Adamson, S. | bioRxiv Science

bioRxiv2017

Genome Sequencing Links Persistent Outbreak Of Legionellosis In Sydney To An Emerging Clone Of Legionella pneumophila ST211

The city of Sydney, Australia, experienced a persistent outbreak of Legionella pneumophila serogroup 1 (Lp1) pneumonia in 2016. To elucidate the source and bring the outbreak to a close we examined the genomes of clinical and environmental Lp1 isolates recovered over 7 weeks. A total of 48 isolates from patients and cooling towers were sequenced and compared using SNP-based, core-genome MLST and pangenome approaches. All three methods confirmed phylogenetic relatedness between isolates associated with outbreaks in the Central Business District (March and May) and Suburb 1. These isolates were designated \"Main cluster\" and consisted of isolates from two patients from the CBD March outbreak, one patient and one tower isolate from Suburb 1 and isolates from two cooling towers and three patients from the CDB May outbreak. All main cluster isolates were sequence type ST211 which has only ever been reported in Canada. Significantly, pangenome analysis identified mobile genetic elements containing a unique T4ASS that was specific to the main cluster and co-circulating clinical strains, suggesting a potential mechanism for increased fitness and persistence of the outbreak clone. Genome sequencing was key in deciphering the environmental sources of infection among the spatially and temporally coinciding cases of legionellosis in this highly populated urban setting. Further, the discovery of a unique T4ASS emphasises the potential contribution of genome recombination in the emergence of successful Lp1 clones.

genomics↗

bioRxiv2017

SARNAclust: Semi-Automatic Detection Of RNA Protein Binding Motifs From Immunoprecipitation Data

RNA-protein binding is critical to gene regulation, controlling fundamental processes including splicing, translation, localization and stability, and aberrant RNA-protein interactions are known to play a role in a wide variety of diseases. However, molecular understanding of RNA-protein interactions remains limited, and in particular identification of the RNA motifs that bind proteins has long been a difficult problem. To address this challenge, we have developed a novel semi-automatic algorithm, SARNAclust, to computationally identify combined structure/sequence motifs from immunoprecipitation data. SARNAclust is, to our knowledge, the first unsupervised method that can identify RNA motifs at full structural resolution while also being able to simultaneously deconvolve multiple motifs. SARNAclust makes use of a graph kernel to evaluate similarity between sequence/structure objects, and provides the ability to isolate the impact of specific features through the bulge graph formalism. SARNAclust includes a key method for predicting RNA secondary structure at CLIP peaks, RNApeakFold, which we have verified to be effective on synthetic motif data. We applied SARNAclust to 30 ENCODE eCLIP datasets, identifying known motifs and novel predictions. Notably, we predicted a new motif for the protein ILF3 similar to that for the splicing factor hnRNPC, providing evidence for interaction between these two proteins. To validate our predictions, we performed a directed RNA bind-n-seq assay for two proteins: ILF3 and SLBP, in each case revealing the effectiveness of SARNAclust in predicting RNA sequence and structure elements important to protein binding. Availability: https://github.com/idotu/SARNAclust

bioinformatics↗