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Abstract 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.

We describe a novel, severe autoinflammatory syndrome characterized by neuroinflammation, systemic autoinflammation, splenomegaly, and anemia (NASA) caused by bi-allelic mutations in IRAK4. IRAK-4 is a serine/threonine kinase with a pivotal role in innate immune signaling from toll-like receptors and production of pro-inflammatory cytokines. In humans, bi-allelic mutations in IRAK4 result in IRAK-4 deficiency and increased susceptibility to pyogenic bacterial infections, but autoinflammation has never been described. We describe 5 affected patients from 2 unrelated families with compound heterozygous mutations in IRAK4 (c.C877T (p.Q293*)/c.G958T (p.D320Y); and c.A86C (p.Q29P)/c.161 + 1G>A) resulting in severe systemic autoinflammation, massive splenomegaly and severe transfusion dependent anemia and, in 3/5 cases, severe neuroinflammation and seizures. IRAK-4 protein expression was reduced in peripheral blood mononuclear cells (PBMC) in affected patients. Immunological analysis demonstrated elevated serum tumor necrosis factor (TNF), interleukin (IL) 1 beta (IL-1β), IL-6, IL-8, interferon α2a (IFN-α2a), and interferon β (IFN-β); and elevated cerebrospinal fluid (CSF) IL-6 without elevation of CSF IFN-α despite perturbed interferon gene signature. Mutations were located within the death domain (DD; p.Q29P and splice site mutation c.161 + 1G>A) and kinase domain (p.Q293*/p.D320Y) of IRAK-4. Structure-based modeling of the DD mutation p.Q29P showed alteration in the alignment of a loop within the DD with loss of contact distance and hydrogen bond interactions with IRAK-1/2 within the myddosome complex. The kinase domain mutation p.D320Y was predicted to stabilize interactions within the kinase active site. While precise mechanisms of autoinflammation in NASA remain uncertain, we speculate that loss of negative regulation of IRAK-4 and IRAK-1; dysregulation of myddosome assembly and disassembly; or kinase active site instability may drive dysregulated IL-6 and TNF production. Blockade of IL-6 resulted in immediate and complete amelioration of systemic autoinflammation and anemia in all 5 patients treated; however, neuroinflammation has, so far proven recalcitrant to IL-6 blockade and the janus kinase (JAK) inhibitor baricitinib, likely due to lack of central nervous system penetration of both drugs. We therefore highlight that bi-allelic mutation in IRAK4 may be associated with a severe and complex autoinflammatory and neuroinflammatory phenotype that we have called NASA (neuroinflammation, autoinflammation, splenomegaly and anemia), in addition to immunodeficiency in humans.

Summary: Mechanisms of infection and pathogenesis have predominantly been studied based on differential gene or protein expression. Less is known about posttranslational modifications, which are essential for protein functional diversity. We applied an innovative glycoproteomics method to study the systemic proteome-wide glycosylation in response to infection. The protein site-specific glycosylation was characterized in plasma derived from well-defined controls and patients. We found 3862 unique features, of which we identified 463 distinct intact glycopeptides, that could be mapped to more than 30 different proteins. Statistical analyses were used to derive a glycopeptide signature that enabled significant differentiation between patients with a bacterial or viral infection. Furthermore, supported by a machine learning algorithm, we demonstrated the ability to identify the causative pathogens based on the distinctive host blood plasma glycopeptide signatures. These results illustrate that glycoproteomics holds enormous potential as an innovative approach to improve the interpretation of relevant biological changes in response to infection.

Abstract Infection with SARS-CoV-2 has highly variable clinical manifestations, ranging from asymptomatic infection through to life-threatening disease. Host whole blood transcriptomics can offer unique insights into the biological processes underpinning infection and disease, as well as severity. We performed whole blood RNA Sequencing of individuals with varying degrees of COVID-19 severity. We used differential expression analysis and pathway enrichment analysis to explore how the blood transcriptome differs between individuals with mild, moderate, and severe COVID-19, performing pairwise comparisons between groups. Increasing COVID-19 severity was characterised by an abundance of inflammatory immune response genes and pathways, including many related to neutrophils and macrophages, in addition to an upregulation of immunoglobulin genes. In this study, for the first time, we show how immunomodulatory treatments commonly administered to COVID-19 patients greatly alter the transcriptome. Our insights into COVID-19 severity reveal the role of immune dysregulation in the progression to severe disease and highlight the need for further research exploring the interplay between SARS-CoV-2 and the inflammatory immune response.

BackgroundPediatric osteoarticular infections (POAIs) are serious diseases requiring early diagnosis and treatment.MethodsIn this prospective multicenter cohort study, children with POAIs were selected from the European Union Childhood Life-threatening Infectious Diseases Study (EUCLIDS) database to analyze their demographic, clinical, and microbiological data.ResultsA cohort of 380 patients with POAIs, 203 with osteomyelitis (OM), 158 with septic arthritis (SA), and 19 with both OM and SA, was analyzed. Thirty-five patients were admitted to the Pediatric Intensive Care Unit; out of these, six suffered from shock, one needed an amputation of the right foot and of four left toes, and two had skin transplantation. According to the Pediatric Overall Performance Score, 36 (10.5%) showed a mild overall disability, 3 (0.8%) a moderate, and 1 (0.2%) a severe overall disability at discharge. A causative organism was detected in 65% (247/380) of patients. Staphylococcus aureus (S. aureus) was identified in 57.1% (141/247) of microbiological confirmed cases, including 1 (0.7%) methicillin-resistant S. aureus (MRSA) and 6 (4.2%) Panton-Valentine leukocidin (PVL)-producing S. aureus, followed by Group A Streptococcus (18.2%) and Kingella kingae (8.9%). K. kingae and PVL production in S. aureus were less frequently reported than expected from the literature.ConclusionPOAIs are associated with a substantial morbidity in European children, with S. aureus being the major detected pathogen. In one-third of patients, no causative organism is identified. Our observations show an urgent need for the development of a vaccine against S. aureus and for the development of new microbiologic diagnostic guidelines for POAIs in European pediatric hospitals.

IMPORTANCE:. A Disintegrin and Metalloproteinase with Thrombospondin Motifs-1 is hypothesized to play a role in the pathogenesis of invasive infection, but studies in sepsis are lacking. OBJECTIVES:. To study A Disintegrin and Metalloproteinase with Thrombospondin Motifs-1 protein level in pediatric sepsis and to study the association with outcome. DESIGN:. Data from two prospective cohort studies. SETTING AND PARTICIPANTS:. Cohort 1 is from a single-center study involving children admitted to PICU with meningococcal sepsis (samples obtained at three time points). Cohort 2 includes patients from a multicenter study involving children admitted to the hospital with invasive bacterial infections of differing etiologies (samples obtained within 48 hr after hospital admission). MAIN OUTCOMES AND MEASURES:. Primary outcome measure was mortality. Secondary outcome measures were PICU-free days at day 28 and hospital length of stay. RESULTS:. In cohort 1 (n = 59), nonsurvivors more frequently had A Disintegrin and Metalloproteinase with Thrombospondin Motifs-1 levels above the detection limit than survivors at admission to PICU (8/11 [73%] and 6/23 [26%], respectively; p = 0.02) and at t = 24 hours (2/3 [67%] and 3/37 [8%], respectively; p = 0.04). In cohort 2 (n = 240), A Disintegrin and Metalloproteinase with Thrombospondin Motifs-1 levels in patients within 48 hours after hospital admission were more frequently above the detection limit than in healthy controls (110/240 [46%] and 14/64 [22%], respectively; p = 0.001). Nonsurvivors more often had detectable A Disintegrin and Metalloproteinase with Thrombospondin Motifs-1 levels than survivors (16/21 [76%] and 94/219 [43%], respectively; p = 0.003), which was mostly attributable to patients with Neisseria meningitidis. CONCLUSIONS AND RELEVANCE:. In children with bacterial infection, detection of A Disintegrin and Metalloproteinase with Thrombospondin Motifs-1 within 48 hours after hospital admission is associated with death, particularly in meningococcal sepsis. Future studies should confirm the prognostic value of A Disintegrin and Metalloproteinase with Thrombospondin Motifs-1 and should study pathophysiologic mechanisms.

Background: The limited diagnostic accuracy of biomarkers in children at risk of a serious bacterial infection (SBI) might be due to the imperfect reference standard of SBI. We aimed to evaluate the diagnostic performance of a new classification algorithm for biomarker discovery in children at risk of SBI.Methods: We used data from five previously published, prospective observational biomarker discovery studies, which included patients aged 0–

Background: The complement system is a central component of the innate immune system. Constitutive biosynthesis of complement proteins is essential for homeostasis. Dysregulation as a consequence of genetic or environmental cues can lead to inflammatory syndromes or increased susceptibility to infection. However, very little is known about steady state levels in children or its kinetics during infection. Methods: With a newly developed multiplex mass spectrometry-based method we analyzed the levels of 32 complement proteins in healthy individuals and in a group of pediatric patients infected with bacterial or viral pathogens. Findings: In plasma from young infants we found reduced levels of C4BP, ficolin-3, factor B, classical pathway components C1QA, C1QB, C1QC, C1R, and terminal pathway components C5, C8, C9, as compared to healthy adults; whereas the majority of complement regulating (inhibitory) proteins reach adult levels at very young age. Both viral and bacterial infections in children generally lead to a slight overall increase in complement levels, with some exceptions. The kinetics of complement levels during invasive bacterial infections only showed minor changes, except for a significant increase and decrease of CRP and clusterin, respectively. Interpretation: The combination of lower levels of activating and higher levels of regulating complement proteins, would potentially raise the threshold of activation, which might lead to suppressed complement activation in the first phase of life. There is hardly any measurable complement consumption during bacterial or viral infection. Altogether, expression of the complement proteins appears surprisingly stable, which suggests that the system is continuously replenished. Fund: European Union's Horizon 2020, project PERFORM, grant agreement No. 668303. Keywords: Targeted mass spectrometry, Multiple reaction monitoring (MRM), Complement system, Infectious disease, C-reactive protein (CRP), Clusterin

BackgroundRotavirus (RV) is an enteric pathogen that has devastating impact on childhood morbidity and mortality worldwide. The immunologic mechanism underlying the protection achieved after RV vaccination is not yet fully understood.MethodsWe compared the transcriptome of children affected by community-acquired RV infection and children immunized with a live attenuated RV vaccine (RotaTeq®).ResultsRV vaccination mimics the wild type infection causing similar changes in children’s transcriptome, including transcripts associated with cell cycle, diarrhea, nausea, vomiting, intussusception, and abnormal morphology of midgut. A machine learning approach allowed to detect a combination of nine-transcripts that differentiates vaccinated from convalescent-naturally infected children (AUC: 90%; 95%CI: 70–100) and distinguishes between acute-infected and healthy control children (in both cases, AUC: 100%; 95%CI: 100–100). We identified a miRNA hsa-mir-149 that seems to play a role in the host defense against viral pathogens and may have an antiviral role.DiscussionOur findings might shed further light in the understanding of RV infection, its functional link to intussusception causes, as well as guide development of antiviral treatments and safer and more effective vaccines. The nine-transcript signature may constitute a marker of vaccine protection and helps to differentiate vaccinated from naturally infected or susceptible children.

Abstract Background Sepsis is one of the main reasons for non-elective admission to pediatric intensive care units (PICUs), but little is known about determinants influencing outcome. We characterized children admitted with community-acquired sepsis to European PICUs and studied risk factors for mortality and disability. Methods Data were collected within the collaborative Seventh Framework Programme (FP7)-funded EUCLIDS study, which is a prospective multicenter cohort study aiming to evaluate genetic determinants of susceptibility and/or severity in sepsis. This report includes 795 children admitted with community-acquired sepsis to 52 PICUs from seven European countries between July 2012 and January 2016. The primary outcome measure was in-hospital death. Secondary outcome measures were PICU-free days censured at day 28, hospital length of stay, and disability. Independent predictors were identified by multivariate regression analysis. Results Patients most commonly presented clinically with sepsis without a source (n = 278, 35%), meningitis/encephalitis (n = 182, 23%), or pneumonia (n = 149, 19%). Of 428 (54%) patients with confirmed bacterial infection, Neisseria meningitidis (n = 131, 31%) and Streptococcus pneumoniae (n = 78, 18%) were the main pathogens. Mortality was 6% (51/795), increasing to 10% in the presence of septic shock (45/466). Of the survivors, 31% were discharged with disability, including 24% of previously healthy children who survived with disability. Mortality and disability were independently associated with S. pneumoniae infections (mortality OR 4.1, 95% CI 1.1–16.0, P = 0.04; disability OR 5.4, 95% CI 1.8–15.8, P

ABSTRACT Greater understanding of the functions of host gene products in response to infection is required. While many of these genes enable pathogen clearance, some enhance pathogen growth or contribute to disease symptoms. Many studies have profiled transcriptomic and proteomic responses to infection, generating large data sets, but selecting targets for further study is challenging. Here we propose a novel data-mining approach combining multiple heterogeneous data sets to prioritize genes for further study by using respiratory syncytial virus (RSV) infection as a model pathogen with a significant health care impact. The assumption was that the more frequently a gene is detected across multiple studies, the more important its role is. A literature search was performed to find data sets of genes and proteins that change after RSV infection. The data sets were standardized, collated into a single database, and then panned to determine which genes occurred in multiple data sets, generating a candidate gene list. This candidate gene list was validated by using both a clinical cohort and in vitro screening. We identified several genes that were frequently expressed following RSV infection with no assigned function in RSV control, including IFI27, IFIT3, IFI44L, GBP1, OAS3, IFI44, and IRF7. Drilling down into the function of these genes, we demonstrate a role in disease for the gene for interferon regulatory factor 7, which was highly ranked on the list, but not for IRF1, which was not. Thus, we have developed and validated an approach for collating published data sets into a manageable list of candidates, identifying novel targets for future analysis. IMPORTANCE Making the most of “big data” is one of the core challenges of current biology. There is a large array of heterogeneous data sets of host gene responses to infection, but these data sets do not inform us about gene function and require specialized skill sets and training for their utilization. Here we describe an approach that combines and simplifies these data sets, distilling this information into a single list of genes commonly upregulated in response to infection with RSV as a model pathogen. Many of the genes on the list have unknown functions in RSV disease. We validated the gene list with new clinical, in vitro, and in vivo data. This approach allows the rapid selection of genes of interest for further, more-detailed studies, thus reducing time and costs. Furthermore, the approach is simple to use and widely applicable to a range of diseases.

Summary: Background: Emergency admissions for infection often lack initial diagnostic certainty. COVID-19 has highlighted a need for novel diagnostic approaches to indicate likelihood of viral infection in a pandemic setting. We aimed to derive and validate a blood transcriptional signature to detect viral infections, including COVID-19, among adults with suspected infection who presented to the emergency department. Methods: Individuals (aged ≥18 years) presenting with suspected infection to an emergency department at a major teaching hospital in the UK were prospectively recruited as part of the Bioresource in Adult Infectious Diseases (BioAID) discovery cohort. Whole-blood RNA sequencing was done on samples from participants with subsequently confirmed viral, bacterial, or no infection diagnoses. Differentially expressed host genes that met additional filtering criteria were subjected to feature selection to derive the most parsimonious discriminating signature. We validated the signature via RT-qPCR in a prospective validation cohort of participants who presented to an emergency department with undifferentiated fever, and a second case-control validation cohort of emergency department participants with PCR-positive COVID-19 or bacterial infection. We assessed signature performance by calculating the area under receiver operating characteristic curves (AUROCs), sensitivities, and specificities. Findings: A three-gene transcript signature, comprising HERC6, IGF1R, and NAGK, was derived from the discovery cohort of 56 participants with bacterial infections and 27 with viral infections. In the validation cohort of 200 participants, the signature differentiated bacterial from viral infections with an AUROC of 0·976 (95% CI 0·919−1·000), sensitivity of 97·3% (85·8−99·9), and specificity of 100% (63·1−100). The AUROC for C-reactive protein (CRP) was 0·833 (0·694−0·944) and for leukocyte count was 0·938 (0·840−0·986). The signature achieved higher net benefit in decision curve analysis than either CRP or leukocyte count for discriminating viral infections from all other infections. In the second validation analysis, which included SARS-CoV-2-positive participants, the signature discriminated 35 bacterial infections from 34 SARS-CoV-2-positive COVID-19 infections with AUROC of 0·953 (0·893−0·992), sensitivity 88·6%, and specificity of 94·1%. Interpretation: This novel three-gene signature discriminates viral infections, including COVID-19, from other emergency infection presentations in adults, outperforming both leukocyte count and CRP, thus potentially providing substantial clinical utility in managing acute presentations with infection. Funding: National Institute for Health Research, Medical Research Council, Wellcome Trust, and EU-FP7.

Abstract Objectives Complement deficiencies are difficult to diagnose because of the variability of symptoms and the complexity of the diagnostic process. Here, we applied a novel ‘complementomics’ approach to study the impact of various complement deficiencies on circulating complement levels. Methods Using a quantitative multiplex mass spectrometry assay, we analysed 44 peptides to profile 34 complement proteins simultaneously in 40 healthy controls and 83 individuals with a diagnosed deficiency or a potential pathogenic variant in 14 different complement proteins. Results Apart from confirming near or total absence of the respective protein in plasma of complement‐deficient patients, this mass spectrometry‐based profiling method led to the identification of additional deficiencies. In many cases, partial depletion of the pathway up‐ and/or downstream of the absent protein was measured. This was especially found in patients deficient for complement inhibitors, such as angioedema patients with a C1‐inhibitor deficiency. The added value of complementomics was shown in three patients with poorly defined complement deficiencies. Conclusion Our study shows the potential clinical utility of profiling circulating complement proteins as a comprehensive read‐out of various complement deficiencies. Particularly, our approach provides insight into the intricate interplay between complement proteins due to functional coupling, which contributes to the better understanding of the various disease phenotypes and improvement of care for patients with complement‐mediated diseases.