An efficient system for the generation of marked genetic mutants in members of the genus Burkholderia

To elucidate the function of a gene in bacteria it is vital that targeted gene inactivation (allelic replacement) can be achieved. Allelic replacement is often carried out by disruption of the gene of interest by insertion of an antibiotic-resistance marker followed by subsequent transfer of the mutant allele to the genome of the host organism in place of the wild-type gene. However, due to their intrinsic resistance to many antibiotics only selected antibiotic-resistance markers can be used in members of the genus Burkholderia, including the Burkholderia cepacia complex (Bcc). Here we describe the construction of improved antibiotic-resistance cassettes that specify resistance to kanamycin, chloramphenicol or trimethoprim effectively in the Bcc and related species. These were then used in combination with and/or to construct a series enhanced suicide vectors, pSHAFT2, pSHAFT3 and pSHAFT-GFP to facilitate effective allelic replacement in the Bcc. Validation of these improved suicide vectors was demonstrated by the genetic inactivation of selected genes in the Bcc species Burkholderia cenocepacia and B. lata, and in the non-Bcc species, B. thailandensis

Mouse genetics reveals Barttin as a genetic modifier of Joubert syndrome

International audienceGenetic and phenotypic heterogeneity and the lack of sufficiently large patient cohorts pose a significant challenge to understanding genetic associations in rare disease. Here we identify Bsnd (alias Barttin) as a genetic modifier of cystic kidney disease in Joubert syndrome, using a Cep290-deficient mouse model to recapitulate the phenotypic variability observed in patients by mixing genetic backgrounds in a controlled manner and performing genome-wide analysis of these mice. Experimental down-regulation of Bsnd in the parental mouse strain phenocopied the severe cystic kidney phenotype. A common polymorphism within human BSND significantly associates with kidney disease severity in a patient cohort with CEP290 mutations. The striking phenotypic modifications we describe are a timely reminder of the value of mouse models and highlight the significant contribution of genetic background. Furthermore, if appropriately managed, this can be exploited as a powerful tool to elucidate mechanisms underlying human disease heterogeneity

Am J Hum Genet

While common obesity accounts for an increasing global health burden, its monogenic forms have taught us underlying mechanisms via more than 20 single-gene disorders. Among these, the most common mechanism is central nervous system dysregulation of food intake and satiety, often accompanied by neurodevelopmental delay (NDD) and autism spectrum disorder. In a family with syndromic obesity, we identified a monoallelic truncating variant in POU3F2 (alias BRN2) encoding a neural transcription factor, which has previously been suggested as a driver of obesity and NDD in individuals with the 6q16.1 deletion. In an international collaboration, we identified ultra-rare truncating and missense variants in another ten individuals sharing autism spectrum disorder, NDD, and adolescent-onset obesity. Affected individuals presented with low-to-normal birth weight and infantile feeding difficulties but developed insulin resistance and hyperphagia during childhood. Except for a variant leading to early truncation of the protein, identified variants showed adequate nuclear translocation but overall disturbed DNA-binding ability and promotor activation. In a cohort with common non-syndromic obesity, we independently observed a negative correlation of POU3F2 gene expression with BMI, suggesting a role beyond monogenic obesity. In summary, we propose deleterious intragenic variants of POU3F2 to cause transcriptional dysregulation associated with hyperphagic obesity of adolescent onset with variable NDD

Implementation of Recommendations on the Use of Corticosteroids in Severe COVID-19

Importance: Research diversity and representativeness are paramount in building trust, generating valid biomedical knowledge, and possibly in implementing clinical guidelines. Objectives: To compare variations over time and across World Health Organization (WHO) geographic regions of corticosteroid use for treatment of severe COVID-19; secondary objectives were to evaluate the association between the timing of publication of the RECOVERY (Randomised Evaluation of COVID-19 Therapy) trial (June 2020) and the WHO guidelines for corticosteroids (September 2020) and the temporal trends observed in corticosteroid use by region and to describe the geographic distribution of the recruitment in clinical trials that informed the WHO recommendation. Design, setting, and participants: This prospective cohort study of 434 851 patients was conducted between January 31, 2020, and September 2, 2022, in 63 countries worldwide. The data were collected under the auspices of the International Severe Acute Respiratory and Emerging Infections Consortium (ISARIC)-WHO Clinical Characterisation Protocol for Severe Emerging Infections. Analyses were restricted to patients hospitalized for severe COVID-19 (a subset of the ISARIC data set). Exposure: Corticosteroid use as reported to the ISARIC-WHO Clinical Characterisation Protocol for Severe Emerging Infections. Main outcomes and measures: Number and percentage of patients hospitalized with severe COVID-19 who received corticosteroids by time period and by WHO geographic region. Results: Among 434 851 patients with confirmed severe or critical COVID-19 for whom receipt of corticosteroids could be ascertained (median [IQR] age, 61.0 [48.0-74.0] years; 53.0% male), 174 307 (40.1%) received corticosteroids during the study period. Of the participants in clinical trials that informed the guideline, 91.6% were recruited from the United Kingdom. In all regions, corticosteroid use for severe COVID-19 increased, but this increase corresponded to the timing of the RECOVERY trial (time-interruption coefficient 1.0 [95% CI, 0.9-1.2]) and WHO guideline (time-interruption coefficient 1.9 [95% CI, 1.7-2.0]) publications only in Europe. At the end of the study period, corticosteroid use for treatment of severe COVID-19 was highest in the Americas (5421 of 6095 [88.9%]; 95% CI, 87.7-90.2) and lowest in Africa (31 588 of 185 191 [17.1%]; 95% CI, 16.8-17.3). Conclusions and relevance: The results of this cohort study showed that implementation of the guidelines for use of corticosteroids in the treatment of severe COVID-19 varied geographically. Uptake of corticosteroid treatment was lower in regions with limited clinical trial involvement. Improving research diversity and representativeness may facilitate timely knowledge uptake and guideline implementation

At-admission prediction of mortality and pulmonary embolism in an international cohort of hospitalised patients with COVID-19 using statistical and machine learning methods

By September 2022, more than 600 million cases of SARS-CoV-2 infection have been reported globally, resulting in over 6.5 million deaths. COVID-19 mortality risk estimators are often, however, developed with small unrepresentative samples and with methodological limitations. It is highly important to develop predictive tools for pulmonary embolism (PE) in COVID-19 patients as one of the most severe preventable complications of COVID-19. Early recognition can help provide life-saving targeted anti-coagulation therapy right at admission. Using a dataset of more than 800,000 COVID-19 patients from an international cohort, we propose a cost-sensitive gradient-boosted machine learning model that predicts occurrence of PE and death at admission. Logistic regression, Cox proportional hazards models, and Shapley values were used to identify key predictors for PE and death. Our prediction model had a test AUROC of 75.9% and 74.2%, and sensitivities of 67.5% and 72.7% for PE and all-cause mortality respectively on a highly diverse and held-out test set. The PE prediction model was also evaluated on patients in UK and Spain separately with test results of 74.5% AUROC, 63.5% sensitivity and 78.9% AUROC, 95.7% sensitivity. Age, sex, region of admission, comorbidities (chronic cardiac and pulmonary disease, dementia, diabetes, hypertension, cancer, obesity, smoking), and symptoms (any, confusion, chest pain, fatigue, headache, fever, muscle or joint pain, shortness of breath) were the most important clinical predictors at admission. Age, overall presence of symptoms, shortness of breath, and hypertension were found to be key predictors for PE using our extreme gradient boosted model. This analysis based on the, until now, largest global dataset for this set of problems can inform hospital prioritisation policy and guide long term clinical research and decision-making for COVID-19 patients globally. Our machine learning model developed from an international cohort can serve to better regulate hospital risk prioritisation of at-risk patients

Characteristics and outcomes of an international cohort of 600 000 hospitalized patients with COVID-19

Background: We describe demographic features, treatments and clinical outcomes in the International Severe Acute Respiratory and emerging Infection Consortium (ISARIC) COVID-19 cohort, one of the world's largest international, standardized data sets concerning hospitalized patients. Methods: The data set analysed includes COVID-19 patients hospitalized between January 2020 and January 2022 in 52 countries. We investigated how symptoms on admission, co-morbidities, risk factors and treatments varied by age, sex and other characteristics. We used Cox regression models to investigate associations between demographics, symptoms, co-morbidities and other factors with risk of death, admission to an intensive care unit (ICU) and invasive mechanical ventilation (IMV). Results: Data were available for 689 572 patients with laboratory-confirmed (91.1%) or clinically diagnosed (8.9%) SARS-CoV-2 infection from 52 countries. Age [adjusted hazard ratio per 10 years 1.49 (95% CI 1.48, 1.49)] and male sex [1.23 (1.21, 1.24)] were associated with a higher risk of death. Rates of admission to an ICU and use of IMV increased with age up to age 60 years then dropped. Symptoms, co-morbidities and treatments varied by age and had varied associations with clinical outcomes. The case-fatality ratio varied by country partly due to differences in the clinical characteristics of recruited patients and was on average 21.5%. Conclusions: Age was the strongest determinant of risk of death, with a ∼30-fold difference between the oldest and youngest groups; each of the co-morbidities included was associated with up to an almost 2-fold increase in risk. Smoking and obesity were also associated with a higher risk of death. The size of our international database and the standardized data collection method make this study a comprehensive international description of COVID-19 clinical features. Our findings may inform strategies that involve prioritization of patients hospitalized with COVID-19 who have a higher risk of death

The value of open-source clinical science in pandemic response: lessons from ISARIC

International audienc

ISARIC-COVID-19 dataset: A Prospective, Standardized, Global Dataset of Patients Hospitalized with COVID-19

The International Severe Acute Respiratory and Emerging Infection Consortium (ISARIC) COVID-19 dataset is one of the largest international databases of prospectively collected clinical data on people hospitalized with COVID-19. This dataset was compiled during the COVID-19 pandemic by a network of hospitals that collect data using the ISARIC-World Health Organization Clinical Characterization Protocol and data tools. The database includes data from more than 705,000 patients, collected in more than 60 countries and 1,500 centres worldwide. Patient data are available from acute hospital admissions with COVID-19 and outpatient follow-ups. The data include signs and symptoms, pre-existing comorbidities, vital signs, chronic and acute treatments, complications, dates of hospitalization and discharge, mortality, viral strains, vaccination status, and other data. Here, we present the dataset characteristics, explain its architecture and how to gain access, and provide tools to facilitate its use