Backgrounds:

Multisystem Inflammatory Syndrome in Children (MIS-C) occurs several weeks after SARS-CoV-2 infection with symptoms including fever, shock and multiorgan failure. Clinical features of MIS-C overlap with Kawasaki Disease (KD), bacterial, and viral infections, making accurate diagnosis challenging. Host genes, measurable through whole blood transcriptomics, are an alternative tool for diagnosing infectious and inflammatory diseases.

Methods

Patients with MIS-C, KD, bacterial, and viral infections were recruited to the EU-funded PERFORM and DIAMONDS studies and the NIH-funded PREVAIL study. Patients were phenotyped using a standardised algorithm. Genome wide RNA sequencing of whole blood was undertaken, and feature selection was performed to identify a diagnostic signature for distinguishing between MIS-C and other infectious and inflammatory conditions. The expression levels of the genes identified were measured using RT-qPCR assays in an independent validation cohort.

Results:

Through feature selection and differential expression analysis, 11 genes with diagnostic potential were identified and taken forward into cross-platform validation using RT-qPCR. With up to 11 genes, it was possible to distinguish between MIS-C vs. KD, bacterial, and viral infections with high accuracy, with an AUC of 92.9% (95% CI: 88.2%-97.6%) in the validation cohort. The diagnostic gene signature retained its high performance when tested within the groups separately in the validation cohort: MIS-C vs. bacterial infections (AUC: 94.6%), vs. viral infections (AUC: 93.1%), and vs. KD (AUC: 89.8%).

Conclusions/Learning Points:

Despite the clinical similarities between MIS-C and other infectious and inflammatory conditions, there are key differences in gene expression profiles that can be used in diagnostic contexts. It will be necessary for the genes reported here to undergo further validation prior to their development into tests with clinical utility.

Backgrounds:

Determining the key genomic characteristics of childhood pneumonia may inform the development of new clinical interventions for the disease. We used a genome-wide association study (GWAS) to identify variants associated with all-cause pneumonia and subsequently used these data to identify eQTL.

Methods

DNA and peripheral blood RNA were collected from healthy Nepalese children and Nepalese children admitted to Patan Hospital, Kathmandu with clinician diagnosed pneumonia. Samples were genotyped using Illumina Global Screening Arrays. Array data underwent QC and filtering before imputation using the HRC R1.1 2016 reference panel. Association analysis, by conducting a logistic regression using multidimensional scaling values, was performed using PLINK 2. RNA underwent whole-transcriptome sequencing using Illumina HiSeq4000, 75bp paired-end reads. Count data underwent filtering, normalisation, and batch correction, before eQTL were identified using MatrixeQTL.

Results:

A GWAS of 773 children with pneumonia (cases) and 2121 healthy community-based children (controls) identified rs79755386, proximal to B3GLT (involved in glycosylating o-linked mucins) to be associated with all-cause pneumonia (p=1.1x10^-10, MAF cases = 0.09 vs MAF controls = 0.04, OR 2.3, 95% CI 1.8-2.9). Children with pneumonia who possessed the alternate allele, compared with those who had the reference, were more likely to have end-point consolidation on their chest x-ray (p=0.0492).

220 RNA samples from children with pneumonia were analysed. No cis eQTL were identified however, 230 significant trans eQTL to rs79755386 were identified with expression of HIC1 (p=8.9x10^-5) which regulates T-cell differentiation, and SSH3 (p=0.0001) which plays a role in actin filament dynamics, having the most significant associations.

Conclusions/Learning Points:

rs79755386 is associated with childhood pneumonia and the expression of genes which likely affect both immune and respiratory epithelial function. Further examination of the role rs79755386 plays in respiratory epithelium function is required.

Backgrounds:

Cardiomyopathy is one of the significant features of SARS-CoV-2 induced multi-system inflammatory syndrome in children (MIS-C) and it is also a rare complication of mRNA COVID-19 vaccination in young adults. MIS-C occurs 4-6 weeks following SARS-CoV-2 infection; we therefore postulated that antibodies might play a role in the cardiac immunopathology. The mechanism of vaccine-induced myocarditis is currently being explored. We investigated the role of anti-cardiac antibodies in post SARS-CoV-2 vaccine myocarditis and MIS-C.

Methods:

Clinical cohort: Pre-treatment acute MIS-C (n=10), acute COVID-19 vaccination-induced myocarditis (n=10), Pre-COVID-19 pandemic healthy children (n=10) and healthy COVID-19 vaccinated adults (10).

Immunohistochemistry was performed on human left ventricular tissue sections from 2 donor hearts (deemed unsuitable for donation) for assessment of auto-antibody binding. Sera from patients and controls were used as primary antibodies. FITC-conjugated anti-human-IgG (1:150), IgM (1:150) and IgA (1:50) were used for detection. 10 images were taken at random from each section and immunoglobulin deposition was quantified by calculating mean fluorescent intensity using ImageJ/Fiji. This method has previously shown specific binding of IgG to cardiac tissue following treatment with serum obtained from an adult myocarditis patient.

Results:

No specific binding was seen in left ventricular tissue treated with sera from paediatric patients with either MIS-C or COVID-19 vaccine-induced myocarditis. There was no significant difference in the mean fluorescent intensity of IgG, IgM and IgA in patients compared to controls (Figure 1).

Conclusions/Learning Points:

Our study did not find evidence for a role of an anti-cardiac antibody-mediated inflammatory process in MIS-C cardiomyopathy and COVID-19 vaccine induced myocarditis.

Backgrounds:

The ongoing COVID-19 pandemic has led to over five million deaths worldwide highlighting an unprecedented need for rapid diagnostic screening. The gold standard for COVID-19 diagnosis is the collection of a nasopharyngeal swab subsequently processed with an RNA extraction kit requiring electricity and expensive laboratory equipment. Therefore, the diagnosis of COVID-19 in low- and middle-income countries (LMIC) is rarely achievable at a point-of-care (POC) and instead relegated to remote centralized laboratories. To address this need, our team has developed an innovative, rapid and easy to use sample preparation method for RNA extraction allowing for true POC application.

Methods

The SmartLid^TM extraction method utilizes a custom 3D printed magnetic lid, designed to work with standard Eppendorf tubes, to transfer magnetic nanoparticles and attached RNA through three sample preparation steps. This is in contrast to all other manual extraction methods, which require expensive micropipettes, lab training and electrical power. The whole extraction process is performed within five minutes providing pure RNA adequate for downstream applications.

Results:

A total of 410 nasopharyngeal swabs has been tested (including 150 COVID-positive subjects). All clinical isolates were extracted by the SmartLid and the gold standard QIAmp Viral RNA methods and tested by the CDC RT-qPCR assays. The SmartLid method achieved 93.9% sensitivity and 99.5% specificity compared to the QIAmp Viral RNA showing equivalent performance.

Conclusions/Learning Points:

The extraction method presented can compete favourably with conventional laboratory-based extraction techniques which require expensive equipment and electricity, and which incur delays of over 45 minutes. The method has received overwhelmingly positive feedback from collaborators, who have tested it in CAT3 laboratory environments, and from visiting clinicians with experience in LMIC diagnostics. Thus, there is a clear interest for implementation in a domestic clinical/laboratory setting (NHS),and LMIC remote point-of-care setting.

Backgrounds:

T cell lymphopaenia, and activation are well described features of MIS-C. We evaluated whether T cell exhaustion alongside T cell activation reported in childhood viral infections, occurs in children with a clinical diagnosis of MIS-C.

Methods

Clinical cohort: Children with febrile illnesses and healthy controls were recruited from two tertiary London hospitals between 01/09/2020-31/12/21, as part of the EU-funded DIAMONDS study. 162 participants were included: MIS-C (n=80), COVID-19 pneumonitis (n=7), Kawasaki disease (n=3), severe viral (n=7), and bacterial (n=19) illness, other inflammatory (n=8), paediatric controls (n=13), and adult vaccinated controls (n=25). Eighty-two of 124 patients (66%) required intensive care.

Samples were collected at two time-points: acute illness at admission and convalescence. Qiagen SARS-CoV-2 QuantiFERON kits were used for stimulation of whole blood with mitogen and SARS-CoV-2 antigens, to stimulate CD4+ and CD8+ T cells, and subsequent measurement in the supernatant of IFNγ levels.

Results:

The QuantiFERON assay was performed on 132 samples (acute n=59, convalescence n=19, paediatric controls n=11, adult controls n=25). MIS-C patients had raised baseline IFNγ levels, with lower increase after stimulation with SARS-CoV-2 antigens compared to vaccinated adults. The absolute increase in IFNγ in response to mitogen was lower in MIS-C compared to vaccinated adults, healthy children or bacterial infection. There was recovery in mitogen response in MIS-C in convalescence (Figure 1).

Conclusions/Learning Points:

We report a high baseline IFNγ and low activation by SARS-CoV-2 peptides and mitogen, which suggests T cell exhaustion coexists with features of T cell activation in MIS-C. Further studies on molecular mechanisms of T cell exhaustion are warranted.

Backgrounds:

Febrile children below three months have a higher risk of serious bacterial infections, which often leads to extensive diagnostics and treatment. However, there is practice variation in management due to differences in guidelines and the usage and adherence. We aimed to assess whether management in febrile children below three months attending European Emergency Departments (EDs) was according to the available guidelines for fever.

Methods

This study is part of the MOFICHE study, which is an observational multicenter study including routine data of febrile children (0-18 years) attending twelve European EDs. In febrile children <3 months (excluding bronchiolitis), we analyzed actual management compared to the available guidelines for fever. Ten EDs applied the (adapted) NICE guideline and two EDs applied local guidelines. Management included diagnostic tests, antibiotic treatment and admission. Subgroup analyses in children <1 month and 1-3 months were performed. Data on follow-up was not collected.

Results:

We included 913 children (median age 1.7 months) with the majority triaged as intermediate/high urgent (53%), 40% having a respiratory tract infection and 56% having a viral illness. Management per ED varied: diagnostic tests 14-83%, antibiotic treatment 23-54%, admission 34-86%. Adherence to the guidelines varied: blood cultures were obtained in 43% (374/868), lumbar punctures in 30% (144/488), antibiotics were prescribed in 55% (270/492) and 67% (573/859) were admitted. Full adherence to all these four components occurred in 15% (132/868, range 0-38%), 31% (71/223) in children <1 month and 10% (61/645) in children 1-3 months respectively.

Conclusions/Learning Points:

There is large practice variation in management and guideline adherence was limited, but highest for admission which implies good safety netting. Future studies should focus on guideline revision with new biomarkers in order to optimize management in young febrile children.

Backgrounds:

Many host transcript signatures for paediatric inflammatory and infectious diseases are in development, but require validation in independent cohorts; their translation to clinically useful test platforms lags behind discovery. We used NanoString technology to efficiently validate multiple signatures in parallel and explore the potential for more sophisticated multi-class classification models.

Methods

We validated five transcriptomic diagnostic signatures using prospectively recruited patients from multiple paediatric cohorts. Final phenotypes were assigned using pre-agreed definitions after review of clinical and laboratory data. We quantified 69 transcripts on a custom NanoString nCounter cartridge, normalising expression values using reference genes. Signature performance was assessed using Area Under ROC Curve (AUC) statistics. We explored two approaches to multiclassification diagnostics to develop proof-of-concept methods: a mixed test combining four independent one-vs-all models, and a multinomial model.

Results:

Our cohort of 92 paediatric patients included 23 definite bacterial and 20 definite viral infections, 15 Kawasaki disease, 18 with tuberculosis and 16 healthy controls. The signatures achieved AUCs above 0.82 (Table 1), with confidence intervals overlapping those of the respective discovery studies. However, performance declined in all signatures when tasked with differentiating additional groups. For example, the single-transcript BATF2 had AUC of 0.910 differentiating TB from healthy individuals, reducing to 0.745 when differentiating TB from other febrile diseases. In comparison, the multinomial approach identified a 24-transcript model that correctly classified all 76 non-control patients (0% in-sample error), outperforming the mixed-model (19 transcripts, 19.8% in-sample error).

Conclusions/Learning Points:

The cross-platform, out-of-sample findings validated 5 signatures, but discriminatory power was reduced in patients drawn from outside their remit. An exploratory 24-transcript model had improved accuracy across all diagnostic groups, demonstrating in principle the utility for one-step multi-class diagnosis in patients with broad diagnostic uncertainty.