| 1. |
- Chen, Ruibing, et al.
(author)
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Engineering cofactor supply and recycling to drive phenolic acid biosynthesis in yeast
- 2022
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In: Nature Chemical Biology. - : Springer Science and Business Media LLC. - 1552-4450 .- 1552-4469. ; 18:5, s. 520-529
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Journal article (peer-reviewed)abstract
- Advances in synthetic biology enable microbial hosts to synthesize valuable natural products in an efficient, cost-competitive and safe manner. However, current engineering endeavors focus mainly on enzyme engineering and pathway optimization, leaving the role of cofactors in microbial production of natural products and cofactor engineering largely ignored. Here we systematically engineered the supply and recycling of three cofactors (FADH2, S-adenosyl-l-methion and NADPH) in the yeast Saccharomyces cerevisiae, for high-level production of the phenolic acids caffeic acid and ferulic acid, the precursors of many pharmaceutical molecules. Tailored engineering strategies were developed for rewiring biosynthesis, compartmentalization and recycling of the cofactors, which enabled the highest production of caffeic acid (5.5 ± 0.2 g l−1) and ferulic acid (3.8 ± 0.3 g l−1) in microbial cell factories. These results demonstrate that cofactors play an essential role in driving natural product biosynthesis and the engineering strategies described here can be easily adopted for regulating the metabolism of other cofactors. [Figure not available: see fulltext.].
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| 2. |
- Bu, Junling, et al.
(author)
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Catalytic promiscuity of O-methyltransferases from Corydalis yanhusuo leading to the structural diversity of benzylisoquinoline alkaloids
- 2022
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In: Horticulture Research. - : Oxford University Press (OUP). - 2662-6810 .- 2052-7276. ; 9
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Journal article (peer-reviewed)abstract
- O-methyltransferases play essential roles in producing structural diversity and improving the biological properties of benzylisoquinoline alkaloids (BIAs) in plants. In this study, Corydalis yanhusuo, a plant used in traditional Chinese medicine due to the analgesic effects of its BIA-active compounds, was employed to analyze the catalytic characteristics of O-methyltransferases in the formation of BIA diversity. Seven genes encoding O-methyltransferases were cloned, and functionally characterized using seven potential BIA substrates. Specifically, an O-methyltransferase (CyOMT2) with highly efficient catalytic activity of both 4′- and 6-O-methylations of 1-BIAs was found. CyOMT6 was found to perform two sequential methylations at both 9- and 2-positions of the essential intermediate of tetrahydroprotoberberines, (S)-scoulerine. Two O-methyltransferases (CyOMT5 and CyOMT7) with wide substrate promiscuity were found, with the 2-position of tetrahydroprotoberberines as the preferential catalytic site for CyOMT5 (named scoulerine 2-O-methyltransferase) and the 6-position of 1-BIAs as the preferential site for CyOMT7. In addition, results of integrated phylogenetic molecular docking analysis and site-directed mutation suggested that residues at sites 172, 306, 313, and 314 in CyOMT5 are important for enzyme promiscuity related to O-methylations at the 6- and 7-positions of isoquinoline. Cys at site 253 in CyOMT2 was proved to promote the methylation activity of the 6-position and to expand substrate scopes. This work provides insight into O-methyltransferases in producing BIA diversity in C. yanhusuo and genetic elements for producing BIAs by metabolic engineering and synthetic biology.
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| 3. |
- Cronin, M. F., et al.
(author)
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Developing an Observing Air-Sea Interactions Strategy (OASIS) for the global ocean
- 2022
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In: Ices Journal of Marine Science. - : Oxford University Press (OUP). - 1054-3139 .- 1095-9289. ; 80:2, s. 367-73
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Journal article (peer-reviewed)abstract
- The Observing Air-Sea Interactions Strategy (OASIS) is a new United Nations Decade of Ocean Science for Sustainable Development programme working to develop a practical, integrated approach for observing air-sea interactions globally for improved Earth system (including ecosystem) forecasts, CO2 uptake assessments called for by the Paris Agreement, and invaluable surface ocean information for decision makers. Our "Theory of Change" relies upon leveraged multi-disciplinary activities, partnerships, and capacity strengthening. Recommendations from >40 OceanObs'19 community papers and a series of workshops have been consolidated into three interlinked Grand Ideas for creating #1: a globally distributed network of mobile air-sea observing platforms built around an expanded array of long-term time-series stations; #2: a satellite network, with high spatial and temporal resolution, optimized for measuring air-sea fluxes; and #3: improved representation of air-sea coupling in a hierarchy of Earth system models. OASIS activities are organized across five Theme Teams: (1) Observing Network Design & Model Improvement; (2) Partnership & Capacity Strengthening; (3) UN Decade OASIS Actions; (4) Best Practices & Interoperability Experiments; and (5) Findable-Accessible-Interoperable-Reusable (FAIR) models, data, and OASIS products. Stakeholders, including researchers, are actively recruited to participate in Theme Teams to help promote a predicted, safe, clean, healthy, resilient, and productive ocean.
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| 4. |
- Chen, Yu, 1990, et al.
(author)
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Genome-scale modeling of yeast metabolism: retrospectives and perspectives
- 2022
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In: FEMS Yeast Research. - : Oxford University Press (OUP). - 1567-1356 .- 1567-1364. ; 22:1
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Research review (peer-reviewed)abstract
- Yeasts have been widely used for production of bread, beer and wine, as well as for production of bioethanol, but they have also been designed as cell factories to produce various chemicals, advanced biofuels and recombinant proteins. To systematically understand and rationally engineer yeast metabolism, genome-scale metabolic models (GEMs) have been reconstructed for the model yeast Saccharomyces cerevisiae and nonconventional yeasts. Here, we review the historical development of yeast GEMs together with their recent applications, including metabolic flux prediction, cell factory design, culture condition optimization and multi-yeast comparative analysis. Furthermore, we present an emerging effort, namely the integration of proteome constraints into yeast GEMs, resulting in models with improved performance. At last, we discuss challenges and perspectives on the development of yeast GEMs and the integration of proteome constraints.
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| 5. |
- Chen, Yu, 1990, et al.
(author)
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Yeast has evolved to minimize protein resource cost for synthesizing amino acids
- 2022
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In: Proceedings of the National Academy of Sciences of the United States of America. - : Proceedings of the National Academy of Sciences. - 0027-8424 .- 1091-6490. ; 119:4
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Journal article (peer-reviewed)abstract
- Proteins, as essential biomolecules, account for a large fraction of cell mass, and thus the synthesis of the complete set of proteins (i.e., the proteome) represents a substantial part of the cellular resource budget. Therefore, cells might be under selective pressures to optimize the resource costs for protein synthesis, particularly the biosynthesis of the 20 proteinogenic amino acids. Previous studies showed that less energetically costly amino acids are more abundant in the proteomes of bacteria that survive under energy-limited conditions, but the energy cost of synthesizing amino acids was reported to be weakly associated with the amino acid usage in Saccharomyces cerevisiae. Here we present a modeling framework to estimate the protein cost of synthesizing each amino acid (i.e., the protein mass required for supporting one unit of amino acid biosynthetic flux) and the glucose cost (i.e., the glucose consumed per amino acid synthesized). We show that the logarithms of the relative abundances of amino acids in S. cerevisiae's proteome correlate well with the protein costs of synthesizing amino acids (Pearson's r = 20.89), which is better than that with the glucose costs (Pearson's r = 20.5). Therefore, we demonstrate that S. cerevisiae tends to minimize protein resource, rather than glucose or energy, for synthesizing amino acids.
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| 6. |
- Li, Feiran, 1993, et al.
(author)
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Deep learning-based k(cat) prediction enables improved enzyme-constrained model reconstruction
- 2022
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In: Nature Catalysis. - : Springer Science and Business Media LLC. - 2520-1158. ; 5:8, s. 662-672
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Journal article (peer-reviewed)abstract
- Enzyme turnover numbers (k(cat)) are key to understanding cellular metabolism, proteome allocation and physiological diversity, but experimentally measured k(cat) data are sparse and noisy. Here we provide a deep learning approach (DLKcat) for high-throughput k(cat) prediction for metabolic enzymes from any organism merely from substrate structures and protein sequences. DLKcat can capture k(cat) changes for mutated enzymes and identify amino acid residues with a strong impact on k(cat) values. We applied this approach to predict genome-scale k(cat) values for more than 300 yeast species. Additionally, we designed a Bayesian pipeline to parameterize enzyme-constrained genome-scale metabolic models from predicted k(cat) values. The resulting models outperformed the corresponding original enzyme-constrained genome-scale metabolic models from previous pipelines in predicting phenotypes and proteomes, and enabled us to explain phenotypic differences. DLKcat and the enzyme-constrained genome-scale metabolic model construction pipeline are valuable tools to uncover global trends of enzyme kinetics and physiological diversity, and to further elucidate cellular metabolism on a large scale.
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| 7. |
- Li, Feiran, 1993, et al.
(author)
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Improving recombinant protein production by yeast through genome-scale modeling using proteome constraints
- 2022
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In: Nature Communications. - : Springer Science and Business Media LLC. - 2041-1723 .- 2041-1723. ; 13:1
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Journal article (peer-reviewed)abstract
- Eukaryotic cells are used as cell factories to produce and secrete multitudes of recombinant pharmaceutical proteins, including several of the current top-selling drugs. Due to the essential role and complexity of the secretory pathway, improvement for recombinant protein production through metabolic engineering has traditionally been relatively ad-hoc; and a more systematic approach is required to generate novel design principles. Here, we present the proteome-constrained genome-scale protein secretory model of yeast Saccharomyces cerevisiae (pcSecYeast), which enables us to simulate and explain phenotypes caused by limited secretory capacity. We further apply the pcSecYeast model to predict overexpression targets for the production of several recombinant proteins. We experimentally validate many of the predicted targets for alpha-amylase production to demonstrate pcSecYeast application as a computational tool in guiding yeast engineering and improving recombinant protein production. Due to the complexity of the protein secretory pathway, strategy suitable for the production of a certain recombination protein cannot be generalized. Here, the authors construct a proteome-constrained genome-scale protein secretory model for yeast and show its application in the production of different misfolded or recombinant proteins.
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| 8. |
- Needham, Jessica F., et al.
(author)
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Demographic composition, not demographic diversity, predicts biomass and turnover across temperate and tropical forests
- 2022
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In: Global Change Biology. - : Wiley. - 1354-1013 .- 1365-2486. ; 28, s. 2895-2909
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Journal article (peer-reviewed)abstract
- The growth and survival of individual trees determine the physical structure of a forest with important consequences for forest function. However, given the diversity of tree species and forest biomes, quantifying the multitude of demographic strategies within and across forests and the way that they translate into forest structure and function remains a significant challenge. Here, we quantify the demographic rates of 1961 tree species from temperate and tropical forests and evaluate how demographic diversity (DD) and demographic composition (DC) differ across forests, and how these differences in demography relate to species richness, aboveground biomass (AGB), and carbon residence time. We find wide variation in DD and DC across forest plots, patterns that are not explained by species richness or climate variables alone. There is no evidence that DD has an effect on either AGB or carbon residence time. Rather, the DC of forests, specifically the relative abundance of large statured species, predicted both biomass and carbon residence time. Our results demonstrate the distinct DCs of globally distributed forests, reflecting biogeography, recent history, and current plot conditions. Linking the DC of forests to resilience or vulnerability to climate change, will improve the precision and accuracy of predictions of future forest composition, structure, and function.
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| 9. |
- Xia, Jianye, 1980, et al.
(author)
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Proteome allocations change linearly with the specific growth rate of Saccharomyces cerevisiae under glucose limitation
- 2022
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In: Nature Communications. - : Springer Science and Business Media LLC. - 2041-1723 .- 2041-1723. ; 13:1
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Journal article (peer-reviewed)abstract
- Saccharomyces cerevisiae is a widely used cell factory; therefore, it is important to understand how it organizes key functional parts when cultured under different conditions. Here, we perform a multiomics analysis of S. cerevisiae by culturing the strain with a wide range of specific growth rates using glucose as the sole limiting nutrient. Under these different conditions, we measure the absolute transcriptome, the absolute proteome, the phosphoproteome, and the metabolome. Most functional protein groups show a linear dependence on the specific growth rate. Proteins engaged in translation show a perfect linear increase with the specific growth rate, while glycolysis and chaperone proteins show a linear decrease under respiratory conditions. Glycolytic enzymes and chaperones, however, show decreased phosphorylation with increasing specific growth rates; at the same time, an overall increased flux through these pathways is observed. Further analysis show that even though mRNA levels do not correlate with protein levels for all individual genes, the transcriptome level of functional groups correlates very well with its corresponding proteome. Finally, using enzyme-constrained genome-scale modeling, we find that enzyme usage plays an important role in controlling flux in amino acid biosynthesis. Understanding how yeast organizes its functional proteome is a fundamental task in systems biology. Here, the authors conduct a multiomics analysis on yeast cells cultured with different growth rates, identifying a linear dependence of the functional proteome on the growth rate.
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