PLoS Computational BiologyGenomicsPeer-reviewed journal article
The super repertoire of type IV effectors in the pangenome of Ehrlichia spp. provides insights into host-specificity and pathogenesis
Abstract
The identification of bacterial effectors is essential to understand how obligatory intracellular bacteria such as Ehrlichia spp. manipulate the host cell for survival and replication. Infection of mammals - including humans - by the intracellular pathogenic bacteria Ehrlichia spp. depends largely on the injection of virulence proteins that hijack host cell processes. Several hypothetical virulence proteins have been identified in Ehrlichia spp., but one so far has been experimentally shown to translocate into host cells via the type IV secretion system. However, the current challenge is to identify most of the type IV effectors (T4Es) to fully understand their role in Ehrlichia spp. virulence and host adaptation. Here, we predict the T4E repertoires of four sequenced Ehrlichia spp. and four other Anaplasmataceae as comparative models (pathogenic Anaplasma spp. and Wolbachia endosymbiont) using previously developed S4TE 2.0 software. This analysis identified 579 predicted T4Es (228 pT4Es for Ehrlichia spp. only). The effector repertoires of Ehrlichia spp. overlapped, thereby defining a conserved core effectome of 92 effectors shared by all strains. In addition, 69 species-specific T4Es were predicted with non-canonical GC% mostly in gene sparse regions of the genomes and we observed a bias in pT4Es according to host-specificity. We also identified new protein domain combinations, suggesting novel effector functions. This work presenting the predicted effector collection of Ehrlichia spp. can serve as a guide for future functional characterisation of effectors and design of alternative control strategies against these bacteria.
- organism: E. coli
- assay: proteomics
- research focus: gene regulatory network inference
- research focus: benchmarking
- research focus: database / resource
- research focus: software / tool
- approach: statistical model
- Citations
- 12
- FWCI
- 1.51
- Field percentile
- top 22%
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