# Comparative analysis of Legionella lytica genome identifies specific metabolic traits and virulence factors

## TL;DR

This study sequenced the genome of Legionella lytica, revealing unique genes and virulence traits that highlight its evolutionary and pathogenic potential.

## Contribution

The study identifies 383 unique genes in L. lytica, including plasmid-encoded effectors, and introduces a web tool for exploring the Legionella pangenome.

## Key findings

- L. lytica has a 3.7 Mbp genome with 3,165 coding sequences, including virulence-related genes like MOMP and Mip.
- The species contributes 383 unique genes to the Legionella pangenome, with 35 plasmid-encoded effector proteins.
- A custom R Shiny web app was developed to explore the expanded Legionella pangenome interactively.

## Abstract

The complete genome of Legionella lytica PCM 2298 was sequenced and analyzed to provide insights into its genomic structure, virulence potential, and evolutionary position within the Legionella genus. The genome comprised a 3.2 Mbp chromosome and two plasmids, pLlyPCM2298_1 and pLlyPCM2298_2, contributing to a total genome size of 3.7 Mbp. Functional annotation identified 3,165 coding sequences, including genes associated with known virulence factors such as the major outer membrane protein (MOMP), the macrophage infectivity potentiator (Mip), and a comprehensive set of secretion systems (type II, type IVA, and type IVB Dot/Icm type IV secretion system). Notably, L. lytica contributed 383 unique genes to the Legionella pangenome, with 232 identified effector proteins, of which 35 were plasmid-encoded. The identification of unique genes, particularly those on plasmids, suggests an evolutionary strategy favoring horizontal gene transfer and niche adaptation. The effector repertoire included proteins with domains characteristic of host interaction strategies, such as ankyrin repeats and protein kinases. Comparative analyses showed that while L. lytica shares core virulence traits with other Legionella species, it has distinct features that may contribute to its adaptability and pathogenic potential. These findings underscore the genomic diversity within the genus and contribute to a deeper understanding of Legionella’s ecological and clinical significance. A custom web application was developed using the R Shiny library, enabling users to interactively explore the expanded Legionella pangenome through UpSet plots.