Genomic analysis of 38 Legionella species identifies large and diverse effector repertoires

TLDR

Legionella pneumophila delivers ~300 virulence effectors to manipulate host cells, yet little is known about effector repertoires in other Legionella species. The study aimed to sequence and analyze the genomes of 38 Legionella species to predict their effector repertoires. Researchers sequenced 38 Legionella genomes and used a validated machine‑learning model to identify 5,885 predicted effectors. They found a core set of seven effectors shared by all species, highly diverse and largely non‑overlapping repertoires, species‑specific effectors with low GC content suggesting horizontal acquisition, and numerous novel conserved domains and domain combinations that provide a roadmap for future functional studies.

Abstract

Gil Segal, Howard Shuman and colleagues sequence the genomes of 38 Legionella species and analyze 5,885 predicted effector proteins. Their analysis identifies a core set of seven effectors shared by all 38 species and numerous previously unidentified conserved effector domains. Infection by the human pathogen Legionella pneumophila relies on the translocation of ∼300 virulence proteins, termed effectors, which manipulate host cell processes. However, almost no information exists regarding effectors in other Legionella pathogens. Here we sequenced, assembled and characterized the genomes of 38 Legionella species and predicted their effector repertoires using a previously validated machine learning approach. This analysis identified 5,885 predicted effectors. The effector repertoires of different Legionella species were found to be largely non-overlapping, and only seven core effectors were shared by all species studied. Species-specific effectors had atypically low GC content, suggesting exogenous acquisition, possibly from the natural protozoan hosts of these species. Furthermore, we detected numerous new conserved effector domains and discovered new domain combinations, which allowed the inference of as yet undescribed effector functions. The effector collection and network of domain architectures described here can serve as a roadmap for future studies of effector function and evolution.