Biology subjects

Shaw, L. P.

Publications and source records attributed to Shaw, L. P..

3 recordsLinked to original sources

bioRxiv2018

Whole genome sequencing Mycobacterium tuberculosis directly from sputum identifies more genetic diversity than sequencing from culture

BackgroundRepeated culture reduces within-sample Mycobacterium tuberculosis genetic diversity due to selection of clones suited to growth in culture and/or random loss of lineages, but it is not known to what extent omitting the culture step altogether alters genetic diversity. We compared M. tuberculosis whole genome sequences generated from 33 paired clinical samples using two methods. In one method DNA was extracted directly from sputum then enriched with custom-designed SureSelect (Agilent) oligonucleotide baits and in the other it was extracted from mycobacterial growth indicator tube (MGIT) culture.\n\nResultsDNA directly sequenced from sputum showed significantly more within-sample diversity than that from MGIT culture (median 5.0 vs 4.5 heterozygous alleles per sample, p=0.04). Resistance associated variants present as HAs occurred in four patients, and in two cases may provide a genotypic explanation for phenotypic resistance.\n\nConclusionsCulture-free M. tuberculosis whole genome sequencing detects more within-sample diversity than a leading culture-based method and may allow detection of mycobacteria that are not actively replicating.

genomics↗

bioRxiv2018

Children with cystic fibrosis are infected with multiple subpopulations of Mycobacterium abscessus with different antimicrobial resistance profiles

BackgroundChildren with cystic fibrosis (CF) can develop life-threatening infections of Mycobacterium abscessus. These present a significant clinical challenge, particularly when the strains involved are resistant to antibiotics. Recent evidence of within-patient subclones of M. abscessus in adults with CF suggests the possibility that within-patient diversity may be relevant for the treatment of pediatric CF patients.\n\nMethodsWe performed whole genome sequencing (WGS) on 32 isolates of M. abscessus from multiple body sites for two patients with CF undergoing treatment at Great Ormond Street Hospital, UK, in 2015.\n\nResultsWe found evidence of extensive diversity within patients over time. Clustering analysis of single nucleotide variants (SNVs) revealed that each patient harboured multiple subpopulations, which were differentially abundant between sputum, lung samples, chest wounds, and pleural fluid. Sputum isolates did not reflect overall within-patient diversity, including failing to detect subclones with mutations previously associated with macrolide resistance (rrl 2058/2059). Some variants were present at intermediate frequencies before lung transplant. The time of transplant coincided with extensive variation, suggesting that this event is particularly disruptive for the microbial community, but transplant did not clear the M. abscessus infection and both patients died as a result of this infection.\n\nConclusionsIsolates of M. abscessus from sputum do not always reflect the entire diversity present within the patient, which can include subclones with differing AMR profiles. Awareness of this phenotypic variability, with sampling of multiple body sites in conjunction with WGS, may be necessary to ensure the best treatment for this vulnerable patient group.\n\nKey point summaryO_LIChildren with cystic fibrosis undergoing lung transplant harbour multiple subpopulations of M. abscessus.\nC_LIO_LISubpopulations can have different antimicrobial resistance genotypes.\nC_LIO_LISputum isolates do not reflect the genetic diversity within a patient.\nC_LI

microbiology↗

bioRxiv2017

A perturbation model of the gut microbiome’s response to antibiotics

Treatment with antibiotics is one of the most extreme perturbations to the human microbiome. Even standard courses of antibiotics dramatically reduce the microbiomes diversity and can cause transitions to dysbiotic states. Conceptually, this is often described as a stability landscape: the microbiome sits in a landscape with multiple stable equilibria, and sufficiently strong perturbations can shift the microbiome from its normal equilibrium to another state. However, this picture is only qualitative and has not been incorporated in previous mathematical models of the effects of antibiotics. Here, we outline a simple quantitative model based on the stability landscape concept and demonstrate its success on real data. Our analytical impulse-response model has minimal assumptions with three parameters. We fit this model in a Bayesian framework to previously published data on the year-long effects of four common antibiotics (ciprofloxacin, clindamycin, minocycline, and amoxicillin) on the gut and oral microbiomes, allowing us to compare parameters between antibiotics and microbiomes. Furthermore, using Bayesian model selection we find support for a long-term transition to an alternative microbiome state after courses of ciprofloxacin and clindamycin in both the gut and salivary microbiomes. Quantitative stability landscape frameworks are an exciting avenue for future microbiome modelling.

microbiology↗