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Bacterial networks and co‐occurrence relationships in the lettuce root microbiota
Author(s) -
Cardinale Massimiliano,
Grube Martin,
Erlacher Armin,
Quehenberger Julian,
Berg Gabriele
Publication year - 2015
Publication title -
environmental microbiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.954
H-Index - 188
eISSN - 1462-2920
pISSN - 1462-2912
DOI - 10.1111/1462-2920.12686
Subject(s) - biology , pyrosequencing , domestication , proteobacteria , microbiome , amplicon , cultivar , botany , rhizosphere , 16s ribosomal rna , ecology , bacteria , genetics , gene , polymerase chain reaction
Summary 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 L actuca sativa cultivars and the wild ancestor L actuca serriola by pyrosequencing of 16 S rRNA gene amplicon libraries. The lettuce microbiota was dominated by P roteobacteria and B acteriodetes, as well as abundant C hloroflexi and A ctinobacteria. 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 P seudomonas , F lavobacterium and S phingomonadaceae 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.