Combined Metabarcoding and Co-occurrence Network Analysis to Profile the Bacterial, Fungal and Fusarium Communities and Their Interactions in Maize Stalks

Cobo‐Díaz, José F.; Baroncelli, Riccardo; Floch, Gaétan Le; Picot, Adeline

doi:10.3389/fmicb.2019.00261

Cited by 60 publications

(43 citation statements)

References 111 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A recent study combining metabarcoding and co-occurrence network analysis profiled microbial communities presents in maize residues and their potential interactions with the different pathogenic Fusarium species (Cobo-Díaz et al, 2019). The microbial communities present in the maize residues may represent important taxa that could lead to biocontrol strategies against Fusarium Head Blight.…”

Section: Residue Microbiota and The Interface Between The Plant And Smentioning

confidence: 99%

Microbiomes and Pathogen Survival in Crop Residues, an Ecotone Between Plant and Soil

Kerdraon

Laval

Suffert

2019

Phytobiomes Journal

View full text Add to dashboard Cite

The negative contribution of crop residues as a source of inoculum for plant diseases is well established. However, microbial ecologists have long reported positive effects of residues on the stability of agrosystems and conservation tillage practices have become increasingly widespread. Most studies have suggested that large microbial communities should be taken into account in plant disease management, but we know little about their ecological interaction with pathogens in the crop residue compartment. This review focuses on microbiomes associated with residues within the context of other microbial habitats in cereal-producing agroecosystems such as phyllosphere or rhizosphere. We connect residue microbiome with the survival of residue-borne fungal plant pathogens, thus combining knowledge in microbial ecology and epidemiology, two disciplines still not sufficiently connected. We provide an overview of the impact of residues on cereal disease epidemics and how dynamic interactions between microbial communities of non-buried residues during their degradation, along with soil and multitude of abiotic factors, can contribute to innovative disease management strategies, including next-generation microbiome-based biocontrol strategies. Starting from the classical but still relevant view of crop residues as a source of pathogen inoculum, we first consider possibilities for limiting the amount of residues on the soil surface to reduce the pathogen pressure. We then describe residues as a transient half-plant/half-soil compartment constituting a key fully fledged microbial ecosystem: in other words, an ecotone which deserves special attention. We focus on microbial communities, the changes in these communities over time and the factors influencing them. Finally, we discuss how the interactions between the microbial communities and the pathogens present on residues could be used: identification of keystone taxa and beneficial assemblages, then preservation of these taxa by adapted agronomic practices or development of synthetic communities, rather than the introduction of a single exogenous biocontrol species designed as a "treatment product".

show abstract

Section: Residue Microbiota and The Interface Between The Plant And Smentioning

confidence: 99%

Microbiomes and Pathogen Survival in Crop Residues, an Ecotone Between Plant and Soil

Kerdraon

Laval

Suffert

2019

Phytobiomes Journal

View full text Add to dashboard Cite

show abstract

“…There are many real bipartite networks such as actor-movie network [35] which illustrates the movies and the actors playing in them, author-article network [36] which shows the articles and theirs authors, gene-disease network [37][38][39][40][41][42][43] which reveals diseases and their targeted genes, metabolite-enzyme network [44][45][46] which shows metabolites and their corresponding enzymes, and host-pathogen PPI network such as fungal pathogen scedosporium aurantiacum with human long epithelial cells [47], Leptospira interrogans and Homo sapiens [48], extracellular bacterial pathogen and human host [49], zika virus non-structural proteins and human host proteins [50], bacterial and fungal pathogens and maize stalks host [51].…”

Section: Preliminaries and Definitionsmentioning

confidence: 99%

Comprehensive host-pathogen protein-protein interaction network analysis

2020

View full text Add to dashboard Cite

Background Infectious diseases are a cruel assassin with millions of victims around the world each year. Understanding infectious mechanism of viruses is indispensable for their inhibition. One of the best ways of unveiling this mechanism is to investigate the host-pathogen protein-protein interaction network. In this paper we try to disclose many properties of this network. We focus on human as host and integrate experimentally 32,859 interaction between human proteins and virus proteins from several databases. We investigate different properties of human proteins targeted by virus proteins and find that most of them have a considerable high centrality scores in human intra protein-protein interaction network. Investigating human proteins network properties which are targeted by different virus proteins can help us to design multipurpose drugs. Results As host-pathogen protein-protein interaction network is a bipartite network and centrality measures for this type of networks are scarce, we proposed seven new centrality measures for analyzing bipartite networks. Applying them to different virus strains reveals unrandomness of attack strategies of virus proteins which could help us in drug design hence elevating the quality of life. They could also be used in detecting host essential proteins. Essential proteins are those whose functions are critical for survival of its host. One of the proposed centralities named diversity of predators, outperforms the other existing centralities in terms of detecting essential proteins and could be used as an optimal essential proteins’ marker. Conclusions Different centralities were applied to analyze human protein-protein interaction network and to detect characteristics of human proteins targeted by virus proteins. Moreover, seven new centralities were proposed to analyze host-pathogen protein-protein interaction network and to detect pathogens’ favorite host protein victims. Comparing different centralities in detecting essential proteins reveals that diversity of predator (one of the proposed centralities) is the best essential protein marker.

show abstract

“…The raw sequences, after Q-score filtering performed by Genome Quebec (reads with Q-score higher than 25 were kept), were processed and analyzed with QIIME v1.9.1 (Quantitative Insights Into Microbial Ecology) (Caporaso et al, 2010), according to Legrand et al (2018) and Cobo-Díaz et al (2019). For 16S rRNA amplicons, the forward (R1) and reverse (R2) pairedend sequences were joined using multiple_join_paired_ends.py, followed by multiple_split_libraries_fastq.py for demultiplexing.…”

Section: S Rrna Read Filteringmentioning

confidence: 99%

Water Microbiota in Greenhouses With Soilless Cultures of Tomato by Metabarcoding and Culture-Dependent Approaches

et al. 2020

Self Cite

View full text Add to dashboard Cite

Water supply, in hydroponic greenhouses, can originate from groundwater, surface water or rainwater stored in open tanks. To limit contamination of water supply, several methods have been used including active and passive methods such as slow filtration techniques which consist in passing the nutrient solutions slowly through filters. The purpose of this study was to describe the microbiota associated with water sampled before entering greenhouses and in recirculating nutrient solutions, either before or after running through a biofiltration system. Metabarcoding analysis revealed that water ecosystems were unique niches for diverse bacterial and fungal communities. Microbial composition varied greatly across storage conditions (groundwater vs. rainwater) and among greenhouses, suggesting that water microbiota is site-and storagecondition-specific. Nonetheless, we found that microbiota structure in open-stored water (either coming from ground or rain) shared a higher degree of similarity than with water directly pumped out of the ground. Open-stored waters were characterized by predominant taxa, notably those involved in aerobic chemoheterotrophy, such as the Sphingomonadaceae and Hyphomicrobiaceae families. Water directly collected from the ground showed the lowest levels of fungal and bacterial richness while also characterized by a significantly higher level of bacterial equitability and an enrichment in taxa involved in N-cycling. Slow filtration allowed reducing cultivable bacterial loads as well Pythium spp. and Fusarium oxysporum propagules, based on culture-dependent results, without compromising microbiota richness and diversity. Although compositional structure was similar following biofiltration, significant differences in bacterial (but not fungal) taxa abundance were reported, with primarily an enrichment of Chelativorans, Mycobacterium, and Gemmata as well as a depletion of Rhodobacter, Aminobacter, and Ellin329. The exact mechanisms by which such taxa would be favored at the expense of other remained unknown. Besides the accurate description of microbiota found in water at both taxonomical and predicted functional levels, our study allowed comparing the water microbiota between various storage system and following biofiltration. Although preliminary, our results provide a first insight into the potential microbial diversity, which can increase ecosystem functionality.

show abstract

Combined Metabarcoding and Co-occurrence Network Analysis to Profile the Bacterial, Fungal and Fusarium Communities and Their Interactions in Maize Stalks

Cited by 60 publications

References 111 publications

Microbiomes and Pathogen Survival in Crop Residues, an Ecotone Between Plant and Soil

Microbiomes and Pathogen Survival in Crop Residues, an Ecotone Between Plant and Soil

Comprehensive host-pathogen protein-protein interaction network analysis

Water Microbiota in Greenhouses With Soilless Cultures of Tomato by Metabarcoding and Culture-Dependent Approaches

Contact Info

Product

Resources

About