Koehorst, J. | Search bioRxiv

bioRxiv2024

Comparative genomics of Rickettsiella bacteria reveal variable metabolic traits involved in symbiotic interactions with arthropods

Members of the Rickettsiella genus (order: Legionellales) are emerging as widespread bacteria associated with insects, arachnids, and crustaceans. While some Rickettsiella strains are highly virulent pathogens, others are maternally inherited endosymbionts that manipulate arthropod phenotypes, including the induction of defensive symbiosis and cytoplasmic incompatibility. However, the genomic diversity of Rickettsiella remains largely unexplored, and their genetic potential to induce complex phenotypes in arthropods is only partially understood. In this study, we sequenced five new Rickettsiella genomes isolated from three tick species. Through comparative genomics, we observed that Rickettsiella members share similar metabolic capabilities, and collectively lack virulence genes from pathogenic Legionellales. Additional analysis of Rickettsiella genomes revealed significant variability in metabolic properties related to endosymbiosis. Specifically, their capacity to biosynthesize certain B vitamins and heme varies, suggesting a functional role of some Rickettsiella strains in the nutrition of their arthropod hosts. Some Rickettsiella genomes harbour homologs of Wolbachia cif genes, the cause of Wolbachia-induced cytoplasmic incompatibility,, suggesting that Rickettsiella may use a similar molecular mechanism to manipulate the reproduction of their arthropod hosts. Phylogenomics further revealed that tick-borne Rickettsiella exhibit distinct evolutionary origins within the genus, indicating that Rickettsiella have undergone repeated horizontal transfers between ticks and other arthropods.

evolutionary biology↗

bioRxiv2021

Machine learning approaches to predict the plant-associated phenotype of Xanthomonas strains

The genus Xanthomonas has long been considered to consist predominantly of plant pathogens, but over the last decade there has been an increasing number of reports on non-pathogenic and endophytic members. As Xanthomonas species are prevalent pathogens on a wide variety of important crops around the world, there is a need to distinguish between these plant-associated phenotypes. To date a large number of Xanthomonas genomes have been sequenced, which enables the application of machine learning (ML) approaches on the genome content to predict this phenotype. Until now such approaches to the pathogenomics of Xanthomonas strains have been hampered by the fragmentation of information regarding strain pathogenicity over many studies. Unification of this information into a single resource was therefore considered to be an essential step. Mining of 39 papers considering both plant-associated phenotypes, allowed for a phenotypic classification of 578 Xanthomonas strains. For 65 plant-pathogenic and 53 non-pathogenic strains the corresponding genomes were available and de novo annotated for the presence of Pfam protein domains used as features to train and compare three ML classification algorithms; CART, Lasso and Random Forest. Recursive feature extraction provided further insights into the virulence enabling factors, but also yielded domains linked to traits not present in pathogenic strains.

microbiology↗