Lipidomics is an emerging field with great promise for biomarker and mechanistic studies due to lipids diverse biological roles. Clinical research applying lipidomics is drastically increasing, with research methods and tools developed for clinical applications equally promising for wildlife studies.
Limited research to date has applied lipidomics, especially of the intact lipidome, to wildlife studies. Therefore, we examine the application of lipidomics for in situ studies on Mozambique tilapia (Oreochromis mossambicus) in Loskop Dam, South Africa. Wide-scale mortality events of aquatic life associated with an environmentally-derived inflammatory disease, pansteatitis, have occurred in this area.
The lipidome of adipose tissue (n = 31) and plasma (n = 51) from tilapia collected from Loskop Dam were characterized using state of the art liquid chromatography coupled to high-resolution tandem mass spectrometry.
Lipid profiles reflected pansteatitis severity and were significantly different between diseased and healthy individuals. Over 13 classes of lipids associated with inflammation, cell death, and/or oxidative damage were upregulated in pansteatitis-affected adipose tissue, including ether-lipids, short-chained triglyceride oxidation products, sphingolipids, and acylcarnitines. Ceramides showed a 1000-fold increase in the most affected adipose tissues and were sensitive to disease severity. In plasma, triglycerides were found to be downregulated in pansteatitis-affected tilapia.
Intact lipidomics provided useful mechanistic data and possible biomarkers of pansteatitis. Lipids pointed to upregulated inflammatory pathways, and ceramides serve as promising biomarker candidates for pansteatitis. As comprehensive coverage of the lipidome aids in the elucidation of possible disease mechanisms, application of lipidomics could be applied to the understanding of other environmentally-derived inflammatory conditions, such as those caused by obesogens.
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Ethylene bridged hybrid
Data-dependent tandem mass spectrometry
False discovery rate
Free fatty acid
Heated electrospray ionization
High resolution mass spectrometry
Principle components analysis
Ultra-high performance liquid chromatography
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Authors would like to thank Andrew C. Patt for developing the R script for combining negative and positive polarity data and combining multiple adducts representing one lipid species. Mr. AC Hoffman is sincerely thanked for his assistance with fish collection and his keen interested in our research. Part of this work is based on the research supported by the South African Research Chairs Initiative of the Department of Science and Technology and National Research Foundation of South Africa (Grant No 101054). In addition, part of this work was funded by Core 1 of the Southeast Center for Metabolomics (SECIM) ( http://secim.ufl.edu/) (National Institute for Health Grant #U24 DK097209), the U.S. National Science Foundation under Award No. DBI-1359079, and the Medical University of South Carolina Center for Global Health.
This study was funded in by the South African Research Chairs Initiative of the Department of Science and Technology and National Research Foundation of South Africa (Grant No 101054), by Core 1 of the Southeast Center for Metabolomics (SECIM) ( http://secim.ufl.edu/) (National Institute for Health Grant #U24 DK097209), the U.S. National Science Foundation under Award No. DBI-1359079, and the Medical University of South Carolina Center for Global Health.
Conflict of interest
Jeremy P. Koelmel declares no conflict of interest, Candice Z. Ulmer declares no conflict of interest, Susan Fogelson declares no conflict of interest, Christina M. Jones declares no conflict of interest, Hannes Botha declares no conflict of interest, Jacqueline T. Bangma declares no conflict of interest, Theresa C. Guillette declares no conflict of interest, Wilmien J. Luus-Powell declares no conflict of interest, Joseph R. Sara declares no conflict of interest, Willem J. Smit declares no conflict of interest, Korin Albert declares no conflict of interest, Harmony A. Miller declares no conflict of interest, Matthew P. Guillette declares no conflict of interest, Berkley C. Olsen declares no conflict of interest, Jason A. Cochran declares no conflict of interest, Timothy J. Garrett declares no conflict of interest, Richard A. Yost declares no conflict of interest, and John A. Bowden declares no conflict of interest.
All applicable international, national, and/or institutional guidelines for the care and use of animals were followed.
The lipidomics .raw files, normalized feature tables, and metadata reported in this paper are available via the National Institute for Health’s (NIH) metabolomics workbench: (http://www.metabolomicsworkbench.org/data/DRCCStudySummary.php) under ST001052 (plasma samples) and ST001059 (tissue samples).
Software availability statement:
All “in-house” software used and/or developed during this study are available at: http://secim.ufl.edu/secim-tools/.
Certain commercial equipment or instruments are identified in the paper to specify adequately the experimental procedures. Such identification does not imply recommendations or endorsement by NIST; nor does it imply that the equipment or instruments are the best available for the purpose. Any opinion, finding and conclusion or recommendation expressed in this material is that of the author(s) and the NRF does not accept any liability in this regard.
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Koelmel, J.P., Ulmer, C.Z., Fogelson, S. et al. Lipidomics for wildlife disease etiology and biomarker discovery: a case study of pansteatitis outbreak in South Africa. Metabolomics 15, 38 (2019). https://doi.org/10.1007/s11306-019-1490-9
- Environmental metabolomics