Metagenome-assembled genomes reveal greatly expanded taxonomic and functional diversification of the abundant marine Roseobacter RCA cluster

BACKGROUND

The RCA (Roseobacter clade affiliated) cluster belongs to the family Roseobacteracea and represents a major Roseobacter lineage in temperate to polar oceans. Despite its prevalence and abundance, only a few genomes and one described species, Planktomarina temperata, exist. To gain more insights into our limited understanding of this cluster and its taxonomic and functional diversity and biogeography, we screened metagenomic datasets from the global oceans and reconstructed metagenome-assembled genomes (MAG) affiliated to this cluster.

RESULTS

The total of 82 MAGs, plus five genomes of isolates, reveal an unexpected diversity and novel insights into the genomic features, the functional diversity, and greatly refined biogeographic patterns of the RCA cluster. This cluster is subdivided into three genera: Planktomarina, Pseudoplanktomarina, and the most deeply branching Candidatus Paraplanktomarina. Six of the eight Planktomarina species have larger genome sizes (2.44-3.12 Mbp) and higher G + C contents (46.36-53.70%) than the four Pseudoplanktomarina species (2.26-2.72 Mbp, 42.22-43.72 G + C%). Cand. Paraplanktomarina is represented only by one species with a genome size of 2.40 Mbp and a G + C content of 45.85%. Three novel species of the genera Planktomarina and Pseudoplanktomarina are validly described according to the SeqCode nomenclature for prokaryotic genomes. Aerobic anoxygenic photosynthesis (AAP) is encoded in three Planktomarina species. Unexpectedly, proteorhodopsin (PR) is encoded in the other Planktomarina and all Pseudoplanktomarina species, suggesting that this light-driven proton pump is the most important mode of acquiring complementary energy of the RCA cluster. The Pseudoplanktomarina species exhibit differences in functional traits compared to Planktomarina species and adaptations to more resource-limited conditions. An assessment of the global biogeography of the different species greatly expands the range of occurrence and shows that the different species exhibit distinct biogeographic patterns. They partially reflect the genomic features of the species.

CONCLUSIONS

Our detailed MAG-based analyses shed new light on the diversification, environmental adaptation, and global biogeography of a major lineage of pelagic bacteria. The taxonomic delineation and validation by the SeqCode nomenclature of prominent genera and species of the RCA cluster may be a promising way for a refined taxonomic identification of major prokaryotic lineages and sublineages in marine and other prokaryotic communities assessed by metagenomics approaches. Video Abstract.

Detection of Genetic Rearrangements in the Regulators of Complement Activation RCA Cluster by High-Throughput Sequencing and MLPA

The regulators of complement activation (RCA) gene cluster in 1q31-1q32 includes most of the genes encoding complement regulatory proteins. Genetic variability in the RCA gene cluster frequently involve copy number variations (CNVs), a type of chromosome structural variation causing alterations in the number of copies of specific regions of DNA. CNVs in the RCA gene cluster often relate with gene rearrangements that result in the generation of novel genes, carrying internal duplications or deletions, and hybrid genes, resulting from the fusion or exchange of genetic material between two different genes. These gene rearrangements are strongly associated with a number of rare and common diseases characterized by complement dysregulation. Identification of CNVs in the RCA gene cluster is critical in the molecular diagnostic of these diseases. It can be done by bioinformatics analysis of DNA sequence data generated by massive parallel sequencing techniques (NGS, next generation sequencing) but often requires special techniques like multiplex ligation-dependent probe amplification (MLPA). This is because the currently used massive parallel DNA sequencing approaches do not easily identify all the structural variations in the RCA gene cluster. We will describe here how to use the MLPA assays and two computational tools to analyze NGS data, NextGENe and ONCOCNV, to detect CNVs and gene rearrangements in the RCA gene cluster.