Biology subjects

Nicholas J Croucher

Publications and source records attributed to Nicholas J Croucher.

3 recordsLinked to original sources

bioRxiv2016

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↗

bioRxiv2016

Large scale genomic analysis shows no evidence for repeated pathogen adaptation during the invasive phase of bacterial meningitis in humans

Recent studies have provided evidence for rapid pathogen genome variation, some of which could potentially affect the course of disease. We have previously detected such variation by comparing isolates infecting the blood and cerebrospinal fluid (CSF) of a single patient during a case of bacterial meningitis.\n\nTo determine whether the observed variation repeatedly occurs in cases of disease, we performed whole genome sequencing of paired isolates from blood and CSF of 938 meningitis patients. We also applied the same techniques to 54 paired isolates from the nasopharynx and CSF.\n\nUsing a combination of reference-free variant calling approaches we show that no genetic adaptation occurs in the invasive phase of bacterial meningitis for four major pathogen species: Streptococcus pneumoniae, Neisseria meningitidis, Listeria monocytogenes and Haemophilus influenzae. From nasopharynx to CSF, no adaptation was seen in S. pneumoniae, but in N. meningitidis mutations potentially mediating adaptation to the invasive niche were occasionally observed in the dca gene.\n\nThis study therefore shows that the bacteria capable of causing meningitis are already able to do this upon entering the blood, and no further sequence change is necessary to cross the blood-brain barrier. The variation discovered from nasopharyngeal isolates suggest that larger studies comparing carriage and invasion may help determine the likely mechanisms of invasiveness.\n\nAuthor SummaryWe have analysed the entire DNA sequence from bacterial pathogen isolates from cases of meningitis in 938 Dutch adults, focusing on comparing pairs of isolates from the patients blood and their cerebrospinal fluid. Previous research has been on only a single patient, but showed possible signs of adaptation to treatment within the host over the course of a single case of disease.\n\nBy sequencing many more such paired samples, and including four different bacterial species, we were able to determine that adaptation of the pathogen does not occur after bloodstream invasion during bacterial meningitis.\n\nWe also analysed 54 pairs of isolates from pre- and post-invasive niches from the same patient. In N. meningitidis we found variation in the sequence of one gene which appears to provide bacteria with an advantage after invasion of the bloodstream.\n\nOverall, our findings indicate that evolution after invasion in bacterial meningitis is not a major contribution to disease pathogenesis. Future studies should involve more extensive sampling between the carriage and disease niches, or on variation of the host.

Genomics↗

bioRxiv2016

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↗