Biology subjects

Watts, S. C.

Publications and source records attributed to Watts, S. C..

7 recordsLinked to original sources

bioRxiv2023

Bactabolize: A tool for high-throughput generation of bacterial strain-specific metabolic models

Metabolic capacity can vary substantially within a bacterial species, leading to ecological niche separation, as well as differences in virulence and antimicrobial susceptibility. Genome-scale metabolic models are useful tools for studying the metabolic potential of individuals, and with the rapid expansion of genomic sequencing there is a wealth of data that can be leveraged for comparative analysis. However, there exist few tools to construct strain-specific metabolic models at scale. Here we describe Bactabolize (github.com/kelwyres/Bactabolize), a reference-based tool which rapidly produces strain-specific metabolic models and growth phenotype predictions. We describe a pan reference model for the priority antimicrobial-resistant pathogen, Klebsiella pneumoniae (github.com/kelwyres/KpSC-pan-metabolic-model), and a quality control framework for using draft genome assemblies as input for Bactabolize. The Bactabolize-derived model for K. pneumoniae reference strain KPPR1 performed comparatively or better than currently available automated approaches CarveMe and gapseq across 507 substrate and 2317 knockout mutant growth predictions. Novel draft genomes passing our systematically-defined quality control criteria resulted in models with a high degree of completeness ([≥]99% genes and reactions captured compared to models derived from matched complete genomes) and high accuracy (mean 0.97, n=10). We anticipate the tools and framework described herein will facilitate large-scale metabolic modelling analyses that broaden our understanding of diversity within bacterial species and inform novel control strategies for priority pathogens.

bioinformatics↗

bioRxiv2020

Genomic diversity and antimicrobial resistance of Haemophilus colonising the airways of young children with cystic fibrosis

Respiratory infection during childhood is a key risk factor in early cystic fibrosis (CF) lung disease progression. Haemophilus influenzae (Hi) and Haemophilus parainfluenzae (Hpi) are routinely isolated from the lungs of children with CF, however little is known about the frequency and characteristics of Haemophilus colonisation in this context. Here, we describe detection, antimicrobial resistance (AMR) and genome sequencing of Hi/Hpi isolated from sputum, cough swab, and bronchoalveolar lavage samples regularly collected from 147 participants aged [≤]12 years enrolled in the Australian Respiratory Early Surveillance Team for Cystic Fibrosis (AREST CF) program. The frequency of colonisation per visit was 4.6% for Hi and 32.1% for Hpi, 80.3% of participants had Hi and/or Hpi detected on at least one visit, and using genomic data we estimate 15.6% of participants had persistent colonisation with the same strain for at least two consecutive visits. Colonising strains were genetically highly diverse and AMR was common, with 52% of Hi and 82% of Hpi displaying resistance to at least one drug. The genetic basis for AMR could be identified in most cases; novel determinants include a new plasmid encoding blaTEM-1 (ampicillin resistance), a new inhibitor-resistant blaTEM allele (augmentin resistance), and previously unreported mutations in chromosomally-encoded genes (pbp3, ampicillin resistance; folA and folP, co-trimoxazole resistance; rpoB, rifampicin resistance). Acquired AMR genes were significantly more common in Hpi than Hi (51% vs 21%, p=0.0107) and were mostly associated with the ICEHin mobile element carrying blaTEM-1, resulting in higher rates of ampicillin resistance in Hpi (73% vs 30%, p=0.0004). The genome data identified six potential instances of Haemophilus transmission between participants, three of which involved participant pairs who attended the clinic on the same day. The high prevalence of Haemophilus colonisation and high burden of antimicrobial use in children with CF likely provides a reservoir for emergence and spread of AMR as well as a source of infections.

microbiology↗

bioRxiv2020

Klebsiella MALDI TypeR: a web-based tool for Klebsiella identification based on MALDI-TOF mass spectrometry

MotivationKlebsiella species are increasingly multidrug resistant pathogens affecting human and animal health and are widely distributed in the environment. Among these, the Klebsiella pneumoniae species complex (KpSC), which includes seven phylogroups, is an important cause of community and hospital infections. In addition, the Klebsiella oxytoca species complex (KoSC) also causes hospital infections and antibiotic-associated haemorrhagic colitis. The unsuitability of widely used clinical microbiology methods to distinguish species within each of these species complexes leads to high rates of misidentifications that are masking the true clinical significance and potential epidemiological specificities of individual species. ResultsWe developed a web-based tool, Klebsiella MALDI TypeR, a platform-independent and user-friendly application that enables uploading raw data from MALDI-TOF mass spectrometer to identify Klebsiella isolates at the species complex and phylogroup levels. The tool is based on a database of previously identified biomarkers that are specific for either the species complex, individual phylogroups, or related phylogroups, and is available at https://maldityper.pasteur.fr.

bioinformatics↗

bioRxiv2019

Correcting index databases improves metagenomic studies

Assessing the taxonomic composition of metagenomic samples is an important first step in understanding the biology and ecology of microbial communities in complex environments. Despite a wealth of algorithms and tools for metagenomic classification, relatively little effort has been put into the critical task of improving the quality of reference indices to which metagenomic reads are assigned. Here, we inferred the taxonomic composition of 404 publicly available metagenomes from human, marine and soil environments, using custom index databases modified according to two factors: the number of reference genomes used to build the databases, and the monophyletic strictness of species definitions. Index databases built following the NCBI taxonomic system were also compared to others using Genome Taxonomy Database (GTDB) taxonomic redefinitions. We observed a considerable increase in the rate of read classification using modified reference index databases as compared to a default NCBI RefSeq database, with up to a 4.4-, 6.4- and 2.2-fold increase in classified reads per sample for human, marine and soil metagenomes, respectively. Importantly, targeted correction for 70 common human pathogens and bacterial genera in the index database increased their specific detection levels in human metagenomes. We also show the choice of index database can influence downstream diversity and distance estimates for microbiome data. Overall, the study shows a large amount of accessible information in metagenomes remains unexploited using current methods, and that the same data analysed using different index databases could potentially lead to different conclusions. These results have implications for the power and design of individual microbiome studies, and for comparison and meta-analysis of microbiome datasets.

bioinformatics↗

bioRxiv2019

hicap: in silico serotyping of the Haemophilus influenzae capsule locus

Haemophilus influenzae exclusively colonises the human nasopharynx and can cause a variety of respiratory infections as well as invasive diseases including meningitis and sepsis. A key virulence determinant of H. influenzae is the polysaccharide capsule of which six serotypes are known, each encoded by a distinct variation of the capsule biosynthesis locus (cap-a to cap-f). H. influenzae type b (Hib) was historically responsible for the majority of invasive H. influenzae disease and prevalence has been markedly reduced in countries that have implemented vaccination programs targeting this serotype. In the postvaccine era, non-typeable H. influenzae emerged as the most dominant group causing disease but in recent years a resurgence of encapsulated H. influenzae strains has also been observed, most notably serotype a. Given the increasing incidence of encapsulated strains and the high frequency of Hib in countries without vaccination programs, there is growing interest in genomic epidemiology of H. influenzae. Here we present hicap, a software tool for rapid in silico serotype prediction from H. influenzae genome sequences. hicap is written using Python3 and is freely available at github.com/scwatts/hicap under a GPLv3 license. To demonstrate the utility of hicap, we used it to investigate the cap locus diversity and distribution in 691 high-quality H. influenzae genomes from GenBank. These analyses identified cap loci in 95 genomes and confirmed the general association of each serotype with a unique clonal lineage and also identified occasional recombination between lineages giving rise to hybrid cap loci (2% of encapsulated strains).

bioinformatics↗

bioRxiv2018

FastSpar: Rapid and scalable correlation estimation for compositional data

A common goal of microbiome studies is the elucidation of community composition and member interactions using counts of taxonomic units extracted from sequence data. Inference of interaction networks from sparse and compositional data requires specialised statistical approaches. A popular solution is SparCC, however its performance limits the calculation of interaction networks for very high-dimensional datasets. Here we introduce FastSpar, an efficient and parallelisable implementation of the SparCC algorithm which rapidly infers correlation networks and calculates p-values using an unbiased estimator. We further demonstrate that FastSpar reduces network inference wall time by 2-3 orders of magnitude compared to SparCC. FastSpar source code, precompiled binaries, and platform packages are freely available on GitHub: github.com/scwatts/FastSpar

bioinformatics↗

bioRxiv2017

Dynamics of the upper airway microbiome in the pathogenesis of asthma-associated persistent wheeze in preschool children

Repeated cycles of infection-associated lower airway inflammation drives the pathogenesis of persistent wheezing disease in children. Tracking these events across a birth cohort during their first five years, we demonstrate that >80% of infectious events indeed involve viral pathogens, but are accompanied by a shift in the nasopharyngeal microbiome (NPM) towards dominance by a small range of pathogenic bacterial genera. Unexpectedly, this change in NPM frequently precedes the appearance of viral pathogens and acute symptoms. In non-sensitized children these events are associated only with \"transient wheeze\" that resolves after age three. In contrast, in children developing early allergic sensitization, they are associated with ensuing development of persistent wheeze, which is the hallmark of the asthma phenotype. This suggests underlying pathogenic interactions between allergic sensitization and antibacterial mechanisms.

genetics↗