bioRxivMicrobiologyPreprint
Closed genomes uncover a saltwater species of Candidatus Electronema and shed new light on the boundary between marine and freshwater cable bacteria
M. Sereika, F. Petriglieri, T. B. N. Jensen, A. Sannikov, M. Hoppe, P. H. Nielsen, I. P. G. Marshall, A. Schramm, M. Albertsen
Abstract
Cable bacteria of the Desulfobulbaceae family are centimeter-long filamentous bacteria, which are capable of conducting long-distance electron transfer. Currently, all cable bacteria are classified into two candidate genera: Candidatus Electronema, typically found in freshwater environments, and Candidatus Electrothrix, typically found in saltwater environments. This taxonomic framework is based on both 16S rRNA gene sequences and metagenome-assembled genome (MAG) phylogenies. However, most of the currently available MAGs are highly fragmented, incomplete, and thus likely miss key genes essential for deciphering the physiology of cable bacteria. To address this, we performed Nanopore long read (total 162.4 Gbp) and Illumina short read (total 148.3 Gbp) shotgun sequencing of selected environmental samples and a single-strain enrichment of Ca. Electronema aureum. We recovered multiple cable bacteria MAGs, including two circular and one single-contig. Phylogenomic analysis, also confirmed by 16S rRNA gene-based phylogeny, classified one circular MAG and the single-contig MAG as novel species of cable bacteria, which we propose to name Ca. Electronema halotolerans and Ca. Electrothrix laxa, respectively. The Ca. Electronema halotolerans, despite belonging to the previously recognized freshwater genus of cable bacteria, was retrieved from brackish-water sediment. Metabolic predictions showed several adaptations to a high salinity environment, similar to the "saltwater" Ca. Electrothrix species, indicating how Ca. Electronema halotolerans may be the evolutionary link between marine and freshwater cable bacteria lineages.
- assay: long-read sequencing
- assay: bulk RNA-seq
- research focus: clustering
- research focus: gene regulatory network inference
- approach: statistical model
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