Combining whole-genome shotgun sequencing and rRNA gene amplicon analyses to improve detection of microbe-microbe interaction networks in plant leaves

ISME J. 2020 Aug;14(8):2116-2130. doi: 10.1038/s41396-020-0665-8. Epub 2020 May 13.


Microorganisms from all domains of life establish associations with plants. Although some harm the plant, others antagonize pathogens or prime the plant immune system, support the acquisition of nutrients, tune plant hormone levels, or perform additional services. Most culture-independent plant microbiome research has focused on amplicon sequencing of the 16S rRNA gene and/or the internal transcribed spacer (ITS) of rRNA genomic loci, which show the relative abundance of the microbes to each other. Here, we describe shotgun sequencing of 275 wild Arabidopsis thaliana leaf microbiomes from southwest Germany, with additional bacterial 16S and eukaryotic ITS1 rRNA amplicon data from 176 of these samples. Shotgun data, which unlike the amplicon data capture the ratio of microbe to plant DNA, enable scaling of microbial read abundances to reflect the microbial load on the host. In a more cost-effective hybrid strategy, we show they also allow a similar scaling of amplicon data to overcome compositionality problems. Our wild plants were dominated by bacterial sequences, with eukaryotes contributing only a minority of reads. Microbial membership showed weak associations with both site of origin and plant genotype, both of which were highly confounded in this dataset. There was large variation among microbiomes, with one extreme comprising samples of low complexity and a high load of microorganisms typical of infected plants, and the other extreme being samples of high complexity and a low microbial load. Critically, considering absolute microbial load led to fundamentally different conclusions about microbiome assembly and the interaction networks among major taxa.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Genes, rRNA
  • Germany
  • Microbiota*
  • Plant Leaves
  • RNA, Ribosomal, 16S / genetics


  • RNA, Ribosomal, 16S