Morphological and epigenetic variation in mussels from contrasting environments
The impact of contrasting environments on organisms can result in the establishment of distinct phenotypic traits. Environmentally induced epigenetic mechanisms directly regulate gene expression and potentially lead to long-lasting effects. How phenotypic, epigenetic, and genetic components of wild populations relate to each other is, however, still largely debated. We examined the effect of broad coastline topography (as bay versus open coast) on the morphological, genetic, and epigenetic (i.e., DNA methylation) traits of the brown mussel Perna perna from four natural populations along the south coast of South Africa (between 33.9 S, 25.7 E and 34.2 S, 22.1 E) collected in April 2014. Several morphometric measurements were taken on the mussel body and byssal thread. The epigenetic and genetic structure of P. perna was assessed using the methylation sensitive analysis of polymorphisms technique. Morphological traits differed among populations, but no clear effect of topography on both morphology and genetics was found. Bay and Open Coast sites differed in the patterns of DNA methylation of selected loci, suggesting that topography shaped the epigenetic profile of populations of P. perna. The environmentally induced changes in the DNA methylation of selected loci were neither correlated with the morphological traits analysed, nor explained by the underlying genetic variance among populations. The relationship amongst epigenetics, morphology, and genetics of P. perna populations was shown to be complex and dynamic. Although inconsistent, the topographically linked variability in epigenetic and the phenotypic differences in genetically close populations of mussels highlights the potential role of the local environment in driving mesoscale differences among populations.
We thank N. Weidberg, D. Sousoni, and J. Bueno for assistance during the field collection and T. Bodill for assistance in the molecular laboratory. FP wishes to thank Dr Dittmar Eichoff for early discussions on epigenetics and sharing ideas and applications on orthodontics and marine ecology. We finally thank two anonymous reviewers for their valuable contribution on the revision of this paper.
This contribution is based upon research supported by funds provided by the South African Institute for Aquatic Biodiversity.
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest
All applicable international, national, and institutional guidelines for the care and use of animals were followed.
- Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Series B Methodol 57(1):289–300Google Scholar
- Branch GM, Griffiths CL, Branch M, Beckley L (2010) Two Oceans: a guide to the marine life of southern Africa. Struik Nature, GardensGoogle Scholar
- Hoffmann AA, Willi Y (2008) Detecting genetic responses to environmental change. Nature 9:421–432Google Scholar
- Jablonka E, Lamb MJ (2005) Evolution in four dimensions: genetic, epigenetic, behavioral, and symbolic variation in the history of life. MIT Press, CambridgeGoogle Scholar
- Kageyama S, Shinmura K, Yamamoto H, Goto M, Suzuki K, Tanioka F, Tsuneyoshi T, Sugimura H (2008) Fluorescence-labeled methylation-sensitive amplified fragment length polymorphism (FL-MS-AFLP) analysis for quantitative determination of DNA methylation and demethylation status. Jpn J Clin Oncol 38(4):317–322. https://doi.org/10.1093/jjco/hyn021 CrossRefPubMedGoogle Scholar
- Katolikova M, Khaitov V, Väinölä R, Gantsevich M, Strelkov P (2016) Genetic, ecological and morphological distinctness of the blue mussels Mytilus trossulus Gould and M. edulis L. in the white sea. PLoS One 11(4):e0152963. https://doi.org/10.1371/journal.pone.0152963 CrossRefPubMedPubMedCentralGoogle Scholar
- Kroeker KJ, Sanford E, Rose JM, Blanchette CA, Chan F, Chavez FP, Gaylord B, Helmuth B, Hill TM, Hofmann GE, McManus MA, Menge BA, Nielsen KJ, Raimondi PT, Russell AD, Washburn L (2016) Interacting environmental mosaics drive geographic variation in mussel performance and predation vulnerability. Ecol Lett 19:771–779CrossRefPubMedGoogle Scholar
- R Core Team (2014) R: a language and environment for statistical computing. R Foundation for Statistical Computing, ViennaGoogle Scholar
- StatSoft Inc (2012) STATISTICA (data analysis software system), version 12.0. http://www.statsoft.com/
- Suchanek TH (1985) Mussels and their role in structuring rocky shore communities. In: Moore PG, Seed R (eds) The ecology of rocky coasts. Hodder and Stoughton Press, London, pp 70–96Google Scholar
- Tunley K (2009) State of management of South Africa’s Marine protected areas. WWF South Africa report series—2009/Marine/001, pp 1–209Google Scholar
- Underwood AJ (1997) Experiments in ecology. Their logical design and interpretation using analysis of variance. Cambridge University Press, CambridgeGoogle Scholar
- Underwood AJ, Keough MJ (2001) Supply-side ecology: the nature and consequences of variations in recruitment of intertidal organisms. In: Bertness MD, Gaines SD, Hay ME (eds) Marine community ecology. Sinauer Associates, Sunderland, pp 183–200Google Scholar
- Winer BJ (1971) Statistical principles in experimental designs, 2nd edn. McGraw-Hill–Kogakusha, TokyoGoogle Scholar
- Zardi GI, Nicastro KR, McQuaid CD, Rius M, Porri F (2006) Hydrodynamic stress and habitat partitioning between indigenous (Perna perna) and invasive (Mytilus galloprovincialis) mussels: constraints of an evolutionary strategy. Mar Biol 150:79–88. https://doi.org/10.1007/s00227-006-0328-y CrossRefGoogle Scholar
- Zardi GI, Nicastro KR, McQuaid CD, Castilho R, Costa J, Serrão EA, Pearson GA (2015) Intraspecific genetic lineages of a marine mussel show behavioural divergence and spatial segregation over a tropical/subtropical biogeographic transition. BMC Evol Biol 15(100):1–11Google Scholar