The Expanding Computational Toolbox for Engineering Microbial Phenotypes at the Genome Scale

Zielinski, Daniel C.; Patel, Arjun; Palsson, Bernhard Ø.

doi:10.3390/microorganisms8122050

Cited by 13 publications

(14 citation statements)

References 136 publications

(174 reference statements)

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“… 37 Finally, the meta-analysis of aggregated ALE mutations may also reveal general strain design principles that could inform strain design strategies. 38 …”

Section: Introductionmentioning

confidence: 99%

Escherichia coli Data-Driven Strain Design Using Aggregated Adaptive Laboratory Evolution Mutational Data

et al. 2021

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Microbes are being engineered for an increasingly large and diverse set of applications. However, the designing of microbial genomes remains challenging due to the general complexity of biological systems. Adaptive Laboratory Evolution (ALE) leverages nature’s problem-solving processes to generate optimized genotypes currently inaccessible to rational methods. The large amount of public ALE data now represents a new opportunity for data-driven strain design. This study describes how novel strain designs, or genome sequences not yet observed in ALE experiments or published designs, can be extracted from aggregated ALE data and demonstrates this by designing, building, and testing three novel Escherichia coli strains with fitnesses comparable to ALE mutants. These designs were achieved through a meta-analysis of aggregated ALE mutations data (63 Escherichia coli K-12 MG1655 based ALE experiments, described by 93 unique environmental conditions, 357 independent evolutions, and 13 957 observed mutations), which additionally revealed global ALE mutation trends that inform on ALE-derived strain design principles. Such informative trends anticipate ALE-derived strain designs as largely gene-centric, as opposed to noncoding, and composed of a relatively small number of beneficial variants (approximately 6). These results demonstrate how strain design efforts can be enhanced by the meta-analysis of aggregated ALE data.

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“… 37 Finally, the meta-analysis of aggregated ALE mutations may also reveal general strain design principles that could inform strain design strategies. 38 …”

Section: Introductionmentioning

confidence: 99%

Escherichia coli Data-Driven Strain Design Using Aggregated Adaptive Laboratory Evolution Mutational Data

et al. 2021

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“…Modern methods, especially those recognising the potential of synthetic biology and host engineering to make 'anything' (e.g. [14][15][16][17][18][19][20][21][22][23]), are improving both computational [24] and experimental approaches. The main means of making such navigation more effective is by seeking to recognise those areas that are most 'important' or 'difficult' for the problem of interest, and focusing on them; this is generally true of combinatorial search problems (and to illustrate this, a nice example is given by the means by which the Eternity puzzle https://en.wikipedia.org/wiki/Eternity_puzzle was solved).…”

Section: Introductionmentioning

confidence: 99%

Intelligent host engineering for metabolic flux optimisation in biotechnology

Munro

Kell

2021

Biochemical Journal

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Optimising the function of a protein of length N amino acids by directed evolution involves navigating a ‘search space’ of possible sequences of some 20N. Optimising the expression levels of P proteins that materially affect host performance, each of which might also take 20 (logarithmically spaced) values, implies a similar search space of 20P. In this combinatorial sense, then, the problems of directed protein evolution and of host engineering are broadly equivalent. In practice, however, they have different means for avoiding the inevitable difficulties of implementation. The spare capacity exhibited in metabolic networks implies that host engineering may admit substantial increases in flux to targets of interest. Thus, we rehearse the relevant issues for those wishing to understand and exploit those modern genome-wide host engineering tools and thinking that have been designed and developed to optimise fluxes towards desirable products in biotechnological processes, with a focus on microbial systems. The aim throughput is ‘making such biology predictable’. Strategies have been aimed at both transcription and translation, especially for regulatory processes that can affect multiple targets. However, because there is a limit on how much protein a cell can produce, increasing kcat in selected targets may be a better strategy than increasing protein expression levels for optimal host engineering.

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“…Indeed, it has been recently acknowledged that the most innovative approach currently available to improving the yield of recombinant proteins, while minimising wet-lab costs, relies on the combination of in silico studies to reduce the experimental search space [ 10 ]. Among all the available in silico approaches, genome-scale metabolic models (GEMs) offer the possibility to predict a cellular phenotype from a genotype under certain environmental conditions and, importantly, to identify possible metabolic targets to improve the production of valuable compounds, while ensuring sufficiently high growth rates [ 11 , 12 , 13 ]. GEMs can also be used for descriptive purposes, including the identification of specific metabolic rewiring strategies following external perturbations and/or a nutrient switch [ 14 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

Modelling hCDKL5 Heterologous Expression in Bacteria

et al. 2021

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hCDKL5 refers to the human cyclin-dependent kinase like 5 that is primarily expressed in the brain. Mutations in its coding sequence are often causative of hCDKL5 deficiency disorder, a devastating neurodevelopmental disorder currently lacking a cure. The large-scale recombinant production of hCDKL5 is desirable to boost the translation of preclinical therapeutic approaches into the clinic. However, this is hampered by the intrinsically disordered nature of almost two-thirds of the hCDKL5 sequence, making this region more susceptible to proteolytic attack, and the observed toxicity when the enzyme is accumulated in the cytoplasm of eukaryotic host cells. The bacterium Pseudoalteromonas haloplanktis TAC125 (PhTAC125) is the only prokaryotic host in which the full-length production of hCDKL5 has been demonstrated. To date, a system-level understanding of the metabolic burden imposed by hCDKL5 production is missing, although it would be crucial for upscaling of the production process. Here, we combined experimental data on protein production and nutrients assimilation with metabolic modelling to infer the global consequences of hCDKL5 production in PhTAC125 and to identify potential overproduction targets. Our analyses showed a remarkable accuracy of the model in simulating the recombinant strain phenotype and also identified priority targets for optimised protein production.

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The Expanding Computational Toolbox for Engineering Microbial Phenotypes at the Genome Scale

Cited by 13 publications

References 136 publications

Escherichia coli Data-Driven Strain Design Using Aggregated Adaptive Laboratory Evolution Mutational Data

Escherichia coli Data-Driven Strain Design Using Aggregated Adaptive Laboratory Evolution Mutational Data

Intelligent host engineering for metabolic flux optimisation in biotechnology

Modelling hCDKL5 Heterologous Expression in Bacteria

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