LCMS data processing and analysis
Mass peak picking and alignment were performed using Metalign software
(Lommen 2009). Mass features in the resulting peak list were considered
as a real signal if they were detected with an intensity of more than 3
times the noise value and in 3 out of the 4 biological replicates of at
least one treatment. Mass features originating from the same metabolites
were subsequently reconstituted based on their similar retention window
and their intensity correlation across all measured samples, using
MSClust software (Tikunov et al. 2012). This resulted in the
relative intensity of 725 putative metabolites in Arabidopsis, 868 in
Artemisia and 1908 in broccoli detected in positive and negative
ionization mode, in which the metabolite abundance was represented by
the Measured Ion Count (MIC), i.e the sum of the corrected intensity
values of all mass features ions within the corresponding cluster. ANOVA
and a threshold of at least a 2-fold change were applied to pinpoint
compounds that were significantly different between
rhizobacteria-treated and control samples. Log transformation and
scaling of the data was performed in GeneMaths XT 1.6
(www.applied-maths.com). Transformed and scaled values were used for
hierarchical cluster analysis using Pearson’s correlation coefficient
and Unweighted Pair Group Method with Arithmetic Mean (UPGMA).
Annotation of differential metabolites was performed after manually
identifying the putative molecular ions within the clustered masses.
In-house databases were used to annotate metabolites detected in
Arabidopsis and Broccoli by considering the observed accurate masses and
retention times of the molecular ions. If selected compounds were not
yet present in this experimentally obtained database, detected masses
were matched with compound libraries, including Metabolomics Japan
(www.metabolomics.jp), the
Dictionary of Natural Products (www.dnp.chemnetbase.com), KNApSAcK
(www.knapsackfamily.com), and Metlin (www.metlin.scripps.edu) using a
maximum mass deviation of 5 ppm. To annotate metabolites detected in
Artemisia, we used the online Magma (Ridder et al. 2013) in
combination with the above-mentioned publicly available databases.