Novel approaches for metabolic engineering of yeast at multiple scales (2024)

ACS Synthetic Biology, 2018

Genetics, 2006

Frontiers in Bioengineering and Biotechnology, 2020

Over the past 15 years, the seismic shifts caused by the convergence of biomolecular, chemical, physical, mathematical and computational sciences alongside cutting-edge developments in information technology and engineering have erupted into a new field of scientific endeavor dubbed Synthetic Biology. Recent rapid advances in high-throughput DNA sequencing and DNA synthesis techniques are enabling the design and construction of new biological parts (genes), devices (gene networks) and modules (biosynthetic pathways), and the redesign of biological systems (cells and organisms) for useful purposes. In 2014, the budding yeast Saccharomyces cerevisiae became the first eukaryotic cell to be equipped with a fully functional synthetic chromosome. This was achieved following the synthesis of the first viral (poliovirus in 2002 and bacteriophage X174 in 2003) and bacterial (Mycoplasma genitalium in 2008 and Mycoplasma mycoides in 2010) genomes, and less than two decades after revealing the full genome sequence of a laboratory (S288c in 1996) and wine (AWRI1631 in 2008) yeast strain. A large international project – the Synthetic Yeast Genome (Sc2.0) Project – is now underway to synthesize all 16 chromosomes (12 Mb carrying 6,000 genes) of the sequenced S288c lab strain by the end of 2017. If successful, S. cerevisiae will become the first eukaryote to cross the horizon of in silico design of complex cells through de novo synthesis, reshuffling and editing of genomes. In the meantime, yeast are being used as cell factories for the semi-synthetic production of high-value compounds, such as the potent antimalarial artemisinin, and food ingredients like resveratrol, vanillin, stevia, nootkatone and saffron. As a continuum of previously genetically-engineered industrially-important yeast strains, precision genome engineering is bound to also impact the study and development of wine yeast strains supercharged with synthetic DNA. The first taste of what the future holds is the de novo production of the raspberry ketone aroma compound, 4-[4-hydroxyphenyl]butan-2-one, in a wine yeast strain (AWRI1631), which was recently achieved via metabolic pathway engineering and synthetic enzyme fusion. A peek over the horizon is revealing that the future of ‘Wine Yeast 2.0’ is already here. Therefore, this article seeks to help prepare the wine industry – an industry rich in history and tradition on the one hand, and innovation on the other – for the inevitable intersection of the ancient art practiced by winemakers and inventive science of pioneering ‘synthetic genomicists’. It would be prudent to proactively engage all stakeholders – researchers, industry practitioners, policymakers, regulators, commentators and consumers – in a meaningful dialogue about the potential challenges and opportunities emanating from Synthetic Biology. To capitalize on the new vistas of synthetic yeast genomics, this paper presents wine yeast research in a fresh context, raises important questions and proposes new directions.

bioRxiv (Cold Spring Harbor Laboratory), 2023

Nature biotechnology, 2018

Precise control over microbial cell growth conditions could enable detection of minute phenotypic changes, which would improve our understanding of how genotypes are shaped by adaptive selection. Although automated cell-culture systems such as bioreactors offer strict control over liquid culture conditions, they often do not scale to high-throughput or require cumbersome redesign to alter growth conditions. We report the design and validation of eVOLVER, a scalable do-it-yourself (DIY) framework, which can be configured to carry out high-throughput growth experiments in molecular evolution, systems biology, and microbiology. High-throughput evolution of yeast populations grown at different densities reveals that eVOLVER can be applied to characterize adaptive niches. Growth selection on a genome-wide yeast knockout library, using temperatures varied over different timescales, finds strains sensitive to temperature changes or frequency of temperature change. Inspired by large-scale i...

Metabolic Engineering, 2013

Cell Biology Education, 2005

BMC Bioinformatics, 2005

Background Through genetic engineering it is possible to introduce targeted genetic changes and hereby engineer the metabolism of microbial cells with the objective to obtain desirable phenotypes. However, owing to the complexity of metabolic networks, both in terms of structure and regulation, it is often difficult to predict the effects of genetic modifications on the resulting phenotype. Recently genome-scale metabolic models have been compiled for several different microorganisms where structural and stoichiometric complexity is inherently accounted for. New algorithms are being developed by using genome-scale metabolic models that enable identification of gene knockout strategies for obtaining improved phenotypes. However, the problem of finding optimal gene deletion strategy is combinatorial and consequently the computational time increases exponentially with the size of the problem, and it is therefore interesting to develop new faster algorithms. Results In this study we report an evolutionary programming based method to rapidly identify gene deletion strategies for optimization of a desired phenotypic objective function. We illustrate the proposed method for two important design parameters in industrial fermentations, one linear and other non-linear, by using a genome-scale model of the yeast Saccharomyces cerevisiae. Potential metabolic engineering targets for improved production of succinic acid, glycerol and vanillin are identified and underlying flux changes for the predicted mutants are discussed. Conclusion We show that evolutionary programming enables solving large gene knockout problems in relatively short computational time. The proposed algorithm also allows the optimization of non-linear objective functions or incorporation of non-linear constraints and additionally provides a family of close to optimal solutions. The identified metabolic engineering strategies suggest that non-intuitive genetic modifications span several different pathways and may be necessary for solving challenging metabolic engineering problems.

ACS Synthetic Biology, 2018

The Yale journal of biology and medicine, 2007

Nucleic Acids Research, 2019

Applied microbiology and biotechnology, 2015

Metabolic engineering facilitates the rational development of recombinant bacterial strains for metabolite overproduction. Building on enormous advances in system biology and synthetic biology, novel strategies have been established for multivariate optimization of metabolic networks in ensemble, spatial, and dynamic manners such as modular pathway engineering, compartmentalization metabolic engineering, and metabolic engineering guided by genome-scale metabolic models, in vitro reconstitution, and systems and synthetic biology. Herein, we summarize recent advances in novel metabolic engineering strategies. Combined with advancing kinetic models and synthetic biology tools, more efficient new strategies for improving cellular properties can be established and applied for industrially important biochemical production.

Microbiology Australia

One of the key challenges for industrial yeast strain development is to obtain a thorough understanding of the biology of yeast and to apply this knowledge to develop novel strains with improved features. The detailed study of individual biological components and the use of metabolic engineering have benefited the development of industrial strains enormously; however, such approaches have failed to describe yeast behaviour in the detail required to reveal the complex interactions operating within such biological systems. How can we accurately describe the biological processes and the interactions that occur during fermentation or cell growth?

BMC Genomics, 2010

Biochemical Society transactions, 2016

GeneMill officially launched on 4th February 2016 and is an open access academic facility located at The University of Liverpool that has been established for the high-throughput construction and testing of synthetic DNA constructs. GeneMill provides end-to-end design, construction and phenotypic characterization of small to large gene constructs or genetic circuits/pathways for academic and industrial applications. Thus, GeneMill is equipping the scientific community with easy access to the validated tools required to explore the possibilities of Synthetic Biology.