MultiOmics integration
Following the holo-omics approach, we integrated the metabolomics and
root metabarcoding datasets using the DIABLO framework (Singh et
al. 2019) with multiblock sparse PLS-DA (partial least squares
discriminant analysis). Model tuning helped us to select features from
metabarcoding and metabolomics to improve the modelling of differences
between groups. Noteworthy, the datasets are highly correlated for all
three components (Figure 6, components 1 and 2, supplementary figure S5,
components 2 and 3). The agreement between metabarcoding and
metabolomics is high for all samples and treatments. The combined
drought and heat treatment separates from the other three treatments on
the first axis, and heat treatment on the second axis, while the drought
and control treatments are not well separated by components 1 and 2, but
are separated by component 3 (Supplementary figure S5.
As a follow-up analysis to DIABLO, Pearson correlation network analysis
was performed. We aimed to identify positive correlations between
features assigned to the same treatment by DIABLO. These pairs likely
resolve the underlying interaction between the plant metabolome and the
rhizosphere microbiota.
Only bacterial genera and plant metabolites significantly assigned to a
feature by DIABLO were included in network construction. Three
clustering methods (Optimal, Louvain, fast greedy clustering) reproduced
four similar modules. The largest was mainly constituted by correlations
between features of the control treatment and several heat or drought
metabolites. The latter were likely false positives of the DIABLO
analyses.
Besides the control module, only metabolites and genera assigned as
features of the H+D treatment by DIABLO co-occurred in a single module.
Thus, crosslinks between metabolite and bacterial datasets were
characteristic of the H+D treatment appearing as chords within Figure 7
. Additionally, a fraction of drought-associated bacterial genera and
heat-associated metabolites formed connections within this majorly
H+D-associated module. The module consisted of 24 nodes in total (Table
1). Among them were predominantly Nitrogen-Containing Secondary
Compounds for biosynthesis, Phenylpropanoid Derivative biosynthesis,
Polyketide biosynthesis and Proteobacteria .
In contrast, the majority of drought-associated metabolites or
heat-associated bacterial genera formed no crosslinks between the same
feature category across the metabolite and metabarcoding datasets.
Instead, drought-associated metabolites correlated with other
metabolites and heat-associated bacterial genera interacted with other
bacteria, as indicated by characteristic arcs within Figure 7.
In conclusion, a small core of Proteobacteria was associated with
Nitrogen-Containing Secondary Compounds in response to combined H+D
stress. The combined treatment appears to further select plant
metabolism responsive to heat metabolism, but rhizosphere bacteria
responsive to D.