We collected plasma samples from foraging green (Chelonia mydas, n = 27) and hawksbill (Eretmochelys imbricate, n = 16) turtles live captured in North Pacific Costa Rica in 2017. From these samples, we identified 623 MRMs belonging to 10 lipid classes (sphingomyelin, phosphatidylcholine, free fatty acids, cholesteryl esters, phosphatidylserine, phosphatidylinositol, phosphatidylglycerol, phosphatidylethanolamine, ceramides, and triacylglycerides) and one metabolite group (acyl-carnitines) present in sea turtle plasma. The relative ion intensities of most lipids (80%) were consistent between species, across seasons, and were not correlated to body size or estimated sex. Of the differences we observed, the most pronounced was the differences in relative ion intensity between species. We identified 114 lipids that had species-specific relative ion intensities. While some of this variability is likely due to green and hawksbill turtles consuming different food items, we found indications of a phylogenetic component as well. In addition, we identified 46 lipids that varied by season, most belonging to the structural phospholipid classes. Overall, more lipids (n = 27) had higher relative ion intensity in the upwelling (colder) season compared to the non-upwelling season (n = 6). Further, we found more variability in hawksbill turtles than green turtles. 

Researchers should cite this work as follows:

• Clyde-Brockway, C. E.; Ferreira C. R.; Flaherty, E. A.; Paladino, F. (2021). Lipid profiles of Pacific green and hawksbill sea turtle plasma. Purdue University Research Repository. doi:10.4231/93P2-GY15

  BibTex | EndNote

1. Chelonia myda
2. Eretmochelys imbricata
3. Forestry and Natural Resources
4. Forestry and Natural Resources (Dept)
5. lipid profiles
6. Mass Spectrometry
7. MRM-profiling

Data were produced using an Agilent 6410 QQQ mass spectrometer (Agilent Technologies, Santa Clara, CA, USA). Raw MRM mass spectrometry data was processed using an in-house script and MRM transition. Samples that had a sample ion intensity higher than the blank were selected for further analysis. Statistical analysis was conducted using MetaboAnalyst 4.0 (http://www.metaboanalyst.ca, Rv3.4.3).

Chong J, Xia J. MetaboAnalystR: an R package for flexible and reproducible analysis of metabolomics data. Bioinformatics. 2018; 34(24): 4313-4314.

Chong J, Soufan O, Li C, Caraus I, Li S, Bourque G, et al. MetaboAnalyst 4.0: towards more transparent and integrative metabolomics analysis. Nucl Acids Res. 2018; 46: W486–W494.