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Zhao M, Ma J, Zhang L, Qi H. Engineering strategies for enhanced heterologous protein production by Saccharomyces cerevisiae. Microb Cell Fact 2024; 23:32. [PMID: 38247006 PMCID: PMC10801990 DOI: 10.1186/s12934-024-02299-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Accepted: 01/05/2024] [Indexed: 01/23/2024] Open
Abstract
Microbial proteins are promising substitutes for animal- and plant-based proteins. S. cerevisiae, a generally recognized as safe (GRAS) microorganism, has been frequently employed to generate heterologous proteins. However, constructing a universal yeast chassis for efficient protein production is still a challenge due to the varying properties of different proteins. With progress in synthetic biology, a multitude of molecular biology tools and metabolic engineering strategies have been employed to alleviate these issues. This review first analyses the advantages of protein production by S. cerevisiae. The most recent advances in improving heterologous protein yield are summarized and discussed in terms of protein hyperexpression systems, protein secretion engineering, glycosylation pathway engineering and systems metabolic engineering. Furthermore, the prospects for efficient and sustainable heterologous protein production by S. cerevisiae are also provided.
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Affiliation(s)
- Meirong Zhao
- Department of Biochemical Engineering, School of Chemical Engineering and Technology, Frontier Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (MOE), Tianjin University, Tianjin, 300350, China
| | - Jianfan Ma
- Department of Biochemical Engineering, School of Chemical Engineering and Technology, Frontier Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (MOE), Tianjin University, Tianjin, 300350, China
| | - Lei Zhang
- Department of Biochemical Engineering, School of Chemical Engineering and Technology, Frontier Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (MOE), Tianjin University, Tianjin, 300350, China
| | - Haishan Qi
- Department of Biochemical Engineering, School of Chemical Engineering and Technology, Frontier Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (MOE), Tianjin University, Tianjin, 300350, China.
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2
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Jiang Y, MacNeil LT. Simple model systems reveal conserved mechanisms of Alzheimer's disease and related tauopathies. Mol Neurodegener 2023; 18:82. [PMID: 37950311 PMCID: PMC10638731 DOI: 10.1186/s13024-023-00664-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2023] [Accepted: 10/04/2023] [Indexed: 11/12/2023] Open
Abstract
The lack of effective therapies that slow the progression of Alzheimer's disease (AD) and related tauopathies highlights the need for a more comprehensive understanding of the fundamental cellular mechanisms underlying these diseases. Model organisms, including yeast, worms, and flies, provide simple systems with which to investigate the mechanisms of disease. The evolutionary conservation of cellular pathways regulating proteostasis and stress response in these organisms facilitates the study of genetic factors that contribute to, or protect against, neurodegeneration. Here, we review genetic modifiers of neurodegeneration and related cellular pathways identified in the budding yeast Saccharomyces cerevisiae, the nematode Caenorhabditis elegans, and the fruit fly Drosophila melanogaster, focusing on models of AD and related tauopathies. We further address the potential of simple model systems to better understand the fundamental mechanisms that lead to AD and other neurodegenerative disorders.
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Affiliation(s)
- Yuwei Jiang
- Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, Canada
| | - Lesley T MacNeil
- Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, Canada.
- Farncombe Family Digestive Health Research Institute, McMaster University, Hamilton, Canada.
- Michael G. DeGroote Institute for Infectious Disease Research, McMaster University, 1280 Main St W, Hamilton, ON, L8S 4K1, Canada.
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3
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Rader JA, Pivovarnik MA, Vantilburg ME, Whitehouse LS. PhyloMatcher: a tool for resolving conflicts in taxonomic nomenclature. BIOINFORMATICS ADVANCES 2023; 3:vbad144. [PMID: 37840907 PMCID: PMC10576170 DOI: 10.1093/bioadv/vbad144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 08/31/2023] [Accepted: 10/03/2023] [Indexed: 10/17/2023]
Abstract
Summary Large-scale comparative studies rely on the application of both phylogenetic trees and phenotypic data, both of which come from a variety of sources, but due to the changing nature of phylogenetic classification over time, many taxon names in comparative datasets do not match the nomenclature in phylogenetic trees. Manual curation of taxonomic synonyms in large comparative datasets can be daunting. To address this issue, we introduce PhyloMatcher, a tool which allows for programmatic querying of the National Center for Biotechnology Information Taxonomy and Global Biodiversity Information Facility databases to find associated synonyms with given target species names. Availability and implementation PhyloMatcher is easily installed as a Python package with pip, or as a standalone GUI application. PhyloMatcher source code and documentation are freely available at https://github.com/Lswhiteh/PhyloMatcher, the GUI application can be downloaded from the Releases page.
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Affiliation(s)
- Jonathan A Rader
- Department of Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599-3280, United States
| | - Madelyn A Pivovarnik
- Department of Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599-3280, United States
| | - Matias E Vantilburg
- Department of Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599-3280, United States
| | - Logan S Whitehouse
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599-7264, United States
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4
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Rader JA, Pivovarnik MA, Vantilburg ME, Whitehouse LS. PhyloMatcher: a tool for resolving conflicts in taxonomic nomenclature. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.07.552263. [PMID: 37609275 PMCID: PMC10441299 DOI: 10.1101/2023.08.07.552263] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/24/2023]
Abstract
Summary Large-scale comparative studies rely on the application of both phylogenetic trees and phenotypic data, both of which come from a variety of sources, but due to the changing nature of phylogenetic classification over time, many taxon names in comparative datasets do not match the nomenclature in phylogenetic trees. Manual curation of taxonomic synonyms in large comparative datasets can be daunting. To address this issue, we introduce PhyloMatcher, a tool which allows for programmatic querying of two commonly used taxonomic databases to find associated synonyms with given target species names. Availability and implementation PhyloMatcher is easily installed as a Python package with pip, or as a standalone GUI application. PhyloMatcher source code and documentation are freely available at https://github.com/Lswhiteh/PhyloMatcher, the GUI application can be downloaded from the Releases page. Contact Lswhiteh@unc.edu. Supplemental Information We provide documentation for PhyloMatcher, including walkthrough instructions for the GUI application on the Releases page of https://github.com/Lswhiteh/PhyloMatcher.
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Affiliation(s)
- Jonathan A. Rader
- Dept. of Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | | | - Matias E. Vantilburg
- Dept. of Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Logan S. Whitehouse
- Dept. of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
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5
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Parikh SB, Houghton C, Van Oss SB, Wacholder A, Carvunis A. Origins, evolution, and physiological implications of de novo genes in yeast. Yeast 2022; 39:471-481. [PMID: 35959631 PMCID: PMC9544372 DOI: 10.1002/yea.3810] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Revised: 08/08/2022] [Accepted: 08/09/2022] [Indexed: 12/03/2022] Open
Abstract
De novo gene birth is the process by which new genes emerge in sequences that were previously noncoding. Over the past decade, researchers have taken advantage of the power of yeast as a model and a tool to study the evolutionary mechanisms and physiological implications of de novo gene birth. We summarize the mechanisms that have been proposed to explicate how noncoding sequences can become protein-coding genes, highlighting the discovery of pervasive translation of the yeast transcriptome and its presumed impact on evolutionary innovation. We summarize current best practices for the identification and characterization of de novo genes. Crucially, we explain that the field is still in its nascency, with the physiological roles of most young yeast de novo genes identified thus far still utterly unknown. We hope this review inspires researchers to investigate the true contribution of de novo gene birth to cellular physiology and phenotypic diversity across yeast strains and species.
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Affiliation(s)
- Saurin B. Parikh
- Department of Computational and Systems Biology, School of Medicine, Pittsburgh Center for Evolutionary Biology and EvolutionUniversity of PittsburghPittsburghPennsylvaniaUSA
| | - Carly Houghton
- Department of Computational and Systems Biology, School of Medicine, Pittsburgh Center for Evolutionary Biology and EvolutionUniversity of PittsburghPittsburghPennsylvaniaUSA
| | - S. Branden Van Oss
- Department of Computational and Systems Biology, School of Medicine, Pittsburgh Center for Evolutionary Biology and EvolutionUniversity of PittsburghPittsburghPennsylvaniaUSA
| | - Aaron Wacholder
- Department of Computational and Systems Biology, School of Medicine, Pittsburgh Center for Evolutionary Biology and EvolutionUniversity of PittsburghPittsburghPennsylvaniaUSA
| | - Anne‐Ruxandra Carvunis
- Department of Computational and Systems Biology, School of Medicine, Pittsburgh Center for Evolutionary Biology and EvolutionUniversity of PittsburghPittsburghPennsylvaniaUSA
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6
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OUP accepted manuscript. Brief Funct Genomics 2022; 21:243-269. [DOI: 10.1093/bfgp/elac007] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Revised: 03/17/2022] [Accepted: 03/18/2022] [Indexed: 11/14/2022] Open
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Otto M, Liu D, Siewers V. Saccharomyces cerevisiae as a Heterologous Host for Natural Products. Methods Mol Biol 2022; 2489:333-367. [PMID: 35524059 DOI: 10.1007/978-1-0716-2273-5_18] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Cell factories can provide a sustainable supply of natural products with applications as pharmaceuticals, food-additives or biofuels. Besides being an important model organism for eukaryotic systems, Saccharomyces cerevisiae is used as a chassis for the heterologous production of natural products. Its success as a cell factory can be attributed to the vast knowledge accumulated over decades of research, its overall ease of engineering and its robustness. Many methods and toolkits have been developed by the yeast metabolic engineering community with the aim of simplifying and accelerating the engineering process.In this chapter, a range of methodologies are highlighted, which can be used to develop novel natural product cell factories or to improve titer, rate and yields of an existing cell factory with the goal of developing an industrially relevant strain. The addressed topics are applicable for different stages of a cell factory engineering project and include the choice of a natural product platform strain, expression cassette design for heterologous or native genes, basic and advanced genetic engineering strategies, and library screening methods using biosensors. The many engineering methods available and the examples of yeast cell factories underline the importance and future potential of this host for industrial production of natural products.
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Affiliation(s)
- Maximilian Otto
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
- Novo Nordisk Foundation Center for Biosustainability, Chalmers University of Technology, Gothenburg, Sweden
| | - Dany Liu
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
- Novo Nordisk Foundation Center for Biosustainability, Chalmers University of Technology, Gothenburg, Sweden
| | - Verena Siewers
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden.
- Novo Nordisk Foundation Center for Biosustainability, Chalmers University of Technology, Gothenburg, Sweden.
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Zeng H, Rohani R, Huang WE, Yang A. Understanding and mathematical modelling of cellular resource allocation in microorganisms: a comparative synthesis. BMC Bioinformatics 2021; 22:467. [PMID: 34583645 PMCID: PMC8479906 DOI: 10.1186/s12859-021-04382-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Accepted: 09/20/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND The rising consensus that the cell can dynamically allocate its resources provides an interesting angle for discovering the governing principles of cell growth and metabolism. Extensive efforts have been made in the past decade to elucidate the relationship between resource allocation and phenotypic patterns of microorganisms. Despite these exciting developments, there is still a lack of explicit comparison between potentially competing propositions and a lack of synthesis of inter-related proposals and findings. RESULTS In this work, we have reviewed resource allocation-derived principles, hypotheses and mathematical models to recapitulate important achievements in this area. In particular, the emergence of resource allocation phenomena is deciphered by the putative tug of war between the cellular objectives, demands and the supply capability. Competing hypotheses for explaining the most-studied phenomenon arising from resource allocation, i.e. the overflow metabolism, have been re-examined towards uncovering the potential physiological root cause. The possible link between proteome fractions and the partition of the ribosomal machinery has been analysed through mathematical derivations. Finally, open questions are highlighted and an outlook on the practical applications is provided. It is the authors' intention that this review contributes to a clearer understanding of the role of resource allocation in resolving bacterial growth strategies, one of the central questions in microbiology. CONCLUSIONS We have shown the importance of resource allocation in understanding various aspects of cellular systems. Several important questions such as the physiological root cause of overflow metabolism and the correct interpretation of 'protein costs' are shown to remain open. As the understanding of the mechanisms and utility of resource application in cellular systems further develops, we anticipate that mathematical modelling tools incorporating resource allocation will facilitate the circuit-host design in synthetic biology.
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Affiliation(s)
- Hong Zeng
- Beijing Advanced Innovation Center for Food Nutrition and Human Health, Beijing Technology and Business University, Beijing, 100048, China
| | - Reza Rohani
- Department of Engineering Science, University of Oxford, Parks Road, Oxford, OX1 3PJ, UK
| | - Wei E Huang
- Department of Engineering Science, University of Oxford, Parks Road, Oxford, OX1 3PJ, UK
| | - Aidong Yang
- Department of Engineering Science, University of Oxford, Parks Road, Oxford, OX1 3PJ, UK.
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Yu R, Vorontsov E, Sihlbom C, Nielsen J. Quantifying absolute gene expression profiles reveals distinct regulation of central carbon metabolism genes in yeast. eLife 2021; 10:e65722. [PMID: 33720010 PMCID: PMC8016476 DOI: 10.7554/elife.65722] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2020] [Accepted: 03/13/2021] [Indexed: 12/18/2022] Open
Abstract
In addition to controlled expression of genes by specific regulatory circuits, the abundance of proteins and transcripts can also be influenced by physiological states of the cell such as growth rate and metabolism. Here we examine the control of gene expression by growth rate and metabolism, by analyzing a multi-omics dataset consisting of absolute-quantitative abundances of the transcriptome, proteome, and amino acids in 22 steady-state yeast cultures. We find that transcription and translation are coordinately controlled by the cell growth rate via RNA polymerase II and ribosome abundance, but they are independently controlled by nitrogen metabolism via amino acid and nucleotide availabilities. Genes in central carbon metabolism, however, are distinctly regulated and do not respond to the cell growth rate or nitrogen metabolism as all other genes. Understanding these effects allows the confounding factors of growth rate and metabolism to be accounted for in gene expression profiling studies.
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Affiliation(s)
- Rosemary Yu
- Department of Biology and Biological Engineering, Chalmers University of TechnologyGothenburgSweden
- Novo Nordisk Foundation Center for Biosustainability, Chalmers University of TechnologyGothenburgSweden
| | - Egor Vorontsov
- Proteomics Core Facility, Sahlgrenska Academy, University of GothenburgGothenburgSweden
| | - Carina Sihlbom
- Proteomics Core Facility, Sahlgrenska Academy, University of GothenburgGothenburgSweden
| | - Jens Nielsen
- Department of Biology and Biological Engineering, Chalmers University of TechnologyGothenburgSweden
- Novo Nordisk Foundation Center for Biosustainability, Chalmers University of TechnologyGothenburgSweden
- Novo Nordisk Foundation Center for Biosustainability, Technical University of DenmarkLyngbyDenmark
- BioInnovation InstituteCopenhagenDenmark
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10
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Hsu IS, Moses AM. Stochastic models for single-cell data: Current challenges and the way forward. FEBS J 2021; 289:647-658. [PMID: 33570798 DOI: 10.1111/febs.15760] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Revised: 12/22/2020] [Accepted: 02/10/2021] [Indexed: 11/28/2022]
Abstract
Although the quantity and quality of single-cell data have progressed rapidly, making quantitative predictions with single-cell stochastic models remains challenging. The stochastic nature of cellular processes leads to at least three challenges in building models with single-cell data: (a) because variability in single-cell data can be attributed to multiple different sources, it is difficult to rule out conflicting mechanistic models that explain the same data equally well; (b) the distinction between interesting biological variability and experimental variability is sometimes ambiguous; (c) the nonstandard distributions of single-cell data can lead to violations of the assumption of symmetric errors in least-squares fitting. In this review, we first discuss recent studies that overcome some of the challenges or set up a promising direction and then introduce some powerful statistical approaches utilized in these studies. We conclude that applying and developing statistical approaches could lead to further progress in building stochastic models for single-cell data.
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Affiliation(s)
- Ian S Hsu
- Department of Cell & Systems Biology, University of Toronto, ON, Canada
| | - Alan M Moses
- Department of Cell & Systems Biology, University of Toronto, ON, Canada
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11
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Patra P, Das M, Kundu P, Ghosh A. Recent advances in systems and synthetic biology approaches for developing novel cell-factories in non-conventional yeasts. Biotechnol Adv 2021; 47:107695. [PMID: 33465474 DOI: 10.1016/j.biotechadv.2021.107695] [Citation(s) in RCA: 70] [Impact Index Per Article: 23.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2020] [Revised: 12/14/2020] [Accepted: 01/09/2021] [Indexed: 12/14/2022]
Abstract
Microbial bioproduction of chemicals, proteins, and primary metabolites from cheap carbon sources is currently an advancing area in industrial research. The model yeast, Saccharomyces cerevisiae, is a well-established biorefinery host that has been used extensively for commercial manufacturing of bioethanol from myriad carbon sources. However, its Crabtree-positive nature often limits the use of this organism for the biosynthesis of commercial molecules that do not belong in the fermentative pathway. To avoid extensive strain engineering of S. cerevisiae for the production of metabolites other than ethanol, non-conventional yeasts can be selected as hosts based on their natural capacity to produce desired commodity chemicals. Non-conventional yeasts like Kluyveromyces marxianus, K. lactis, Yarrowia lipolytica, Pichia pastoris, Scheffersomyces stipitis, Hansenula polymorpha, and Rhodotorula toruloides have been considered as potential industrial eukaryotic hosts owing to their desirable phenotypes such as thermotolerance, assimilation of a wide range of carbon sources, as well as ability to secrete high titers of protein and lipid. However, the advanced metabolic engineering efforts in these organisms are still lacking due to the limited availability of systems and synthetic biology methods like in silico models, well-characterised genetic parts, and optimized genome engineering tools. This review provides an insight into the recent advances and challenges of systems and synthetic biology as well as metabolic engineering endeavours towards the commercial usage of non-conventional yeasts. Particularly, the approaches in emerging non-conventional yeasts for the production of enzymes, therapeutic proteins, lipids, and metabolites for commercial applications are extensively discussed here. Various attempts to address current limitations in designing novel cell factories have been highlighted that include the advances in the fields of genome-scale metabolic model reconstruction, flux balance analysis, 'omics'-data integration into models, genome-editing toolkit development, and rewiring of cellular metabolisms for desired chemical production. Additionally, the understanding of metabolic networks using 13C-labelling experiments as well as the utilization of metabolomics in deciphering intracellular fluxes and reactions have also been discussed here. Application of cutting-edge nuclease-based genome editing platforms like CRISPR/Cas9, and its optimization towards efficient strain engineering in non-conventional yeasts have also been described. Additionally, the impact of the advances in promising non-conventional yeasts for efficient commercial molecule synthesis has been meticulously reviewed. In the future, a cohesive approach involving systems and synthetic biology will help in widening the horizon of the use of unexplored non-conventional yeast species towards industrial biotechnology.
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Affiliation(s)
- Pradipta Patra
- School of Energy Science and Engineering, Indian Institute of Technology Kharagpur, West Bengal 721302, India
| | - Manali Das
- School of Bioscience, Indian Institute of Technology Kharagpur, West Bengal 721302, India
| | - Pritam Kundu
- School of Energy Science and Engineering, Indian Institute of Technology Kharagpur, West Bengal 721302, India
| | - Amit Ghosh
- School of Energy Science and Engineering, Indian Institute of Technology Kharagpur, West Bengal 721302, India; P.K. Sinha Centre for Bioenergy and Renewables, Indian Institute of Technology Kharagpur, West Bengal 721302, India.
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12
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Chen Y, Banerjee D, Mukhopadhyay A, Petzold CJ. Systems and synthetic biology tools for advanced bioproduction hosts. Curr Opin Biotechnol 2020; 64:101-109. [PMID: 31927061 DOI: 10.1016/j.copbio.2019.12.007] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2019] [Revised: 11/27/2019] [Accepted: 12/08/2019] [Indexed: 02/07/2023]
Abstract
The genomic revolution ushered in an era of discovery and characterization of enzymes from novel organisms that fueled engineering of microbes to produce commodity and high-value compounds. Over the past decade advances in synthetic biology tools in recent years contributed to significant progress in metabolic engineering efforts to produce both biofuels and bioproducts resulting in several such related items being brought to market. These successes represent a burgeoning bio-economy; however, significant resources and time are still necessary to progress a system from proof-of-concept to market. In order to fully realize this potential, methods that examine biological systems in a comprehensive, systematic and high-throughput manner are essential. Recent success in synthetic biology has coincided with the development of systems biology and analytical approaches that kept pace and scaled with technology development. Here, we review a selection of systems biology methods and their use in synthetic biology approaches for microbial biotechnology platforms.
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Affiliation(s)
- Yan Chen
- Joint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA, USA; Agile BioFoundry, Lawrence Berkeley National Laboratory, Emeryville, CA, USA; Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Deepanwita Banerjee
- Joint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA, USA; Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Aindrila Mukhopadhyay
- Joint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA, USA; Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA; Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Christopher J Petzold
- Joint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA, USA; Agile BioFoundry, Lawrence Berkeley National Laboratory, Emeryville, CA, USA; Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.
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