Integration of metabolome and microbiota datasets
The rhizosphere microbiota dataset was taken from our previous experiment (Vescio et al., 2021), where additional maize plants from the present experiment (and the same pot) were used. In the previous work, libraries targeting the V4 region of the bacterial 16S rRNA were obtained from DNA extracted from rhizosphere soil (McPherson et al., 2018), and sequenced using an Illumina MiSeq platform (Illumina, San Diego, CA, USA) using the 300PE chemistry. De-multiplexed forward and reverse reads were merged using the PEAR 0.9.1 algorithm using default parameters (Zhang et al. 2014). Data handling was carried out using VSEARCH 2.14.2 (Rognes et al. 2016) to quality-filtered reads, discard chimeric sequences, bin OTUs and assign taxonomy by querying the SILVA database (v. 132) (Quast et al. 2012).
Data analysis was performed using R statistical software 3.5 (R Core Team 2013) (Supplementary material 1). The microbiota dataset was processed to remove singletons and OTUs generated from the amplification of plastidial rRNA. This dataset was then normalized using DESeq2 (Love, Huber & Anders 2014). The metabolome dataset was also processed to remove those metabolites that were not found in at least 25% of the samples. Then, a framework that considers both metabolomic and metabarcoding datasets was built to finely investigate the interaction between root exudates and rhizosphere microbial community. This framework has been built using MOFA+ (Argelaguet et al. 2018). MOFA+ captured three factors that explain a high proportion of variation in both molecules and taxa in the three different treatments (Supplementary material 1). This information allowed identifying, for each treatment, the top 10% molecules and 0.1% microbial taxa that mostly contribute to explain the variation due to the treatments. Then, their relative abundance was correlated (Two tails, Pearson’s correlation) and significant correlations (p<0.05) were recorded.