Volume 12 Issue 5
Nov.  2022
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Nguyen Hoang Anh, Young Cheol Yoon, Young Jin Min, Nguyen Phuoc Long, Cheol Woon Jung, Sun Jo Kim, Suk Won Kim, Eun Goo Lee, Daijie Wang, Xiao Wang, Sung Won Kwon. Caenorhabditis elegans deep lipidome profiling by using integrative mass spectrometry acquisitions reveals significantly altered lipid networks[J]. Journal of Pharmaceutical Analysis, 2022, 12(5): 743-754. doi: 10.1016/j.jpha.2022.06.006
Citation: Nguyen Hoang Anh, Young Cheol Yoon, Young Jin Min, Nguyen Phuoc Long, Cheol Woon Jung, Sun Jo Kim, Suk Won Kim, Eun Goo Lee, Daijie Wang, Xiao Wang, Sung Won Kwon. Caenorhabditis elegans deep lipidome profiling by using integrative mass spectrometry acquisitions reveals significantly altered lipid networks[J]. Journal of Pharmaceutical Analysis, 2022, 12(5): 743-754. doi: 10.1016/j.jpha.2022.06.006

Caenorhabditis elegans deep lipidome profiling by using integrative mass spectrometry acquisitions reveals significantly altered lipid networks

doi: 10.1016/j.jpha.2022.06.006
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This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (Grant Nos.: NRF-2018R1A5A2024425, NRF-2012M3A9C4048796, and NRF-2021R1I1A4A01057387). Graphic was created using Biorender. C. elegans N2 strain was provided by the Caenorhabditis Genetic Center, which is funded by the National Institutes of Health Office of Research Infrastructure Programs (Grant No.: P40 OD010440). Language editing service was supported by Plant Genomics and Breeding Institute at Seoul National University.

  • Received Date: Dec. 17, 2021
  • Accepted Date: Jun. 15, 2022
  • Rev Recd Date: Jun. 14, 2022
  • Publish Date: Jun. 24, 2022
  • Lipidomics coverage improvement is essential for functional lipid and pathway construction. A powerful approach to discovering organism lipidome is to combine various data acquisitions, such as full scan mass spectrometry (full MS), data-dependent acquisition (DDA), and data-independent acquisition (DIA). Caenorhabditis elegans (C. elegans) is a useful model for discovering toxic-induced metabolism, high-throughput drug screening, and a variety of human disease pathways. To determine the lipidome of C. elegans and investigate lipid disruption from the molecular level to the system biology level, we used integrative data acquisition. The methyl-tert-butyl ether method was used to extract L4 stage C. elegans after exposure to triclosan (TCS), perfluorooctanoic acid, and nanopolystyrene (nPS). Full MS, DDA, and DIA integrations were performed to comprehensively profile the C. elegans lipidome by Q-Exactive Plus MS. All annotated lipids were then analyzed using lipid ontology and pathway analysis. We annotated up to 940 lipids from 20 lipid classes involved in various functions and pathways. The biological investigations revealed that when C. elegans were exposed to nPS, lipid droplets were disrupted, whereas plasma membrane-functionalized lipids were likely to be changed in the TCS treatment group. The nPS treatment caused a significant disruption in lipid storage. Triacylglycerol, glycerophospholipid, and ether class lipids were those primarily hindered by toxicants. Finally, toxicant exposure frequently involved numerous lipid-related pathways, including the phosphoinositide 3-kinase/protein kinase B pathway. In conclusion, an integrative data acquisition strategy was used to characterize the C. elegans lipidome, providing valuable biological insights into hypothesis generation and validation.
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  • A. Shevchenko, K. Simons, Lipidomics: Coming to grips with lipid diversity, Nat. Rev. Mol. Cell Biol. 11 (2010) 593-598
    M.R. Wenk, Lipidomics: New tools and applications, Cell 143 (2010) 888-895
    J. Mill, V. Patel, O. Okonkwo, et al., Erythrocyte sphingolipid species as biomarkers of Alzheimer's disease, J. Pharm. Anal. 12 (2022) 178-185
    K. Yang, X. Han, Lipidomics: Techniques, applications, and outcomes related to biomedical sciences, Trends Biochem. Sci. 41 (2016) 954-969
    K. Saito, Application of comprehensive lipidomics to biomarker research on adverse drug reactions, Drug Metab. Pharmacokinet. 37 (2021), 100377
    J.J. Aristizabal-Henao, A. Ahmadireskety, E.K. Griffin, et al., Lipidomics and environmental toxicology: Recent trends, Curr. Opin. Environ. Sci. Health 15 (2020) 26-31
    F. Wei, S. Lamichhane, M. Oresic, et al., Lipidomes in health and disease: Analytical strategies and considerations, TrAC, Trends Anal. Chem. 120 (2019), 115664
    M. Holcapek, G. Liebisch, K. Ekroos, Lipidomic analysis, Anal. Chem. 90 (2018) 4249-4257
    K. Huynh, C.K. Barlow, K.S. Jayawardana, et al., High-throughput plasma lipidomics: Detailed mapping of the associations with cardiometabolic risk factors, Cell Chem. Biol. 26 (2019) 71-84.e4
    S.A. Murphy, A. Nicolaou, Lipidomics applications in health, disease and nutrition research, Mol. Nutr. Food Res. 57 (2013) 1336-1346
    H. Zhang, Y. Wang, L. Guan, et al., Lipidomics reveals carnitine palmitoyltransferase 1C protects cancer cells from lipotoxicity and senescence, J. Pharm. Anal. 11 (2021) 340-350
    C.S. Field, F. Baixauli, R.L. Kyle, et al., Mitochondrial integrity regulated by lipid metabolism is a cell-intrinsic checkpoint for Treg suppressive function, Cell Metab. 31 (2020) 422-437.e5
    Q. Wu, H. Zhang, X. Dong, et al., UPLC-Q-TOF/MS based metabolomic profiling of serum and urine of hyperlipidemic rats induced by high fat diet, J. Pharm. Anal. 4 (2014) 360-367
    L. Goracci, A. Valeri, S. Sciabola, et al., A novel lipidomics-based approach to evaluating the risk of clinical hepatotoxicity potential of drugs in 3D human microtissues, Chem. Res. Toxicol. 33 (2020) 258-270
    A. Triebl, J. Hartler, M. Trotzmuller, et al., Lipidomics: Prospects from a technological perspective, Biochim. Biophys. Acta Mol. Cell Biol. Lipids 1862 (2017) 740-746
    J. Guo, T. Huan, Comparison of full-scan, data-dependent, and data-independent acquisition modes in liquid chromatography-mass spectrometry based untargeted metabolomics, Anal. Chem. 92 (2020) 8072-8080
    P. Barbier Saint Hilaire, K. Rousseau, A. Seyer, et al., Comparative evaluation of data dependent and data independent acquisition workflows implemented on an orbitrap fusion for untargeted metabolomics, Metabolites 10 (2020), 158
    T. Züllig, M. Trötzmüller, H.C. Kofeler, Lipidomics from sample preparation to data analysis: a primer, Anal. Bioanal.Chem. 412 (2020) 2191-2209
    T. Xu, C. Hu, Q. Xuan, et al., Recent advances in analytical strategies for mass spectrometry-based lipidomics, Anal. Chim. Acta 1137 (2020) 156-169
    M.I. Alcoriza-Balaguer, J.C. García-Cañaveras, A. López, et al., LipidMS: An R package for lipid annotation in untargeted liquid chromatography-data independent acquisition-mass spectrometry lipidomics, Anal. Chem. 91 (2019) 836-845
    B. Drotleff, J. Illison, J. Schlotterbeck, et al., Comprehensive lipidomics of mouse plasma using class-specific surrogate calibrants and SWATH acquisition for large-scale lipid quantification in untargeted analysis, Anal. Chim. Acta 1086 (2019) 90-102
    P. Davis, M. Zarowiecki, V. Arnaboldi, et al., WormBase in 2022-data, processes, and tools for analyzing Caenorhabditis elegans, Genetics 220 (2022), iyac003
    H. Tsugawa, K. Ikeda, M. Arita, The importance of bioinformatics for connecting data-driven lipidomics and biological insights, Biochim. Biophys. Acta Mol. Cell Biol. Lipids 1862 (2017) 762-765
    J. Kim, B.-K. Koo, J.A. Knoblich, Human organoids: Model systems for human biology and medicine, Nat. Rev. Mol. Cell Biol. 21 (2020) 571-584
    H. Li, S. Ning, M. Ghandi, et al., The landscape of cancer cell line metabolism, Nat. Med. 25 (2019) 850-860
    D.K. Barupal, Y. Zhang, T. Shen, et al., A comprehensive plasma metabolomics dataset for a cohort of mouse knockouts within the international mouse phenotyping consortium, Metabolites 9 (2019), 101
    P. Wittkowski, P. Marx-Stoelting, N. Violet, et al., Caenorhabditis elegans as a promising alternative model for environmental chemical mixture effect assessment-A comparative study, Environ. Sci. Technol. 53 (2019) 12725-12733
    W.A. Boyd, M.V. Smith, J.H. Freedman, Caenorhabditis elegans as a model in developmental toxicology, Developmental Toxicology: Methods and Protocols, Humana Totowa, NJ, 2012, pp. 15–24
    J.L. Watts, M. Ristow, Lipid and carbohydrate metabolism in Caenorhabditis elegans, Genetics 207 (2017) 413-446
    T.D. Admasu, K.C. Batchu, L.F. Ng, et al., Lipid profiling of C. elegans strains administered pro-longevity drugs and drug combinations, Sci. Data 5 (2018), 180231
    J. Yoshimura, K. Ichikawa, M.J. Shoura, et al., Recompleting the Caenorhabditis elegans genome, Genome Res. 29 (2019) 1009-1022
    J. Nance, C. Frøkjær-Jensen, The Caenorhabditis elegans transgenic toolbox, Genetics 212 (2019) 959-990
    Y. Zhang, X. Zou, Y. Ding, et al., Comparative genomics and functional study of lipid metabolic genes in Caenorhabditis elegans, BMC Genomics 14 (2013), 164
    S. Giunti, N. Andersen, D. Rayes, et al., Drug discovery: Insights from the invertebrate Caenorhabditis elegans, Pharmacol. Res. Perspect. 9 (2021), e00721
    H.M. Kim, N.P. Long, S.J. Yoon, et al., Metabolomics and phenotype assessment reveal cellular toxicity of triclosan in Caenorhabditis elegans, Chemosphere 236 (2019), 124306
    H.M. Kim, N.P. Long, J.E. Min, et al., Comprehensive phenotyping and multi-omic profiling in the toxicity assessment of nanopolystyrene with different surface properties, J. Hazard. Mater. 399 (2020), 123005
    H.M. Kim, N.P. Long, S.J. Yoon, et al., Omics approach reveals perturbation of metabolism and phenotype in Caenorhabditis elegans triggered by perfluorinated compounds, Sci. Total Environ. 703 (2020), 135500
    H. Tsugawa, K. Ikeda, M. Takahashi, et al., A lipidome atlas in MS-DIAL 4, Nat. Biotechnol. 38 (2020) 1159-1163
    M.R. Molenaar, A. Jeucken, T.A. Wassenaar, et al., LION/web: A web-based ontology enrichment tool for lipidomic data analysis, GigaScience 8 (2019), giz061
    W.-J. Lin, P.-C. Shen, H.-C. Liu, et al., LipidSig: A web-based tool for lipidomic data analysis, Nucleic Acids Res. 49 (2021) W336-W345
    H. Wickham, ggplot2: Elegant Graphics for Data Analysis, Springer, New York, NY, 2016
    The R Core Team, R: A Language and Environment for Statistical Computing, Version 3.6.2, R Foundation for Statistical Computing, 2020
    J.R. Conway, A. Lex, N. Gehlenborg, UpSetR: an R package for the visualization of intersecting sets and their properties, Bioinformatics 33 (2017) 2938-2940
    Z. Pang, J. Chong, G. Zhou, et al., MetaboAnalyst 5.0: narrowing the gap between raw spectra and functional insights, Nucleic Acids Res. 49 (2021) W388-W396
    I. Blaženović, T. Kind, M.R. Sa, et al., Structure annotation of all mass spectra in untargeted metabolomics, Anal. Chem. 91 (2019) 2155-2162
    J. Guo, S. Shen, S. Xing, et al., DaDIA: Hybridizing data-dependent and data-independent acquisition modes for generating high-quality metabolomic data, Anal. Chem. 93 (2021) 2669-2677
    F. Zheng, X. Zhao, Z. Zeng, et al., Development of a plasma pseudotargeted metabolomics method based on ultra-high-performance liquid chromatography-mass spectrometry, Nat. Protoc. 15 (2020) 2519-2537
    A. Jankevics, A. Jenkins, W.B. Dunn, et al., An improved strategy for analysis of lipid molecules utilising a reversed phase C30 UHPLC column and scheduled MS/MS acquisition, Talanta 229 (2021), 122262
    S.M. Lam, Z. Wang, B. Li, et al., High-coverage lipidomics for functional lipid and pathway analyses, Anal. Chimica Acta 1147 (2021) 199-210
    L.P. O'Reilly, C.J. Luke, D.H. Perlmutter, et al., C. elegans in high-throughput drug discovery, Adv. Drug Deliv. Rev. 69-70 (2014) 247-253
    K. Strange, Drug discovery in fish, flies, and worms, ILAR J. 57 (2016) 133-143
    S. Bulterijs, B.P. Braeckman, Phenotypic screening in C. elegans as a tool for the discovery of new geroprotective drugs, Pharmaceuticals (Basel) 13 (2020), 164
    A.W. Gao, I.A. Chatzispyrou, R. Kamble, et al., A sensitive mass spectrometry platform identifies metabolic changes of life history traits in C. elegans, Sci. Rep. 7 (2017), 2408
    J.K. Prasain, L. Wilson, H.D. Hoang, et al., Comparative lipidomics of Caenorhabditis elegans metabolic disease models by SWATH non-targeted tandem mass spectrometry, Metabolites 5 (2015) 677-696
    H.C. Köfeler, T.O. Eichmann, R. Ahrends, et al., Quality control requirements for the correct annotation of lipidomics data, Nat. Commun. 12 (2021), 4771
    T. Cajka, J.T. Smilowitz, O. Fiehn, Validating quantitative untargeted lipidomics across nine liquid chromatography-high-resolution mass spectrometry platforms, Anal. Chem. 89 (2017) 12360-12368
    E. Fahy, M. Sud, D. Cotter, et al., LIPID MAPS online tools for lipid research, Nucleic Acids Res. 35 (2007) W606-W612
    T. Kind, K.-H. Liu, D.Y. Lee, et al., LipidBlast in silico tandem mass spectrometry database for lipid identification, Nat. Methods 10 (2013) 755-758
    H. Horai, M. Arita, S. Kanaya, et al., MassBank: A public repository for sharing mass spectral data for life sciences, J. Mass Spectrom. 45 (2010) 703-714
    G. Liebisch, E. Fahy, J. Aoki, et al., Update on LIPID MAPS classification, nomenclature, and shorthand notation for MS-derived lipid structures, J. Lipid Res. 61 (2020) 1539-1555
    M. Witting, P. Schmitt-Kopplin, The Caenorhabditis elegans lipidome: A primer for lipid analysis in Caenorhabditis elegans, Arch. Biochem. Biophys. 589 (2016) 27-37
    Q.-L. Wan, Z.-L. Yang, X.-G. Zhou, et al., The effects of age and reproduction on the lipidome of Caenorhabditis elegans, Oxid. Med. Cell. Longev. 2019 (2019), 5768953
    M. Molenaars, B.V. Schomakers, H.L. Elfrink, et al., Metabolomics and lipidomics in Caenorhabditis elegans using a single-sample preparation, Dis. Model. Mech. 14 (2021), dmm047746
    J.A. Olzmann, P. Carvalho, Dynamics and functions of lipid droplets, Nat. Rev. Mol. Cell Biol. 20 (2019) 137-155
    A.L.S. Cruz, E.A. Barreto, N.P.B. Fazolini, et al., Lipid droplets: Platforms with multiple functions in cancer hallmarks, Cell Death Dis. 11 (2020), 105
    Z. Liu, Y. Li, E. Pérez, et al., Polystyrene nanoplastic induces oxidative stress, immune defense, and glycometabolism change in Daphnia pulex: Application of transcriptome profiling in risk assessment of nanoplastics, J. Hazard. Mater. 402 (2021), 123778
    K. Li, P. Gao, P. Xiang, et al., Molecular mechanisms of PFOA-induced toxicity in animals and humans: Implications for health risks, Environ. Int. 99 (2017) 43-54
    Q. Huang, L. Luo, X. Han, et al., Low-dose perfluorooctanoic acid stimulates steroid hormone synthesis in Leydig cells: Integrated proteomics and metabolomics evidence, J. Hazard. Mater. 424 (2022), 127656
    M.A. Alfhili, M.-H. Lee, Triclosan: An update on biochemical and molecular mechanisms, Oxid. Med. Cell. Longev. 2019 (2019), 1607304
    D.A. Fruman, H. Chiu, B.D. Hopkins, et al., The PI3K pathway in human disease, Cell 170 (2017) 605-635
    S. Peng, L. Yan, J. Zhang, et al., An integrated metabonomics and transcriptomics approach to understanding metabolic pathway disturbance induced by perfluorooctanoic acid, J. Pharm. Biomed. Anal. 86 (2013) 56-64
    X. Song, X. Wang, X. Li, et al., Histopathology and transcriptome reveals the tissue-specific hepatotoxicity and gills injury in mosquitofish (Gambusia affinis) induced by sublethal concentration of triclosan, Ecotoxicol. Environ. Saf. 220 (2021), 112325
    A.P. Davis, C.J. Grondin, R.J. Johnson, et al., Comparative toxicogenomics database (CTD): Update 2021, Nucleic Acids Res. 49 (2021) D1138-D1143
    P. More, L. Bindila, P. Wild, et al., LipiDisease: associate lipids to diseases using literature mining, Bioinformatics 37 (2021) 3981-3982
    T.-C. Kuo, Y.J. Tseng, LipidPedia: A comprehensive lipid knowledgebase, Bioinformatics 34 (2018) 2982-2987
    L. Wadi, M. Meyer, J. Weiser, et al., Impact of outdated gene annotations on pathway enrichment analysis, Nat. Methods 13 (2016) 705-706
    M.A. Alves, S. Lamichhane, A. Dickens, et al., Systems biology approaches to study lipidomes in health and disease, Biochim. Biophys. Acta Mol. Cell Biol. Lipids 1866 (2021), 158857
    B.A. Martinez, K.A. Caldwell, G.A. Caldwell, C. elegans as a model system to accelerate discovery for Parkinson disease, Curr. Opin. Genet. Dev. 44 (2017) 102-109
    E.K. Marsh, R.C. May, Caenorhabditis elegans, a model organism for investigating immunity, Appl. Environ. Microbiol. 78 (2012) 2075-2081
    S. Srinivasan, Neuroendocrine control of lipid metabolism: lessons from C. elegans, J. Neurogenet. 34 (2020) 482-488.
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