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Article

Bacterial networks and co‐occurrence relationships in the lettuce root microbiota

Massimiliano CardinaleInstitute of Environmental Biotechnology Graz University of Technology Graz AustriaMartín GrubeInstitute of Plant Sciences University of Graz Graz AustriaArmin ErlacherInstitute of Environmental Biotechnology Graz University of Technology Graz AustriaJulian QuehenbergerInstitute of Environmental Biotechnology Graz University of Technology Graz AustriaGabriele BergInstitute of Environmental Biotechnology Graz University of Technology Graz Austria
2014en
ABI

Abstract

Lettuce is one of the most common raw foods worldwide, but occasionally also involved in pathogen outbreaks. To understand the correlative structure of the bacterial community as a network, we studied root microbiota of eight ancient and modern Lactuca sativa cultivars and the wild ancestor Lactuca serriola by pyrosequencing of 16S rRNA gene amplicon libraries. The lettuce microbiota was dominated by Proteobacteria and Bacteriodetes, as well as abundant Chloroflexi and Actinobacteria. Cultivar specificity comprised 12.5% of the species. Diversity indices were not different between lettuce cultivar groups but higher than in L. serriola, suggesting that domestication lead to bacterial diversification in lettuce root system. Spearman correlations between operational taxonomic units (OTUs) showed that co-occurrence prevailed over co-exclusion, and complementary fluorescence in situ hybridization-confocal laser scanning microscopy (FISH-CLSM) analyses revealed that this pattern results from both potential interactions and habitat sharing. Predominant taxa, such as Pseudomonas, Flavobacterium and Sphingomonadaceae rather suggested interactions, even though these are not necessarily part of significant modules in the co-occurrence networks. Without any need for complex interactions, single organisms are able to invade into this microbial network and to colonize lettuce plants, a fact that can influence the susceptibility to pathogens. The approach to combine co-occurrence analysis and FISH-CLSM allows reliably reconstructing and interpreting microbial interaction networks.

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Cited by 20 references