The use of prescription medications and non-prescription medications during lactation in a prospective Canadian cohort study.

BACKGROUND: A lack of safety data on postpartum medication use presents a potential barrier to breastfeeding and may result in infant exposure to medications in breastmilk. The type and extent of medication use by lactating women requires investigation. METHODS: Data were collected from the CHILD Cohort Study which enrolled pregnant women across Canada between 2008 and 2012. Participants completed questionnaires regarding medications and non-prescription medications used and breastfeeding status at 3, 6 and 12 months postpartum. Medications, along with self-reported reasons for medication use, were categorized by ontologies [hierarchical controlled vocabulary] as part of a large-scale curation effort to enable more robust investigations of reasons for medication use. RESULTS: A total of 3542 mother-infant dyads were recruited to the CHILD study. Breastfeeding rates were 87.4%, 75.3%, 45.5% at 3, 6 and 12 months respectively. About 40% of women who were breastfeeding at 3 months used at least one prescription medication during the first three months postpartum; this proportion decreased over time to 29.5% % at 6 months and 32.8% at 12 months. The most commonly used prescription medication by breastfeeding women was domperidone at 3 months (9.0%, n = 229/2540) and 6 months (5.6%, n = 109/1948), and norethisterone at 12 months (4.1%, n = 48/1180). The vast majority of domperidone use by breastfeeding women (97.3%) was for lactation purposes which is off-label (signifying unapproved use of an approved medication). Non-prescription medications were more often used among breastfeeding than non-breastfeeding women (67.6% versus 48.9% at 3 months, p < 0.0001), The most commonly used non-prescription medications were multivitamins and Vitamin D at 3, 6 and 12 months postpartum. CONCLUSIONS: In Canada, medication use is common postpartum; 40% of breastfeeding women use prescription medications in the first 3 months postpartum. A diverse range of medications were used, with many women taking more than one prescription and non-prescription medicines. The most commonly used prescription medication by breastfeeding women were domperidone for off-label lactation support, signalling a need for more data on the efficacy of domperidone for this indication. This data should inform research priorities and communication strategies developed to optimize care during lactation.

Cohort profile: investigating SARS-CoV-2 infection and the health and psychosocial impact of the COVID-19 pandemic in the Canadian CHILD Cohort.

The coronavirus disease 2019 (COVID-19) pandemic has affected all Canadian families, with some impacted differently than others. Our study aims to: (1) determine the prevalence and transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection among Canadian families, (2) identify predictors of infection susceptibility and severity of SARS-CoV-2, and (3) identify health and psychosocial impacts of the COVID-19 pandemic. This study builds upon the CHILD Cohort Study, an ongoing multi-ethnic general population prospective cohort consisting of 3,454 Canadian families with children born in Vancouver, Edmonton, Manitoba, and Toronto between 2009 and 2012. During the pandemic, CHILD households were invited to participate in the CHILD COVID-19 Add-On Study involving: (1) brief biweekly surveys about COVID-19 symptoms and testing; (2) quarterly questionnaires assessing COVID-19 exposure and testing, vaccination status, physical and mental health, and pandemic-driven life changes; and (3) in-home biological sampling kits to collect blood and stool. In total, 1,462 households (5,378 participants) consented to the CHILD COVID-19 Add-On Study: 2,803 children (mean±standard deviation [SD], 9.0±2.7 years; range, 0-17 years) and 2,576 adults (mean±SD, 43.0±6.5 years; range, 18-85 years). We will leverage the wealth of pre-pandemic CHILD data to identify risk and resilience factors for susceptibility and severity to the direct and indirect pandemic effects. Our short-term findings will inform key stakeholders and knowledge users to shape current and future pandemic responses. Additionally, this study provides a unique resource to study the long-term impacts of the pandemic as the CHILD Cohort Study continues.

Pathogen-associated gene discovery workflows for novel antivirulence therapeutic development.

Novel therapeutics to manage bacterial infections are urgently needed as the impact and prevalence of antimicrobial resistance (AMR) grows. Antivirulence therapeutics are an alternative approach to antibiotics that aim to attenuate virulence rather than target bacterial essential functions, while minimizing microbiota perturbation and the risk of AMR development. Beyond known virulence factors, pathogen-associated genes (PAGs; genes found only in pathogens to date) may play an important role in virulence or host association. Many identified PAGs encode uncharacterized hypothetical proteins and represent an untapped wealth of novel drug targets. Here, we review current advances in antivirulence drug research and development, including PAG identification, and provide a comprehensive workflow from the discovery of antivirulence drug targets to drug discovery. We highlight the importance of integrating bioinformatic/genomic-based methods for novel virulence factor discovery, coupled with experimental characterization, into existing drug screening platforms to develop novel and effective antivirulence drugs.

GlobeCorr: interactive globe-based visualization for correlation datasets.

Motivation: Increasingly complex omics datasets are being generated, along with associated diverse categories of metadata (environmental, clinical, etc.). Looking at the correlation between these variables can be critical to identify potential confounding factors and novel relationships. To date, some correlation globe software has been developed to aid investigations; however, they lack secure, dynamic visualization capability. Results: GlobeCorr.ca is a web-based application designed to provide user-friendly, interactive visualization and analysis of correlation datasets. Users load tabular data listing pairwise variables and their correlation values, and GlobeCorr creates a dynamic visualization using ribbons to represent positive and negative correlations, optionally grouped by domain/category (such as microbiome taxa against other metadata). GlobeCorr runs securely (locally on a user's computer) and provides a simple method for users to visualize and summarize complex datasets. This tool is applicable to a wide range of disciplines and domains of interest, including the bioinformatics/microbiome and metadata examples provided within. Availability and Implementation: See https://GlobeCorr.ca; Code provided under an open source MIT license: https://github.com/brinkmanlab/globecorr.

CARD 2023: expanded curation, support for machine learning, and resistome prediction at the Comprehensive Antibiotic Resistance Database.

The Comprehensive Antibiotic Resistance Database (CARD; card.mcmaster.ca) combines the Antibiotic Resistance Ontology (ARO) with curated AMR gene (ARG) sequences and resistance-conferring mutations to provide an informatics framework for annotation and interpretation of resistomes. As of version 3.2.4, CARD encompasses 6627 ontology terms, 5010 reference sequences, 1933 mutations, 3004 publications, and 5057 AMR detection models that can be used by the accompanying Resistance Gene Identifier (RGI) software to annotate genomic or metagenomic sequences. Focused curation enhancements since 2020 include expanded ß-lactamase curation, incorporation of likelihood-based AMR mutations for Mycobacterium tuberculosis, addition of disinfectants and antiseptics plus their associated ARGs, and systematic curation of resistance-modifying agents. This expanded curation includes 180 new AMR gene families, 15 new drug classes, 1 new resistance mechanism, and two new ontological relationships: evolutionary_variant_of and is_small_molecule_inhibitor. In silico prediction of resistomes and prevalence statistics of ARGs has been expanded to 377 pathogens, 21,079 chromosomes, 2,662 genomic islands, 41,828 plasmids and 155,606 whole-genome shotgun assemblies, resulting in collation of 322,710 unique ARG allele sequences. New features include the CARD:Live collection of community submitted isolate resistome data and the introduction of standardized 15 character CARD Short Names for ARGs to support machine learning efforts.

From Prescription Drugs to Natural Health Products: Medication Use in Canadian Infants.

Limited data exist on pharmaceutical product use by infants, although available data suggests higher prevalence of use among children under 12 months of age. We conducted a descriptive study of 3050 infants recruited in the CHILD Cohort Study, a prospective, multicenter, longitudinal cohort following children from pregnancy through childhood. Parents were surveyed for use of prescription and over-the-counter drugs, and natural health products (NHPs, including homeopathic products and vitamins) at 3, 6, and 12 months after delivery. By one year of age, 96.0% of children had taken at least one pharmaceutical product. Among 307 reported products, 32 were given to at least 1% of cohort infants. Vitamin D, acetaminophen, ibuprofen, topical hydrocortisone, amoxicillin, and nystatin were the most common medications and natural health products (NHPs) received, with 8/32 of the most frequently used products being NHPs. Overall, 14.7% of pharmaceutical products administered to children were off-label and 35.8% were NHPs or products without a Drug Identification Number (DIN). The use of over-the-counter medications and NHPs is common and off-label use of drugs is frequent, even in the first year of life. This study highlights the importance of conducting studies on medication use in infants, and of infant medication use monitoring by healthcare providers.

Exploring the mobilome and resistome of Enterococcus faecium in a One Health context across two continents.

Enterococcus faecium is a ubiquitous opportunistic pathogen that is exhibiting increasing levels of antimicrobial resistance (AMR). Many of the genes that confer resistance and pathogenic functions are localized on mobile genetic elements (MGEs), which facilitate their transfer between lineages. Here, features including resistance determinants, virulence factors and MGEs were profiled in a set of 1273 E. faecium genomes from two disparate geographic locations (in the UK and Canada) from a range of agricultural, clinical and associated habitats. Neither lineages of E. faecium, type A and B, nor MGEs are constrained by geographic proximity, but our results show evidence of a strong association of many profiled genes and MGEs with habitat. Many features were associated with a group of clinical and municipal wastewater genomes that are likely forming a new human-associated ecotype within type A. The evolutionary dynamics of E. faecium make it a highly versatile emerging pathogen, and its ability to acquire, transmit and lose features presents a high risk for the emergence of new pathogenic variants and novel resistance combinations. This study provides a workflow for MGE-centric surveillance of AMR in Enterococcus that can be adapted to other pathogens.

Enabling genomic island prediction and comparison in multiple genomes to investigate bacterial evolution and outbreaks.

Outbreaks of virulent and/or drug-resistant bacteria have a significant impact on human health and major economic consequences. Genomic islands (GIs; defined as clusters of genes of probable horizontal origin) are of high interest because they disproportionately encode virulence factors, some antimicrobial-resistance (AMR) genes, and other adaptations of medical or environmental interest. While microbial genome sequencing has become rapid and inexpensive, current computational methods for GI analysis are not amenable for rapid, accurate, user-friendly and scalable comparative analysis of sets of related genomes. To help fill this gap, we have developed IslandCompare, an open-source computational pipeline for GI prediction and comparison across several to hundreds of bacterial genomes. A dynamic and interactive visualization strategy displays a bacterial core-genome phylogeny, with bacterial genomes linearly displayed at the phylogenetic tree leaves. Genomes are overlaid with GI predictions and AMR determinants from the Comprehensive Antibiotic Resistance Database (CARD), and regions of similarity between the genomes are also displayed. GI predictions are performed using Sigi-HMM and IslandPath-DIMOB, the two most precise GI prediction tools based on nucleotide composition biases, as well as a novel blast-based consistency step to improve cross-genome prediction consistency. GIs across genomes sharing sequence similarity are grouped into clusters, further aiding comparative analysis and visualization of acquisition and loss of mobile GIs in specific sub-clades. IslandCompare is an open-source software that is containerized for local use, plus available via a user-friendly, web-based interface to allow direct use by bioinformaticians, biologists and clinicians (at https://islandcompare.ca).

Dysbiosis of the Female Murine Gut Microbiome Exacerbates Neutrophil-mediated Vascular Allograft Injury by Affecting Immunoregulation by Acetate.

BACKGROUND: The gut microbiota affects immune responses that cause organ transplant rejection, but the mechanisms by which this occurs remain poorly understood. METHODS: We have examined, in a murine model, how disruption of the gut microbiota with antibiotics early in life alters this microbial community later in life to affect immune responses that injure vascular allografts. RESULTS: Analysis of 16S rRNA and whole genome sequencing of the gut microbiota demonstrated that early life disruption of this microbial community with antibiotics caused a reduction in taxa and enzymatic genes involved in the synthesis of acetate, an immunoregulatory metabolite in mice and humans. When allograft vascular injury was examined, early life disruption of the gut microbiota increased neutrophil accumulation and related medial injury of transplanted arteries. Normalizing the gut microbiota by co-housing and oral administration of acetate prevented neutrophil-mediated vascular allograft injury. CONCLUSIONS: Dysbiosis of the gut microbiome that reduces its production of the immunoregulatory metabolite acetate exacerbates neutrophil-mediated allograft vascular injury.

Similarities between bacterial GAD and human GAD65: Implications in gut mediated autoimmune type 1 diabetes.

A variety of islet autoantibodies (AAbs) can predict and possibly dictate eventual type 1 diabetes (T1D) diagnosis. Upwards of 75% of those with T1D are positive for AAbs against glutamic acid decarboxylase (GAD65 or GAD), a producer of gamma-aminobutyric acid (GABA) in human pancreatic beta cells. Interestingly, bacterial populations within the human gut also express GAD and produce GABA. Evidence suggests that dysbiosis of the microbiome may correlate with T1D pathogenesis and physiology. Therefore, autoimmune linkages between the gut microbiome and islets susceptible to autoimmune attack need to be further elucidated. Utilizing in silico analyses, we show that 25 GAD sequences from human gut bacterial sources show sequence and motif similarities to human beta cell GAD65. Our motif analyses determined that most gut GAD sequences contain the pyroxical dependent decarboxylase (PDD) domain of human GAD65, which is important for its enzymatic activity. Additionally, we showed overlap with known human GAD65 T cell receptor epitopes, which may implicate the immune destruction of beta cells. Thus, we propose a physiological hypothesis in which changes in the gut microbiome in those with T1D result in a release of bacterial GAD, thus causing miseducation of the host immune system. Due to the notable similarities we found between human and bacterial GAD, these deputized immune cells may then target human beta cells leading to the development of T1D.

The Pseudomonas aeruginosa whole genome sequence: A 20th anniversary celebration.

Toward the end of August 2000, the 6.3 Mbp whole genome sequence of Pseudomonas aeruginosa strain PAO1 was published. With 5570 open reading frames (ORFs), PAO1 had the largest microbial genome sequenced up to that point in time-including a large proportion of metabolic, transport and antimicrobial resistance genes supporting its ability to colonize diverse environments. A remarkable 9% of its ORFs were predicted to encode proteins with regulatory functions, providing new insight into bacterial network complexity as a function of network size. In this celebratory article, we fast forward 20 years, and examine how access to this resource has transformed our understanding of P. aeruginosa. What follows is more than a simple review or commentary; we have specifically asked some of the leaders in the field to provide personal reflections on how the PAO1 genome sequence, along with the Pseudomonas Community Annotation Project (PseudoCAP) and Pseudomonas Genome Database (pseudomonas.com), have contributed to the many exciting discoveries in this field. In addition to bringing us all up to date with the latest developments, we also ask our contributors to speculate on how the next 20 years of Pseudomonas research might pan out.

Composition and Associations of the Infant Gut Fungal Microbiota with Environmental Factors and Childhood Allergic Outcomes.

Although often neglected in gut microbiota studies, recent evidence suggests that imbalanced, or dysbiotic, gut mycobiota (fungal microbiota) communities in infancy coassociate with states of bacterial dysbiosis linked to inflammatory diseases such as asthma. In the present study, we (i) characterized the infant gut mycobiota at 3 months and 1 year of age in 343 infants from the CHILD Cohort Study, (ii) defined associations among gut mycobiota community composition and environmental factors for the development of inhalant allergic sensitization (atopy) at age 5 years, and (iii) built a predictive model for inhalant atopy status at age 5 years using these data. We show that in Canadian infants, fungal communities shift dramatically in composition over the first year of life. Early-life environmental factors known to affect gut bacterial communities were also associated with differences in gut fungal community alpha diversity, beta diversity, and/or the relative abundance of specific fungal taxa. Moreover, these metrics differed among healthy infants and those who developed inhalant allergic sensitization (atopy) by age 5 years. Using a rationally selected set of early-life environmental factors in combination with fungal community composition at 1 year of age, we developed a machine learning logistic regression model that predicted inhalant atopy status at 5 years of age with 81% accuracy. Together, these data suggest an important role for the infant gut mycobiota in early-life immune development and indicate that early-life behavioral or therapeutic interventions have the potential to modify infant gut fungal communities, with implications for an infant's long-term health. IMPORTANCE Recent evidence suggests an immunomodulatory role for commensal fungi (mycobiota) in the gut, yet little is known about the composition and dynamics of early-life gut fungal communities. In this work, we show for the first time that the composition of the gut mycobiota of Canadian infants changes dramatically over the course of the first year of life, is associated with environmental factors such as geographical location, diet, and season of birth, and can be used in conjunction with knowledge of a small number of key early-life factors to predict inhalant atopy status at age 5 years. Our study highlights the importance of considering fungal communities as indicators or inciters of immune dysfunction preceding the onset of allergic disease and can serve as a benchmark for future studies aiming to examine infant gut fungal communities across birth cohorts.

Bacterial cyclic diguanylate signaling networks sense temperature.

Many bacteria use the second messenger cyclic diguanylate (c-di-GMP) to control motility, biofilm production and virulence. Here, we identify a thermosensory diguanylate cyclase (TdcA) that modulates temperature-dependent motility, biofilm development and virulence in the opportunistic pathogen Pseudomonas aeruginosa. TdcA synthesizes c-di-GMP with catalytic rates that increase more than a hundred-fold over a ten-degree Celsius change. Analyses using protein chimeras indicate that heat-sensing is mediated by a thermosensitive Per-Arnt-SIM (PAS) domain. TdcA homologs are widespread in sequence databases, and a distantly related, heterologously expressed homolog from the Betaproteobacteria order Gallionellales also displayed thermosensitive diguanylate cyclase activity. We propose, therefore, that thermotransduction is a conserved function of c-di-GMP signaling networks, and that thermosensitive catalysis of a second messenger constitutes a mechanism for thermal sensing in bacteria.

Addressing Privacy Concerns in Sharing Viral Sequences and Minimum Contextual Data in a Public Repository During the COVID-19 Pandemic.

COVID-19 was declared to be a pandemic in March 2020 by the World Health Organization. Timely sharing of viral genomic sequencing data accompanied by a minimal set of contextual data is essential for informing regional, national, and international public health responses. Such contextual data is also necessary for developing, and improving clinical therapies and vaccines, and enhancing the scientific community's understanding of the SARS-CoV-2 virus. The Canadian COVID-19 Genomics Network (CanCOGeN) was launched in April 2020 to coordinate and upscale existing genomics-based COVID-19 research and surveillance efforts. CanCOGeN is performing large-scale sequencing of both the genomes of SARS-CoV-2 virus samples (VirusSeq) and affected Canadians (HostSeq). This paper addresses the privacy concerns associated with sharing the viral sequence data with a pre-defined set of contextual data describing the sample source and case attribute of the sequence data in the Canadian context. Currently, the viral genome sequences are shared by provincial public health laboratories and their healthcare and academic partners, with the Canadian National Microbiology Laboratory and with publicly accessible databases. However, data sharing delays and the provision of incomplete contextual data often occur because publicly releasing such data triggers privacy and data governance concerns. The CanCOGeN Ethics and Governance Expert Working Group thus has investigated several privacy issues cited by CanCOGeN data providers/stewards. This paper addresses these privacy concerns and offers insights primarily in the Canadian context, although similar privacy considerations also exist in other jurisdictions. We maintain that sharing viral sequencing data and its limited associated contextual data in the public domain generally does not pose insurmountable privacy challenges. However, privacy risks associated with reidentification should be actively monitored due to advancements in reidentification methods and the evolving pandemic landscape. We also argue that during a global health emergency such as COVID-19, privacy should not be used as a blanket measure to prevent such genomic data sharing due to the significant benefits it provides towards public health responses and ongoing research activities.

PSORTdb 4.0: expanded and redesigned bacterial and archaeal protein subcellular localization database incorporating new secondary localizations.

Protein subcellular localization (SCL) is important for understanding protein function, genome annotation, and aids identification of potential cell surface diagnostic markers, drug targets, or vaccine components. PSORTdb comprises ePSORTdb, a manually curated database of experimentally verified protein SCLs, and cPSORTdb, a pre-computed database of PSORTb-predicted SCLs for NCBI's RefSeq deduced bacterial and archaeal proteomes. We now report PSORTdb 4.0 (http://db.psort.org/). It features a website refresh, in particular a more user-friendly database search. It also addresses the need to uniquely identify proteins from NCBI genomes now that GI numbers have been retired. It further expands both ePSORTdb and cPSORTdb, including additional data about novel secondary localizations, such as proteins found in bacterial outer membrane vesicles. Protein predictions in cPSORTdb have increased along with the number of available microbial genomes, from approximately 13 million when PSORTdb 3.0 was released, to over 66 million currently. Now, analyses of both complete and draft genomes are included. This expanded database will be of wide use to researchers developing SCL predictors or studying diverse microbes, including medically, agriculturally and industrially important species that have both classic or atypical cell envelope structures or vesicles.

Metagenome-assembled genome binning methods with short reads disproportionately fail for plasmids and genomic Islands.

Metagenomic methods enable the simultaneous characterization of microbial communities without time-consuming and bias-inducing culturing. Metagenome-assembled genome (MAG) binning methods aim to reassemble individual genomes from this data. However, the recovery of mobile genetic elements (MGEs), such as plasmids and genomic islands (GIs), by binning has not been well characterized. Given the association of antimicrobial resistance (AMR) genes and virulence factor (VF) genes with MGEs, studying their transmission is a public-health priority. The variable copy number and sequence composition of MGEs makes them potentially problematic for MAG binning methods. To systematically investigate this issue, we simulated a low-complexity metagenome comprising 30 GI-rich and plasmid-containing bacterial genomes. MAGs were then recovered using 12 current prediction pipelines and evaluated. While 82-94 % of chromosomes could be correctly recovered and binned, only 38-44 % of GIs and 1-29 % of plasmid sequences were found. Strikingly, no plasmid-borne VF nor AMR genes were recovered, and only 0-45 % of AMR or VF genes within GIs. We conclude that short-read MAG approaches, without further optimization, are largely ineffective for the analysis of mobile genes, including those of public-health importance, such as AMR and VF genes. We propose that researchers should explore developing methods that optimize for this issue and consider also using unassembled short reads and/or long-read approaches to more fully characterize metagenomic data.

Decreasing antibiotic use, the gut microbiota, and asthma incidence in children: evidence from population-based and prospective cohort studies.

BACKGROUND: Childhood asthma incidence is decreasing in some parts of Europe and North America. Antibiotic use in infancy has been associated with increased asthma risk. In the present study, we tested the hypothesis that decreases in asthma incidence are linked to reduced antibiotic prescribing and mediated by changes in the gut bacterial community. METHODS: This study comprised population-based and prospective cohort analyses. At the population level, we used administrative data from British Columbia, Canada (population 4·7 million), on annual rates of antibiotic prescriptions and asthma diagnoses, to assess the association between antibiotic prescribing (at age <1 year) and asthma incidence (at age 1-4 years). At the individual level, 2644 children from the Canadian Healthy Infant Longitudinal Development (CHILD) prospective birth cohort were examined for the association of systemic antibiotic use (at age <1 year) with the diagnosis of asthma (at age 5 years). In the same cohort, we did a mechanistic investigation of 917 children with available 16S rRNA gene sequencing data from faecal samples (at age ≤1 year), to assess how composition of the gut microbiota relates to antibiotic exposure and asthma incidence. FINDINGS: At the population level between 2000 and 2014, asthma incidence in children (aged 1-4 years) showed an absolute decrease of 7·1 new diagnoses per 1000 children, from 27·3 (26·8-28·3) per 1000 children to 20·2 (19·5-20·8) per 1000 children (a relative decrease of 26·0%). Reduction in incidence over the study period was associated with decreasing antibiotic use in infancy (age <1 year), from 1253·8 prescriptions (95% CI 1219·3-1288·9) per 1000 infants to 489·1 (467·6-511·2) per 1000 infants (Spearman's r=0·81; p<0·0001). Asthma incidence increased by 24% with each 10% increase in antibiotic prescribing (adjusted incidence rate ratio 1·24 [95% CI 1·20-1·28]; p<0·0001). In the CHILD cohort, after excluding children who received antibiotics for respiratory symptoms, asthma diagnosis in childhood was associated with infant antibiotic use (adjusted odds ratio [aOR] 2·15 [95% CI 1·37-3·39]; p=0·0009), with a significant dose-response; 114 (5·2%) of 2182 children unexposed to antibiotics had asthma by age 5 years, compared with 23 (8·1%) of 284 exposed to one course, five (10·2%) of 49 exposed to two courses, and six (17·6%) of 34 exposed to three or more courses (aOR 1·44 [1·16-1·79]; p=0·0008). Increasing α-diversity of the gut microbiota, defined as an IQR increase (25th to 75th percentile) in the Chao1 index, at age 1 year was associated with a 32% reduced risk of asthma at age 5 years (aOR for IQR increase 0·68 [0·46-0·99]; p=0·046). In a structural equation model, we found the gut microbiota at age 1 year, characterised by α-diversity, ß-diversity, and amplicon sequence variants modified by antibiotic exposure, to be a significant mediator between outpatient antibiotic exposure in the first year of life and asthma diagnosis at age 5 years (ß=0·08; p=0·027). INTERPRETATION: Our findings suggest that the reduction in the incidence of paediatric asthma observed in recent years might be an unexpected benefit of prudent antibiotic use during infancy, acting via preservation of the gut microbial community. FUNDING: British Columbia Ministry of Health, Pharmaceutical Services Branch; Canadian Institutes of Health Research; Allergy, Genes and Environment (AllerGen) Network of Centres of Excellence; Genome Canada; and Genome British Columbia.

AB569, a nontoxic chemical tandem that kills major human pathogenic bacteria.

Antibiotic-resistant superbug bacteria represent a global health problem with no imminent solutions. Here we demonstrate that the combination (termed AB569) of acidified nitrite (A-NO2-) and Na2-EDTA (disodium ethylenediaminetetraacetic acid) inhibited all Gram-negative and Gram-positive bacteria tested. AB569 was also efficacious at killing the model organism Pseudomonas aeruginosa in biofilms and in a murine chronic lung infection model. AB569 was not toxic to human cell lines at bactericidal concentrations using a basic viability assay. RNA-Seq analyses upon treatment of P. aeruginosa with AB569 revealed a catastrophic loss of the ability to support core pathways encompassing DNA, RNA, protein, ATP biosynthesis, and iron metabolism. Electrochemical analyses elucidated that AB569 produced more stable SNO proteins, potentially explaining one mechanism of bacterial killing. Our data implicate that AB569 is a safe and effective means to kill pathogenic bacteria, suggesting that simple strategies could be applied with highly advantageous therapeutic/toxicity index ratios to pathogens associated with a myriad of periepithelial infections and related disease scenarios.

PSORTm: a bacterial and archaeal protein subcellular localization prediction tool for metagenomics data.

MOTIVATION: Many methods for microbial protein subcellular localization (SCL) prediction exist; however, none is readily available for analysis of metagenomic sequence data, despite growing interest from researchers studying microbial communities in humans, agri-food relevant organisms and in other environments (e.g. for identification of cell-surface biomarkers for rapid protein-based diagnostic tests). We wished to also identify new markers of water quality from freshwater samples collected from pristine versus pollution-impacted watersheds. RESULTS: We report PSORTm, the first bioinformatics tool designed for prediction of diverse bacterial and archaeal protein SCL from metagenomics data. PSORTm incorporates components of PSORTb, one of the most precise and widely used protein SCL predictors, with an automated classification by cell envelope. An evaluation using 5-fold cross-validation with in silico-fragmented sequences with known localization showed that PSORTm maintains PSORTb's high precision, while sensitivity increases proportionately with metagenomic sequence fragment length. PSORTm's read-based analysis was similar to PSORTb-based analysis of metagenome-assembled genomes (MAGs); however, the latter requires non-trivial manual classification of each MAG by cell envelope, and cannot make use of unassembled sequences. Analysis of the watershed samples revealed the importance of normalization and identified potential biomarkers of water quality. This method should be useful for examining a wide range of microbial communities, including human microbiomes, and other microbiomes of medical, environmental or industrial importance. AVAILABILITY AND IMPLEMENTATION: Documentation, source code and docker containers are available for running PSORTm locally at https://www.psort.org/psortm/ (freely available, open-source software under GNU General Public License Version 3). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

CARD 2020: antibiotic resistome surveillance with the comprehensive antibiotic resistance database.

The Comprehensive Antibiotic Resistance Database (CARD; https://card.mcmaster.ca) is a curated resource providing reference DNA and protein sequences, detection models and bioinformatics tools on the molecular basis of bacterial antimicrobial resistance (AMR). CARD focuses on providing high-quality reference data and molecular sequences within a controlled vocabulary, the Antibiotic Resistance Ontology (ARO), designed by the CARD biocuration team to integrate with software development efforts for resistome analysis and prediction, such as CARD's Resistance Gene Identifier (RGI) software. Since 2017, CARD has expanded through extensive curation of reference sequences, revision of the ontological structure, curation of over 500 new AMR detection models, development of a new classification paradigm and expansion of analytical tools. Most notably, a new Resistomes & Variants module provides analysis and statistical summary of in silico predicted resistance variants from 82 pathogens and over 100 000 genomes. By adding these resistance variants to CARD, we are able to summarize predicted resistance using the information included in CARD, identify trends in AMR mobility and determine previously undescribed and novel resistance variants. Here, we describe updates and recent expansions to CARD and its biocuration process, including new resources for community biocuration of AMR molecular reference data.