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Lange E, Kranert L, Krüger J, Benndorf D, Heyer R. Microbiome modeling: a beginner's guide. Front Microbiol 2024; 15:1368377. [PMID: 38962127 PMCID: PMC11220171 DOI: 10.3389/fmicb.2024.1368377] [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: 01/10/2024] [Accepted: 05/27/2024] [Indexed: 07/05/2024] Open
Abstract
Microbiomes, comprised of diverse microbial species and viruses, play pivotal roles in human health, environmental processes, and biotechnological applications and interact with each other, their environment, and hosts via ecological interactions. Our understanding of microbiomes is still limited and hampered by their complexity. A concept improving this understanding is systems biology, which focuses on the holistic description of biological systems utilizing experimental and computational methods. An important set of such experimental methods are metaomics methods which analyze microbiomes and output lists of molecular features. These lists of data are integrated, interpreted, and compiled into computational microbiome models, to predict, optimize, and control microbiome behavior. There exists a gap in understanding between microbiologists and modelers/bioinformaticians, stemming from a lack of interdisciplinary knowledge. This knowledge gap hinders the establishment of computational models in microbiome analysis. This review aims to bridge this gap and is tailored for microbiologists, researchers new to microbiome modeling, and bioinformaticians. To achieve this goal, it provides an interdisciplinary overview of microbiome modeling, starting with fundamental knowledge of microbiomes, metaomics methods, common modeling formalisms, and how models facilitate microbiome control. It concludes with guidelines and repositories for modeling. Each section provides entry-level information, example applications, and important references, serving as a valuable resource for comprehending and navigating the complex landscape of microbiome research and modeling.
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Affiliation(s)
- Emanuel Lange
- Multidimensional Omics Data Analysis, Department for Bioanalytics, Leibniz-Institut für Analytische Wissenschaften - ISAS - e.V., Dortmund, Germany
- Graduate School Digital Infrastructure for the Life Sciences, Bielefeld Institute for Bioinformatics Infrastructure (BIBI), Faculty of Technology, Bielefeld University, Bielefeld, Germany
| | - Lena Kranert
- Institute for Automation Engineering, Otto von Guericke University Magdeburg, Magdeburg, Germany
| | - Jacob Krüger
- Engineering of Software-Intensive Systems, Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, Netherlands
| | - Dirk Benndorf
- Applied Biosciences and Bioprocess Engineering, Anhalt University of Applied Sciences, Köthen, Germany
| | - Robert Heyer
- Multidimensional Omics Data Analysis, Department for Bioanalytics, Leibniz-Institut für Analytische Wissenschaften - ISAS - e.V., Dortmund, Germany
- Graduate School Digital Infrastructure for the Life Sciences, Bielefeld Institute for Bioinformatics Infrastructure (BIBI), Faculty of Technology, Bielefeld University, Bielefeld, Germany
- Multidimensional Omics Data Analysis, Faculty of Technology, Bielefeld University, Bielefeld, Germany
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2
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Huang Y, Mao Z, Zhang Y, Zhao J, Luan X, Wu K, Yun L, Yu J, Shi Z, Liao X, Ma H. Omics data analysis reveals the system-level constraint on cellular amino acid composition. Synth Syst Biotechnol 2024; 9:304-311. [PMID: 38510205 PMCID: PMC10951587 DOI: 10.1016/j.synbio.2024.03.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Revised: 03/01/2024] [Accepted: 03/01/2024] [Indexed: 03/22/2024] Open
Abstract
Proteins play a pivotal role in coordinating the functions of organisms, essentially governing their traits, as the dynamic arrangement of diverse amino acids leads to a multitude of folded configurations within peptide chains. Despite dynamic changes in amino acid composition of an individual protein (referred to as AAP) and great variance in protein expression levels under different conditions, our study, utilizing transcriptomics data from four model organisms uncovers surprising stability in the overall amino acid composition of the total cellular proteins (referred to as AACell). Although this value may vary between different species, we observed no significant differences among distinct strains of the same species. This indicates that organisms enforce system-level constraints to maintain a consistent AACell, even amid fluctuations in AAP and protein expression. Further exploration of this phenomenon promises insights into the intricate mechanisms orchestrating cellular protein expression and adaptation to varying environmental challenges.
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Affiliation(s)
- Yuanyuan Huang
- College of Biotechnology, Tianjin University of Science and Technology, Tianjin, 300457, China
- Biodesign Center, Key Laboratory of Engineering Biology for Low-carbon Manufacturing, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China
- National Center of Technology Innovation for Synthetic Biology, Tianjin, 300308, China
| | - Zhitao Mao
- Biodesign Center, Key Laboratory of Engineering Biology for Low-carbon Manufacturing, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China
- National Center of Technology Innovation for Synthetic Biology, Tianjin, 300308, China
| | - Yue Zhang
- College of Biotechnology, Tianjin University of Science and Technology, Tianjin, 300457, China
- Biodesign Center, Key Laboratory of Engineering Biology for Low-carbon Manufacturing, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China
- National Center of Technology Innovation for Synthetic Biology, Tianjin, 300308, China
| | - Jianxiao Zhao
- Biodesign Center, Key Laboratory of Engineering Biology for Low-carbon Manufacturing, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China
- National Center of Technology Innovation for Synthetic Biology, Tianjin, 300308, China
- Frontier Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), Tianjin University, Tianjin, 300072, China
| | - Xiaodi Luan
- College of Biotechnology, Tianjin University of Science and Technology, Tianjin, 300457, China
- Biodesign Center, Key Laboratory of Engineering Biology for Low-carbon Manufacturing, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China
- National Center of Technology Innovation for Synthetic Biology, Tianjin, 300308, China
| | - Ke Wu
- Biodesign Center, Key Laboratory of Engineering Biology for Low-carbon Manufacturing, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China
- National Center of Technology Innovation for Synthetic Biology, Tianjin, 300308, China
| | - Lili Yun
- Tianjin Medical Laboratory, BGI-Tianjin, BGI-Shenzhen, Tianjin, 300308, China
| | - Jing Yu
- Biodesign Center, Key Laboratory of Engineering Biology for Low-carbon Manufacturing, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China
- National Center of Technology Innovation for Synthetic Biology, Tianjin, 300308, China
| | - Zhenkun Shi
- Biodesign Center, Key Laboratory of Engineering Biology for Low-carbon Manufacturing, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China
- National Center of Technology Innovation for Synthetic Biology, Tianjin, 300308, China
| | - Xiaoping Liao
- Haihe Laboratory of Synthetic Biology, Tianjin, 300308, China
- Biodesign Center, Key Laboratory of Engineering Biology for Low-carbon Manufacturing, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China
- National Center of Technology Innovation for Synthetic Biology, Tianjin, 300308, China
| | - Hongwu Ma
- Biodesign Center, Key Laboratory of Engineering Biology for Low-carbon Manufacturing, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China
- National Center of Technology Innovation for Synthetic Biology, Tianjin, 300308, China
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3
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Fifer LM, Wong ML. Quantifying the Potential for Nitrate-Dependent Iron Oxidation on Early Mars: Implications for the Interpretation of Gale Crater Organics. ASTROBIOLOGY 2024; 24:590-603. [PMID: 38805190 DOI: 10.1089/ast.2023.0109] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
Geological evidence and atmospheric and climate models suggest habitable conditions occurred on early Mars, including in a lake in Gale crater. Instruments aboard the Curiosity rover measured organic compounds of unknown provenance in sedimentary mudstones at Gale crater. Additionally, Curiosity measured nitrates in Gale crater sediments, which suggests that nitrate-dependent Fe2+ oxidation (NDFO) may have been a viable metabolism for putative martian life. Here, we perform the first quantitative assessment of an NDFO community that could have existed in an ancient Gale crater lake and quantify the long-term preservation of biological necromass in lakebed mudstones. We find that an NDFO community would have the capacity to produce cell concentrations of up to 106 cells mL-1, which is comparable to microbes in Earth's oceans. However, only a concentration of <104 cells mL-1, due to organisms that inefficiently consume less than 10% of precipitating nitrate, would be consistent with the abundance of organics found at Gale. We also find that meteoritic sources of organics would likely be insufficient as a sole source for the Gale crater organics, which would require a separate source, such as abiotic hydrothermal or atmospheric production or possibly biological production from a slowly turning over chemotrophic community.
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Affiliation(s)
- Lucas M Fifer
- Department of Earth and Space Sciences, University of Washington, Seattle, Washington, USA
- Astrobiology Program, University of Washington, Seattle, Washington, USA
| | - Michael L Wong
- Earth and Planets Laboratory, Carnegie Institution for Science, Washington, DC, USA
- NHFP Sagan Fellow, NASA Hubble Fellowship Program, Space Telescope Science Institute, Baltimore, Maryland, USA
- NASA Nexus for Exoplanet System Science, Virtual Planetary Laboratory Team, University of Washington, Seattle, Washington, USA
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Mahout M, Carlson RP, Simon L, Peres S. Logic programming-based Minimal Cut Sets reveal consortium-level therapeutic targets for chronic wound infections. NPJ Syst Biol Appl 2024; 10:34. [PMID: 38565568 PMCID: PMC10987626 DOI: 10.1038/s41540-024-00360-6] [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: 07/29/2023] [Accepted: 03/13/2024] [Indexed: 04/04/2024] Open
Abstract
Minimal Cut Sets (MCSs) identify sets of reactions which, when removed from a metabolic network, disable certain cellular functions. The traditional search for MCSs within genome-scale metabolic models (GSMMs) targets cellular growth, identifies reaction sets resulting in a lethal phenotype if disrupted, and retrieves a list of corresponding gene, mRNA, or enzyme targets. Using the dual link between MCSs and Elementary Flux Modes (EFMs), our logic programming-based tool aspefm was able to compute MCSs of any size from GSMMs in acceptable run times. The tool demonstrated better performance when computing large-sized MCSs than the mixed-integer linear programming methods. We applied the new MCSs methodology to a medically-relevant consortium model of two cross-feeding bacteria, Staphylococcus aureus and Pseudomonas aeruginosa. aspefm constraints were used to bias the computation of MCSs toward exchanged metabolites that could complement lethal phenotypes in individual species. We found that interspecies metabolite exchanges could play an essential role in rescuing single-species growth, for instance inosine could complement lethal reaction knock-outs in the purine synthesis, glycolysis, and pentose phosphate pathways of both bacteria. Finally, MCSs were used to derive a list of promising enzyme targets for consortium-level therapeutic applications that cannot be circumvented via interspecies metabolite exchange.
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Affiliation(s)
- Maxime Mahout
- Université Paris-Saclay, CNRS, Laboratoire Interdisciplinaire des Sciences du Numérique, 91405, Orsay, France
| | - Ross P Carlson
- Department of Chemical and Biological Engineering, Center for Biofilm Engineering, Microbiology and Immunology, Montana State University, Bozeman, MT, 59717, USA
| | - Laurent Simon
- Bordeaux-INP, Université Bordeaux, LaBRI, 33405, Talence Cedex, France
| | - Sabine Peres
- UMR CNRS 5558, Laboratoire de Biométrie et de Biologie Évolutive, Université Claude Bernard Lyon 1, 69100, Villeurbanne, France.
- INRIA Lyon Centre, 69100, Villeurbanne, France.
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Hasan R, Kasera N, Beck AE, Hall SG. Potential of Synechococcus elongatus UTEX 2973 as a feedstock for sugar production during mixed aquaculture and swine wastewater bioremediation. Heliyon 2024; 10:e24646. [PMID: 38314264 PMCID: PMC10837500 DOI: 10.1016/j.heliyon.2024.e24646] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Revised: 12/14/2023] [Accepted: 01/11/2024] [Indexed: 02/06/2024] Open
Abstract
The demand for protein is increasing with an expanding world population and is influencing the rapid growth of fish and animal agriculture. These sectors are becoming a significant source of water pollution and need to develop environmentally sustainable techniques that are cost-effective, ideally with potential for downstream value-added production. This study investigated the potential of one of the fastest-growing cyanobacterial species, Synechococcus elongatus UTEX 2973, for bioremediation of mixed wastewater (combination of sturgeon and swine wastewater). Three different mixing ratios (25:75, 50:50, and 75:25 sturgeon:swine) were compared to find a suitable combination for the growth of S. elongatus as well as carbohydrate accumulation in biomass. The final biomass production was found to be 0.65 ± 0.03 g Dry cell Weight (DW)/L for 75%-25 %, 0.90 ± 0.004 g DW/L for 50%-50 %, and 0.71 ± 0.04 g DW/L for 25%-75 % sturgeon-swine wastewater combination. Cyanobacteria cultivated in 50%-50 % sturgeon-swine wastewater also accumulated 70 % total carbohydrate of DW, whereas 75%-25 % sturgeon-swine and 25%-75 % sturgeon-swine accumulated 53 % and 45 %, respectively. Subsequently, the S. elongatus cells were grown in a separate batch of 50%-50 % sturgeon-swine wastewater and compared with cells grown in BG11 synthetic growth media. Cultivation in BG11 resulted in higher biomass production but lower carbohydrate accumulation than 50%-50 % mixed wastewater. Final biomass production was 0.85 ± 0.08 g DW/L for BG11 and 0.65 ± 0.04 g DW/L for 50%-50 % sturgeon-swine wastewater. Total carbohydrate accumulated was 75 % and 64 % of DW for 50%-50 % sturgeon-swine mixed wastewater and BG11 growth media, respectively, where glycogen was the main carbohydrate component (90 %). The nutrient removal efficiencies of S. elongatus were 67.15 % for orthophosphate, 93.39 % for nitrate-nitrite, and 97.98 % for ammonia. This study suggested that S. elongatus is a promising candidate for enabling simultaneous bioremediation of mixed wastewater and the production of value-added biochemicals.
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Affiliation(s)
- Rifat Hasan
- Department of Biological and Agricultural Engineering, North Carolina State University, Raleigh, NC, USA
| | - Nitesh Kasera
- Department of Biological and Agricultural Engineering, North Carolina State University, Raleigh, NC, USA
| | - Ashley E. Beck
- Department of Biological and Environmental Sciences, Carroll College, Helena, MT, USA
| | - Steven G. Hall
- Department of Biological and Agricultural Engineering, North Carolina State University, Raleigh, NC, USA
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Choi YM, Choi DH, Lee YQ, Koduru L, Lewis NE, Lakshmanan M, Lee DY. Mitigating biomass composition uncertainties in flux balance analysis using ensemble representations. Comput Struct Biotechnol J 2023; 21:3736-3745. [PMID: 37547082 PMCID: PMC10400880 DOI: 10.1016/j.csbj.2023.07.025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Revised: 07/04/2023] [Accepted: 07/19/2023] [Indexed: 08/08/2023] Open
Abstract
The biomass equation is a critical component in genome-scale metabolic models (GEMs): it is used as the de facto objective function in flux balance analysis (FBA). This equation accounts for the quantities of all known biomass precursors that are required for cell growth based on the macromolecular and monomer compositions measured at certain conditions. However, it is often reported that the macromolecular composition of cells could change across different environmental conditions and thus the use of the same single biomass equation in FBA, under multiple conditions, is questionable. Herein, we first investigated the qualitative and quantitative variations of macromolecular compositions of three representative host organisms, Escherichia coli, Saccharomyces cerevisiae and Cricetulus griseus, across different environmental/genetic variations. While macromolecular building blocks such as RNA, protein, and lipid composition vary notably, changes in fundamental biomass monomer units such as nucleotides and amino acids are not appreciable. We also observed that flux predictions through FBA is quite sensitive to macromolecular compositions but not the monomer compositions. Based on these observations, we propose ensemble representations of biomass equation in FBA to account for the natural variation of cellular constituents. Such ensemble representations of biomass better predicted the flux through anabolic reactions as it allows for the flexibility in the biosynthetic demands of the cells. The current study clearly highlights that certain component of the biomass equation indeed vary across different conditions, and the ensemble representation of biomass equation in FBA by accounting for such natural variations could avoid inaccuracies that may arise from in silico simulations.
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Affiliation(s)
- Yoon-Mi Choi
- School of Chemical Engineering, Sungkyunkwan University, Suwon-si, Gyeonggi-do, Republic of Korea
- Bioprocessing Technology Institute (BTI), Agency for Science, Technology and Research (A⁎STAR), Singapore
| | - Dong-Hyuk Choi
- School of Chemical Engineering, Sungkyunkwan University, Suwon-si, Gyeonggi-do, Republic of Korea
| | - Yi Qing Lee
- School of Chemical Engineering, Sungkyunkwan University, Suwon-si, Gyeonggi-do, Republic of Korea
| | - Lokanand Koduru
- Institute of Molecular and Cell Biology (IMCB), Agency for Science, Technology and Research (A⁎STAR), Singapore
| | - Nathan E. Lewis
- Departments of Pediatrics and Bioengineering, University of California, La Jolla, San Diego, USA
| | - Meiyappan Lakshmanan
- Bioprocessing Technology Institute (BTI), Agency for Science, Technology and Research (A⁎STAR), Singapore
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, and Centre for Integrative Biology and Systems medicinE (IBSE), Indian Institute of Technology Madras, Chennai, Tamil Nadu, India
| | - Dong-Yup Lee
- School of Chemical Engineering, Sungkyunkwan University, Suwon-si, Gyeonggi-do, Republic of Korea
- Bitwinners Pte. Ltd., Singapore
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García-Calvo L, Rane DV, Everson N, Humlebrekk ST, Mathiassen LF, Mæhlum AHM, Malmo J, Bruheim P. Central carbon metabolite profiling reveals vector-associated differences in the recombinant protein production host Escherichia coli BL21. FRONTIERS IN CHEMICAL ENGINEERING 2023. [DOI: 10.3389/fceng.2023.1142226] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/16/2023] Open
Abstract
The Gram-negative bacterium Escherichia coli is the most widely used host for recombinant protein production, both as an industrial expression platform and as a model system at laboratory scale. The recombinant protein production industry generates proteins with direct applications as biopharmaceuticals and in technological processes central to a plethora of fields. Despite the increasing economic significance of recombinant protein production, and the importance of E. coli as an expression platform and model organism, only few studies have focused on the central carbon metabolic landscape of E. coli during high-level recombinant protein production. In the present work, we applied four targeted CapIC- and LC-MS/MS methods, covering over 60 metabolites, to perform an in-depth metabolite profiling of the effects of high-level recombinant protein production in strains derived from E. coli BL21, carrying XylS/Pm vectors with different characteristics. The mass-spectrometric central carbon metabolite profiling was complemented with the study of growth kinetics and protein production in batch bioreactors. Our work shows the robustness in E. coli central carbon metabolism when introducing increased plasmid copy number, as well as the greater importance of induction of recombinant protein production as a metabolic challenge, especially when strong promoters are used.
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Rios Garza D, Gonze D, Zafeiropoulos H, Liu B, Faust K. Metabolic models of human gut microbiota: Advances and challenges. Cell Syst 2023; 14:109-121. [PMID: 36796330 DOI: 10.1016/j.cels.2022.11.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Revised: 08/24/2022] [Accepted: 11/04/2022] [Indexed: 02/17/2023]
Abstract
The human gut is a complex ecosystem consisting of hundreds of microbial species interacting with each other and with the human host. Mathematical models of the gut microbiome integrate our knowledge of this system and help to formulate hypotheses to explain observations. The generalized Lotka-Volterra model has been widely used for this purpose, but it does not describe interaction mechanisms and thus does not account for metabolic flexibility. Recently, models that explicitly describe gut microbial metabolite production and consumption have become popular. These models have been used to investigate the factors that shape gut microbial composition and to link specific gut microorganisms to changes in metabolite concentrations found in diseases. Here, we review how such models are built and what we have learned so far from their application to human gut microbiome data. In addition, we discuss current challenges of these models and how these can be addressed in the future.
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Affiliation(s)
- Daniel Rios Garza
- KU Leuven, Department of Microbiology, Immunology and Transplantation, Rega Institute for Medical Research, Laboratory of Molecular Bacteriology, 3000 Leuven, Belgium
| | - Didier Gonze
- Unité de Chronobiologie Théorique, Faculté des Sciences, CP 231, Université Libre de Bruxelles, Bvd du Triomphe, 1050 Bruxelles, Belgium
| | - Haris Zafeiropoulos
- Biology Department, University of Crete, Heraklion 700 13, Greece; Institute of Marine Biology, Biotechnology and Aquaculture (IMBBC), Hellenic Centre for Marine Research (HCMR), Former U.S. Base of Gournes P.O. Box 2214, 71003, Heraklion, Crete, Greece
| | - Bin Liu
- KU Leuven, Department of Microbiology, Immunology and Transplantation, Rega Institute for Medical Research, Laboratory of Molecular Bacteriology, 3000 Leuven, Belgium
| | - Karoline Faust
- KU Leuven, Department of Microbiology, Immunology and Transplantation, Rega Institute for Medical Research, Laboratory of Molecular Bacteriology, 3000 Leuven, Belgium.
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Li F, Li Y, Novoselov KS, Liang F, Meng J, Ho SH, Zhao T, Zhou H, Ahmad A, Zhu Y, Hu L, Ji D, Jia L, Liu R, Ramakrishna S, Zhang X. Bioresource Upgrade for Sustainable Energy, Environment, and Biomedicine. NANO-MICRO LETTERS 2023; 15:35. [PMID: 36629933 PMCID: PMC9833044 DOI: 10.1007/s40820-022-00993-4] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Accepted: 11/29/2022] [Indexed: 06/17/2023]
Abstract
We conceptualize bioresource upgrade for sustainable energy, environment, and biomedicine with a focus on circular economy, sustainability, and carbon neutrality using high availability and low utilization biomass (HALUB). We acme energy-efficient technologies for sustainable energy and material recovery and applications. The technologies of thermochemical conversion (TC), biochemical conversion (BC), electrochemical conversion (EC), and photochemical conversion (PTC) are summarized for HALUB. Microalgal biomass could contribute to a biofuel HHV of 35.72 MJ Kg-1 and total benefit of 749 $/ton biomass via TC. Specific surface area of biochar reached 3000 m2 g-1 via pyrolytic carbonization of waste bean dregs. Lignocellulosic biomass can be effectively converted into bio-stimulants and biofertilizers via BC with a high conversion efficiency of more than 90%. Besides, lignocellulosic biomass can contribute to a current density of 672 mA m-2 via EC. Bioresource can be 100% selectively synthesized via electrocatalysis through EC and PTC. Machine learning, techno-economic analysis, and life cycle analysis are essential to various upgrading approaches of HALUB. Sustainable biomaterials, sustainable living materials and technologies for biomedical and multifunctional applications like nano-catalysis, microfluidic and micro/nanomotors beyond are also highlighted. New techniques and systems for the complete conversion and utilization of HALUB for new energy and materials are further discussed.
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Affiliation(s)
- Fanghua Li
- Center for Nanofibers and Nanotechnology, National University of Singapore, Singapore, 119260, Singapore
- State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, Harbin, 150090, People's Republic of China
| | - Yiwei Li
- School of Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- John A Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, 02138, USA
- The Key Laboratory for Biomedical Photonics of MOE at Wuhan National Laboratory for Optoelectronics - Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, 430074, People's Republic of China
| | - K S Novoselov
- Centre for Advanced 2D Materials, National University of Singapore, Singapore, 117546, Singapore
- School of Physics and Astronomy, The University of Manchester, Manchester, M13 9PL, UK
| | - Feng Liang
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA
| | - Jiashen Meng
- School of Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Shih-Hsin Ho
- State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, Harbin, 150090, People's Republic of China
| | - Tong Zhao
- State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, Harbin, 150090, People's Republic of China
| | - Hui Zhou
- Department of Energy and Power Engineering, Tsinghua University, Beijing, 100084, People's Republic of China
| | - Awais Ahmad
- Departamento de Quimica Organica, Universidad de Cordoba, Edificio Marie Curie (C-3), Ctra Nnal IV-A, Km 396, 14014, Cordoba, Spain
| | - Yinlong Zhu
- Department of Chemical Engineering, Monash University, Clayton, VIC, 3800, Australia
| | - Liangxing Hu
- School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore, 639798, Singapore
| | - Dongxiao Ji
- Center for Nanofibers and Nanotechnology, National University of Singapore, Singapore, 119260, Singapore
| | - Litao Jia
- State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, Harbin, 150090, People's Republic of China
| | - Rui Liu
- State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, Harbin, 150090, People's Republic of China
| | - Seeram Ramakrishna
- Center for Nanofibers and Nanotechnology, National University of Singapore, Singapore, 119260, Singapore
| | - Xingcai Zhang
- John A Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, 02138, USA.
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10
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Karlsen E, Gylseth M, Schulz C, Almaas E. A study of a diauxic growth experiment using an expanded dynamic flux balance framework. PLoS One 2023; 18:e0280077. [PMID: 36607958 PMCID: PMC9821518 DOI: 10.1371/journal.pone.0280077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Accepted: 12/20/2022] [Indexed: 01/07/2023] Open
Abstract
Flux balance analysis (FBA) remains one of the most used methods for modeling the entirety of cellular metabolism, and a range of applications and extensions based on the FBA framework have been generated. Dynamic flux balance analysis (dFBA), the expansion of FBA into the time domain, still has issues regarding accessibility limiting its widespread adoption and application, such as a lack of a consistently rigid formalism and tools that can be applied without expert knowledge. Recent work has combined dFBA with enzyme-constrained flux balance analysis (decFBA), which has been shown to greatly improve accuracy in the comparison of computational simulations and experimental data, but such approaches generally do not take into account the fact that altering the enzyme composition of a cell is not an instantaneous process. Here, we have developed a decFBA method that explicitly takes enzyme change constraints (ecc) into account, decFBAecc. The resulting software is a simple yet flexible framework for using genome-scale metabolic modeling for simulations in the time domain that has full interoperability with the COBRA Toolbox 3.0. To assess the quality of the computational predictions of decFBAecc, we conducted a diauxic growth fermentation experiment with Escherichia coli BW25113 in glucose minimal M9 medium. The comparison of experimental data with dFBA, decFBA and decFBAecc predictions demonstrates how systematic analyses within a fixed constraint-based framework can aid the study of model parameters. Finally, in explaining experimentally observed phenotypes, our computational analysis demonstrates the importance of non-linear dependence of exchange fluxes on medium metabolite concentrations and the non-instantaneous change in enzyme composition, effects of which have not previously been accounted for in constraint-based analysis.
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Affiliation(s)
- Emil Karlsen
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
| | - Marianne Gylseth
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
| | - Christian Schulz
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
| | - Eivind Almaas
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
- K. G. Jebsen Center for Genetic Epidemiology Department of Public Health and General Practice, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
- * E-mail:
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11
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Salter TL, Magee BA, Waite JH, Sephton MA. Mass Spectrometric Fingerprints of Bacteria and Archaea for Life Detection on Icy Moons. ASTROBIOLOGY 2022; 22:143-157. [PMID: 35021862 DOI: 10.1089/ast.2020.2394] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
The icy moons of the outer Solar System display evidence of subsurface liquid water and, therefore, potential habitability for life. Flybys of Saturn's moon Enceladus by the Cassini spacecraft have provided measurements of material from plumes that suggest hydrothermal activity and the presence of organic matter. Jupiter's moon Europa may have similar plumes and is the target for the forthcoming Europa Clipper mission that carries a high mass resolution and high sensitivity mass spectrometer, called the MAss Spectrometer for Planetary EXploration (MASPEX), with the capability for providing detailed characterization of any organic materials encountered. We have performed a series of experiments using pyrolysis-gas chromatography-mass spectrometry to characterize the mass spectrometric fingerprints of microbial life. A range of extremophile Archaea and Bacteria have been analyzed and the laboratory data converted to MASPEX-type signals. Molecular characteristics of protein, carbohydrate, and lipid structures were detected, and the characteristic fragmentation patterns corresponding to these different biological structures were identified. Protein pyrolysis fragments included phenols, nitrogen heterocycles, and cyclic dipeptides. Oxygen heterocycles, such as furans, were detected from carbohydrates. Our data reveal how mass spectrometry on Europa Clipper can aid in the identification of the presence of life, by looking for characteristic bacterial fingerprints that are similar to those from simple Earthly organisms.
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Affiliation(s)
- Tara L Salter
- Impacts and Astromaterials Research Centre, Department of Earth Science and Engineering, Imperial College London, London, United Kingdom
| | - Brian A Magee
- Space Science and Engineering Division, Southwest Research Institute, Boulder, Colorado, USA
| | - J Hunter Waite
- Space Science and Engineering Division, Southwest Research Institute, San Antonio, Texas, USA
| | - Mark A Sephton
- Impacts and Astromaterials Research Centre, Department of Earth Science and Engineering, Imperial College London, London, United Kingdom
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12
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Simensen V, Schulz C, Karlsen E, Bråtelund S, Burgos I, Thorfinnsdottir LB, García-Calvo L, Bruheim P, Almaas E. Experimental determination of Escherichia coli biomass composition for constraint-based metabolic modeling. PLoS One 2022; 17:e0262450. [PMID: 35085271 PMCID: PMC8794083 DOI: 10.1371/journal.pone.0262450] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Accepted: 12/24/2021] [Indexed: 12/03/2022] Open
Abstract
Genome-scale metabolic models (GEMs) are mathematical representations of metabolism that allow for in silico simulation of metabolic phenotypes and capabilities. A prerequisite for these predictions is an accurate representation of the biomolecular composition of the cell necessary for replication and growth, implemented in GEMs as the so-called biomass objective function (BOF). The BOF contains the metabolic precursors required for synthesis of the cellular macro- and micromolecular constituents (e.g. protein, RNA, DNA), and its composition is highly dependent on the particular organism, strain, and growth condition. Despite its critical role, the BOF is rarely constructed using specific measurements of the modeled organism, drawing the validity of this approach into question. Thus, there is a need to establish robust and reliable protocols for experimental condition-specific biomass determination. Here, we address this challenge by presenting a general pipeline for biomass quantification, evaluating its performance on Escherichia coli K-12 MG1655 sampled during balanced exponential growth under controlled conditions in a batch-fermentor set-up. We significantly improve both the coverage and molecular resolution compared to previously published workflows, quantifying 91.6% of the biomass. Our measurements display great correspondence with previously reported measurements, and we were also able to detect subtle characteristics specific to the particular E. coli strain. Using the modified E. coli GEM iML1515a, we compare the feasible flux ranges of our experimentally determined BOF with the original BOF, finding that the changes in BOF coefficients considerably affect the attainable fluxes at the genome-scale.
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Affiliation(s)
- Vetle Simensen
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
| | - Christian Schulz
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
| | - Emil Karlsen
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
| | - Signe Bråtelund
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
| | - Idun Burgos
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
| | - Lilja Brekke Thorfinnsdottir
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
| | - Laura García-Calvo
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
| | - Per Bruheim
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
| | - Eivind Almaas
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
- K.G. Jebsen Center for Genetic Epidemiology Department of Public Health and General Practice, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
- * E-mail:
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13
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Quantifying the propagation of parametric uncertainty on flux balance analysis. Metab Eng 2021; 69:26-39. [PMID: 34718140 DOI: 10.1016/j.ymben.2021.10.012] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 10/21/2021] [Accepted: 10/24/2021] [Indexed: 12/27/2022]
Abstract
Flux balance analysis (FBA) and associated techniques operating on stoichiometric genome-scale metabolic models play a central role in quantifying metabolic flows and constraining feasible phenotypes. At the heart of these methods lie two important assumptions: (i) the biomass precursors and energy requirements neither change in response to growth conditions nor environmental/genetic perturbations, and (ii) metabolite production and consumption rates are equal at all times (i.e., steady-state). Despite the stringency of these two assumptions, FBA has been shown to be surprisingly robust at predicting cellular phenotypes. In this paper, we formally assess the impact of these two assumptions on FBA results by quantifying how uncertainty in biomass reaction coefficients, and departures from steady-state due to temporal fluctuations could propagate to FBA results. In the first case, conditional sampling of parameter space is required to re-weigh the biomass reaction so as the molecular weight remains equal to 1 g mmol-1, and in the second case, metabolite (and elemental) pool conservation must be imposed under temporally varying conditions. Results confirm the importance of enforcing the aforementioned constraints and explain the robustness of FBA biomass yield predictions.
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14
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Sert D, Mercan E, Dinkul M, Aydemir S. Processing of skim milk powder made using sonicated milk concentrates: A study of physicochemical, functional, powder flow and microbiological characteristics. Int Dairy J 2021. [DOI: 10.1016/j.idairyj.2021.105080] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
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15
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Wiechert W, Nöh K. Quantitative Metabolic Flux Analysis Based on Isotope Labeling. Metab Eng 2021. [DOI: 10.1002/9783527823468.ch3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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16
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Schulz C, Kumelj T, Karlsen E, Almaas E. Genome-scale metabolic modelling when changes in environmental conditions affect biomass composition. PLoS Comput Biol 2021; 17:e1008528. [PMID: 34029317 PMCID: PMC8177628 DOI: 10.1371/journal.pcbi.1008528] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Revised: 06/04/2021] [Accepted: 04/27/2021] [Indexed: 11/29/2022] Open
Abstract
Genome-scale metabolic modeling is an important tool in the study of metabolism by enhancing the collation of knowledge, interpretation of data, and prediction of metabolic capabilities. A frequent assumption in the use of genome-scale models is that the in vivo organism is evolved for optimal growth, where growth is represented by flux through a biomass objective function (BOF). While the specific composition of the BOF is crucial, its formulation is often inherited from similar organisms due to the experimental challenges associated with its proper determination. A cell’s macro-molecular composition is not fixed and it responds to changes in environmental conditions. As a consequence, initiatives for the high-fidelity determination of cellular biomass composition have been launched. Thus, there is a need for a mathematical and computational framework capable of using multiple measurements of cellular biomass composition in different environments. Here, we propose two different computational approaches for directly addressing this challenge: Biomass Trade-off Weighting (BTW) and Higher-dimensional-plane InterPolation (HIP). In lieu of experimental data on biomass composition-variation in response to changing nutrient environment, we assess the properties of BTW and HIP using three hypothetical, yet biologically plausible, BOFs for the Escherichia coli genome-scale metabolic model iML1515. We find that the BTW and HIP formulations have a significant impact on model performance and phenotypes. Furthermore, the BTW method generates larger growth rates in all environments when compared to HIP. Using acetate secretion and the respiratory quotient as proxies for phenotypic changes, we find marked differences between the methods as HIP generates BOFs more similar to a reference BOF than BTW. We conclude that the presented methods constitute a conceptual step in developing genome-scale metabolic modelling approaches capable of addressing the inherent dependence of cellular biomass composition on nutrient environments. Changes in the environment promote changes in an organism’s metabolism. To achieve balanced growth states for near-optimal function, cells respond through metabolic rearrangements, which may influence the biosynthesis of metabolic precursors for building a cell’s molecular constituents. Therefore, it is necessary to take the dependence of biomass composition on environmental conditions into consideration. While measuring the biomass composition for some environments is possible, and should be done, it cannot be completed for all possible environments. In this work, we propose two main approaches, BTW and HIP, for addressing the challenge of estimating biomass composition in response to environmental changes. We evaluate the phenotypic consequences of BTW and HIP by characterizing their effect on growth, secretion potential, respiratory efficiency, and gene essentiality of a cell. Our work constitutes a first conceptual step in accounting for the influence of growth conditions on biomass composition, and in turn the biomass composition’s effect on metabolic phenotypic traits, within constraint-based modelling. As such, we believe it will improve the relevance of constraint-based methods in metabolic engineering and drug discovery, since the biosynthetic potential of microbes for generating industrially relevant products or drugs often is closely linked to their biomass composition.
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Affiliation(s)
- Christian Schulz
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
| | - Tjasa Kumelj
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
| | - Emil Karlsen
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
| | - Eivind Almaas
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and General Practice, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
- * E-mail:
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17
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Schinn SM, Morrison C, Wei W, Zhang L, Lewis NE. Systematic evaluation of parameters for genome-scale metabolic models of cultured mammalian cells. Metab Eng 2021; 66:21-30. [PMID: 33771719 DOI: 10.1016/j.ymben.2021.03.013] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Revised: 11/25/2020] [Accepted: 03/17/2021] [Indexed: 10/21/2022]
Abstract
Genome-scale metabolic models describe cellular metabolism with mechanistic detail. Given their high complexity, such models need to be parameterized correctly to yield accurate predictions and avoid overfitting. Effective parameterization has been well-studied for microbial models, but it remains unclear for higher eukaryotes, including mammalian cells. To address this, we enumerated model parameters that describe key features of cultured mammalian cells - including cellular composition, bioprocess performance metrics, mammalian-specific pathways, and biological assumptions behind model formulation approaches. We tested these parameters by building thousands of metabolic models and evaluating their ability to predict the growth rates of a panel of phenotypically diverse Chinese Hamster Ovary cell clones. We found the following considerations to be most critical for accurate parameterization: (1) cells limit metabolic activity to maintain homeostasis, (2) cell morphology and viability change dynamically during a growth curve, and (3) cellular biomass has a particular macromolecular composition. Depending on parameterization, models predicted different metabolic phenotypes, including contrasting mechanisms of nutrient utilization and energy generation, leading to varying accuracies of growth rate predictions. Notably, accurate parameter values broadly agreed with experimental measurements. These insights will guide future investigations of mammalian metabolism.
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Affiliation(s)
- Song-Min Schinn
- Department of Pediatrics, University of California, San Diego, USA
| | - Carly Morrison
- Pfizer, Biotherapeutics Pharmaceutical Sciences, Andover, MA, USA
| | - Wei Wei
- Pfizer, Biotherapeutics Pharmaceutical Sciences, Andover, MA, USA
| | - Lin Zhang
- Pfizer, Biotherapeutics Pharmaceutical Sciences, Andover, MA, USA
| | - Nathan E Lewis
- Department of Pediatrics, University of California, San Diego, USA; Department of Bioengineering, University of California, San Diego, USA; Novo Nordisk Foundation Center for Biosustainability at UC, San Diego, USA.
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18
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Bernstein DB, Sulheim S, Almaas E, Segrè D. Addressing uncertainty in genome-scale metabolic model reconstruction and analysis. Genome Biol 2021; 22:64. [PMID: 33602294 PMCID: PMC7890832 DOI: 10.1186/s13059-021-02289-z] [Citation(s) in RCA: 63] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Accepted: 02/04/2021] [Indexed: 02/07/2023] Open
Abstract
The reconstruction and analysis of genome-scale metabolic models constitutes a powerful systems biology approach, with applications ranging from basic understanding of genotype-phenotype mapping to solving biomedical and environmental problems. However, the biological insight obtained from these models is limited by multiple heterogeneous sources of uncertainty, which are often difficult to quantify. Here we review the major sources of uncertainty and survey existing approaches developed for representing and addressing them. A unified formal characterization of these uncertainties through probabilistic approaches and ensemble modeling will facilitate convergence towards consistent reconstruction pipelines, improved data integration algorithms, and more accurate assessment of predictive capacity.
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Affiliation(s)
- David B Bernstein
- Department of Biomedical Engineering and Biological Design Center, Boston University, Boston, MA, USA
| | - Snorre Sulheim
- Bioinformatics Program, Boston University, Boston, MA, USA
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
- Department of Biotechnology and Nanomedicine, SINTEF Industry, Trondheim, Norway
| | - Eivind Almaas
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
- K.G. Jebsen Center for Genetic Epidemiology, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
| | - Daniel Segrè
- Department of Biomedical Engineering and Biological Design Center, Boston University, Boston, MA, USA.
- Bioinformatics Program, Boston University, Boston, MA, USA.
- Department of Biology and Department of Physics, Boston University, Boston, MA, USA.
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19
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Experimentally Validated Reconstruction and Analysis of a Genome-Scale Metabolic Model of an Anaerobic Neocallimastigomycota Fungus. mSystems 2021; 6:6/1/e00002-21. [PMID: 33594000 PMCID: PMC8561657 DOI: 10.1128/msystems.00002-21] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023] Open
Abstract
Anaerobic gut fungi in the phylum Neocallimastigomycota typically inhabit the digestive tracts of large mammalian herbivores, where they play an integral role in the decomposition of raw lignocellulose into its constitutive sugar monomers. However, quantitative tools to study their physiology are lacking, partially due to their complex and unresolved metabolism that includes the largely uncharacterized fungal hydrogenosome. Modern omics approaches combined with metabolic modeling can be used to establish an understanding of gut fungal metabolism and develop targeted engineering strategies to harness their degradation capabilities for lignocellulosic bioprocessing. Here, we introduce a high-quality genome of the anaerobic fungus Neocallimastix lanati from which we constructed the first genome-scale metabolic model of an anaerobic fungus. Relative to its size (200 Mbp, sequenced at 62× depth), it is the least fragmented publicly available gut fungal genome to date. Of the 1,788 lignocellulolytic enzymes annotated in the genome, 585 are associated with the fungal cellulosome, underscoring the powerful lignocellulolytic potential of N. lanati. The genome-scale metabolic model captures the primary metabolism of N. lanati and accurately predicts experimentally validated substrate utilization requirements. Additionally, metabolic flux predictions are verified by 13C metabolic flux analysis, demonstrating that the model faithfully describes the underlying fungal metabolism. Furthermore, the model clarifies key aspects of the hydrogenosomal metabolism and can be used as a platform to quantitatively study these biotechnologically important yet poorly understood early-branching fungi. IMPORTANCE Recent genomic analyses have revealed that anaerobic gut fungi possess both the largest number and highest diversity of lignocellulolytic enzymes of all sequenced fungi, explaining their ability to decompose lignocellulosic substrates, e.g., agricultural waste, into fermentable sugars. Despite their potential, the development of engineering methods for these organisms has been slow due to their complex life cycle, understudied metabolism, and challenging anaerobic culture requirements. Currently, there is no framework that can be used to combine multi-omic data sets to understand their physiology. Here, we introduce a high-quality PacBio-sequenced genome of the anaerobic gut fungus Neocallimastix lanati. Beyond identifying a trove of lignocellulolytic enzymes, we use this genome to construct the first genome-scale metabolic model of an anaerobic gut fungus. The model is experimentally validated and sheds light on unresolved metabolic features common to gut fungi. Model-guided analysis will pave the way for deepening our understanding of anaerobic gut fungi and provides a systematic framework to guide strain engineering efforts of these organisms for biotechnological use.
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20
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Széliová D, Ruckerbauer DE, Galleguillos SN, Petersen LB, Natter K, Hanscho M, Troyer C, Causon T, Schoeny H, Christensen HB, Lee DY, Lewis NE, Koellensperger G, Hann S, Nielsen LK, Borth N, Zanghellini J. What CHO is made of: Variations in the biomass composition of Chinese hamster ovary cell lines. Metab Eng 2020; 61:288-300. [DOI: 10.1016/j.ymben.2020.06.002] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
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21
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Macklin DN, Ahn-Horst TA, Choi H, Ruggero NA, Carrera J, Mason JC, Sun G, Agmon E, DeFelice MM, Maayan I, Lane K, Spangler RK, Gillies TE, Paull ML, Akhter S, Bray SR, Weaver DS, Keseler IM, Karp PD, Morrison JH, Covert MW. Simultaneous cross-evaluation of heterogeneous E. coli datasets via mechanistic simulation. Science 2020; 369:eaav3751. [PMID: 32703847 PMCID: PMC7990026 DOI: 10.1126/science.aav3751] [Citation(s) in RCA: 85] [Impact Index Per Article: 21.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2018] [Revised: 10/28/2019] [Accepted: 05/26/2020] [Indexed: 12/24/2022]
Abstract
The extensive heterogeneity of biological data poses challenges to analysis and interpretation. Construction of a large-scale mechanistic model of Escherichia coli enabled us to integrate and cross-evaluate a massive, heterogeneous dataset based on measurements reported by various groups over decades. We identified inconsistencies with functional consequences across the data, including that the total output of the ribosomes and RNA polymerases described by data are not sufficient for a cell to reproduce measured doubling times, that measured metabolic parameters are neither fully compatible with each other nor with overall growth, and that essential proteins are absent during the cell cycle-and the cell is robust to this absence. Finally, considering these data as a whole leads to successful predictions of new experimental outcomes, in this case protein half-lives.
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Affiliation(s)
- Derek N Macklin
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
- Allen Discovery Center at Stanford University, Stanford University, Stanford, CA 94305, USA
| | - Travis A Ahn-Horst
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
- Allen Discovery Center at Stanford University, Stanford University, Stanford, CA 94305, USA
| | - Heejo Choi
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
- Allen Discovery Center at Stanford University, Stanford University, Stanford, CA 94305, USA
| | - Nicholas A Ruggero
- Allen Discovery Center at Stanford University, Stanford University, Stanford, CA 94305, USA
- Department of Chemical Engineering, Stanford University, Stanford, CA 94305, USA
| | - Javier Carrera
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
- Allen Discovery Center at Stanford University, Stanford University, Stanford, CA 94305, USA
| | - John C Mason
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
- Allen Discovery Center at Stanford University, Stanford University, Stanford, CA 94305, USA
| | - Gwanggyu Sun
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
- Allen Discovery Center at Stanford University, Stanford University, Stanford, CA 94305, USA
| | - Eran Agmon
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
- Allen Discovery Center at Stanford University, Stanford University, Stanford, CA 94305, USA
| | - Mialy M DeFelice
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
- Allen Discovery Center at Stanford University, Stanford University, Stanford, CA 94305, USA
| | - Inbal Maayan
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
- Allen Discovery Center at Stanford University, Stanford University, Stanford, CA 94305, USA
| | - Keara Lane
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
- Allen Discovery Center at Stanford University, Stanford University, Stanford, CA 94305, USA
| | - Ryan K Spangler
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
- Allen Discovery Center at Stanford University, Stanford University, Stanford, CA 94305, USA
| | - Taryn E Gillies
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
- Allen Discovery Center at Stanford University, Stanford University, Stanford, CA 94305, USA
| | - Morgan L Paull
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| | - Sajia Akhter
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| | - Samuel R Bray
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| | | | | | | | - Jerry H Morrison
- Allen Discovery Center at Stanford University, Stanford University, Stanford, CA 94305, USA
| | - Markus W Covert
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA.
- Allen Discovery Center at Stanford University, Stanford University, Stanford, CA 94305, USA
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22
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Ahmad A, Pathania R, Srivastava S. Biochemical Characteristics and a Genome-Scale Metabolic Model of an Indian Euryhaline Cyanobacterium with High Polyglucan Content. Metabolites 2020; 10:metabo10050177. [PMID: 32365713 PMCID: PMC7281201 DOI: 10.3390/metabo10050177] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2020] [Revised: 01/28/2020] [Accepted: 02/05/2020] [Indexed: 12/16/2022] Open
Abstract
Marine cyanobacteria are promising microbes to capture and convert atmospheric CO2 and light into biomass and valuable industrial bio-products. Yet, reports on metabolic characteristics of non-model cyanobacteria are scarce. In this report, we show that an Indian euryhaline Synechococcus sp. BDU 130192 has biomass accumulation comparable to a model marine cyanobacterium and contains approximately double the amount of total carbohydrates, but significantly lower protein levels compared to Synechococcus sp. PCC 7002 cells. Based on its annotated chromosomal genome sequence, we present a genome scale metabolic model (GSMM) of this cyanobacterium, which we have named as iSyn706. The model includes 706 genes, 908 reactions, and 900 metabolites. The difference in the flux balance analysis (FBA) predicted flux distributions between Synechococcus sp. PCC 7002 and Synechococcus sp. BDU130192 strains mimicked the differences in their biomass compositions. Model-predicted oxygen evolution rate for Synechococcus sp. BDU130192 was found to be close to the experimentally-measured value. The model was analyzed to determine the potential of the strain for the production of various industrially-useful products without affecting growth significantly. This model will be helpful to researchers interested in understanding the metabolism as well as to design metabolic engineering strategies for the production of industrially-relevant compounds.
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Affiliation(s)
- Ahmad Ahmad
- DBT-ICGEB Center for Advanced Bioenergy Research, International Centre for Genetic Engineering and Biotechnology, New Delhi 110067, India;
- Department of Biotechnology, Noida International University, Noida, U.P. 203201, India
| | - Ruchi Pathania
- Systems Biology for Biofuels Group, International Centre for Genetic Engineering and Biotechnology, New Delhi 110067, India;
| | - Shireesh Srivastava
- DBT-ICGEB Center for Advanced Bioenergy Research, International Centre for Genetic Engineering and Biotechnology, New Delhi 110067, India;
- Systems Biology for Biofuels Group, International Centre for Genetic Engineering and Biotechnology, New Delhi 110067, India;
- Correspondence: ; Tel.: +91-11-26741361 (ext. 450)
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23
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Metabolic Efficiency of Sugar Co-Metabolism and Phenol Degradation in Alicyclobacillus acidocaldarius for Improved Lignocellulose Processing. Processes (Basel) 2020. [DOI: 10.3390/pr8050502] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
Abstract
Substrate availability plays a key role in dictating metabolic strategies. Most microorganisms consume carbon/energy sources in a sequential, preferential order. The presented study investigates metabolic strategies of Alicyclobacillus acidocaldarius, a thermoacidophilic bacterium that has been shown to co-utilize glucose and xylose, as well as degrade phenolic compounds. An existing metabolic model was expanded to include phenol degradation and was analyzed with both metabolic pathway and constraint-based analysis methods. Elementary flux mode analysis was used in conjunction with resource allocation theory to investigate ecologically optimal metabolic pathways for different carbon substrate combinations. Additionally, a dynamic version of flux balance analysis was used to generate time-resolved simulations of growth on phenol and xylose. Results showed that availability of xylose along with glucose did not predict enhanced growth efficiency beyond that of glucose alone, but did predict some differences in pathway utilization and byproduct profiles. In contrast, addition of phenol as a co-substrate with xylose predicted lower growth efficiency. Dynamic simulations predicted co-consumption of xylose and phenol in a similar pattern as previously reported experiments. Altogether, this work serves as a case study for combining both elementary flux mode and flux balance analyses to probe unique metabolic features, and also demonstrates the versatility of A. acidocaldarius for lignocellulosic biomass processing applications.
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24
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Lin DS, Lee CH, Yang YT. Wireless bioreactor for anaerobic cultivation of bacteria. Biotechnol Prog 2020; 36:e3009. [PMID: 32329232 DOI: 10.1002/btpr.3009] [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: 10/23/2019] [Revised: 03/24/2020] [Accepted: 04/21/2020] [Indexed: 11/07/2022]
Abstract
Anaerobic cultivation methods of bacteria are indispensable in microbiology. One methodology is to cultivate the microbes in anaerobic enclosure with oxygen-adosrbing chemicals. Here, we report an electronic extension of such strategy for facultative anaerobic bacteria. The technique is based a bioreactor with entire operation including turbidity measurement, fluidic mixing, and gas delivery in an anaerobic enclosure. Wireless data transmission is employed and the anaerobic condition is achieved with gas pack. Although the technique is not meant to completely replace the anaerobic chamber for strict anaerobic bacteria, it provides a convenient way to bypass the cumbersome operation in anaerobic chamber for facultative anaerobic bacteria. Such a cultivation strategy is demonstrated with Escherichia coli with different carbon sources and hydrogen as energy source.
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Affiliation(s)
- Ding-Shun Lin
- Department of Electrical Engineering, National Tsing Hua University, Hsinchu, Taiwan
| | - Chih-Hsien Lee
- Department of Electrical Engineering, National Tsing Hua University, Hsinchu, Taiwan
| | - Ya-Tang Yang
- Department of Electrical Engineering, National Tsing Hua University, Hsinchu, Taiwan
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25
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Hatzenpichler R, Krukenberg V, Spietz RL, Jay ZJ. Next-generation physiology approaches to study microbiome function at single cell level. Nat Rev Microbiol 2020; 18:241-256. [PMID: 32055027 DOI: 10.1038/s41579-020-0323-1] [Citation(s) in RCA: 151] [Impact Index Per Article: 37.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/06/2020] [Indexed: 12/14/2022]
Abstract
The function of cells in their native habitat often cannot be reliably predicted from genomic data or from physiology studies of isolates. Traditional experimental approaches to study the function of taxonomically and metabolically diverse microbiomes are limited by their destructive nature, low spatial resolution or low throughput. Recently developed technologies can offer new insights into cellular function in natural and human-made systems and how microorganisms interact with and shape the environments that they inhabit. In this Review, we provide an overview of these next-generation physiology approaches and discuss how the non-destructive analysis of cellular phenotypes, in combination with the separation of the target cells for downstream analyses, provide powerful new, complementary ways to study microbiome function. We anticipate that the widespread application of next-generation physiology approaches will transform the field of microbial ecology and dramatically improve our understanding of how microorganisms function in their native environment.
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Affiliation(s)
- Roland Hatzenpichler
- Department of Chemistry and Biochemistry, Center for Biofilm Engineering, and Thermal Biology Institute, Montana State University, Bozeman, MT, USA.
| | - Viola Krukenberg
- Department of Chemistry and Biochemistry, Center for Biofilm Engineering, and Thermal Biology Institute, Montana State University, Bozeman, MT, USA
| | - Rachel L Spietz
- Department of Chemistry and Biochemistry, Center for Biofilm Engineering, and Thermal Biology Institute, Montana State University, Bozeman, MT, USA
| | - Zackary J Jay
- Department of Chemistry and Biochemistry, Center for Biofilm Engineering, and Thermal Biology Institute, Montana State University, Bozeman, MT, USA
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Park H, McGill SL, Arnold AD, Carlson RP. Pseudomonad reverse carbon catabolite repression, interspecies metabolite exchange, and consortial division of labor. Cell Mol Life Sci 2020; 77:395-413. [PMID: 31768608 PMCID: PMC7015805 DOI: 10.1007/s00018-019-03377-x] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2019] [Revised: 11/04/2019] [Accepted: 11/12/2019] [Indexed: 10/25/2022]
Abstract
Microorganisms acquire energy and nutrients from dynamic environments, where substrates vary in both type and abundance. The regulatory system responsible for prioritizing preferred substrates is known as carbon catabolite repression (CCR). Two broad classes of CCR have been documented in the literature. The best described CCR strategy, referred to here as classic CCR (cCCR), has been experimentally and theoretically studied using model organisms such as Escherichia coli. cCCR phenotypes are often used to generalize universal strategies for fitness, sometimes incorrectly. For instance, extremely competitive microorganisms, such as Pseudomonads, which arguably have broader global distributions than E. coli, have achieved their success using metabolic strategies that are nearly opposite of cCCR. These organisms utilize a CCR strategy termed 'reverse CCR' (rCCR), because the order of preferred substrates is nearly reverse that of cCCR. rCCR phenotypes prefer organic acids over glucose, may or may not select preferred substrates to optimize growth rates, and do not allocate intracellular resources in a manner that produces an overflow metabolism. cCCR and rCCR have traditionally been interpreted from the perspective of monocultures, even though most microorganisms live in consortia. Here, we review the basic tenets of the two CCR strategies and consider these phenotypes from the perspective of resource acquisition in consortia, a scenario that surely influenced the evolution of cCCR and rCCR. For instance, cCCR and rCCR metabolism are near mirror images of each other; when considered from a consortium basis, the complementary properties of the two strategies can mitigate direct competition for energy and nutrients and instead establish cooperative division of labor.
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Affiliation(s)
- Heejoon Park
- Department of Chemical and Biological Engineering, Montana State University, Bozeman, USA
- Center for Biofilm Engineering, Montana State University, Bozeman, USA
| | - S Lee McGill
- Department of Microbiology and Immunology, Montana State University, Bozeman, USA
- Center for Biofilm Engineering, Montana State University, Bozeman, USA
| | - Adrienne D Arnold
- Department of Microbiology and Immunology, Montana State University, Bozeman, USA
- Center for Biofilm Engineering, Montana State University, Bozeman, USA
| | - Ross P Carlson
- Department of Chemical and Biological Engineering, Montana State University, Bozeman, USA.
- Department of Microbiology and Immunology, Montana State University, Bozeman, USA.
- Center for Biofilm Engineering, Montana State University, Bozeman, USA.
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27
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Relevance and Regulation of Cell Density. Trends Cell Biol 2020; 30:213-225. [PMID: 31980346 DOI: 10.1016/j.tcb.2019.12.006] [Citation(s) in RCA: 55] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2019] [Revised: 12/17/2019] [Accepted: 12/17/2019] [Indexed: 01/05/2023]
Abstract
Cell density shows very little variation within a given cell type. For example, in humans variability in cell density among cells of a given cell type is 100 times smaller than variation in cell mass. This tight control indicates that maintenance of a cell type-specific cell density is important for cell function. Indeed, pathological conditions such as cellular senescence are accompanied by changes in cell density. Despite the apparent importance of cell-type-specific density, we know little about how cell density affects cell function, how it is controlled, and how it sometimes changes as part of a developmental process or in response to changes in the environment. The recent development of new technologies to accurately measure the cell density of single cells in suspension and in tissues is likely to provide answers to these important questions.
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Széliová D, Schoeny H, Knez Š, Troyer C, Coman C, Rampler E, Koellensperger G, Ahrends R, Hann S, Borth N, Zanghellini J, Ruckerbauer DE. Robust Analytical Methods for the Accurate Quantification of the Total Biomass Composition of Mammalian Cells. Methods Mol Biol 2020; 2088:119-160. [PMID: 31893373 DOI: 10.1007/978-1-0716-0159-4_7] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Biomass composition is an important input for genome-scale metabolic models and has a big impact on their predictive capabilities. However, researchers often rely on generic data for biomass composition, e.g. collected from similar organisms. This leads to inaccurate predictions, because biomass composition varies between different cell lines, conditions, and growth phases. In this chapter we present protocols for the determination of the biomass composition of Chinese Hamster Ovary (CHO) cells. These methods can easily be adapted to other types of mammalian cells. The protocols include the quantification of cell dry mass and of the main biomass components, namely protein, lipid, DNA, RNA, and carbohydrates. Cell dry mass is determined gravimetrically by weighing a defined number of cells. Amino acid composition and protein content are measured by gas chromatography mass spectrometry. Lipids are quantified by shotgun mass spectrometry, which provides quantities for the different lipid classes and also the distribution of fatty acids. RNA is purified and then quantified spectrophotometrically. The methods for DNA and carbohydrates are simple fluorometric and colorimetric assays adapted to a 96-well plate format. To ensure quantitative results, internal standards or spike-in controls are used in all methods, e.g. to account for possible matrix effects or loss of material. Finally, the last section provides a guide on how to convert the measured data into biomass equations, which can then be integrated into a metabolic model.
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Affiliation(s)
- Diana Széliová
- Austrian Centre of Industrial Biotechnology, Vienna, Austria
- University of Natural Resources and Life Sciences, Vienna, Austria
| | | | - Špela Knez
- University of Ljubljana, Ljubljana, Slovenia
| | - Christina Troyer
- University of Natural Resources and Life Sciences, Vienna, Austria
| | - Cristina Coman
- Leibniz Institut für Analytische Wissenschaften - e.V., Dortmund, Germany
| | | | | | - Robert Ahrends
- Leibniz Institut für Analytische Wissenschaften - e.V., Dortmund, Germany
| | - Stephen Hann
- Austrian Centre of Industrial Biotechnology, Vienna, Austria
- University of Natural Resources and Life Sciences, Vienna, Austria
| | - Nicole Borth
- Austrian Centre of Industrial Biotechnology, Vienna, Austria
- University of Natural Resources and Life Sciences, Vienna, Austria
| | - Jürgen Zanghellini
- University of Natural Resources and Life Sciences, Vienna, Austria
- Austrian Biotech University of Applied Sciences, Tulln, Austria
- Austrian Centre of Industrial Biotechnology, Vienna, Austria
| | - David E Ruckerbauer
- Austrian Centre of Industrial Biotechnology, Vienna, Austria.
- University of Natural Resources and Life Sciences, Vienna, Austria.
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BOFdat: Generating biomass objective functions for genome-scale metabolic models from experimental data. PLoS Comput Biol 2019; 15:e1006971. [PMID: 31009451 PMCID: PMC6497307 DOI: 10.1371/journal.pcbi.1006971] [Citation(s) in RCA: 51] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2018] [Revised: 05/02/2019] [Accepted: 03/21/2019] [Indexed: 12/12/2022] Open
Abstract
Genome-scale metabolic models (GEMs) are mathematically structured knowledge bases of metabolism that provide phenotypic predictions from genomic information. GEM-guided predictions of growth phenotypes rely on the accurate definition of a biomass objective function (BOF) that is designed to include key cellular biomass components such as the major macromolecules (DNA, RNA, proteins), lipids, coenzymes, inorganic ions and species-specific components. Despite its importance, no standardized computational platform is currently available to generate species-specific biomass objective functions in a data-driven, unbiased fashion. To fill this gap in the metabolic modeling software ecosystem, we implemented BOFdat, a Python package for the definition of a Biomass Objective Function from experimental data. BOFdat has a modular implementation that divides the BOF definition process into three independent modules defined here as steps: 1) the coefficients for major macromolecules are calculated, 2) coenzymes and inorganic ions are identified and their stoichiometric coefficients estimated, 3) the remaining species-specific metabolic biomass precursors are algorithmically extracted in an unbiased way from experimental data. We used BOFdat to reconstruct the BOF of the Escherichia coli model iML1515, a gold standard in the field. The BOF generated by BOFdat resulted in the most concordant biomass composition, growth rate, and gene essentiality prediction accuracy when compared to other methods. Installation instructions for BOFdat are available in the documentation and the source code is available on GitHub (https://github.com/jclachance/BOFdat).
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Special Issue: Methods in Computational Biology. Processes (Basel) 2019. [DOI: 10.3390/pr7040205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Biological systems are multiscale with respect to time and space, exist at the interface of biological and physical constraints, and their interactions with the environment are often nonlinear [...]
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Starke R, Jehmlich N, Alfaro T, Dohnalkova A, Capek P, Bell SL, Hofmockel KS. Incomplete cell disruption of resistant microbes. Sci Rep 2019; 9:5618. [PMID: 30948770 PMCID: PMC6449382 DOI: 10.1038/s41598-019-42188-9] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2019] [Accepted: 03/22/2019] [Indexed: 11/08/2022] Open
Abstract
Biomolecules for OMIC analysis of microbial communities are commonly extracted by bead-beating or ultra-sonication, but both showed varying yields. In addition to that, different disruption pressures are necessary to lyse bacteria and fungi. However, the disruption efficiency and yields comparing bead-beating and ultra-sonication of different biological material have not yet been demonstrated. Here, we show that ultra-sonication in a bath transfers three times more energy than bead-beating over 10 min. TEM imaging revealed intact gram-positive bacterial and fungal cells whereas the gram-negative bacterial cells were destroyed beyond recognition after 10 min of ultra-sonication. DNA extraction using 10 min of bead-beating revealed higher yields for fungi but the extraction efficiency was at least three-fold lower considering its larger genome. By our critical viewpoint, we encourage the review of the commonly used extraction techniques as we provide evidence for a potential underrepresentation of resistant microbes, particularly fungi, in ecological studies.
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Affiliation(s)
- Robert Starke
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington, USA.
- Laboratory of Environmental Microbiology, Institute of Microbiology of the CAS, Praha, Czech Republic.
| | - Nico Jehmlich
- Helmholtz-Center for Environmental Research, UFZ, Leipzig, Germany
| | - Trinidad Alfaro
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington, USA
| | - Alice Dohnalkova
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington, USA
| | - Petr Capek
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington, USA
| | - Sheryl L Bell
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington, USA
| | - Kirsten S Hofmockel
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington, USA
- Department of Ecology, Evolution and Organismal Biology, Iowa State University, Iowa, USA
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