Genome Sequencing of a Historic Staphylococcus aureus Collection Reveals New Enterotoxin Genes and Sheds Light on the Evolution and Genomic Organization of This Key Virulence Gene Family.

We take advantage of a historic collection of 133 Staphylococcus aureus strains accessioned between 1924 and 2016, whose genomes have been long-read sequenced as part of a major National Collection of Type Cultures (NCTC) initiative, to conduct a gene family-wide computational analysis of enterotoxin genes. We identify two novel staphylococcal enterotoxin (pseudo)genes (sel29p and sel30), the former of which has not been observed in any contemporary strain to date. We provide further information on five additional enterotoxin genes or gene variants that either have recently entered the literature or for which the nomenclature or description is currently unclear (selz, sel26, sel27, sel28, and ses-2p). An examination of over 11,000 RefSeq genomes in search of wider support for these seven (pseudo)genes led to the identification of an additional three novel enterotoxin gene family members (sel31, sel32, and sel33) plus two new variants (seh-2p and ses-3p). We cast light on the genomic distribution of the enterotoxin genes, further defining their arrangement in gene clusters. Finally, we show that cooccurrence of enterotoxin genes is prevalent, with individual NCTC strains possessing as many as 18 enterotoxin genes and pseudogenes, and that clonal complex membership rather than time of isolation is the key factor in determining enterotoxin load.IMPORTANCEStaphylococcus aureus strains pose a significant health risk to both human and animal populations. Key among this species' virulence factors is the staphylococcal enterotoxin gene family. Certain enterotoxin forms can induce a potentially life-threatening immune response, while others are implicated in less fatal though often severe conditions such as food poisoning. Genetic characterization of staphylococcal enterotoxin gene family members has steadily accumulated over recent decades, with over 20 genes now established in the literature. Despite the current wealth of knowledge on this important gene family, questions remain about the presence of additional enterotoxin genes and the genomic composition of family members. This study further expands knowledge of the staphylococcal enterotoxins while shedding light on their evolution over the last century

Whole-Genome Sequences of Five Strains of Kocuria rosea, NCTC2676, NCTC7514, NCTC7512, NCTC7528, and NCTC7511.

Kocuria rosea is a Gram-positive coccus found in the environment and within normal human skin microbiota, and more recently, it has been potentially implicated as an opportunistic pathogen. Here, we describe the genome sequences of five strains of K. rosea (NCTC2676, NCTC7514, NCTC7512, NCTC7528, and NCTC7511)

The history, genome and biology of NCTC 30: a non-pandemic Vibrio cholerae isolate from World War One.

The sixth global cholera pandemic lasted from 1899 to 1923. However, despite widespread fear of the disease and of its negative effects on troop morale, very few soldiers in the British Expeditionary Forces contracted cholera between 1914 and 1918. Here, we have revived and sequenced the genome of NCTC 30, a 102-year-old Vibrio cholerae isolate, which we believe is the oldest publicly available live V. cholerae strain in existence. NCTC 30 was isolated in 1916 from a British soldier convalescent in Egypt. We found that this strain does not encode cholera toxin, thought to be necessary to cause cholera, and is not part of V. cholerae lineages responsible for the pandemic disease. We also show that NCTC 30, which predates the introduction of penicillin-based antibiotics, harbours a functional β-lactamase antibiotic resistance gene. Our data corroborate and provide molecular explanations for previous phenotypic studies of NCTC 30 and provide a new high-quality genome sequence for historical, non-pandemic V. cholerae

Genetic mechanisms of critical illness in COVID-19.

Host-mediated lung inflammation is present1, and drives mortality2, in the critical illness caused by coronavirus disease 2019 (COVID-19). Host genetic variants associated with critical illness may identify mechanistic targets for therapeutic development3. Here we report the results of the GenOMICC (Genetics Of Mortality In Critical Care) genome-wide association study in 2,244 critically ill patients with COVID-19 from 208 UK intensive care units. We have identified and replicated the following new genome-wide significant associations: on chromosome 12q24.13 (rs10735079, P = 1.65 × 10-8) in a gene cluster that encodes antiviral restriction enzyme activators (OAS1, OAS2 and OAS3); on chromosome 19p13.2 (rs74956615, P = 2.3 × 10-8) near the gene that encodes tyrosine kinase 2 (TYK2); on chromosome 19p13.3 (rs2109069, P = 3.98 ×  10-12) within the gene that encodes dipeptidyl peptidase 9 (DPP9); and on chromosome 21q22.1 (rs2236757, P = 4.99 × 10-8) in the interferon receptor gene IFNAR2. We identified potential targets for repurposing of licensed medications: using Mendelian randomization, we found evidence that low expression of IFNAR2, or high expression of TYK2, are associated with life-threatening disease; and transcriptome-wide association in lung tissue revealed that high expression of the monocyte-macrophage chemotactic receptor CCR2 is associated with severe COVID-19. Our results identify robust genetic signals relating to key host antiviral defence mechanisms and mediators of inflammatory organ damage in COVID-19. Both mechanisms may be amenable to targeted treatment with existing drugs. However, large-scale randomized clinical trials will be essential before any change to clinical practice

A prenylated dsRNA sensor protects against severe COVID-19

Inherited genetic factors can influence the severity of COVID-19, but the molecular explanation underpinning a genetic association is often unclear. Intracellular antiviral defenses can inhibit the replication of viruses and reduce disease severity. To better understand the antiviral defenses relevant to COVID-19, we used interferon-stimulated gene (ISG) expression screening to reveal that OAS1, through RNase L, potently inhibits SARS-CoV-2. We show that a common splice-acceptor SNP (Rs10774671) governs whether people express prenylated OAS1 isoforms that are membrane-associated and sense specific regions of SARS-CoV-2 RNAs, or only express cytosolic, nonprenylated OAS1 that does not efficiently detect SARS-CoV-2. Importantly, in hospitalized patients, expression of prenylated OAS1 was associated with protection from severe COVID-19, suggesting this antiviral defense is a major component of a protective antiviral response

Additional materials from The history, genome and biology of NCTC 30: a non-pandemic Vibrio cholerae isolate from World War One

The sixth global cholera pandemic lasted from 1899 to 1923. However, despite widespread fear of the disease and of its negative effects on troop morale, very few soldiers in the British Expeditionary Forces contracted cholera between 1914 and 1918. Here, we have revived and sequenced the genome of NCTC 30, a 102-year-old Vibrio cholerae isolate, which we believe is the oldest publicly available live V. cholerae strain in existence. NCTC 30 was isolated in 1916 from a British soldier convalescent in Egypt. We found that this strain does not encode cholera toxin, thought to be necessary to cause cholera, and is not part of V. cholerae lineages responsible for the pandemic disease. We also show that NCTC 30, which predates the introduction of penicillin-based antibiotics, harbours a functional β-lactamase antibiotic resistance gene. Our data corroborate and provide molecular explanations for previous phenotypic studies of NCTC 30 and provide a new high-quality genome sequence for historical, non-pandemic V. cholerae

NCTC3000: a century of bacterial strain collecting leads to a rich genomic data resource.

The National Collection of Type Cultures (NCTC) was founded on 1 January 1920 in order to fulfil a recognized need for a centralized repository for bacterial and fungal strains within the UK. It is among the longest-established collections of its kind anywhere in the world and today holds approximately 6000 type and reference bacterial strains - many of medical, scientific and veterinary importance - available to academic, health, food and veterinary institutions worldwide. Recently, a collaboration between NCTC, Pacific Biosciences and the Wellcome Sanger Institute established the NCTC3000 project to long-read sequence and assemble the genomes of up to 3000 NCTC strains. Here, at the beginning of the collection's second century, we introduce the resulting NCTC3000 sequence read datasets, genome assemblies and annotations as a unique, historically and scientifically relevant resource for the benefit of the international bacterial research community

Comparative performances of machine learning methods for classifying Crohn Disease patients using genome-wide genotyping data

Abstract: Crohn Disease (CD) is a complex genetic disorder for which more than 140 genes have been identified using genome wide association studies (GWAS). However, the genetic architecture of the trait remains largely unknown. The recent development of machine learning (ML) approaches incited us to apply them to classify healthy and diseased people according to their genomic information. The Immunochip dataset containing 18,227 CD patients and 34,050 healthy controls enrolled and genotyped by the international Inflammatory Bowel Disease genetic consortium (IIBDGC) has been re-analyzed using a set of ML methods: penalized logistic regression (LR), gradient boosted trees (GBT) and artificial neural networks (NN). The main score used to compare the methods was the Area Under the ROC Curve (AUC) statistics. The impact of quality control (QC), imputing and coding methods on LR results showed that QC methods and imputation of missing genotypes may artificially increase the scores. At the opposite, neither the patient/control ratio nor marker preselection or coding strategies significantly affected the results. LR methods, including Lasso, Ridge and ElasticNet provided similar results with a maximum AUC of 0.80. GBT methods like XGBoost, LightGBM and CatBoost, together with dense NN with one or more hidden layers, provided similar AUC values, suggesting limited epistatic effects in the genetic architecture of the trait. ML methods detected near all the genetic variants previously identified by GWAS among the best predictors plus additional predictors with lower effects. The robustness and complementarity of the different methods are also studied. Compared to LR, non-linear models such as GBT or NN may provide robust complementary approaches to identify and classify genetic markers

Description and Cross-Sectional Analyses of 25,880 Adults and Children in the UK National Registry of Rare Kidney Diseases Cohort

Introduction: The National Registry of Rare Kidney Diseases (RaDaR) collects data from people living with rare kidney diseases across the UK, and is the world's largest, rare kidney disease registry. We present the clinical demographics and renal function of 25,880 prevalent patients and sought evidence of bias in recruitment to RaDaR. Methods: RaDaR is linked with the UK Renal Registry (UKRR, with which all UK patients receiving kidney replacement therapy [KRT] are registered). We assessed ethnicity and socioeconomic status in the following: (i) prevalent RaDaR patients receiving KRT compared with patients with eligible rare disease diagnoses receiving KRT in the UKRR, (ii) patients recruited to RaDaR compared with all eligible unrecruited patients at 2 renal centers, and (iii) the age-stratified ethnicity distribution of RaDaR patients with autosomal dominant polycystic kidney disease (ADPKD) was compared to that of the English census. Results: We found evidence of disparities in ethnicity and social deprivation in recruitment to RaDaR; however, these were not consistent across comparisons. Compared with either adults recruited to RaDaR or the English population, children recruited to RaDaR were more likely to be of Asian ethnicity (17.3% vs. 7.5%, P-value < 0.0001) and live in more socially deprived areas (30.3% vs. 17.3% in the most deprived Index of Multiple Deprivation (IMD) quintile, P-value < 0.0001). Conclusion: We observed no evidence of systematic biases in recruitment of patients into RaDaR; however, the data provide empirical evidence of negative economic and social consequences (across all ethnicities) experienced by families with children affected by rare kidney diseases

Effects of rare kidney diseases on kidney failure: a longitudinal analysis of the UK National Registry of Rare Kidney Diseases (RaDaR) cohort

Individuals with rare kidney diseases account for 5-10% of people with chronic kidney disease, but constitute more than 25% of patients receiving kidney replacement therapy. The National Registry of Rare Kidney Diseases (RaDaR) gathers longitudinal data from patients with these conditions, which we used to study disease progression and outcomes of death and kidney failure.People aged 0-96 years living with 28 types of rare kidney diseases were recruited from 108 UK renal care facilities. The primary outcomes were cumulative incidence of mortality and kidney failure in individuals with rare kidney diseases, which were calculated and compared with that of unselected patients with chronic kidney disease. Cumulative incidence and Kaplan-Meier survival estimates were calculated for the following outcomes: median age at kidney failure; median age at death; time from start of dialysis to death; and time from diagnosis to estimated glomerular filtration rate (eGFR) thresholds, allowing calculation of time from last eGFR of 75 mL/min per 1·73 m2 or more to first eGFR of less than 30 mL/min per 1·73 m2 (the therapeutic trial window).Between Jan 18, 2010, and July 25, 2022, 27 285 participants were recruited to RaDaR. Median follow-up time from diagnosis was 9·6 years (IQR 5·9-16·7). RaDaR participants had significantly higher 5-year cumulative incidence of kidney failure than 2·81 million UK patients with all-cause chronic kidney disease (28% vs 1%; p Background Methods Findings Interpretation Funding</p