Diagnosing enterovirus meningitis via blood transcriptomics: an alternative for lumbar puncture?

Use of transcriptomics for diagnosis of infections and sepsis in children: A narrative review

AIM

The aim of this review was to summarise the most recent evidence about the use of omics-based techniques as an instrument for a more rapid and accurate characterisation of respiratory tract infections, neurological infections and sepsis in paediatrics.

METHODS

We performed a narrative review using PubMed and a set of inclusion criteria: English language articles, clinical trials, meta-analysis and reviews including only paediatric population inherited to this topic in the last 15 years.

RESULTS

The examined studies suggest that host gene expression signatures are an effective method to characterise the different types of infections, to distinguish infection from colonisation and, in some cases, to assess the severity of the disease in children.

CONCLUSIONS

'Omics-based techniques' may help to define the aetiology of infections in paediatrics, representing a useful tool to choose the most appropriate therapies and limit antibiotic resistance.

Whole blood stimulation as a tool for studying the human immune system

The human immune system is best accessible via tissues and organs not requiring major surgical intervention, such as blood. In many circumstances, circulating immune cells correlate with an individual's health state and give insight into physiological and pathophysiological processes. Stimulating whole blood ex vivo is a powerful tool to investigate immune responses. In the context of clinical research, the applications of whole blood stimulation include host immunity, disease characterization, diagnosis, treatment, and drug development. Here, we summarize different setups and readouts of whole blood assays and discuss applications for preclinical research and clinical practice. Finally, we propose combining whole blood stimulation with high-throughput technologies, such as single-cell RNA-sequencing, to comprehensively analyze the human immune system for the identification of biomarkers, therapeutic interventions as well as companion diagnostics.

Current methods for microbiological diagnosis of acute central nervous system infections

The incidence of infections affecting the central nervous system has increased in recent years, making neuroinfections a current global health problem. The central nervous system is quite well protected from the external and internal environments, although it is susceptible to infection by a wide variety of pathogens. The etiological diversity further complicates the management of such infections because it is important to identify correctly the specific cause in order to choose the most appropriate antimicrobial therapy. Diagnosis is made not only based on clinical and epidemiological data but also on the results of clinical laboratory and microbiological examination of cerebrospinal fluid. This article aims to review current microbiological methods in the diagnosis of acute central nervous system infections and help healthcare providers to recognize their advantages and limitations in order to manage their patients appropriately.

Blood transcriptomics to facilitate diagnosis and stratification in pediatric rheumatic diseases - a proof of concept study

BACKGROUND

Transcriptome profiling of blood cells is an efficient tool to study the gene expression signatures of rheumatic diseases. This study aims to improve the early diagnosis of pediatric rheumatic diseases by investigating patients' blood gene expression and applying machine learning on the transcriptome data to develop predictive models.

METHODS

RNA sequencing was performed on whole blood collected from children with rheumatic diseases. Random Forest classification models were developed based on the transcriptome data of 48 rheumatic patients, 46 children with viral infection, and 35 controls to classify different disease groups. The performance of these classifiers was evaluated by leave-one-out cross-validation. Analyses of differentially expressed genes (DEG), gene ontology (GO), and interferon-stimulated gene (ISG) score were also conducted.

RESULTS

Our first classifier could differentiate pediatric rheumatic patients from controls and infection cases with high area-under-the-curve (AUC) values (AUC = 0.8 ± 0.1 and 0.7 ± 0.1, respectively). Three other classifiers could distinguish chronic recurrent multifocal osteomyelitis (CRMO), juvenile idiopathic arthritis (JIA), and interferonopathies (IFN) from control and infection cases with AUC ≥ 0.8. DEG and GO analyses reveal that the pathophysiology of CRMO, IFN, and JIA involves innate immune responses including myeloid leukocyte and granulocyte activation, neutrophil activation and degranulation. IFN is specifically mediated by antibacterial and antifungal defense responses, CRMO by cellular response to cytokine, and JIA by cellular response to chemical stimulus. IFN patients particularly had the highest mean ISG score among all disease groups.

CONCLUSION

Our data show that blood transcriptomics combined with machine learning is a promising diagnostic tool for pediatric rheumatic diseases and may assist physicians in making data-driven and patient-specific decisions in clinical practice.

Systematic review of host genomic biomarkers of invasive bacterial disease: Distinguishing bacterial from non-bacterial causes of acute febrile illness

Background

Infectious diseases play a significant role in the global burden of disease. The gold standard for the diagnosis of bacterial infection, bacterial culture, can lead to diagnostic delays and inappropriate antibiotic use. The advent of high- throughput technologies has led to the discovery of host-based genomic biomarkers of infection, capable of differentiating bacterial from other causes of infection, but few have achieved validation for use in a clinical setting.

Methods

A systematic review was performed. PubMed/Ovid Medline, Ovid Embase and Scopus databases were searched for relevant studies from inception up to 30/03/2022 with forward and backward citation searching of key references. Studies assessing the diagnostic performance of human host genomic biomarkers of bacterial infection were included. Study selection and assessment of quality were conducted by two independent reviewers. A meta-analysis was undertaken using a diagnostic random-effects model. The review was registered with PROSPERO (ID: CRD42021208462).

Findings

Seventy-two studies evaluating the performance of 116 biomarkers in 16,216 patients were included. Forty-six studies examined TB-specific biomarker performance and twenty-four studies assessed biomarker performance in a paediatric population. The results of pooled sensitivity, specificity, negative and positive likelihood ratio, and diagnostic odds ratio of genomic biomarkers of bacterial infection were 0.80 (95% CI 0.78 to 0.82), 0.86 (95% CI 0.84 to 0.88), 0.18 (95% CI 0.16 to 0.21), 5.5 (95% CI 4.9 to 6.3), 30.1 (95% CI 24 to 37), respectively. Significant between-study heterogeneity (I² 77%) was present.

Interpretation

Host derived genomic biomarkers show significant potential for clinical use as diagnostic tests of bacterial infection however, further validation and attention to test platform is warranted before clinical implementation can be achieved.

Funding

No funding received.

Artificial Intelligence in Differential Diagnostics of Meningitis: A Nationwide Study

Differential diagnosis between bacterial and viral meningitis is crucial. In our study, to differentiate bacterial vs. viral meningitis, three machine learning (ML) algorithms (multiple logistic regression (MLR), random forest (RF), and naïve-Bayes (NB)) were applied for the two age groups (0-14 and >14 years) of patients with meningitis by both conventional (culture) and molecular (PCR) methods. Cerebrospinal fluid (CSF) neutrophils, CSF lymphocytes, neutrophil-to-lymphocyte ratio (NLR), blood albumin, blood C-reactive protein (CRP), glucose, blood soluble urokinase-type plasminogen activator receptor (suPAR), and CSF lymphocytes-to-blood CRP ratio (LCR) were used as predictors for the ML algorithms. The performance of the ML algorithms was evaluated through a cross-validation procedure, and optimal predictions of the type of meningitis were above 95% for viral and 78% for bacterial meningitis. Overall, MLR and RF yielded the best performance when using CSF neutrophils, CSF lymphocytes, NLR, albumin, glucose, gender, and CRP. Also, our results reconfirm the high diagnostic accuracy of NLR in the differential diagnosis between bacterial and viral meningitis.

Disease severity-specific neutrophil signatures in blood transcriptomes stratify COVID-19 patients

BACKGROUND

The SARS-CoV-2 pandemic is currently leading to increasing numbers of COVID-19 patients all over the world. Clinical presentations range from asymptomatic, mild respiratory tract infection, to severe cases with acute respiratory distress syndrome, respiratory failure, and death. Reports on a dysregulated immune system in the severe cases call for a better characterization and understanding of the changes in the immune system.

METHODS

In order to dissect COVID-19-driven immune host responses, we performed RNA-seq of whole blood cell transcriptomes and granulocyte preparations from mild and severe COVID-19 patients and analyzed the data using a combination of conventional and data-driven co-expression analysis. Additionally, publicly available data was used to show the distinction from COVID-19 to other diseases. Reverse drug target prediction was used to identify known or novel drug candidates based on finding from data-driven findings.

RESULTS

Here, we profiled whole blood transcriptomes of 39 COVID-19 patients and 10 control donors enabling a data-driven stratification based on molecular phenotype. Neutrophil activation-associated signatures were prominently enriched in severe patient groups, which was corroborated in whole blood transcriptomes from an independent second cohort of 30 as well as in granulocyte samples from a third cohort of 16 COVID-19 patients (44 samples). Comparison of COVID-19 blood transcriptomes with those of a collection of over 3100 samples derived from 12 different viral infections, inflammatory diseases, and independent control samples revealed highly specific transcriptome signatures for COVID-19. Further, stratified transcriptomes predicted patient subgroup-specific drug candidates targeting the dysregulated systemic immune response of the host.

CONCLUSIONS

Our study provides novel insights in the distinct molecular subgroups or phenotypes that are not simply explained by clinical parameters. We show that whole blood transcriptomes are extremely informative for COVID-19 since they capture granulocytes which are major drivers of disease severity.

Disease severity-specific neutrophil signatures in blood transcriptomes stratify COVID-19 patients

BACKGROUND

The SARS-CoV-2 pandemic is currently leading to increasing numbers of COVID-19 patients all over the world. Clinical presentations range from asymptomatic, mild respiratory tract infection, to severe cases with acute respiratory distress syndrome, respiratory failure, and death. Reports on a dysregulated immune system in the severe cases call for a better characterization and understanding of the changes in the immune system.

METHODS

In order to dissect COVID-19-driven immune host responses, we performed RNA-seq of whole blood cell transcriptomes and granulocyte preparations from mild and severe COVID-19 patients and analyzed the data using a combination of conventional and data-driven co-expression analysis. Additionally, publicly available data was used to show the distinction from COVID-19 to other diseases. Reverse drug target prediction was used to identify known or novel drug candidates based on finding from data-driven findings.

RESULTS

Here, we profiled whole blood transcriptomes of 39 COVID-19 patients and 10 control donors enabling a data-driven stratification based on molecular phenotype. Neutrophil activation-associated signatures were prominently enriched in severe patient groups, which was corroborated in whole blood transcriptomes from an independent second cohort of 30 as well as in granulocyte samples from a third cohort of 16 COVID-19 patients (44 samples). Comparison of COVID-19 blood transcriptomes with those of a collection of over 3100 samples derived from 12 different viral infections, inflammatory diseases, and independent control samples revealed highly specific transcriptome signatures for COVID-19. Further, stratified transcriptomes predicted patient subgroup-specific drug candidates targeting the dysregulated systemic immune response of the host.

CONCLUSIONS

Our study provides novel insights in the distinct molecular subgroups or phenotypes that are not simply explained by clinical parameters. We show that whole blood transcriptomes are extremely informative for COVID-19 since they capture granulocytes which are major drivers of disease severity.

Immune Transcriptome of Cells Infected with Enterovirus Strains Obtained from Cases of Type 1 Diabetes

Enterovirus (EV) infection of insulin-producing pancreatic beta cells is associated with type 1 diabetes (T1D), but little is known about the mechanisms that lead the virus to cause a persistent infection and, possibly, to induce beta cell autoimmunity. A cell line susceptible to most enterovirus types was infected with EV isolates from cases of T1D and, for comparison, with a replication-competent strain of coxsackievirus B3. The transcription of immune-related genes and secretion of cytokines was evaluated in infected vs. uninfected cells. Acutely infected cells showed the preserved transcription of type I interferon (IFN) pathways and the enhanced transcription/secretion of IL6, IL8, LIF, MCP1, and TGFB1. On the other hand, infection by defective EV strains obtained from diabetic subjects suppressed IFN pathways and the transcription of most cytokines, while enhancing the expression of IL8, IL18, IL32, and MCP1. IL18 and IL32 are known for their pathogenic role in autoimmune diabetes. Thus, the cytokine profile of AV3 cells infected by diabetes-derived EV strains closely matches that observed in patients at the early stages of T1D. The concordance of our results with clinically verified information reinforces the hypothesis that the immune changes observed in type 1 diabetic patients are due to a hardly noticeable virus infection.