FEMS Microbiology Ecology · 2022 · 14 citations · 65 references
Life SciencesCrop SciencesMicrobiologyMicrobial EcologyMicrobiome ResearchPlant-microbe InteractionsMicrobial DiversityMicrobiome DiversityPlant MicrobiomePlant-associated Microbial CommunitiesRhizosphere MicrobiologyRoot MicrobiomeMicrobial Co-occurrence NetworksPlant Microbiome DiversityRhizosphere Bacterial CommunitiesRhizosphere BiodiversityRhizosphere Fungal CommunitiesRhizosphere MicrobiomesRoot-associated Bacterial CommunitiesRoot-associated Microbial Communities
Due to their potential applications for food safety, there is a growing interest in rice root-associated microbial communities, but some systems remain understudied. Here, we compare the assemblage of root-associated microbiota in rice sampled in 19 small farmer's fields from irrigated and rainfed lowlands in Burkina Faso, using an amplicon metabarcoding approach of the 16S rRNA gene (prokaryotes, three plant samples per field) and ITS (fungi, one sample per field). In addition to the expected structure by root compartments (root vs rhizosphere) and geographical zones, we showed that the rice production system is a major driver of microbiome structure. In irrigated systems, we found a higher diversity of prokaryotic communities from the rhizosphere and more complex co-occurrence networks, compared to rainfed lowlands, while fungal communities exhibited an opposite pattern (higher richness in rainfed lowlands). Core taxa were different between the two systems, and indicator species were identified: mostly within Bacillaceae in rainfed lowlands, and within Burkholderiaceae and Moraxellaceae in irrigated areas. Finally, a higher abundance in rainfed lowlands was found for mycorrhizal fungi (both compartments) and rhizobia (rhizosphere only). Our results highlight deep microbiome differences induced by contrasted rice production systems that should consequently be considered for microbial engineering applications.
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R: A Language and Environment for Statistical Computing
R Core Team · 2000 · 353K citations · Full text
Natural Sciences, Computer Science, Statistical Computing +10
DADA2: High-resolution sample inference from Illumina amplicon data
Benjamin J. Callahan, Paul J. McMurdie, Michael Rosen et al. · Nature Methods · 2016 · 36.3K citations
Cutadapt removes adapter sequences from high-throughput sequencing reads
Marcel Martin · EMBnet journal · 2011 · 35.8K citations · Full text