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Biology subjects

Arkhipova, K.

Publications and source records attributed to Arkhipova, K..

2 recordsLinked to original sources

A symbiont phage protein aids in eukaryote immune evasion

Phages are increasingly recognized as important members of host associated microbial communities. While recent studies have revealed vast genomic diversity in the virosphere, the new frontier is to understand how newly discovered phages may affect higher order processes, such as in the context of host-microbe interactions. Here, we aim to understand the tripartite interplay between phages, bacterial symbionts and marine sponges. In a viromics approach, we discover 491 novel viral clusters and show that sponges, as filter-feeding organisms, are distinct viral niches. By using a nested sampling design, we show that each sponge individual of the four species investigated harbours its own unique virome, regardless of the tissue investigated. We further discover a novel, symbiont phage-encoded ankyrin domain-containing protein which appears to be widely spread in phages of many host-associated contexts including human. The ankyrin protein (ANKp) modulates the eukaryotic immune response against bacteria as confirmed in macrophage infection assays. We predict that the role of ANKp in nature is to facilitate co-existence in the tripartite interplay between phages, symbionts and sponges and possibly in many other host-microbe associations.

microbiology

Robust taxonomic classification of uncharted microbial sequences and bins with CAT and BAT

Current-day metagenomics increasingly requires taxonomic classification of long DNA sequences and metagenome-assembled genomes (MAGs) of unknown microorganisms. We show that the standard best-hit approach often leads to classifications that are too specific. We present tools to classify high-quality metagenomic contigs (Contig Annotation Tool, CAT) and MAGs (Bin Annotation Tool, BAT) and thoroughly benchmark them with simulated metagenomic sequences that are classified against a reference database where related sequences are increasingly removed, thereby simulating increasingly unknown queries. We find that the query sequences are correctly classified at low taxonomic ranks if closely related organisms are present in the reference database, while classifications are made higher in the taxonomy when closely related organisms are absent, thus avoiding spurious classification specificity. In a real-world challenge, we apply BAT to over 900 MAGs from a recent rumen metagenomics study and classified 97% consistently with prior phylogeny-based classifications, but in a fully automated fashion.

bioinformatics