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Changes in Fatty Acid Composition During Starvation in Vertebrates: Mechanisms and Questions

  • Edwin R. Price
  • Teresa G. Valencak
Chapter

Abstract

Animals often rely heavily on stored lipids as a fuel source during extended periods of fasting/starvation; this results in notable decreases in lipid content during the fast. Additionally, the composition of stored lipids often changes during periods of fasting, although the reasons for these compositional changes have not been fully explored. We examine the changes in fatty acid composition that occur during starvation through the lens of two important processes: (1) changes in the triacylglycerol to phospholipid ratio and (2) selective mobilization and oxidation of particular fatty acids. As triacylglycerols are oxidized, the ratio of triacylglycerols to phospholipids should decrease, resulting in higher overall proportions of polyunsaturated fatty acids, which are more abundant in phospholipids. Selective mobilization of fatty acids results in the preferential oxidation of short-chained and highly unsaturated fatty acids, the proportions of which should therefore decrease during starvation. In general, decreases in the triacylglycerol to phospholipid ratio appear to explain observed changes in fatty acid composition of whole animals and some tissues. On the other hand, selective mobilization of fatty acids can explain many of the compositional changes observed in adipose tissue. Together, these two processes should be considered when seeking to identify exceptional species or examples of unique lipid regulation. One notable exception is hibernating mammals, which do not exhibit standard selective mobilization patterns, possibly in order to conserve certain essential polyunsaturated fatty acids during their hibernation fast.

Keywords

Adipose Tissue Fatty Acid Composition Brown Adipose Tissue White Adipose Tissue Liver Phospholipid 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

We thank Tess Killpack, Heidi Bissell, Tawnya Cary, Jian-Nan Liu, Jeff Lorch, Thomas Ruf, and Agus Muñoz-Garcia for helpful comments on the manuscript. ERP is supported by the National Science Foundation (IOS-1025886). TGV is grateful for funding of FWF (projects T 376-B17 and V 197-B17).

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© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Department of Forest and Wildlife EcologyUniversity of Wisconsin-MadisonMadisonUSA
  2. 2.Research Institute of Wildlife EcologyUniversity of Veterinary MedicineViennaAustria
  3. 3.Institute of Biological and Environmental SciencesUniversity of AberdeenAberdeenScotland, UK

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