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Wang J, Shi L, Zhang X, Hu R, Yue Z, Zou H, Peng Q, Jiang Y, Wang Z. Metabolomics and proteomics insights into subacute ruminal acidosis etiology and inhibition of proliferation of yak rumen epithelial cells in vitro. BMC Genomics 2024; 25:394. [PMID: 38649832 PMCID: PMC11036571 DOI: 10.1186/s12864-024-10242-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2023] [Accepted: 03/19/2024] [Indexed: 04/25/2024] Open
Abstract
BACKGROUND Untargeted metabolomics and proteomics were employed to investigate the intracellular response of yak rumen epithelial cells (YRECs) to conditions mimicking subacute rumen acidosis (SARA) etiology, including exposure to short-chain fatty acids (SCFA), low pH5.5 (Acid), and lipopolysaccharide (LPS) exposure for 24 h. RESULTS These treatments significantly altered the cellular morphology of YRECs. Metabolomic analysis identified significant perturbations with SCFA, Acid and LPS treatment affecting 259, 245 and 196 metabolites (VIP > 1, P < 0.05, and fold change (FC) ≥ 1.5 or FC ≤ 0.667). Proteomic analysis revealed that treatment with SCFA, Acid, and LPS resulted in differential expression of 1251, 1396, and 242 proteins, respectively (FC ≥ 1.2 or ≤ 0.83, P < 0.05, FDR < 1%). Treatment with SCFA induced elevated levels of metabolites involved in purine metabolism, glutathione metabolism, and arginine biosynthesis, and dysregulated proteins associated with actin cytoskeleton organization and ribosome pathways. Furthermore, SCFA reduced the number, morphology, and functionality of mitochondria, leading to oxidative damage and inhibition of cell survival. Gene expression analysis revealed a decrease the genes expression of the cytoskeleton and cell cycle, while the genes expression associated with inflammation and autophagy increased (P < 0.05). Acid exposure altered metabolites related to purine metabolism, and affected proteins associated with complement and coagulation cascades and RNA degradation. Acid also leads to mitochondrial dysfunction, alterations in mitochondrial integrity, and reduced ATP generation. It also causes actin filaments to change from filamentous to punctate, affecting cellular cytoskeletal function, and increases inflammation-related molecules, indicating the promotion of inflammatory responses and cellular damage (P < 0.05). LPS treatment induced differential expression of proteins involved in the TNF signaling pathway and cytokine-cytokine receptor interaction, accompanied by alterations in metabolites associated with arachidonic acid metabolism and MAPK signaling (P < 0.05). The inflammatory response and activation of signaling pathways induced by LPS treatment were also confirmed through protein interaction network analysis. The integrated analysis reveals co-enrichment of proteins and metabolites in cellular signaling and metabolic pathways. CONCLUSIONS In summary, this study contributes to a comprehensive understanding of the detrimental effects of SARA-associated factors on YRECs, elucidating their molecular mechanisms and providing potential therapeutic targets for mitigating SARA.
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Affiliation(s)
- JunMei Wang
- Key Laboratory of Low Carbon Culture and Safety Production in Cattle in Sichuan, Animal Nutrition Institute, Sichuan Agricultural University, Chengdu, 611130, China
| | - Liyuan Shi
- Key Laboratory of Low Carbon Culture and Safety Production in Cattle in Sichuan, Animal Nutrition Institute, Sichuan Agricultural University, Chengdu, 611130, China
| | - Xiaohong Zhang
- Key Laboratory of Low Carbon Culture and Safety Production in Cattle in Sichuan, Animal Nutrition Institute, Sichuan Agricultural University, Chengdu, 611130, China
| | - Rui Hu
- Key Laboratory of Low Carbon Culture and Safety Production in Cattle in Sichuan, Animal Nutrition Institute, Sichuan Agricultural University, Chengdu, 611130, China
| | - Ziqi Yue
- Key Laboratory of Low Carbon Culture and Safety Production in Cattle in Sichuan, Animal Nutrition Institute, Sichuan Agricultural University, Chengdu, 611130, China
| | - Huawei Zou
- Key Laboratory of Low Carbon Culture and Safety Production in Cattle in Sichuan, Animal Nutrition Institute, Sichuan Agricultural University, Chengdu, 611130, China
| | - Quanhui Peng
- Key Laboratory of Low Carbon Culture and Safety Production in Cattle in Sichuan, Animal Nutrition Institute, Sichuan Agricultural University, Chengdu, 611130, China
| | - Yahui Jiang
- Key Laboratory of Low Carbon Culture and Safety Production in Cattle in Sichuan, Animal Nutrition Institute, Sichuan Agricultural University, Chengdu, 611130, China
| | - Zhisheng Wang
- Key Laboratory of Low Carbon Culture and Safety Production in Cattle in Sichuan, Animal Nutrition Institute, Sichuan Agricultural University, Chengdu, 611130, China.
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Weiszmann J, Walther D, Clauw P, Back G, Gunis J, Reichardt I, Koemeda S, Jez J, Nordborg M, Schwarzerova J, Pierides I, Nägele T, Weckwerth W. Metabolome plasticity in 241 Arabidopsis thaliana accessions reveals evolutionary cold adaptation processes. PLANT PHYSIOLOGY 2023; 193:980-1000. [PMID: 37220420 PMCID: PMC10517190 DOI: 10.1093/plphys/kiad298] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 04/28/2023] [Accepted: 05/03/2023] [Indexed: 05/25/2023]
Abstract
Acclimation and adaptation of metabolism to a changing environment are key processes for plant survival and reproductive success. In the present study, 241 natural accessions of Arabidopsis (Arabidopsis thaliana) were grown under two different temperature regimes, 16 °C and 6 °C, and growth parameters were recorded, together with metabolite profiles, to investigate the natural genome × environment effects on metabolome variation. The plasticity of metabolism, which was captured by metabolic distance measures, varied considerably between accessions. Both relative growth rates and metabolic distances were predictable by the underlying natural genetic variation of accessions. Applying machine learning methods, climatic variables of the original growth habitats were tested for their predictive power of natural metabolic variation among accessions. We found specifically habitat temperature during the first quarter of the year to be the best predictor of the plasticity of primary metabolism, indicating habitat temperature as the causal driver of evolutionary cold adaptation processes. Analyses of epigenome- and genome-wide associations revealed accession-specific differential DNA-methylation levels as potentially linked to the metabolome and identified FUMARASE2 as strongly associated with cold adaptation in Arabidopsis accessions. These findings were supported by calculations of the biochemical Jacobian matrix based on variance and covariance of metabolomics data, which revealed that growth under low temperatures most substantially affects the accession-specific plasticity of fumarate and sugar metabolism. Our findings indicate that the plasticity of metabolic regulation is predictable from the genome and epigenome and driven evolutionarily by Arabidopsis growth habitats.
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Affiliation(s)
- Jakob Weiszmann
- Molecular Systems Biology (MOSYS), Department of Functional and Evolutionary Ecology, Faculty of Life Sciences, University of Vienna, 1030 Vienna, Austria
- Vienna Metabolomics Center (VIME), University of Vienna, 1030 Vienna, Austria
| | - Dirk Walther
- Bioinformatics, Max-Planck-Institute of Molecular Plant Physiology, 14476 Potsdam, Germany
| | - Pieter Clauw
- Austrian Academy of Sciences, Gregor Mendel Institute (GMI), 1030 Vienna, Austria
| | - Georg Back
- Bioinformatics, Max-Planck-Institute of Molecular Plant Physiology, 14476 Potsdam, Germany
| | - Joanna Gunis
- Austrian Academy of Sciences, Gregor Mendel Institute (GMI), 1030 Vienna, Austria
| | - Ilka Reichardt
- Genome Engineering Facility, Max Planck Institute of Molecular Cell Biology and Genetics, 01307 Dresden, Germany
| | - Stefanie Koemeda
- Plant Sciences Facility, Vienna BioCenter Core Facilities GmbH (VBCF), 1030 Vienna, Austria
| | - Jakub Jez
- Plant Sciences Facility, Vienna BioCenter Core Facilities GmbH (VBCF), 1030 Vienna, Austria
| | - Magnus Nordborg
- Austrian Academy of Sciences, Gregor Mendel Institute (GMI), 1030 Vienna, Austria
| | - Jana Schwarzerova
- Molecular Systems Biology (MOSYS), Department of Functional and Evolutionary Ecology, Faculty of Life Sciences, University of Vienna, 1030 Vienna, Austria
- Brno University of Technology, Faculty of Electrical Engineering and Communication, Department of Biomedical Engineering, Technická 12, 616 00 Brno, Czech Republic
| | - Iro Pierides
- Molecular Systems Biology (MOSYS), Department of Functional and Evolutionary Ecology, Faculty of Life Sciences, University of Vienna, 1030 Vienna, Austria
| | - Thomas Nägele
- LMU Munich, Faculty of Biology, Plant Evolutionary Cell Biology, 82152 Planegg, Germany
| | - Wolfram Weckwerth
- Molecular Systems Biology (MOSYS), Department of Functional and Evolutionary Ecology, Faculty of Life Sciences, University of Vienna, 1030 Vienna, Austria
- Vienna Metabolomics Center (VIME), University of Vienna, 1030 Vienna, Austria
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Mo J, Ma Z, Yan S, Cheung NK, Yang F, Yao X, Guo J. Metabolomic profiles in a green alga (Raphidocelis subcapitata) following erythromycin treatment: ABC transporters and energy metabolism. J Environ Sci (China) 2023; 124:591-601. [PMID: 36182165 DOI: 10.1016/j.jes.2021.12.001] [Citation(s) in RCA: 21] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Revised: 12/01/2021] [Accepted: 12/01/2021] [Indexed: 06/16/2023]
Abstract
A recent study showed that erythromycin (ERY) exposure caused hormesis in a model alga (Raphidocelis subcapitata) where the growth was promoted at an environmentally realistic concentration (4 µg/L) but inhibited at two higher concentrations (80 and 120 µg/L), associated with opposite actions of certain signaling pathways (e.g., xenobiotic metabolism, DNA replication). However, these transcriptional alterations remain to be investigated and verified at the metabolomic level. This study uncovered metabolomic profiles and detailed toxic mechanisms of ERY in R. subcapitata using untargeted metabolomics. The metabolomic analysis showed that metabolomic pathways including ABC transporters, fatty acid biosynthesis and purine metabolism were associated with growth promotion in algae treated with 4 µg/L ERY. An overcompensation was possibly activated by the low level of ERY in algae where more resources were reallocated to efficiently restore the temporary impairments, ultimately leading to the outperformance of growth. By contrast, algal growth inhibition in the 80 and 120 µg/L ERY treatments was likely attributed to the dysfunction of metabolomic pathways related to ABC transporters, energy metabolism and metabolism of nucleosides. Apart from binding of ERY to the 50S subunit of ribosomes to inhibit protein translation as in bacteria, the data presented here indicate that inhibition of protein translation and growth performance of algae by ERY may also result from the suppression of amino acid biosynthesis and aminoacyl-tRNA biosynthesis. This study provides novel insights into the dose-dependent toxicity of ERY on R. subcapitata.
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Affiliation(s)
- Jiezhang Mo
- Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity, College of Urban and Environmental Sciences, Northwest University, Xi'an 710127, China; State Key Laboratory of Marine Pollution and Department of Chemistry, City University of Hong Kong, Kowloon, Hong Kong, China
| | - Zhihua Ma
- Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity, College of Urban and Environmental Sciences, Northwest University, Xi'an 710127, China
| | - Shiwei Yan
- Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity, College of Urban and Environmental Sciences, Northwest University, Xi'an 710127, China
| | - Napo Km Cheung
- State Key Laboratory of Marine Pollution and Department of Chemistry, City University of Hong Kong, Kowloon, Hong Kong, China
| | - Fangshe Yang
- Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity, College of Urban and Environmental Sciences, Northwest University, Xi'an 710127, China
| | - Xiunan Yao
- Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity, College of Urban and Environmental Sciences, Northwest University, Xi'an 710127, China
| | - Jiahua Guo
- Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity, College of Urban and Environmental Sciences, Northwest University, Xi'an 710127, China.
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Jiang RW, Jaroch K, Pawliszyn J. Solid-phase microextraction of endogenous metabolites from intact tissue validated using a Biocrates standard reference method kit. J Pharm Anal 2023; 13:55-62. [PMID: 36816540 PMCID: PMC9937786 DOI: 10.1016/j.jpha.2022.09.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Revised: 09/22/2022] [Accepted: 09/22/2022] [Indexed: 11/06/2022] Open
Abstract
Improved analytical methods for the metabolomic profiling of tissue samples are constantly needed. Currently, conventional sample preparation methods often involve tissue biopsy and/or homogenization, which disrupts the endogenous metabolome. In this study, solid-phase microextraction (SPME) fibers were used to monitor changes in endogenous compounds in homogenized and intact ovine lung tissue. Following SPME, a Biocrates AbsoluteIDQ assay was applied to make a downstream targeted metabolomics analysis and confirm the advantages of in vivo SPME metabolomics. The AbsoluteIDQ kit enabled the targeted analysis of over 100 metabolites via solid-liquid extraction and SPME. Statistical analysis revealed significant differences between conventional liquid extractions from homogenized tissue and SPME results for both homogenized and intact tissue samples. In addition, principal component analysis revealed separated clustering among all the three sample groups, indicating changes in the metabolome due to tissue homogenization and the chosen sample preparation method. Furthermore, clear differences in free metabolites were observed when extractions were performed on the intact and homogenized tissue using identical SPME procedures. Specifically, a direct comparison showed that 47 statistically distinct metabolites were detected between the homogenized and intact lung tissue samples (P < 0.05) using mixed-mode SPME fibers. These changes were probably due to the disruptive homogenization of the tissue. This study's findings highlight both the importance of sample preparation in tissue-based metabolomics studies and SPME's unique ability to perform minimally invasive extractions without tissue biopsy or homogenization while providing broad metabolite coverage.
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Affiliation(s)
- Runshan Will Jiang
- Department of Chemistry, University of Waterloo, Waterloo, N2L 3G1, Canada
| | - Karol Jaroch
- Department of Chemistry, University of Waterloo, Waterloo, N2L 3G1, Canada,Department of Pharmacodynamics and Molecular Pharmacology, Faculty of Pharmacy, Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Toruń, Bydgoszcz, 85-089, Poland
| | - Janusz Pawliszyn
- Department of Chemistry, University of Waterloo, Waterloo, N2L 3G1, Canada,Corresponding author.
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Krantz M, Zimmer D, Adler SO, Kitashova A, Klipp E, Mühlhaus T, Nägele T. Data Management and Modeling in Plant Biology. FRONTIERS IN PLANT SCIENCE 2021; 12:717958. [PMID: 34539712 PMCID: PMC8446634 DOI: 10.3389/fpls.2021.717958] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Accepted: 07/29/2021] [Indexed: 05/25/2023]
Abstract
The study of plant-environment interactions is a multidisciplinary research field. With the emergence of quantitative large-scale and high-throughput techniques, amount and dimensionality of experimental data have strongly increased. Appropriate strategies for data storage, management, and evaluation are needed to make efficient use of experimental findings. Computational approaches of data mining are essential for deriving statistical trends and signatures contained in data matrices. Although, current biology is challenged by high data dimensionality in general, this is particularly true for plant biology. Plants as sessile organisms have to cope with environmental fluctuations. This typically results in strong dynamics of metabolite and protein concentrations which are often challenging to quantify. Summarizing experimental output results in complex data arrays, which need computational statistics and numerical methods for building quantitative models. Experimental findings need to be combined by computational models to gain a mechanistic understanding of plant metabolism. For this, bioinformatics and mathematics need to be combined with experimental setups in physiology, biochemistry, and molecular biology. This review presents and discusses concepts at the interface of experiment and computation, which are likely to shape current and future plant biology. Finally, this interface is discussed with regard to its capabilities and limitations to develop a quantitative model of plant-environment interactions.
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Affiliation(s)
- Maria Krantz
- Theoretical Biophysics, Institute of Biology, Humboldt-Universität zu Berlin, Berlin, Germany
| | - David Zimmer
- Computational Systems Biology, Technische Universität Kaiserslautern, Kaiserslautern, Germany
| | - Stephan O. Adler
- Theoretical Biophysics, Institute of Biology, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Anastasia Kitashova
- Plant Evolutionary Cell Biology, Faculty of Biology, Ludwig-Maximilians-Universität München, Planegg-Martinsried, Germany
| | - Edda Klipp
- Theoretical Biophysics, Institute of Biology, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Timo Mühlhaus
- Computational Systems Biology, Technische Universität Kaiserslautern, Kaiserslautern, Germany
| | - Thomas Nägele
- Plant Evolutionary Cell Biology, Faculty of Biology, Ludwig-Maximilians-Universität München, Planegg-Martinsried, Germany
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Use of metabolomics to identify strategies to improve and prolong ex vivo lung perfusion for lung transplants. J Heart Lung Transplant 2021; 40:525-535. [PMID: 33849769 DOI: 10.1016/j.healun.2021.02.002] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Revised: 02/03/2021] [Accepted: 02/09/2021] [Indexed: 11/21/2022] Open
Abstract
BACKGROUND Normothermic ex vivo lung perfusion (EVLP) allows for functional assessment of donor lungs; thus has increased the use of marginal lungs for transplantation. To extend EVLP for advanced organ reconditioning and regenerative interventions, cellular metabolic changes need to be understood. We sought to comprehensively characterize the dynamic metabolic changes of the lungs during EVLP, and to identify strategies to improve EVLP. METHODS Human donor lungs (n = 50) were assessed under a 4-hour Toronto EVLP protocol. EVLP perfusate was sampled at first (EVLP-1h) and fourth hour (EVLP-4h) of perfusion and were submitted for mass spectrometry-based untargeted metabolic profiling. Differentially expressed metabolites between the 2 timepoints were identified and analyzed from the samples of lungs transplanted post-EVLP (n = 42) to determine the underlying molecular mechanisms. RESULTS Of the total 312 detected metabolites, 84 were up-regulated and 103 were down-regulated at EVLP-4h relative to 1h (FDR adjusted p < .05, fold change ≥ |1.1|). At EVLP-4h, markedly decreased energy substrates were observed, accompanied by the increase in fatty acid β-oxidation. Concurrently, accumulation of amino acids and nucleic acids was evident, indicative of increased protein and nucleotide catabolism. The uniform decrease in free lysophospholipids and polyunsaturated fatty acids at EVLP-4h suggests cell membrane remodeling. CONCLUSIONS Untargeted metabolomics revealed signs of energy substrate consumption and metabolic by-product accumulation under current EVLP protocols. Strategies to supplement nutrients and to maintain homeostasis will be vital in improving the current clinical practice and prolonging organ perfusion for therapeutic application to further enhance donor lung utilization.
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Aggarwal S, Acharjee A, Mukherjee A, Baker MS, Srivastava S. Role of Multiomics Data to Understand Host-Pathogen Interactions in COVID-19 Pathogenesis. J Proteome Res 2021; 20:1107-1132. [PMID: 33426872 PMCID: PMC7805606 DOI: 10.1021/acs.jproteome.0c00771] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Indexed: 12/15/2022]
Abstract
Human infectious diseases are contributed equally by the host immune system's efficiency and any pathogens' infectivity. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the coronavirus strain causing the respiratory pandemic coronavirus disease 2019 (COVID-19). To understand the pathobiology of SARS-CoV-2, one needs to unravel the intricacies of host immune response to the virus, the viral pathogen's mode of transmission, and alterations in specific biological pathways in the host allowing viral survival. This review critically analyzes recent research using high-throughput "omics" technologies (including proteomics and metabolomics) on various biospecimens that allow an increased understanding of the pathobiology of SARS-CoV-2 in humans. The altered biomolecule profile facilitates an understanding of altered biological pathways. Further, we have performed a meta-analysis of significantly altered biomolecular profiles in COVID-19 patients using bioinformatics tools. Our analysis deciphered alterations in the immune response, fatty acid, and amino acid metabolism and other pathways that cumulatively result in COVID-19 disease, including symptoms such as hyperglycemic and hypoxic sequelae.
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Affiliation(s)
- Shalini Aggarwal
- Department of Biosciences and
Bioengineering, Indian Institute of Technology
Bombay, Mumbai 400076,
India
| | - Arup Acharjee
- Department of Biosciences and
Bioengineering, Indian Institute of Technology
Bombay, Mumbai 400076,
India
| | - Amrita Mukherjee
- Department of Biosciences and
Bioengineering, Indian Institute of Technology
Bombay, Mumbai 400076,
India
| | - Mark S. Baker
- Department of Biomedical Science,
Faculty of Medicine, Health and Human Sciences, Macquarie
University, Sydney 2109,
Australia
| | - Sanjeeva Srivastava
- Department of Biosciences and
Bioengineering, Indian Institute of Technology
Bombay, Mumbai 400076,
India
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Shi D, Xia X, Cui A, Xiong Z, Yan Y, Luo J, Chen G, Zeng Y, Cai D, Hou L, McDermott J, Li Y, Zhang H, Han JDJ. The precursor of PI(3,4,5)P 3 alleviates aging by activating daf-18(Pten) and independent of daf-16. Nat Commun 2020; 11:4496. [PMID: 32901024 PMCID: PMC7479145 DOI: 10.1038/s41467-020-18280-4] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2019] [Accepted: 08/04/2020] [Indexed: 01/31/2023] Open
Abstract
Aging is characterized by the loss of homeostasis and the general decline of physiological functions, accompanied by various degenerative diseases and increased rates of mortality. Aging targeting small molecule screens have been performed many times, however, few have focused on endogenous metabolic intermediates-metabolites. Here, using C. elegans lifespan assays, we conducted a worm metabolite screen and identified an eukaryotes conserved metabolite, myo-inositol (MI), to extend lifespan, increase mobility and reduce fat content. Genetic analysis of enzymes in MI metabolic pathway suggest that MI alleviates aging through its derivative PI(4,5)P2. MI and PI(4,5)P2 are precursors of PI(3,4,5)P3, which is negatively related to longevity. The longevity effect of MI is dependent on the tumor suppressor gene, daf-18 (homologous to mouse Pten), independent of its classical pathway downstream genes, akt or daf-16. Furthermore, we found MI effects on aging and lifespan act through mitophagy regulator PTEN induced kinase-1 (pink-1) and mitophagy. MI's anti-aging effect is also conserved in mouse, indicating a conserved mechanism in mammals.
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Affiliation(s)
- Dawei Shi
- Key Laboratory of Computational Biology, Chinese Academy of Sciences-Max Planck Partner Institute for Computational Biology (PICB), Shanghai Institute of Nutrition and Health (SINH), Chinese Academy of Sciences Center for Excellence in Molecular Cell Science, Collaborative Innovation Center for Genetics and Developmental Biology, Shanghai Institutes for Biological Sciences (SIBS), Chinese Academy of Sciences (CAS), Shanghai, 200031, P.R. China
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Quantitative Biology (CQB), Peking University, Beijing, 100871, P.R. China
- University of Chinese Academy of Sciences, Beijing, 100049, P.R. China
| | - Xian Xia
- Key Laboratory of Computational Biology, Chinese Academy of Sciences-Max Planck Partner Institute for Computational Biology (PICB), Shanghai Institute of Nutrition and Health (SINH), Chinese Academy of Sciences Center for Excellence in Molecular Cell Science, Collaborative Innovation Center for Genetics and Developmental Biology, Shanghai Institutes for Biological Sciences (SIBS), Chinese Academy of Sciences (CAS), Shanghai, 200031, P.R. China
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Quantitative Biology (CQB), Peking University, Beijing, 100871, P.R. China
- University of Chinese Academy of Sciences, Beijing, 100049, P.R. China
| | - Aoyuan Cui
- University of Chinese Academy of Sciences, Beijing, 100049, P.R. China
- CAS Key Laboratory of Nutrition, Metabolism and Food Safety, SINH, SIBS, CAS, Shanghai, 200031, P.R. China
| | - Zhongxiang Xiong
- Key Laboratory of Computational Biology, Chinese Academy of Sciences-Max Planck Partner Institute for Computational Biology (PICB), Shanghai Institute of Nutrition and Health (SINH), Chinese Academy of Sciences Center for Excellence in Molecular Cell Science, Collaborative Innovation Center for Genetics and Developmental Biology, Shanghai Institutes for Biological Sciences (SIBS), Chinese Academy of Sciences (CAS), Shanghai, 200031, P.R. China
- School of Life Science and Technology, ShanghaiTech University, Shanghai, 201210, P.R. China
| | - Yizhen Yan
- Key Laboratory of Computational Biology, Chinese Academy of Sciences-Max Planck Partner Institute for Computational Biology (PICB), Shanghai Institute of Nutrition and Health (SINH), Chinese Academy of Sciences Center for Excellence in Molecular Cell Science, Collaborative Innovation Center for Genetics and Developmental Biology, Shanghai Institutes for Biological Sciences (SIBS), Chinese Academy of Sciences (CAS), Shanghai, 200031, P.R. China
- University of Chinese Academy of Sciences, Beijing, 100049, P.R. China
| | - Jing Luo
- Key Laboratory of Computational Biology, Chinese Academy of Sciences-Max Planck Partner Institute for Computational Biology (PICB), Shanghai Institute of Nutrition and Health (SINH), Chinese Academy of Sciences Center for Excellence in Molecular Cell Science, Collaborative Innovation Center for Genetics and Developmental Biology, Shanghai Institutes for Biological Sciences (SIBS), Chinese Academy of Sciences (CAS), Shanghai, 200031, P.R. China
- College of Biological Sciences and Technology, Beijing Forestry University, Beijing, P.R. China
| | - Guoyu Chen
- Key Laboratory of Computational Biology, Chinese Academy of Sciences-Max Planck Partner Institute for Computational Biology (PICB), Shanghai Institute of Nutrition and Health (SINH), Chinese Academy of Sciences Center for Excellence in Molecular Cell Science, Collaborative Innovation Center for Genetics and Developmental Biology, Shanghai Institutes for Biological Sciences (SIBS), Chinese Academy of Sciences (CAS), Shanghai, 200031, P.R. China
- University of Chinese Academy of Sciences, Beijing, 100049, P.R. China
| | - Yingying Zeng
- Key Laboratory of Computational Biology, Chinese Academy of Sciences-Max Planck Partner Institute for Computational Biology (PICB), Shanghai Institute of Nutrition and Health (SINH), Chinese Academy of Sciences Center for Excellence in Molecular Cell Science, Collaborative Innovation Center for Genetics and Developmental Biology, Shanghai Institutes for Biological Sciences (SIBS), Chinese Academy of Sciences (CAS), Shanghai, 200031, P.R. China
- School of Life Science and Technology, ShanghaiTech University, Shanghai, 201210, P.R. China
| | - Donghong Cai
- Key Laboratory of Computational Biology, Chinese Academy of Sciences-Max Planck Partner Institute for Computational Biology (PICB), Shanghai Institute of Nutrition and Health (SINH), Chinese Academy of Sciences Center for Excellence in Molecular Cell Science, Collaborative Innovation Center for Genetics and Developmental Biology, Shanghai Institutes for Biological Sciences (SIBS), Chinese Academy of Sciences (CAS), Shanghai, 200031, P.R. China
- University of Chinese Academy of Sciences, Beijing, 100049, P.R. China
| | - Lei Hou
- Key Laboratory of Computational Biology, Chinese Academy of Sciences-Max Planck Partner Institute for Computational Biology (PICB), Shanghai Institute of Nutrition and Health (SINH), Chinese Academy of Sciences Center for Excellence in Molecular Cell Science, Collaborative Innovation Center for Genetics and Developmental Biology, Shanghai Institutes for Biological Sciences (SIBS), Chinese Academy of Sciences (CAS), Shanghai, 200031, P.R. China
- University of Chinese Academy of Sciences, Beijing, 100049, P.R. China
| | - Joseph McDermott
- Key Laboratory of Computational Biology, Chinese Academy of Sciences-Max Planck Partner Institute for Computational Biology (PICB), Shanghai Institute of Nutrition and Health (SINH), Chinese Academy of Sciences Center for Excellence in Molecular Cell Science, Collaborative Innovation Center for Genetics and Developmental Biology, Shanghai Institutes for Biological Sciences (SIBS), Chinese Academy of Sciences (CAS), Shanghai, 200031, P.R. China
| | - Yu Li
- CAS Key Laboratory of Nutrition, Metabolism and Food Safety, SINH, SIBS, CAS, Shanghai, 200031, P.R. China
| | - Hong Zhang
- University of Chinese Academy of Sciences, Beijing, 100049, P.R. China
- National Laboratory of Biomacromolecules, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, CAS, 100101, Beijing, P.R. China
| | - Jing-Dong J Han
- Key Laboratory of Computational Biology, Chinese Academy of Sciences-Max Planck Partner Institute for Computational Biology (PICB), Shanghai Institute of Nutrition and Health (SINH), Chinese Academy of Sciences Center for Excellence in Molecular Cell Science, Collaborative Innovation Center for Genetics and Developmental Biology, Shanghai Institutes for Biological Sciences (SIBS), Chinese Academy of Sciences (CAS), Shanghai, 200031, P.R. China.
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Quantitative Biology (CQB), Peking University, Beijing, 100871, P.R. China.
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Benítez Del Castillo JM, Pinazo-Duran MD, Sanz-González SM, Muñoz-Hernández AM, Garcia-Medina JJ, Zanón-Moreno V. Tear 1H Nuclear Magnetic Resonance-Based Metabolomics Application to the Molecular Diagnosis of Aqueous Tear Deficiency and Meibomian Gland Dysfunction. Ophthalmic Res 2020; 64:297-309. [PMID: 32674101 DOI: 10.1159/000510211] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2019] [Accepted: 07/11/2020] [Indexed: 11/19/2022]
Abstract
PURPOSE Meibomian gland dysfunction (MGD) is a major cause of signs and symptoms related to dry eyes (DE) and eyelid inflammation. We investigated the composition of human tears by metabolomic approaches in patients with aqueous tear deficiency and MGD. METHODS Participants in this prospective, case-control pilot study were split into patients with aqueous tear deficiency and MGD (DE-MGD [n = 15]) and healthy controls (CG; n = 20). Personal interviews, ocular surface disease index (OSDI), and ophthalmic examinations were performed. Reflex tears collected by capillarity were processed to 1H nuclear magnetic resonance (NMR) spectroscopy and quantitative data analysis to identify molecules by spectra comparison to library entries of purified standards and/or unknown entities. Statistical analyses were made by the SPSS 22.0 program. RESULTS Chemometric analysis and 1H NMR spectra comparison revealed the presence of 60 metabolites in tears. Differentiating features were evident in the NMR spectra of the 2 clinical groups, characterized by significant upregulation of phenylalanine, glycerol, and isoleucine, and downregulation of glycoproteins, leucine, and -CH3 lipids, as compared to the CG. The 1H NMR metabolomic analyses of human tears confirmed the applicability of this platform with high predictive accuracy/reliability. CONCLUSIONS Our key distinctive findings support that DE-MGD induces tear metabolomics profile changes. Metabolites contributing to a higher separation from the CG can presumably be used, in the foreseeable future, as DE-MGD biomarkers for better managing the diagnosis and therapy of this disease.
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Affiliation(s)
- José Manuel Benítez Del Castillo
- Department of Ophthalmology, San Carlos Clinic Hospital, Madrid, Spain.,Spanish Net of Ophthalmic Pathology (OFTARED) of the Institute of Health Carlos III, Madrid, Spain
| | - Maria Dolores Pinazo-Duran
- Spanish Net of Ophthalmic Pathology (OFTARED) of the Institute of Health Carlos III, Madrid, Spain.,Ophthalmic Research Unit "Santiago Grisolía"/FISABIO, Valencia, Spain.,Cellular and Molecular Ophthalmo-Biology Group, Department of Surgery (Ophthalmology), Faculty of Medicine and Odontology, University of Valencia, Valencia, Spain
| | - Silvia M Sanz-González
- Spanish Net of Ophthalmic Pathology (OFTARED) of the Institute of Health Carlos III, Madrid, Spain.,Ophthalmic Research Unit "Santiago Grisolía"/FISABIO, Valencia, Spain.,Cellular and Molecular Ophthalmo-Biology Group, Department of Surgery (Ophthalmology), Faculty of Medicine and Odontology, University of Valencia, Valencia, Spain
| | - Ana M Muñoz-Hernández
- Department of Ophthalmology, San Carlos Clinic Hospital, Madrid, Spain.,Spanish Net of Ophthalmic Pathology (OFTARED) of the Institute of Health Carlos III, Madrid, Spain
| | - Jose J Garcia-Medina
- Spanish Net of Ophthalmic Pathology (OFTARED) of the Institute of Health Carlos III, Madrid, Spain.,Ophthalmic Research Unit "Santiago Grisolía"/FISABIO, Valencia, Spain.,Cellular and Molecular Ophthalmo-Biology Group, Department of Surgery (Ophthalmology), Faculty of Medicine and Odontology, University of Valencia, Valencia, Spain.,Department of Ophthalmology, University Hospital Morales Meseguer, Murcia, Spain
| | - Vicente Zanón-Moreno
- Spanish Net of Ophthalmic Pathology (OFTARED) of the Institute of Health Carlos III, Madrid, Spain, .,Ophthalmic Research Unit "Santiago Grisolía"/FISABIO, Valencia, Spain, .,Cellular and Molecular Ophthalmo-Biology Group, Department of Surgery (Ophthalmology), Faculty of Medicine and Odontology, University of Valencia, Valencia, Spain, .,International University of Valencia, Valencia, Spain,
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10
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Fürtauer L, Nägele T. Mathematical Modeling of Plant Metabolism in a Changing Temperature Regime. Methods Mol Biol 2020; 2156:277-287. [PMID: 32607988 DOI: 10.1007/978-1-0716-0660-5_19] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Changes in environmental temperature regimes significantly affect plant growth, development and reproduction. Within a multigenic process termed acclimation, many plant species of the temperate region are able to adjust their metabolism to low and high temperature. Temperature-induced metabolic reprogramming is a nonlinear process affecting numerous enzyme kinetic reactions and pathways. The analysis of metabolic reprogramming during temperature acclimation is essentially supported by mathematical modeling which enables the study of nonlinear enzyme kinetics in context of metabolic networks and pathway regulation. This chapter introduces mathematical modeling of plant metabolism during a dynamic environmental temperature regime. A focus is laid on kinetic modeling and thermodynamic constraints.
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Affiliation(s)
- Lisa Fürtauer
- Evolutionäre Zellbiologie der Pflanzen, Ludwig-Maximilians-Universität München, Planegg, Germany
| | - Thomas Nägele
- Evolutionäre Zellbiologie der Pflanzen, Ludwig-Maximilians-Universität München, Planegg, Germany.
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11
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Fürtauer L, Weiszmann J, Weckwerth W, Nägele T. Dynamics of Plant Metabolism during Cold Acclimation. Int J Mol Sci 2019; 20:E5411. [PMID: 31671650 PMCID: PMC6862541 DOI: 10.3390/ijms20215411] [Citation(s) in RCA: 71] [Impact Index Per Article: 14.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2019] [Revised: 10/28/2019] [Accepted: 10/29/2019] [Indexed: 12/26/2022] Open
Abstract
Plants have evolved strategies to tightly regulate metabolism during acclimation to a changing environment. Low temperature significantly constrains distribution, growth and yield of many temperate plant species. Exposing plants to low but non-freezing temperature induces a multigenic processes termed cold acclimation, which eventually results in an increased freezing tolerance. Cold acclimation comprises reprogramming of the transcriptome, proteome and metabolome and affects communication and signaling between subcellular organelles. Carbohydrates play a central role in this metabolic reprogramming. This review summarizes current knowledge about the role of carbohydrate metabolism in plant cold acclimation with a focus on subcellular metabolic reprogramming, its thermodynamic constraints under low temperature and mathematical modelling of metabolism.
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Affiliation(s)
- Lisa Fürtauer
- Plant Evolutionary Cell Biology, Department Biology I, Ludwig-Maximilians-Universität München, 82152 Planegg-Martinsried, Bavaria, Germany.
| | - Jakob Weiszmann
- Department of Ecogenomics and Systems Biology, University of Vienna, Vienna 1090, Austria.
- Vienna Metabolomics Center, University of Vienna, Vienna 1090, Austria.
| | - Wolfram Weckwerth
- Department of Ecogenomics and Systems Biology, University of Vienna, Vienna 1090, Austria.
- Vienna Metabolomics Center, University of Vienna, Vienna 1090, Austria.
| | - Thomas Nägele
- Plant Evolutionary Cell Biology, Department Biology I, Ludwig-Maximilians-Universität München, 82152 Planegg-Martinsried, Bavaria, Germany.
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12
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Tugizimana F, Mhlongo MI, Piater LA, Dubery IA. Metabolomics in Plant Priming Research: The Way Forward? Int J Mol Sci 2018; 19:ijms19061759. [PMID: 29899301 PMCID: PMC6032392 DOI: 10.3390/ijms19061759] [Citation(s) in RCA: 54] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2018] [Revised: 06/02/2018] [Accepted: 06/04/2018] [Indexed: 12/26/2022] Open
Abstract
A new era of plant biochemistry at the systems level is emerging, providing detailed descriptions of biochemical phenomena at the cellular and organismal level. This new era is marked by the advent of metabolomics—the qualitative and quantitative investigation of the entire metabolome (in a dynamic equilibrium) of a biological system. This field has developed as an indispensable methodological approach to study cellular biochemistry at a global level. For protection and survival in a constantly-changing environment, plants rely on a complex and multi-layered innate immune system. This involves surveillance of ‘self’ and ‘non-self,’ molecule-based systemic signalling and metabolic adaptations involving primary and secondary metabolites as well as epigenetic modulation mechanisms. Establishment of a pre-conditioned or primed state can sensitise or enhance aspects of innate immunity for faster and stronger responses. Comprehensive elucidation of the molecular and biochemical processes associated with the phenotypic defence state is vital for a better understanding of the molecular mechanisms that define the metabolism of plant–pathogen interactions. Such insights are essential for translational research and applications. Thus, this review highlights the prospects of metabolomics and addresses current challenges that hinder the realisation of the full potential of the field. Such limitations include partial coverage of the metabolome and maximising the value of metabolomics data (extraction of information and interpretation). Furthermore, the review points out key features that characterise both the plant innate immune system and enhancement of the latter, thus underlining insights from metabolomic studies in plant priming. Future perspectives in this inspiring area are included, with the aim of stimulating further studies leading to a better understanding of plant immunity at the metabolome level.
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Affiliation(s)
- Fidele Tugizimana
- Department of Biochemistry, Research Centre for Plant Metabolomics, University of Johannesburg, Auckland Park 2006, South Africa.
| | - Msizi I Mhlongo
- Department of Biochemistry, Research Centre for Plant Metabolomics, University of Johannesburg, Auckland Park 2006, South Africa.
| | - Lizelle A Piater
- Department of Biochemistry, Research Centre for Plant Metabolomics, University of Johannesburg, Auckland Park 2006, South Africa.
| | - Ian A Dubery
- Department of Biochemistry, Research Centre for Plant Metabolomics, University of Johannesburg, Auckland Park 2006, South Africa.
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13
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Nagler M, Nägele T, Gilli C, Fragner L, Korte A, Platzer A, Farlow A, Nordborg M, Weckwerth W. Eco-Metabolomics and Metabolic Modeling: Making the Leap From Model Systems in the Lab to Native Populations in the Field. FRONTIERS IN PLANT SCIENCE 2018; 9:1556. [PMID: 30459786 PMCID: PMC6232504 DOI: 10.3389/fpls.2018.01556] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/30/2018] [Accepted: 10/04/2018] [Indexed: 05/05/2023]
Abstract
Experimental high-throughput analysis of molecular networks is a central approach to characterize the adaptation of plant metabolism to the environment. However, recent studies have demonstrated that it is hardly possible to predict in situ metabolic phenotypes from experiments under controlled conditions, such as growth chambers or greenhouses. This is particularly due to the high molecular variance of in situ samples induced by environmental fluctuations. An approach of functional metabolome interpretation of field samples would be desirable in order to be able to identify and trace back the impact of environmental changes on plant metabolism. To test the applicability of metabolomics studies for a characterization of plant populations in the field, we have identified and analyzed in situ samples of nearby grown natural populations of Arabidopsis thaliana in Austria. A. thaliana is the primary molecular biological model system in plant biology with one of the best functionally annotated genomes representing a reference system for all other plant genome projects. The genomes of these novel natural populations were sequenced and phylogenetically compared to a comprehensive genome database of A. thaliana ecotypes. Experimental results on primary and secondary metabolite profiling and genotypic variation were functionally integrated by a data mining strategy, which combines statistical output of metabolomics data with genome-derived biochemical pathway reconstruction and metabolic modeling. Correlations of biochemical model predictions and population-specific genetic variation indicated varying strategies of metabolic regulation on a population level which enabled the direct comparison, differentiation, and prediction of metabolic adaptation of the same species to different habitats. These differences were most pronounced at organic and amino acid metabolism as well as at the interface of primary and secondary metabolism and allowed for the direct classification of population-specific metabolic phenotypes within geographically contiguous sampling sites.
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Affiliation(s)
- Matthias Nagler
- Department of Ecogenomics and Systems Biology, University of Vienna, Vienna, Austria
| | - Thomas Nägele
- Department of Ecogenomics and Systems Biology, University of Vienna, Vienna, Austria
- LMU Munich, Plant Evolutionary Cell Biology, Munich, Germany
| | - Christian Gilli
- Department of Ecogenomics and Systems Biology, University of Vienna, Vienna, Austria
| | - Lena Fragner
- Department of Ecogenomics and Systems Biology, University of Vienna, Vienna, Austria
- Vienna Metabolomics Center (VIME), University of Vienna, Vienna, Austria
| | - Arthur Korte
- Center for Computational and Theoretical Biology, University of Würzburg, Würzburg, Germany
| | - Alexander Platzer
- Gregor Mendel Institute of Molecular Plant Biology, Austrian Academy of Sciences, Vienna, Austria
| | - Ashley Farlow
- Gregor Mendel Institute of Molecular Plant Biology, Austrian Academy of Sciences, Vienna, Austria
| | - Magnus Nordborg
- Gregor Mendel Institute of Molecular Plant Biology, Austrian Academy of Sciences, Vienna, Austria
| | - Wolfram Weckwerth
- Department of Ecogenomics and Systems Biology, University of Vienna, Vienna, Austria
- Vienna Metabolomics Center (VIME), University of Vienna, Vienna, Austria
- *Correspondence: Wolfram Weckwerth,
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14
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Fürtauer L, Weiszmann J, Weckwerth W, Nägele T. Mathematical Modeling Approaches in Plant Metabolomics. Methods Mol Biol 2018; 1778:329-347. [PMID: 29761450 DOI: 10.1007/978-1-4939-7819-9_24] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
The experimental analysis of a plant metabolome typically results in a comprehensive and multidimensional data set. To interpret metabolomics data in the context of biochemical regulation and environmental fluctuation, various approaches of mathematical modeling have been developed and have proven useful. In this chapter, a general introduction to mathematical modeling is presented and discussed in context of plant metabolism. A particular focus is laid on the suitability of mathematical approaches to functionally integrate plant metabolomics data in a metabolic network and combine it with other biochemical or physiological parameters.
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Affiliation(s)
- Lisa Fürtauer
- Department of Ecogenomics and Systems Biology, Faculty of Life Sciences, University of Vienna, Vienna, Austria
| | - Jakob Weiszmann
- Department of Ecogenomics and Systems Biology, Faculty of Life Sciences, University of Vienna, Vienna, Austria
- Vienna Metabolomics Center, University of Vienna, Vienna, Austria
| | - Wolfram Weckwerth
- Department of Ecogenomics and Systems Biology, Faculty of Life Sciences, University of Vienna, Vienna, Austria
- Vienna Metabolomics Center, University of Vienna, Vienna, Austria
| | - Thomas Nägele
- Department of Ecogenomics and Systems Biology, Faculty of Life Sciences, University of Vienna, Vienna, Austria.
- Vienna Metabolomics Center, University of Vienna, Vienna, Austria.
- Department Biology I, Ludwig-Maximilians-Universität München, Planegg-Martinsried, Austria.
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15
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Quantitative phosphoproteomics reveals the role of the AMPK plant ortholog SnRK1 as a metabolic master regulator under energy deprivation. Sci Rep 2016; 6:31697. [PMID: 27545962 PMCID: PMC4992866 DOI: 10.1038/srep31697] [Citation(s) in RCA: 205] [Impact Index Per Article: 25.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2016] [Accepted: 07/25/2016] [Indexed: 01/11/2023] Open
Abstract
Since years, research on SnRK1, the major cellular energy sensor in plants, has tried to define its role in energy signalling. However, these attempts were notoriously hampered by the lethality of a complete knockout of SnRK1. Therefore, we generated an inducible amiRNA::SnRK1α2 in a snrk1α1 knock out background (snrk1α1/α2) to abolish SnRK1 activity to understand major systemic functions of SnRK1 signalling under energy deprivation triggered by extended night treatment. We analysed the in vivo phosphoproteome, proteome and metabolome and found that activation of SnRK1 is essential for repression of high energy demanding cell processes such as protein synthesis. The most abundant effect was the constitutively high phosphorylation of ribosomal protein S6 (RPS6) in the snrk1α1/α2 mutant. RPS6 is a major target of TOR signalling and its phosphorylation correlates with translation. Further evidence for an antagonistic SnRK1 and TOR crosstalk comparable to the animal system was demonstrated by the in vivo interaction of SnRK1α1 and RAPTOR1B in the cytosol and by phosphorylation of RAPTOR1B by SnRK1α1 in kinase assays. Moreover, changed levels of phosphorylation states of several chloroplastic proteins in the snrk1α1/α2 mutant indicated an unexpected link to regulation of photosynthesis, the main energy source in plants.
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16
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Wang L, Nägele T, Doerfler H, Fragner L, Chaturvedi P, Nukarinen E, Bellaire A, Huber W, Weiszmann J, Engelmeier D, Ramsak Z, Gruden K, Weckwerth W. System level analysis of cacao seed ripening reveals a sequential interplay of primary and secondary metabolism leading to polyphenol accumulation and preparation of stress resistance. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2016; 87:318-32. [PMID: 27136060 DOI: 10.1111/tpj.13201] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/20/2015] [Revised: 03/04/2016] [Accepted: 04/22/2016] [Indexed: 05/19/2023]
Abstract
Theobroma cacao and its popular product, chocolate, are attracting attention due to potential health benefits including antioxidative effects by polyphenols, anti-depressant effects by high serotonin levels, inhibition of platelet aggregation and prevention of obesity-dependent insulin resistance. The development of cacao seeds during fruit ripening is the most crucial process for the accumulation of these compounds. In this study, we analyzed the primary and the secondary metabolome as well as the proteome during Theobroma cacao cv. Forastero seed development by applying an integrative extraction protocol. The combination of multivariate statistics and mathematical modelling revealed a complex consecutive coordination of primary and secondary metabolism and corresponding pathways. Tricarboxylic acid (TCA) cycle and aromatic amino acid metabolism dominated during the early developmental stages (stages 1 and 2; cell division and expansion phase). This was accompanied with a significant shift of proteins from phenylpropanoid metabolism to flavonoid biosynthesis. At stage 3 (reserve accumulation phase), metabolism of sucrose switched from hydrolysis into raffinose synthesis. Lipids as well as proteins involved in lipid metabolism increased whereas amino acids and N-phenylpropenoyl amino acids decreased. Purine alkaloids, polyphenols, and raffinose as well as proteins involved in abiotic and biotic stress accumulated at stage 4 (maturation phase) endowing cacao seeds the characteristic astringent taste and resistance to stress. In summary, metabolic key points of cacao seed development comprise the sequential coordination of primary metabolites, phenylpropanoid, N-phenylpropenoyl amino acid, serotonin, lipid and polyphenol metabolism thereby covering the major compound classes involved in cacao aroma and health benefits.
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Affiliation(s)
- Lei Wang
- Department of Ecogenomics and Systems Biology, University of Vienna, Althanstrasse 14, 1090, Vienna, Austria
| | - Thomas Nägele
- Department of Ecogenomics and Systems Biology, University of Vienna, Althanstrasse 14, 1090, Vienna, Austria
- Vienna Metabolomics Center (VIME); University of Vienna, Althanstrasse 14, 1090, Vienna, Austria
| | - Hannes Doerfler
- Department of Ecogenomics and Systems Biology, University of Vienna, Althanstrasse 14, 1090, Vienna, Austria
| | - Lena Fragner
- Department of Ecogenomics and Systems Biology, University of Vienna, Althanstrasse 14, 1090, Vienna, Austria
| | - Palak Chaturvedi
- Department of Ecogenomics and Systems Biology, University of Vienna, Althanstrasse 14, 1090, Vienna, Austria
| | - Ella Nukarinen
- Department of Ecogenomics and Systems Biology, University of Vienna, Althanstrasse 14, 1090, Vienna, Austria
| | - Anke Bellaire
- Department of Ecogenomics and Systems Biology, University of Vienna, Althanstrasse 14, 1090, Vienna, Austria
- Department of Botany and Biodiversity Research, University of Vienna, Rennweg 14, 1030, Vienna, Austria
| | - Werner Huber
- Department of Botany and Biodiversity Research, University of Vienna, Rennweg 14, 1030, Vienna, Austria
| | - Jakob Weiszmann
- Department of Ecogenomics and Systems Biology, University of Vienna, Althanstrasse 14, 1090, Vienna, Austria
| | - Doris Engelmeier
- Department of Ecogenomics and Systems Biology, University of Vienna, Althanstrasse 14, 1090, Vienna, Austria
| | - Ziva Ramsak
- Department of Systems Biology and Biotechnology, National Institute of Biology, Vecna pot 111, 1000, Ljubljana, Slovenia
| | - Kristina Gruden
- Department of Systems Biology and Biotechnology, National Institute of Biology, Vecna pot 111, 1000, Ljubljana, Slovenia
| | - Wolfram Weckwerth
- Department of Ecogenomics and Systems Biology, University of Vienna, Althanstrasse 14, 1090, Vienna, Austria.
- Vienna Metabolomics Center (VIME); University of Vienna, Althanstrasse 14, 1090, Vienna, Austria.
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17
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Nägele T, Fürtauer L, Nagler M, Weiszmann J, Weckwerth W. A Strategy for Functional Interpretation of Metabolomic Time Series Data in Context of Metabolic Network Information. Front Mol Biosci 2016; 3:6. [PMID: 27014700 PMCID: PMC4779852 DOI: 10.3389/fmolb.2016.00006] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2015] [Accepted: 02/19/2016] [Indexed: 12/01/2022] Open
Abstract
The functional connection of experimental metabolic time series data with biochemical network information is an important, yet complex, issue in systems biology. Frequently, experimental analysis of diurnal, circadian, or developmental dynamics of metabolism results in a comprehensive and multidimensional data matrix comprising information about metabolite concentrations, protein levels, and/or enzyme activities. While, irrespective of the type of organism, the experimental high-throughput analysis of the transcriptome, proteome, and metabolome has become a common part of many systems biological studies, functional data integration in a biochemical and physiological context is still challenging. Here, an approach is presented which addresses the functional connection of experimental time series data with biochemical network information which can be inferred, for example, from a metabolic network reconstruction. Based on a time-continuous and variance-weighted regression analysis of experimental data, metabolic functions, i.e., first-order derivatives of metabolite concentrations, were related to time-dependent changes in other biochemically relevant metabolic functions, i.e., second-order derivatives of metabolite concentrations. This finally revealed time points of perturbed dependencies in metabolic functions indicating a modified biochemical interaction. The approach was validated using previously published experimental data on a diurnal time course of metabolite levels, enzyme activities, and metabolic flux simulations. To support and ease the presented approach of functional time series analysis, a graphical user interface including a test data set and a manual is provided which can be run within the numerical software environment Matlab®.
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Affiliation(s)
- Thomas Nägele
- Department of Ecogenomics and Systems Biology, University of ViennaVienna, Austria; Vienna Metabolomics Center, University of ViennaVienna, Austria
| | - Lisa Fürtauer
- Department of Ecogenomics and Systems Biology, University of Vienna Vienna, Austria
| | - Matthias Nagler
- Department of Ecogenomics and Systems Biology, University of Vienna Vienna, Austria
| | - Jakob Weiszmann
- Department of Ecogenomics and Systems Biology, University of Vienna Vienna, Austria
| | - Wolfram Weckwerth
- Department of Ecogenomics and Systems Biology, University of ViennaVienna, Austria; Vienna Metabolomics Center, University of ViennaVienna, Austria
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18
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Fürtauer L, Weckwerth W, Nägele T. A Benchtop Fractionation Procedure for Subcellular Analysis of the Plant Metabolome. FRONTIERS IN PLANT SCIENCE 2016; 7:1912. [PMID: 28066469 PMCID: PMC5177628 DOI: 10.3389/fpls.2016.01912] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/12/2016] [Accepted: 12/02/2016] [Indexed: 05/06/2023]
Abstract
Although compartmentation is a key feature of eukaryotic cells, biological research is frequently limited by methods allowing for the comprehensive subcellular resolution of the metabolome. It has been widely accepted that such a resolution would be necessary in order to approximate cellular biochemistry and metabolic regulation, yet technical challenges still limit both the reproducible subcellular fractionation and the sample throughput being necessary for a statistically robust analysis. Here, we present a method and a detailed protocol which is based on the non-aqueous fractionation technique enabling the assignment of metabolites to their subcellular localization. The presented benchtop method aims at unraveling subcellular metabolome dynamics in a precise and statistically robust manner using a relatively small amount of tissue material. The method is based on the separation of cellular fractions via density gradients consisting of organic, non-aqueous solvents. By determining the relative distribution of compartment-specific marker enzymes together with metabolite profiles over the density gradient it is possible to estimate compartment-specific metabolite concentrations by correlation. To support this correlation analysis, a spreadsheet is provided executing a calculation algorithm to determine the distribution of metabolites over subcellular compartments. The calculation algorithm performs correlation of marker enzyme activity and metabolite abundance accounting for technical errors, reproducibility and the resulting error propagation. The method was developed, tested and validated in three natural accessions of Arabidopsis thaliana showing different ability to acclimate to low temperature. Particularly, amino acids were strongly shuffled between subcellular compartments in a cold-sensitive accession while a cold-tolerant accession was characterized by a stable subcellular metabolic homeostasis. Finally, we conclude that subcellular metabolome analysis is essential to unambiguously unravel regulatory strategies being involved in plant-environment interactions.
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Affiliation(s)
- Lisa Fürtauer
- Department of Ecogenomics and Systems Biology, University of ViennaVienna, Austria
| | - Wolfram Weckwerth
- Department of Ecogenomics and Systems Biology, University of ViennaVienna, Austria
- Vienna Metabolomics Center, University of ViennaVienna, Austria
| | - Thomas Nägele
- Department of Ecogenomics and Systems Biology, University of ViennaVienna, Austria
- Vienna Metabolomics Center, University of ViennaVienna, Austria
- *Correspondence: Thomas Nägele
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19
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Martínez-González MÁ, Ruiz-Canela M, Hruby A, Liang L, Trichopoulou A, Hu FB. Intervention Trials with the Mediterranean Diet in Cardiovascular Prevention: Understanding Potential Mechanisms through Metabolomic Profiling. J Nutr 2015; 146:913S-919S. [PMID: 26962184 PMCID: PMC4807639 DOI: 10.3945/jn.115.219147] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2015] [Revised: 08/06/2015] [Accepted: 09/09/2015] [Indexed: 12/15/2022] Open
Abstract
Large observational epidemiologic studies and randomized trials support the benefits of a Mediterranean dietary pattern on cardiovascular disease (CVD). Mechanisms postulated to mediate these benefits include the reduction of low-grade inflammation, increased adiponectin concentrations, decreased blood coagulation, enhanced endothelial function, lower oxidative stress, lower concentrations of oxidized LDL, and improved apolipoprotein profiles. However, the metabolic pathways through which the Mediterranean diet influences CVD risk remain largely unknown. Investigating specific mechanisms in the context of a large intervention trial with the use of high-throughput metabolomic profiling will provide more solid public health messages and may help to identify key molecular targets for more effective prevention and management of CVD. Although metabolomics is not without its limitations, the techniques allow for an assessment of thousands of metabolites, providing wide-ranging profiling of small molecules related to biological status. Specific candidate plasma metabolites that may be associated with CVD include branched-chain and aromatic amino acids; the glutamine-to-glutamate ratio; some short- to medium-chain acylcarnitines; gut flora metabolites (choline, betaine, and trimethylamine N-oxide); urea cycle metabolites (citrulline and ornithine); and specific lipid subclasses. In addition to targeted metabolites, the role of a large number of untargeted metabolites should also be assessed. Large intervention trials with the use of food patterns for the prevention of CVD provide an unparalleled opportunity to examine the effects of these interventions on plasma concentrations of specific metabolites and determine whether such changes mediate the benefits of the dietary interventions on CVD risk.
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Affiliation(s)
- Miguel Á Martínez-González
- Department of Preventive Medicine and Public Health, University of Navarra-Navarra Institute for Health Research, Pamplona, Spain
- Biomedical Research Networking Center Consortium-Physiopathology of Obesity and Nutrition, Institute of Health Carlos III, Madrid, Spain
| | - Miguel Ruiz-Canela
- Department of Preventive Medicine and Public Health, University of Navarra-Navarra Institute for Health Research, Pamplona, Spain
- Biomedical Research Networking Center Consortium-Physiopathology of Obesity and Nutrition, Institute of Health Carlos III, Madrid, Spain
| | | | - Liming Liang
- Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA; and
| | | | - Frank B Hu
- Departments of Nutrition and
- Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA; and
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