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Kaushal M, Upton DJ, Gupta JK, Wood AJ, Srivastava S. Reconstruction of a genome-scale metabolic model and in-silico flux analysis of Aspergillus tubingensis: a non-mycotoxinogenic citric acid-producing fungus. BIOTECHNOLOGY FOR BIOFUELS AND BIOPRODUCTS 2024; 17:70. [PMID: 38807234 PMCID: PMC11134751 DOI: 10.1186/s13068-024-02506-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Accepted: 04/20/2024] [Indexed: 05/30/2024]
Abstract
BACKGROUND Aspergillus tubingensis is a citric acid-producing fungus that can utilize sugars in hydrolysate of lignocellulosic biomass such as sugarcane bagasse and, unlike A. niger, does not produce mycotoxins. To date, no attempt has been made to model its metabolism at genome scale. RESULTS Here, we utilized the whole-genome sequence (34.96 Mb length) and the measured biomass composition to reconstruct a genome-scale metabolic model (GSMM) of A. tubingensis DJU120 strain. The model, named iMK1652, consists of 1652 genes, 1657 metabolites and 2039 reactions distributed over four cellular compartments. The model has been extensively curated manually. This included removal of dead-end metabolites and generic reactions, addition of secondary metabolite pathways and several transporters. Several mycotoxin synthesis pathways were either absent or incomplete in the genome, providing a genomic basis for the non-toxinogenic nature of this species. The model was further refined based on the experimental phenotypic microarray (Biolog) data. The model closely captured DJU120 fermentative data on glucose, xylose, and phosphate consumption, as well as citric acid and biomass production, showing its applicability to capture citric acid fermentation of lignocellulosic biomass hydrolysate. CONCLUSIONS The model offers a framework to conduct metabolic systems biology investigations and can act as a scaffold for integrative modelling of A. tubingensis.
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Affiliation(s)
- Mehak Kaushal
- Systems Biology for Biofuel Group, International Centre for Genetic Engineering and Biotechnology, ICGEB Campus, Aruna Asaf Ali Marg, New Delhi, 110067, India
- Perfect Day India Pvt. Ltd., Bangalore, India
| | - Daniel J Upton
- Department of Biology, University of York, Wentworth Way, York, YO10 5DD, UK
| | - Jai K Gupta
- Systems Biology for Biofuel Group, International Centre for Genetic Engineering and Biotechnology, ICGEB Campus, Aruna Asaf Ali Marg, New Delhi, 110067, India
- JKG: Zero Cow Factory, Surat, India
| | - A Jamie Wood
- Department of Biology, University of York, Wentworth Way, York, YO10 5DD, UK
- Department of Mathematics, University of York, York, YO10 5DD, UK
| | - Shireesh Srivastava
- Systems Biology for Biofuel Group, International Centre for Genetic Engineering and Biotechnology, ICGEB Campus, Aruna Asaf Ali Marg, New Delhi, 110067, India.
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2
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Gong Z, Chen J, Jiao X, Gong H, Pan D, Liu L, Zhang Y, Tan T. Genome-scale metabolic network models for industrial microorganisms metabolic engineering: Current advances and future prospects. Biotechnol Adv 2024; 72:108319. [PMID: 38280495 DOI: 10.1016/j.biotechadv.2024.108319] [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: 12/03/2023] [Revised: 01/04/2024] [Accepted: 01/18/2024] [Indexed: 01/29/2024]
Abstract
The construction of high-performance microbial cell factories (MCFs) is the centerpiece of biomanufacturing. However, the complex metabolic regulatory network of microorganisms poses great challenges for the efficient design and construction of MCFs. The genome-scale metabolic network models (GSMs) can systematically simulate the metabolic regulation process of microorganisms in silico, providing effective guidance for the rapid design and construction of MCFs. In this review, we summarized the development status of 16 important industrial microbial GSMs, and further outline the technologies or methods that continuously promote high-quality GSMs construction from five aspects: I) Databases and modeling tools facilitate GSMs reconstruction; II) evolving gap-filling technologies; III) constraint-based model reconstruction; IV) advances in algorithms; and V) developed visualization tools. In addition, we also summarized the applications of GSMs in guiding metabolic engineering from four aspects: I) exploring and explaining metabolic features; II) predicting the effects of genetic perturbations on metabolism; III) predicting the optimal phenotype; IV) guiding cell factories construction in practical experiment. Finally, we discussed the development of GSMs, aiming to provide a reference for efficiently reconstructing GSMs and guiding metabolic engineering.
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Affiliation(s)
- Zhijin Gong
- National Energy R&D Center for Biorefinery, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China; Beijing Key Laboratory of Bioprocess, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
| | - Jiayao Chen
- National Energy R&D Center for Biorefinery, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China; Beijing Key Laboratory of Bioprocess, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
| | - Xinyu Jiao
- National Energy R&D Center for Biorefinery, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China; Beijing Key Laboratory of Bioprocess, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
| | - Hao Gong
- National Energy R&D Center for Biorefinery, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China; Beijing Key Laboratory of Bioprocess, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China; College of Mathematics and Physics, Beijing University of Chemical Technology, Beijing 100029, China
| | - Danzi Pan
- National Energy R&D Center for Biorefinery, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China; Beijing Key Laboratory of Bioprocess, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China; College of Mathematics and Physics, Beijing University of Chemical Technology, Beijing 100029, China
| | - Lingli Liu
- National Energy R&D Center for Biorefinery, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China; Beijing Key Laboratory of Bioprocess, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China; College of Mathematics and Physics, Beijing University of Chemical Technology, Beijing 100029, China
| | - Yang Zhang
- National Energy R&D Center for Biorefinery, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China; Beijing Key Laboratory of Bioprocess, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
| | - Tianwei Tan
- National Energy R&D Center for Biorefinery, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China; Beijing Key Laboratory of Bioprocess, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China.
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3
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Li YW, Qian JY, Huang JC, Guo DS, Nie ZK, Ye C, Shi TQ. Improving Gibberellin GA 3 Production with the Construction of a Genome-Scale Metabolic Model of Fusarium fujikuroi. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2023; 71:18890-18897. [PMID: 37931026 DOI: 10.1021/acs.jafc.3c05309] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2023]
Abstract
Liquid fermentation is the primary method for GA3 production usingFusarium fujikuroi. However, production capacity is limited due to unknown metabolic pathways. To address this, we constructed a genome-scale metabolic model (iCY1235) with 1753 reactions, 1979 metabolites, and 1235 genes to understand the GA3 regulation mechanisms. The model was validated by analyzing growth rates under different glucose uptake rates and identifying essential genes. We used the model to optimize fermentation conditions, including carbon sources and dissolved oxygen. Through the OptForce algorithm, we identified 20 reactions as targets. Overexpressing FFUJ_02053 and FFUJ_14337 resulted in a 37.5 and 75% increase in GA3 titers, respectively. These targets enhance carbon flux toward GA3 production. Our model holds promise for guiding the metabolic engineering of F. fujikuroi to achieve targeted overproduction. In summary, our study utilizes the iCY1235 model to understand GA3 regulation, optimize fermentation conditions, and identify specific targets for enhancing GA3 production through metabolic engineering.
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Affiliation(s)
- Ya-Wen Li
- School of Food Science and Pharmaceutical Engineering, Nanjing Normal University, 2 Xuelin Road, Qixia District, Nanjing 210023, People's Republic of China
| | - Jin-Yi Qian
- School of Food Science and Pharmaceutical Engineering, Nanjing Normal University, 2 Xuelin Road, Qixia District, Nanjing 210023, People's Republic of China
| | - Jia-Cong Huang
- School of Food Science and Pharmaceutical Engineering, Nanjing Normal University, 2 Xuelin Road, Qixia District, Nanjing 210023, People's Republic of China
| | - Dong-Sheng Guo
- School of Food Science and Pharmaceutical Engineering, Nanjing Normal University, 2 Xuelin Road, Qixia District, Nanjing 210023, People's Republic of China
| | - Zhi-Kui Nie
- Jiangxi New Reyphon Biochemical Co., Ltd., Salt and Chemical Industry, Ji'an 331300, People's Republic of China
| | - Chao Ye
- School of Food Science and Pharmaceutical Engineering, Nanjing Normal University, 2 Xuelin Road, Qixia District, Nanjing 210023, People's Republic of China
| | - Tian-Qiong Shi
- School of Food Science and Pharmaceutical Engineering, Nanjing Normal University, 2 Xuelin Road, Qixia District, Nanjing 210023, People's Republic of China
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4
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Naderi A, Vakilchap F, Motamedian E, Mousavi SM. Regulatory-systemic approach in Aspergillus niger for bioleaching improvement by controlling precipitation. Appl Microbiol Biotechnol 2023; 107:7331-7346. [PMID: 37736792 DOI: 10.1007/s00253-023-12776-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Revised: 08/28/2023] [Accepted: 09/04/2023] [Indexed: 09/23/2023]
Abstract
In the context of e-waste recycling by fungal bioleaching, nickel and cobalt precipitate as toxic metals by oxalic acid, whereas organic acids, such as citric, act as a high-performance chelating agent in dissolving these metals. Oxalic acid elimination requires an excess and uneconomical carbon source concentration in culture media. To resolve this issue, a novel and straightforward systems metabolic engineering method was devised to switch metabolic flux from oxalic acid to citric acid. In this technique, the genome-scale metabolic model of Aspergillus niger was applied to predicting flux variability and key reactions through the calculation of multiple optimal solutions for cellular regulation. Accordingly, BRENDA regulators and a novel molecular docking-oriented approach were defined a regulatory medium for this end. Then, ligands were evaluated in fungal culture to assess their impact on organic acid production for bioleaching of copper and nickel from waste telecommunication printed circuit boards. The protein structure of oxaloacetate hydrolase was modeled based on homology modeling for molecular docking. Metformin, glutathione, and sodium fluoride were found to be effective as inhibitors of oxalic acid production, enabling the production of 8100 ppm citric acid by controlling cellular metabolism. Indirect bioleaching demonstrated that nickel did not precipitate, and the bioleaching efficiency of copper and nickel increased from 40% and 24% to 61% and 100%, respectively. Bioleaching efficiency was evaluated qualitatively by FE-SEM, EDX, mapping, and XRD analysis. KEY POINTS: • A regulatory-systemic procedure for controlling cellular metabolism was introduced • Metformin inhibited oxalic acid, leading to 8100 ppm citric acid production • Bioleaching of copper and nickel in TPCBs improved by 21% and 76.
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Affiliation(s)
- Ali Naderi
- Biotechnology Group, Chemical Engineering Department, Tarbiat Modares University, Tehran, Iran
| | - Farzane Vakilchap
- Biotechnology Group, Chemical Engineering Department, Tarbiat Modares University, Tehran, Iran
| | - Ehsan Motamedian
- Biotechnology Group, Chemical Engineering Department, Tarbiat Modares University, Tehran, Iran
| | - Seyyed Mohammad Mousavi
- Biotechnology Group, Chemical Engineering Department, Tarbiat Modares University, Tehran, Iran.
- Modares Environmental Research Institute, Tarbiat Modares University, Tehran, Iran.
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5
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Han Y, Tafur Rangel A, Pomraning KR, Kerkhoven EJ, Kim J. Advances in genome-scale metabolic models of industrially important fungi. Curr Opin Biotechnol 2023; 84:103005. [PMID: 37797483 DOI: 10.1016/j.copbio.2023.103005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 09/04/2023] [Accepted: 09/05/2023] [Indexed: 10/07/2023]
Abstract
Many fungal species have been used industrially for production of biofuels and bioproducts. Developing strains with better performance in biomanufacturing contexts requires a systematic understanding of cellular metabolism. Genome-scale metabolic models (GEMs) offer a comprehensive view of interconnected pathways and a mathematical framework for downstream analysis. Recently, GEMs have been developed or updated for several industrially important fungi. Some of them incorporate enzyme constraints, enabling improved predictions of cell states and proteome allocation. Here, we provide an overview of these newly developed GEMs and computational methods that facilitate construction of enzyme-constrained GEMs and utilize flux predictions from GEMs. Furthermore, we highlight the pivotal roles of these GEMs in iterative design-build-test-learn cycles, ultimately advancing the field of fungal biomanufacturing.
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Affiliation(s)
- Yichao Han
- Energy and Environment Directorate, Pacific Northwest National Laboratory, Richland, WA, USA; Agile BioFoundry, Department of Energy, Emeryville, CA, USA
| | - Albert Tafur Rangel
- Department of Life Sciences, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden; Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark
| | - Kyle R Pomraning
- Energy and Environment Directorate, Pacific Northwest National Laboratory, Richland, WA, USA; Agile BioFoundry, Department of Energy, Emeryville, CA, USA
| | - Eduard J Kerkhoven
- Department of Life Sciences, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden; Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark; SciLifeLab, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden
| | - Joonhoon Kim
- Energy and Environment Directorate, Pacific Northwest National Laboratory, Richland, WA, USA; Agile BioFoundry, Department of Energy, Emeryville, CA, USA; Joint BioEnergy Institute, Department of Energy, Emeryville, CA, USA.
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6
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Bilbao A, Munoz N, Kim J, Orton DJ, Gao Y, Poorey K, Pomraning KR, Weitz K, Burnet M, Nicora CD, Wilton R, Deng S, Dai Z, Oksen E, Gee A, Fasani RA, Tsalenko A, Tanjore D, Gardner J, Smith RD, Michener JK, Gladden JM, Baker ES, Petzold CJ, Kim YM, Apffel A, Magnuson JK, Burnum-Johnson KE. PeakDecoder enables machine learning-based metabolite annotation and accurate profiling in multidimensional mass spectrometry measurements. Nat Commun 2023; 14:2461. [PMID: 37117207 PMCID: PMC10147702 DOI: 10.1038/s41467-023-37031-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2022] [Accepted: 02/24/2023] [Indexed: 04/30/2023] Open
Abstract
Multidimensional measurements using state-of-the-art separations and mass spectrometry provide advantages in untargeted metabolomics analyses for studying biological and environmental bio-chemical processes. However, the lack of rapid analytical methods and robust algorithms for these heterogeneous data has limited its application. Here, we develop and evaluate a sensitive and high-throughput analytical and computational workflow to enable accurate metabolite profiling. Our workflow combines liquid chromatography, ion mobility spectrometry and data-independent acquisition mass spectrometry with PeakDecoder, a machine learning-based algorithm that learns to distinguish true co-elution and co-mobility from raw data and calculates metabolite identification error rates. We apply PeakDecoder for metabolite profiling of various engineered strains of Aspergillus pseudoterreus, Aspergillus niger, Pseudomonas putida and Rhodosporidium toruloides. Results, validated manually and against selected reaction monitoring and gas-chromatography platforms, show that 2683 features could be confidently annotated and quantified across 116 microbial sample runs using a library built from 64 standards.
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Affiliation(s)
- Aivett Bilbao
- Pacific Northwest National Laboratory, Richland, WA, USA.
- US Department of Energy, Agile BioFoundry, Emeryville, CA, USA.
| | - Nathalie Munoz
- Pacific Northwest National Laboratory, Richland, WA, USA
- US Department of Energy, Agile BioFoundry, Emeryville, CA, USA
| | - Joonhoon Kim
- Pacific Northwest National Laboratory, Richland, WA, USA
- US Department of Energy, Agile BioFoundry, Emeryville, CA, USA
| | - Daniel J Orton
- Pacific Northwest National Laboratory, Richland, WA, USA
| | - Yuqian Gao
- Pacific Northwest National Laboratory, Richland, WA, USA
- US Department of Energy, Agile BioFoundry, Emeryville, CA, USA
| | | | - Kyle R Pomraning
- Pacific Northwest National Laboratory, Richland, WA, USA
- US Department of Energy, Agile BioFoundry, Emeryville, CA, USA
| | - Karl Weitz
- Pacific Northwest National Laboratory, Richland, WA, USA
| | - Meagan Burnet
- Pacific Northwest National Laboratory, Richland, WA, USA
| | | | - Rosemarie Wilton
- US Department of Energy, Agile BioFoundry, Emeryville, CA, USA
- Argonne National Laboratory, Lemont, IL, USA
| | - Shuang Deng
- Pacific Northwest National Laboratory, Richland, WA, USA
- US Department of Energy, Agile BioFoundry, Emeryville, CA, USA
| | - Ziyu Dai
- Pacific Northwest National Laboratory, Richland, WA, USA
- US Department of Energy, Agile BioFoundry, Emeryville, CA, USA
| | - Ethan Oksen
- Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Aaron Gee
- Agilent Research Laboratories, Agilent Technologies, Santa Clara, CA, USA
| | - Rick A Fasani
- Agilent Research Laboratories, Agilent Technologies, Santa Clara, CA, USA
| | - Anya Tsalenko
- Agilent Research Laboratories, Agilent Technologies, Santa Clara, CA, USA
| | - Deepti Tanjore
- US Department of Energy, Agile BioFoundry, Emeryville, CA, USA
- Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - James Gardner
- US Department of Energy, Agile BioFoundry, Emeryville, CA, USA
- Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | | | - Joshua K Michener
- US Department of Energy, Agile BioFoundry, Emeryville, CA, USA
- Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - John M Gladden
- US Department of Energy, Agile BioFoundry, Emeryville, CA, USA
- Sandia National Laboratory, Livermore, CA, USA
| | - Erin S Baker
- Department of Chemistry, University of North Carolina, Chapel Hill, NC, USA
| | - Christopher J Petzold
- US Department of Energy, Agile BioFoundry, Emeryville, CA, USA
- Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Young-Mo Kim
- Pacific Northwest National Laboratory, Richland, WA, USA
- US Department of Energy, Agile BioFoundry, Emeryville, CA, USA
| | - Alex Apffel
- Agilent Research Laboratories, Agilent Technologies, Santa Clara, CA, USA
| | - Jon K Magnuson
- Pacific Northwest National Laboratory, Richland, WA, USA
- US Department of Energy, Agile BioFoundry, Emeryville, CA, USA
| | - Kristin E Burnum-Johnson
- Pacific Northwest National Laboratory, Richland, WA, USA.
- US Department of Energy, Agile BioFoundry, Emeryville, CA, USA.
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7
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Wang X, Jarmusch SA, Frisvad JC, Larsen TO. Current status of secondary metabolite pathways linked to their related biosynthetic gene clusters in Aspergillus section Nigri. Nat Prod Rep 2023; 40:237-274. [PMID: 35587705 DOI: 10.1039/d1np00074h] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
Covering: up to the end of 2021Aspergilli are biosynthetically 'talented' micro-organisms and therefore the natural products community has continually been interested in the wealth of biosynthetic gene clusters (BGCs) encoding numerous secondary metabolites related to these fungi. With the rapid increase in sequenced fungal genomes combined with the continuous development of bioinformatics tools such as antiSMASH, linking new structures to unknown BGCs has become much easier when taking retro-biosynthetic considerations into account. On the other hand, in most cases it is not as straightforward to prove proposed biosynthetic pathways due to the lack of implemented genetic tools in a given fungal species. As a result, very few secondary metabolite biosynthetic pathways have been characterized even amongst some of the most well studied Aspergillus spp., section Nigri (black aspergilli). This review will cover all known biosynthetic compound families and their structural diversity known from black aspergilli. We have logically divided this into sub-sections describing major biosynthetic classes (polyketides, non-ribosomal peptides, terpenoids, meroterpenoids and hybrid biosynthesis). Importantly, we will focus the review on metabolites which have been firmly linked to their corresponding BGCs.
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Affiliation(s)
- Xinhui Wang
- DTU Bioengineering, Technical University of Denmark, DK-2800, Kgs. Lyngby, Denmark.
| | - Scott A Jarmusch
- DTU Bioengineering, Technical University of Denmark, DK-2800, Kgs. Lyngby, Denmark.
| | - Jens C Frisvad
- DTU Bioengineering, Technical University of Denmark, DK-2800, Kgs. Lyngby, Denmark.
| | - Thomas O Larsen
- DTU Bioengineering, Technical University of Denmark, DK-2800, Kgs. Lyngby, Denmark.
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8
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Fan X, Zhou J, Xia J, Yan X. Genome-Scale Metabolic Model's multi-objective solving algorithm based on the inflexion point of Pareto front including maximum energy utilization and its application in A.niger DS03043. Biotechnol Bioeng 2022; 119:1539-1555. [PMID: 35274299 DOI: 10.1002/bit.28078] [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: 11/30/2021] [Revised: 01/20/2022] [Accepted: 03/03/2022] [Indexed: 11/06/2022]
Abstract
The solution of genome-scale metabolic model (GSMM) directly affects the simulation accuracy of the metabolic process in digital cells. Single-objective optimization methods, such as Flux Balance Analysis (FBA) which is widely used in solving GSMM, have limitations when simulating actual biological processes, which leads to unrealistic results due to other biological constraints being ignored. A novel multi-objective Differential Evolution algorithm based on general FBA (i.e., DEFBA) is hence proposed to solve GSMM. First, in accordance with to the assumption that cells minimize resource consumption and maximize resource utilization, the maximum specific growth rate and the minimum cellular production rate of ATP, NADPH, and NADH are defined as the multi-objective functions of DEFBA. Second, FBA is used to produce the initial individuals of DEFBA by changing the upper bound of biomass reaction in GSMM. Third, mutation and selection operations help in generating new individuals in the solution space to search the Pareto front. Finally, the optimal solution is selected by analyzing the inflexion point of the Pareto front. In DEFBA, multi-objective technology and optimal solution judging technology can introduce the biological constraints into the GSMM solving method, such that the solution can be more consistent with the essential biological mechanism. DEFBA is applied to solve Aspergillus niger's GSMM. The improved results show that DEFBA can be an effective general solving algorithm for GSMM. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Xingcun Fan
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, 200237, P. R. China
| | - Jingru Zhou
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai, 200237, P. R. China
| | - Jianye Xia
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai, 200237, P. R. China
| | - Xuefeng Yan
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, 200237, P. R. China
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9
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Liu D, Xu Z, Li J, Liu Q, Yuan Q, Guo Y, Ma H, Tian C. Reconstruction and analysis of genome-scale metabolic model for thermophilic fungus Myceliophthora thermophila. Biotechnol Bioeng 2022; 119:1926-1937. [PMID: 35257374 DOI: 10.1002/bit.28080] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 01/24/2022] [Accepted: 03/03/2022] [Indexed: 11/11/2022]
Abstract
Myceliophthora thermophila, a thermophilic fungus that can degrade and utilize all major polysaccharides in plant biomass, has great potential in biotechnological industries. Here, the first manually curated genome-scale metabolic model iDL1450 for M. thermophila was reconstructed using an auto-generating pipeline with thorough manual curation. The model contains 1450 genes, 2592 reactions and 1784 unique metabolites. High accuracy was shown in predictions related to carbon and nitrogen source utilization based on data obtained from Biolog experiments. Besides, metabolism profiles were analyzed using iDL1450 integrated with transcriptomics data of M. thermophila at various growth temperatures. The refined model provides new insights into thermophilic fungi metabolism and sheds light on model-driven strain design to improve biotechnological applications of this thermophilic lignocellulosic fungus. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Defei Liu
- Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China.,National Technology Innovation Center of Synthetic Biology, Tianjin, 300308, China
| | - Zixiang Xu
- National Technology Innovation Center of Synthetic Biology, Tianjin, 300308, China.,National Engineering Laboratory for Industrial Enzymes and Tianjin Engineering Research Center of Biocatalytic Technology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China
| | - Jingen Li
- Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China.,National Technology Innovation Center of Synthetic Biology, Tianjin, 300308, China
| | - Qian Liu
- Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China.,National Technology Innovation Center of Synthetic Biology, Tianjin, 300308, China
| | - Qianqian Yuan
- National Technology Innovation Center of Synthetic Biology, Tianjin, 300308, China.,Biodesign Center, Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China
| | - Yanmei Guo
- Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China.,National Technology Innovation Center of Synthetic Biology, Tianjin, 300308, China
| | - Hongwu Ma
- National Technology Innovation Center of Synthetic Biology, Tianjin, 300308, China.,Biodesign Center, Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China
| | - Chaoguang Tian
- Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China.,National Technology Innovation Center of Synthetic Biology, Tianjin, 300308, China
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10
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Meyer V, Cairns T, Barthel L, King R, Kunz P, Schmideder S, Müller H, Briesen H, Dinius A, Krull R. Understanding and controlling filamentous growth of fungal cell factories: novel tools and opportunities for targeted morphology engineering. Fungal Biol Biotechnol 2021; 8:8. [PMID: 34425914 PMCID: PMC8383395 DOI: 10.1186/s40694-021-00115-6] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Accepted: 08/02/2021] [Indexed: 02/20/2023] Open
Abstract
Filamentous fungal cell factories are efficient producers of platform chemicals, proteins, enzymes and natural products. Stirred-tank bioreactors up to a scale of several hundred m³ are commonly used for their cultivation. Fungal hyphae self-assemble into various cellular macromorphologies ranging from dispersed mycelia, loose clumps, to compact pellets. Development of these macromorphologies is so far unpredictable but strongly impacts productivities of fungal bioprocesses. Depending on the strain and the desired product, the morphological forms vary, but no strain- or product-related correlations currently exist to improve
process understanding of fungal production systems. However, novel genomic, genetic, metabolic, imaging and modelling tools have recently been established that will provide fundamental new insights into filamentous fungal growth and how it is balanced with product formation. In this primer, these tools will be highlighted and their revolutionary impact on rational morphology engineering and bioprocess control will be discussed.
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Affiliation(s)
- Vera Meyer
- Chair of Applied and Molecular Microbiology, Institute of Biotechnology, Technische Universität Berlin, Straße des 17. Juni 135, 10623, Berlin, Germany.
| | - Timothy Cairns
- Chair of Applied and Molecular Microbiology, Institute of Biotechnology, Technische Universität Berlin, Straße des 17. Juni 135, 10623, Berlin, Germany
| | - Lars Barthel
- Chair of Applied and Molecular Microbiology, Institute of Biotechnology, Technische Universität Berlin, Straße des 17. Juni 135, 10623, Berlin, Germany
| | - Rudibert King
- Chair of Measurement and Control, Institute of Chemical and Process Engineering, Technische Universität Berlin, Straße des 17. Juni 135, 10623, Berlin, Germany
| | - Philipp Kunz
- Chair of Measurement and Control, Institute of Chemical and Process Engineering, Technische Universität Berlin, Straße des 17. Juni 135, 10623, Berlin, Germany
| | - Stefan Schmideder
- Chair of Process Systems Engineering, School of Life Sciences, Technical University of Munich, Gregor- Mendel-Str. 4, 85354, Freising, Germany
| | - Henri Müller
- Chair of Process Systems Engineering, School of Life Sciences, Technical University of Munich, Gregor- Mendel-Str. 4, 85354, Freising, Germany
| | - Heiko Briesen
- Chair of Process Systems Engineering, School of Life Sciences, Technical University of Munich, Gregor- Mendel-Str. 4, 85354, Freising, Germany
| | - Anna Dinius
- Institute of Biochemical Engineering, Technische Universität Braunschweig, Rebenring 56, 38106, Brunswick, Germany.,Center of Pharmaceutical Engineering, Technische Universität Braunschweig, Franz-Liszt-Str. 35a, 38106, Brunswick, Germany
| | - Rainer Krull
- Institute of Biochemical Engineering, Technische Universität Braunschweig, Rebenring 56, 38106, Brunswick, Germany.,Center of Pharmaceutical Engineering, Technische Universität Braunschweig, Franz-Liszt-Str. 35a, 38106, Brunswick, Germany
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11
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Zhou J, Zhuang Y, Xia J. Integration of enzyme constraints in a genome-scale metabolic model of Aspergillus niger improves phenotype predictions. Microb Cell Fact 2021; 20:125. [PMID: 34193117 PMCID: PMC8247156 DOI: 10.1186/s12934-021-01614-2] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Accepted: 06/16/2021] [Indexed: 11/26/2022] Open
Abstract
Background Genome-scale metabolic model (GSMM) is a powerful tool for the study of cellular metabolic characteristics. With the development of multi-omics measurement techniques in recent years, new methods that integrating multi-omics data into the GSMM show promising effects on the predicted results. It does not only improve the accuracy of phenotype prediction but also enhances the reliability of the model for simulating complex biochemical phenomena, which can promote theoretical breakthroughs for specific gene target identification or better understanding the cell metabolism on the system level. Results Based on the basic GSMM model iHL1210 of Aspergillus niger, we integrated large-scale enzyme kinetics and proteomics data to establish a GSMM based on enzyme constraints, termed a GEM with Enzymatic Constraints using Kinetic and Omics data (GECKO). The results show that enzyme constraints effectively improve the model’s phenotype prediction ability, and extended the model’s potential to guide target gene identification through predicting metabolic phenotype changes of A. niger by simulating gene knockout. In addition, enzyme constraints significantly reduced the solution space of the model, i.e., flux variability over 40.10% metabolic reactions were significantly reduced. The new model showed also versatility in other aspects, like estimating large-scale \documentclass[12pt]{minimal}
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\begin{document}$$k_{{cat}}$$\end{document}kcat values, predicting the differential expression of enzymes under different growth conditions. Conclusions This study shows that incorporating enzymes’ abundance information into GSMM is very effective for improving model performance with A. niger. Enzyme-constrained model can be used as a powerful tool for predicting the metabolic phenotype of A. niger by incorporating proteome data. In the foreseeable future, with the fast development of measurement techniques, and more precise and rich proteomics quantitative data being obtained for A. niger, the enzyme-constrained GSMM model will show greater application space on the system level. Supplementary Information The online version contains supplementary material available at 10.1186/s12934-021-01614-2.
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Affiliation(s)
- Jingru Zhou
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai, 200237, China
| | - Yingping Zhuang
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai, 200237, China
| | - Jianye Xia
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai, 200237, China. .,Tianjin Institute of Industrial Biotechnology, Chinese Academy of Science, Tianjin, 300308, China.
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12
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13
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Pomraning KR, Dai Z, Munoz N, Kim YM, Gao Y, Deng S, Kim J, Hofstad BA, Swita MS, Lemmon T, Collett JR, Panisko EA, Webb-Robertson BJM, Zucker JD, Nicora CD, De Paoli H, Baker SE, Burnum-Johnson KE, Hillson NJ, Magnuson JK. Integration of Proteomics and Metabolomics Into the Design, Build, Test, Learn Cycle to Improve 3-Hydroxypropionic Acid Production in Aspergillus pseudoterreus. Front Bioeng Biotechnol 2021; 9:603832. [PMID: 33898398 PMCID: PMC8058442 DOI: 10.3389/fbioe.2021.603832] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2020] [Accepted: 03/16/2021] [Indexed: 11/13/2022] Open
Abstract
Biological engineering of microorganisms to produce value-added chemicals is a promising route to sustainable manufacturing. However, overproduction of metabolic intermediates at high titer, rate, and yield from inexpensive substrates is challenging in non-model systems where limited information is available regarding metabolic flux and its control in production conditions. Integrated multi-omic analyses of engineered strains offers an in-depth look at metabolites and proteins directly involved in growth and production of target and non-target bioproducts. Here we applied multi-omic analyses to overproduction of the polymer precursor 3-hydroxypropionic acid (3HP) in the filamentous fungus Aspergillus pseudoterreus. A synthetic pathway consisting of aspartate decarboxylase, beta-alanine pyruvate transaminase, and 3HP dehydrogenase was designed and built for A. pseudoterreus. Strains with single- and multi-copy integration events were isolated and multi-omics analysis consisting of intracellular and extracellular metabolomics and targeted and global proteomics was used to interrogate the strains in shake-flask and bioreactor conditions. Production of a variety of co-products (organic acids and glycerol) and oxidative degradation of 3HP were identified as metabolic pathways competing with 3HP production. Intracellular accumulation of nitrogen as 2,4-diaminobutanoate was identified as an off-target nitrogen sink that may also limit flux through the engineered 3HP pathway. Elimination of the high-expression oxidative 3HP degradation pathway by deletion of a putative malonate semialdehyde dehydrogenase improved the yield of 3HP by 3.4 × after 10 days in shake-flask culture. This is the first report of 3HP production in a filamentous fungus amenable to industrial scale biomanufacturing of organic acids at high titer and low pH.
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Affiliation(s)
- Kyle R Pomraning
- Pacific Northwest National Laboratory, Richland, WA, United States
| | - Ziyu Dai
- Pacific Northwest National Laboratory, Richland, WA, United States
| | - Nathalie Munoz
- Pacific Northwest National Laboratory, Richland, WA, United States
| | - Young-Mo Kim
- Pacific Northwest National Laboratory, Richland, WA, United States
| | - Yuqian Gao
- Pacific Northwest National Laboratory, Richland, WA, United States
| | - Shuang Deng
- Pacific Northwest National Laboratory, Richland, WA, United States
| | - Joonhoon Kim
- Pacific Northwest National Laboratory, Richland, WA, United States.,Joint BioEnergy Institute, Emeryville, CA, United States
| | - Beth A Hofstad
- Pacific Northwest National Laboratory, Richland, WA, United States
| | - Marie S Swita
- Pacific Northwest National Laboratory, Richland, WA, United States
| | - Teresa Lemmon
- Pacific Northwest National Laboratory, Richland, WA, United States
| | - James R Collett
- Pacific Northwest National Laboratory, Richland, WA, United States
| | - Ellen A Panisko
- Pacific Northwest National Laboratory, Richland, WA, United States
| | | | - Jeremy D Zucker
- Pacific Northwest National Laboratory, Richland, WA, United States
| | - Carrie D Nicora
- Pacific Northwest National Laboratory, Richland, WA, United States
| | | | - Scott E Baker
- Pacific Northwest National Laboratory, Richland, WA, United States
| | | | - Nathan J Hillson
- Lawrence Berkeley National Laboratory, Berkeley, CA, United States
| | - Jon K Magnuson
- Pacific Northwest National Laboratory, Richland, WA, United States.,Joint BioEnergy Institute, Emeryville, CA, United States
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14
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Chroumpi T, Mäkelä MR, de Vries RP. Engineering of primary carbon metabolism in filamentous fungi. Biotechnol Adv 2020; 43:107551. [DOI: 10.1016/j.biotechadv.2020.107551] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2020] [Revised: 04/28/2020] [Accepted: 04/29/2020] [Indexed: 10/24/2022]
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15
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Fatma Z, Schultz JC, Zhao H. Recent advances in domesticating non‐model microorganisms. Biotechnol Prog 2020; 36:e3008. [DOI: 10.1002/btpr.3008] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2020] [Revised: 04/14/2020] [Accepted: 04/18/2020] [Indexed: 12/24/2022]
Affiliation(s)
- Zia Fatma
- Department of Chemical and Biomolecular Engineering, Carl R. Woese Institute for Genomic Biology University of Illinois at Urbana‐Champaign Urbana Illinois USA
| | - J. Carl Schultz
- Department of Chemical and Biomolecular Engineering, Carl R. Woese Institute for Genomic Biology University of Illinois at Urbana‐Champaign Urbana Illinois USA
| | - Huimin Zhao
- Department of Chemical and Biomolecular Engineering, Carl R. Woese Institute for Genomic Biology University of Illinois at Urbana‐Champaign Urbana Illinois USA
- Departments of Chemistry, Biochemistry, and Bioengineering University of Illinois at Urbana‐Champaign Urbana Illinois USA
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16
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Perez R, Luccioni M, Kamakaka R, Clamons S, Gaut N, Stirling F, Adamala KP, Silver PA, Endy D. Enabling community-based metrology for wood-degrading fungi. Fungal Biol Biotechnol 2020; 7:2. [PMID: 32206323 PMCID: PMC7081594 DOI: 10.1186/s40694-020-00092-2] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2019] [Accepted: 02/25/2020] [Indexed: 12/19/2022] Open
Abstract
BACKGROUND Lignocellulosic biomass could support a greatly-expanded bioeconomy. Current strategies for using biomass typically rely on single-cell organisms and extensive ancillary equipment to produce precursors for downstream manufacturing processes. Alternative forms of bioproduction based on solid-state fermentation and wood-degrading fungi could enable more direct means of manufacture. However, basic methods for cultivating wood-degrading fungi are often ad hoc and not readily reproducible. Here, we developed standard reference strains, substrates, measurements, and methods sufficient to begin to enable reliable reuse of mycological materials and products in simple laboratory settings. RESULTS We show that a widely-available and globally-regularized consumer product (Pringles™) can support the growth of wood-degrading fungi, and that growth on Pringles™-broth can be correlated with growth on media made from a fully-traceable and compositionally characterized substrate (National Institute of Standards and Technology Reference Material 8492 Eastern Cottonwood Whole Biomass Feedstock). We also establish a Relative Extension Unit (REU) framework that is designed to reduce variation in quantification of radial growth measurements. So enabled, we demonstrate that five laboratories were able to compare measurements of wood-fungus performance via a simple radial extension growth rate assay, and that our REU-based approach reduced variation in reported measurements by up to ~ 75%. CONCLUSIONS Reliable reuse of materials, measures, and methods is necessary to enable distributed bioproduction processes that can be adopted at all scales, from local to industrial. Our community-based measurement methods incentivize practitioners to coordinate the reuse of standard materials, methods, strains, and to share information supporting work with wood-degrading fungi.
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Affiliation(s)
- Rolando Perez
- Department of Bioengineering, Schools of Engineering and Medicine, Stanford University, Room 252, Shriram Center, 443 Via Ortega, Stanford, CA 94305 USA
| | - Marina Luccioni
- Department of Bioengineering, Schools of Engineering and Medicine, Stanford University, Room 252, Shriram Center, 443 Via Ortega, Stanford, CA 94305 USA
| | - Rohinton Kamakaka
- Department of MCD Biology, University of California, Santa Cruz, 1156 High Street, Santa Cruz, CA 95064 USA
| | - Samuel Clamons
- Department of Chemistry and Molecular Biophysics, California Institute of Technology, 1200 E. California Blvd, MC 138-78, Pasadena, CA 91125 USA
- Department of Control and Dynamical Systems, California Institute of Technology, 1200 E. California Blvd, MC 138-78, Pasadena, CA 91125 USA
| | - Nathaniel Gaut
- Department of Genetics, Cell Biology, and Development, College of Biological Sciences, University of Minnesota, 420 Washington Ave. SE, 5-178 MCB, Minneapolis, MN 55455 USA
| | - Finn Stirling
- Department of Systems Biology, Harvard Medical School, 200 Longwood Avenue, Warren Alpert Building, Boston, MA 02115 USA
- Wyss Institute for Biologically Inspired Engineering, Harvard University, 200 Longwood Avenue, Warren Alpert Building, Boston, MA 02115 USA
| | - Katarzyna P. Adamala
- Department of Genetics, Cell Biology, and Development, College of Biological Sciences, University of Minnesota, 420 Washington Ave. SE, 5-178 MCB, Minneapolis, MN 55455 USA
| | - Pamela A. Silver
- Department of Systems Biology, Harvard Medical School, 200 Longwood Avenue, Warren Alpert Building, Boston, MA 02115 USA
- Wyss Institute for Biologically Inspired Engineering, Harvard University, 200 Longwood Avenue, Warren Alpert Building, Boston, MA 02115 USA
| | - Drew Endy
- Department of Bioengineering, Schools of Engineering and Medicine, Stanford University, Room 252, Shriram Center, 443 Via Ortega, Stanford, CA 94305 USA
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17
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Upton DJ, McQueen-Mason SJ, Wood AJ. In silico evolution of Aspergillus niger organic acid production suggests strategies for switching acid output. BIOTECHNOLOGY FOR BIOFUELS 2020; 13:27. [PMID: 32123544 PMCID: PMC7038614 DOI: 10.1186/s13068-020-01678-z] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/24/2019] [Accepted: 02/06/2020] [Indexed: 05/19/2023]
Abstract
BACKGROUND The fungus Aspergillus niger is an important industrial organism for citric acid fermentation; one of the most efficient biotechnological processes. Previously we introduced a dynamic model that captures this process in the industrially relevant batch fermentation setting, providing a more accurate predictive platform to guide targeted engineering. In this article we exploit this dynamic modelling framework, coupled with a robust genetic algorithm for the in silico evolution of A. niger organic acid production, to provide solutions to complex evolutionary goals involving a multiplicity of targets and beyond the reach of simple Boolean gene deletions. We base this work on the latest metabolic models of the parent citric acid producing strain ATCC1015 dedicated to organic acid production with the required exhaustive genomic coverage needed to perform exploratory in silico evolution. RESULTS With the use of our informed evolutionary framework, we demonstrate targeted changes that induce a complete switch of acid output from citric to numerous different commercially valuable target organic acids including succinic acid. We highlight the key changes in flux patterns that occur in each case, suggesting potentially valuable targets for engineering. We also show that optimum acid productivity is achieved through a balance of organic acid and biomass production, requiring finely tuned flux constraints that give a growth rate optimal for productivity. CONCLUSIONS This study shows how a genome-scale metabolic model can be integrated with dynamic modelling and metaheuristic algorithms to provide solutions to complex metabolic engineering goals of industrial importance. This framework for in silico guided engineering, based on the dynamic batch growth relevant to industrial processes, offers considerable potential for future endeavours focused on the engineering of organisms to produce valuable products.
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Affiliation(s)
- Daniel J. Upton
- Department of Biology, University of York, Wentworth Way, York, YO10 5DD UK
| | | | - A. Jamie Wood
- Department of Biology, University of York, Wentworth Way, York, YO10 5DD UK
- Department of Mathematics, University of York, Heslington, York, YO10 5DD UK
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18
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Cairns TC, Feurstein C, Zheng X, Zhang LH, Zheng P, Sun J, Meyer V. Functional exploration of co-expression networks identifies a nexus for modulating protein and citric acid titres in Aspergillus niger submerged culture. Fungal Biol Biotechnol 2019; 6:18. [PMID: 31728200 PMCID: PMC6842248 DOI: 10.1186/s40694-019-0081-x] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2019] [Accepted: 10/21/2019] [Indexed: 01/10/2023] Open
Abstract
BACKGROUND Filamentous fungal cell factories are used to produce numerous proteins, enzymes, and organic acids. Protein secretion and filamentous growth are tightly coupled at the hyphal tip. Additionally, both these processes require ATP and amino acid precursors derived from the citric acid cycle. Despite this interconnection of organic acid production and protein secretion/filamentous growth, few studies in fungi have identified genes which may concomitantly impact all three processes. RESULTS We applied a novel screen of a global co-expression network in the cell factory Aspergillus niger to identify candidate genes which may concomitantly impact macromorphology, and protein/organic acid fermentation. This identified genes predicted to encode the Golgi localized ArfA GTPase activating protein (GAP, AgeB), and ArfA guanine nucleotide exchange factors (GEFs SecG and GeaB) to be co-expressed with citric acid cycle genes. Consequently, we used CRISPR-based genome editing to place the titratable Tet-on expression system upstream of ageB, secG, and geaB in A. niger. Functional analysis revealed that ageB and geaB are essential whereas secG was dispensable for early filamentous growth. Next, gene expression was titrated during submerged cultivations under conditions for either protein or organic acid production. ArfA regulators played varied and culture-dependent roles on pellet formation. Notably, ageB or geaB expression levels had major impacts on protein secretion, whereas secG was dispensable. In contrast, reduced expression of each predicted ArfA regulator resulted in an absence of citric acid in growth media. Finally, titrated expression of either GEFs resulted in an increase in oxaloacetic acid concentrations in supernatants. CONCLUSION Our data suggest that the Golgi may play an underappreciated role in modulating organic acid titres during industrial applications, and that this is SecG, GeaB and AgeB dependent in A. niger. These data may lead to novel avenues for strain optimization in filamentous fungi for improved protein and organic acid titres.
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Affiliation(s)
- Timothy C. Cairns
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308 People’s Republic of China
- Key Laboratory of Systems Microbial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308 People’s Republic of China
| | - Claudia Feurstein
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308 People’s Republic of China
- Key Laboratory of Systems Microbial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308 People’s Republic of China
- Institute of Biotechnology, Chair of Applied and Molecular Microbiology, Technische Universität Berlin, Straße des 17. Juni 135, 10623 Berlin, Germany
| | - Xiaomei Zheng
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308 People’s Republic of China
- Key Laboratory of Systems Microbial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308 People’s Republic of China
- University of Chinese Academy of Sciences, Beijing, 100049 China
| | - Li Hui Zhang
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308 People’s Republic of China
- Key Laboratory of Systems Microbial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308 People’s Republic of China
- College of Biotechnology, Tianjin University of Science & Technology, Tianjin, 300457 China
| | - Ping Zheng
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308 People’s Republic of China
- Key Laboratory of Systems Microbial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308 People’s Republic of China
- University of Chinese Academy of Sciences, Beijing, 100049 China
| | - Jibin Sun
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308 People’s Republic of China
- Key Laboratory of Systems Microbial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308 People’s Republic of China
- University of Chinese Academy of Sciences, Beijing, 100049 China
| | - Vera Meyer
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308 People’s Republic of China
- Key Laboratory of Systems Microbial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308 People’s Republic of China
- Institute of Biotechnology, Chair of Applied and Molecular Microbiology, Technische Universität Berlin, Straße des 17. Juni 135, 10623 Berlin, Germany
- University of Chinese Academy of Sciences, Beijing, 100049 China
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19
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Cairns TC, Zheng X, Zheng P, Sun J, Meyer V. Moulding the mould: understanding and reprogramming filamentous fungal growth and morphogenesis for next generation cell factories. BIOTECHNOLOGY FOR BIOFUELS 2019; 12:77. [PMID: 30988699 PMCID: PMC6446404 DOI: 10.1186/s13068-019-1400-4] [Citation(s) in RCA: 77] [Impact Index Per Article: 15.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/08/2019] [Accepted: 03/09/2019] [Indexed: 05/21/2023]
Abstract
Filamentous fungi are harnessed as cell factories for the production of a diverse range of organic acids, proteins, and secondary metabolites. Growth and morphology have critical implications for product titres in both submerged and solid-state fermentations. Recent advances in systems-level understanding of the filamentous lifestyle and development of sophisticated synthetic biological tools for controlled manipulation of fungal genomes now allow rational strain development programs based on data-driven decision making. In this review, we focus on Aspergillus spp. and other industrially utilised fungi to summarise recent insights into the multifaceted and dynamic relationship between filamentous growth and product titres from genetic, metabolic, modelling, subcellular, macromorphological and process engineering perspectives. Current progress and knowledge gaps with regard to mechanistic understanding of product secretion and export from the fungal cell are discussed. We highlight possible strategies for unlocking lead genes for rational strain optimizations based on omics data, and discuss how targeted genetic manipulation of these candidates can be used to optimise fungal morphology for improved performance. Additionally, fungal signalling cascades are introduced as critical processes that can be genetically targeted to control growth and morphology during biotechnological applications. Finally, we review progress in the field of synthetic biology towards chassis cells and minimal genomes, which will eventually enable highly programmable filamentous growth and diversified production capabilities. Ultimately, these advances will not only expand the fungal biotechnology portfolio but will also significantly contribute to a sustainable bio-economy.
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Affiliation(s)
- Timothy C. Cairns
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308 China
- Key Laboratory of Systems Microbial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308 People’s Republic of China
| | - Xiaomei Zheng
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308 China
- Key Laboratory of Systems Microbial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308 People’s Republic of China
| | - Ping Zheng
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308 China
- Key Laboratory of Systems Microbial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308 People’s Republic of China
| | - Jibin Sun
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308 China
- Key Laboratory of Systems Microbial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308 People’s Republic of China
| | - Vera Meyer
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308 China
- Department of Applied and Molecular Microbiology, Institute of Biotechnology, Technische Universität Berlin, 13355 Berlin, Germany
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