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Simon R Harris

Publications and source records attributed to Simon R Harris.

5 recordsLinked to original sources

Interacting networks of resistance, virulence and core machinery genes identified by genome-wide epistasis analysis

Recent advances in the scale and diversity of population genomic datasets for bacteria now provide the potential for genome-wide patterns of co-evolution to be studied at the resolution of individual bases. The major human pathogen Streptococcus pneumoniae represents the first bacterial organism for which densely enough sampled population data became available for such an analysis. Here we describe a new statistical method, genomeDCA, which uses recent advances in computational structural biology to identify the polymorphic loci under the strongest co-evolutionary pressures. Genome data from over three thousand pneumococcal isolates identified 5,199 putative epistatic interactions between 1,936 sites. Over three-quarters of the links were between sites within the pbp2x, pbp1a and pbp2b genes, the sequences of which are critical in determining non-susceptibility to beta-lactam antibiotics. A network-based analysis found these genes were also coupled to that encoding dihydrofolate reductase, changes to which underlie trimethoprim resistance. Distinct from these resistance genes, a large network component of 384 protein coding sequences encompassed many genes critical in basic cellular functions, while another distinct component included genes associated with virulence. These results have the potential both to identify previously unsuspected protein-protein interactions, as well as genes making independent contributions to the same phenotype. This approach greatly enhances the future potential of epistasis analysis for systems biology, and can complement genome-wide association studies as a means of formulating hypotheses for experimental work.\n\nAuthor SummaryEpistatic interactions between polymorphisms in DNA are recognized as important drivers of evolution in numerous organisms. Study of epistasis in bacteria has been hampered by the lack of both densely sampled population genomic data, suitable statistical models and powerful inference algorithms for extremely high-dimensional parameter spaces. We introduce the first model-based method for genome-wide epistasis analysis and use the largest available bacterial population genome data set on Streptococcus pneumoniae (the pneumococcus) to demonstrate its potential for biological discovery. Our approach reveals interacting networks of resistance, virulence and core machinery genes in the pneumococcus, which highlights putative candidates for novel drug targets. Our method significantly enhances the future potential of epistasis analysis for systems biology, and can complement genome-wide association studies as a means of formulating hypotheses for experimental work.

Genetics

Genomic dissection of an Icelandic epidemic of equine respiratory disease

The native horse population of Iceland has remained free of major infectious diseases. Between May and July 2010 an epidemic of respiratory disease swept through the population. Initial microbiological investigations ruled out known equine viral agents as the cause of the infections, but identified the opportunistic pathogen Streptococcus zooepidemicus as being frequently isolated from diseased animals. This diverse bacterial species has a broad host range and is usually regarded as a commensal of horses. By genome sequencing S. zooepidemicus recovered from horses during the epidemic we show that although multiple clones of S. zooepidemicus were present in the population, one particular clone, ST209, was responsible for the epidemic. Concurrent with the epidemic, ST209 caused zoonotic infections, highlighting the pathogenic potential of this clone. Phylogenetic analysis suggests that the original ST209 strain entered Iceland in late 2008 or early 2009. Epidemiological investigation revealed that the incursion of this strain into a training yard that utilized a submerged treadmill between the 5th and 19th of February 2010 was a critical trigger for the ensuing epidemic of disease, provided a nidus for the infection of multiple horses, and subsequent distribution of these animals to multiple sites in Iceland.

Microbiology

Sequence element enrichment analysis to determine the genetic basis of bacterial phenotypes

Bacterial genomes vary extensively in terms of both gene content and gene sequence - this plasticity hampers the use of traditional SNP-based methods for identifying all genetic associations with phenotypic variation. Here we introduce a computationally scalable and widely applicable statistical method (SEER) for the identification of sequence elements that are significantly enriched in a phenotype of interest. SEER is applicable to even tens of thousands of genomes by counting variable-length k-mers using a distributed string-mining algorithm. Robust options are provided for association analysis that also correct for the clonal population structure of bacteria. Using large collections of genomes of the major human pathogens Streptococcus pneumoniae and Streptococcus pyogenes, SEER identifies relevant previously characterised resistance determinants for several antibiotics and discovers potential novel factors related to the invasiveness of S. pyogenes. We thus demonstrate that our method can answer important biologically and medically relevant questions.

Genomics

Circlator: automated circularization of genome assemblies using long sequencing reads

The assembly of DNA sequence data into finished genomes is undergoing a renais-sance thanks to emerging technologies producing reads of tens of kilobases. Assembling complete bacterial and small eukaryotic genomes is now possible, but the final step of circularizing sequences remains unsolved. Here we present Circlator, the first tool to automate assembly circularization and produce accurate linear rep-resentations of circular sequences. Using Pacific Biosciences and Oxford Nanopore data, Circlator correctly circularized 26 of 27 circularizable sequences, comprising 11 chromosomes and 12 plasmids from bacteria, the apicoplast and mitochondrion of Plasmodium falciparum and a human mitochondrion. Circlator is available at http://sanger-pathogens.github.io/circlator/.

Bioinformatics

Genome specialization and decay of the strangles pathogen, Streptococcus equi, is driven by persistent infection

Strangles, the most frequently diagnosed infectious disease of horses worldwide, is caused by Streptococcus equi. Despite its prevalence, the global diversity and mechanisms underlying the evolution of S. equi as a host-restricted pathogen remain poorly understood. Here we define the global population structure of this important pathogen and reveal a population replacement in the late 19th or early 20th century, contemporaneous with a spate of global conflicts. Our data reveal a dynamic genome that continues to mutate and decay, but also to amplify and acquire genes despite the organism having lost its natural competence and become host-restricted.\n\nThe lifestyle of S. equi within the horse is defined by short-term acute disease, strangles, followed by long-term carriage. Population analysis reveals evidence of convergent evolution in isolates from post-acute disease samples, as a result of niche adaptation to persistent carriage within a host. Mutations that lead to metabolic streamlining and the loss of virulence determinants are more frequently found in carriage isolates, suggesting that the pathogenic potential of S. equi reduces as a consequence of long term residency within the horse post acute disease. An example of this is the deletion of the equibactin siderophore locus that is associated with iron acquisition, which occurs exclusively in carrier isolates, and renders S. equi significantly less able to cause acute disease in the natural host. We identify several loci that may similarly be required for the full virulence of S. equi, directing future research towards the development of new vaccines against this host-restricted pathogen.

Microbiology