Coupling of metataxonomics and culturing improves bacterial diversity characterization and identifies a novel Rhizorhapis sp. with metal resistance potential in a multi-contaminated waste sediment

AbstractLong-term contaminated environments have been recognized as potential hotspots for bacterial discovery in taxonomic and functional terms for bioremediation purposes. Here, bacterial diversity in waste sediment collected from a former industrial dumpsite and contaminated with petroleum hydrocarbon and heavy metals was investigated through the parallel application of culture-independent (16S rRNA gene amplicon sequencing) and -dependent (plate culturing followed by colony picking and identification of isolates by 16S rRNA gene Sanger sequencing) approaches. The bacterial diversities retrieved by both approaches greatly differed. Bacteroidetes and Proteobacteria were dominant in the culture-independent community, while Firmicutes and Actinobacteria were the main culturable groups. Only 2.7% of OTUs (operational taxonomic units) in the culture-independent dataset were cultured. Most of the culturable OTUs were absent or in very low abundances in the culture-independent dataset, revealing that culturing is a useful tool to study the rare bacterial biosphere. One culturable OTUs (comprising only the isolate SPR117) was identified as a potential new species in the genus Rhizorhapis (class Alphaproteobacteria) and was selected for further characterization. Phytopathogenicity tests showed that Rhizorhapis sp. strain SPR117 (ATCC TSD-228) is not pathogenic for lettuce, despite the only described species within this genus, Rhizorhapis suberifaciens, is causal agent of the lettuce corky root disease. The genome of the strain SPR117 was sequenced, assembled in 256 contigs, with a length of 4,419,522 bp and a GC content of 59.9%), and its further annotation revealed the presence of genes related to the resistance to arsenic, copper, iron, and mercury, among other metals. Therefore, the coupling of metataxonomics and culturing is a useful tool to obtain not only an improved description of bacterial communities in contaminated environments, but also to isolate microorganisms with bioremediation potential.