Kawasaki disease: the role of immune complexes revisited

Kawasaki disease (KD) is an inflammatory disease in children associated with vasculitis affecting predominantly the coronary arteries and is now the most common cause of acquired heart disease in children in developed countries. The etiology of KD is unknown but epidemiological studies implicate an infectious agent or toxin, which causes disease in genetically predisposed individuals. The presence of immune complexes (ICs) in the serum of children with KD was established in numerous studies during the 1970s and 80s. More recent genetic studies have identified variation in Fcγ receptors and genes controlling immunoglobulin production associated with KD. In this review we link the genetic findings and IC studies and suggest a key role for their interaction in pathophysiology of the disease

Diagnosis of Kawasaki disease using a minimal whole blood gene expression signature

Importance There is no diagnostic test for Kawasaki disease (KD). Diagnosis is based on clinical features shared with other febrile conditions, frequently resulting in delayed or missed treatment and an increased risk of coronary artery aneurysms. Objective To identify a whole blood gene expression signature that distinguishes children with KD in the first week of illness from other febrile conditions. Design Case-control discovery study groups comprising training, test, and validation groups of children with KD or comparator febrile illness. Setting Hospitals in the UK, Spain, Netherlands and USA. Participants The training and test discovery group comprised 404 children with infectious and inflammatory conditions (78 KD, 84 other inflammatory diseases, 242 bacterial or viral infections) and 55 healthy controls. The independent validation group included 130 febrile children and 102 KD patients, including 72 in the first 7 days of illness. Exposures Whole blood gene expression was evaluated using microarrays, and minimal transcript sets distinguishing KD were identified using a novel variable selection method (Parallel Deterministic Model Search). Main outcomes and measures The ability of transcript signatures - implemented as Disease Risk Scores - to discriminate KD cases from controls, was assessed by Area Under the Curve (AUC), sensitivity, and specificity at the optimal cut-point according to Youden’s index. Results A 13-transcript signature identified in the discovery training set distinguished KD from other infectious and inflammatory conditions in the discovery test set with AUC, sensitivity, and specificity (95% confidence intervals (CI)) of 96.2% (92.5-99.9), 81.7% (60.0-94.8), and 92.1% (84.0-97.0), respectively. In the validation set, the signature distinguished KD from febrile controls with AUC, sensitivity, and specificity (95% CI) of 94.6% (91.3-98.0), 85.9% (76.8-92.6), and 89.1% (83.0-93.7) respectively. The signature was applied to clinically defined categories of Definite, Highly Probable and Possible KD resulting in AUCs of 98.1%, 96.3% and 70.0% respectively, mirroring clinical certainty. Conclusions and relevance A 13-transcript blood gene expression signature distinguished KD from other febrile conditions. Diagnostic accuracy increased with certainty of clinical diagnosis. A test incorporating the 13-transcript Disease Risk Score may enable earlier diagnosis and treatment of KD, and reduce inappropriate treatment in those with other diagnoses

Mental Health Status of Healthcare Workers During the COVID-19 Outbreak An International Study

Background: The COVID-19 pandemic is a massive health crisis that has exerted enormous physical and psychological pressure. Mental healthcare for healthcare workers (HCWs) should receive serious consideration. This study served to determine the mental-health outcomes of 1,556 HCWs from 45 countries who participated in the COVID-19 IMPACT project, and to examine the predictors of the outcomes during the first pandemic wave. Methods: Outcomes assessed were self-reported perceived stress, depression symptom, and sleep changes. The predictors examined included sociodemographic factors and perceived social support. Results: The results demonstrated that half of the HCWs had moderate levels of perceived stress and symptoms of depression. Half of the HCWs (n = 800, 51.4%) had similar sleeping patterns since the pandemic started, and one in four slept more or slept less. HCWs reported less perceived stress and depression symptoms and higher levels of perceived social support than the general population who participated in the same project. Predictors associated with higher perceived stress and symptoms of depression among HCWs included female sex, not having children, living with parents, lower educational level, and lower social support. Discussion: The need for establishing ways to mitigate mental-health risks and adjusting psychological interventions and support for HCWs seems to be significant as the pandemic continues

A multi-platform approach to identify a blood-based host protein signature for distinguishing between bacterial and viral infections in febrile children (PERFORM): a multi-cohort machine learning study

BACKGROUND: Differentiating between self-resolving viral infections and bacterial infections in children who are febrile is a common challenge, causing difficulties in identifying which individuals require antibiotics. Studying the host response to infection can provide useful insights and can lead to the identification of biomarkers of infection with diagnostic potential. This study aimed to identify host protein biomarkers for future development into an accurate, rapid point-of-care test that can distinguish between bacterial and viral infections, by recruiting children presenting to health-care settings with fever or a history of fever in the previous 72 h. METHODS: In this multi-cohort machine learning study, patient data were taken from EUCLIDS, the Swiss Pediatric Sepsis study, the GENDRES study, and the PERFORM study, which were all based in Europe. We generated three high-dimensional proteomic datasets (SomaScan and two via liquid chromatography tandem mass spectrometry, referred to as MS-A and MS-B) using targeted and untargeted platforms (SomaScan and liquid chromatography mass spectrometry). Protein biomarkers were then shortlisted using differential abundance analysis, feature selection using forward selection-partial least squares (FS-PLS; 100 iterations), along with a literature search. Identified proteins were tested with Luminex and ELISA and iterative FS-PLS was done again (25 iterations) on the Luminex results alone, and the Luminex and ELISA results together. A sparse protein signature for distinguishing between bacterial and viral infections was identified from the selected proteins. The performance of this signature was finally tested using Luminex assays and by calculating disease risk scores. FINDINGS: 376 children provided serum or plasma samples for use in the discovery of protein biomarkers. 79 serum samples were collected for the generation of the SomaScan dataset, 147 plasma samples for the MS-A dataset, and 150 plasma samples for the MS-B dataset. Differential abundance analysis, and the first round of feature selection using FS-PLS identified 35 protein biomarker candidates, of which 13 had commercial ELISA or Luminex tests available. 16 proteins with ELISA or Luminex tests available were identified by literature review. Further evaluation via Luminex and ELISA and the second round of feature selection using FS-PLS revealed a six-protein signature: three of the included proteins are elevated in bacterial infections (SELE, NGAL, and IFN-γ), and three are elevated in viral infections (IL18, NCAM1, and LG3BP). Performance testing of the signature using Luminex assays revealed area under the receiver operating characteristic curve values between 89·4% and 93·6%. INTERPRETATION: This study has led to the identification of a protein signature that could be ultimately developed into a blood-based point-of-care diagnostic test for rapidly diagnosing bacterial and viral infections in febrile children. Such a test has the potential to greatly improve care of children who are febrile, ensuring that the correct individuals receive antibiotics. FUNDING: European Union's Horizon 2020 research and innovation programme, the European Union's Seventh Framework Programme (EUCLIDS), Imperial Biomedical Research Centre of the National Institute for Health Research, the Wellcome Trust and Medical Research Foundation, Instituto de Salud Carlos III, Consorcio Centro de Investigación Biomédica en Red de Enfermedades Respiratorias, Grupos de Refeencia Competitiva, Swiss State Secretariat for Education, Research and Innovation

Identification of regulatory variants associated with genetic susceptibility to meningococcal disease

Non-coding genetic variants play an important role in driving susceptibility to complex diseases but their characterization remains challenging. Here, we employed a novel approach to interrogate the genetic risk of such polymorphisms in a more systematic way by targeting specific regulatory regions relevant for the phenotype studied. We applied this method to meningococcal disease susceptibility, using the DNA binding pattern of RELA - a NF-kB subunit, master regulator of the response to infection - under bacterial stimuli in nasopharyngeal epithelial cells. We designed a custom panel to cover these RELA binding sites and used it for targeted sequencing in cases and controls. Variant calling and association analysis were performed followed by validation of candidate polymorphisms by genotyping in three independent cohorts. We identified two new polymorphisms, rs4823231 and rs11913168, showing signs of association with meningococcal disease susceptibility. In addition, using our genomic data as well as publicly available resources, we found evidences for these SNPs to have potential regulatory effects on ATXN10 and LIF genes respectively. The variants and related candidate genes are relevant for infectious diseases and may have important contribution for meningococcal disease pathology. Finally, we described a novel genetic association approach that could be applied to other phenotypes

Plasma lipid profiles discriminate bacterial from viral infection in febrile children

Fever is the most common reason that children present to Emergency Departments. Clinical signs and symptoms suggestive of bacterial infection are often non-specific, and there is no definitive test for the accurate diagnosis of infection. The 'omics' approaches to identifying biomarkers from the host-response to bacterial infection are promising. In this study, lipidomic analysis was carried out with plasma samples obtained from febrile children with confirmed bacterial infection (n = 20) and confirmed viral infection (n = 20). We show for the first time that bacterial and viral infection produces distinct profile in the host lipidome. Some species of glycerophosphoinositol, sphingomyelin, lysophosphatidylcholine and cholesterol sulfate were higher in the confirmed virus infected group, while some species of fatty acids, glycerophosphocholine, glycerophosphoserine, lactosylceramide and bilirubin were lower in the confirmed virus infected group when compared with confirmed bacterial infected group. A combination of three lipids achieved an area under the receiver operating characteristic (ROC) curve of 0.911 (95% CI 0.81 to 0.98). This pilot study demonstrates the potential of metabolic biomarkers to assist clinicians in distinguishing bacterial from viral infection in febrile children, to facilitate effective clinical management and to the limit inappropriate use of antibiotics

Identification of regulatory variants associated with genetic susceptibility to meningococcal disease.

Non-coding genetic variants play an important role in driving susceptibility to complex diseases but their characterization remains challenging. Here, we employed a novel approach to interrogate the genetic risk of such polymorphisms in a more systematic way by targeting specific regulatory regions relevant for the phenotype studied. We applied this method to meningococcal disease susceptibility, using the DNA binding pattern of RELA - a NF-kB subunit, master regulator of the response to infection - under bacterial stimuli in nasopharyngeal epithelial cells. We designed a custom panel to cover these RELA binding sites and used it for targeted sequencing in cases and controls. Variant calling and association analysis were performed followed by validation of candidate polymorphisms by genotyping in three independent cohorts. We identified two new polymorphisms, rs4823231 and rs11913168, showing signs of association with meningococcal disease susceptibility. In addition, using our genomic data as well as publicly available resources, we found evidences for these SNPs to have potential regulatory effects on ATXN10 and LIF genes respectively. The variants and related candidate genes are relevant for infectious diseases and may have important contribution for meningococcal disease pathology. Finally, we described a novel genetic association approach that could be applied to other phenotypes

Life-threatening infections in children in Europe (the EUCLIDS Project): a prospective cohort study

Background: Sepsis and severe focal infections represent a substantial disease burden in children admitted to hospital. We aimed to understand the burden of disease and outcomes in children with life-threatening bacterial infections in Europe. Methods: The European Union Childhood Life-threatening Infectious Disease Study (EUCLIDS) was a prospective, multicentre, cohort study done in six countries in Europe. Patients aged 1 month to 18 years with sepsis (or suspected sepsis) or severe focal infections, admitted to 98 participating hospitals in the UK, Austria, Germany, Lithuania, Spain, and the Netherlands were prospectively recruited between July 1, 2012, and Dec 31, 2015. To assess disease burden and outcomes, we collected demographic and clinical data using a secured web-based platform and obtained microbiological data using locally available clinical diagnostic procedures. Findings: 2844 patients were recruited and included in the analysis. 1512 (53·2%) of 2841 patients were male and median age was 39·1 months (IQR 12·4–93·9). 1229 (43·2%) patients had sepsis and 1615 (56·8%) had severe focal infections. Patients diagnosed with sepsis had a median age of 27·6 months (IQR 9·0–80·2), whereas those diagnosed with severe focal infections had a median age of 46·5 months (15·8–100·4; p<0·0001). Of 2844 patients in the entire cohort, the main clinical syndromes were pneumonia (511 [18·0%] patients), CNS infection (469 [16·5%]), and skin and soft tissue infection (247 [8·7%]). The causal microorganism was identified in 1359 (47·8%) children, with the most prevalent ones being Neisseria meningitidis (in 259 [9·1%] patients), followed by Staphylococcus aureus (in 222 [7·8%]), Streptococcus pneumoniae (in 219 [7·7%]), and group A streptococcus (in 162 [5·7%]). 1070 (37·6%) patients required admission to a paediatric intensive care unit. Of 2469 patients with outcome data, 57 (2·2%) deaths occurred: seven were in patients with severe focal infections and 50 in those with sepsis. Interpretation: Mortality in children admitted to hospital for sepsis or severe focal infections is low in Europe. The disease burden is mainly in children younger than 5 years and is largely due to vaccine-preventable meningococcal and pneumococcal infections. Despite the availability and application of clinical procedures for microbiological diagnosis, the causative organism remained unidentified in approximately 50% of patients

Plasma lipid profiles discriminate bacterial from viral infection in febrile children

Fever is the most common reason that children present to Emergency Departments. Clinical signs and symptoms suggestive of bacterial infection ar