Bacterial richness and biodiversity
In soil and root samples, 1455 and 915 bacterial ASVs remained after
filtering out sparse ASVs and outliers, respectively. These AVSs were
affiliated with 20 phyla, 39 classes, 87 orders, 130 families and 184
genera in root and 27 phyla, 51 classes, 114 orders, 170 Families and
238 Genera in soil.
Both soil and root samples were dominated by Proteobacteria, which made
up, on average, 89% of classified reads in root samples and 53% in
soil samples (Figure 4A). Taking a more detailed look at the
distribution of orders within the Proteobacteria (Figure 4B), pronounced
different patterns between the compartments (root, soil) as well as the
treatments (C, H, H+D, D). In soil samples, a large fraction of
Micropepsales (average of 11.5 % of reads) was detected, which is
substantially less frequent in root samples (1.2% of reads). In the
roots, treatment differences also refer to proteobacteria, mainly
classified as Rhizobiales and Burkolderiales. A large fraction of
Pseudomonadales occurs in both the C and D treatments. The combined H+D
treatment only was characterised by a substantial increase in
Enterobacteriales (26.4% of reads), which were also present in the heat
treatment to a lesser extent (mean 1.56%).
A decrease in bacterial alpha diversity was observed from the
rhizosphere to the root. Considering the different stress, differences
in alpha-diversity were evident under drought stress (with lower values
than the not stressed control). Analysis of variance (ANOVA) followed by
Tukey’s honestly significant difference (HSD) post hoc test revealed no
effect of the treatments on the richness in either compartment. However,
a significant interaction between treatment and Inverse Simpson index
for the root samples was identified (p≥0.05).
The beta-diversity was determined using principal coordinate analysis
(PCoA) with Bray-Curtis dissimilarities. The first two axes accounted
for over 72% and 58 % of the variance between treatments of root and
soil samples, respectively. PERMANOVA analysis on the root and soil
datasets indicated a significant effect of all treatments, where drought
alone explains 23.2% of the variance, heat 38.7% and the combination
22.2% in root samples. In soil samples, D explains 29.1% of the
variance, heat only 18.9 % and combined H+D only 13.6% of the variance
(p≥0.001) ( Supplementary Table S4). Then, linear discriminant analysis
effect size (LEfSe) was carried out to identify the taxa driving the
differences between treatments (Supplementary Figure S2) and the
ANCOM-BC differential analysis was used to identify the taxa
differentially abundant between the treatments. Both methods perform a
similar analysis but look at the problem differently. As expected, the
marker taxa established as significant were largely shared between the
methods, being 131 in roots and 138 in soil (Supplementary Figure S3).
For interpretation, we focused then on LefSE results to identify
keystone taxa that explain differences between sample groups.
In root samples, LefSe analysis revealed 21 microbial biomarkers in C,
11 in D, 71 in H and 14 in H+D.The most discriminant taxa in H belonged
to the phyla of Actinobacteriota and Proteobacteria , in
particular, Rhizobiales , Xanthomonadales andStreptomycetales, while Burkholderiales characterized the
D samples. The combined stress (H+D) was characterized by the
presence of Gammaproteobacteria, being the Enterobacterales
and Azospirillaleles the most discriminating taxa. However,
non-stressed roots (C) were characterized by Pseudomanadales andCaulobacterales . (Supplementary Figure S4A)
In Soil samples there were 12 microbial biomarkers detected in C, 9 in
D, 20 in H and 25 in H+D. Here the most discriminant taxa for H were theAlphaproteobacterium Azospirillum and the PhylumFirmicutes member of Bacillales and Clostridiales .
At the same time, H+D was most defined by a taxon fromPolyangiales , followed by Rhizobiales, Fibrobacterales,
Sphingobacteriales and Chitinophagales, and member from the
phylum Elusimicrobiota. In D differences were driven byDevosiaceae and Cryseobacterium and C byGeobacterales and Ktedonobacterales (Supplementary Figure
S4B).