Detecting the effects of predator-induced stress on the global metabolism of an ungulate prey using fecal metabolomic fingerprinting.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
17 03 2021
17 03 2021
Historique:
received:
18
11
2020
accepted:
01
03
2021
entrez:
18
3
2021
pubmed:
19
3
2021
medline:
21
10
2021
Statut:
epublish
Résumé
Few field tests have assessed the effects of predator-induced stress on prey fitness, particularly in large carnivore-ungulate systems. Because traditional measures of stress present limitations when applied to free-ranging animals, new strategies and systemic methodologies are needed. Recent studies have shown that stress and anxiety related behaviors can influence the metabolic activity of the gut microbiome in mammal hosts, and these metabolic alterations may aid in identification of stress. In this study, we used NMR-based fecal metabolomic fingerprinting to compare the fecal metabolome, a functional readout of the gut microbiome, of cattle herds grazing in low vs. high wolf-impacted areas within three wolf pack territories. Additionally, we evaluated if other factors (e.g., cattle nutritional state, climate, landscape) besides wolf presence were related to the variation in cattle metabolism. By collecting longitudinal fecal samples from GPS-collared cattle, we found relevant metabolic differences between cattle herds in areas where the probability of wolf pack interaction was higher. Moreover, cattle distance to GPS-collared wolves was the factor most correlated with this difference in cattle metabolism, potentially reflecting the variation in wolf predation risk. We further validated our results through a regression model that reconstructed cattle distances to GPS-collared wolves based on the metabolic difference between cattle herds. Although further research is needed to explore if similar patterns also hold at a finer scale, our results suggests that fecal metabolomic fingerprinting is a promising tool for assessing the physiological responses of prey to predation risk. This novel approach will help improve our knowledge of the consequences of predators beyond the direct effect of predation.
Identifiants
pubmed: 33731769
doi: 10.1038/s41598-021-85600-z
pii: 10.1038/s41598-021-85600-z
pmc: PMC7971053
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
6129Références
Schmitz, O. J., Krivan, V. & Ovadia, O. Trophic cascades: the primacy of trait-mediated indirect interactions. Ecol. Lett. 7, 153–163 (2004).
doi: 10.1111/j.1461-0248.2003.00560.x
Creel, S. & Christianson, D. Relationships between direct predation and risk effects. Trends Ecol. Evol. 23(4), 194–201 (2008).
pubmed: 18308423
doi: 10.1016/j.tree.2007.12.004
Ritchie, E. G. et al. Ecosystem restoration with teeth: what role for predators?. Trends Ecol. Evol. 27(5), 265–271 (2012).
pubmed: 22321653
doi: 10.1016/j.tree.2012.01.001
Terborgh, J. & Estes, J. A. Trophic Cascades: Predators, Prey, and the Changing Dynamics of Nature (Island Press, 2010).
Creel, S. & Winnie, J. A. Responses of elk herd size to fine scale spatial and temporal variation in the risk of predation by wolves. Anim. Behav. 69, 1181–1189 (2005).
doi: 10.1016/j.anbehav.2004.07.022
Fischhoff, I. R., Sundaresan, S. R., Cordingley, J. & Rubenstein, D. I. Habitat use and movements of plains zebra (Equus burchelli) in response to predation danger from lions. Behav. Ecol. 18, 725–729 (2007).
doi: 10.1093/beheco/arm036
Latombe, G., Fortin, D. & Parrott, L. Spatio-temporal dynamics in the response of woodland caribou and moose to the passage of grey wolves. J. Anim. Ecol. 83, 185–198 (2014).
pubmed: 23859231
doi: 10.1111/1365-2656.12108
Prugh, L. R. et al. Designing studies of predation risk for improved inference in carnivore-ungulate systems. Biol. Conserv. 232, 194–207 (2019).
doi: 10.1016/j.biocon.2019.02.011
Creel, S., Winnie, J. A. & Christianson, D. Glucocorticoid stress hormones and the effect of predation risk on elk reproduction. PNAS 106(30), 12388–12393 (2009).
pubmed: 19617549
doi: 10.1073/pnas.0902235106
pmcid: 2718336
Dulude-de Broin, F., Hamel, S., Mastromonaco, G. F. & Côté, S. D. Predation risk and mountain goat reproduction: evidence for stress-induced breeding suppression in a wild ungulate. Funct. Ecol. 34(5), 1003–1014 (2020).
doi: 10.1111/1365-2435.13514
Moberg, G. P. & Mench, J. A. The Biology of Animal Stress: Basic Principles and Implications for Animal Welfare (CABI Publishing, 2000).
doi: 10.1079/9780851993591.0000
Boonstra, R. The ecology of stress: a marriage of disciplines. Funct. Ecol. 27, 7–10 (2013).
doi: 10.1111/1365-2435.12048
Sheriff, M. J., Dantzer, B., Delehanty, B., Palme, R. & Boonstra, R. Measuring stress in wildlife: techniques for quantifying glucocorticoids. Oecologia 166, 869–887 (2011).
pubmed: 21344254
doi: 10.1007/s00442-011-1943-y
Kelley, K. W. Immunological consequences of changing environmental stimuli. In Animal Stress (ed. Moberg, G. P.) 193–223 (American Physiological Society, Bethesda, 1985).
Mӧstl, E. & Palme, R. Hormones as indicators of stress. Domest. Anim. Endocrinol. 23, 67–74 (2002).
doi: 10.1016/S0739-7240(02)00146-7
Ursin, H. & Eriksen, H. R. The cognitive activation theory of stress. Psychoneuroendocrinology 29(5), 567–592 (2004).
pubmed: 15041082
doi: 10.1016/S0306-4530(03)00091-X
Lovallo, W. R. Individual differences in reactivity to stress. In Stress and Health. Biological and Psychological Interactions (ed. Lovallo, W. R.) 203–225 (Sage, 2016).
Patchev, V. K. & Patchev, A. V. Experimental models of stress. Dialogues Clin. Neurosci. 8(4), 417–432 (2006).
pubmed: 17290800
pmcid: 3181831
doi: 10.31887/DCNS.2006.8.4/vpatchev
Mills, J. L. Scientific Principles of Stress (University of the West Indie Press, 2012).
Henry, J. P. Biological basis of the stress response. Integr. Physiol. Behav. Sci. 27, 66–83 (1992).
pubmed: 1576090
doi: 10.1007/BF02691093
Wu, Y., Patchev, A. V., Daniel, G., Almeida, O. F. X. & Spengler, D. Early-life stress reduces DNA methylation of the Pomc gene in male mice. Endocrinology 155(5), 1751–1762 (2014).
pubmed: 24506071
doi: 10.1210/en.2013-1868
Novais, A., Monteiro, S., Roque, S., Correia-Neves, M. & Sousa, N. How age, sex and genotype shape the stress response. Neurob. Stress 6, 44–56 (2017).
doi: 10.1016/j.ynstr.2016.11.004
Romero, L. M. & Gormally, B. M. G. How truly conserved is the “well-conserved” vertebrate stress response?. Integr. Comp. Biol. 59(2), 273–281 (2019).
pubmed: 30907954
doi: 10.1093/icb/icz011
Millspaugh, J. J. & Washburn, B. E. Use of fecal glucocorticoid metabolite measures in conservation biology research: considerations for application and interpretation. Gen. Comp. Endocrinol. 138, 189–199 (2004).
pubmed: 15364201
doi: 10.1016/j.ygcen.2004.07.002
Romero, L. M. Physiological stress in ecology: lessons from biomedical research. Trends Ecol. Evol. 19(5), 249–255 (2004).
pubmed: 16701264
doi: 10.1016/j.tree.2004.03.008
Johnstone, C. P., Reina, R. D. & Lill, A. Interpreting indices of physiological stress in free-living vertebrates. J. Comp. Physiol. B 182, 861–879 (2012).
pubmed: 22415475
doi: 10.1007/s00360-012-0656-9
Mayer, E. A., Knight, R., Mazmanian, S. K., Cryan, J. F. & Tillisch, K. Gut microbes and the brain: paradigm shift in neuroscience. J. Neurosci. 34, 15490–15496 (2014).
pubmed: 25392516
pmcid: 4228144
doi: 10.1523/JNEUROSCI.3299-14.2014
Sharon, G., Sampson, T. R., Geschwind, D. H. & Mazmanian, S. K. The central nervous system and the gut microbiome. Cell 167, 915–932 (2016).
pubmed: 27814521
pmcid: 5127403
doi: 10.1016/j.cell.2016.10.027
Mohajeri, M. H., La Fata, G., Steinert, R. E. & Weber, P. Relationship between the gut microbiome and brain function. Nutr. Rev. 76, 481–496 (2018).
pubmed: 29701810
doi: 10.1093/nutrit/nuy009
Bravo, J. A., Forsythe, P., Chew, M. V., Escaravage, E. & Savignac, H. M. Ingestion of Lactobacillus strain regulates emotional behavior and central GABA receptor expression in a mouse via the vagus nerve. PNAS 108(38), 16050–16055 (2011).
pubmed: 21876150
doi: 10.1073/pnas.1102999108
pmcid: 3179073
Beauclercq, S. et al. A multiplatform metabolomic approach to characterize fecal signatures of negative postnatal events in chicks: a pilot study. J Anim. Sci. Biotechnol. 10, 21 (2019).
pubmed: 31007908
pmcid: 6454711
doi: 10.1186/s40104-019-0335-8
Jianguo, L., Xueyang, J., Cui, W., Changxin, W. & Xuemei, Q. Altered gut metabolome contributes to depression-like behaviors in rats exposed to chronic unpredictable mild stress. Transl. Psychiatry 9, 1–14 (2019).
Valerio, A., Casadei, L., Giuliani, A. & Valerio, M. Fecal metabolomics as a novel non-invasive method for short-term stress monitoring in beef cattle. J. Proteome Res. 19(2), 845–853 (2020).
pubmed: 31873020
doi: 10.1021/acs.jproteome.9b00655
Nicholson, J. K. et al. Host-gut microbiota metabolic interactions. Science 336, 1262–1267 (2012).
doi: 10.1126/science.1223813
pubmed: 22674330
Nicholson, J. K., Connelly, J., Lindon, J. C. & Holmes, E. Metabolomics: a platform for studying drug toxicity and gene function. Nat. Rev. Drug Discov. 1, 153–161 (2002).
pubmed: 12120097
doi: 10.1038/nrd728
Lindon, J. C., Nicholson, J. K. & Holmes, E. The Handbook of Metabonomics and Metabolomics (Elsevier, 2007).
Matysik, S., Le Roy, C. I., Liebisch, G. & Claus, S. P. Metabolomics of fecal samples: a practical consideration. Trends Food Sci. Technol. 57, 244–255 (2016).
doi: 10.1016/j.tifs.2016.05.011
Nicholson, J. K. & Lindon, J. C. Metabonomics. Nature 455, 1054–1056 (2008).
pubmed: 18948945
doi: 10.1038/4551054a
Viant, M. R. Environmental metabolomics using
pubmed: 18642599
doi: 10.1007/978-1-59745-548-0_9
Ellis, D. I., Dunn, W. B., Griffin, J. L., Allwood, J. W. & Goodacre, R. Metabolic fingerprinting as a diagnostic tool. Pharmacogenomics 8(9), 1243–1266 (2007).
pubmed: 17924839
doi: 10.2217/14622416.8.9.1243
Worley, B. & Powers, R. Multivariate analysis in metabolomics. Curr. Metabolomics 1(1), 92–107 (2013).
pubmed: 26078916
pmcid: 4465187
Rivas-Ubach, A. et al. Ecometabolomics: optimized NMR-based method. Methods Ecol. Evol. 4(5), 464–473 (2013).
doi: 10.1111/2041-210X.12028
Chen, M. X., Wang, S. Y., Kuo, C. H. & Tsai, I. L. Metabolome analysis for investigating host-gut microbiota interactions. JFMA 118(1), S10–S22 (2019).
Emwas, A. H. M. The Strengths and weaknesses of NMR spectroscopy and mass spectrometry with particular focus on metabolomics research. In Metabonomics. Methods in Molecular Biology (ed. Bjerrum, J. T.) 1277, 161–193 (Human Press, 2015).
Emwas, A. H. M. et al. NMR spectroscopy for metabolomics research. Metabolites 9(7), 123 (2019).
pmcid: 6680826
doi: 10.3390/metabo9070123
Nicholson, J. K., Connelly, J., Lindon, J. C. & Holmes, E. Metabonomics: a platform for studying drug toxicity and gene function. Nat. Rev. Drug Discov. 1, 153–161 (2002).
pubmed: 12120097
doi: 10.1038/nrd728
Wiles, G. J., Allen, H. L. & Hayes, G. E. Wolf Conservation and Management Plan for Washington (Washington Department of Fish and Wildlife, 2011).
Schmitz, O. J. & Trussell, G. C. Multiple stressors, state-dependence and predation risk-foraging trade-offs: toward a modern concept of trait-mediated indirect effects in communities and ecosystems. Curr. Opin. Behav. 12, 6–11 (2016).
doi: 10.1016/j.cobeha.2016.08.003
Brown, J. A. Mortality of Range Livestock in Wolf-Occupied Areas of Washington. Thesis. Washington State University, Pullman, WA, USA (2015).
Fieberg, J. & Kochanny, C. O. Quantification of home range overlap: the importance of the utilization distribution. J. Wildl. Manag. 69, 1346–1359 (2005).
doi: 10.2193/0022-541X(2005)69[1346:QHOTIO]2.0.CO;2
Robert, K., Garant, D. & Pelletier, F. Keep in touch: does spatial overlap correlate with contact rate frequency?. J. Wildl. Manag. 76(8), 1670–1675 (2012).
doi: 10.1002/jwmg.435
Angel, S. P. et al. Climate change and cattle production: impact and adaptation. J. Vet. Med. Res. 5(4), 1134 (2018).
Brosh, A. et al. Energy cost of cows’ grazing activity: use of the heart rate method and the global positioning system for direct field estimation. J. Anim. Sci. 84, 1951–1967 (2006).
pubmed: 16775080
doi: 10.2527/jas.2005-315
Provenza, F. D. Postingestive feed-back as an elemental determinant of food preference and intake in ruminants. J. Range Manag. 48, 2–17 (1995).
doi: 10.2307/4002498
Provenza, F. D. Acquired aversions as the basis for varied diets of ruminants foraging on rangelands. J. Anim. Sci. 74, 2010–2020 (1996).
pubmed: 8856457
doi: 10.2527/1996.7482010x
Howery, L. D., Provenza, F. D., Ruyle, G. B. & Jordan, N. C. How do animals learn if rangeland plants are toxic or nutritious?. Rangelands 20, 4–9 (1998).
Davitt, B. B. & Nelson, J. R. Methodology for the determination of DAPA in feces of large ruminants. In Proceedings of the Western States and Provinces Elk Workshop (ed. Nelson, R.W.) 133–147 (Edmonton, 1984).
Church, D. C. Digestive Physiology and Nutrition of Ruminants I (Oxford Press, 1969).
Sato, S. Leadership during actual grazing in a small herd of cattle. Appl. Anim. Ethol. 8, 53–65 (1982).
doi: 10.1016/0304-3762(82)90132-8
Frair, J. L. et al. Resolving issues of imprecise and habitat-biased locations in ecological analyses using GPS telemetry data. Philos. Trans. R. Soc. B 365, 2187–2200 (2010).
doi: 10.1098/rstb.2010.0084
Deda, O., Gika, H. G., Wilson, I. D. & Theodoridis, G. A. An overview of fecal preparation for global metabolic profiling. J. Pharm. Biomed. 113, 137–150 (2015).
doi: 10.1016/j.jpba.2015.02.006
Landakadurai, B. P., Nagato, E. G. & Simpson, M. J. Environmental metabolomics: an emerging approach to study organism responses to environmental stressors. Environ. Rev. 21, 180–205 (2013).
doi: 10.1139/er-2013-0011
Wiklund, S. et al. Visualization of GC/TOF-MS-based metabolomics data for identification of biochemically interesting compounds using OPLS class models. Anal. Chem. 80, 115–122 (2008).
pubmed: 18027910
doi: 10.1021/ac0713510
Wishart, D. S. et al. HMDB: a knowledgebase for the human metabolome. Nucl. Acids Res. 37, D603–D610 (2009).
pubmed: 18953024
doi: 10.1093/nar/gkn810
Frair, J. L. et al. Scale of movement by elk (Cervus elaphus) in response to heterogeneity in forage resources and predation risk. Landsc. Ecol. 20, 273–287 (2005).
doi: 10.1007/s10980-005-2075-8
Valerio, A. Stress-Mediated and Habitat-Mediated Risk Effects of Free-Ranging Cattle in Washington. Dissertation. Washington State University, Pullman, WA (2019).
Winnie, J. & Creel, S. Sex-specific behavioral responses of elk to spatial and temporal variation in the threat of wolf predation. Anim. Behav. 73, 215–225 (2007).
doi: 10.1016/j.anbehav.2006.07.007
Bundy, J. G., Davey, M. P. & Viant, M. R. Environmental metabolomics: a critical review and future perspectives. Metabolomics 5, 3–21 (2009).
doi: 10.1007/s11306-008-0152-0