Abstract

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.

Graphical abstract

Tipping points of oyster transcriptome. (a–c) Frequency distribution of tipping point for piecewise linear relationships (left side). Linear and log-linear models (no tipping point) are under “Lin” name. Genes are grouped into three clusters of genes that co-vary together. The gray line indicates the distribution frequency of all genes irrespective of clusters. Groups of genes that exhibit neighboring tipping points with distribution frequencies >5% (shown as a dotted line), were grouped together. The segments above the bars indicate the groups of genes on which GO analyses were conducted. In each case, the gene that best represents the cluster according to module membership, gene significance for pH and R2 is presented as a function of pH as an example (right side). Tipping points and their 95% confidence intervals are shown in gray. The significance levels of the slopes are presented using stars (p < .001***, p < .01**, p < .05*). Gene names are as follows: LOC117690205: monocarboxylate transporter 12-like, LOC105317113: 60S ribosomal protein L10a, LOC105331560: protocadherin Fat 4

Reference

Abstract
In bivalves, no clear-cut functional role of microbiota has yet been identified, although many publications suggest that they could be involved in nutrition or immunity of their host. In the context of climate change, integrative approaches at the crossroads of disciplines have been developed to explore the environment-host-pathogen-microbiota system. Here, we attempt to synthesize work on (1) the current methodologies to analyse bivalve microbiota, (2) the comparison of microbiota between species, between host compartments and their surrounding habitat, (3) how the bivalve microbiota are governed by environmental factors and host genetics and (4) how host-associated microorganisms act as a buffer against pathogens and/or promote recovery, and could thereby play a role in the prevention of disease or mortalities.

Graphical abstract

Figure 2. Host and environmental factors acting on bivalve microbiota.
Some of these factors, alone or in combination, have been shown to lead to dysbiosis, diseases and recovery.

Highlights :

• High-throughput sequencing technologies enabled research on bivalve-microbiota interactions.
• Bivalve-microbiota interactions depend on host factors nested within each other.
• Microbiota interaction with surrounding environment is important for bivalve health.
• Recent studies highlight the involvement of oyster microbiota in disease resistance.
• Microbiome studies will provide new strategies to improve bivalve aquaculture.

Référence :

Christine Paillard, Yannick Gueguen, K Mathias Wegner, David Bass, Alberto Pallavicini, Luigi Vezzulli, Isabelle Arzul. Recent advances in bivalve-microbiota interactions for disease prevention in aquaculture. Current Opinion in Biotechnology, Volume 73, 2022, Pages 225-232. ISSN 0958-1669. https://doi.org/10.1016/j.copbio.2021.07.026.