Bactabolize is 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, 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, 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.

Genomic Diversity and Antimicrobial Resistance of Haemophilus Colonizing 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 and Haemophilus parainfluenzae are routinely isolated from the lungs of children with CF; however, little is known about the frequency and characteristics of Haemophilus colonization in this context. Here, we describe the detection, antimicrobial resistance (AMR), and genome sequencing of H. influenzae and H. parainfluenzae isolated from airway samples of 147 participants aged ≤12 years enrolled in the Australian Respiratory Early Surveillance Team for Cystic Fibrosis (AREST CF) program, Melbourne, Australia. The frequency of colonization per visit was 4.6% for H. influenzae and 32.1% for H. parainfluenzae, 80.3% of participants had H. influenzae and/or H. parainfluenzae detected on at least one visit, and using genomic data, we estimate 15.6% of participants had persistent colonization with the same strain for at least two consecutive visits. Isolates were genetically diverse and AMR was common, with 52% of H. influenzae and 82% of H. parainfluenzae displaying resistance to at least one drug. The genetic basis for AMR could be identified in most cases; putative 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 carried genes (pbp3, ampicillin resistance; folA/folP, cotrimoxazole resistance; rpoB, rifampicin resistance). Acquired AMR genes were more common in H. parainfluenzae than H. influenzae (51% versus 21%, P = 0.0107) and were mostly associated with the ICEHin mobile element carrying blaTEM-1, resulting in more ampicillin resistance in H. parainfluenzae (73% versus 30%, P = 0.0004). Genomic data identified six potential instances of Haemophilus transmission between participants, of which three involved participants who shared clinic visit days. IMPORTANCE Cystic fibrosis (CF) lung disease begins during infancy, and acute respiratory infections increase the risk of early disease development and progression. Microbes involved in advanced stages of CF are well characterized, but less is known about early respiratory colonizers. We report the population dynamics and genomic determinants of AMR in two early colonizer species, namely, Haemophilus influenzae and Haemophilus parainfluenzae, collected from a pediatric CF cohort. This investigation also reveals that H. parainfluenzae has a high frequency of AMR carried on mobile elements that may act as a potential reservoir for the emergence and spread of AMR to H. influenzae, which has greater clinical significance as a respiratory pathogen in children. This study provides insight into the evolution of AMR and the colonization of H. influenzae and H. parainfluenzae in a pediatric CF cohort, which will help inform future treatment.

A genomic surveillance framework and genotyping tool for Klebsiella pneumoniae and its related species complex.

Klebsiella pneumoniae is a leading cause of antimicrobial-resistant (AMR) healthcare-associated infections, neonatal sepsis and community-acquired liver abscess, and is associated with chronic intestinal diseases. Its diversity and complex population structure pose challenges for analysis and interpretation of K. pneumoniae genome data. Here we introduce Kleborate, a tool for analysing genomes of K. pneumoniae and its associated species complex, which consolidates interrogation of key features of proven clinical importance. Kleborate provides a framework to support genomic surveillance and epidemiology in research, clinical and public health settings. To demonstrate its utility we apply Kleborate to analyse publicly available Klebsiella genomes, including clinical isolates from a pan-European study of carbapenemase-producing Klebsiella, highlighting global trends in AMR and virulence as examples of what could be achieved by applying this genomic framework within more systematic genomic surveillance efforts. We also demonstrate the application of Kleborate to detect and type K. pneumoniae from gut metagenomes.

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

Klebsiella pathogens affect human and animal health and are widely distributed in the environment. Among these, the Klebsiella pneumoniae species complex, which includes seven phylogroups, is an important cause of community and hospital infections. The Klebsiella oxytoca species complex also causes hospital infections and antibiotic-associated haemorrhagic colitis. The unsuitability of currently 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. We developed a web-based tool, Klebsiella MALDI TypeR, a platform-independent and user-friendly application that enables uploading MALDI-TOF mass spectrometry data in order to identify Klebsiella isolates at the species complex and phylogroup levels. The tool, available at https://maldityper.pasteur.fr/, leverages a database of previously identified biomarkers that are specific for species complexes, individual phylogroups, or related phylogroups. We obtained 84%-100% identification accuracy depending on phylogroup. Identification results are obtained in a few seconds from batches of uploaded spectral data. Klebsiella MALDI TypeR enables fast and reliable identification of Klebsiella strains that are often misidentified with standard microbiological methods. This web-based identification tool may be extended in the future to other human bacterial pathogens.

hicap: In Silico Serotyping of the Haemophilus influenzae Capsule Locus.

Haemophilus influenzae exclusively colonizes 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 its prevalence has been markedly reduced in countries that have implemented vaccination programs targeting this serotype. In the postvaccine era, nontypeable 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 https://github.com/scwatts/hicap under the GNU General Public License v3 (GPL3). 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 they also identified occasional recombination between lineages that gave rise to hybrid cap loci (2% of encapsulated strains).

FastSpar: rapid and scalable correlation estimation for compositional data.

SUMMARY: 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 specialized 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 parallelizable 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. AVAILABILITY AND IMPLEMENTATION: FastSpar source code, precompiled binaries and platform packages are freely available on GitHub: github.com/scwatts/FastSpar. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Airway Microbiota Dynamics Uncover a Critical Window for Interplay of Pathogenic Bacteria and Allergy in Childhood Respiratory Disease.

Repeated cycles of infection-associated lower airway inflammation drive the pathogenesis of persistent wheezing disease in children. In this study, the occurrence of acute respiratory tract illnesses (ARIs) and the nasopharyngeal microbiome (NPM) were characterized in 244 infants through their first five years of life. Through this analysis, we demonstrate that >80% of infectious events involve viral pathogens, but are accompanied by a shift in the NPM toward dominance by a small range of pathogenic bacterial genera. Unexpectedly, this change frequently precedes the detection of viral pathogens and acute symptoms. Colonization of illness-associated bacteria coupled with early allergic sensitization is associated with persistent wheeze in school-aged children, which is the hallmark of the asthma phenotype. In contrast, these bacterial genera are associated with "transient wheeze" that resolves after age 3 years in non-sensitized children. Thus, to complement early allergic sensitization, monitoring NPM composition may enable early detection and intervention in high-risk children.