Biology subjects

Vallance, J.

Publications and source records attributed to Vallance, J..

3 recordsLinked to original sources

bioRxiv2025

Differential impacts of drought and esca expression on Ascomycota fungi in the trunks and young organs of mature grapevines

Perennial plant decline is increasingly threatening the profitability and sustainability of agriculture and forestry worldwide. It results from intricate interactions between microbial communities, the plant host, and abiotic stressors. We investigated the effects of drought and esca disease on mature grapevine phytobiomes. Grapevines display no esca leaf symptoms during droughts, but the impacts of drought and esca expression on fungal communities and wood health in mature plants remain poorly understood. We studied 43 uprooted 30-year-old naturally infected vines in three experimental conditions: well-watered asymptomatic (Control) vines, vines with esca symptoms (Esca), and vines subjected to water deficit (WD) over two consecutive summers. We profiled trunk, cane, stem and petiole Ascomycota communities by DNA metabarcoding with primers specifically designed for grapevine trunk-associated Ascomycota, and quantified wood necrosis. The Ascomycota communities of trunks and younger organs clearly differed, and drought and esca had different impacts on the Ascomycota communities of perennial and young organs. In the trunk, drought significantly decreased fungal diversity in healthy wood and increased the abundance of wood pathogens (e.g. Phaeomoniella chlamydospora, Botryosphaeria dothidea). In young organs, esca expression decreased the species richness and diversity of the Ascomycota community to a greater extent than drought. We also found that the relative proportion of healthy wood was smaller in plants with esca symptoms than in control plants. Thus, drought increased Ascomycota pathogen abundance in the trunk but did not increase wood degradation and esca expression, highlighting the need to investigate the molecular basis of plant-microbiome interactions under multi-stress conditions.

plant biology↗

bioRxiv2020

Microbial association networks give relevant insights into plant pathobiomes

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.

ecology↗

bioRxiv2019

Microbial networks inferred from metabarcoding data lack replicability: consequences for next-generation biomonitoring

Plant-associated microbial interaction networks protect plants against disease. There is, therefore, a need to monitor in real time their responses to environmental changes to predict disease risk and adjust crop protection strategies. Next-Generation Biomonitoring (NGB) proposes to reconstruct automatically these networks from metabarcoding data, to complement ecological community properties commonly used for ecosystem health assessment. This study aimed to evaluate the benefits and shortcomings of community-level and network-level properties for biomonitoring. We specifically investigated whether microbial networks inferred from metabarcoding data show robust responses to agricultural practices, using the grapevine microbiota as a study system. Our results demonstrate a strong footprint of the agricultural practice on the metabarcoding data, when analyzed at the community level. The richness, diversity and evenness of fungal communities were significantly higher in organic than conventional plots. The cropping system also affected the composition of grapevine foliar fungal communities significantly. Contrary to our expectations, microbial networks were less sensitive to changes in agricultural practices than microbial communities, confirming that NGB should not only consider network-level properties but also community-level properties. Moreover, we found that microbial networks lacked replicability within a cropping system but that consensus networks, built from several network replicates, could generate relevant hypotheses of microbial interactions. As things stand, community-level properties appear to be a more reliable and statistically powerful monitoring option than network-level properties. Future developments, especially in network inference methods, are likely to challenge our findings and help to improve the monitoring of the ecosystem services provided by the plant microbiota.

ecology↗