS synthetic biology.

[24]  Daniel I Bolnick,et al.  Evaluation of TagSeq, a reliable low‐cost alternative for RNAseq , 2016, Molecular ecology resources.

[25]  W. Huber,et al.  Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2 , 2014, Genome Biology.

[26]  Alfonso Jaramillo,et al.  Empirical model and in vivo characterization of the bacterial response to synthetic gene expression show that ribosome allocation limits growth rate. , 2011, Biotechnology journal.

[27]  J. Gierse,et al.  Two novel heat shock genes encoding proteins produced in response to heterologous protein expression in Escherichia coli , 1992, Journal of bacteriology.

[28]  Thomas E Gorochowski,et al.  DNAplotlib: Programmable Visualization of Genetic Designs and Associated Data. , 2017, ACS synthetic biology.

[29]  Richard M Myers,et al.  Transposase mediated construction of RNA-seq libraries. , 2012, Genome research.

[30]  M. Gerstein,et al.  RNA-Seq: a revolutionary tool for transcriptomics , 2009, Nature Reviews Genetics.

[31]  Domitilla Del Vecchio,et al.  Resource Competition Shapes the Response of Genetic Circuits. , 2017, ACS synthetic biology.

[32]  J. Weissman,et al.  Ribosome profiling reveals the what, when, where and how of protein synthesis , 2015, Nature Reviews Molecular Cell Biology.

[33]  Z. Su,et al.  Directional RNA-seq reveals highly complex condition-dependent transcriptomes in E. coli K12 through accurate full-length transcripts assembling , 2013, BMC Genomics.

[34]  Ron Weiss,et al.  Isocost Lines Describe the Cellular Economy of Genetic Circuits , 2015, Biophysical journal.

[35]  J. Collins,et al.  Tunable protein degradation in bacteria , 2014, Nature Biotechnology.

[36]  Christopher A. Voigt,et al.  Internal workings of a genetic circuit , 2017 .

[37]  L. You,et al.  Emergent bistability by a growth-modulating positive feedback circuit. , 2009, Nature chemical biology.

[38]  Christopher A. Voigt,et al.  Genetic circuit design automation , 2016, Science.