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Liu Y, LaBonte S, Brake C, LaFayette C, Rosebrock AP, Caudy AA, Straight PD. MOB rules: Antibiotic Exposure Reprograms Metabolism to Mobilize Bacillus subtilis in Competitive Interactions. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.20.585991. [PMID: 38562742 PMCID: PMC10983992 DOI: 10.1101/2024.03.20.585991] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Antibiotics have dose-dependent effects on exposed bacteria. The medicinal use of antibiotics relies on their growth-inhibitory activities at sufficient concentrations. At subinhibitory concentrations, exposure effects vary widely among different antibiotics and bacteria. Bacillus subtilis responds to bacteriostatic translation inhibitors by mobilizing a population of cells (MOB-Mobilized Bacillus) to spread across a surface. How B. subtilis regulates the antibiotic-induced mobilization is not known. In this study, we used chloramphenicol to identify regulatory functions that B. subtilis requires to coordinate cell mobilization following subinhibitory exposure. We measured changes in gene expression and metabolism and mapped the results to a network of regulatory proteins that direct the mobile response. Our data reveal that several transcriptional regulators coordinately control the reprogramming of metabolism to support mobilization. The network regulates changes in glycolysis, nucleotide metabolism, and amino acid metabolism that are signature features of the mobilized population. Among the hundreds of genes with changing expression, we identified two, pdhA and pucA, where the magnitudes of their changes in expression, and in the abundance of associated metabolites, reveal hallmark metabolic features of the mobilized population. Using reporters of pdhA and pucA expression, we visualized the separation of major branches of metabolism in different regions of the mobilized population. Our results reveal a regulated response to chloramphenicol exposure that enables a population of bacteria in different metabolic states to mount a coordinated mobile response.
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Affiliation(s)
- Yongjin Liu
- Biochemistry and Biophysics Department, Texas A&M University, AgriLife Research, College Station, Texas, USA
| | - Sandra LaBonte
- Biochemistry and Biophysics Department, Texas A&M University, AgriLife Research, College Station, Texas, USA
- Interdisciplinary Program in Genetics and Genomics,Texas A&M University, College Station, Texas, USA
| | - Courtney Brake
- Department of Visualization, Institute for Applied Creativity, Texas A&M University, College Station, Texas, USA
| | - Carol LaFayette
- Department of Visualization, Institute for Applied Creativity, Texas A&M University, College Station, Texas, USA
| | | | - Amy A. Caudy
- Maple Flavored Solutions, LLC, Indianapolis, Indiana, USA
| | - Paul D. Straight
- Biochemistry and Biophysics Department, Texas A&M University, AgriLife Research, College Station, Texas, USA
- Interdisciplinary Program in Genetics and Genomics,Texas A&M University, College Station, Texas, USA
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2
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Jiang S, Luo Z, Wu J, Yu K, Zhao S, Cai Z, Yu W, Wang H, Cheng L, Liang Z, Gao H, Monti M, Schindler D, Huang L, Zeng C, Zhang W, Zhou C, Tang Y, Li T, Ma Y, Cai Y, Boeke JD, Zhao Q, Dai J. Building a eukaryotic chromosome arm by de novo design and synthesis. Nat Commun 2023; 14:7886. [PMID: 38036514 PMCID: PMC10689750 DOI: 10.1038/s41467-023-43531-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Accepted: 11/13/2023] [Indexed: 12/02/2023] Open
Abstract
The genome of an organism is inherited from its ancestor and continues to evolve over time, however, the extent to which the current version could be altered remains unknown. To probe the genome plasticity of Saccharomyces cerevisiae, here we replace the native left arm of chromosome XII (chrXIIL) with a linear artificial chromosome harboring small sets of reconstructed genes. We find that as few as 12 genes are sufficient for cell viability, whereas 25 genes are required to recover the partial fitness defects observed in the 12-gene strain. Next, we demonstrate that these genes can be reconstructed individually using synthetic regulatory sequences and recoded open-reading frames with a "one-amino-acid-one-codon" strategy to remain functional. Finally, a synthetic neochromsome with the reconstructed genes is assembled which could substitute chrXIIL for viability. Together, our work not only highlights the high plasticity of yeast genome, but also illustrates the possibility of making functional eukaryotic chromosomes from entirely artificial sequences.
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Grants
- National Natural Science Foundation of China (31725002), Shenzhen Science and Technology Program (KQTD20180413181837372), Guangdong Provincial Key Laboratory of Synthetic Genomics (2019B030301006),Bureau of International Cooperation,Chinese Academy of Sciences (172644KYSB20180022) and Shenzhen Outstanding Talents Training Fund.
- National Key Research and Development Program of China (2018YFA0900100),National Natural Science Foundation of China (31800069),Guangdong Basic and Applied Basic Research Foundation (2023A1515030285)
- National Key Research and Development Program of China (2018YFA0900100), National Natural Science Foundation of China (31800082 and 32122050),Guangdong Natural Science Funds for Distinguished Young Scholar (2021B1515020060)
- UK Biotechnology and Biological Sciences Research Council (BBSRC) grants BB/M005690/1, BB/P02114X/1 and BB/W014483/1, and a Volkswagen Foundation “Life? Initiative” Grant (Ref. 94 771)
- US NSF grants MCB-1026068, MCB-1443299, MCB-1616111 and MCB-1921641
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Affiliation(s)
- Shuangying Jiang
- CAS Key Laboratory of Quantitative Engineering Biology, Guangdong Provincial Key Laboratory of Synthetic Genomics and Shenzhen Key Laboratory of Synthetic Genomics, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Zhouqing Luo
- CAS Key Laboratory of Quantitative Engineering Biology, Guangdong Provincial Key Laboratory of Synthetic Genomics and Shenzhen Key Laboratory of Synthetic Genomics, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- State Key Laboratory of Cellular Stress Biology, Innovation Center for Cell Signaling Network, School of Life Sciences, Faculty of Medicine and Life Sciences, Xiamen University, Xiamen, Fujian, 361102, China
| | - Jie Wu
- CAS Key Laboratory of Quantitative Engineering Biology, Guangdong Provincial Key Laboratory of Synthetic Genomics and Shenzhen Key Laboratory of Synthetic Genomics, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Kang Yu
- CAS Key Laboratory of Quantitative Engineering Biology, Guangdong Provincial Key Laboratory of Synthetic Genomics and Shenzhen Key Laboratory of Synthetic Genomics, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Shijun Zhao
- CAS Key Laboratory of Quantitative Engineering Biology, Guangdong Provincial Key Laboratory of Synthetic Genomics and Shenzhen Key Laboratory of Synthetic Genomics, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Zelin Cai
- CAS Key Laboratory of Quantitative Engineering Biology, Guangdong Provincial Key Laboratory of Synthetic Genomics and Shenzhen Key Laboratory of Synthetic Genomics, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Wenfei Yu
- CAS Key Laboratory of Quantitative Engineering Biology, Guangdong Provincial Key Laboratory of Synthetic Genomics and Shenzhen Key Laboratory of Synthetic Genomics, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Hui Wang
- State Key Laboratory of Cellular Stress Biology, Innovation Center for Cell Signaling Network, School of Life Sciences, Faculty of Medicine and Life Sciences, Xiamen University, Xiamen, Fujian, 361102, China
| | - Li Cheng
- CAS Key Laboratory of Quantitative Engineering Biology, Guangdong Provincial Key Laboratory of Synthetic Genomics and Shenzhen Key Laboratory of Synthetic Genomics, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Zhenzhen Liang
- CAS Key Laboratory of Quantitative Engineering Biology, Guangdong Provincial Key Laboratory of Synthetic Genomics and Shenzhen Key Laboratory of Synthetic Genomics, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Hui Gao
- CAS Key Laboratory of Quantitative Engineering Biology, Guangdong Provincial Key Laboratory of Synthetic Genomics and Shenzhen Key Laboratory of Synthetic Genomics, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- Institute of Molecular Physiology, Shenzhen Bay Laboratory, Shenzhen, China
| | - Marco Monti
- Manchester Institute of Biotechnology, University of Manchester, Manchester, M1 7DN, UK
| | - Daniel Schindler
- Manchester Institute of Biotechnology, University of Manchester, Manchester, M1 7DN, UK
| | - Linsen Huang
- CAS Key Laboratory of Quantitative Engineering Biology, Guangdong Provincial Key Laboratory of Synthetic Genomics and Shenzhen Key Laboratory of Synthetic Genomics, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Cheng Zeng
- CAS Key Laboratory of Quantitative Engineering Biology, Guangdong Provincial Key Laboratory of Synthetic Genomics and Shenzhen Key Laboratory of Synthetic Genomics, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Weimin Zhang
- Institute for Systems Genetics and Department of Biochemistry and Molecular Pharmacology, NYU Langone Health, New York, NY, 10016, USA
| | - Chun Zhou
- CAS Key Laboratory of Quantitative Engineering Biology, Guangdong Provincial Key Laboratory of Synthetic Genomics and Shenzhen Key Laboratory of Synthetic Genomics, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Yuanwei Tang
- CAS Key Laboratory of Quantitative Engineering Biology, Guangdong Provincial Key Laboratory of Synthetic Genomics and Shenzhen Key Laboratory of Synthetic Genomics, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Tianyi Li
- CAS Key Laboratory of Quantitative Engineering Biology, Guangdong Provincial Key Laboratory of Synthetic Genomics and Shenzhen Key Laboratory of Synthetic Genomics, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Yingxin Ma
- CAS Key Laboratory of Quantitative Engineering Biology, Guangdong Provincial Key Laboratory of Synthetic Genomics and Shenzhen Key Laboratory of Synthetic Genomics, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Yizhi Cai
- CAS Key Laboratory of Quantitative Engineering Biology, Guangdong Provincial Key Laboratory of Synthetic Genomics and Shenzhen Key Laboratory of Synthetic Genomics, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- Manchester Institute of Biotechnology, University of Manchester, Manchester, M1 7DN, UK
| | - Jef D Boeke
- Institute for Systems Genetics and Department of Biochemistry and Molecular Pharmacology, NYU Langone Health, New York, NY, 10016, USA
- Department of Biomedical Engineering, NYU Tandon School of Engineering, Brooklyn, NY, 11201, USA
| | - Qiao Zhao
- CAS Key Laboratory of Quantitative Engineering Biology, Guangdong Provincial Key Laboratory of Synthetic Genomics and Shenzhen Key Laboratory of Synthetic Genomics, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
| | - Junbiao Dai
- CAS Key Laboratory of Quantitative Engineering Biology, Guangdong Provincial Key Laboratory of Synthetic Genomics and Shenzhen Key Laboratory of Synthetic Genomics, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
- University of Chinese Academy of Sciences, Beijing, China.
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Synthetic Biology, Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China.
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3
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Li K, Mocciaro G, Griffin JL, Zhang N. The Saccharomyces cerevisiae acetyltransferase Gcn5 exerts antagonistic pleiotropic effects on chronological ageing. Aging (Albany NY) 2023; 15:10915-10937. [PMID: 37874684 PMCID: PMC10637828 DOI: 10.18632/aging.205109] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Accepted: 09/18/2023] [Indexed: 10/26/2023]
Abstract
Compared to replicative lifespan, epigenetic regulation of chronological lifespan (CLS) is less well understood in yeast. Here, by screening all the viable mutants of histone acetyltransferase (HAT) and histone deacetylase (HDAC), we demonstrate that Gcn5, functioning in the HAT module of the SAGA/SLIK complex, exhibits an epistatic relationship with the HDAC Hda1 to control the expression of starvation-induced stress response and respiratory cell growth. Surprisingly, the gcn5Δ mutants lose their colony-forming potential early in the stationary phase but display a longer maximum CLS than their WT counterparts, suggesting the contradictory roles of Gcn5 in lifespan regulation. Integrative analyses of the transcriptome, metabolome and ChIP assays reveal that Gcn5 is necessary for the activation of two regulons upon glucose starvation: the Msn2/4-/Gis1-dependent stress response and the Cat8-/Adr1-mediated metabolic reprogramming, to enable pro-longevity characteristics, including redox homeostasis, stress resistance and maximal storage of carbohydrates. The activation of Cat8-/Adr1-dependent regulon also promotes the pyruvate dehydrogenase (PDH) bypass, leading to acetyl-CoA synthesis, global and targeted H3K9 acetylation. Global H3K9 acetylation levels mediated by Gcn5 and Hda1 during the transition into stationary phase are positively correlated with senescent cell populations accumulated in the aged cell cultures. These data suggest that Gcn5 lies in the centre of a feed-forward loop between histone acetylation and starvation-induced gene expression, enabling stress resistance and homeostasis but also promoting chronological ageing concomitantly.
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Affiliation(s)
- Kaiqiang Li
- Department of Biochemistry, University of Cambridge, Cambridge CB2 1GA, UK
| | - Gabriele Mocciaro
- Department of Biochemistry, University of Cambridge, Cambridge CB2 1GA, UK
| | - Jules L. Griffin
- Department of Biochemistry, University of Cambridge, Cambridge CB2 1GA, UK
- The Rowett Institute, University of Aberdeen, Foresterhill Campus, Aberdeen AB25 2ZD, UK
| | - Nianshu Zhang
- Department of Biochemistry, University of Cambridge, Cambridge CB2 1GA, UK
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4
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Green R, Sonal, Wang L, Hart SFM, Lu W, Skelding D, Burton JC, Mi H, Capel A, Chen HA, Lin A, Subramaniam AR, Rabinowitz JD, Shou W. Metabolic excretion associated with nutrient-growth dysregulation promotes the rapid evolution of an overt metabolic defect. PLoS Biol 2020; 18:e3000757. [PMID: 32833957 PMCID: PMC7470746 DOI: 10.1371/journal.pbio.3000757] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2019] [Revised: 09/03/2020] [Accepted: 07/20/2020] [Indexed: 01/19/2023] Open
Abstract
In eukaryotes, conserved mechanisms ensure that cell growth is coordinated with nutrient availability. Overactive growth during nutrient limitation ("nutrient-growth dysregulation") can lead to rapid cell death. Here, we demonstrate that cells can adapt to nutrient-growth dysregulation by evolving major metabolic defects. Specifically, when yeast lysine-auxotrophic mutant lys- encountered lysine limitation, an evolutionarily novel stress, cells suffered nutrient-growth dysregulation. A subpopulation repeatedly evolved to lose the ability to synthesize organosulfurs (lys-orgS-). Organosulfurs, mainly reduced glutathione (GSH) and GSH conjugates, were released by lys- cells during lysine limitation when growth was dysregulated, but not during glucose limitation when growth was regulated. Limiting organosulfurs conferred a frequency-dependent fitness advantage to lys-orgS- by eliciting a proper slow growth program, including autophagy. Thus, nutrient-growth dysregulation is associated with rapid organosulfur release, which enables the selection of organosulfur auxotrophy to better tune cell growth to the metabolic environment. We speculate that evolutionarily novel stresses can trigger atypical release of certain metabolites, setting the stage for the evolution of new ecological interactions.
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Affiliation(s)
- Robin Green
- Division of Basic Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
| | - Sonal
- Division of Basic Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
| | - Lin Wang
- Department of Chemistry and Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, United States of America
| | - Samuel F. M. Hart
- Division of Basic Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
| | - Wenyun Lu
- Department of Chemistry and Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, United States of America
| | - David Skelding
- Division of Basic Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
| | - Justin C. Burton
- Division of Basic Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
| | - Hanbing Mi
- Division of Basic Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
| | - Aric Capel
- Division of Basic Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
| | - Hung Alex Chen
- Division of Basic Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
| | - Aaron Lin
- Division of Basic Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
| | - Arvind R. Subramaniam
- Division of Basic Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
| | - Joshua D. Rabinowitz
- Department of Chemistry and Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, United States of America
| | - Wenying Shou
- Division of Basic Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
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5
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Gmelch L, Wirtz D, Witting M, Weber N, Striegel L, Schmitt-Kopplin P, Rychlik M. Comprehensive Vitamer Profiling of Folate Mono- and Polyglutamates in Baker's Yeast ( Saccharomyces cerevisiae) as a Function of Different Sample Preparation Procedures. Metabolites 2020; 10:E301. [PMID: 32717862 PMCID: PMC7464241 DOI: 10.3390/metabo10080301] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2020] [Revised: 07/13/2020] [Accepted: 07/16/2020] [Indexed: 11/22/2022] Open
Abstract
Folates are a group of B9 vitamins playing an important role in many metabolic processes such as methylation reactions, nucleotide synthesis or oxidation and reduction processes. However, humans are not able to synthesize folates de novo and thus rely on external sources thereof. Baker's yeast (Saccharomyces cerevisiae) has been shown to produce high amounts of this vitamin but extensive identification of its folate metabolism is still lacking. Therefore, we optimized and compared different sample preparation and purification procedures applying solid phase extraction (SPE). Strong anion exchange (SAX), C18 and hydrophilic-lipophilic-balanced (HLB) materials were tested for their applicability in future metabolomics studies. SAX turned out to be the preferred material for the quantitative purification of folates. Qualification of several folate vitamers was achieved by ultra-high pressure liquid chromatography quadrupole time of flight mass spectrometry (UHPLC-Q-ToF-MS) measurements and quantification was performed by liquid chromatography tandem mass spectrometry (LC-MS/MS) applying stable isotope dilution assays (SIDAs). The oxidation product s-pyrazino-triazine (MeFox) was included into the SIDA method for total folate determination and validation. Applying the best protocol (SAX) in regard to folate recovery, we analyzed 32 different vitamers in different polyglutamate states up to nonaglutamates, of which we could further identify 26 vitamers based on tandem-MS (MS2) spectra. Total folate quantification revealed differences in formyl folate contents depending on the cartridge chemistry used for purification. These are supposedly a result of interconversion reactions occurring during sample preparation due to variation in pH adjustments for the different purification protocols. The occurrence of interconversion and oxidation reactions should be taken into consideration in sample preparation procedures for metabolomics analyses with a focus on folates.
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Affiliation(s)
- Lena Gmelch
- Chair of Analytical Food Chemistry, Technical University of Munich, 85354 Freising-Weihenstephan, Germany; (L.G.); (D.W.); (M.W.); (N.W.); (L.S.)
| | - Daniela Wirtz
- Chair of Analytical Food Chemistry, Technical University of Munich, 85354 Freising-Weihenstephan, Germany; (L.G.); (D.W.); (M.W.); (N.W.); (L.S.)
| | - Michael Witting
- Chair of Analytical Food Chemistry, Technical University of Munich, 85354 Freising-Weihenstephan, Germany; (L.G.); (D.W.); (M.W.); (N.W.); (L.S.)
- Research Unit BioGeoChemistry, Helmholtz Zentrum Munich, 85764 Neuherberg, Germany
| | - Nadine Weber
- Chair of Analytical Food Chemistry, Technical University of Munich, 85354 Freising-Weihenstephan, Germany; (L.G.); (D.W.); (M.W.); (N.W.); (L.S.)
| | - Lisa Striegel
- Chair of Analytical Food Chemistry, Technical University of Munich, 85354 Freising-Weihenstephan, Germany; (L.G.); (D.W.); (M.W.); (N.W.); (L.S.)
| | - Philippe Schmitt-Kopplin
- Chair of Analytical Food Chemistry, Technical University of Munich, 85354 Freising-Weihenstephan, Germany; (L.G.); (D.W.); (M.W.); (N.W.); (L.S.)
- Research Unit BioGeoChemistry, Helmholtz Zentrum Munich, 85764 Neuherberg, Germany
| | - Michael Rychlik
- Chair of Analytical Food Chemistry, Technical University of Munich, 85354 Freising-Weihenstephan, Germany; (L.G.); (D.W.); (M.W.); (N.W.); (L.S.)
- Research Unit BioGeoChemistry, Helmholtz Zentrum Munich, 85764 Neuherberg, Germany
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6
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Gulli J, Cook E, Kroll E, Rosebrock A, Caudy A, Rosenzweig F. Diverse conditions support near-zero growth in yeast: Implications for the study of cell lifespan. MICROBIAL CELL 2019; 6:397-413. [PMID: 31528631 PMCID: PMC6717879 DOI: 10.15698/mic2019.09.690] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Baker's yeast has a finite lifespan and ages in two ways: a mother cell can only divide so many times (its replicative lifespan), and a non-dividing cell can only live so long (its chronological lifespan). Wild and laboratory yeast strains exhibit natural variation for each type of lifespan, and the genetic basis for this variation has been generalized to other eukaryotes, including metazoans. To date, yeast chronological lifespan has chiefly been studied in relation to the rate and mode of functional decline among non-dividing cells in nutrient-depleted batch culture. However, this culture method does not accurately capture two major classes of long-lived metazoan cells: cells that are terminally differentiated and metabolically active for periods that approximate animal lifespan (e.g. cardiac myocytes), and cells that are pluripotent and metabolically quiescent (e.g. stem cells). Here, we consider alternative ways of cultivating Saccharomyces cerevisiae so that these different metabolic states can be explored in non-dividing cells: (i) yeast cultured as giant colonies on semi-solid agar, (ii) yeast cultured in retentostats and provided sufficient nutrients to meet minimal energy requirements, and (iii) yeast encapsulated in a semisolid matrix and fed ad libitum in bioreactors. We review the physiology of yeast cultured under each of these conditions, and explore their potential to provide unique insights into determinants of chronological lifespan in the cells of higher eukaryotes.
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Affiliation(s)
- Jordan Gulli
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA 30332
| | - Emily Cook
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA 30332
| | - Eugene Kroll
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA 30332
| | - Adam Rosebrock
- Donnelly Centre for Cellular and Biological Research and Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.,Present address: Stony Brook School of Medicine, Stony Brook University, Stony Brook, NY 11794
| | - Amy Caudy
- Donnelly Centre for Cellular and Biological Research and Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
| | - Frank Rosenzweig
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA 30332
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7
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Caudy AA, Hanchard JA, Hsieh A, Shaan S, Rosebrock AP. Functional genetic discovery of enzymes using full-scan mass spectrometry metabolomics. Biochem Cell Biol 2019; 97:73-84. [DOI: 10.1139/bcb-2018-0058] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
Our understanding of metabolic networks is incomplete, and new enzymatic activities await discovery in well-studied organisms. Mass spectrometric measurement of cellular metabolites reveals compounds inside cells that are unexplained by current maps of metabolic reactions, and existing computational models are unable to account for all activities observed within cells. Additional large-scale genetic and biochemical approaches are required to elucidate metabolic gene function. We have used full-scan mass spectrometry metabolomics of polar small molecules to examine deletion mutants of candidate enzymes in the model yeast Saccharomyces cerevisiae. We report the identification of 25 genes whose deletion results in focal metabolic changes consistent with loss of enzymatic activity and describe the informatic approaches used to enrich for candidate enzymes from uncharacterized open reading frames. Triumphs and pitfalls of metabolic phenotyping screens are discussed, including estimates of the frequency of uncharacterized eukaryotic genes that affect metabolism and key issues to consider when searching for new enzymatic functions in other organisms.
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Affiliation(s)
- Amy A. Caudy
- Donnelly Centre for Cellular and Biomolecular Research and Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 3E1, Canada
- Donnelly Centre for Cellular and Biomolecular Research and Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 3E1, Canada
| | - Julia A. Hanchard
- Donnelly Centre for Cellular and Biomolecular Research and Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 3E1, Canada
- Donnelly Centre for Cellular and Biomolecular Research and Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 3E1, Canada
| | - Alan Hsieh
- Donnelly Centre for Cellular and Biomolecular Research and Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 3E1, Canada
- Donnelly Centre for Cellular and Biomolecular Research and Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 3E1, Canada
| | - Saravannan Shaan
- Donnelly Centre for Cellular and Biomolecular Research and Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 3E1, Canada
- Donnelly Centre for Cellular and Biomolecular Research and Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 3E1, Canada
| | - Adam P. Rosebrock
- Donnelly Centre for Cellular and Biomolecular Research and Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 3E1, Canada
- Donnelly Centre for Cellular and Biomolecular Research and Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 3E1, Canada
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8
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Abstract
Budding yeast has from the beginning been a major eukaryotic model for the study of metabolic network structure and function. This is attributable to both its genetic and biochemical capacities and its role as a workhorse in food production and biotechnology. New inventions in analytical technologies allow accurate, simultaneous detection and quantification of metabolites, and a series of recent findings have placed the metabolic network at center stage in the physiology of the cell. For example, metabolism might have facilitated the origin of life, and in modern organisms it not only provides nutrients to the cell but also serves as a buffer to changes in the cellular environment, a regulator of cellular processes, and a requirement for cell growth. These findings have triggered a rapid and massive renaissance in this important field. Here, we provide an introduction to analysis of metabolomics in yeast.
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Affiliation(s)
- Amy A Caudy
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S3E1, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S3E1, Canada
| | - Michael Mülleder
- Department of Biochemistry and Cambridge Systems Biology Centre, University of Cambridge, Cambridge CB2 1GA, United Kingdom
| | - Markus Ralser
- Department of Biochemistry and Cambridge Systems Biology Centre, University of Cambridge, Cambridge CB2 1GA, United Kingdom
- The Francis Crick Institute, Mill Hill Laboratory, London NW7 1AA, United Kingdom
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