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Background: Drivers of COVID-19 severity are multifactorial and include multidimensional and potentially interacting factors encompassing viral determinants and host-related factors (i.e., demographics, pre-existing conditions and/or genetics), thus complicating the prediction of clinical outcomes for different severe acute respiratory syndrome coronavirus (SARS-CoV-2) variants. Although millions of SARS-CoV-2 genomes have been publicly shared in global databases, linkages with detailed clinical data are scarce. Therefore, we aimed to establish a COVID-19 patient dataset with linked clinical and viral genomic data to then examine associations between SARS-CoV-2 genomic signatures and clinical disease phenotypes., Methods: A cohort of adult patients with laboratory confirmed SARS-CoV-2 from 11 participating healthcare institutions in the Greater Toronto Area (GTA) were recruited from March 2020 to April 2022. Supervised machine learning (ML) models were developed to predict hospitalization using SARS-CoV-2 lineage-specific genomic signatures, patient demographics, symptoms, and pre-existing comorbidities. The relative importance of these features was then evaluated., Results: Complete clinical data and viral whole genome level information were obtained from 617 patients, 50.4% of whom were hospitalized. Notably, inpatients were older with a mean age of 66.67 years (SD ± 17.64 years), whereas outpatients had a mean age of 44.89 years (SD ± 16.00 years). SHapley Additive exPlanations (SHAP) analyses revealed that underlying vascular disease, underlying pulmonary disease, and fever were the most significant clinical features associated with hospitalization. In models built on the amino acid sequences of functional regions including spike, nucleocapsid, ORF3a, and ORF8 proteins, variants preceding the emergence of variants of concern (VOCs) or pre-VOC variants, were associated with hospitalization., Conclusions: Viral genomic features have limited utility in predicting hospitalization across SARS-CoV-2 diversity. Combining clinical and viral genomic datasets provides perspective on patient specific and virus-related factors that impact COVID-19 disease severity. Overall, clinical features had greater discriminatory power than viral genomic features in predicting hospitalization., Competing Interests: Declarations. Ethics approval and consent to participate: This study was approved by the ethics committee of all participating TIBDN hospitals (REB# 02-0118-U/05/0016-C) and Sunnybrook Health Sciences Centre’s ethics board (REB# 5533). All participants provided informed consent to partake in this study. All research was conducted in accordance with the second edition of the Tri-Council Policy Statement: Ethical Conduct for Research Involving Humans standard. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests., (© 2025. The Author(s).)

The incorporation of sequencing technologies in frontline and public health healthcare settings was vital in developing virus surveillance programs during the Coronavirus Disease 2019 (COVID-19) pandemic caused by transmission of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, increased data acquisition poses challenges for both rapid and accurate analyses. To overcome these hurdles, we developed the SARS-CoV-2 Illumina GeNome Assembly Line (SIGNAL) for quick bulk analyses of Illumina short-read sequencing data. SIGNAL is a Snakemake workflow that seamlessly manages parallel tasks to process large volumes of sequencing data. A series of outputs are generated, including consensus genomes, variant calls, lineage assessments and identified variants of concern (VOCs). Compared to other existing SARS-CoV-2 sequencing workflows, SIGNAL is one of the fastest-performing analysis tools while maintaining high accuracy. The source code is publicly available (github.com/jaleezyy/covid-19-signal) and is optimized to run on various systems, with software compatibility and resource management all handled within the workflow. Overall, SIGNAL illustrated its capacity for high-volume analyses through several contributions to publicly funded government public health surveillance programs and can be a valuable tool for continuing SARS-CoV-2 Illumina sequencing efforts and will inform the development of similar strategies for rapid viral sequence assessment., (© The Author(s) 2024. Published by Oxford University Press on behalf of NAR Genomics and Bioinformatics.)

Interpreting the phenotypes of bla SHV alleles in Klebsiella pneumoniae genomes is complex. Whilst all strains are expected to carry a chromosomal copy conferring resistance to ampicillin, they may also carry mutations in chromosomal bla SHV alleles or additional plasmid-borne bla SHV alleles that have extended-spectrum β-lactamase (ESBL) activity and/or β-lactamase inhibitor (BLI) resistance activity. In addition, the role of individual mutations/a changes is not completely documented or understood. This has led to confusion in the literature and in antimicrobial resistance (AMR) gene databases [e.g. the National Center for Biotechnology Information (NCBI) Reference Gene Catalog and the β-lactamase database (BLDB)] over the specific functionality of individual sulfhydryl variable (SHV) protein variants. Therefore, the identification of ESBL-producing strains from K. pneumoniae genome data is complicated. Here, we reviewed the experimental evidence for the expansion of SHV enzyme function associated with specific aa substitutions. We then systematically assigned SHV alleles to functional classes (WT, ESBL and BLI resistant) based on the presence of these mutations. This resulted in the re-classification of 37 SHV alleles compared with the current assignments in the NCBI's Reference Gene Catalog and/or BLDB (21 to WT, 12 to ESBL and 4 to BLI resistant). Phylogenetic and comparative genomic analyses support that (i) SHV-1 (encoded by bla SHV-1 ) is the ancestral chromosomal variant, (ii) ESBL- and BLI-resistant variants have evolved multiple times through parallel substitution mutations, (iii) ESBL variants are mostly mobilized to plasmids and (iv) BLI-resistant variants mostly result from mutations in chromosomal bla SHV . We used matched genome-phenotype data from the KlebNET-GSP AMR Genotype-Phenotype Group to identify 3999 K . pneumoniae isolates carrying one or more bla SHV alleles but no other acquired β-lactamases to assess genotype-phenotype relationships for bla SHV . This collection includes human, animal and environmental isolates collected between 2001 and 2021 from 24 countries. Our analysis supports that mutations at Ambler sites 238 and 179 confer ESBL activity, whilst most omega-loop substitutions do not. Our data also provide support for the WT assignment of 67 protein variants, including 8 that were noted in public databases as ESBL. These eight variants were reclassified as WT because they lack ESBL-associated mutations, and our phenotype data support susceptibility to third-generation cephalosporins (SHV-27, SHV-38, SHV-40, SHV-41, SHV-42, SHV-65, SHV-164 and SHV-187). The approach and results outlined here have been implemented in Kleborate v2.4.1 (a software tool for genotyping K. pneumoniae ), whereby known and novel bla SHV alleles are classified based on causative mutations. Kleborate v2.4.1 was updated to include ten novel protein variants from the KlebNET-GSP dataset and all alleles in public databases as of November 2023. This study demonstrates the power of sharing AMR phenotypes alongside genome data to improve the understanding of resistance mechanisms.

Background: Macrolide antibiotics, including azithromycin, can reduce under 5 years of age mortality rates and treat various infections in children in sub-Saharan Africa. These exposures, however, can select for antibiotic-resistant bacteria in the gut microbiota., Methods: Our previous randomized controlled trial (RCT) of a rapid-test-and-treat strategy for severe acute diarrheal disease in children in Botswana included an intervention (3-day azithromycin dose) group and a control group that received supportive treatment. In this prospective matched cohort study using stools collected at baseline and 60 days after treatment from RCT participants, the collection of antibiotic resistance genes or resistome was compared between groups., Results: Certain macrolide resistance genes increased in prevalence by 13%-55% at 60 days, without differences in gene presence between the intervention and control groups. These genes were linked to tetracycline resistance genes and mobile genetic elements., Conclusions: Azithromycin treatment for bacterial diarrhea for young children in Botswana resulted in similar effects on the gut resistome as the supportive treatment and did not provide additional selective pressure for macrolide resistance gene maintenance. The gut microbiota of these children contains diverse macrolide resistance genes that may be transferred within the gut upon repeated exposures to azithromycin or coselected by other antibiotics., Clinical Trials Registration: NCT02803827., Competing Interests: Potential conflicts of interest . J. M. P.'s institution has received grant funding from medImmune and Merck for a study outside the submitted work. All other authors report no potential conflicts. All authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Conflicts that the editors consider relevant to the content of the manuscript have been disclosed., (© The Author(s) 2024. Published by Oxford University Press on behalf of Infectious Diseases Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Antimicrobial resistant (AMR) pathogens represent urgent threats to human health, and their surveillance is of paramount importance. Metagenomic next generation sequencing (mNGS) has revolutionized such efforts, but remains challenging due to the lack of open-access bioinformatics tools capable of simultaneously analyzing both microbial and AMR gene sequences. To address this need, we developed the Chan Zuckerberg ID (CZ ID) AMR module, an open-access, cloud-based workflow designed to integrate detection of both microbes and AMR genes in mNGS and whole-genome sequencing (WGS) data. It leverages the Comprehensive Antibiotic Resistance Database and associated Resistance Gene Identifier software, and works synergistically with the CZ ID short-read mNGS module to enable broad detection of both microbes and AMR genes. We highlight diverse applications of the AMR module through analysis of both publicly available and newly generated mNGS and WGS data from four clinical cohort studies and an environmental surveillance project. Through genomic investigations of bacterial sepsis and pneumonia cases, hospital outbreaks, and wastewater surveillance data, we gain a deeper understanding of infectious agents and their resistomes, highlighting the value of integrating microbial identification and AMR profiling for both research and public health. We leverage additional functionalities of the CZ ID mNGS platform to couple resistome profiling with the assessment of phylogenetic relationships between nosocomial pathogens, and further demonstrate the potential to capture the longitudinal dynamics of pathogen and AMR genes in hospital acquired bacterial infections. In sum, the new AMR module advances the capabilities of the open-access CZ ID microbial bioinformatics platform by integrating pathogen detection and AMR profiling from mNGS and WGS data. Its development represents a critical step toward democratizing pathogen genomic analysis and supporting collaborative efforts to combat the growing threat of AMR., Competing Interests: Competing interests The authors declare that they have no competing interests.

Background: The COVID-19 pandemic, caused by the Severe Acute Respiratory Syndrome Coronavirus 2 virus, emerged in late 2019 and spready globally. Many effects of infection with this pathogen are still unknown, with both chronic and repeated COVID-19 infection producing novel pathologies., Case Presentation: An immunocompromised patient presented with chronic COVID-19 infection. The patient had history of Hodgkin's lymphoma, treated with chemotherapy and stem cell transplant. During the course of their treatment, eleven respiratory samples from the patient were analyzed by whole-genome sequencing followed by lineage identification. Whole-genome sequencing of the virus present in the patient over time revealed that the patient at various timepoints harboured three different lineages of the virus. The patient was initially infected with the B.1.1.176 lineage before coinfection with BA.1. When the patient was coinfected with both B.1.1.176 and BA.1, the viral populations were found in approximately equal proportions within the patient based on sequencing read abundance. Upon further sampling, the lineage present within the patient during the final two timepoints was found to be BA.2.9. The patient eventually developed respiratory failure and died., Conclusions: This case study shows an example of the changes that can happen within an immunocompromised patient who is infected with COVID-19 multiple times. Furthermore, this case demonstrates how simultaneous coinfection with two lineages of COVID-19 can lead to unclear lineage assignment by standard methods, which are resolved by further investigation. When analyzing chronic COVID-19 infection and reinfection cases, care must be taken to properly identify the lineages of the virus present., (© 2024. The Author(s).)

Importance: While increasing rates of antimicrobial resistance undermine our current arsenal of antibiotics, resistance-modifying agents (RMAs) hold promise to extend the lifetime of these important molecules. We here provide a standardized nomenclature for RMAs within the Comprehensive Antibiotic Resistance Database in aid of RMA discovery, data curation, and genome mining., Competing Interests: The authors declare no conflict of interest.

Atherosclerotic cardiovascular disease is characterized by both chronic low-grade inflammation and dyslipidemia. The AMP-activated protein kinase (AMPK) inhibits cholesterol synthesis and dampens inflammation but whether pharmacological activation reduces atherosclerosis is equivocal. In the current study, we found that the orally bioavailable and highly selective activator of AMPKβ1 complexes, PF-06409577, reduced atherosclerosis in two mouse models in a myeloid-derived AMPKβ1 dependent manner, suggesting a critical role for macrophages. In bone marrow-derived macrophages (BMDMs), PF-06409577 dose dependently activated AMPK as indicated by increased phosphorylation of downstream substrates ULK1 and acetyl-CoA carboxylase (ACC), which are important for autophagy and fatty acid oxidation/ de novo lipogenesis, respectively. Treatment of BMDMs with PF-06409577 suppressed fatty acid and cholesterol synthesis and transcripts related to the inflammatory response while increasing transcripts important for autophagy through AMPKβ1. These data indicate that pharmacologically targeting macrophage AMPKβ1 may be a promising strategy for reducing atherosclerosis., Competing Interests: G.R.S. has received research funding from Esperion Therapeutics, Espervita Therapeutics, Poxel Pharmaceuticals and Novo Nordisk, honoraria and/or consulting fees from Astra Zeneca, Eli-Lilly, Esperion Therapeutics, Poxel Pharmaceuticals, Merck and is a founder and shareholder of Espervita Therapeutics., (© 2023 The Author(s).)

Isolating and characterizing emerging SARS-CoV-2 variants is key to understanding virus pathogenesis. In this study, we isolated samples of the SARS-CoV-2 R.1 lineage, categorized as a variant under monitoring by the World Health Organization, and evaluated their sensitivity to neutralizing antibodies and type I interferons. We used convalescent serum samples from persons in Canada infected either with ancestral virus (wave 1) or the B.1.1.7 (Alpha) variant of concern (wave 3) for testing neutralization sensitivity. The R.1 isolates were potently neutralized by both the wave 1 and wave 3 convalescent serum samples, unlike the B.1.351 (Beta) variant of concern. Of note, the R.1 variant was significantly more resistant to type I interferons (IFN-α/β) than was the ancestral isolate. Our study demonstrates that the R.1 variant retained sensitivity to neutralizing antibodies but evolved resistance to type I interferons. This critical driving force will influence the trajectory of the pandemic.

Genomic epidemiology can facilitate an understanding of evolutionary history and transmission dynamics of a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) outbreak. We used next-generation sequencing techniques to study SARS-CoV-2 genomes isolated from patients and health care workers (HCWs) across five wards of a Canadian hospital with an ongoing SARS-CoV-2 outbreak. Using traditional contact tracing methods, we show transmission events between patients and HCWs, which were also supported by the SARS-CoV-2 lineage assignments. The outbreak predominantly involved SARS-CoV-2 B.1.564.1 across all five wards, but we also show evidence of community introductions of lineages B.1, B.1.1.32, and B.1.231, falsely assumed to be outbreak related. Altogether, our study exemplifies the value of using contact tracing in combination with genomic epidemiology to understand the transmission dynamics and genetic underpinnings of a SARS-CoV-2 outbreak. IMPORTANCE Our manuscript describes a SARS-CoV-2 outbreak investigation in an Ontario tertiary care hospital. We use traditional contract tracing paired with whole-genome sequencing to facilitate an understanding of the evolutionary history and transmission dynamics of this SARS-CoV-2 outbreak in a clinical setting. These advancements have enabled the incorporation of phylogenetics and genomic epidemiology into the understanding of clinical outbreaks. We show that genomic epidemiology can help to explore the genetic evolution of a pathogen in real time, enabling the identification of the index case and helping understand its transmission dynamics to develop better strategies to prevent future spread of SARS-CoV-2 in congregate, clinical settings such as hospitals., Competing Interests: The authors declare no conflict of interest.

Scientific literature is published at a rate that makes manual data extraction a highly time-consuming task. The Comprehensive Antibiotic Resistance Database (CARD) utilizes literature to curate information on antimicrobial resistance genes and to enable time-efficient triage of publications we have developed a classification algorithm for identifying publications describing first reports of new resistance genes. Trained on publications contained in the CARD, CARD*Shark downloads, processes and identifies publications recently added to PubMed that should be reviewed by biocurators. With CARD*Shark, we can minimize the monthly scope of articles a biocurator reviews from hundreds of articles to a few dozen, drastically improving the speed of curation while ensuring no relevant publications are overlooked. Database URL http://card.mcmaster.ca., (© The Author(s) 2023. Published by Oxford University Press.)

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 β-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., (© The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Pathogen genomics is a critical tool for public health surveillance, infection control, outbreak investigations as well as research. In order to make use of pathogen genomics data, they must be interpreted using contextual data (metadata). Contextual data include sample metadata, laboratory methods, patient demographics, clinical outcomes and epidemiological information. However, the variability in how contextual information is captured by different authorities and how it is encoded in different databases poses challenges for data interpretation, integration and their use/re-use. The DataHarmonizer is a template-driven spreadsheet application for harmonizing, validating and transforming genomics contextual data into submission-ready formats for public or private repositories. The tool's web browser-based JavaScript environment enables validation and its offline functionality and local installation increases data security. The DataHarmonizer was developed to address the data sharing needs that arose during the COVID-19 pandemic, and was used by members of the Canadian COVID Genomics Network (CanCOGeN) to harmonize SARS-CoV-2 contextual data for national surveillance and for public repository submission. In order to support coordination of international surveillance efforts, we have partnered with the Public Health Alliance for Genomic Epidemiology to also provide a template conforming to its SARS-CoV-2 contextual data specification for use worldwide. Templates are also being developed for One Health and foodborne pathogens. Overall, the DataHarmonizer tool improves the effectiveness and fidelity of contextual data capture as well as its subsequent usability. Harmonization of contextual information across authorities, platforms and systems globally improves interoperability and reusability of data for concerted public health and research initiatives to fight the current pandemic and future public health emergencies. While initially developed for the COVID-19 pandemic, its expansion to other data management applications and pathogens is already underway.

Antimicrobial resistance (AMR) is a global health crisis that poses a great threat to modern medicine. Effective prevention strategies are urgently required to slow the emergence and further dissemination of AMR. Given the availability of data sets encompassing hundreds or thousands of pathogen genomes, machine learning (ML) is increasingly being used to predict resistance to different antibiotics in pathogens based on gene content and genome composition. A key objective of this work is to advocate for the incorporation of ML into front-line settings but also highlight the further refinements that are necessary to safely and confidently incorporate these methods. The question of what to predict is not trivial given the existence of different quantitative and qualitative laboratory measures of AMR. ML models typically treat genes as independent predictors, with no consideration of structural and functional linkages; they also may not be accurate when new mutational variants of known AMR genes emerge. Finally, to have the technology trusted by end users in public health settings, ML models need to be transparent and explainable to ensure that the basis for prediction is clear. We strongly advocate that the next set of AMR-ML studies should focus on the refinement of these limitations to be able to bridge the gap to diagnostic implementation.

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.

Background: Probiotic use in preterm infants can mitigate the impact of antibiotic exposure and reduce rates of certain illnesses; however, the benefit on the gut resistome, the collection of antibiotic resistance genes, requires further investigation. We hypothesized that probiotic supplementation of early preterm infants (born < 32-week gestation) while in hospital reduces the prevalence of antibiotic resistance genes associated with pathogenic bacteria in the gut. We used a targeted capture approach to compare the resistome from stool samples collected at the term corrected age of 40 weeks for two groups of preterm infants (those that routinely received a multi-strain probiotic during hospitalization and those that did not) with samples from full-term infants at 10 days of age to identify if preterm birth or probiotic supplementation impacted the resistome. We also compared the two groups of preterm infants up to 5 months of age to identify persistent antibiotic resistance genes., Results: At the term corrected age, or 10 days of age for the full-term infants, we found over 80 antibiotic resistance genes in the preterm infants that did not receive probiotics that were not identified in either the full-term or probiotic-supplemented preterm infants. More genes associated with antibiotic inactivation mechanisms were identified in preterm infants unexposed to probiotics at this collection time-point compared to the other infants. We further linked these genes to mobile genetic elements and Enterobacteriaceae, which were also abundant in their gut microbiomes. Various genes associated with aminoglycoside and beta-lactam resistance, commonly found in pathogenic bacteria, were retained for up to 5 months in the preterm infants that did not receive probiotics., Conclusions: This pilot survey of preterm infants shows that probiotics administered after preterm birth during hospitalization reduced the diversity and prevented persistence of antibiotic resistance genes in the gut microbiome. The benefits of probiotic use on the microbiome and the resistome should be further explored in larger groups of infants. Due to its high sensitivity and lower sequencing cost, our targeted capture approach can facilitate these surveys to further address the implications of resistance genes persisting into infancy without the need for large-scale metagenomic sequencing. Video Abstract., (© 2022. The Author(s).)

Background/purpose: Type 2 diabetes and obesity increase the risk of developing colorectal cancer. Metformin may reduce colorectal cancer but the mechanisms mediating this effect remain unclear. In mice and humans, a high-fat diet (HFD), obesity and metformin are known to alter the gut microbiome but whether this is important for influencing tumor growth is not known., Methods: Mice with syngeneic MC38 colon adenocarcinomas were treated with metformin or feces obtained from control or metformin treated mice., Results: We find that compared to chow-fed controls, tumor growth is increased when mice are fed a HFD and that this acceleration of tumor growth can be partially recapitulated through transfer of the fecal microbiome or in vitro treatment of cells with fecal filtrates from HFD-fed animals. Treatment of HFD-fed mice with orally ingested, but not intraperitoneally injected, metformin suppresses tumor growth and increases the expression of short-chain fatty acid (SCFA)-producing microbes Alistipes, Lachnospiraceae and Ruminococcaceae. The transfer of the gut microbiome from mice treated orally with metformin to drug naïve, conventionalized HFD-fed mice increases circulating propionate and butyrate, reduces tumor proliferation, and suppresses the expression of sterol response element binding protein (SREBP) gene targets in the tumor., Conclusion: These data indicate that in obese mice fed a HFD, metformin reduces tumor burden through changes in the gut microbiome., (Copyright © 2022 The Author(s). Published by Elsevier GmbH.. All rights reserved.)

Short-read sequencing can provide detection of multiple genomic determinants of antimicrobial resistance from single bacterial genomes and metagenomic samples. Despite its increasing application in human, animal, and environmental microbiology, including human clinical trials, the performance of short-read Illumina sequencing for antimicrobial resistance gene (ARG) detection, including resistance-conferring single nucleotide polymorphisms (SNPs), has not been systematically characterized. Using paired-end 2 × 150 bp (base pair) Illumina sequencing and an assembly-based method for ARG prediction, we determined sensitivity, positive predictive value (PPV), and sequencing depths required for ARG detection in an Escherichia coli isolate of sequence type (ST) 38 spiked into a synthetic microbial community at varying abundances. Approximately 300,000 reads or 15× genome coverage was sufficient to detect ARGs in E. coli ST38, with comparable sensitivity and PPV to ~100× genome coverage. Using metagenome assembly of mixed microbial communities, ARG detection at E. coli relative abundances of 1% would require assembly of approximately 30 million reads to achieve 15× target coverage. The minimum sequencing depths were validated using public data sets of 948 E. coli genomes and 10 metagenomic rectal swab samples. A read-based approach using k-mer alignment (KMA) for ARG prediction did not substantially improve minimum sequencing depths for ARG detection compared to assembly of the E. coli ST38 genome or the combined metagenomic samples. Analysis of sequencing depths from recent studies assessing ARG content in metagenomic samples demonstrated that sequencing depths had a median estimated detection frequency of 84% (interquartile range: 30%-92%) for a relative abundance of 1%. IMPORTANCE Systematically determining Illumina sequencing performance characteristics for detection of ARGs in metagenomic samples is essential to inform study design and appraisal of human, animal, and environmental metagenomic antimicrobial resistance studies. In this study, we quantified the performance characteristics of ARG detection in E. coli genomes and metagenomes and established a benchmark of ~15× coverage for ARG detection for E. coli in metagenomes. We demonstrate that for low relative abundances, sequencing depths of ~30 million reads or more may be required for adequate sensitivity for many applications.

Whole genome sequencing (WGS) is a key tool in identifying and characterising disease-associated bacteria across clinical, agricultural, and environmental contexts. One increasingly common use of genomic and metagenomic sequencing is in identifying the type and range of antimicrobial resistance (AMR) genes present in bacterial isolates in order to make predictions regarding their AMR phenotype. However, there are a large number of alternative bioinformatics software and pipelines available, which can lead to dissimilar results. It is, therefore, vital that researchers carefully evaluate their genomic and metagenomic AMR analysis methods using a common dataset. To this end, as part of the Microbial Bioinformatics Hackathon and Workshop 2021, a 'gold standard' reference genomic and simulated metagenomic dataset was generated containing raw sequence reads mapped against their corresponding reference genome from a range of 174 potentially pathogenic bacteria. These datasets and their accompanying metadata are freely available for use in benchmarking studies of bacteria and their antimicrobial resistance genes and will help improve tool development for the identification of AMR genes in complex samples., (© 2022. The Author(s).)

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).

Background: We determined the burden of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in air and on surfaces in rooms of patients hospitalized with coronavirus disease 2019 (COVID-19) and investigated patient characteristics associated with SARS-CoV-2 environmental contamination., Methods: Nasopharyngeal swabs, surface, and air samples were collected from the rooms of 78 inpatients with COVID-19 at 6 acute care hospitals in Toronto from March to May 2020. Samples were tested for SARS-CoV-2 ribonucleic acid (RNA), cultured to determine potential infectivity, and whole viral genomes were sequenced. Association between patient factors and detection of SARS-CoV-2 RNA in surface samples were investigated., Results: Severe acute respiratory syndrome coronavirus 2 RNA was detected from surfaces (125 of 474 samples; 42 of 78 patients) and air (3 of 146 samples; 3 of 45 patients); 17% (6 of 36) of surface samples from 3 patients yielded viable virus. Viral sequences from nasopharyngeal and surface samples clustered by patient. Multivariable analysis indicated hypoxia at admission, polymerase chain reaction-positive nasopharyngeal swab (cycle threshold of ≤30) on or after surface sampling date, higher Charlson comorbidity score, and shorter time from onset of illness to sampling date were significantly associated with detection of SARS-CoV-2 RNA in surface samples., Conclusions: The infrequent recovery of infectious SARS-CoV-2 virus from the environment suggests that the risk to healthcare workers from air and near-patient surfaces in acute care hospital wards is likely limited., (© The Author(s) 2021. Published by Oxford University Press for the Infectious Diseases Society of America. All rights reserved. For permissions, e-mail: journals.permissions@oup.com.)

Background: As the COVID-19 pandemic heightened, infection control and prevention experts recommended clinical training opportunities be modified or discontinued, substantially impacting the function of clinical or medical teaching units (CTU). A CTU is structured to involve medical learners such that they become active participants of the health care team. Since a review of the literature demonstrates a paucity of data to guide pediatric CTU implementation during pandemic phases, we developed and disseminated a survey to assess Canadian practices., Method: A group of infectious disease specialists and pediatric hospitalists developed, tested, and disseminated surveys to understand CTU clinical rounding and teaching practices during the waves of the COVID-19 pandemic., Result: Our surveys demonstrate the variability in adapting rounding practices during this pandemic and highlights the opportunities to share our approaches and lessons learned to optimize learner experience and patient centered care during unprecedented times in our academic hospitals. We also show the pragmatic implementation of our new pediatric hospital CTU process that was informed by our survey results., Conclusion: Our study demonstrates the variability in adapting rounding practices during this pandemic and highlights the opportunities to share our approaches and lessons learned to optimize learner experience and patient centered care during unprecedented times in our academic hospitals., (© 2021. The Author(s).)

Obesity results from a caloric imbalance between energy intake, absorption and expenditure. In both rodents and humans, diet-induced thermogenesis contributes to energy expenditure and involves the activation of brown adipose tissue (BAT). We hypothesize that environmental toxicants commonly used as food additives or pesticides might reduce BAT thermogenesis through suppression of uncoupling protein 1 (UCP1) and this may contribute to the development of obesity. Using a step-wise screening approach, we discover that the organophosphate insecticide chlorpyrifos suppresses UCP1 and mitochondrial respiration in BAT at concentrations as low as 1 pM. In mice housed at thermoneutrality and fed a high-fat diet, chlorpyrifos impairs BAT mitochondrial function and diet-induced thermogenesis, promoting greater obesity, non-alcoholic fatty liver disease (NAFLD) and insulin resistance. This is associated with reductions in cAMP; activation of p38MAPK and AMPK; protein kinases critical for maintaining UCP1 and mitophagy, respectively in BAT. These data indicate that the commonly used pesticide chlorpyrifos, suppresses diet-induced thermogenesis and the activation of BAT, suggesting its use may contribute to the obesity epidemic., (© 2021. The Author(s).)

The emergence of a novel coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and more recently, the independent evolution of multiple SARS-CoV-2 variants has generated renewed interest in virus evolution and cross-species transmission. While all known human coronaviruses (HCoVs) are speculated to have originated in animals, very little is known about their evolutionary history and factors that enable some CoVs to co-exist with humans as low pathogenic and endemic infections (HCoV-229E, HCoV-NL63, HCoV-OC43, HCoV-HKU1), while others, such as SARS-CoV, MERS-CoV and SARS-CoV-2 have evolved to cause severe disease. In this review, we highlight the origins of all known HCoVs and map positively selected for mutations within HCoV proteins to discuss the evolutionary trajectory of SARS-CoV-2. Furthermore, we discuss emerging mutations within SARS-CoV-2 and variants of concern (VOC), along with highlighting the demonstrated or speculated impact of these mutations on virus transmission, pathogenicity, and neutralization by natural or vaccine-mediated immunity., (© 2021. The Author(s).)

Type I interferons (IFNs) are our first line of defense against virus infection. Recent studies have suggested the ability of SARS-CoV-2 proteins to inhibit IFN responses. Emerging data also suggest that timing and extent of IFN production is associated with manifestation of COVID-19 severity. In spite of progress in understanding how SARS-CoV-2 activates antiviral responses, mechanistic studies into wild-type SARS-CoV-2-mediated induction and inhibition of human type I IFN responses are scarce. Here we demonstrate that SARS-CoV-2 infection induces a type I IFN response in vitro and in moderate cases of COVID-19. In vitro stimulation of type I IFN expression and signaling in human airway epithelial cells is associated with activation of canonical transcriptions factors, and SARS-CoV-2 is unable to inhibit exogenous induction of these responses. Furthermore, we show that physiological levels of IFNα detected in patients with moderate COVID-19 is sufficient to suppress SARS-CoV-2 replication in human airway cells., Competing Interests: The authors declare no competing interests., (© 2021 The Author(s).)

The cytochrome P450 (CYP) superfamily is a diverse and important enzyme family, playing a central role in chemical defense and in synthesis and metabolism of major biological signaling molecules. The CYPomes of four cnidarian genomes (Hydra vulgaris, Acropora digitifera, Aurelia aurita, Nematostella vectensis) were annotated; phylogenetic analyses determined the evolutionary relationships amongst the sequences and with existing metazoan CYPs. 155 functional CYPs were identified and 90 fragments. Genes were from 24 new CYP families and several new subfamilies; genes were in 9 of the 12 established metazoan CYP clans. All species had large expansions of clan 2 diversity, with H. vulgaris having reduced diversity for both clan 3 and mitochondrial clan. We identified potential candidates for xenobiotic metabolism and steroidogenesis. That each genome contained multiple, novel CYP families may reflect the large evolutionary distance within the cnidarians, unique physiology in the cnidarian classes, and/or different ecology of the individual species.

Genome-wide variation in SARS-CoV-2 reveals evolution and transmission dynamics which are critical considerations for disease control and prevention decisions. Here, we review estimates of the genome-wide viral mutation rates, summarize current COVID-19 case load in the province of Ontario, Canada (5 January 2021), and analyze published SARS-CoV-2 genomes from Ontario (collected prior to 24 November 2020) to test for more infectious genetic variants or lineages. The reported mutation rate (∼10 -6  nucleotide [nt] -1  cycle -1 ) for SARS-CoV-2 is typical for coronaviruses. Analysis of published SARS-CoV-2 genomes revealed that the G614 spike protein mutation has dominated infections in Ontario and that SARS-CoV-2 lineages present in Ontario have not differed significantly in their rate of spread. These results suggest that the SARS-CoV-2 population circulating in Ontario has not changed significantly to date. However, ongoing genome monitoring is essential for identification of new variants and lineages that may contribute to increased viral transmission., (Copyright © 2021 Sjaarda et al.)

Diagnosing antimicrobial resistance (AMR) in the clinic is based on empirical evidence and current gold standard laboratory phenotypic methods. Genotypic methods have the potential advantages of being faster and cheaper, and having improved mechanistic resolution over phenotypic methods. We generated and applied rule-based and logistic regression models to predict the AMR phenotype from Escherichia coli and Pseudomonas aeruginosa multidrug-resistant clinical isolate genomes. By inspecting and evaluating these models, we identified previously unknown β-lactamase substrate activities. In total, 22 unknown β-lactamase substrate activities were experimentally validated using targeted gene expression studies. Our results demonstrate that generating and analysing predictive models can help guide researchers to the mechanisms driving resistance and improve annotation of AMR genes and phenotypic prediction, and suggest that we cannot solely rely on curated knowledge to predict resistance phenotypes.

This case report describes a 68-year-old male with recurrent multidrug-resistant Salmonella enterica serovar Typhimurium bacteremia acquired during travel abroad. He experienced a recurrence of bacteremia without a clear source and was successfully treated with 10 weeks of intravenous ertapenem. Post hoc genome sequencing revealed an isolate bearing class A, C, and D extended-spectrum β-lactamases (ESBLs). A review of English- and French-language literature since 2000 revealed eight publications that discussed recurrent S. enterica serovar Typhimurium bacteremia. Patients with multidrug-resistant S. enterica serovar Typhimurium should be monitored frequently for recrudescence, even in the absence of risk factors., Competing Interests: The authors have nothing to disclose., (Copyright © 2020, Association of Medical Microbiology and Infectious Disease Canada (AMMI Canada).)

Type VI secretion systems (T6SSs) deliver antibacterial effector proteins between neighboring bacteria. Many effectors harbor N-terminal t rans m embrane d omains (TMDs) implicated in effector translocation across target cell membranes. However, the distribution of these TMD-containing effectors remains unknown. Here, we discover prePAAR, a conserved motif found in over 6000 putative TMD-containing effectors encoded predominantly by 15 genera of Proteobacteria. Based on differing numbers of TMDs, effectors group into two distinct classes that both require a member of the Eag family of T6SS chaperones for export. Co-crystal structures of class I and class II effector TMD-chaperone complexes from Salmonella Typhimurium and Pseudomonas aeruginosa , respectively, reveals that Eag chaperones mimic transmembrane helical packing to stabilize effector TMDs. In addition to participating in the chaperone-TMD interface, we find that prePAAR residues mediate effector-VgrG spike interactions. Taken together, our findings reveal mechanisms of chaperone-mediated stabilization and secretion of two distinct families of T6SS membrane protein effectors., Competing Interests: SA, KT, KS, DQ, TT, NB, SR, AM, GP, JW No competing interests declared, (© 2020, Ahmad et al.)

RNA sequencing (RNA-Seq) is a complicated protocol, both in the laboratory in generation of data and at the computer in analysis of results. Several decisions during RNA-Seq library construction have important implications for analysis, most notably strandedness during complementary DNA library construction. Here, we clarify bioinformatic decisions related to strandedness in both alignment of DNA sequencing reads to reference genomes and subsequent determination of transcript abundance., (© The Author(s) 2020. Published by Oxford University Press.)

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) recently emerged to cause widespread infections in humans. SARS-CoV-2 infections have been reported in the Kingdom of Saudi Arabia, where Middle East respiratory syndrome coronavirus (MERS-CoV) causes seasonal outbreaks with a case fatality rate of ~37 %. Here we show that there exists a theoretical possibility of future recombination events between SARS-CoV-2 and MERS-CoV RNA. Through computational analyses, we have identified homologous genomic regions within the ORF1ab and S genes that could facilitate recombination, and have analysed co-expression patterns of the cellular receptors for SARS-CoV-2 and MERS-CoV, ACE2 and DPP4, respectively, to identify human anatomical sites that could facilitate co-infection. Furthermore, we have investigated the likely susceptibility of various animal species to MERS-CoV and SARS-CoV-2 infection by comparing known virus spike protein-receptor interacting residues. In conclusion, we suggest that a recombination between SARS-CoV-2 and MERS-CoV RNA is possible and urge public health laboratories in high-risk areas to develop diagnostic capability for the detection of recombined coronaviruses in patient samples.

Cancer immunotherapies using monoclonal antibodies to block inhibitory checkpoints are showing durable remissions in many types of cancer patients, although the majority of breast cancer patients acquire little benefit. Human melanoma and lung cancer patient studies suggest that immune checkpoint inhibitors are often potent in patients that already have intratumoral T cell infiltrate; although it remains unknown what types of interventions can result in an intratumoral T cell infiltrate in breast cancer. Using non-T cell-inflamed mammary tumors, we assessed what biological processes and downstream inflammation can overcome the barriers to spontaneous T cell priming. Here we show a specific type of combination therapy, consisting of oncolytic virus and chemotherapy, activates necroptosis and limits tumor growth in autochthonous tumors. Combination therapy activates proinflammatory cytokines; intratumoral influx of myeloid cells and cytotoxic T cell infiltrate in locally treated and distant autochthonous tumors to render them susceptible to immune checkpoint inhibitors.

The growing challenge of microbial resistance emphasizes the importance of new antibiotics or reviving strategies for the use of old ones. Macrolide antibiotics are potent bacterial protein synthesis inhibitors with a formidable capacity to treat life-threatening bacterial infections; however, acquired and intrinsic resistance limits their clinical application. In the work presented here, we reveal that bicarbonate is a potent enhancer of the activity of macrolide antibiotics that overcomes both acquired and intrinsic resistance mechanisms. With a focus on azithromycin, a highly prescribed macrolide antibiotic, and using clinically relevant pathogens, we show that physiological concentrations of bicarbonate overcome drug resistance by increasing the intracellular concentration of azithromycin. We demonstrate the potential of bicarbonate as a formulation additive for topical use of azithromycin in treating a murine wound infection caused by Pseudomonas aeruginosa . Further, using a systemic murine model of methicillin-resistant Staphylococcus aureus (MRSA) infection, we demonstrate the potential role of physiological bicarbonate, naturally abundant in the host, to enhance the activity of azithromycin against macrolide-resistant MRSA. In all, our findings suggest that macrolide resistance, observed in the clinical microbiology laboratory using standard culturing techniques, is a poor predictor of efficacy in the clinic and that observed resistance should not necessarily hamper the use of macrolides. Whether as a formulation additive for topical use or as a natural component of host tissues, bicarbonate is a powerful potentiator of macrolides with the capacity to overcome drug resistance in life-threatening bacterial infections.

Since its emergence in Wuhan, China, in December 2019, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has infected ≈6 million persons worldwide. As SARS-CoV-2 spreads across the planet, we explored the range of human cells that can be infected by this virus. We isolated SARS-CoV-2 from 2 infected patients in Toronto, Canada; determined the genomic sequences; and identified single-nucleotide changes in representative populations of our virus stocks. We also tested a wide range of human immune cells for productive infection with SARS-CoV-2. We confirm that human primary peripheral blood mononuclear cells are not permissive for SARS-CoV-2. As SARS-CoV-2 continues to spread globally, it is essential to monitor single-nucleotide polymorphisms in the virus and to continue to isolate circulating viruses to determine viral genotype and phenotype by using in vitro and in vivo infection models.

Genome sequencing of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is increasingly important to monitor the transmission and adaptive evolution of the virus. The accessibility of high-throughput methods and polymerase chain reaction (PCR) has facilitated a growing ecosystem of protocols. Two differing protocols are tiling multiplex PCR and bait capture enrichment. Each method has advantages and disadvantages but a direct comparison with different viral RNA concentrations has not been performed to assess the performance of these approaches. Here we compare Liverpool amplification, ARTIC amplification, and bait capture using clinical diagnostics samples. All libraries were sequenced using an Illumina MiniSeq with data analyzed using a standardized bioinformatics workflow (SARS-CoV-2 Illumina GeNome Assembly Line; SIGNAL). One sample showed poor SARS-CoV-2 genome coverage and consensus, reflective of low viral RNA concentration. In contrast, the second sample had a higher viral RNA concentration, which yielded good genome coverage and consensus. ARTIC amplification showed the highest depth of coverage results for both samples, suggesting this protocol is effective for low concentrations. Liverpool amplification provided a more even read coverage of the SARS-CoV-2 genome, but at a lower depth of coverage. Bait capture enrichment of SARS-CoV-2 cDNA provided results on par with amplification. While only two clinical samples were examined in this comparative analysis, both the Liverpool and ARTIC amplification methods showed differing efficacy for high and low concentration samples. In addition, amplification-free bait capture enriched sequencing of cDNA is a viable method for generating a SARS-CoV-2 genome sequence and for identification of amplification artifacts.

Vaccination has transformed public health, most notably including the eradication of smallpox. Despite its profound historical importance, little is known of the origins and diversity of the viruses used in smallpox vaccination. Prior to the twentieth century, the method, source and origin of smallpox vaccinations remained unstandardised and opaque. We reconstruct and analyse viral vaccine genomes associated with smallpox vaccination from historical artefacts. Significantly, we recover viral molecules through non-destructive sampling of historical materials lacking signs of biological residues. We use the authenticated ancient genomes to reveal the evolutionary relationships of smallpox vaccination viruses within the poxviruses as a whole.

Purpose: Antimicrobial resistance (AMR), especially multidrug resistance, is one of the most serious global threats facing public health. The authors proof-of-concept study assessing the suitability of shotgun proteomics as an additional approach to whole-genome sequencing (WGS) for detecting AMR determinants., Experimental Design: Previously published shotgun proteomics and WGS data on four isolates of Campylobacter jejuni are used to perform AMR detection by searching the Comprehensive Antibiotic Resistance Database, and their detection ability relative to genomics screening and traditional phenotypic testing measured by minimum inhibitory concentration is assessed., Results: Both genomic and proteomic approaches identify the wild-type and variant molecular determinants responsible for resistance to tetracycline and ciprofloxacin, in agreement with phenotypic testing. In contrast, the genomic method identifies the presence of the β-lactamase gene, bla OXA - 61 , in three isolates. However, its corresponding protein product is detected in only a single isolate, consistent with results obtained from phenotypic testing., (© 2019 Her Majesty the Queen in Right of Canada. Proteomics - Clinical Applications published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

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., (© The Author(s) 2019. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Identification of the nucleotide sequences encoding antibiotic resistance elements and determination of their association with antibiotic resistance are critical to improve surveillance and monitor trends in antibiotic resistance. Current methods to study antibiotic resistance in various environments rely on extensive deep sequencing or laborious culturing of fastidious organisms, both of which are heavily time-consuming operations. An accurate and sensitive method to identify both rare and common resistance elements in complex metagenomic samples is needed. Referencing the sequences in the Comprehensive Antibiotic Resistance Database, we designed a set of 37,826 probes to specifically target over 2,000 nucleotide sequences associated with antibiotic resistance in clinically relevant bacteria. Testing of this probe set on DNA libraries generated from multidrug-resistant bacteria to selectively capture resistance genes reproducibly produced higher numbers of reads on target at a greater length of coverage than shotgun sequencing. We also identified additional resistance gene sequences from human gut microbiome samples that sequencing alone was not able to detect. Our method to capture the resistome enables a sensitive means of gene detection in diverse environments where genes encoding antibiotic resistance represent less than 0.1% of the metagenome., (Copyright © 2019 Guitor et al.)

Metformin is the most commonly prescribed medication for type 2 diabetes, owing to its glucose-lowering effects, which are mediated through the suppression of hepatic glucose production (reviewed in refs. 1-3 ). However, in addition to its effects on the liver, metformin reduces appetite and in preclinical models exerts beneficial effects on ageing and a number of diverse diseases (for example, cognitive disorders, cancer, cardiovascular disease) through mechanisms that are not fully understood 1-3 . Given the high concentration of metformin in the liver and its many beneficial effects beyond glycemic control, we reasoned that metformin may increase the secretion of a hepatocyte-derived endocrine factor that communicates with the central nervous system 4 . Here we show, using unbiased transcriptomics of mouse hepatocytes and analysis of proteins in human serum, that metformin induces expression and secretion of growth differentiating factor 15 (GDF15). In primary mouse hepatocytes, metformin stimulates the secretion of GDF15 by increasing the expression of activating transcription factor 4 (ATF4) and C/EBP homologous protein (CHOP; also known as DDIT3). In wild-type mice fed a high-fat diet, oral administration of metformin increases serum GDF15 and reduces food intake, body mass, fasting insulin and glucose intolerance; these effects are eliminated in GDF15 null mice. An increase in serum GDF15 is also associated with weight loss in patients with type 2 diabetes who take metformin. Although further studies will be required to determine the tissue source(s) of GDF15 produced in response to metformin in vivo, our data indicate that the therapeutic benefits of metformin on appetite, body mass and serum insulin depend on GDF15.

Bacteria have evolved sophisticated mechanisms to inhibit the growth of competitors 1 . One such mechanism involves type VI secretion systems, which bacteria can use to inject antibacterial toxins directly into neighbouring cells. Many of these toxins target the integrity of the cell envelope, but the full range of growth inhibitory mechanisms remains unknown 2 . Here we identify a type VI secretion effector, Tas1, in the opportunistic pathogen Pseudomonas aeruginosa. The crystal structure of Tas1 shows that it is similar to enzymes that synthesize (p)ppGpp, a broadly conserved signalling molecule in bacteria that modulates cell growth rate, particularly in response to nutritional stress 3 . However, Tas1 does not synthesize (p)ppGpp; instead, it pyrophosphorylates adenosine nucleotides to produce (p)ppApp at rates of nearly 180,000 molecules per minute. Consequently, the delivery of Tas1 into competitor cells drives rapid accumulation of (p)ppApp, depletion of ATP, and widespread dysregulation of essential metabolic pathways, thereby resulting in target cell death. Our findings reveal a previously undescribed mechanism for interbacterial antagonism and demonstrate a physiological role for the metabolite (p)ppApp in bacteria.

Glycopeptide antibiotics are produced by Actinobacteria through biosynthetic gene clusters that include genes supporting their regulation, synthesis, export and resistance. The chemical and biosynthetic diversities of glycopeptides are the product of an intricate evolutionary history. Extracting this history from genome sequences is difficult as conservation of the individual components of these gene clusters is variable and each component can have a different trajectory. We show that glycopeptide biosynthesis and resistance in Actinobacteria maps to approximately 150-400 million years ago. Phylogenetic reconciliation reveals that the precursors of glycopeptide biosynthesis are far older than other components, implying that these clusters arose from a pre-existing pool of genes. We find that resistance appeared contemporaneously with biosynthetic genes, raising the possibility that the mechanism of action of glycopeptides was a driver of diversification in these gene clusters. Our results put antibiotic biosynthesis and resistance into an evolutionary context and can guide the future discovery of compounds possessing new mechanisms of action, which are especially needed as the usefulness of the antibiotics available at present is imperilled by human activity.

The structures and properties of thirty-three of the thirty-four possible chlorato- Cl -, chlorato- O - and perchlorato-derivatives of methane have been computed using the range-separated hybrid generalized gradient approximation density functional method ωB97X-D with the 6-311+G(2d,p) basis set. These results indicate that the chlorato- O -substituent confers more stability to a molecule than does the chlorato- Cl -substituent; the perchlorato-substituent is approximately intermediate in this regard when decomposition energies are calculated. The C-Cl bond lengths in the chlorato- Cl -substituents generally elongate and the C-O distances in the chlorato- O - and perchlorato-substituents tend to shorten as the number of chlorate/perchlorate substituents increases. In addition, as the C-O bond shortens, the CO-Cl bond lengthens. The calculated Mulliken and Löwdin bond orders for these bonds exhibit the opposite of the trends exhibited by the bond lengths, as expected: As the bond lengthens, the bond order decreases, and vice versa. The single molecule that could not be optimized as a stable methane derivative, (chlorato -Cl- )tris(chlorato -O -)methane, rearranged during all optimization attempts to an isomer of the neutral Cl 2 O 5 molecule and a hitherto unknown molecule, bis(chlorato- O -)carbonyl, (O 2 ClO) 2 C=O., (© 2019 Published by Elsevier Ltd.)

Background: Physical inactivity impairs insulin sensitivity, which is exacerbated with aging. We examined the impact of 2 wk of acute inactivity and recovery on glycemic control, and integrated rates of muscle protein synthesis in older men and women., Methods: Twenty-two overweight, prediabetic older adults (12 men, 10 women, 69 ± 4 y) undertook 7 d of habitual activity (baseline; BL), step reduction (SR; <1,000 steps.d-1 for 14 d), followed by 14 d of recovery (RC). An oral glucose tolerance test was used to assess glycemic control and deuterated water ingestion to measure integrated rates of muscle protein synthesis., Results: Daily step count was reduced (all p < .05) from BL at SR (7362 ± 3294 to 991 ± 97) and returned to BL levels at RC (7117 ± 3819). Homeostasis model assessment-insulin resistance increased from BL to SR and Matsuda insulin sensitivity index decreased and did not return to BL in RC. Glucose and insulin area under the curve were elevated from BL to SR and did not recover in RC. Integrated muscle protein synthesis was reduced during SR and did not return to BL in RC., Conclusions: Our findings demonstrate that 2 wk of SR leads to lowered rates of muscle protein synthesis and a worsening of glycemic control that unlike younger adults is not recovered during return to normal activity in overweight, prediabetic elderly humans., Clinical Trials Registration: ClinicalTrials.gov identifier: NCT03039556.