Biology subjects

Lawley, T.

Publications and source records attributed to Lawley, T..

5 recordsLinked to original sources

bioRxiv2026

MxSure: a mixture model for inferring within-host substitution rates and transmission SNP thresholds

Quantifying short-term evolutionary rates of microbial genomes is essential for understanding the processes that shape within-host evolution and for establishing thresholds needed to track transmission. In studies of short-term evolutionary rates, samples are often collected from closely related clusters (e.g. longitudinally from the same host or from transmission pairs), with substantial time intervals separating genomes between clusters. Distinguishing strain replacement from persistence presents is also difficult in these studies. In addition, many public health and metagenomic bacterial strain tracking pipelines output pairwise SNP distances rather than the multiple sequence alignments required by common substitution rate estimation pipelines. This makes it hard to estimate within-host evolutionary rates in many commensal bacterial species that are difficult to culture and isolate. To address these challenges, we introduce MxSure, a tool for estimating substitution rates and transmission thresholds while accounting for strain replacement from pairwise SNP distance data, as commonly generated by transmission tracking and metagenomic analysis pipelines. We demonstrate the accuracy of MxSure through extensive simulations and by analysing species with previously estimated substitution rates from longitudinal metagenomic datasets. Using MxSure, we estimated within-host substitution rates and transmission SNP thresholds for multiple commensal bacterial species including Bifidobacterium longum and Bifidobacterium bifidum from a longitudinal study of the infant gut microbiome.

bioinformatics↗

bioRxiv2022

Multi-omic analysis along the gut-brain axis points to a functional architecture of autism

Autism is a highly heritable neurodevelopmental disorder characterized by heterogeneous cognitive, behavioral and communication impairments. Disruption of the gut-brain axis (GBA) has been implicated in autism, with dozens of cross-sectional microbiome and other omic studies revealing autism-specific profiles along the GBA albeit with little agreement in composition or magnitude. To explore the functional architecture of autism, we developed an age and sex-matched Bayesian differential ranking algorithm that identified autism-specific profiles across 10 cross-sectional microbiome datasets and 15 other omic datasets, including dietary patterns, metabolomics, cytokine profiles, and human brain expression profiles. The analysis uncovered a highly significant, functional architecture along the GBA that encapsulated the overall heterogeneity of autism phenotypes. This architecture was determined by autism-specific amino acid, carbohydrate and lipid metabolism profiles predominantly encoded by microbial species in the genera Prevotella, Enterococcus, Bifidobacterium, and Desulfovibrio, and was mirrored in brain-associated gene expression profiles and restrictive dietary patterns in individuals with autism. Pro-inflammatory cytokine profiling and virome association analysis further supported the existence of an autism-specific architecture associated with particular microbial genera. Re-analysis of a longitudinal intervention study in autism recapitulated the cross-sectional profiles, and showed a strong association between temporal changes in microbiome composition and autism symptoms. Further elucidation of the functional architecture of autism, including of the role the microbiome plays in it, will require deep, multi-omic longitudinal intervention studies on well-defined stratified cohorts to support causal and mechanistic inference.

microbiology↗

bioRxiv2019

Distinct microbial and immune niches of the human colon

Gastrointestinal microbiota and immune cells interact closely and display regional specificity, but little is known about how these communities differ with location. Here, we simultaneously assess microbiota and single immune cells across the healthy, adult human colon, with paired characterisation of immune cells in the mesenteric lymph nodes, to delineate colonic immune niches at steady-state. We describe distinct T helper cell activation and migration profiles along the colon and characterise the transcriptional adaptation trajectory of T regulatory cells between lymphoid tissue and colon. Finally, we show increasing B cell accumulation, clonal expansion and mutational frequency from caecum to sigmoid colon, and link this to the increasing number of reactive bacterial species.

immunology↗

bioRxiv2019

CarboLogR: a Shiny/R application for statistical analysis of bacterial utilisation of carbon sources

SummaryThe Biolog Phenotype Microarray (PM) and Anaerobic MicroPlates (AN) 96-well plates utilise colorimetric redox reactions to rapidly screen bacteria for the ability to utilise different carbon sources and other metabolites. Measurement of substrate utilisation as bacterial growth curves typically involves extended data normalization, outlier detection, and statistical analysis. The CarboLogR package streamlines this process with a Shiny application, guiding users from raw data generated from Biolog assays to growth profile comparison. We applied chemoinformatics approaches to define clusters of carbon sources, based on molecular similarities, increasing statistical power. Altogether, CarboLogR is a novel integrated tool providing automatic and high-level resolution for bacterial growth patterns and carbon source usage.\n\nAvailability and ImplementationCarboLogR application can be downloaded and installed from Github repository https://github.com/kevinVervier/CarboLogR. Tutorial, data, and examples can be downloaded at https://github.com/kevinVervier/CarboLogR/vignettes.\n\nContactkv4@sanger.ac.uk\n\nSupplementary InformationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2018

Haemophilus, antibiotic therapy and the airway microbiome in chronic obstructive pulmonary disease

Chronic obstructive pulmonary disease (COPD) is a smoking-related illness affecting 64 million people worldwide. Airway infection drives recurrent exacerbations and lung function decline. Prophylactic antibiotics may prevent exacerbations but their use is a significant cause of population antimicrobial resistance.\n\nWe characterised the sputum microbiome by 16S rRNA gene analysis using 138 samples collected during a randomised controlled trial of prophylactic antibiotics in 71 patients with stable COPD. On comparing the profile of the microbiome obtained by sequencing to the isolates grown from samples using standard culture, there were similarities overall, although with a much narrower spectrum of genera on culture with under-representation of certain genera including Veillonella and Prevotella. There was concordance in the most abundant genera within samples and the number of isolates cultured reflected the measured bacterial diversity.\n\nWe found that at baseline the microbiota of 17 (24%) patients were dominated by Haemophilus influenzae, accompanied by narrowed microbial diversity and higher levels of sputum inflammatory cytokines. Different H. influenzae strains co-existed within individuals. Opportunistic whole genome sequencing of six H. influenzae isolates obtained during the study revealed that all were non-typeable H. influenzae (NTHI), with a range of different antibiotic resistance gene profiles, but an identical complement of virulence genes.\n\nAdministration of 13 weeks prophylaxis with moxifloxacin, azithromycin or doxycycline revealed distinctive changes in microbial communities for each group. Haemophilus numbers reduced by 90% compared to placebo only after moxifloxacin, and significant reduction in sputum cytokines occurred in patients dominated by Haemophilus at baseline. Haemophilus influenzae dominance defines COPD patients with active disease who may particularly benefit from antibiotics or vaccination.

microbiology↗