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Genome Biol Evol
2016 Dec 01;812:3672-3684. doi: 10.1093/gbe/evw272.
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Genomic Characterization of the Evolutionary Potential of the Sea Urchin Strongylocentrotus droebachiensis Facing Ocean Acidification.
Runcie DE
,
Dorey N
,
Garfield DA
,
Stumpp M
,
Dupont S
,
Wray GA
.
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Ocean acidification (OA) is increasing due to anthropogenic CO2 emissions and poses a threat to marine species and communities worldwide. To better project the effects of acidification on organisms'' health and persistence, an understanding is needed of the 1) mechanisms underlying developmental and physiological tolerance and 2) potential populations have for rapid evolutionary adaptation. This is especially challenging in nonmodel species where targeted assays of metabolism and stress physiology may not be available or economical for large-scale assessments of genetic constraints. We used mRNA sequencing and a quantitative genetics breeding design to study mechanisms underlying genetic variability and tolerance to decreased seawater pH (-0.4 pH units) in larvae of the sea urchin Strongylocentrotus droebachiensis. We used a gene ontology-based approach to integrate expression profiles into indirect measures of cellular and biochemical traits underlying variation in larval performance (i.e., growth rates). Molecular responses to OA were complex, involving changes to several functions such as growth rates, cell division, metabolism, and immune activities. Surprisingly, the magnitude of pH effects on molecular traits tended to be small relative to variation attributable to segregating functional genetic variation in this species. We discuss how the application of transcriptomics and quantitative genetics approaches across diverse species can enrich our understanding of the biological impacts of climate change.
Fig. 1.—. Experimental layout. We used a three-way factorial layout to assess the effects of pH (control vs low pH), male parent (seven individuals), and female parent (three individuals). Cultures were created representing all 42 combinations of these three factors. Larvae were raised for 14 days, and monitored daily for mortality, growth rate (days 1−8), and seawater chemistry (table 1).
Fig. 2.—. Larval growth rates are reduced in low pH seawater. Boxplots show median and quartiles of the distributions of growth rates for cultures grown in control or low pH seawater (N = 21 for each). The effect of pH treatment on growth rate was significant (P = 2.78 × 10−8). Growth rates (mm/day) were calculated using linear regression of daily measures of ∼10 larvae/culture over the first 6–9 days of development.
Fig. 3.—.
Physiological and molecular responses attributed to seawater pH are common but subtle relative to variation attributed to father or mother effects. (A) Mean (across all cultures) log2 responses to low pH seawater for 22,430 genes plotted against mean log2(expression). Genes with significant responses to low pH seawater (5% FDR) are highlighted in blue. (B) Boxplots show median and quartiles of the distributions of percentage of total among-culture variation in each gene expression trait (n = 22,430) or each MF gene set (n = 128) trait that could be attributed to each experimental factor (mother, father or pH).
Fig. 4.—. Summary of the response to pH in Molecular Function gene set traits. Bubble plot of MF gene set variation traits with significant (5% FDR) responses to low pH seawater. Bubble plots represent: (i) The a priori relationship among the MF gene sets. Bubble centers are arranged based on a multidimensional scaling projection (R function sammon) of the SimREL distances (Schlicker et al. 2006) among the PANTHER MF ontology terms. This distance takes into account both the tree-relationships among terms and the number of genes in each category. (ii) The number of genes linked to each MF term (bubble area is proportional to gene number). (iii) The percentage of variation in each gene set explained by the pH treatment and the mean direction of the response to low pH (orange = increase in expression, blue = decrease in expression). This plot is based on the REVIGO scatter plot (Supek et al. 2011).
Fig. 5.—. Biological process gene set traits associated with variation in larval growth rates. Bubble plot show the 22 BP gene set traits selected by the LASSO penalized regression as important predictors of growth rate variation. Color hue and intensity represents the sign and magnitude of each of the non-zero regression coefficients in the best model. The lambda parameter was chosen by leave-one-out cross validation.
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