Looking for the pick of the bunch: high-throughput screening of producing microorganisms with biosensors

Schallmey, Marcus; Frunzke, Julia; Eggeling, Lothar; Marienhagen, Jan

doi:10.1016/j.copbio.2014.01.005

Cited by 126 publications

(90 citation statements)

References 36 publications

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“….) [2,71] or the presence of key metabolites [13,72]. These systems are valuable for the determination of the metabolic potential of microbial cell factories, and have notably been exploited for high-throughput selection of a high amino acid producer for Corynebacterium glutamicum [73].…”

Section: Opinionmentioning

confidence: 99%

Metabolic variability in bioprocessing: implications of microbial phenotypic heterogeneity

Delvigne

Zune

Lara

et al. 2014

Trends in Biotechnology

102

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Section: Opinionmentioning

confidence: 99%

Metabolic variability in bioprocessing: implications of microbial phenotypic heterogeneity

Delvigne

Zune

Lara

et al. 2014

Trends in Biotechnology

102

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“…[1][2][3][4] Here, the natural link between molecular recognition and gene expression is used to report the presence and production of a metabolite of interest. For cases where there is no known transcriptional regulatory protein that responds to a desired compound, an existing transcriptional regulatory protein may be engineered to exhibit altered specificity toward the compound of interest.…”

Section: Introductionmentioning

confidence: 99%

Analysis of amino acid substitutions in AraC variants that respond to triacetic acid lactone

et al. 2016

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The Escherichia coli regulatory protein AraC regulates expression of ara genes in response to L-arabinose. In efforts to develop genetically encoded molecular reporters, we previously engineered an AraC variant that responds to the compound triacetic acid lactone (TAL). This variant (named "AraC-TAL1") was isolated by screening a library of AraC variants, in which five amino acid positions in the ligand-binding pocket were simultaneously randomized. Screening was carried out through multiple rounds of alternating positive and negative fluorescence-activated cell sorting. Here we show that changing the screening protocol results in the identification of different TAL-responsive variants (nine new variants). Individual substituted residues within these variants were found to primarily act cooperatively toward the gene expression response. Finally, X-ray diffraction was used to solve the crystal structure of the apo AraC-TAL1 ligand-binding domain. The resolved crystal structure confirms that this variant takes on a structure nearly identical to the apo wild-type AraC ligand-binding domain (root-mean-square deviation 0.93 Å ), suggesting that AraC-TAL1 behaves similar to wild-type with regard to ligand recognition and gene regulation. Our results provide amino acid sequence-function data sets for training and validating AraC modeling studies, and contribute to our understanding of how to design new biosensors based on AraC.

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“…While biosensors have been reviewed previously (Gredell et al, 2012;Palmer et al, 2011;Schallmey et al, 2014;Su et al, 2011;Zhang and Keasling, 2011), this review focuses on recent advances in metabolite biosensors and emphasizes their applications for metabolic engineering. Here we classify metabolite biosensors into five categories based on their diverse mechanisms of sensing and functional output, including (1) metabolite-responsive transcription factors, (2) two-component systems, (3) cellular stress response, (4) regulatory RNAs, and (5) protein activities.…”

Section: Introductionmentioning

confidence: 99%

Applications and advances of metabolite biosensors for metabolic engineering

Liu

Evans

Zhang

2015

Metabolic Engineering

168

138

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Quantification and regulation of pathway metabolites is crucial for optimization of microbial production bioprocesses. Genetically encoded biosensors provide the means to couple metabolite sensing to several outputs invaluable for metabolic engineering. These include semi-quantification of metabolite concentrations to screen or select strains with desirable metabolite characteristics, and construction of dynamic metabolite-regulated pathways to enhance production. Taking inspiration from naturally occurring systems, biosensor functions are based on highly diverse mechanisms including metabolite responsive transcription factors, two component systems, cellular stress responses, regulatory RNAs, and protein activities. We review recent developments in biosensors in each of these mechanistic classes, with considerations towards how these sensors are engineered, how new sensing mechanisms have led to improved function, and the advantages and disadvantages of each of these sensing mechanisms in relevant applications. We particularly highlight recent examples directly using biosensors to improve microbial production, and the great potential for biosensors to further inform metabolic engineering practices.

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Looking for the pick of the bunch: high-throughput screening of producing microorganisms with biosensors

Cited by 126 publications

References 36 publications

Metabolic variability in bioprocessing: implications of microbial phenotypic heterogeneity

Metabolic variability in bioprocessing: implications of microbial phenotypic heterogeneity

Analysis of amino acid substitutions in AraC variants that respond to triacetic acid lactone

Applications and advances of metabolite biosensors for metabolic engineering

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