Population-specificity of heat stress gene induction in northern and southern eelgrass Zostera marina populations under simulated global warming
Article first published online: 1 JUL 2010
© 2010 Blackwell Publishing Ltd
Volume 19, Issue 14, pages 2870–2883, July 2010
How to Cite
BERGMANN, N., WINTERS, G., RAUCH, G., EIZAGUIRRE, C., GU, J., NELLE, P., FRICKE, B. and REUSCH, T. B. H. (2010), Population-specificity of heat stress gene induction in northern and southern eelgrass Zostera marina populations under simulated global warming. Molecular Ecology, 19: 2870–2883. doi: 10.1111/j.1365-294X.2010.04731.x
- Issue published online: 12 JUL 2010
- Article first published online: 1 JUL 2010
- Received 21 December 2009; revised 20 May 2010; accepted 21 May 2010
S1 Genes, reaction conditions and accession numbers for quantitative polymerase chain reaction QPCR assays of stress gene expression (including Table S1).
S2 Long-term temperature & salinity data at the collection sites of experimental plants (including Figures S1, S2).
S3 Effects of different sediment types on Z. marina growth (including Figure S3).
S4 Sample pooling approach in order to minimize the number of Q-PCR reactions (including Figures S4, S5, S6).
S5 Statistical tables on the effects of the experimental heat wave (including Tables S2, S3, S4).
S6 Comparison of the Zostera marina heat stress response to Arabidopsis thaliana (including Table S5).
S7 Comparison of experimental leaf growth rates to rates measured in the field.
Fig. S1 Surface water temperatures at the collection sites; grey: long term data; black recorded field data, this study; a) Aarhus, 15 km away from Ebeltoft, Denmark, daily long term data: station 22331; N 56° 09 E 10°13, source: Bettina Evers-Jansen, Danish Meterological Institute (DMI); b) Doverodde, Denmark, long term data: station VIB 3221, N 56°41.87 E 08°35.61, source: Marie-Louise Maarup, Agency for Spatial & Environmental Planning, Ministry of the Environment, Denmark; c) Gabicce Mare, Italy, long term data recorded every 2nd week: Station 10, Cattolica: N43°58.29, E12°44.46, source: Stefano Serra, Agenzia Regionale per la Prevenzione e l′Ambiente dell′Emilia-Romagna (ARPA), Italy.
Fig. S2 Salinity in Arhus Bay, Denmark (close to station Ebeltoft) from 1990 to 2005; data from the national database for marine data (MADS); National Environmental Research Institute (NERI), Aarhus University; Denmark; http://www2.dmu.dk/.
Fig. S3 Mean growth/ 24h of Z. marina ramets planted into different sediment types: black: beach sand, white: sediment originating from seagrass meadow; grey: beach sand inoculated with sediment of the seagrass meadow; +SE.
Fig. S4 Gene expression in the seagrass Zostera marina -ΔΔCT values of 12 target gene were assessed in a pooled approach; (a) temperature course of the experiment (grey: heat treatment, black: control) and RNA sampling time points T1 – T9; (b) – (d) -ΔΔCT values of 5 pooled samples for Ebeltoft (b), Doverodde (c) and Italy (d) at 4 time points in the simulated heat wave (T1-T4), directly after the heat wave (T5) and after 4 weeks of recovery (T9); Target gene identity is indicated on the y axes; note that MT3 and CuChap were never responsive. A linear regression model conducted in software package ‘R’ on expression data of the pooled versus single approach revealed that -ΔCT values of the pooled approach and mean –dCT values of the replicated approach were highly correlated (P<0.0001; R²= 0.90) (Fig. S5), demonstrating that the pooling procedure is a valid approach for target gene selection.
Fig. S5 Gene expression of Z. marina; a linear regression of mean -ΔCT of the mean values of the replicated approach (x-axis) versus -ΔCT of the pooled approach (y-axis) is given. Using above approach, we also identified two time points with similar gene expression patterns. As a linear regression model conducted in R revealed that -ΔCT values of T3 and T4 (14d; 28d after the onset of the heat wave respectively) highly correlated (P<0.0001; R²=0.97) (Fig. S6), we decided to omit T4 from measurement in the replicated approach for data reduction.
Fig. S6 Gene expression of Z. marina; linear regression of -ΔCT of T3 (seven days of 26°C stress treatment) (x-axis) versus -ΔCT of T4 (28 days of 26° stress treatment) (y-axis).
Table S1 Genes in Zostera marina assessed using quantitative PCR, their primer sequences and primer concentrations in the respective QPCR assay. The Genbank accession numbers of all reads forming the contig of the tentative unigene are given. Each reaction consisted of 10μl QPCR Master Mix and variable primer and 1:50 diluted cDNA concentrations. See Table 1 for full gene names and homologues in other plant species. Thermocycling was performed using the following conditions: 20s at 95°C, 45 cycles of 5s at 95°C, 30s at 60°C with subsequent melting point analysis
Table S2 General linear model assessing the effects of heat stress treatment, population, and time point on leaf growth rates and shoot count; d.f., degrees of freedom; MS, mean square
Table S3 MANOVA (multivariant analysis of variance) assessing the effects of heat stress treatment and population on gene expression (as -ΔΔCT) in Z. marina; d.f., degrees of freedom
Table S4 Matrix of pair-wise comparison of gene expression (-ΔΔCT) in eelgrass (Zostera marina) among five time points during and after an experimental heat wave. Given are P-values of an ANOSIM analysis. The global model fit R=0.39, global P<0.001. Comparisons with an asterisk (*) indicate a poor model fit (R<0.2)
Table S5 Comparison of heat stress gene regulation in leaf tissue of Z. marina and A. thaliana; values for Z marina are mean fold changes; data for A thaliana are qualitative expression changes derived from the database AtGenExpress. Gene up-regulation is highlighted in bold. NA: data not available
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