Environmental Metabarcoding Reveals Contrasting Belowground and Aboveground Fungal Communities from Poplar at a Hg Phytomanagement Site

Abstract

Characterization of microbial communities in stressful conditions at a field level is rather scarce, especially when considering fungal communities from aboveground habitats. We aimed at characterizing fungal communities from different poplar habitats at a Hg-contaminated phytomanagement site by using Illumina-based sequencing, network analysis approach, and direct isolation of Hg-resistant fungal strains. The highest diversity estimated by the Shannon index was found for soil communities, which was negatively affected by soil Hg concentration. Among the significant correlations between soil operational taxonomic units (OTUs) in the co-occurrence network, 80% were negatively correlated revealing dominance of a pattern of mutual exclusion. The fungal communities associated with Populus roots mostly consisted of OTUs from the symbiotic guild, such as members of the Thelephoraceae, thus explaining the lowest diversity found for root communities. Additionally, root communities showed the highest network connectivity index, while rarely detected OTUs from the Glomeromycetes may have a central role in the root network. Unexpectedly high richness and diversity were found for aboveground habitats, compared to the root habitat. The aboveground habitats were dominated by yeasts from the Lalaria, Davidiella, and Bensingtonia genera, not detected in belowground habitats. Leaf and stem habitats were characterized by few dominant OTUs such as those from the Dothideomycete class producing mutual exclusion with other OTUs. Aureobasidium pullulans, one of the dominating OTUs, was further isolated from the leaf habitat, in addition to Nakazawaea populi species, which were found to be Hg resistant. Altogether, these findings will provide an improved point of reference for microbial research on inoculation-based programs of tailings dumps.

Fungal community assembly in drought-stressed sorghum shows stochasticity, selection, and universal ecological dynamics

“…2). The gap between the very strong compartment effect in the first Populus study and our results may be explained by the low overlap of EM fungal OTUs in Populus roots and soil 39,41 compared to the greater overlap of AM fungi in these two compartments in sorghum 11 .…”

“…While acknowledging that comparison among studies is never simple, comparison of our results with those two studies may help explain the more than twofold difference in Populus compartment effects. The strong compartment effects seen in our study and the first of the Populus studies 39 rely, in part, on an abundance of yeasts in leaves, which was smaller in the second Populus study 40 ( Supplementary Fig. 2).…”

Native stinging nettle (Urtica dioica L.) growing spontaneously under short rotation coppice for phytomanagement of trace element contaminated soils: Fibre yield, processability and quality

“…Soils at both sites differed in their composition. The full description of the Tavaux and Fresnes sur Escaut sites and soils are provided in (Durand et al, 2017) and (Ciadamidaro et al, 2017), respectively. The main meteorological parameters during the growth period at the two sites are summarized in Table 1.…”

“…It aims at investigating the potentialities of nettle fibres growing spontaneously under SRC grown poplar at two phytomanagement sites, for material application. Hg is the main contaminant at the first site due to the chlor alkali process (Durand et al, 2017), whereas As, Cd, Pb and Zn are the main contaminants at the second site due to contamination of deposit sediments from the canal Escaut. (Ciadamidaro et al, 2017).…”

Biases in the metabarcoding of plant pathogens using rust fungi as a model system

“…DNA metabarcoding seems especially promising for the monitoring of potential plant pathogens (hereafter pathogens), because it bypasses the need for cultivation and isolation of species, and is able to detect plant pathogenic species when they occur asymptomatically (Malcolm, Kuldau, Gugino, & Jiménez-Gasco, 2013;Stergiopoulos & Gordon, 2014) or at barely discernible levels. While DNA metabarcoding holds great potential for detecting and monitoring fungi in their environment (Durand et al, 2017;Miller, Hopkins, Inward, & Vogler, 2016;Schmidt et al, 2013), it has not yet been widely applied to pathogens specifically (Abdelfattah, Nicosia, Cacciola, Droby, & Schena, 2015;Merges et al, 2018). It is therefore crucial to more fully understand the potential limitations of this new approach.…”