bioRxivEvolutionary BiologyPreprint
A new Serratia taxon found in a trombidiform mite shows genomic evidence of host adaptation but is phylogenetically distinct from Serratia symbiotica
Jia ChangiD, Ary HoffmanniD, Neil David YoungiD, Sunita SumanamiD, Thomas SchmidtiD, Joshua A ThiaiD
Abstract
Abstract The evolutionary transition from free-living bacteria to host-adapted symbionts is typically accompanied by substantial genomic changes. Within the genus Serratia, this transition has so far been documented only in the aphid-associated species, Serratia symbiotica, whereas all other Serratia species are free-living. In this study, we identify a putatively new Serratia taxon using metagenomic approaches in the trombidiform mite, Halotydeus destructor. We provide genomic evidence for a possible transition to a host-adapted lifestyle in this new Serratia sp., which sits phylogenetically within a clade of free-living Serratia species. The genomic signatures of evolving toward a host-adapted lifestyle include a reduced genome size and coding capacity, an elevated transposase fraction, and detectable chromosomal rearrangements, relative to other free-living Serratia species. However, the species does not show the same structure of genome reduction as S. symbiotica. Functional gene content analyses including COG categories and virulence factor repertoires also showed that our new Serratia sp. had a distinct gene profile from both free-living Serratia and S. symbiotica. Additional genomic screening suggests that infections of the new Serratia sp. are common in wild populations of H. destructor across its invasive Australian range, warranting future investigations on the possible role this symbiont plays in the evolutionary ecology of its mite host. Our work not only highlights a potential parallel transition to a host-adapted lifestyle in the Serratia genus, but also represents the first characterisation of a possible host-adapted Serratia taxon in a trombidiform mite.
- assay: metagenomics
- research focus: cell type annotation
- research focus: clustering
- research focus: differential expression
- approach: genetic perturbation / CRISPR
- approach: statistical model
- approach: machine learning (classical)
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