Deciphering the Pathobiome: Intra- and Interkingdom Interactions Involving the Pathogen Erysiphe alphitoides

Abstract

Plant-inhabiting microorganisms interact directly with each other, forming complex microbial interaction networks. These interactions can either prevent or facilitate the establishment of new microbial species, such as a pathogen infecting the plant. Here, our aim was to identify the most likely interactions between Erysiphe alphitoides, the causal agent of oak powdery mildew, and other foliar microorganisms of pedunculate oak (Quercus robur L.). We combined metabarcoding techniques and a Bayesian method of network inference to decipher these interactions. Our results indicate that infection with E. alphitoides is accompanied by significant changes in the composition of the foliar fungal and bacterial communities. They also highlight 13 fungal operational taxonomic units (OTUs) and 13 bacterial OTUs likely to interact directly with E. alphitoides. Half of these OTUs, including the fungal endophytes Mycosphaerella punctiformis and Monochaetia kansensis, could be antagonists of E. alphitoides according to the inferred microbial network. Further studies will be required to validate these potential interactions experimentally. Overall, we showed that a combination of metabarcoding and network inference, by highlighting potential antagonists of pathogen species, could potentially improve the biological control of plant diseases.

Foliar fungal communities strongly differ between habitat patches in a landscape mosaic

“…It is difficult to distinguish real contaminations (sequences originating from the people who performed the experiments, the laboratory environment and the DNA extraction kit) from cross-contaminations between samples, occurring during the DNA extraction, amplification and sequencing (Esling, Lejzerowicz & Pawlowski, 2015; Galan et al, 2016). It is highly probable that OTUs assigned to Erysiphe alphitoides , the agent responsible for the oak powdery mildew (1.5% of the negative control sequences; Jakuschkin et al, 2016) or Botrytis cinerea , responsible for the grey mold on grapes (1.2%; Jaspers et al, 2016) are likely cross-contaminations because they are strongly related to a specific host. Moreover, the removal of very abundant OTUs strongly altered the taxonomic composition of the samples, and removed some species known to be abundant on leaves such as Aureobasidium pullulans, known as very abundant on grapevine (Pinto & Gomes, 2016).…”

“…We computed 100 random rarefied OTU matrices, using the smallest number of sequences per sample as a threshold. The number of OTUs per sample (OTU richness) and the dissimilarity between samples (Bray-Curtis index based on abundances and Jaccard index based on occurrences) were calculated for each rarefied matrix and averaged (Cordier et al, 2012; Jakuschkin et al, 2016). However, because the relevance of rarefaction is debated in the scientific community (Hughes & Hellmann, 2005; McMurdie & Holmes, 2014), we also performed the analyses on the raw OTU matrix by including the square root of the total number of sequences per sample (abundance) as first explanatory variable in all the models.…”

Amphibian skin fungal communities vary across host species and do not correlate with infection by a pathogenic fungus

“…There is some evidence that amplicon sequencing data might sometimes provide a reliable indicator of pathogen occurrence on hosts. For instance, in plant systems, several studies have found correlations between the number of amplicon reads associated with fungal pathogens and disease severity in host tissues (Sapkota et al ., ; Jakuschkin et al ., ).…”

Potentials and pitfalls in the analysis of bipartite networks to understand plant–microbe interactions in changing environments

“…These networks are characterised based on link direction (i.e., directed or undirected ) and weighting (see below). Co‐occurrence networks can be used to understand microbial species interactions and soil (particularly rhizosphere) microbial community, mostly through construction of unipartite networks in which all nodes (species) are categorised equally (Jakuschkin et al, ; Shi et al, ; Toju, Kishida, Katayama, & Takagi, ). By contrast, bipartite networks describe interactions between two guilds of organisms (e.g., plants and microbes) and interactions within guild (e.g., plants) are not analysed (Figure a).…”