Biology subjects

Vacher, C.

Publications and source records attributed to Vacher, C..

5 recordsLinked to original sources

bioRxiv2022

NGS data reveals the phyllosphere microbiome of wheat plants infected by the fungal pathogen Zymoseptoria tritici

The fungal pathogen Zymoseptoria tritici is the causal agent of Septoria tritici blotch (STB), a major wheat disease in Western Europe. Microorganisms inhabiting wheat leaves might act as beneficial, biocontrol or facilitating agents that could limit or stimulate the development of Z. tritici. Improving our understanding of microbial communities in the wheat phyllosphere would lead to new insights into STB management. This resource announcement provides fungal and bacterial metabarcoding datasets obtained by sampling wheat leaves differing in the presence of symptoms caused by Z. tritici. Tissues were sampled from three wheat commercial varieties on three sampling dates during a cropping season. Weeds around wheat fields were sampled as well. In total, more than 450 leaf samples were collected. The pathogen Z. tritici was quantified using qPCR. We provide the raw metabarcoding datasets, the Amplicon Sequence Variant (ASV) tables obtained after bioinformatic processing, the metadata associated to each sample (sampling date, wheat variety and tissue health condition), a preliminary descriptive analysis of the data, and the code used for bioinformatic and descriptive statistical analysis.

microbiology↗

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

Maternal effects and environmental filtering shape seed fungal communities in oak trees

O_LITrees, as foundation species, play a pivotal role in the species interaction networks that constitute forest ecosystems. From the seed stage, they interact with microbial communities that affect their growth, health and fitness. Despite their eco-evolutionary importance, the processes shaping seed microbial communities in natural forests have received little attention.\nC_LIO_LITo unravel these processes, we analyzed the microbial communities of seeds collected in populations of sessile oak (Quercus petraea) growing along elevation gradients. We focused on the fungal communities as this group includes seed pathogens. Ecological processes shaping the communities were quantified using joint species distribution models.\nC_LIO_LIFungi were present in all seed tissues, including the embryo. Fungal communities differed significantly among oak populations along the elevation gradients, and among mother trees within the same population. These maternal effects remained significant after seed fall, despite colonization by fungal species on the ground. Associations between tree pathogens and their antagonists were detected in the seeds.\nC_LIO_LIOur results demonstrate that both maternal effects and environmental filtering shape seed microbial communities of sessile oak. They provide a starting point for future research aimed at identifying the seed extended phenotypic traits that influence seed dispersal and germination, and seedling survival and growth across environments.\nC_LI

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↗

bioRxiv2018

Network-aware mutation clustering of cancer

The grouping of cancers across tissue boundaries is central to precision oncology, but remains a difficult problem. Here we present EPICC (Experimental Protein Interaction Clustering of Cancer), a novel technique to cluster cancer patients based on DNA mutation profile, that leverages knowledge of protein-protein interactions to reduce noise and amplify biological signal. We applied EPICC to data from The Cancer Genome Atlas (TCGA), and both recapitulated known cancer clusterings, and identified new cross-tissue cancer groups that may indicate novel cancer molecular subtypes. Investigation of EPICC clusters revealed new protein modules which were recurrently mutated across cancers, and indicate new avenues for research into cancer biology. EPICC leveraged the Vodafone DreamLab citizen science platform, and we provide our results as a resource for researchers to investigate the role of protein modules in cancer.

bioinformatics↗