Engineering Sensors for Gene Expression Burden.

Cell Engineering Escherichia coli / genetics Escherichia coli Proteins / genetics Gene Expression Profiling / methods Gene Expression Regulation, Bacterial Sequence Analysis, RNA Synthetic Biology
Feedback Gene expression burden RNA-seq Sensor Synthetic construct

Journal

Methods in molecular biology (Clifton, N.J.)
ISSN: 1940-6029
Titre abrégé: Methods Mol Biol
Pays: United States
ID NLM: 9214969

Informations de publication

Date de publication:
2021
Historique:
entrez: 6 1 2021
pubmed: 7 1 2021
medline: 30 3 2021
Statut: ppublish

Résumé

RNA-seq enables the analysis of gene expression profiles across different conditions and organisms. Gene expression burden slows down growth, which results in poor predictability of gene constructs and product yields. Here, we describe how we applied RNA-seq to study the transcriptional profiles of Escherichia coli when burden is elicited during heterologous gene expression. We then present how we selected early responsive promoters from our RNA-seq results to design sensors for gene expression burden. Finally, we describe how we used one of these sensors to develop a burden-driven feedback regulator to improve cellular fitness in engineered E. coli.

Identifiants

DOI: 10.1007/978-1-0716-1032-9_15 PMID: 33405229
pubmed: 33405229
doi: 10.1007/978-1-0716-1032-9_15
doi:

Substances chimiques

Escherichia coli Proteins 0

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

313-330

Références

Ceroni F, Algar R, Stan G-B, Ellis T (2015) Quantifying cellular capacity identifies gene expression designs with reduced burden. Nat Methods 12:415–418. https://doi.org/10.1038/nmeth.3339
doi: 10.1038/nmeth.3339 pubmed: 25849635
Borkowski O, Ceroni F, Stan GB, Ellis T (2016) Overloaded and stressed: whole-cell considerations for bacterial synthetic biology. Curr Opin Microbiol 33:123–130. https://doi.org/10.1016/j.mib.2016.07.009
doi: 10.1016/j.mib.2016.07.009 pubmed: 27494248
Ellis T (2018) Predicting how evolution will beat us. Microb Biotechnol 12(1):41–43. https://doi.org/10.1111/1751-7915.13327
doi: 10.1111/1751-7915.13327 pubmed: 30461203 pmcid: 6302733
Martin VJJ, Pitera DJ, Withers ST et al (2003) Engineering a mevalonate pathway in Escherichia coli for production of terpenoids. Nat Biotechnol 21:796–802. https://doi.org/10.1038/nbt833
doi: 10.1038/nbt833 pubmed: 12778056
Gyorgy A, Jiménez JI, Yazbek J et al (2015) Isocost lines describe the cellular economy of genetic circuits. Biophys J 109:639–646. https://doi.org/10.1016/j.bpj.2015.06.034
doi: 10.1016/j.bpj.2015.06.034 pubmed: 26244745 pmcid: 4572570
Shachrai I, Zaslaver A, Alon U, Dekel E (2010) Cost of unneeded proteins in E. coli is reduced after several generations in exponential growth. Mol Cell 38:758–767. https://doi.org/10.1016/j.molcel.2010.04.015
doi: 10.1016/j.molcel.2010.04.015 pubmed: 20434381
Gertz J, Varley KE, Davis NS et al (2012) Transposase mediated construction of RNA-seq libraries. Genome Res 22:134–141. https://doi.org/10.1101/gr.127373.111.134
doi: 10.1101/gr.127373.111.134 pubmed: 22128135 pmcid: 3246200
Gorochowski TE, Espah Borujeni A, Park Y et al (2017) Genetic circuit characterization and debugging using RNA-seq. Mol Syst Biol 13:952. https://doi.org/10.15252/msb.20167461
doi: 10.15252/msb.20167461 pubmed: 29122925 pmcid: 5731345
Der BS, Glassey E, Bartley BA et al (2017) DNAplotlib: programmable visualization of genetic designs and associated data. ACS Synth Biol 6:1115–1119. https://doi.org/10.1021/acssynbio.6b00252
doi: 10.1021/acssynbio.6b00252 pubmed: 27744689
Myers CJ, Beal J, Gorochowski TE et al (2017) A standard-enabled workflow for synthetic biology. Biochem Soc Trans 45:793–803. https://doi.org/10.1042/BST20160347
doi: 10.1042/BST20160347 pubmed: 28620041
Ceroni F, Boo A, Furini S et al (2018) Burden-driven feedback control of gene expression. Nat Methods 15:387–393. https://doi.org/10.1038/nmeth.4635
doi: 10.1038/nmeth.4635 pubmed: 29578536
Love MI, Huber W, Anders S (2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15:1–21. https://doi.org/10.1186/s13059-014-0550-8
doi: 10.1186/s13059-014-0550-8
Keseler IM, Mackie A, Santos-Zavaleta A et al (2017) The EcoCyc database: reflecting new knowledge about Escherichia coli K-12. Nucleic Acids Res 45:D543–D550. https://doi.org/10.1093/nar/gkw1003
doi: 10.1093/nar/gkw1003 pubmed: 27899573
Farasat I, Salis HM (2016) A biophysical model of CRISPR/Cas9 activity for rational design of genome editing and gene regulation. PLoS Comput Biol 12:1–33. https://doi.org/10.1371/journal.pcbi.1004724
doi: 10.1371/journal.pcbi.1004724
Petrova OE, Garcia-Alcalde F, Zampaloni C, Sauer K (2017) Comparative evaluation of rRNA depletion procedures for the improved analysis of bacterial biofilm and mixed pathogen culture transcriptomes. Sci Rep 7:1–15. https://doi.org/10.1038/srep41114
doi: 10.1038/srep41114
Haldimann A, Wanner BL (2001) Conditional-replication, integration, excision, and retrieval plasmid-host systems for gene structure-function studies of bacteria. J Bacteriol 183:6384–6393. https://doi.org/10.1128/JB.183.21.6384
doi: 10.1128/JB.183.21.6384 pubmed: 11591683 pmcid: 100134
Algar RJR (2013) Understanding, characterising and modelling the interactions between synthetic genetic circuits and their host chassis Rhys James Richmond Algar, MA (Oxon), MRes Submission for the degree of PhD. Imperial College London

Auteurs

Alice Boo (A)

Department of Bioengineering, Imperial College London, London, UK.
Imperial College Centre for Synthetic Biology, Imperial College London, London, UK.

Francesca Ceroni (F)

Imperial College Centre for Synthetic Biology, Imperial College London, London, UK. f.ceroni@imperial.ac.uk.
Department of Chemical Engineering, Imperial College London, London, UK. f.ceroni@imperial.ac.uk.

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Classifications MeSH

questionsmedicales.fr › Technologie, industrie et agriculture › Ingénierie › Bioingénierie › Ingénierie cellulaire › Engineering Sensors for Gene Expression Burden.
questionsmedicales.fr › Bactéries › Proteobacteria › Gammaproteobacteria › Enterobacteriaceae › Escherichia › Escherichia coli › Engineering Sensors for Gene Expression Burden.
questionsmedicales.fr › Acides aminés, peptides et protéines › Protéines › Protéines bactériennes › Protéines Escherichia coli › Engineering Sensors for Gene Expression Burden.
questionsmedicales.fr › Techniques d'investigation › Techniques génétiques › Analyse de profil d'expression de gènes › Engineering Sensors for Gene Expression Burden.
questionsmedicales.fr › Phénomènes génétiques › Régulation de l'expression des gènes › Régulation de l'expression des gènes bactériens › Engineering Sensors for Gene Expression Burden.
questionsmedicales.fr › Techniques d'investigation › Techniques génétiques › Analyse de séquence › Analyse de séquence d'ARN › Engineering Sensors for Gene Expression Burden.
questionsmedicales.fr › Technologie, industrie et agriculture › Ingénierie › Bioingénierie › Biologie synthétique › Engineering Sensors for Gene Expression Burden.