Integration and validation of host transcript signatures, including a novel 3-transcript tuberculosis signature, to enable one-step multiclass diagnosis of childhood febrile disease

Background: Whole blood host transcript signatures show great potential for diagnosis of infectious and inflammatory illness, with most published signatures performing binary classification tasks. Barriers to clinical implementation include validation studies, and development of strategies that enable simultaneous, multiclass diagnosis of febrile illness based on gene expression. Methods: We validated five distinct diagnostic signatures for paediatric infectious diseases in parallel using a single NanoString nCounter® experiment. We included a novel 3-transcript signature for childhood tuberculosis, and four published signatures which differentiate bacterial infection, viral infection, or Kawasaki disease from other febrile illnesses. Signature performance was assessed using receiver operating characteristic curve statistics. We also explored conceptual frameworks for multiclass diagnostic signatures, including additional transcripts found to be significantly differentially expressed in previous studies. Relaxed, regularised logistic regression models were used to derive two novel multiclass signatures: a mixed One-vs-All model (MOVA), running multiple binomial models in parallel, and a full-multiclass model. In-sample performance of these models was compared using radar-plots and confusion matrix statistics. Results: Samples from 91 children were included in the study: 23 bacterial infections (DB), 20 viral infections (DV), 14 Kawasaki disease (KD), 18 tuberculosis disease (TB), and 16 healthy controls. The five signatures tested demonstrated cross-platform performance similar to their primary discovery-validation cohorts. The signatures could differentiate: KD from other diseases with area under ROC curve (AUC) of 0.897 [95% confidence interval: 0.822–0.972]; DB from DV with AUC of 0.825 [0.691–0.959] (signature-1) and 0.867 [0.753–0.982] (signature-2); TB from other diseases with AUC of 0.882 [0.787–0.977] (novel signature); TB from healthy children with AUC of 0.910 [0.808–1.000]. Application of signatures outside of their designed context reduced performance. In-sample error rates for the multiclass models were 13.3% for the MOVA model and 0.0% for the full-multiclass model. The MOVA model misclassified DB cases most frequently (18.7%) and TB cases least (2.7%). Conclusions: Our study demonstrates the feasibility of NanoString technology for cross-platform validation of multiple transcriptomic signatures in parallel. This external cohort validated performance of all five signatures, including a novel sparse TB signature. Two exploratory multi-class models showed high potential accuracy across four distinct diagnostic groups

Diagnosis of Multisystem Inflammatory Syndrome in Children by a Whole-Blood Transcriptional Signature

Background: To identify a diagnostic blood transcriptomic signature that distinguishes multisystem inflammatory syndrome in children (MIS-C) from Kawasaki disease (KD), bacterial infections, and viral infections. Methods: Children presenting with MIS-C to participating hospitals in the United Kingdom and the European Union between April 2020 and April 2021 were prospectively recruited. Whole-blood RNA Sequencing was performed, contrasting the transcriptomes of children with MIS-C (n = 38) to those from children with KD (n = 136), definite bacterial (DB; n = 188) and viral infections (DV; n = 138). Genes significantly differentially expressed (SDE) between MIS-C and comparator groups were identified. Feature selection was used to identify genes that optimally distinguish MIS-C from other diseases, which were subsequently translated into RT-qPCR assays and evaluated in an independent validation set comprising MIS-C (n = 37), KD (n = 19), DB (n = 56), DV (n = 43), and COVID-19 (n = 39). Results: In the discovery set, 5696 genes were SDE between MIS-C and combined comparator disease groups. Five genes were identified as potential MIS-C diagnostic biomarkers (HSPBAP1, VPS37C, TGFB1, MX2, and TRBV11-2), achieving an AUC of 96.8% (95% CI: 94.6%–98.9%) in the discovery set, and were translated into RT-qPCR assays. The RT-qPCR 5-gene signature achieved an AUC of 93.2% (95% CI: 88.3%–97.7%) in the independent validation set when distinguishing MIS-C from KD, DB, and DV. Conclusions: MIS-C can be distinguished from KD, DB, and DV groups using a 5-gene blood RNA expression signature. The small number of genes in the signature and good performance in both discovery and validation sets should enable the development of a diagnostic test for MIS-C

Diagnosis of Multisystem Inflammatory Syndrome in Children by a Whole-Blood Transcriptional Signature

Background: To identify a diagnostic blood transcriptomic signature that distinguishes multisystem inflammatory syndrome in children (MIS-C) from Kawasaki disease (KD), bacterial infections, and viral infections. Methods: Children presenting with MIS-C to participating hospitals in the United Kingdom and the European Union between April 2020 and April 2021 were prospectively recruited. Whole-blood RNA Sequencing was performed, contrasting the transcriptomes of children with MIS-C (n = 38) to those from children with KD (n = 136), definite bacterial (DB; n = 188) and viral infections (DV; n = 138). Genes significantly differentially expressed (SDE) between MIS-C and comparator groups were identified. Feature selection was used to identify genes that optimally distinguish MIS-C from other diseases, which were subsequently translated into RT-qPCR assays and evaluated in an independent validation set comprising MIS-C (n = 37), KD (n = 19), DB (n = 56), DV (n = 43), and COVID-19 (n = 39). Results: In the discovery set, 5696 genes were SDE between MIS-C and combined comparator disease groups. Five genes were identified as potential MIS-C diagnostic biomarkers (HSPBAP1, VPS37C, TGFB1, MX2, and TRBV11-2), achieving an AUC of 96.8% (95% CI: 94.6%-98.9%) in the discovery set, and were translated into RT-qPCR assays. The RT-qPCR 5-gene signature achieved an AUC of 93.2% (95% CI: 88.3%-97.7%) in the independent validation set when distinguishing MIS-C from KD, DB, and DV. Conclusions: MIS-C can be distinguished from KD, DB, and DV groups using a 5-gene blood RNA expression signature. The small number of genes in the signature and good performance in both discovery and validation sets should enable the development of a diagnostic test for MIS-C

Diagnosis of multisystem inflammatory syndrome in children by a whole-blood transcriptional signature.

ObjectiveTo identify a diagnostic blood transcriptomic signature that distinguishes multisystem inflammatory syndrome in children (MIS-C) from Kawasaki Disease (KD), bacterial infections and viral infections.Study designChildren presenting with MIS-C to participating hospitals in the United Kingdom and the European Union between April 2020-April 2021 were prospectively recruited. Whole blood RNA Sequencing was performed, contrasting the transcriptomes of children with MIS-C (n=38) to those from children with KD (n=136), definite bacterial (DB; n=188) and viral infections (DV; n=138). Genes significantly differentially expressed (SDE) between MIS-C and comparator groups were identified. Feature selection was used to identify genes that optimally distinguish MIS-C from other diseases, which were subsequently translated into RT-qPCR assays and evaluated in an independent validation set comprising MIS-C (n=37), KD (n=19), DB (n=56), DV (n=43), and COVID-19 (n=39).ResultsIn the discovery set, 5,696 genes were SDE between MIS-C and combined comparator disease groups. Five genes were identified as potential MIS-C diagnostic biomarkers (HSPBAP1, VPS37C, TGFB1, MX2, TRBV11-2), achieving an AUC of 96.8% (95% CI: 94.6%-98.9%) in the discovery set, and were translated into RT-qPCR assays. The RT-qPCR 5-gene signature achieved an AUC of 93.2% (95% CI: 88.3%-97.7%) in the independent validation set when distinguishing MIS-C from KD, DB, and DV.ConclusionMIS-C can be distinguished from KD, DB, and DV groups using a 5-gene blood RNA expression signature. The small number of genes in the signature, and good performance in both discovery and validation sets should enable the development of a diagnostic test for MIS-C

Relationship between molecular pathogen detection and clinical disease in febrile children across Europe: a multicentre, prospective observational study

BackgroundThe PERFORM study aimed to understand causes of febrile childhood illness by comparing molecular pathogen detection with current clinical practice.MethodsFebrile children and controls were recruited on presentation to hospital in 9 European countries 2016-2020. Each child was assigned a standardized diagnostic category based on retrospective review of local clinical and microbiological data. Subsequently, centralised molecular tests (CMTs) for 19 respiratory and 27 blood pathogens were performed.FindingsOf 4611 febrile children, 643 (14%) were classified as definite bacterial infection (DB), 491 (11%) as definite viral infection (DV), and 3477 (75%) had uncertain aetiology. 1061 controls without infection were recruited. CMTs detected blood bacteria more frequently in DB than DV cases for N. meningitidis (OR: 3.37, 95% CI: 1.92-5.99), S. pneumoniae (OR: 3.89, 95% CI: 2.07-7.59), Group A streptococcus (OR 2.73, 95% CI 1.13-6.09) and E. coli (OR 2.7, 95% CI 1.02-6.71). Respiratory viruses were more common in febrile children than controls, but only influenza A (OR 0.24, 95% CI 0.11-0.46), influenza B (OR 0.12, 95% CI 0.02-0.37) and RSV (OR 0.16, 95% CI: 0.06-0.36) were less common in DB than DV cases. Of 16 blood viruses, enterovirus (OR 0.43, 95% CI 0.23-0.72) and EBV (OR 0.71, 95% CI 0.56-0.90) were detected less often in DB than DV cases. Combined local diagnostics and CMTs respectively detected blood viruses and respiratory viruses in 360 (56%) and 161 (25%) of DB cases, and virus detection ruled-out bacterial infection poorly, with predictive values of 0.64 and 0.68 respectively.InterpretationMost febrile children cannot be conclusively defined as having bacterial or viral infection when molecular tests supplement conventional approaches. Viruses are detected in most patients with bacterial infections, and the clinical value of individual pathogen detection in determining treatment is low. New approaches are needed to help determine which febrile children require antibiotics.FundingEU Horizon 2020 grant 668303

Immunoglobulin, glucocorticoid, or combination therapy for multisystem inflammatory syndrome in children: a propensity-weighted cohort study.

BACKGROUND: Multisystem inflammatory syndrome in children (MIS-C), a hyperinflammatory condition associated with SARS-CoV-2 infection, has emerged as a serious illness in children worldwide. Immunoglobulin or glucocorticoids, or both, are currently recommended treatments. METHODS: The Best Available Treatment Study evaluated immunomodulatory treatments for MIS-C in an international observational cohort. Analysis of the first 614 patients was previously reported. In this propensity-weighted cohort study, clinical and outcome data from children with suspected or proven MIS-C were collected onto a web-based Research Electronic Data Capture database. After excluding neonates and incomplete or duplicate records, inverse probability weighting was used to compare primary treatments with intravenous immunoglobulin, intravenous immunoglobulin plus glucocorticoids, or glucocorticoids alone, using intravenous immunoglobulin as the reference treatment. Primary outcomes were a composite of inotropic or ventilator support from the second day after treatment initiation, or death, and time to improvement on an ordinal clinical severity scale. Secondary outcomes included treatment escalation, clinical deterioration, fever, and coronary artery aneurysm occurrence and resolution. This study is registered with the ISRCTN registry, ISRCTN69546370. FINDINGS: We enrolled 2101 children (aged 0 months to 19 years) with clinically diagnosed MIS-C from 39 countries between June 14, 2020, and April 25, 2022, and, following exclusions, 2009 patients were included for analysis (median age 8·0 years [IQR 4·2-11·4], 1191 [59·3%] male and 818 [40·7%] female, and 825 [41·1%] White). 680 (33·8%) patients received primary treatment with intravenous immunoglobulin, 698 (34·7%) with intravenous immunoglobulin plus glucocorticoids, 487 (24·2%) with glucocorticoids alone; 59 (2·9%) patients received other combinations, including biologicals, and 85 (4·2%) patients received no immunomodulators. There were no significant differences between treatments for primary outcomes for the 1586 patients with complete baseline and outcome data that were considered for primary analysis. Adjusted odds ratios for ventilation, inotropic support, or death were 1·09 (95% CI 0·75-1·58; corrected p value=1·00) for intravenous immunoglobulin plus glucocorticoids and 0·93 (0·58-1·47; corrected p value=1·00) for glucocorticoids alone, versus intravenous immunoglobulin alone. Adjusted average hazard ratios for time to improvement were 1·04 (95% CI 0·91-1·20; corrected p value=1·00) for intravenous immunoglobulin plus glucocorticoids, and 0·84 (0·70-1·00; corrected p value=0·22) for glucocorticoids alone, versus intravenous immunoglobulin alone. Treatment escalation was less frequent for intravenous immunoglobulin plus glucocorticoids (OR 0·15 [95% CI 0·11-0·20]; p<0·0001) and glucocorticoids alone (0·68 [0·50-0·93]; p=0·014) versus intravenous immunoglobulin alone. Persistent fever (from day 2 onward) was less common with intravenous immunoglobulin plus glucocorticoids compared with either intravenous immunoglobulin alone (OR 0·50 [95% CI 0·38-0·67]; p<0·0001) or glucocorticoids alone (0·63 [0·45-0·88]; p=0·0058). Coronary artery aneurysm occurrence and resolution did not differ significantly between treatment groups. INTERPRETATION: Recovery rates, including occurrence and resolution of coronary artery aneurysms, were similar for primary treatment with intravenous immunoglobulin when compared to glucocorticoids or intravenous immunoglobulin plus glucocorticoids. Initial treatment with glucocorticoids appears to be a safe alternative to immunoglobulin or combined therapy, and might be advantageous in view of the cost and limited availability of intravenous immunoglobulin in many countries. FUNDING: Imperial College London, the European Union's Horizon 2020, Wellcome Trust, the Medical Research Foundation, UK National Institute for Health and Care Research, and National Institutes of Health

Genomic investigations of unexplained acute hepatitis in children

Since its first identification in Scotland, over 1,000 cases of unexplained paediatric hepatitis in children have been reported worldwide, including 278 cases in the UK1. Here we report an investigation of 38 cases, 66 age-matched immunocompetent controls and 21 immunocompromised comparator participants, using a combination of genomic, transcriptomic, proteomic and immunohistochemical methods. We detected high levels of adeno-associated virus 2 (AAV2) DNA in the liver, blood, plasma or stool from 27 of 28 cases. We found low levels of adenovirus (HAdV) and human herpesvirus 6B (HHV-6B) in 23 of 31 and 16 of 23, respectively, of the cases tested. By contrast, AAV2 was infrequently detected and at low titre in the blood or the liver from control children with HAdV, even when profoundly immunosuppressed. AAV2, HAdV and HHV-6 phylogeny excluded the emergence of novel strains in cases. Histological analyses of explanted livers showed enrichment for T cells and B lineage cells. Proteomic comparison of liver tissue from cases and healthy controls identified increased expression of HLA class 2, immunoglobulin variable regions and complement proteins. HAdV and AAV2 proteins were not detected in the livers. Instead, we identified AAV2 DNA complexes reflecting both HAdV-mediated and HHV-6B-mediated replication. We hypothesize that high levels of abnormal AAV2 replication products aided by HAdV and, in severe cases, HHV-6B may have triggered immune-mediated hepatic disease in genetically and immunologically predisposed children

Analisi di pattern di segnali fisiologici a seguito di stimolazione edonica olfattiva.

L’olfatto è considerato il più antico e primordiale dei sensi, come dimostra il suo legame con le parti più remote e primitive del cervello. La corteccia olfattiva primaria, di cui la parte più estesa è la corteccia piriforme, ha infatti diverse proiezioni su altre regioni cerebrali, quali ad esempio l’amigdala e la corteccia orbitofrontale, che creano uno stretto collegamento tra stimoli olfattivi, memoria e stati emotivi. Più di altri stimoli sensoriali, quelli olfattivi hanno la capacità di evocare o arricchire risposte emozionali studiate solitamente attraverso l’analisi di specifiche features di segnali provenienti dal sistema nervoso autonomico e centrale. Nel presente studio alcuni volontari sono stati sottoposti a cinque stimoli olfattivi di cui due piacevoli, vanillina e benzaldeide, uno neutro, n-butanolo, e due spiacevoli, acido butirrico e acido isovalerico. Lo scopo della presente tesi è stato studiare la variazione delle suddette features in base al valore edonico delle sostanze odorose in esame

A computational framework to improve cross-platform implementation of transcriptomics signatures

Summary: The emergence of next-generation sequencing technologies and computational advances have expanded our understanding of gene expression regulation (i.e., the transcriptome). This has also led to an increased interest in using transcriptomic biomarkers to improve disease diagnosis and stratification, to assess prognosis and predict the response to treatment. Significant progress in identifying transcriptomic signatures for various clinical needs has been made, with large discovery studies accounting for challenges such as patient variability, unwanted batch effects, and data complexities; however, obstacles related to the technical aspects of cross-platform implementation still hinder the successful integration of transcriptomic technologies into standard diagnostic workflows. In this article, we discuss the challenges associated with integrating transcriptomic signatures derived using high-throughput technologies (such as RNA-sequencing) into clinical diagnostic tools using nucleic acid amplification (NAA) techniques. The novelty of the proposed approach lies in our aim to embed constraints related to cross-platform implementation in the process of signature discovery. These constraints could include technical limitations of amplification platform and chemistry, the maximal number of targets imposed by the chosen multiplexing strategy, and the genomic context of identified RNA biomarkers. Finally, we propose to build a computational framework that would integrate these constraints in combination with existing statistical and machine learning models used for signature identification. We envision that this could accelerate the integration of RNA signatures discovered by high-throughput technologies into NAA-based approaches suitable for clinical applications

Treatment of Multisystem Inflammatory Syndrome in Children

BACKGROUND Evidence is urgently needed to support treatment decisions for children with multisystem inflammatory syndrome (MIS-C) associated with severe acute respiratory syndrome coronavirus 2. METHODS We performed an international observational cohort study of clinical and outcome data regarding suspected MIS-C that had been uploaded by physicians onto a Web-based database. We used inverse-probability weighting and generalized linear models to evaluate intravenous immune globulin (IVIG) as a reference, as compared with IVIG plus glucocorticoids and glucocorticoids alone. There were two primary outcomes: the first was a composite of inotropic support or mechanical ventilation by day 2 or later or death; the second was a reduction in disease severity on an ordinal scale by day 2. Secondary outcomes included treatment escalation and the time until a reduction in organ failure and inflammation. RESULTS Data were available regarding the course of treatment for 614 children from 32 countries from June 2020 through February 2021; 490 met the World Health Organization criteria for MIS-C. Of the 614 children with suspected MIS-C, 246 received primary treatment with IVIG alone, 208 with IVIG plus glucocorticoids, and 99 with glucocorticoids alone; 22 children received other treatment combinations, including biologic agents, and 39 received no immunomodulatory therapy. Receipt of inotropic or ventilatory support or death occurred in 56 patients who received IVIG plus glucocorticoids (adjusted odds ratio for the comparison with IVIG alone, 0.77; 95% confidence interval [CI], 0.33 to 1.82) and in 17 patients who received glucocorticoids alone (adjusted odds ratio, 0.54; 95% CI, 0.22 to 1.33). The adjusted odds ratios for a reduction in disease severity were similar in the two groups, as compared with IVIG alone (0.90 for IVIG plus glucocorticoids and 0.93 for glucocorticoids alone). The time until a reduction in disease severity was similar in the three groups. CONCLUSIONS We found no evidence that recovery from MIS-C differed after primary treatment with IVIG alone, IVIG plus glucocorticoids, or glucocorticoids alone, although significant differences may emerge as more data accrue. (Funded by the European Union's Horizon 2020 Program and others; BATS ISRCTN number, ISRCTN69546370.)