Microbial association networks give relevant insights into plant pathobiomes

Abstract

Interactions between plant pathogens and other plant-associated microorganisms regulate disease.Deciphering the networks formed by these interactions, termed pathobiomes, is crucial to disease management. Our aim was to investigate whether microbial association networks inferred from metabarcoding data give relevant insights into pathobiomes, by testing whether inferred associations contain signals of ecological interactions. We used Poisson Lognormal Models to construct microbial association networks from metabarcoding data and then investigated whether some of these associations corresponded to interactions measurable in co-cultures or known in the literature, by using grapevine (Vitis vinifera) and the fungal pathogen causing powdery mildew (Erysiphe necator) as a model system. Our model suggested that the pathogen species was associated with 23 other fungal species, forming its putative pathobiome. These associations were not known as interactions in the literature, but one of them was confirmed by our co-culture experiments. The yeast Buckleyzyma aurantiaca impeded pathogen growth and reproduction, in line with the negative association found in the microbial network. Co-cultures also supported another association involving two yeast species. Together, these findings indicate that microbial networks can provide plausible hypotheses of ecological interactions that could be used to develop microbiome-based strategies for crop protection. Pauvert et al. 2020. Pathobiome networks 3

Response of the plant core microbiome toFusarium oxysporuminfection and identification of the pathobiome

“…While positive associations between pathogens and other microbiota may help pathogens to cause disease (Hoffman & Arnold, 2010; Jakuschkin et al ., 2016), they may also represent a common response in the stressed plants under pathogen attack (Sweet et al ., 2019). On the other hand, negative associations can be considered as potential biocontrol agents against specific pathogens (Pauvert et al ., 2020). Following the network analysis approach (Pauvert et al ., 2020), we extracted 78 bacterial and fungal OTUs from core network as the putative pathobiome of F. oxysporum , of which 32 bacterial taxa were identified as key members of the pathobiome, including 19 taxa from the phylum Actinobacteria, suggesting these taxa potentially provide antifungal activities at early stages of the FOV infection (de Jesus Sousa & Olivares, 2016; Goudjal et al ., 2016).…”

“…On the other hand, negative associations can be considered as potential biocontrol agents against specific pathogens (Pauvert et al ., 2020). Following the network analysis approach (Pauvert et al ., 2020), we extracted 78 bacterial and fungal OTUs from core network as the putative pathobiome of F. oxysporum , of which 32 bacterial taxa were identified as key members of the pathobiome, including 19 taxa from the phylum Actinobacteria, suggesting these taxa potentially provide antifungal activities at early stages of the FOV infection (de Jesus Sousa & Olivares, 2016; Goudjal et al ., 2016). We were able to isolate five members of the pathobiome and they all showed antifungal activities against FOV suggesting these microbes have a direct role in pathogenesis and that negatively associated microbial taxa of the pathobiome can be considered as candidate biocontrol agents relevant to disease suppression.…”

“…To determine the cotton rhizosphere pathobiome, we followed previously reported approaches (Jakuschkin et al ., 2016; Pauvert et al ., 2020) using network analysis to subset significant correlations between pathogen and associated microorganisms at the interkingdom scale. To investigate the OTUs closely linked with the pathogen as cotton pathobiome, bacterial and fungal species that correlated with F. oxysporum in F and FB treatments were extracted.…”

“…The pathobiome, which is an emerging concept, considers disease as the manifestation of multiple interactions among several microbial species, including pathogens, which affect the health and disease status of the host (Vayssier-Taussat et al ., 2014; Sweet & Bulling, 2017; Bass et al ., 2019). Specifically, co-occurrence network analysis has been used previously to understand microbial interactions at interkingdom scales and to decode the influence of pathogen invasion on the indigenous microbial communities (Erlacher et al ., 2014; Wei et al ., 2015; Bass et al ., 2019; Pauvert et al ., 2020). Given that the pathobiome can be dynamic under different host and environmental conditions, using core members of the rhizosphere soil microbiome to identify the pathobiome could pinpoint microbial species that interact consistently with the target pathogen.…”

Yeast diversity in the vineyard: how it is defined, measured and influenced by fungicides

“…Multiple pipelines for fungal biodiversity analysis. 1 Escribano‐Viana et al (2018), 2 Agarbati et al (2019b), 3 Sirén et al (2019), 4 Vorholt et al (2017), 5 Caporaso et al (2010), 6 Schloss et al (2009), 7 López‐García et al (2018), 8 Edgar (2013), 9 Lucaciu et al (2019), 10 Whittaker (1972), 11 Morris et al (2014), 12 Libis et al (2019), 13 Pauvert et al (2020), 14 Zhang et al (2019), 15 Abdelfattah et al (2019), 16 Nerva et al (2019), 17 Pascual‐García et al (2020). …”