Statistical analyses
To assess the effects of fish kairomones and subpopulation on the
metabolome of D. magna , we applied two parallel multivariate
analyses: a principal component analysis (PCA) coupled with two-way
ANOVA and an ANOVA-simultaneous component analysis (ASCA). The results
of both analyses were very similar and here we only show the PCA - ANOVA
results (see details for the ASCA results in the Supplementary
Information). PCA was conducted on the processed data matrices to assess
the broad-scale variation between the two treatments using the PLS
Toolbox (version 5.5.1, Eigenvector Research, Manson, WA, USA) within
Matlab (version 7.8; The MathsWorks, Natick, MA, USA) following mean
centring of the processed DIMS data. We extracted the first two PC axes
for both ion modes; these explained 43.1 % (positive ion mode) and 42.9
% (negative ion mode) of the total variation. In order to test the
effects of fish kairomones and subpopulation on the metabolome ofD. magna , we then applied two-way ANOVAs on the generated PC
scores in Statistica v12.0. In each analysis, the fish kairomone
treatment, subpopulation and their interaction were included as fixed
factors and clone was nested in subpopulation as a random factor. A
significant effect of the fish kairomone treatment indicates plasticity,
while a subpopulation effect indicates rapid evolution of the trait
means, and a fish kairomone × subpopulation interaction indicates rapid
evolution of plasticity.
As we found strong fish kairomone × subpopulation interactions on the
metabolome, we applied partial least squares discriminant analysis
(PLS-DA) to each subpopulation separately to identify the specific
metabolic responses to fish kairomones for each subpopulation. PLS-DA
uses prior knowledge of the sample classes (here the fish kairomone
treatments) to maximize separation of the metabolic profiles of the
different classes and to derive predictive models (Nicholson et
al. 2002). Internal cross-validation and permutation testing (see
details in Supplementary Information) were employed to prevent
over-fitting of the data (Westerhuis et al. 2008). Putative
marker metabolites in response to fish kairomones for each subpopulation
were screened using as criterion a Variable Importance in Projection
(VIP) threshold greater than 1 (Xuan et al. 2011). All putative
marker metabolites for each subpopulation were compared to screen for
the general and subpopulation-specific metabolites responsive to fish
kairomones. PLS-DA was conducted using in-house scripts with the
PLS-Toolbox in Matlab.
In addition, changes in the intensities of individual m/z peaks were
also assessed using t-tests for each subpopulation separately. All
t-tests were corrected using a false discovery rate (FDR, Benjamini &
Hochberg, 1995) of 5% to account for multiple testing and adjusted
p-values are reported. Differences in the number of significantly
changed peaks among subpopulations were tested using a chi-square test.
Pathway analyses
We used MI-Pack and KEGG to annotate the metabolites (see details in the
Supplementary Information). We then used MetaboAnalyst (Xia & Wishart
2011) to analyse the metabolic pathways that were affected by fish
kairomones. We put all putatively annotated KEGG compounds with VIP
scores > 1 (based on the PLS-DA model including all three
subpopulations) into MetaboAnalyst for metabolic pathway visualisation.
Fisher’s exact tests were used for over-representation analysis (Toyotaet al. 2016) and out-degree centrality was used for pathway
topology analysis (Xia & Wishart 2011). The FDR-corrected p values and
impact values of all annotated pathways were plotted. Pathways were
filtered based on the uncorrected p values (-log p > 0.5)
and impact value (> 0.2) as those pathways were considered
as potentially affected (Ratnasekhar et al. 2015).