Root knot nematodes (RKN,
Meloidogyne
spp.) are serious pathogens of numerous crops worldwide. Understanding the roles plant rhizosphere soil microbiome play during RKN infection is very important. The current study aims at investigating the impacts of soil microbiome on the activity of RKN. In this study, the 16S rRNA genes of the bacterial communities from nematode-infested and non-infested rhizosphere soils from four different plants were sequenced on the Illumina Hi-Seq platform. The soil microbiome effects on RKN infection were tested in a greenhouse assay. The non-infested soils had more microbial diversity than the infested soils from all plant rhizospheres, and both soil types had exclusive microbial communities. The inoculation of the microbiomes from eggplant and cucumber non-infested soils to tomato plants significantly alleviated the RKN infection, while the microbiome from infested soil showed increased the RKN infection. Furthermore, bacteria
Pseudomonas
sp. and
Bacillus
sp. were screened out from non-infested eggplant soil and exhibited biocontrol activity to RKN on tomato. Our findings suggest that microbes may regulate RKN infection in plants and are involved in biocontrol of RKN.
Electronic supplementary material
The online version of this article (10.1007/s00248-019-01319-5) contains supplementary material, which is available to authorized users.
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
This is an open access article under the terms of the Creat ive Commo ns Attri bution-NonCo mmercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
How does the evolution of bioinformatics tools impact the biological interpretation of high-throughput sequencing datasets? For eukaryotic metabarcoding studies, in particular, researchers often rely on tools originally developed for the analysis of 16S ribosomal RNA (rRNA) datasets. Such tools do not adequately account for the complexity of eukaryotic genomes, the ubiquity of intragenomic variation in eukaryotic metabarcoding loci, or the differential evolutionary rates observed across eukaryotic genes and taxa. Recently, metabarcoding workflows have shifted away from the use of Operational Taxonomic Units (OTUs) towards delimitation of Amplicon Sequence Variants (ASVs). We assessed how the choice of bioinformatics algorithm impacts the downstream biological conclusions that are drawn from eukaryotic 18S rRNA metabarcoding studies. We focused on four workflows including UCLUST and VSearch algorithms for OTU clustering, and DADA2 and Deblur algorithms for ASV delimitation. We used two 18S rRNA datasets to further evaluate whether dataset complexity had a major impact on the statistical trends and ecological metrics: a "high complexity" (HC) environmental dataset generated from community DNA in Arctic marine sediments, and a "low complexity" (LC) dataset representing individually-barcoded nematodes. Our results indicate that ASV algorithms produce more biologically realistic metabarcoding outputs, with DADA2 being the most consistent and accurate pipeline regardless of dataset complexity. In contrast, OTU clustering algorithms inflate the metabarcoding-derived estimates of biodiversity, consistently returning a high proportion of "rare" Molecular Operational Taxonomic Units (MOTUs) that appear to represent computational artifacts and sequencing errors. However, species-specific MOTUs with high relative abundance are often recovered regardless of the bioinformatics approach. We also found high concordance across pipelines for downstream ecological analysis based on beta-diversity and alpha-diversity comparisons that utilize taxonomic assignment information. Analyses of LC datasets and rare MOTUs are especially sensitive to the choice of algorithms and better software tools may be needed to address these scenarios.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.