4.8 Article

Revisiting tolerance to ocean acidification: Insights from a new framework combining physiological and molecular tipping points of Pacific oyster

Journal

GLOBAL CHANGE BIOLOGY
Volume 28, Issue 10, Pages 3333-3348

Publisher

WILEY
DOI: 10.1111/gcb.16101

Keywords

acidification; lipidomic; mollusk; reaction norm; threshold; transcriptomic

Funding

  1. Foundation for Research on Biodiversity
  2. French Ministere de la transition ecologique

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The study developed a framework to analyze broad macro-physiological and molecular responses in juvenile oysters over a wide pH range, identifying low tipping points for physiological traits and major reshuffling in membrane lipids and transcriptome. This innovative methodology allowed for the synthesis and identification of main patterns of variations in large -omic data sets, fitting them to pH and identifying molecular tipping points, with potential broad applications to assess the effects of global change on other organisms.
Studies on the impact of ocean acidification on marine organisms involve exposing organisms to future acidification scenarios, which has limited relevance for coastal calcifiers living in a mosaic of habitats. Identification of tipping points beyond which detrimental effects are observed is a widely generalizable proxy of acidification susceptibility at the population level. This approach is limited to a handful of studies that focus on only a few macro-physiological traits, thus overlooking the whole organism response. Here we develop a framework to analyze the broad macro-physiological and molecular responses over a wide pH range in juvenile oyster. We identify low tipping points for physiological traits at pH 7.3-6.9 that coincide with a major reshuffling in membrane lipids and transcriptome. In contrast, a drop in pH affects shell parameters above tipping points, likely impacting animal fitness. These findings were made possible by the development of an innovative methodology to synthesize and identify the main patterns of variations in large -omic data sets, fitting them to pH and identifying molecular tipping points. We propose the broad application of our framework to the assessment of effects of global change on other organisms.

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