Dynamic allocation of orthogonal ribosomes facilitates uncoupling of co-expressed genes

Introduction of synthetic circuits into microbes creates competition between circuit and host genes for shared cellular resources, such as ribosomes. This can lead to the emergence of unwanted coupling between the expression of different circuit genes, complicating the design process and potentially leading to circuit failure. By expressing a synthetic 16S rRNA with altered specificity, we can partition the ribosome pool into host-specific and circuit-specific activities. We show mathematically and experimentally that the effects of resource competition can be alleviated by targeting genes to different ribosomal pools. This division of labour can be used to increase flux through a metabolic pathway. We develop a model of cell physiology which is able to capture these observations and use it to design a dynamic resource allocation controller. When implemented, this controller acts to decouple genes by increasing orthogonal ribosome production as the demand for translational resources by a synthetic circuit increases.Competition between synthetic genetic circuits and host genes for shared resources can complicate circuit design and lead to failure. Here the authors demonstrate, mathematically and experimentally, the use of orthogonal ribosomes to decouple competing genes.

[1]  T. Hwa,et al.  Interdependence of Cell Growth and Gene Expression: Origins and Consequences , 2010, Science.

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

[3]  Domitilla Del Vecchio,et al.  Mitigation of resource competition in synthetic genetic circuits through feedback regulation , 2014, 53rd IEEE Conference on Decision and Control.

[4]  Domitilla Del Vecchio,et al.  Resource Competition Shapes the Response of Genetic Circuits , 2017, bioRxiv.

[5]  H. D. de Boer,et al.  Specialized ribosome system: preferential translation of a single mRNA species by a subpopulation of mutated ribosomes in Escherichia coli. , 1987, Proceedings of the National Academy of Sciences of the United States of America.

[6]  J. Sambrook,et al.  Molecular Cloning: A Laboratory Manual , 2001 .

[7]  Christopher A. Voigt,et al.  Principles of genetic circuit design , 2014, Nature Methods.

[8]  Yizhi Cai,et al.  Orthogonal Ribosome Biofirewall. , 2017, ACS synthetic biology.

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

[10]  Christopher V. Rao,et al.  Computational design of orthogonal ribosomes , 2008, Nucleic acids research.

[11]  Adam P. Arkin,et al.  Programming mRNA decay to modulate synthetic circuit resource allocation , 2016 .

[12]  Jason W. Chin,et al.  Concerted, Rapid, Quantitative, and Site-Specific Dual Labeling of Proteins. , 2014 .

[13]  H. Mori,et al.  Construction of Escherichia coli K-12 in-frame, single-gene knockout mutants: the Keio collection , 2006, Molecular systems biology.

[14]  J. Chin,et al.  A network of orthogonal ribosome·mRNA pairs , 2005, Nature chemical biology.

[15]  Zsuzsanna Gyorfy,et al.  Engineered ribosomal RNA operon copy-number variants of E. coli reveal the evolutionary trade-offs shaping rRNA operon number , 2015, Nucleic acids research.

[16]  Manish Kushwaha,et al.  A portable expression resource for engineering cross-species genetic circuits and pathways , 2015, Nature Communications.

[17]  David Vanderbilt,et al.  Origins and Consequences of Surface Stress , 1996 .

[18]  Mauricio Barahona,et al.  Tuning the dials of Synthetic Biology , 2013, Microbiology.

[19]  H. Salis,et al.  Translation rate is controlled by coupled trade-offs between site accessibility, selective RNA unfolding and sliding at upstream standby sites , 2013, Nucleic acids research.

[20]  Farren J. Isaacs,et al.  Repurposing the translation apparatus for synthetic biology. , 2015, Current opinion in chemical biology.

[21]  Ljubica Tasic,et al.  Advances in Chromobacterium violaceum and properties of violacein-Its main secondary metabolite: A review. , 2016, Biotechnology advances.

[22]  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.

[23]  S. Ho,et al.  Engineering hybrid genes without the use of restriction enzymes: gene splicing by overlap extension. , 1989, Gene.

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

[25]  Robert C. Wolpert,et al.  A Review of the , 1985 .

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

[27]  Yinjie J. Tang,et al.  Decoupling Resource-Coupled Gene Expression in Living Cells. , 2017, ACS synthetic biology.

[28]  A. Malcolm Campbell,et al.  Improving the Lac System for Synthetic Biology , 2010 .

[29]  Michael C. Jewett,et al.  Protein synthesis by ribosomes with tethered subunits , 2015, Nature.

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

[31]  U. Alon,et al.  Cost of unneeded proteins in E. coli is reduced after several generations in exponential growth. , 2010, Molecular cell.

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

[33]  K. Oost,et al.  An assessment of the global impact of 21st century land use change on soil erosion , 2017, Nature Communications.

[34]  Domitilla Del Vecchio,et al.  A quasi-integral controller for adaptation of genetic modules to variable ribosome demand , 2018 .

[35]  Igor L. Medintz,et al.  Exploiting the Feedstock Flexibility of the Emergent Synthetic Biology Chassis Vibrio natriegens for Engineered Natural Product Production , 2019, Marine drugs.

[36]  K. Matthews,et al.  Operator DNA sequence variation enhances high affinity binding by hinge helix mutants of lactose repressor protein. , 2000, Biochemistry.

[37]  D. G. Gibson,et al.  Enzymatic assembly of DNA molecules up to several hundred kilobases , 2009, Nature Methods.

[38]  K. Potrykus,et al.  (p)ppGpp: still magical? , 2008, Annual review of microbiology.

[39]  Juhyun Kim,et al.  The Standard European Vector Architecture (SEVA): a coherent platform for the analysis and deployment of complex prokaryotic phenotypes , 2012, Nucleic Acids Res..

[40]  Domitilla Del Vecchio,et al.  Mitigation of ribosome competition through distributed sRNA feedback , 2016, 2016 IEEE 55th Conference on Decision and Control (CDC).

[41]  B. Wanner,et al.  One-step inactivation of chromosomal genes in Escherichia coli K-12 using PCR products. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[42]  C. Rodríguez-Caso,et al.  Dealing with the genetic load in bacterial synthetic biology circuits: convergences with the Ohm's law , 2015, Nucleic acids research.

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