1
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Mao J, Zhang H, Chen Y, Wei L, Liu J, Nielsen J, Chen Y, Xu N. Relieving metabolic burden to improve robustness and bioproduction by industrial microorganisms. Biotechnol Adv 2024; 74:108401. [PMID: 38944217 DOI: 10.1016/j.biotechadv.2024.108401] [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: 02/01/2024] [Revised: 05/04/2024] [Accepted: 06/25/2024] [Indexed: 07/01/2024]
Abstract
Metabolic burden is defined by the influence of genetic manipulation and environmental perturbations on the distribution of cellular resources. The rewiring of microbial metabolism for bio-based chemical production often leads to a metabolic burden, followed by adverse physiological effects, such as impaired cell growth and low product yields. Alleviating the burden imposed by undesirable metabolic changes has become an increasingly attractive approach for constructing robust microbial cell factories. In this review, we provide a brief overview of metabolic burden engineering, focusing specifically on recent developments and strategies for diminishing the burden while improving robustness and yield. A variety of examples are presented to showcase the promise of metabolic burden engineering in facilitating the design and construction of robust microbial cell factories. Finally, challenges and limitations encountered in metabolic burden engineering are discussed.
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Affiliation(s)
- Jiwei Mao
- Department of Life Sciences, Chalmers University of Technology, SE412 96 Gothenburg, Sweden
| | - Hongyu Zhang
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, PR China; College of Life Sciences, University of Chinese Academy of Sciences, Beijing, 100049, PR China
| | - Yu Chen
- Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, PR China
| | - Liang Wei
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, PR China
| | - Jun Liu
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, PR China; Key Laboratory of Engineering Biology for Low-Carbon Manufacturing, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, PR China
| | - Jens Nielsen
- Department of Life Sciences, Chalmers University of Technology, SE412 96 Gothenburg, Sweden; BioInnovation Institute, Ole Maaløes Vej 3, DK2200 Copenhagen, Denmark.
| | - Yun Chen
- Department of Life Sciences, Chalmers University of Technology, SE412 96 Gothenburg, Sweden; Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK2800 Kongens Lyngby, Denmark.
| | - Ning Xu
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, PR China; College of Life Sciences, University of Chinese Academy of Sciences, Beijing, 100049, PR China; Key Laboratory of Engineering Biology for Low-Carbon Manufacturing, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, PR China.
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2
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Xu L, Bai X, Joong Oh E. Strategic approaches for designing yeast strains as protein secretion and display platforms. Crit Rev Biotechnol 2024:1-18. [PMID: 39138023 DOI: 10.1080/07388551.2024.2385996] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2024] [Revised: 07/03/2024] [Accepted: 07/04/2024] [Indexed: 08/15/2024]
Abstract
Yeast has been established as a versatile platform for expressing functional molecules, owing to its well-characterized biology and extensive genetic modification tools. Compared to prokaryotic systems, yeast possesses advanced cellular mechanisms that ensure accurate protein folding and post-translational modifications. These capabilities are particularly advantageous for the expression of human-derived functional proteins. However, designing yeast strains as an expression platform for proteins requires the integration of molecular and cellular functions. By delving into the complexities of yeast-based expression systems, this review aims to empower researchers with the knowledge to fully exploit yeast as a functional platform to produce a diverse range of proteins. This review includes an exploration of the host strains, gene cassette structures, as well as considerations for maximizing the efficiency of the expression system. Through this in-depth analysis, the review anticipates stimulating further innovation in the field of yeast biotechnology and protein engineering.
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Affiliation(s)
- Luping Xu
- Department of Food Science, Purdue University, West Lafayette, IN, USA
- Whistler Center for Carbohydrate Research, Purdue University, West Lafayette, IN, USA
| | | | - Eun Joong Oh
- Department of Food Science, Purdue University, West Lafayette, IN, USA
- Whistler Center for Carbohydrate Research, Purdue University, West Lafayette, IN, USA
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3
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Brown GW. The cytidine deaminase APOBEC3C has unique sequence and genome feature preferences. Genetics 2024; 227:iyae092. [PMID: 38946641 DOI: 10.1093/genetics/iyae092] [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: 01/17/2024] [Accepted: 05/22/2024] [Indexed: 07/02/2024] Open
Abstract
APOBEC proteins are cytidine deaminases that restrict the replication of viruses and transposable elements. Several members of the APOBEC3 family, APOBEC3A, APOBEC3B, and APOBEC3H-I, can access the nucleus and cause what is thought to be indiscriminate deamination of the genome, resulting in mutagenesis and genome instability. Although APOBEC3C is also present in the nucleus, the full scope of its deamination target preferences is unknown. By expressing human APOBEC3C in a yeast model system, I have defined the APOBEC3C mutation signature, as well as the preferred genome features of APOBEC3C targets. The APOBEC3C mutation signature is distinct from those of the known cancer genome mutators APOBEC3A and APOBEC3B. APOBEC3C produces DNA strand-coordinated mutation clusters, and APOBEC3C mutations are enriched near the transcription start sites of active genes. Surprisingly, APOBEC3C lacks the bias for the lagging strand of DNA replication that is seen for APOBEC3A and APOBEC3B. The unique preferences of APOBEC3C constitute a mutation profile that will be useful in defining sites of APOBEC3C mutagenesis in human genomes.
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Affiliation(s)
- Grant W Brown
- Department of Biochemistry, University of Toronto, 1 King's College Circle, Toronto, ON, Canada M5S 1A8
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, 160 College Street, Toronto, ON, Canada M5S 3E1
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4
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Andreani V, South EJ, Dunlop MJ. Generating information-dense promoter sequences with optimal string packing. PLoS Comput Biol 2024; 20:e1012276. [PMID: 39047028 PMCID: PMC11268586 DOI: 10.1371/journal.pcbi.1012276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2024] [Accepted: 06/25/2024] [Indexed: 07/27/2024] Open
Abstract
Dense arrangements of binding sites within nucleotide sequences can collectively influence downstream transcription rates or initiate biomolecular interactions. For example, natural promoter regions can harbor many overlapping transcription factor binding sites that influence the rate of transcription initiation. Despite the prevalence of overlapping binding sites in nature, rapid design of nucleotide sequences with many overlapping sites remains a challenge. Here, we show that this is an NP-hard problem, coined here as the nucleotide String Packing Problem (SPP). We then introduce a computational technique that efficiently assembles sets of DNA-protein binding sites into dense, contiguous stretches of double-stranded DNA. For the efficient design of nucleotide sequences spanning hundreds of base pairs, we reduce the SPP to an Orienteering Problem with integer distances, and then leverage modern integer linear programming solvers. Our method optimally packs sets of 20-100 binding sites into dense nucleotide arrays of 50-300 base pairs in 0.05-10 seconds. Unlike approximation algorithms or meta-heuristics, our approach finds provably optimal solutions. We demonstrate how our method can generate large sets of diverse sequences suitable for library generation, where the frequency of binding site usage across the returned sequences can be controlled by modulating the objective function. As an example, we then show how adding additional constraints, like the inclusion of sequence elements with fixed positions, allows for the design of bacterial promoters. The nucleotide string packing approach we present can accelerate the design of sequences with complex DNA-protein interactions. When used in combination with synthesis and high-throughput screening, this design strategy could help interrogate how complex binding site arrangements impact either gene expression or biomolecular mechanisms in varied cellular contexts.
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Affiliation(s)
- Virgile Andreani
- Biomedical Engineering Department, Boston University, Boston, Massachusetts, United States of America
- Biological Design Center, Boston University, Boston, Massachusetts, United States of America
| | - Eric J. South
- Biological Design Center, Boston University, Boston, Massachusetts, United States of America
- Molecular Biology, Cell Biology & Biochemistry Program, Boston University, Boston, Massachusetts, United States of America
| | - Mary J. Dunlop
- Biomedical Engineering Department, Boston University, Boston, Massachusetts, United States of America
- Biological Design Center, Boston University, Boston, Massachusetts, United States of America
- Molecular Biology, Cell Biology & Biochemistry Program, Boston University, Boston, Massachusetts, United States of America
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5
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Xia Y, Du X, Liu B, Guo S, Huo YX. Species-specific design of artificial promoters by transfer-learning based generative deep-learning model. Nucleic Acids Res 2024; 52:6145-6157. [PMID: 38783063 PMCID: PMC11194083 DOI: 10.1093/nar/gkae429] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Revised: 04/04/2024] [Accepted: 05/08/2024] [Indexed: 05/25/2024] Open
Abstract
Native prokaryotic promoters share common sequence patterns, but are species dependent. For understudied species with limited data, it is challenging to predict the strength of existing promoters and generate novel promoters. Here, we developed PromoGen, a collection of nucleotide language models to generate species-specific functional promoters, across dozens of species in a data and parameter efficient way. Twenty-seven species-specific models in this collection were finetuned from the pretrained model which was trained on multi-species promoters. When systematically compared with native promoters, the Escherichia coli- and Bacillus subtilis-specific artificial PromoGen-generated promoters (PGPs) were demonstrated to hold all distribution patterns of native promoters. A regression model was developed to score generated either by PromoGen or by another competitive neural network, and the overall score of PGPs is higher. Encouraged by in silico analysis, we further experimentally characterized twenty-two B. subtilis PGPs, results showed that four of tested PGPs reached the strong promoter level while all were active. Furthermore, we developed a user-friendly website to generate species-specific promoters for 27 different species by PromoGen. This work presented an efficient deep-learning strategy for de novo species-specific promoter generation even with limited datasets, providing valuable promoter toolboxes especially for the metabolic engineering of understudied microorganisms.
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Affiliation(s)
- Yan Xia
- Key Laboratory of Molecular Medicine and Biotherapy, School of Life Science, Beijing Institute of Technology, Beijing 100081, China
| | - Xiaowen Du
- Key Laboratory of Molecular Medicine and Biotherapy, School of Life Science, Beijing Institute of Technology, Beijing 100081, China
| | - Bin Liu
- School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China
| | - Shuyuan Guo
- Key Laboratory of Molecular Medicine and Biotherapy, School of Life Science, Beijing Institute of Technology, Beijing 100081, China
| | - Yi-Xin Huo
- Key Laboratory of Molecular Medicine and Biotherapy, School of Life Science, Beijing Institute of Technology, Beijing 100081, China
- Tangshan Research Institute, Beijing Institute of Technology, Hebei 063611, China
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6
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Reddy AJ, Geng X, Herschl MH, Kolli S, Kumar A, Hsu PD, Levine S, Ioannidis NM. Designing Cell-Type-Specific Promoter Sequences Using Conservative Model-Based Optimization. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.23.600232. [PMID: 38948874 PMCID: PMC11213138 DOI: 10.1101/2024.06.23.600232] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/02/2024]
Abstract
Gene therapies have the potential to treat disease by delivering therapeutic genetic cargo to disease-associated cells. One limitation to their widespread use is the lack of short regulatory sequences, or promoters, that differentially induce the expression of delivered genetic cargo in target cells, minimizing side effects in other cell types. Such cell-type-specific promoters are difficult to discover using existing methods, requiring either manual curation or access to large datasets of promoter-driven expression from both targeted and untargeted cells. Model-based optimization (MBO) has emerged as an effective method to design biological sequences in an automated manner, and has recently been used in promoter design methods. However, these methods have only been tested using large training datasets that are expensive to collect, and focus on designing promoters for markedly different cell types, overlooking the complexities associated with designing promoters for closely related cell types that share similar regulatory features. Therefore, we introduce a comprehensive framework for utilizing MBO to design promoters in a data-efficient manner, with an emphasis on discovering promoters for similar cell types. We use conservative objective models (COMs) for MBO and highlight practical considerations such as best practices for improving sequence diversity, getting estimates of model uncertainty, and choosing the optimal set of sequences for experimental validation. Using three relatively similar blood cancer cell lines (Jurkat, K562, and THP1), we show that our approach discovers many novel cell-type-specific promoters after experimentally validating the designed sequences. For K562 cells, in particular, we discover a promoter that has 75.85% higher cell-type-specificity than the best promoter from the initial dataset used to train our models.
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7
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Zelenka NR, Di Cara N, Sharma K, Sarvaharman S, Ghataora JS, Parmeggiani F, Nivala J, Abdallah ZS, Marucci L, Gorochowski TE. Data hazards in synthetic biology. Synth Biol (Oxf) 2024; 9:ysae010. [PMID: 38973982 PMCID: PMC11227101 DOI: 10.1093/synbio/ysae010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Revised: 05/17/2024] [Accepted: 06/19/2024] [Indexed: 07/09/2024] Open
Abstract
Data science is playing an increasingly important role in the design and analysis of engineered biology. This has been fueled by the development of high-throughput methods like massively parallel reporter assays, data-rich microscopy techniques, computational protein structure prediction and design, and the development of whole-cell models able to generate huge volumes of data. Although the ability to apply data-centric analyses in these contexts is appealing and increasingly simple to do, it comes with potential risks. For example, how might biases in the underlying data affect the validity of a result and what might the environmental impact of large-scale data analyses be? Here, we present a community-developed framework for assessing data hazards to help address these concerns and demonstrate its application to two synthetic biology case studies. We show the diversity of considerations that arise in common types of bioengineering projects and provide some guidelines and mitigating steps. Understanding potential issues and dangers when working with data and proactively addressing them will be essential for ensuring the appropriate use of emerging data-intensive AI methods and help increase the trustworthiness of their applications in synthetic biology.
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Affiliation(s)
- Natalie R Zelenka
- Jean Golding Institute, University of Bristol, Bristol, UK
- BrisEngBio, University of Bristol, Bristol, UK
| | - Nina Di Cara
- School of Psychological Science, University of Bristol, Bristol, UK
| | - Kieren Sharma
- School of Engineering Mathematics and Technology, University of Bristol, Bristol, UK
| | | | - Jasdeep S Ghataora
- BrisEngBio, University of Bristol, Bristol, UK
- School of Biological Sciences, University of Bristol, Bristol, UK
| | - Fabio Parmeggiani
- BrisEngBio, University of Bristol, Bristol, UK
- School of Biochemistry, University of Bristol, Bristol, UK
- School of Pharmacy and Pharmaceutical Sciences, Cardiff University, Cardiff, UK
| | - Jeff Nivala
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA
| | - Zahraa S Abdallah
- School of Engineering Mathematics and Technology, University of Bristol, Bristol, UK
| | - Lucia Marucci
- BrisEngBio, University of Bristol, Bristol, UK
- School of Engineering Mathematics and Technology, University of Bristol, Bristol, UK
| | - Thomas E Gorochowski
- BrisEngBio, University of Bristol, Bristol, UK
- School of Biological Sciences, University of Bristol, Bristol, UK
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8
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Lim SR, Lee SJ. Multiplex CRISPR-Cas Genome Editing: Next-Generation Microbial Strain Engineering. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2024; 72:11871-11884. [PMID: 38744727 PMCID: PMC11141556 DOI: 10.1021/acs.jafc.4c01650] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Revised: 05/02/2024] [Accepted: 05/08/2024] [Indexed: 05/16/2024]
Abstract
Genome editing is a crucial technology for obtaining desired phenotypes in a variety of species, ranging from microbes to plants, animals, and humans. With the advent of CRISPR-Cas technology, it has become possible to edit the intended sequence by modifying the target recognition sequence in guide RNA (gRNA). By expressing multiple gRNAs simultaneously, it is possible to edit multiple targets at the same time, allowing for the simultaneous introduction of various functions into the cell. This can significantly reduce the time and cost of obtaining engineered microbial strains for specific traits. In this review, we investigate the resolution of multiplex genome editing and its application in engineering microorganisms, including bacteria and yeast. Furthermore, we examine how recent advancements in artificial intelligence technology could assist in microbial genome editing and engineering. Based on these insights, we present our perspectives on the future evolution and potential impact of multiplex genome editing technologies in the agriculture and food industry.
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Affiliation(s)
- Se Ra Lim
- Department of Systems Biotechnology
and Institute of Microbiomics, Chung-Ang
University, Anseong 17546, Republic
of Korea
| | - Sang Jun Lee
- Department of Systems Biotechnology
and Institute of Microbiomics, Chung-Ang
University, Anseong 17546, Republic
of Korea
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9
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Lin J, Wang X, Liu T, Teng Y, Cui W. Diffusion-Based Generative Network for de Novo Synthetic Promoter Design. ACS Synth Biol 2024; 13:1513-1522. [PMID: 38613497 DOI: 10.1021/acssynbio.4c00041] [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: 04/15/2024]
Abstract
Computer-aided promoter design is a major development trend in synthetic promoter engineering. Various deep learning models have been used to evaluate or screen synthetic promoters, but there have been few works on de novo promoter design. To explore the potential ability of generative models in promoter design, we established a diffusion-based generative model for promoter design in Escherichia coli. The model was completely driven by sequence data and could study the essential characteristics of natural promoters, thus generating synthetic promoters similar to natural promoters in structure and component. We also improved the calculation method of FID indicator, using a convolution layer to extract the feature matrix of the promoter sequence instead. As a result, we got an FID equal to 1.37, which meant synthetic promoters have a distribution similar to that of natural ones. Our work provides a fresh approach to de novo promoter design, indicating that a completely data-driven generative model is feasible for promoter design.
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Affiliation(s)
- Jianfeng Lin
- School of Automation Science and Engineering, South China University of Technology, Guangzhou 510641, China
| | - Xin Wang
- School of Automation Science and Engineering, South China University of Technology, Guangzhou 510641, China
| | - Tuoyu Liu
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing 100071, China
| | - Yue Teng
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing 100071, China
| | - Wei Cui
- School of Automation Science and Engineering, South China University of Technology, Guangzhou 510641, China
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10
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Guo J, Lin LF, Oraskovich SV, Rivera de Jesús JA, Listgarten J, Schaffer DV. Computationally guided AAV engineering for enhanced gene delivery. Trends Biochem Sci 2024; 49:457-469. [PMID: 38531696 DOI: 10.1016/j.tibs.2024.03.002] [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: 11/23/2023] [Revised: 02/22/2024] [Accepted: 03/01/2024] [Indexed: 03/28/2024]
Abstract
Gene delivery vehicles based on adeno-associated viruses (AAVs) are enabling increasing success in human clinical trials, and they offer the promise of treating a broad spectrum of both genetic and non-genetic disorders. However, delivery efficiency and targeting must be improved to enable safe and effective therapies. In recent years, considerable effort has been invested in creating AAV variants with improved delivery, and computational approaches have been increasingly harnessed for AAV engineering. In this review, we discuss how computationally designed AAV libraries are enabling directed evolution. Specifically, we highlight approaches that harness sequences outputted by next-generation sequencing (NGS) coupled with machine learning (ML) to generate new functional AAV capsids and related regulatory elements, pushing the frontier of what vector engineering and gene therapy may achieve.
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Affiliation(s)
- Jingxuan Guo
- California Institute for Quantitative Biosciences (QB3), University of California, Berkeley, CA 94720, USA
| | - Li F Lin
- Department of Chemical and Biomolecular Engineering, University of California, Berkeley, CA 94720, USA
| | - Sydney V Oraskovich
- Department of Bioengineering, University of California, Berkeley, CA 94720, USA; Graduate Program in Bioengineering, University of California, San Francisco and University of California, Berkeley, CA 94720, USA
| | - Julio A Rivera de Jesús
- Department of Bioengineering, University of California, Berkeley, CA 94720, USA; Graduate Program in Bioengineering, University of California, San Francisco and University of California, Berkeley, CA 94720, USA; Department of Neurological Surgery, University of California, San Francisco, CA 94143, USA
| | - Jennifer Listgarten
- Department of Electrical Engineering and Computer Science, University of California, Berkeley, CA 94720, USA
| | - David V Schaffer
- California Institute for Quantitative Biosciences (QB3), University of California, Berkeley, CA 94720, USA; Department of Chemical and Biomolecular Engineering, University of California, Berkeley, CA 94720, USA; Department of Bioengineering, University of California, Berkeley, CA 94720, USA; Department of Molecular and Cell Biology, University of California, Berkeley, CA 94720, USA.
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11
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You J, Wang Y, Wang K, Du Y, Zhang X, Zhang X, Yang T, Pan X, Rao Z. Utilizing 5' UTR Engineering Enables Fine-Tuning of Multiple Genes within Operons to Balance Metabolic Flux in Bacillus subtilis. BIOLOGY 2024; 13:277. [PMID: 38666889 PMCID: PMC11047901 DOI: 10.3390/biology13040277] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/12/2024] [Revised: 04/10/2024] [Accepted: 04/15/2024] [Indexed: 04/28/2024]
Abstract
The application of synthetic biology tools to modulate gene expression to increase yield has been thoroughly demonstrated as an effective and convenient approach in industrial production. In this study, we employed a high-throughput screening strategy to identify a 5' UTR sequence from the genome of B. subtilis 168. This sequence resulted in a 5.8-fold increase in the expression level of EGFP. By utilizing the 5' UTR sequence to overexpress individual genes within the rib operon, it was determined that the genes ribD and ribAB serve as rate-limiting enzymes in the riboflavin synthesis pathway. Constructing a 5' UTR library to regulate EGFP expression resulted in a variation range in gene expression levels exceeding 100-fold. Employing the same 5' UTR library to regulate the expression of EGFP and mCherry within the operon led to a change in the expression ratio of these two genes by over 10,000-fold. So, employing a 5' UTR library to modulate the expression of the rib operon gene and construct a synthetic rib operon resulted in a 2.09-fold increase in riboflavin production. These results indicate that the 5' UTR sequence identified and characterized in this study can serve as a versatile synthetic biology toolkit for achieving complex metabolic network reconstruction. This toolkit can facilitate the fine-tuning of gene expression to produce target products.
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Affiliation(s)
- Jiajia You
- Key Laboratory of Industrial Biotechnology of the Ministry of Education, Laboratory of Applied Microorganisms and Metabolic Engineering, School of Biotechnology, Jiangnan University, Wuxi 214122, China; (J.Y.); (K.W.); (Y.D.); (X.Z.); (X.Z.); (T.Y.)
- Yixing Institute of Food and Biotechnology Co., Ltd., Yixing 214200, China
| | - Yifan Wang
- Department of Food Science and Technology, Texas A & M University, College Station, TX 77843, USA;
| | - Kang Wang
- Key Laboratory of Industrial Biotechnology of the Ministry of Education, Laboratory of Applied Microorganisms and Metabolic Engineering, School of Biotechnology, Jiangnan University, Wuxi 214122, China; (J.Y.); (K.W.); (Y.D.); (X.Z.); (X.Z.); (T.Y.)
| | - Yuxuan Du
- Key Laboratory of Industrial Biotechnology of the Ministry of Education, Laboratory of Applied Microorganisms and Metabolic Engineering, School of Biotechnology, Jiangnan University, Wuxi 214122, China; (J.Y.); (K.W.); (Y.D.); (X.Z.); (X.Z.); (T.Y.)
| | - Xiaoling Zhang
- Key Laboratory of Industrial Biotechnology of the Ministry of Education, Laboratory of Applied Microorganisms and Metabolic Engineering, School of Biotechnology, Jiangnan University, Wuxi 214122, China; (J.Y.); (K.W.); (Y.D.); (X.Z.); (X.Z.); (T.Y.)
| | - Xian Zhang
- Key Laboratory of Industrial Biotechnology of the Ministry of Education, Laboratory of Applied Microorganisms and Metabolic Engineering, School of Biotechnology, Jiangnan University, Wuxi 214122, China; (J.Y.); (K.W.); (Y.D.); (X.Z.); (X.Z.); (T.Y.)
| | - Taowei Yang
- Key Laboratory of Industrial Biotechnology of the Ministry of Education, Laboratory of Applied Microorganisms and Metabolic Engineering, School of Biotechnology, Jiangnan University, Wuxi 214122, China; (J.Y.); (K.W.); (Y.D.); (X.Z.); (X.Z.); (T.Y.)
| | - Xuewei Pan
- Key Laboratory of Industrial Biotechnology of the Ministry of Education, Laboratory of Applied Microorganisms and Metabolic Engineering, School of Biotechnology, Jiangnan University, Wuxi 214122, China; (J.Y.); (K.W.); (Y.D.); (X.Z.); (X.Z.); (T.Y.)
| | - Zhiming Rao
- Key Laboratory of Industrial Biotechnology of the Ministry of Education, Laboratory of Applied Microorganisms and Metabolic Engineering, School of Biotechnology, Jiangnan University, Wuxi 214122, China; (J.Y.); (K.W.); (Y.D.); (X.Z.); (X.Z.); (T.Y.)
- Yixing Institute of Food and Biotechnology Co., Ltd., Yixing 214200, China
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12
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Goshisht MK. Machine Learning and Deep Learning in Synthetic Biology: Key Architectures, Applications, and Challenges. ACS OMEGA 2024; 9:9921-9945. [PMID: 38463314 PMCID: PMC10918679 DOI: 10.1021/acsomega.3c05913] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Revised: 01/19/2024] [Accepted: 01/30/2024] [Indexed: 03/12/2024]
Abstract
Machine learning (ML), particularly deep learning (DL), has made rapid and substantial progress in synthetic biology in recent years. Biotechnological applications of biosystems, including pathways, enzymes, and whole cells, are being probed frequently with time. The intricacy and interconnectedness of biosystems make it challenging to design them with the desired properties. ML and DL have a synergy with synthetic biology. Synthetic biology can be employed to produce large data sets for training models (for instance, by utilizing DNA synthesis), and ML/DL models can be employed to inform design (for example, by generating new parts or advising unrivaled experiments to perform). This potential has recently been brought to light by research at the intersection of engineering biology and ML/DL through achievements like the design of novel biological components, best experimental design, automated analysis of microscopy data, protein structure prediction, and biomolecular implementations of ANNs (Artificial Neural Networks). I have divided this review into three sections. In the first section, I describe predictive potential and basics of ML along with myriad applications in synthetic biology, especially in engineering cells, activity of proteins, and metabolic pathways. In the second section, I describe fundamental DL architectures and their applications in synthetic biology. Finally, I describe different challenges causing hurdles in the progress of ML/DL and synthetic biology along with their solutions.
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Affiliation(s)
- Manoj Kumar Goshisht
- Department of Chemistry, Natural and
Applied Sciences, University of Wisconsin—Green
Bay, Green
Bay, Wisconsin 54311-7001, United States
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13
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Wang H, Du Q, Wang Y, Xu H, Wei Z, Wang X. GPro: generative AI-empowered toolkit for promoter design. Bioinformatics 2024; 40:btae123. [PMID: 38429953 PMCID: PMC10937896 DOI: 10.1093/bioinformatics/btae123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Revised: 02/19/2024] [Accepted: 02/28/2024] [Indexed: 03/03/2024] Open
Abstract
MOTIVATION Promoters with desirable properties are crucial in biotechnological applications. Generative AI (GenAI) has demonstrated potential in creating novel synthetic promoters with significantly enhanced functionality. However, these methods' reliance on various programming frameworks and specific task-oriented contexts limits their flexibilities. Overcoming these limitations is essential for researchers to fully leverage the power of GenAI to design promoters for their tasks. RESULTS Here, we introduce GPro (Generative AI-empowered toolkit for promoter design), a user-friendly toolkit that integrates a collection of cutting-edge GenAI-empowered approaches for promoter design. This toolkit provides a standardized pipeline covering essential promoter design processes, including training, optimization, and evaluation. Several detailed demos are provided to reproduce state-of-the-art promoter design pipelines. GPro's user-friendly interface makes it accessible to a wide range of users including non-AI experts. It also offers a variety of optional algorithms for each design process, and gives users the flexibility to compare methods and create customized pipelines. AVAILABILITY AND IMPLEMENTATION GPro is released as an open-source software under the MIT license. The source code for GPro is available on GitHub for Linux, macOS, and Windows: https://github.com/WangLabTHU/GPro, and is available for download via Zenodo repository at https://zenodo.org/doi/10.5281/zenodo.10681733.
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Affiliation(s)
- Haochen Wang
- Ministry of Education Key Laboratory of Bioinformatics, Tsinghua University, Beijing 100084, China
- Center for Synthetic and Systems Biology, Tsinghua University, Beijing 100084, China
- Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing 100084, China
- Department of Automation, Tsinghua University, Beijing 100084, China
| | - Qixiu Du
- Ministry of Education Key Laboratory of Bioinformatics, Tsinghua University, Beijing 100084, China
- Center for Synthetic and Systems Biology, Tsinghua University, Beijing 100084, China
- Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing 100084, China
- Department of Automation, Tsinghua University, Beijing 100084, China
| | - Ye Wang
- Ministry of Education Key Laboratory of Bioinformatics, Tsinghua University, Beijing 100084, China
- Center for Synthetic and Systems Biology, Tsinghua University, Beijing 100084, China
- Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing 100084, China
- Department of Automation, Tsinghua University, Beijing 100084, China
| | - Hanwen Xu
- Ministry of Education Key Laboratory of Bioinformatics, Tsinghua University, Beijing 100084, China
- Center for Synthetic and Systems Biology, Tsinghua University, Beijing 100084, China
- Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing 100084, China
- Department of Automation, Tsinghua University, Beijing 100084, China
| | - Zheng Wei
- Ministry of Education Key Laboratory of Bioinformatics, Tsinghua University, Beijing 100084, China
- Center for Synthetic and Systems Biology, Tsinghua University, Beijing 100084, China
- Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing 100084, China
- Department of Automation, Tsinghua University, Beijing 100084, China
| | - Xiaowo Wang
- Ministry of Education Key Laboratory of Bioinformatics, Tsinghua University, Beijing 100084, China
- Center for Synthetic and Systems Biology, Tsinghua University, Beijing 100084, China
- Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing 100084, China
- Department of Automation, Tsinghua University, Beijing 100084, China
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14
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Barbosa F, Araújo J, Gonçalves VMF, Palmeira A, Cunha A, Silva PMA, Fernandes C, Pinto M, Bousbaa H, Queirós O, Tiritan ME. Evaluation of Antitumor Activity of Xanthones Conjugated with Amino Acids. Int J Mol Sci 2024; 25:2121. [PMID: 38396802 PMCID: PMC10889492 DOI: 10.3390/ijms25042121] [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: 12/24/2023] [Revised: 02/04/2024] [Accepted: 02/06/2024] [Indexed: 02/25/2024] Open
Abstract
Cancer is a complex disease characterized by several alterations, which confer, to the cells, the capacity to proliferate uncontrollably and to resist cellular death. Multiresistance to conventional chemotherapy drugs is often the cause of treatment failure; thus, the search for natural products or their derivatives with therapeutic action is essential. Chiral derivatives of xanthones (CDXs) have shown potential inhibitory activity against the growth of some human tumor cell lines. This work reports the screening of a library of CDXs, through viability assays, in different cancer cell lines: A375-C5, MCF-7, NCI-H460, and HCT-15. CDXs' effect was analyzed based on several parameters of cancer cells, and it was also verified if these compounds were substrates of glycoprotein-P (Pgp), one of the main mechanisms of resistance in cancer therapy. Pgp expression was evaluated in all cell lines, but no expression was observed, except for HCT-15. Also, when a humanized yeast expressing the human gene MDR1 was used, no conclusions could be drawn about CDXs as Pgp substrates. The selected CDXs did not induce significant differences in the metabolic parameters analyzed. These results show that some CDXs present promising antitumor activity, but other mechanisms should be triggered by these compounds.
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Affiliation(s)
- Flávia Barbosa
- UNIPRO—Oral Pathology and Rehabilitation Research Unit, University Institute of Health Sciences (IUCS-CESPU), 4585-116 Gandra, Portugal; (F.B.); (V.M.F.G.); (A.C.); (P.M.A.S.); (H.B.); (O.Q.)
| | - Joana Araújo
- Laboratory of Organic and Pharmaceutical Chemistry, Department of Chemical Sciences, Faculty of Pharmacy, University of Porto, Rua Jorge Viterbo Ferreira, 228, 4050-313 Porto, Portugal; (J.A.); (A.P.); (C.F.); (M.P.)
| | - Virgínia M. F. Gonçalves
- UNIPRO—Oral Pathology and Rehabilitation Research Unit, University Institute of Health Sciences (IUCS-CESPU), 4585-116 Gandra, Portugal; (F.B.); (V.M.F.G.); (A.C.); (P.M.A.S.); (H.B.); (O.Q.)
- 1H-TOXRUN—One Health Toxicology Research Unit, University Institute of Health Sciences (IUCS), University Institute of Health Sciences-CESPU (IUCS-CESPU), 4585-116 Gandra, Portugal
| | - Andreia Palmeira
- Laboratory of Organic and Pharmaceutical Chemistry, Department of Chemical Sciences, Faculty of Pharmacy, University of Porto, Rua Jorge Viterbo Ferreira, 228, 4050-313 Porto, Portugal; (J.A.); (A.P.); (C.F.); (M.P.)
- CIIMAR-Interdisciplinary Center for Marine and Environmental Research, University of Porto, Avenida General Norton de Matos, 4450-208 Matosinhos, Portugal
| | - Andrea Cunha
- UNIPRO—Oral Pathology and Rehabilitation Research Unit, University Institute of Health Sciences (IUCS-CESPU), 4585-116 Gandra, Portugal; (F.B.); (V.M.F.G.); (A.C.); (P.M.A.S.); (H.B.); (O.Q.)
| | - Patrícia M. A. Silva
- UNIPRO—Oral Pathology and Rehabilitation Research Unit, University Institute of Health Sciences (IUCS-CESPU), 4585-116 Gandra, Portugal; (F.B.); (V.M.F.G.); (A.C.); (P.M.A.S.); (H.B.); (O.Q.)
- 1H-TOXRUN—One Health Toxicology Research Unit, University Institute of Health Sciences (IUCS), University Institute of Health Sciences-CESPU (IUCS-CESPU), 4585-116 Gandra, Portugal
| | - Carla Fernandes
- Laboratory of Organic and Pharmaceutical Chemistry, Department of Chemical Sciences, Faculty of Pharmacy, University of Porto, Rua Jorge Viterbo Ferreira, 228, 4050-313 Porto, Portugal; (J.A.); (A.P.); (C.F.); (M.P.)
- CIIMAR-Interdisciplinary Center for Marine and Environmental Research, University of Porto, Avenida General Norton de Matos, 4450-208 Matosinhos, Portugal
| | - Madalena Pinto
- Laboratory of Organic and Pharmaceutical Chemistry, Department of Chemical Sciences, Faculty of Pharmacy, University of Porto, Rua Jorge Viterbo Ferreira, 228, 4050-313 Porto, Portugal; (J.A.); (A.P.); (C.F.); (M.P.)
- CIIMAR-Interdisciplinary Center for Marine and Environmental Research, University of Porto, Avenida General Norton de Matos, 4450-208 Matosinhos, Portugal
| | - Hassan Bousbaa
- UNIPRO—Oral Pathology and Rehabilitation Research Unit, University Institute of Health Sciences (IUCS-CESPU), 4585-116 Gandra, Portugal; (F.B.); (V.M.F.G.); (A.C.); (P.M.A.S.); (H.B.); (O.Q.)
| | - Odília Queirós
- UNIPRO—Oral Pathology and Rehabilitation Research Unit, University Institute of Health Sciences (IUCS-CESPU), 4585-116 Gandra, Portugal; (F.B.); (V.M.F.G.); (A.C.); (P.M.A.S.); (H.B.); (O.Q.)
| | - Maria Elizabeth Tiritan
- UNIPRO—Oral Pathology and Rehabilitation Research Unit, University Institute of Health Sciences (IUCS-CESPU), 4585-116 Gandra, Portugal; (F.B.); (V.M.F.G.); (A.C.); (P.M.A.S.); (H.B.); (O.Q.)
- Laboratory of Organic and Pharmaceutical Chemistry, Department of Chemical Sciences, Faculty of Pharmacy, University of Porto, Rua Jorge Viterbo Ferreira, 228, 4050-313 Porto, Portugal; (J.A.); (A.P.); (C.F.); (M.P.)
- 1H-TOXRUN—One Health Toxicology Research Unit, University Institute of Health Sciences (IUCS), University Institute of Health Sciences-CESPU (IUCS-CESPU), 4585-116 Gandra, Portugal
- CIIMAR-Interdisciplinary Center for Marine and Environmental Research, University of Porto, Avenida General Norton de Matos, 4450-208 Matosinhos, Portugal
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15
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Cautereels C, Smets J, Bircham P, De Ruysscher D, Zimmermann A, De Rijk P, Steensels J, Gorkovskiy A, Masschelein J, Verstrepen KJ. Combinatorial optimization of gene expression through recombinase-mediated promoter and terminator shuffling in yeast. Nat Commun 2024; 15:1112. [PMID: 38326309 PMCID: PMC10850122 DOI: 10.1038/s41467-024-44997-7] [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: 08/10/2023] [Accepted: 01/12/2024] [Indexed: 02/09/2024] Open
Abstract
Microbes are increasingly employed as cell factories to produce biomolecules. This often involves the expression of complex heterologous biosynthesis pathways in host strains. Achieving maximal product yields and avoiding build-up of (toxic) intermediates requires balanced expression of every pathway gene. However, despite progress in metabolic modeling, the optimization of gene expression still heavily relies on trial-and-error. Here, we report an approach for in vivo, multiplexed Gene Expression Modification by LoxPsym-Cre Recombination (GEMbLeR). GEMbLeR exploits orthogonal LoxPsym sites to independently shuffle promoter and terminator modules at distinct genomic loci. This approach facilitates creation of large strain libraries, in which expression of every pathway gene ranges over 120-fold and each strain harbors a unique expression profile. When applied to the biosynthetic pathway of astaxanthin, an industrially relevant antioxidant, a single round of GEMbLeR improved pathway flux and doubled production titers. Together, this shows that GEMbLeR allows rapid and efficient gene expression optimization in heterologous biosynthetic pathways, offering possibilities for enhancing the performance of microbial cell factories.
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Affiliation(s)
- Charlotte Cautereels
- VIB Laboratory for Systems Biology, VIB-KU Leuven Center for Microbiology, Leuven, 3001, Belgium
- Laboratory of Genetics and Genomics, Center of Microbial and Plant Genetics, Department M2S, KU Leuven, Gaston Geenslaan 1, Leuven, 3001, Belgium
| | - Jolien Smets
- VIB Laboratory for Systems Biology, VIB-KU Leuven Center for Microbiology, Leuven, 3001, Belgium
- Laboratory of Genetics and Genomics, Center of Microbial and Plant Genetics, Department M2S, KU Leuven, Gaston Geenslaan 1, Leuven, 3001, Belgium
| | - Peter Bircham
- VIB Laboratory for Systems Biology, VIB-KU Leuven Center for Microbiology, Leuven, 3001, Belgium
- Laboratory of Genetics and Genomics, Center of Microbial and Plant Genetics, Department M2S, KU Leuven, Gaston Geenslaan 1, Leuven, 3001, Belgium
| | - Dries De Ruysscher
- Molecular Biotechnology of Plants and Micro-organisms, Department of Biology, KU Leuven, Kasteelpark Arenberg 31, box 2438, Leuven, 3001, Belgium
- Laboratory for Biomolecular Discovery & Engineering, VIB-KU Leuven Center for Microbiology, Leuven, 3001, Belgium
| | - Anna Zimmermann
- VIB Laboratory for Systems Biology, VIB-KU Leuven Center for Microbiology, Leuven, 3001, Belgium
- Laboratory of Genetics and Genomics, Center of Microbial and Plant Genetics, Department M2S, KU Leuven, Gaston Geenslaan 1, Leuven, 3001, Belgium
| | - Peter De Rijk
- Neuromics Support Facility, VIB Center for Molecular Neurology, VIB, Antwerp, 2610, Belgium
- Neuromics Support Facility, Department of Biomedical Sciences, University of Antwerp, Antwerp, 2610, Belgium
| | - Jan Steensels
- VIB Laboratory for Systems Biology, VIB-KU Leuven Center for Microbiology, Leuven, 3001, Belgium
- Laboratory of Genetics and Genomics, Center of Microbial and Plant Genetics, Department M2S, KU Leuven, Gaston Geenslaan 1, Leuven, 3001, Belgium
| | - Anton Gorkovskiy
- VIB Laboratory for Systems Biology, VIB-KU Leuven Center for Microbiology, Leuven, 3001, Belgium
- Laboratory of Genetics and Genomics, Center of Microbial and Plant Genetics, Department M2S, KU Leuven, Gaston Geenslaan 1, Leuven, 3001, Belgium
| | - Joleen Masschelein
- Molecular Biotechnology of Plants and Micro-organisms, Department of Biology, KU Leuven, Kasteelpark Arenberg 31, box 2438, Leuven, 3001, Belgium
- Laboratory for Biomolecular Discovery & Engineering, VIB-KU Leuven Center for Microbiology, Leuven, 3001, Belgium
| | - Kevin J Verstrepen
- VIB Laboratory for Systems Biology, VIB-KU Leuven Center for Microbiology, Leuven, 3001, Belgium.
- Laboratory of Genetics and Genomics, Center of Microbial and Plant Genetics, Department M2S, KU Leuven, Gaston Geenslaan 1, Leuven, 3001, Belgium.
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16
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Andreani V, South EJ, Dunlop MJ. Generating information-dense promoter sequences with optimal string packing. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.11.01.565124. [PMID: 37961203 PMCID: PMC10635063 DOI: 10.1101/2023.11.01.565124] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Dense arrangements of binding sites within nucleotide sequences can collectively influence downstream transcription rates or initiate biomolecular interactions. For example, natural promoter regions can harbor many overlapping transcription factor binding sites that influence the rate of transcription initiation. Despite the prevalence of overlapping binding sites in nature, rapid design of nucleotide sequences with many overlapping sites remains a challenge. Here, we show that this is an NP-hard problem, coined here as the nucleotide String Packing Problem (SPP). We then introduce a computational technique that efficiently assembles sets of DNA-protein binding sites into dense, contiguous stretches of double-stranded DNA. For the efficient design of nucleotide sequences spanning hundreds of base pairs, we reduce the SPP to an Orienteering Problem with integer distances, and then leverage modern integer linear programming solvers. Our method optimally packs libraries of 20-100 binding sites into dense nucleotide arrays of 50-300 base pairs in 0.05-10 seconds. Unlike approximation algorithms or meta-heuristics, our approach finds provably optimal solutions. We demonstrate how our method can generate large sets of diverse sequences suitable for library generation, where the frequency of binding site usage across the returned sequences can be controlled by modulating the objective function. As an example, we then show how adding additional constraints, like the inclusion of sequence elements with fixed positions, allows for the design of bacterial promoters. The nucleotide string packing approach we present can accelerate the design of sequences with complex DNA-protein interactions. When used in combination with synthesis and high-throughput screening, this design strategy could help interrogate how complex binding site arrangements impact either gene expression or biomolecular mechanisms in varied cellular contexts. Author Summary The way protein binding sites are arranged on DNA can control the regulation and transcription of downstream genes. Areas with a high concentration of binding sites can enable complex interplay between transcription factors, a feature that is exploited by natural promoters. However, designing synthetic promoters that contain dense arrangements of binding sites is a challenge. The task involves overlapping many binding sites, each typically about 10 nucleotides long, within a constrained sequence area, which becomes increasingly difficult as sequence length decreases, and binding site variety increases. We introduce an approach to design nucleotide sequences with optimally packed protein binding sites, which we call the nucleotide String Packing Problem (SPP). We show that the SPP can be solved efficiently using integer linear programming to identify the densest arrangements of binding sites for a specified sequence length. We show how adding additional constraints, like the inclusion of sequence elements with fixed positions, allows for the design of bacterial promoters. The presented approach enables the rapid design and study of nucleotide sequences with complex, dense binding site architectures.
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17
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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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18
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Boob AG, Chen J, Zhao H. Enabling pathway design by multiplex experimentation and machine learning. Metab Eng 2024; 81:70-87. [PMID: 38040110 DOI: 10.1016/j.ymben.2023.11.006] [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: 09/14/2023] [Revised: 11/01/2023] [Accepted: 11/25/2023] [Indexed: 12/03/2023]
Abstract
The remarkable metabolic diversity observed in nature has provided a foundation for sustainable production of a wide array of valuable molecules. However, transferring the biosynthetic pathway to the desired host often runs into inherent failures that arise from intermediate accumulation and reduced flux resulting from competing pathways within the host cell. Moreover, the conventional trial and error methods utilized in pathway optimization struggle to fully grasp the intricacies of installed pathways, leading to time-consuming and labor-intensive experiments, ultimately resulting in suboptimal yields. Considering these obstacles, there is a pressing need to explore the enzyme expression landscape and identify the optimal pathway configuration for enhanced production of molecules. This review delves into recent advancements in pathway engineering, with a focus on multiplex experimentation and machine learning techniques. These approaches play a pivotal role in overcoming the limitations of traditional methods, enabling exploration of a broader design space and increasing the likelihood of discovering optimal pathway configurations for enhanced production of molecules. We discuss several tools and strategies for pathway design, construction, and optimization for sustainable and cost-effective microbial production of molecules ranging from bulk to fine chemicals. We also highlight major successes in academia and industry through compelling case studies.
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Affiliation(s)
- Aashutosh Girish Boob
- Department of Chemical and Biomolecular Engineering, University of Illinois Urbana-Champaign, Urbana, IL, 61801, United States; Carl R. Woese Institute for Genomic Biology, University of Illinois Urbana-Champaign, Urbana, IL, 61801, United States; DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Junyu Chen
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL, 61801, United States; Carl R. Woese Institute for Genomic Biology, University of Illinois Urbana-Champaign, Urbana, IL, 61801, United States; DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Huimin Zhao
- Department of Chemical and Biomolecular Engineering, University of Illinois Urbana-Champaign, Urbana, IL, 61801, United States; Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL, 61801, United States; Carl R. Woese Institute for Genomic Biology, University of Illinois Urbana-Champaign, Urbana, IL, 61801, United States; DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, United States.
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19
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Haresh Liya D, Elanchezhian M, Pahari M, Mouroug Anand N, Suresh S, Balaji N, Kumar Jainarayanan A. QPromoters: sequence based prediction of promoter strength in Saccharomyces cesrevisiae. ALL LIFE 2023. [DOI: 10.1080/26895293.2023.2168304] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023] Open
Affiliation(s)
- Devang Haresh Liya
- Department of Physical Sciences, Indian Institute of Science Education and Research, Mohali, India
| | - Mirudula Elanchezhian
- Department of Biological Sciences, Indian Institute of Science Education and Research, Mohali, India
| | - Mukulika Pahari
- Department of Computer Engineering, Ramrao Adik Institute of Technology, DY Patil Deemed to be University, Navi Mumbai, India
| | - Nithishwer Mouroug Anand
- Department of Physical Sciences, Indian Institute of Science Education and Research, Mohali, India
| | - Shivani Suresh
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, UK
| | - Nivedha Balaji
- School of Biology and Environmental Sciences (SBES), University College Dublin, Dublin, Ireland
| | - Ashwin Kumar Jainarayanan
- Kennedy Institute of Rheumatology, University of Oxford, Oxford, UK
- Interdisciplinary Bioscience Doctoral Training Program and Exeter College, University of Oxford, Oxford, UK
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20
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Jiang S, Luo Z, Wu J, Yu K, Zhao S, Cai Z, Yu W, Wang H, Cheng L, Liang Z, Gao H, Monti M, Schindler D, Huang L, Zeng C, Zhang W, Zhou C, Tang Y, Li T, Ma Y, Cai Y, Boeke JD, Zhao Q, Dai J. Building a eukaryotic chromosome arm by de novo design and synthesis. Nat Commun 2023; 14:7886. [PMID: 38036514 PMCID: PMC10689750 DOI: 10.1038/s41467-023-43531-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Accepted: 11/13/2023] [Indexed: 12/02/2023] Open
Abstract
The genome of an organism is inherited from its ancestor and continues to evolve over time, however, the extent to which the current version could be altered remains unknown. To probe the genome plasticity of Saccharomyces cerevisiae, here we replace the native left arm of chromosome XII (chrXIIL) with a linear artificial chromosome harboring small sets of reconstructed genes. We find that as few as 12 genes are sufficient for cell viability, whereas 25 genes are required to recover the partial fitness defects observed in the 12-gene strain. Next, we demonstrate that these genes can be reconstructed individually using synthetic regulatory sequences and recoded open-reading frames with a "one-amino-acid-one-codon" strategy to remain functional. Finally, a synthetic neochromsome with the reconstructed genes is assembled which could substitute chrXIIL for viability. Together, our work not only highlights the high plasticity of yeast genome, but also illustrates the possibility of making functional eukaryotic chromosomes from entirely artificial sequences.
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Grants
- National Natural Science Foundation of China (31725002), Shenzhen Science and Technology Program (KQTD20180413181837372), Guangdong Provincial Key Laboratory of Synthetic Genomics (2019B030301006),Bureau of International Cooperation,Chinese Academy of Sciences (172644KYSB20180022) and Shenzhen Outstanding Talents Training Fund.
- National Key Research and Development Program of China (2018YFA0900100),National Natural Science Foundation of China (31800069),Guangdong Basic and Applied Basic Research Foundation (2023A1515030285)
- National Key Research and Development Program of China (2018YFA0900100), National Natural Science Foundation of China (31800082 and 32122050),Guangdong Natural Science Funds for Distinguished Young Scholar (2021B1515020060)
- UK Biotechnology and Biological Sciences Research Council (BBSRC) grants BB/M005690/1, BB/P02114X/1 and BB/W014483/1, and a Volkswagen Foundation “Life? Initiative” Grant (Ref. 94 771)
- US NSF grants MCB-1026068, MCB-1443299, MCB-1616111 and MCB-1921641
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Affiliation(s)
- Shuangying Jiang
- CAS Key Laboratory of Quantitative Engineering Biology, Guangdong Provincial Key Laboratory of Synthetic Genomics and Shenzhen Key Laboratory of Synthetic Genomics, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Zhouqing Luo
- CAS Key Laboratory of Quantitative Engineering Biology, Guangdong Provincial Key Laboratory of Synthetic Genomics and Shenzhen Key Laboratory of Synthetic Genomics, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- State Key Laboratory of Cellular Stress Biology, Innovation Center for Cell Signaling Network, School of Life Sciences, Faculty of Medicine and Life Sciences, Xiamen University, Xiamen, Fujian, 361102, China
| | - Jie Wu
- CAS Key Laboratory of Quantitative Engineering Biology, Guangdong Provincial Key Laboratory of Synthetic Genomics and Shenzhen Key Laboratory of Synthetic Genomics, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Kang Yu
- CAS Key Laboratory of Quantitative Engineering Biology, Guangdong Provincial Key Laboratory of Synthetic Genomics and Shenzhen Key Laboratory of Synthetic Genomics, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Shijun Zhao
- CAS Key Laboratory of Quantitative Engineering Biology, Guangdong Provincial Key Laboratory of Synthetic Genomics and Shenzhen Key Laboratory of Synthetic Genomics, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Zelin Cai
- CAS Key Laboratory of Quantitative Engineering Biology, Guangdong Provincial Key Laboratory of Synthetic Genomics and Shenzhen Key Laboratory of Synthetic Genomics, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Wenfei Yu
- CAS Key Laboratory of Quantitative Engineering Biology, Guangdong Provincial Key Laboratory of Synthetic Genomics and Shenzhen Key Laboratory of Synthetic Genomics, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Hui Wang
- State Key Laboratory of Cellular Stress Biology, Innovation Center for Cell Signaling Network, School of Life Sciences, Faculty of Medicine and Life Sciences, Xiamen University, Xiamen, Fujian, 361102, China
| | - Li Cheng
- CAS Key Laboratory of Quantitative Engineering Biology, Guangdong Provincial Key Laboratory of Synthetic Genomics and Shenzhen Key Laboratory of Synthetic Genomics, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Zhenzhen Liang
- CAS Key Laboratory of Quantitative Engineering Biology, Guangdong Provincial Key Laboratory of Synthetic Genomics and Shenzhen Key Laboratory of Synthetic Genomics, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Hui Gao
- CAS Key Laboratory of Quantitative Engineering Biology, Guangdong Provincial Key Laboratory of Synthetic Genomics and Shenzhen Key Laboratory of Synthetic Genomics, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- Institute of Molecular Physiology, Shenzhen Bay Laboratory, Shenzhen, China
| | - Marco Monti
- Manchester Institute of Biotechnology, University of Manchester, Manchester, M1 7DN, UK
| | - Daniel Schindler
- Manchester Institute of Biotechnology, University of Manchester, Manchester, M1 7DN, UK
| | - Linsen Huang
- CAS Key Laboratory of Quantitative Engineering Biology, Guangdong Provincial Key Laboratory of Synthetic Genomics and Shenzhen Key Laboratory of Synthetic Genomics, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Cheng Zeng
- CAS Key Laboratory of Quantitative Engineering Biology, Guangdong Provincial Key Laboratory of Synthetic Genomics and Shenzhen Key Laboratory of Synthetic Genomics, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Weimin Zhang
- Institute for Systems Genetics and Department of Biochemistry and Molecular Pharmacology, NYU Langone Health, New York, NY, 10016, USA
| | - Chun Zhou
- CAS Key Laboratory of Quantitative Engineering Biology, Guangdong Provincial Key Laboratory of Synthetic Genomics and Shenzhen Key Laboratory of Synthetic Genomics, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Yuanwei Tang
- CAS Key Laboratory of Quantitative Engineering Biology, Guangdong Provincial Key Laboratory of Synthetic Genomics and Shenzhen Key Laboratory of Synthetic Genomics, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Tianyi Li
- CAS Key Laboratory of Quantitative Engineering Biology, Guangdong Provincial Key Laboratory of Synthetic Genomics and Shenzhen Key Laboratory of Synthetic Genomics, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Yingxin Ma
- CAS Key Laboratory of Quantitative Engineering Biology, Guangdong Provincial Key Laboratory of Synthetic Genomics and Shenzhen Key Laboratory of Synthetic Genomics, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Yizhi Cai
- CAS Key Laboratory of Quantitative Engineering Biology, Guangdong Provincial Key Laboratory of Synthetic Genomics and Shenzhen Key Laboratory of Synthetic Genomics, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- Manchester Institute of Biotechnology, University of Manchester, Manchester, M1 7DN, UK
| | - Jef D Boeke
- Institute for Systems Genetics and Department of Biochemistry and Molecular Pharmacology, NYU Langone Health, New York, NY, 10016, USA
- Department of Biomedical Engineering, NYU Tandon School of Engineering, Brooklyn, NY, 11201, USA
| | - Qiao Zhao
- CAS Key Laboratory of Quantitative Engineering Biology, Guangdong Provincial Key Laboratory of Synthetic Genomics and Shenzhen Key Laboratory of Synthetic Genomics, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
| | - Junbiao Dai
- CAS Key Laboratory of Quantitative Engineering Biology, Guangdong Provincial Key Laboratory of Synthetic Genomics and Shenzhen Key Laboratory of Synthetic Genomics, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
- University of Chinese Academy of Sciences, Beijing, China.
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Synthetic Biology, Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China.
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21
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Fu ZH, He SZ, Wu Y, Zhao GR. Design and deep learning of synthetic B-cell-specific promoters. Nucleic Acids Res 2023; 51:11967-11979. [PMID: 37889080 PMCID: PMC10681721 DOI: 10.1093/nar/gkad930] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2023] [Revised: 09/20/2023] [Accepted: 10/10/2023] [Indexed: 10/28/2023] Open
Abstract
Synthetic biology and deep learning synergistically revolutionize our ability for decoding and recoding DNA regulatory grammar. The B-cell-specific transcriptional regulation is intricate, and unlock the potential of B-cell-specific promoters as synthetic elements is important for B-cell engineering. Here, we designed and pooled synthesized 23 640 B-cell-specific promoters that exhibit larger sequence space, B-cell-specific expression, and enable diverse transcriptional patterns in B-cells. By MPRA (Massively parallel reporter assays), we deciphered the sequence features that regulate promoter transcriptional, including motifs and motif syntax (their combination and distance). Finally, we built and trained a deep learning model capable of predicting the transcriptional strength of the immunoglobulin V gene promoter directly from sequence. Prediction of thousands of promoter variants identified in the global human population shows that polymorphisms in promoters influence the transcription of immunoglobulin V genes, which may contribute to individual differences in adaptive humoral immune responses. Our work helps to decipher the transcription mechanism in immunoglobulin genes and offers thousands of non-similar promoters for B-cell engineering.
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Affiliation(s)
- Zong-Heng Fu
- Frontiers Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
- Frontiers Research Institute for Synthetic Biology, Tianjin University, Tianjin 300072, China
| | - Si-Zhe He
- Frontiers Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
- Frontiers Research Institute for Synthetic Biology, Tianjin University, Tianjin 300072, China
| | - Yi Wu
- Frontiers Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
- Frontiers Research Institute for Synthetic Biology, Tianjin University, Tianjin 300072, China
| | - Guang-Rong Zhao
- Frontiers Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
- Frontiers Research Institute for Synthetic Biology, Tianjin University, Tianjin 300072, China
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22
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Yin ZN, Lai FL, Gao F. Unveiling human origins of replication using deep learning: accurate prediction and comprehensive analysis. Brief Bioinform 2023; 25:bbad432. [PMID: 38008420 PMCID: PMC10676776 DOI: 10.1093/bib/bbad432] [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: 09/14/2023] [Revised: 10/11/2023] [Accepted: 11/06/2023] [Indexed: 11/28/2023] Open
Abstract
Accurate identification of replication origins (ORIs) is crucial for a comprehensive investigation into the progression of human cell growth and cancer therapy. Here, we proposed a computational approach Ori-FinderH, which can efficiently and precisely predict the human ORIs of various lengths by combining the Z-curve method with deep learning approach. Compared with existing methods, Ori-FinderH exhibits superior performance, achieving an area under the receiver operating characteristic curve (AUC) of 0.9616 for K562 cell line in 10-fold cross-validation. In addition, we also established a cross-cell-line predictive model, which yielded a further improved AUC of 0.9706. The model was subsequently employed as a fitness function to support genetic algorithm for generating artificial ORIs. Sequence analysis through iORI-Euk revealed that a vast majority of the created sequences, specifically 98% or more, incorporate at least one ORI for three cell lines (Hela, MCF7 and K562). This innovative approach could provide more efficient, accurate and comprehensive information for experimental investigation, thereby further advancing the development of this field.
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Affiliation(s)
- Zhen-Ning Yin
- Department of Physics, School of Science, Tianjin University, Tianjin 300072, China
| | - Fei-Liao Lai
- Department of Physics, School of Science, Tianjin University, Tianjin 300072, China
| | - Feng Gao
- Department of Physics, School of Science, Tianjin University, Tianjin 300072, China
- Frontiers Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), Tianjin University, Tianjin 300072, China
- SynBio Research Platform, Collaborative Innovation Center of Chemical Science and Engineering (Tianjin), Tianjin 300072, China
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23
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Merzbacher C, Oyarzún DA. Applications of artificial intelligence and machine learning in dynamic pathway engineering. Biochem Soc Trans 2023; 51:1871-1879. [PMID: 37656433 PMCID: PMC10657174 DOI: 10.1042/bst20221542] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 08/07/2023] [Accepted: 08/21/2023] [Indexed: 09/02/2023]
Abstract
Dynamic pathway engineering aims to build metabolic production systems embedded with intracellular control mechanisms for improved performance. These control systems enable host cells to self-regulate the temporal activity of a production pathway in response to perturbations, using a combination of biosensors and feedback circuits for controlling expression of heterologous enzymes. Pathway design, however, requires assembling together multiple biological parts into suitable circuit architectures, as well as careful calibration of the function of each component. This results in a large design space that is costly to navigate through experimentation alone. Methods from artificial intelligence (AI) and machine learning are gaining increasing attention as tools to accelerate the design cycle, owing to their ability to identify hidden patterns in data and rapidly screen through large collections of designs. In this review, we discuss recent developments in the application of machine learning methods to the design of dynamic pathways and their components. We cover recent successes and offer perspectives for future developments in the field. The integration of AI into metabolic engineering pipelines offers great opportunities to streamline design and discover control systems for improved production of high-value chemicals.
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Affiliation(s)
| | - Diego A. Oyarzún
- School of Informatics, University of Edinburgh, Edinburgh, U.K
- The Alan Turing Institute, London, U.K
- School of Biological Sciences, University of Edinburgh, Edinburgh, U.K
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24
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Zhang P, Wang H, Xu H, Wei L, Liu L, Hu Z, Wang X. Deep flanking sequence engineering for efficient promoter design using DeepSEED. Nat Commun 2023; 14:6309. [PMID: 37813854 PMCID: PMC10562447 DOI: 10.1038/s41467-023-41899-y] [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: 04/22/2023] [Accepted: 09/20/2023] [Indexed: 10/11/2023] Open
Abstract
Designing promoters with desirable properties is essential in synthetic biology. Human experts are skilled at identifying strong explicit patterns in small samples, while deep learning models excel at detecting implicit weak patterns in large datasets. Biologists have described the sequence patterns of promoters via transcription factor binding sites (TFBSs). However, the flanking sequences of cis-regulatory elements, have long been overlooked and often arbitrarily decided in promoter design. To address this limitation, we introduce DeepSEED, an AI-aided framework that efficiently designs synthetic promoters by combining expert knowledge with deep learning techniques. DeepSEED has demonstrated success in improving the properties of Escherichia coli constitutive, IPTG-inducible, and mammalian cell doxycycline (Dox)-inducible promoters. Furthermore, our results show that DeepSEED captures the implicit features in flanking sequences, such as k-mer frequencies and DNA shape features, which are crucial for determining promoter properties.
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Affiliation(s)
- Pengcheng Zhang
- Ministry of Education Key Laboratory of Bioinformatics; Center for Synthetic and Systems Biology; Bioinformatics Division, Beijing National Research Center for Information Science and Technology; Department of Automation, Tsinghua University, Beijing, China
| | - Haochen Wang
- Ministry of Education Key Laboratory of Bioinformatics; Center for Synthetic and Systems Biology; Bioinformatics Division, Beijing National Research Center for Information Science and Technology; Department of Automation, Tsinghua University, Beijing, China
| | - Hanwen Xu
- Ministry of Education Key Laboratory of Bioinformatics; Center for Synthetic and Systems Biology; Bioinformatics Division, Beijing National Research Center for Information Science and Technology; Department of Automation, Tsinghua University, Beijing, China
| | - Lei Wei
- Ministry of Education Key Laboratory of Bioinformatics; Center for Synthetic and Systems Biology; Bioinformatics Division, Beijing National Research Center for Information Science and Technology; Department of Automation, Tsinghua University, Beijing, China
| | - Liyang Liu
- Ministry of Education Key Laboratory of Bioinformatics; Center for Synthetic and Systems Biology; Bioinformatics Division, Beijing National Research Center for Information Science and Technology; Department of Automation, Tsinghua University, Beijing, China
| | - Zhirui Hu
- Center for Statistical Science, Tsinghua University, Beijing, China
| | - Xiaowo Wang
- Ministry of Education Key Laboratory of Bioinformatics; Center for Synthetic and Systems Biology; Bioinformatics Division, Beijing National Research Center for Information Science and Technology; Department of Automation, Tsinghua University, Beijing, China.
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25
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Chen C, Chen J, Wu G, Li L, Hu Z, Li X. A Blue Light-Responsive Strong Synthetic Promoter Based on Rational Design in Chlamydomonas reinhardtii. Int J Mol Sci 2023; 24:14596. [PMID: 37834043 PMCID: PMC10572394 DOI: 10.3390/ijms241914596] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 09/13/2023] [Accepted: 09/21/2023] [Indexed: 10/15/2023] Open
Abstract
Chlamydomonas reinhardtii (C. reinhardtii) is a single-cell green alga that can be easily genetically manipulated. With its favorable characteristics of rapid growth, low cost, non-toxicity, and the ability for post-translational protein modification, C. reinhardtii has emerged as an attractive option for the biosynthesis of various valuable products. To enhance the expression level of exogenous genes and overcome the silencing of foreign genes by C. reinhardtii, synthetic promoters such as the chimeric promoter AR have been constructed and evaluated. In this study, a synthetic promoter GA was constructed by hybridizing core fragments from the natural promoters of the acyl carrier protein gene (ACP2) and the glutamate dehydrogenase gene (GDH2). The GA promoter exhibited a significant increase (7 times) in expressing GUS, over the AR promoter as positive control. The GA promoter also displayed a strong responsiveness to blue light (BL), where the GUS expression was doubled compared to the white light (WL) condition. The ability of the GA promoter was further tested in the expression of another exogenous cadA gene, responsible for catalyzing the decarboxylation of lysine to produce cadaverine. The cadaverine yield driven by the GA promoter was increased by 1-2 times under WL and 2-3 times under BL as compared to the AR promoter. This study obtained, for the first time, a blue light-responsive GDH2 minimal fragment in C. reinhardtii, which delivered a doubling effect under BL when used alone or in hybrid. Together with the strong GA synthetic promoter, this study offered useful tools of synthetic biology to the algal biotechnology field.
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Affiliation(s)
| | | | | | | | - Zhangli Hu
- Guangdong Technology Research Center for Marine Algal Bioengineering, Guangdong Provincial Key Laboratory for Plant Epigenetics, Shenzhen Engineering Laboratory for Marine Algal Biotechnology, Longhua Innovation Institute for Biotechnology, College of Life Sciences and Oceanography, Shenzhen University, Shenzhen 518060, China
| | - Xiaozheng Li
- Guangdong Technology Research Center for Marine Algal Bioengineering, Guangdong Provincial Key Laboratory for Plant Epigenetics, Shenzhen Engineering Laboratory for Marine Algal Biotechnology, Longhua Innovation Institute for Biotechnology, College of Life Sciences and Oceanography, Shenzhen University, Shenzhen 518060, China
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26
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Han Y, Li W, Filko A, Li J, Zhang F. Genome-wide promoter responses to CRISPR perturbations of regulators reveal regulatory networks in Escherichia coli. Nat Commun 2023; 14:5757. [PMID: 37717013 PMCID: PMC10505187 DOI: 10.1038/s41467-023-41572-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Accepted: 09/08/2023] [Indexed: 09/18/2023] Open
Abstract
Elucidating genome-scale regulatory networks requires a comprehensive collection of gene expression profiles, yet measuring gene expression responses for every transcription factor (TF)-gene pair in living prokaryotic cells remains challenging. Here, we develop pooled promoter responses to TF perturbation sequencing (PPTP-seq) via CRISPR interference to address this challenge. Using PPTP-seq, we systematically measure the activity of 1372 Escherichia coli promoters under single knockdown of 183 TF genes, illustrating more than 200,000 possible TF-gene responses in one experiment. We perform PPTP-seq for E. coli growing in three different media. The PPTP-seq data reveal robust steady-state promoter activities under most single TF knockdown conditions. PPTP-seq also enables identifications of, to the best of our knowledge, previously unknown TF autoregulatory responses and complex transcriptional control on one-carbon metabolism. We further find context-dependent promoter regulation by multiple TFs whose relative binding strengths determined promoter activities. Additionally, PPTP-seq reveals different promoter responses in different growth media, suggesting condition-specific gene regulation. Overall, PPTP-seq provides a powerful method to examine genome-wide transcriptional regulatory networks and can be potentially expanded to reveal gene expression responses to other genetic elements.
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Affiliation(s)
- Yichao Han
- Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, Saint Louis, Missouri, USA
| | - Wanji Li
- Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, Saint Louis, Missouri, USA
| | - Alden Filko
- Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, Saint Louis, Missouri, USA
| | - Jingyao Li
- Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, Saint Louis, Missouri, USA
| | - Fuzhong Zhang
- Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, Saint Louis, Missouri, USA.
- Division of Biological and Biomedical Sciences, Washington University in St. Louis, Saint Louis, Missouri, USA.
- Institute of Materials Science and Engineering, Washington University in St. Louis, Saint Louis, Missouri, USA.
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27
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Kleinschmidt H, Xu C, Bai L. Using Synthetic DNA Libraries to Investigate Chromatin and Gene Regulation. Chromosoma 2023; 132:167-189. [PMID: 37184694 PMCID: PMC10542970 DOI: 10.1007/s00412-023-00796-5] [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: 02/05/2023] [Revised: 04/25/2023] [Accepted: 04/26/2023] [Indexed: 05/16/2023]
Abstract
Despite the recent explosion in genome-wide studies in chromatin and gene regulation, we are still far from extracting a set of genetic rules that can predict the function of the regulatory genome. One major reason for this deficiency is that gene regulation is a multi-layered process that involves an enormous variable space, which cannot be fully explored using native genomes. This problem can be partially solved by introducing synthetic DNA libraries into cells, a method that can test the regulatory roles of thousands to millions of sequences with limited variables. Here, we review recent applications of this method to study transcription factor (TF) binding, nucleosome positioning, and transcriptional activity. We discuss the design principles, experimental procedures, and major findings from these studies and compare the pros and cons of different approaches.
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Affiliation(s)
- Holly Kleinschmidt
- Department of Biochemistry and Molecular Biology, The Pennsylvania State University, University Park, PA, 16802, USA
- Center for Eukaryotic Gene Regulation, The Pennsylvania State University, University Park, PA, 16802, USA
| | - Cheng Xu
- Department of Biochemistry and Molecular Biology, The Pennsylvania State University, University Park, PA, 16802, USA
- Center for Eukaryotic Gene Regulation, The Pennsylvania State University, University Park, PA, 16802, USA
| | - Lu Bai
- Department of Biochemistry and Molecular Biology, The Pennsylvania State University, University Park, PA, 16802, USA.
- Center for Eukaryotic Gene Regulation, The Pennsylvania State University, University Park, PA, 16802, USA.
- Department of Physics, The Pennsylvania State University, University Park, PA, 16802, USA.
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28
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Zhang XE, Liu C, Dai J, Yuan Y, Gao C, Feng Y, Wu B, Wei P, You C, Wang X, Si T. Enabling technology and core theory of synthetic biology. SCIENCE CHINA. LIFE SCIENCES 2023; 66:1742-1785. [PMID: 36753021 PMCID: PMC9907219 DOI: 10.1007/s11427-022-2214-2] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Accepted: 10/04/2022] [Indexed: 02/09/2023]
Abstract
Synthetic biology provides a new paradigm for life science research ("build to learn") and opens the future journey of biotechnology ("build to use"). Here, we discuss advances of various principles and technologies in the mainstream of the enabling technology of synthetic biology, including synthesis and assembly of a genome, DNA storage, gene editing, molecular evolution and de novo design of function proteins, cell and gene circuit engineering, cell-free synthetic biology, artificial intelligence (AI)-aided synthetic biology, as well as biofoundries. We also introduce the concept of quantitative synthetic biology, which is guiding synthetic biology towards increased accuracy and predictability or the real rational design. We conclude that synthetic biology will establish its disciplinary system with the iterative development of enabling technologies and the maturity of the core theory.
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Affiliation(s)
- Xian-En Zhang
- Faculty of Synthetic Biology, Shenzhen Institute of Advanced Technology, Shenzhen, 518055, China.
- National Laboratory of Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Chenli Liu
- Faculty of Synthetic Biology, Shenzhen Institute of Advanced Technology, Shenzhen, 518055, China.
- Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
| | - Junbiao Dai
- Faculty of Synthetic Biology, Shenzhen Institute of Advanced Technology, Shenzhen, 518055, China.
- Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
| | - Yingjin Yuan
- Frontiers Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), School of Chemical Engineering and Technology, Tianjin University, Tianjin, 300072, China.
| | - Caixia Gao
- State Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Yan Feng
- State Key Laboratory of Microbial Metabolism, Shanghai Jiao Tong University, Shanghai, 200240, China.
| | - Bian Wu
- State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Ping Wei
- Faculty of Synthetic Biology, Shenzhen Institute of Advanced Technology, Shenzhen, 518055, China.
- Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
| | - Chun You
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China.
| | - Xiaowo Wang
- Ministry of Education Key Laboratory of Bioinformatics; Center for Synthetic and Systems Biology; Bioinformatics Division, Beijing National Research Center for Information Science and Technology; Department of Automation, Tsinghua University, Beijing, 100084, China.
| | - Tong Si
- Faculty of Synthetic Biology, Shenzhen Institute of Advanced Technology, Shenzhen, 518055, China.
- Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
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29
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Penzar D, Nogina D, Noskova E, Zinkevich A, Meshcheryakov G, Lando A, Rafi AM, de Boer C, Kulakovskiy IV. LegNet: a best-in-class deep learning model for short DNA regulatory regions. Bioinformatics 2023; 39:btad457. [PMID: 37490428 PMCID: PMC10400376 DOI: 10.1093/bioinformatics/btad457] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Revised: 05/28/2023] [Accepted: 07/24/2023] [Indexed: 07/27/2023] Open
Abstract
MOTIVATION The increasing volume of data from high-throughput experiments including parallel reporter assays facilitates the development of complex deep-learning approaches for modeling DNA regulatory grammar. RESULTS Here, we introduce LegNet, an EfficientNetV2-inspired convolutional network for modeling short gene regulatory regions. By approaching the sequence-to-expression regression problem as a soft classification task, LegNet secured first place for the autosome.org team in the DREAM 2022 challenge of predicting gene expression from gigantic parallel reporter assays. Using published data, here, we demonstrate that LegNet outperforms existing models and accurately predicts gene expression per se as well as the effects of single-nucleotide variants. Furthermore, we show how LegNet can be used in a diffusion network manner for the rational design of promoter sequences yielding the desired expression level. AVAILABILITY AND IMPLEMENTATION https://github.com/autosome-ru/LegNet. The GitHub repository includes Jupyter Notebook tutorials and Python scripts under the MIT license to reproduce the results presented in the study.
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Affiliation(s)
- Dmitry Penzar
- Vavilov Institute of General Genetics, Moscow 119991, Russia
- Institute of Protein Research, Pushchino 142290, Russia
- Institute of Translational Medicine, Pirogov Russian National Research Medical University, Moscow 117997, Russia
| | - Daria Nogina
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Moscow 119991, Russia
| | - Elizaveta Noskova
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Moscow 119991, Russia
| | - Arsenii Zinkevich
- Vavilov Institute of General Genetics, Moscow 119991, Russia
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Moscow 119991, Russia
| | | | | | - Abdul Muntakim Rafi
- School of Biomedical Engineering, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
| | - Carl de Boer
- School of Biomedical Engineering, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
| | - Ivan V Kulakovskiy
- Vavilov Institute of General Genetics, Moscow 119991, Russia
- Institute of Protein Research, Pushchino 142290, Russia
- Laboratory of Regulatory Genomics, Institute of Fundamental Medicine and Biology, Kazan Federal University, Kazan 420008, Russia
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30
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Milito A, Aschern M, McQuillan JL, Yang JS. Challenges and advances towards the rational design of microalgal synthetic promoters in Chlamydomonas reinhardtii. JOURNAL OF EXPERIMENTAL BOTANY 2023; 74:3833-3850. [PMID: 37025006 DOI: 10.1093/jxb/erad100] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Accepted: 03/24/2023] [Indexed: 06/19/2023]
Abstract
Microalgae hold enormous potential to provide a safe and sustainable source of high-value compounds, acting as carbon-fixing biofactories that could help to mitigate rapidly progressing climate change. Bioengineering microalgal strains will be key to optimizing and modifying their metabolic outputs, and to render them competitive with established industrial biotechnology hosts, such as bacteria or yeast. To achieve this, precise and tuneable control over transgene expression will be essential, which would require the development and rational design of synthetic promoters as a key strategy. Among green microalgae, Chlamydomonas reinhardtii represents the reference species for bioengineering and synthetic biology; however, the repertoire of functional synthetic promoters for this species, and for microalgae generally, is limited in comparison to other commercial chassis, emphasizing the need to expand the current microalgal gene expression toolbox. Here, we discuss state-of-the-art promoter analyses, and highlight areas of research required to advance synthetic promoter development in C. reinhardtii. In particular, we exemplify high-throughput studies performed in other model systems that could be applicable to microalgae, and propose novel approaches to interrogating algal promoters. We lastly outline the major limitations hindering microalgal promoter development, while providing novel suggestions and perspectives for how to overcome them.
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Affiliation(s)
- Alfonsina Milito
- Centre for Research in Agricultural Genomics (CRAG), CSIC-IRTA-UAB-UB, Campus UAB, Bellaterra, Barcelona, Spain
| | - Moritz Aschern
- Centre for Research in Agricultural Genomics (CRAG), CSIC-IRTA-UAB-UB, Campus UAB, Bellaterra, Barcelona, Spain
| | - Josie L McQuillan
- Department of Chemical and Biological Engineering, University of Sheffield, Mappin Street, Sheffield, S1 3JD, UK
| | - Jae-Seong Yang
- Centre for Research in Agricultural Genomics (CRAG), CSIC-IRTA-UAB-UB, Campus UAB, Bellaterra, Barcelona, Spain
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31
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Yang W, Li D, Huang R. EVMP: enhancing machine learning models for synthetic promoter strength prediction by Extended Vision Mutant Priority framework. Front Microbiol 2023; 14:1215609. [PMID: 37476664 PMCID: PMC10354429 DOI: 10.3389/fmicb.2023.1215609] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Accepted: 06/19/2023] [Indexed: 07/22/2023] Open
Abstract
Introduction In metabolic engineering and synthetic biology applications, promoters with appropriate strengths are critical. However, it is time-consuming and laborious to annotate promoter strength by experiments. Nowadays, constructing mutation-based synthetic promoter libraries that span multiple orders of magnitude of promoter strength is receiving increasing attention. A number of machine learning (ML) methods are applied to synthetic promoter strength prediction, but existing models are limited by the excessive proximity between synthetic promoters. Methods In order to enhance ML models to better predict the synthetic promoter strength, we propose EVMP(Extended Vision Mutant Priority), a universal framework which utilize mutation information more effectively. In EVMP, synthetic promoters are equivalently transformed into base promoter and corresponding k-mer mutations, which are input into BaseEncoder and VarEncoder, respectively. EVMP also provides optional data augmentation, which generates multiple copies of the data by selecting different base promoters for the same synthetic promoter. Results In Trc synthetic promoter library, EVMP was applied to multiple ML models and the model effect was enhanced to varying extents, up to 61.30% (MAE), while the SOTA(state-of-the-art) record was improved by 15.25% (MAE) and 4.03% (R2). Data augmentation based on multiple base promoters further improved the model performance by 17.95% (MAE) and 7.25% (R2) compared with non-EVMP SOTA record. Discussion In further study, extended vision (or k-mer) is shown to be essential for EVMP. We also found that EVMP can alleviate the over-smoothing phenomenon, which may contributes to its effectiveness. Our work suggests that EVMP can highlight the mutation information of synthetic promoters and significantly improve the prediction accuracy of strength. The source code is publicly available on GitHub: https://github.com/Tiny-Snow/EVMP.
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Affiliation(s)
- Weiqin Yang
- Institute of Marine Science and Technology, Shandong University, Qingdao, China
- School of Computer Science and Technology, Shandong University, Qingdao, China
| | - Dexin Li
- Institute of Marine Science and Technology, Shandong University, Qingdao, China
- School of Computer Science and Technology, Shandong University, Qingdao, China
| | - Ranran Huang
- Institute of Marine Science and Technology, Shandong University, Qingdao, China
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Wittkopp PJ. Contributions of mutation and selection to regulatory variation: lessons from the Saccharomyces cerevisiae TDH3 gene. Philos Trans R Soc Lond B Biol Sci 2023; 378:20220057. [PMID: 37004723 PMCID: PMC10067266 DOI: 10.1098/rstb.2022.0057] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Accepted: 02/16/2023] [Indexed: 04/04/2023] Open
Abstract
Heritable variation in gene expression is common within and among species and contributes to phenotypic diversity. Mutations affecting either cis- or trans-regulatory sequences controlling gene expression give rise to variation in gene expression, and natural selection acting on this variation causes some regulatory variants to persist in a population for longer than others. To understand how mutation and selection interact to produce the patterns of regulatory variation we see within and among species, my colleagues and I have been systematically determining the effects of new mutations on expression of the TDH3 gene in Saccharomyces cerevisiae and comparing them to the effects of polymorphisms segregating within this species. We have also investigated the molecular mechanisms by which regulatory variants act. Over the past decade, this work has revealed properties of cis- and trans-regulatory mutations including their relative frequency, effects, dominance, pleiotropy and fitness consequences. Comparing these mutational effects to the effects of polymorphisms in natural populations, we have inferred selection acting on expression level, expression noise and phenotypic plasticity. Here, I summarize this body of work and synthesize its findings to make inferences not readily discernible from the individual studies alone. This article is part of the theme issue 'Interdisciplinary approaches to predicting evolutionary biology'.
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Affiliation(s)
- Patricia J. Wittkopp
- Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, MI 48109, USA
- Department of Molecular, Cellular, and Developmental Biology, University of Michigan, Ann Arbor, MI 48109, USA
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Nikolados EM, Oyarzún DA. Deep learning for optimization of protein expression. Curr Opin Biotechnol 2023; 81:102941. [PMID: 37087839 DOI: 10.1016/j.copbio.2023.102941] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 02/02/2023] [Accepted: 03/17/2023] [Indexed: 04/25/2023]
Abstract
Recent progress in high-throughput DNA synthesis and sequencing has enabled the development of massively parallel reporter assays for strain characterization. These datasets map a large number of DNA sequences to protein expression levels, sparking increased interest in data-driven methods for sequence-to-expression modeling. Here, we highlight advances in deep learning models of protein expression and their potential for optimizing strains engineered to produce recombinant proteins. We review recent works that built highly accurate models and discuss challenges that hinder adoption by end users. There is a need to better align this technology with the constraints encountered in strain engineering, particularly the cost of acquiring large amounts of data and the requirement for interpretable models that generalize beyond the training data. Overcoming these barriers will help to incentivize academic and industrial laboratories to tap into a new era of data-centric strain engineering.
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Affiliation(s)
| | - Diego A Oyarzún
- School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3JH, UK; School of Informatics, University of Edinburgh, Edinburgh EH8 9AB, UK; The Alan Turing Institute, London NW1 2DB, UK.
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Xu H, Li C, Xu C, Zhang J. Chance promoter activities illuminate the origins of eukaryotic intergenic transcriptions. Nat Commun 2023; 14:1826. [PMID: 37005399 PMCID: PMC10067814 DOI: 10.1038/s41467-023-37610-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Accepted: 03/23/2023] [Indexed: 04/04/2023] Open
Abstract
It is debated whether the pervasive intergenic transcription from eukaryotic genomes has functional significance or simply reflects the promiscuity of RNA polymerases. We approach this question by comparing chance promoter activities with the expression levels of intergenic regions in the model eukaryote Saccharomyces cerevisiae. We build a library of over 105 strains, each carrying a 120-nucleotide, chromosomally integrated, completely random sequence driving the potential transcription of a barcode. Quantifying the RNA concentration of each barcode in two environments reveals that 41-63% of random sequences have significant, albeit usually low, promoter activities. Therefore, even in eukaryotes, where the presence of chromatin is thought to repress transcription, chance transcription is prevalent. We find that only 1-5% of yeast intergenic transcriptions are unattributable to chance promoter activities or neighboring gene expressions, and these transcriptions exhibit higher-than-expected environment-specificity. These findings suggest that only a minute fraction of intergenic transcription is functional in yeast.
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Affiliation(s)
- Haiqing Xu
- Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, MI, USA
- Department of Biology, Stanford University, Stanford, CA, USA
| | - Chuan Li
- Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, MI, USA
- Microsoft, Redmond, WA, USA
| | - Chuan Xu
- Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, MI, USA
- Bio-X Institutes, Shanghai Jiao Tong University, Shanghai, China
| | - Jianzhi Zhang
- Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, MI, USA.
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35
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Park JH, Bassalo MC, Lin GM, Chen Y, Doosthosseini H, Schmitz J, Roubos JA, Voigt CA. Design of Four Small-Molecule-Inducible Systems in the Yeast Chromosome, Applied to Optimize Terpene Biosynthesis. ACS Synth Biol 2023; 12:1119-1132. [PMID: 36943773 PMCID: PMC10127285 DOI: 10.1021/acssynbio.2c00607] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/23/2023]
Abstract
The optimization of cellular functions often requires the balancing of gene expression, but the physical construction and screening of alternative designs are costly and time-consuming. Here, we construct a strain of Saccharomyces cerevisiae that contains a "sensor array" containing bacterial regulators that respond to four small-molecule inducers (vanillic acid, xylose, aTc, IPTG). Four promoters can be independently controlled with low background and a 40- to 5000-fold dynamic range. These systems can be used to study the impact of changing the level and timing of gene expression without requiring the construction of multiple strains. We apply this approach to the optimization of a four-gene heterologous pathway to the terpene linalool, which is a flavor and precursor to energetic materials. Using this approach, we identify bottlenecks in the metabolic pathway. This work can aid the rapid automated strain development of yeasts for the bio-manufacturing of diverse products, including chemicals, materials, fuels, and food ingredients.
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Affiliation(s)
- Jong Hyun Park
- Synthetic Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, 500 Technology Square, Cambridge, Massachusetts 02139, United States
| | - Marcelo C Bassalo
- Synthetic Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, 500 Technology Square, Cambridge, Massachusetts 02139, United States
| | - Geng-Min Lin
- Synthetic Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, 500 Technology Square, Cambridge, Massachusetts 02139, United States
| | - Ye Chen
- Synthetic Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, 500 Technology Square, Cambridge, Massachusetts 02139, United States
| | - Hamid Doosthosseini
- Synthetic Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, 500 Technology Square, Cambridge, Massachusetts 02139, United States
| | - Joep Schmitz
- DSM Science & Innovation, Biodata & Translational Sciences, P.O. Box 1, 2600 MA Delft, The Netherlands
| | - Johannes A Roubos
- DSM Science & Innovation, Biodata & Translational Sciences, P.O. Box 1, 2600 MA Delft, The Netherlands
| | - Christopher A Voigt
- Synthetic Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, 500 Technology Square, Cambridge, Massachusetts 02139, United States
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Kang CK, Shin J, Cha Y, Kim MS, Choi MS, Kim T, Park YK, Choi YJ. Machine learning-guided prediction of potential engineering targets for microbial production of lycopene. BIORESOURCE TECHNOLOGY 2023; 369:128455. [PMID: 36503092 DOI: 10.1016/j.biortech.2022.128455] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Revised: 12/02/2022] [Accepted: 12/04/2022] [Indexed: 06/17/2023]
Abstract
The process of designing streamlined workflows for developing microbial strains using classical methods from vast amounts of biological big data has reached its limits. With the continuous increase in the amount of biological big data, data-driven machine learning approaches are being used to overcome the limits of classical approaches for strain development. Here, machine learning-guided engineering of Deinococcus radiodurans R1 for high-yield production of lycopene was demonstrated. The multilayer perceptron models were first trained using the mRNA expression levels of the key genes along with lycopene titers and yields obtained from 17 strains. Then, the potential overexpression targets from 2,047 possible combinations were predicted by the multilayer perceptron combined with a genetic algorithm. Through the machine learning-aided fine-tuning of the predicted genes, the final-engineered LY04 strain resulted in an 8-fold increase in the lycopene production, up to 1.25 g/L from glycerol, and a 6-fold increase in the lycopene yield.
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Affiliation(s)
- Chang Keun Kang
- School of Environmental Engineering, University of Seoul, Seoul 02504, Republic of Korea
| | - Jihoon Shin
- School of Environmental Engineering, University of Seoul, Seoul 02504, Republic of Korea
| | - YoonKyung Cha
- School of Environmental Engineering, University of Seoul, Seoul 02504, Republic of Korea
| | - Min Sun Kim
- School of Environmental Engineering, University of Seoul, Seoul 02504, Republic of Korea
| | - Min Sun Choi
- School of Environmental Engineering, University of Seoul, Seoul 02504, Republic of Korea
| | - TaeHo Kim
- School of Environmental Engineering, University of Seoul, Seoul 02504, Republic of Korea
| | - Young-Kwon Park
- School of Environmental Engineering, University of Seoul, Seoul 02504, Republic of Korea
| | - Yong Jun Choi
- School of Environmental Engineering, University of Seoul, Seoul 02504, Republic of Korea.
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37
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He S, Zhang Z, Lu W. Natural promoters and promoter engineering strategies for metabolic regulation in Saccharomyces cerevisiae. J Ind Microbiol Biotechnol 2023; 50:6986260. [PMID: 36633543 PMCID: PMC9936215 DOI: 10.1093/jimb/kuac029] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Accepted: 12/19/2022] [Indexed: 01/13/2023]
Abstract
Sharomyces cerevisiae is currently one of the most important foreign gene expression systems. S. cerevisiae is an excellent host for high-value metabolite cell factories due to its advantages of simplicity, safety, and nontoxicity. A promoter, as one of the basic elements of gene transcription, plays an important role in regulating gene expression and optimizing metabolic pathways. Promoters control the direction and intensity of transcription, and the application of promoters with different intensities and performances will largely determine the effect of gene expression and ultimately affect the experimental results. Due to its significant role, there have been many studies on promoters for decades. While some studies have explored and analyzed new promoters with different functions, more studies have focused on artificially modifying promoters to meet their own scientific needs. Thus, this article reviews current research on promoter engineering techniques and related natural promoters in S. cerevisiae. First, we introduce the basic structure of promoters and the classification of natural promoters. Then, the classification of various promoter strategies is reviewed. Finally, by grouping related articles together using various strategies, this review anticipates the future development direction of promoter engineering.
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Affiliation(s)
| | - Zhanwei Zhang
- School of Chemical Engineering and Technology, Tianjin University, Tianjin 300350, PR China
| | - Wenyu Lu
- Correspondence should be addressed to: W. Y. Lu, School of Chemical Engineering and Technology, Tianjin University, Tianjin 300350, PR China. Phone: +86-22-853-56523. Fax: +86-22-274-00973. E-mail:
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Abstract
As the global burden of antibiotic resistance continues to grow, creative approaches to antibiotic discovery are needed to accelerate the development of novel medicines. A rapidly progressing computational revolution-artificial intelligence-offers an optimistic path forward due to its ability to alleviate bottlenecks in the antibiotic discovery pipeline. In this review, we discuss how advancements in artificial intelligence are reinvigorating the adoption of past antibiotic discovery models-namely natural product exploration and small molecule screening. We then explore the application of contemporary machine learning approaches to emerging areas of antibiotic discovery, including antibacterial systems biology, drug combination development, antimicrobial peptide discovery, and mechanism of action prediction. Lastly, we propose a call to action for open access of high-quality screening datasets and interdisciplinary collaboration to accelerate the rate at which machine learning models can be trained and new antibiotic drugs can be developed.
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Affiliation(s)
- Telmah Lluka
- Department of Biochemistry and Biomedical Sciences, Michael G. DeGroote Institute for Infectious Disease Research, David Braley Centre for Antibiotic Discovery, McMaster University, Hamilton, Ontario, Canada
| | - Jonathan M Stokes
- Department of Biochemistry and Biomedical Sciences, Michael G. DeGroote Institute for Infectious Disease Research, David Braley Centre for Antibiotic Discovery, McMaster University, Hamilton, Ontario, Canada
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39
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Gilliot PA, Gorochowski TE. Design and Analysis of Massively Parallel Reporter Assays Using FORECAST. Methods Mol Biol 2023; 2553:41-56. [PMID: 36227538 DOI: 10.1007/978-1-0716-2617-7_3] [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] [Indexed: 06/16/2023]
Abstract
Machine learning is revolutionizing molecular biology and bioengineering by providing powerful insights and predictions. Massively parallel reporter assays (MPRAs) have emerged as a particularly valuable class of high-throughput technique to support such algorithms. MPRAs enable the simultaneous characterization of thousands or even millions of genetic constructs and provide the large amounts of data needed to train models. However, while the scale of this approach is impressive, the design of effective MPRA experiments is challenging due to the many factors that can be varied and the difficulty in predicting how these will impact the quality and quantity of data obtained. Here, we present a computational tool called FORECAST, which can simulate MPRA experiments based on fluorescence-activated cell sorting and subsequent sequencing (commonly referred to as Flow-seq or Sort-seq experiments), as well as carry out rigorous statistical estimation of construct performance from this type of experimental data. FORECAST can be used to develop workflows to aid the design of MPRA experiments and reanalyze existing MPRA data sets.
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40
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Sieow BFL, De Sotto R, Seet ZRD, Hwang IY, Chang MW. Synthetic Biology Meets Machine Learning. Methods Mol Biol 2023; 2553:21-39. [PMID: 36227537 DOI: 10.1007/978-1-0716-2617-7_2] [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: 06/16/2023]
Abstract
This chapter outlines the myriad applications of machine learning (ML) in synthetic biology, specifically in engineering cell and protein activity, and metabolic pathways. Though by no means comprehensive, the chapter highlights several prominent computational tools applied in the field and their potential use cases. The examples detailed reinforce how ML algorithms can enhance synthetic biology research by providing data-driven insights into the behavior of living systems, even without detailed knowledge of their underlying mechanisms. By doing so, ML promises to increase the efficiency of research projects by modeling hypotheses in silico that can then be tested through experiments. While challenges related to training dataset generation and computational costs remain, ongoing improvements in ML tools are paving the way for smarter and more streamlined synthetic biology workflows that can be readily employed to address grand challenges across manufacturing, medicine, engineering, agriculture, and beyond.
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Affiliation(s)
- Brendan Fu-Long Sieow
- NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), National University of Singapore, Singapore, Singapore
- Synthetic Biology Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- NUS Graduate School for Integrative Sciences and Engineering Programme, National University of Singapore, Singapore, Singapore
| | - Ryan De Sotto
- NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), National University of Singapore, Singapore, Singapore
- Synthetic Biology Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Zhi Ren Darren Seet
- NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), National University of Singapore, Singapore, Singapore
- Synthetic Biology Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - In Young Hwang
- NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), National University of Singapore, Singapore, Singapore
- Synthetic Biology Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Matthew Wook Chang
- NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), National University of Singapore, Singapore, Singapore.
- Synthetic Biology Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
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41
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Accuracy and data efficiency in deep learning models of protein expression. Nat Commun 2022; 13:7755. [PMID: 36517468 PMCID: PMC9751117 DOI: 10.1038/s41467-022-34902-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Accepted: 11/10/2022] [Indexed: 12/23/2022] Open
Abstract
Synthetic biology often involves engineering microbial strains to express high-value proteins. Thanks to progress in rapid DNA synthesis and sequencing, deep learning has emerged as a promising approach to build sequence-to-expression models for strain optimization. But such models need large and costly training data that create steep entry barriers for many laboratories. Here we study the relation between accuracy and data efficiency in an atlas of machine learning models trained on datasets of varied size and sequence diversity. We show that deep learning can achieve good prediction accuracy with much smaller datasets than previously thought. We demonstrate that controlled sequence diversity leads to substantial gains in data efficiency and employed Explainable AI to show that convolutional neural networks can finely discriminate between input DNA sequences. Our results provide guidelines for designing genotype-phenotype screens that balance cost and quality of training data, thus helping promote the wider adoption of deep learning in the biotechnology sector.
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Duong-Trung N, Born S, Kim JW, Schermeyer MT, Paulick K, Borisyak M, Cruz-Bournazou MN, Werner T, Scholz R, Schmidt-Thieme L, Neubauer P, Martinez E. When Bioprocess Engineering Meets Machine Learning: A Survey from the Perspective of Automated Bioprocess Development. Biochem Eng J 2022. [DOI: 10.1016/j.bej.2022.108764] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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43
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Yap WS, Thibault G. A new combinatorial megaplasmid library assembly method designed to screen for minimal pathways by using SCRaMbLE. MICROPUBLICATION BIOLOGY 2022; 2022:10.17912/micropub.biology.000657. [PMID: 36389121 PMCID: PMC9647409 DOI: 10.17912/micropub.biology.000657] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 10/10/2022] [Accepted: 10/20/2022] [Indexed: 11/23/2022]
Abstract
Human proteins expressed in yeast are common to enhance protein production while the expression of functional human pathways remain challenging. Here, we propose a simple and economical high-throughput gene assembly method to create a yeast megaplasmid library from human cDNA to screen for minimal human functional pathways. We introduced artificial promoters followed by symmetric loxP sites into the megaplasmids using Golden Gate assembly coupled with streptavidin-bead-based purification. The isolated high molecular weight, randomly assembled cDNA megaplasmid library may be useful for high-throughput directed evolution experiments and may be adapted for use in other model organisms.
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Affiliation(s)
- Wei Sheng Yap
- School of Biological Sciences, Nanyang Technological University, Singapore, 637551
,
Current address: Department of Biochemistry, University of Toronto, Toronto, Canada
| | - Guillaume Thibault
- School of Biological Sciences, Nanyang Technological University, Singapore, 637551
,
Mechanobiology Institute, National University of Singapore, Singapore, 117411
,
Institute of Molecular and Cell Biology, A*STAR, Singapore, 138673
,
Correspondence to: Guillaume Thibault (
)
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Yang Y, Shao Y, Chaffin TA, Lee JH, Poindexter MR, Ahkami AH, Blumwald E, Stewart CN. Performance of abiotic stress-inducible synthetic promoters in genetically engineered hybrid poplar ( Populus tremula × Populus alba). FRONTIERS IN PLANT SCIENCE 2022; 13:1011939. [PMID: 36330242 PMCID: PMC9623294 DOI: 10.3389/fpls.2022.1011939] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Accepted: 09/28/2022] [Indexed: 05/27/2023]
Abstract
Abiotic stresses can cause significant damage to plants. For sustainable bioenergy crop production, it is critical to generate resistant crops to such stress. Engineering promoters to control the precise expression of stress resistance genes is a very effective way to address the problem. Here we developed stably transformed Populus tremula × Populus alba hybrid poplar (INRA 717-1B4) containing one-of-six synthetic drought stress-inducible promoters (SDs; SD9-1, SD9-2, SD9-3, SD13-1, SD18-1, and SD18-3) identified previously by transient transformation assays. We screened green fluorescent protein (GFP) induction in poplar under osmotic stress conditions. Of six transgenic lines containing synthetic promoter, three lines (SD18-1, 9-2, and 9-3) had significant GFP expression in both salt and osmotic stress treatments. Each synthetic promoter employed heptamerized repeats of specific and short cis-regulatory elements (7 repeats of 7-8 bases). To verify whether the repeats of longer sequences can improve osmotic stress responsiveness, a transgenic poplar containing the synthetic promoter of the heptamerized entire SD9 motif (20 bases, containing all partial SD9 motifs) was generated and measured for GFP induction under osmotic stress. The heptamerized entire SD9 motif did not result in higher GFP expression than the shorter promoters consisting of heptamerized SD9-1, 9-2, and 9-3 (partial SD9) motifs. This result indicates that shorter synthetic promoters (~50 bp) can be used for versatile control of gene expression in transgenic poplar. These synthetic promoters will be useful tools to engineer stress-resilient bioenergy tree crops in the future.
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Affiliation(s)
- Yongil Yang
- Center for Agricultural Synthetic Biology, University of Tennessee Institute of Agriculture, Knoxville, TN, United States
- Department of Plant Sciences, University of Tennessee Institute of Agriculture, Knoxville, TN, United States
| | - Yuanhua Shao
- Center for Agricultural Synthetic Biology, University of Tennessee Institute of Agriculture, Knoxville, TN, United States
- Department of Plant Sciences, University of Tennessee Institute of Agriculture, Knoxville, TN, United States
| | - Timothy A. Chaffin
- Center for Agricultural Synthetic Biology, University of Tennessee Institute of Agriculture, Knoxville, TN, United States
- Department of Plant Sciences, University of Tennessee Institute of Agriculture, Knoxville, TN, United States
| | - Jun Hyung Lee
- Center for Agricultural Synthetic Biology, University of Tennessee Institute of Agriculture, Knoxville, TN, United States
- Department of Plant Sciences, University of Tennessee Institute of Agriculture, Knoxville, TN, United States
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, United States
| | - Magen R. Poindexter
- Center for Agricultural Synthetic Biology, University of Tennessee Institute of Agriculture, Knoxville, TN, United States
- Department of Plant Sciences, University of Tennessee Institute of Agriculture, Knoxville, TN, United States
| | - Amir H. Ahkami
- Environmental Molecular Sciences Laboratory (EMSL), Pacific Northwest National Laboratory (PNNL), Richland, WA, United States
| | - Eduardo Blumwald
- Department of Plant Sciences, University of California, Davis, Davis, CA, United States
| | - C. Neal Stewart
- Center for Agricultural Synthetic Biology, University of Tennessee Institute of Agriculture, Knoxville, TN, United States
- Department of Plant Sciences, University of Tennessee Institute of Agriculture, Knoxville, TN, United States
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45
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Beardall WA, Stan GB, Dunlop MJ. Deep Learning Concepts and Applications for Synthetic Biology. GEN BIOTECHNOLOGY 2022; 1:360-371. [PMID: 36061221 PMCID: PMC9428732 DOI: 10.1089/genbio.2022.0017] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Accepted: 07/14/2022] [Indexed: 12/24/2022]
Abstract
Synthetic biology has a natural synergy with deep learning. It can be used to generate large data sets to train models, for example by using DNA synthesis, and deep learning models can be used to inform design, such as by generating novel parts or suggesting optimal experiments to conduct. Recently, research at the interface of engineering biology and deep learning has highlighted this potential through successes including the design of novel biological parts, protein structure prediction, automated analysis of microscopy data, optimal experimental design, and biomolecular implementations of artificial neural networks. In this review, we present an overview of synthetic biology-relevant classes of data and deep learning architectures. We also highlight emerging studies in synthetic biology that capitalize on deep learning to enable novel understanding and design, and discuss challenges and future opportunities in this space.
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Affiliation(s)
- William A.V. Beardall
- Department of Bioengineering, Imperial College London, London, United Kingdom
- Imperial College Centre of Excellence in Synthetic Biology, Imperial College London, London, United Kingdom
| | - Guy-Bart Stan
- Department of Bioengineering, Imperial College London, London, United Kingdom
- Imperial College Centre of Excellence in Synthetic Biology, Imperial College London, London, United Kingdom
| | - Mary J. Dunlop
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA
- Biological Design Center, Boston University, Boston, Massachusetts, USA
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Yap WS, Thibault G. Human PERK rescues unfolded protein response-deficient yeast cells. MICROPUBLICATION BIOLOGY 2022; 2022:10.17912/micropub.biology.000592. [PMID: 35845817 PMCID: PMC9277465 DOI: 10.17912/micropub.biology.000592] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/11/2022] [Revised: 06/22/2022] [Accepted: 06/27/2022] [Indexed: 11/23/2022]
Abstract
Protein folding and quality control is tightly regulated at the endoplasmic reticulum (ER), and its disruption is associated with many diseases. In eukaryotes, the accumulation of unfolded protein in the ER is sensed by the three sensors, IRE1, PERK, and ATF6 to activate the unfolded protein response (UPR) to restore ER homeostasis. However, uncoupling the sensing of each sensor and their respective downstream pathways has been challenging as the absence of one is compensated by the remaining two sensors. Here, we report a fully functional human PERK (hPERK) chimeric protein expressed in
Saccharomyces cerevisiae
that could be used for high throughput screen to identify new PERK inhibitory or activating compounds as well as to characterize the PERK stress sensing mechanisms.
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Affiliation(s)
- Wei Sheng Yap
- School of Biological Sciences, Nanyang Technological University, Singapore, 637551
| | - Guillaume Thibault
- School of Biological Sciences, Nanyang Technological University, Singapore, 637551
,
Mechanobiology Institute, National University of Singapore, Singapore, 117411
,
Institute of Molecular and Cell Biology, A*STAR, Singapore, 138673
,
Correspondence to: Guillaume Thibault (
)
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47
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Qin L, Liu X, Xu K, Li C. Mining and design of biosensors for engineering microbial cell factory. Curr Opin Biotechnol 2022; 75:102694. [DOI: 10.1016/j.copbio.2022.102694] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Revised: 01/14/2022] [Accepted: 01/25/2022] [Indexed: 12/14/2022]
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48
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Expanding the promoter toolbox for metabolic engineering of methylotrophic yeasts. Appl Microbiol Biotechnol 2022; 106:3449-3464. [PMID: 35538374 DOI: 10.1007/s00253-022-11948-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 04/28/2022] [Accepted: 04/29/2022] [Indexed: 01/31/2023]
Abstract
Methylotrophic yeasts have been widely recognized as a promising host for production of recombinant proteins and value-added chemicals. Promoters for controlled gene expression are critical for construction of efficient methylotrophic yeasts cell factories. Here, we summarized recent advances in characterizing and engineering promoters in methylotrophic yeasts, such as Komagataella phaffii and Ogataea polymorpha. Constitutive and inducible promoters controlled by methanol or other inducers/repressors were introduced to demonstrate their applications in production of proteins and chemicals. Furthermore, efforts of promoter engineering, including site-directed mutagenesis, hybrid promoter, and transcription factor regulation to expand the promoter toolbox were also summarized. This mini-review also provides useful information on promoters for the application of metabolic engineering in methylotrophic yeasts. KEY POINTS: • The characteristics of six methylotrophic yeasts and their promoters are described. • The applications of Komagataella phaffii and Ogataea polymorpha in metabolic engineeringare expounded. • Three promoter engineering strategies are introduced in order to expand the promoter toolbox.
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49
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Li S, Ma L, Fu W, Su R, Zhao Y, Deng Y. Programmable Synthetic Upstream Activating Sequence Library for Fine-Tuning Gene Expression Levels in Saccharomyces cerevisiae. ACS Synth Biol 2022; 11:1228-1239. [PMID: 35195994 DOI: 10.1021/acssynbio.1c00511] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
A wide dynamic range of promoters is necessary for fine-tuning transcription levels. However, weak intensity and narrow dynamic range limit transcriptional regulation via constitutive promoters. The upstream activation sequence (UAS) located upstream of the core promoter is a crucial region that could obviously enhance promoter strength. Herein, we created a random mutagenesis library consisting of 330 different variants based on the UAS of the TDH3 promoter with an ∼37-fold dynamic range by error-prone polymerase chain reaction (PCR) and obtained strong intensity mutant UAS, which was ∼12-fold greater than the wild-type UASTDH3. Analysis of the mutant library revealed 15 strength-enhancing sites and their corresponding bases of the UASTDH3 regions, which provided the impetus for a synthetic library. The resulting 32 768 mutant UAS library was constructed by permutation and combination of the bases of the 15 enhancing sites. To characterize the library, a strength prediction model was built by correlating DNA structural features and UAS strength, which provided a model between UAS sequence and intensity. Following characterization, the UAS library was applied to precisely regulate gene expression in the production of β-carotene, proving that the UAS library would be a useful tool for gene tuning in metabolic engineering. In summary, we designed, constructed, and characterized a UAS library that facilitated precise tuning of transcription levels of target proteins.
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Affiliation(s)
- Shiyun Li
- National Engineering Laboratory for Cereal Fermentation Technology (NELCF), Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China
| | - Lizhou Ma
- National Engineering Laboratory for Cereal Fermentation Technology (NELCF), Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China
| | - Wenxuan Fu
- Jiangsu Provincial Research Center for Bioactive Product Processing Technology, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China
| | - Ruifang Su
- Jiangsu Provincial Research Center for Bioactive Product Processing Technology, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China
| | - Yunying Zhao
- National Engineering Laboratory for Cereal Fermentation Technology (NELCF), Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China
- Jiangsu Provincial Research Center for Bioactive Product Processing Technology, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China
| | - Yu Deng
- National Engineering Laboratory for Cereal Fermentation Technology (NELCF), Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China
- Jiangsu Provincial Research Center for Bioactive Product Processing Technology, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China
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50
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Engineering eukaryote-like regulatory circuits to expand artificial control mechanisms for metabolic engineering in Saccharomyces cerevisiae. Commun Biol 2022; 5:135. [PMID: 35173283 PMCID: PMC8850539 DOI: 10.1038/s42003-022-03070-z] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Accepted: 01/20/2022] [Indexed: 12/22/2022] Open
Abstract
Temporal control of heterologous pathway expression is critical to achieve optimal efficiency in microbial metabolic engineering. The broadly-used GAL promoter system for engineered yeast (Saccharomyces cerevisiae) suffers from several drawbacks; specifically, unintended induction during laboratory development, and unintended repression in industrial production applications, which decreases overall production capacity. Eukaryotic synthetic circuits have not been well examined to address these problems. Here, we explore a modularised engineering method to deploy new genetic circuits applicable for expanding the control of GAL promoter-driven heterologous pathways in S. cerevisiae. Trans- and cis- modules, including eukaryotic trans-activating-and-repressing mechanisms, were characterised to provide new and better tools for circuit design. A eukaryote-like tetracycline-mediated circuit that delivers stringent repression was engineered to minimise metabolic burden during strain development and maintenance. This was combined with a novel 37 °C induction circuit to relief glucose-mediated repression on the GAL promoter during the bioprocess. This delivered a 44% increase in production of the terpenoid nerolidol, to 2.54 g L-1 in flask cultivation. These negative/positive transcriptional regulatory circuits expand global strategies of metabolic control to facilitate laboratory maintenance and for industry applications.
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