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

ABSTRACT: 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.

SUBMITTER: Burstein D

PROVIDER: S-EPMC5050043 | biostudies-literature | 2016 Feb

REPOSITORIES: biostudies-literature

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

Burstein David D Amaro Francisco F Zusman Tal T Lifshitz Ziv Z Cohen Ofir O Gilbert Jack A JA Pupko Tal T Shuman Howard A HA Segal Gil G

Nature genetics 20160111 2

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 effect ...[more]

PMID: 26752266

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Project description:Aphids are phloem-feeding insects that cause significant economic losses to agriculture worldwide. While feeding and probing these insects deliver molecules, called effectors, inside their host to enable infestation. The identification and characterization of these effectors from different species that vary in their host range is an important step in understanding the infestation success of aphids and aphid host range variation. This study employs a multi-disciplinary approach based on transcriptome sequencing and proteomics to identify and compare effector candidates from the broad host range aphid Myzus persicae (green peach aphid) (genotypes O, J and F), and narrow host range aphids Myzus cerasi (black cherry aphid) and Rhopalosiphum padi (bird-cherry oat aphid).Using a combination of aphid transcriptome sequencing on libraries derived from head versus body tissues as well as saliva proteomics we were able to predict candidate effectors repertoires from the different aphid species and genotypes. Among the identified conserved or core effector sets, we identified a significant number of previously identified aphid candidate effectors indicating these proteins may be involved in general infestation strategies. Moreover, we identified aphid candidate effector sequences that were specific to one species, which are interesting candidates for further validation and characterization with regards to species-specific functions during infestation. We assessed our candidate effector repertoires for evidence of positive selection, and identified 49 candidates with DN/DS ratios >1. We noted higher rates of DN/DS ratios in predicted aphid effectors than non-effectors. Whether this reflects positive selection due to co-evolution with host plants, or increased neofunctionalization upon gene duplication remains to be investigated.Our work provides a comprehensive overview of the candidate effector repertoires from three different aphid species with varying host ranges. Comparative analyses revealed candidate effectors that are most likely are involved in general aspects of infestation, whereas others, that are highly divergent, may be involved in specific processes important for certain aphid species. Insights into the overlap and differences in aphid effector repertoires are important in understanding how different species successfully infest different ranges of plant species.

Dataset Information

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

Publications

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

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