A qPCR expression assay of IFI44L gene differentiates viral from bacterial infections in febrile children

A 5-transcript signature for discriminating viral and bacterial etiology in pediatric pneumonia

Pneumonia stands as the primary cause of death among children under five, yet current diagnosis methods often result in inadequate or unnecessary treatments. Our research seeks to address this gap by identifying host transcriptomic biomarkers in the blood of children with definitive viral and bacterial pneumonia. We performed RNA sequencing on 192 prospectively collected whole blood samples, including 38 controls and 154 pneumonia cases, uncovering a 5-transcript signature (genes FAM20A, BAG3, TDRD9, MXRA7, and KLF14) that effectively distinguishes bacterial from viral pneumonia (area under the curve (AUC): 0.95 [0.88-1.00]). Initial validation using combined definitive and probable cases yielded an AUC of 0.87 [0.77-0.97], while full validation in a new prospective cohort of 32 patients achieved an AUC of 0.92 [0.83-1.00]. This robust signature holds significant potential to enhance diagnostics accuracy for pediatric pneumonia, reducing diagnostic delays and unnecessary treatments and potentially transforming clinical practice.

Recent updates of interferon-derived myxovirus resistance protein A as a biomarker for acute viral infection

BACKGROUND

Antibiotic resistance (AMR) remains a global public health threat with a high burden in sub-Saharan countries. The overuse of antimicrobials in the clinical setting is the main factor for the spread of antibiotic resistance. Diagnostic uncertainty in differentiating between bacterial and viral infections is the major contributor to antimicrobial overuse. The available biomarkers lack specificity in guiding clinicians to make antibiotic decisions and only estimate bacterial infection.

MAIN BODY

Myxovirus resistance (Mx) proteins are a type of interferon (IFN)-inducible protein that belongs to the dynamin superfamily of large guanine triphosphates (GTPases) involved in broad antiviral responses. Myxovirus resistance protein A (MxA) is a host-derived biomarker with antiviral properties against various viruses. It is induced by IFN I and IFN III as part of the innate immune response. Its basal level is < 15 ng/ml and elevated levels are detectable 1-2 h after IFN induction and remain detectable in serum up to 10 days after viral infection. Increased levels in the blood are associated with viral infection and remain low during bacterial infections. This biomarker showed promising performance in diagnosing undifferentiated febrile patients with respiratory tract infections. In this review, we discuss the role of Mx proteins, specifically MxA, in diagnosing acute viral infections, including how they are induced and their potential as diagnostic tools.

METHODS

A comprehensive electronic search was conducted in Scopus and Medline (using the PubMed interface) regarding myxovirus resistance protein A as a biomarker for acute viral infection. In the search strategy, English language was used without date restriction. Manual search was also performed when appropriate.

CONCLUSIONS

Elevated MxA combined with other biomarkers, such as CRP and PCT, is a promising tool for identifying patients with viral infections. Therefore, incorporating MxA in the existing point of care formats help to improve the antibiotic stewardship programs and future randomized controlled trials are recommended to evaluate its utility in medical practice.

Unraveling IFI44L's biofunction in human disease

Interferon-induced protein 44-like (IFI44L) is regarded as an immune-related gene and is a member of interferon-stimulated genes (ISGs). They participate in network transduction, and its own epigenetic modifications, apoptosis, cell-matrix formation, and many other pathways in tumors, autoimmune diseases, and viral infections. The current review provides a comprehensive overview of the onset and biological mechanisms of IFI44L and its potential clinical applications in malignant tumors and non-neoplastic diseases.

SARS-CoV-2 human challenge reveals biomarkers that discriminate early and late phases of respiratory viral infections

Blood transcriptional biomarkers of acute viral infections typically reflect type 1 interferon (IFN) signalling, but it is not known whether there are biological differences in their regulation that can be leveraged for distinct translational applications. We use high frequency sampling in the SARS-CoV-2 human challenge model to show induction of IFN-stimulated gene (ISG) expression with different temporal and cellular profiles. MX1 gene expression correlates with a rapid and transient wave of ISG expression across all cell types, which may precede PCR detection of replicative infection. Another ISG, IFI27, shows a delayed but sustained response restricted to myeloid cells, attributable to gene and cell-specific epigenetic regulation. These findings are reproducible in experimental and naturally acquired infections with influenza, respiratory syncytial virus and rhinovirus. Blood MX1 expression is superior to IFI27 expression for diagnosis of early infection, as a correlate of viral load and for discrimination of virus culture positivity. Therefore, MX1 expression offers potential to stratify patients for antiviral therapy or infection control interventions. Blood IFI27 expression is superior to MX1 expression for diagnostic accuracy across the time course of symptomatic infection and thereby, offers higher diagnostic yield for respiratory virus infections that incur a delay between transmission and testing.

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.

Uncovering hub genes in sepsis through bioinformatics analysis

In-depth studies on the mechanisms of pathogenesis of sepsis and diagnostic biomarkers in the early stages may be the key to developing individualized and effective treatment strategies. This study aimed to identify sepsis-related hub genes and evaluate their diagnostic reliability. The gene expression profiles of GSE4607 and GSE131761 were obtained from the Gene Expression Omnibus. Differentially co-expressed genes between the sepsis and control groups were screened. Single-sample gene set enrichment analysis and gene set variation analysis were performed to investigate the biological functions of the hub genes. A receiver operating characteristic curve was used to evaluate diagnostic value. Datasets GSE154918 and GSE185263 were used as external validation datasets to verify the reliability of the hub genes. Four differentially co-expressed genes, FAM89A, FFAR3, G0S2, and FGF13, were extracted using a weighted gene co-expression network analysis and differential gene expression analysis methods. These 4 genes were upregulated in the sepsis group and were distinct from those in the controls. Moreover, the receiver operating characteristic curves of the 4 genes exhibited considerable diagnostic value in discriminating septic blood samples from those of the non-septic control group. The reliability and consistency of these 4 genes were externally validated. Single-sample gene set enrichment analysis and gene set variation analysis analyses indicated that the 4 hub genes were significantly correlated with the regulation of immunity and metabolism in sepsis. The identified FAM89A, FFAR3, G0S2, and FGF13 genes may help elucidate the molecular mechanisms underlying sepsis and drive the introduction of new biomarkers to advance diagnosis and treatment.

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.

Implications of Using Host Response-Based Molecular Diagnostics on the Management of Bacterial and Viral Infections: A Review

Host-based diagnostics are a rapidly evolving field that may serve as an alternative to traditional pathogen-based diagnostics for infectious diseases. Understanding the exact mechanisms underlying a host-immune response and deriving specific host-response signatures, biomarkers and gene transcripts will potentially achieve improved diagnostics that will ultimately translate to better patient outcomes. Several studies have focused on novel techniques and assays focused on immunodiagnostics. In this review, we will highlight recent publications on the current use of host-based diagnostics alone or in combination with traditional microbiological assays and their potential future implications on the diagnosis and prognostic accuracy for the patient with infectious complications. Finally, we will address the cost-effectiveness implications from a healthcare and public health perspective.

Transcript host-RNA signatures to discriminate bacterial and viral infections in febrile children

Traditional laboratory markers, such as white blood cell count, C-reactive protein, and procalcitonin, failed to discriminate viral and bacterial infections in children. The lack of an accurate diagnostic test has a negative impact on child's care, limiting the ability of early diagnosis and appropriate management of children. This, on the one hand, may lead to delayed recognition of sepsis and severe bacterial infections, which still represent the leading causes of child morbidity and mortality. On the other hand, this may lead to overuse of empiric antibiotic therapies, particularly for specific subgroups of patients, such as infants younger than 90 days of life or neutropenic patients. This approach has an adverse effect on costs, antibiotic resistance, and pediatric microbiota. Transcript host-RNA signatures are a new tool used to differentiate viral from bacterial infections by analyzing the transcriptional biosignatures of RNA in host leukocytes. In this systematic review, we evaluate the efficacy and the possible application of this new diagnostic method in febrile children, along with challenges in its implementation. Our review support the growing evidence that the application of these new tools can improve the characterization of the spectrum of bacterial and viral infections and optimize the use of antibiotics in children. IMPACT: Transcript host RNA signatures may allow to better characterize the spectrum of viral, bacterial, and inflammatory illnesses in febrile children and can be used with traditional diagnostic methods to determine if and when to start antibiotic therapy. This is the first review on the use of transcript RNA signatures in febrile children to distinguish viral from bacterial infections. Our review identified a wide variability of target populations and gold standards used to define sepsis and SBIs, limiting the generalization of our findings.

Novel Biomarkers Differentiating Viral from Bacterial Infection in Febrile Children: Future Perspectives for Management in Clinical Praxis

Differentiating viral from bacterial infections in febrile children is challenging and often leads to an unnecessary use of antibiotics. There is a great need for more accurate diagnostic tools. New molecular methods have improved the particular diagnostics of viral respiratory tract infections, but defining etiology can still be challenging, as certain viruses are frequently detected in asymptomatic children. For the detection of bacterial infections, time consuming cultures with limited sensitivity are still the gold standard. As a response to infection, the immune system elicits a cascade of events, which aims to eliminate the invading pathogen. Recent studies have focused on these host–pathogen interactions to identify pathogen-specific biomarkers (gene expression profiles), or “pathogen signatures”, as potential future diagnostic tools. Other studies have assessed combinations of traditional bacterial and viral biomarkers (C-reactive protein, interleukins, myxovirus resistance protein A, procalcitonin, tumor necrosis factor-related apoptosis-inducing ligand) to establish etiology. In this review we discuss the performance of such novel diagnostics and their potential role in clinical praxis. In conclusion, there are several promising novel biomarkers in the pipeline, but well-designed randomized controlled trials are needed to evaluate the safety of using these novel biomarkers to guide clinical decisions.

SARS-CoV-2 infection and replication in human gastric organoids

COVID-19 typically manifests as a respiratory illness, but several clinical reports have described gastrointestinal symptoms. This is particularly true in children in whom gastrointestinal symptoms are frequent and viral shedding outlasts viral clearance from the respiratory system. These observations raise the question of whether the virus can replicate within the stomach. Here we generate gastric organoids from fetal, pediatric, and adult biopsies as in vitro models of SARS-CoV-2 infection. To facilitate infection, we induce reverse polarity in the gastric organoids. We find that the pediatric and late fetal gastric organoids are susceptible to infection with SARS-CoV-2, while viral replication is significantly lower in undifferentiated organoids of early fetal and adult origin. We demonstrate that adult gastric organoids are more susceptible to infection following differentiation. We perform transcriptomic analysis to reveal a moderate innate antiviral response and a lack of differentially expressed genes belonging to the interferon family. Collectively, we show that the virus can efficiently infect the gastric epithelium, suggesting that the stomach might have an active role in fecal-oral SARS-CoV-2 transmission.

Host Blood Gene Signatures Can Detect the Progression to Severe and Cerebral Malaria

Malaria is a major international public health problem that affects millions of patients worldwide especially in sub-Saharan Africa. Although many tests have been developed to diagnose malaria infections, we still lack reliable diagnostic biomarkers for the identification of disease severity, especially in endemic areas where the diagnosis of cerebral malaria is very difficult and requires the exclusion of all other possible causes. Previous host and pathogen transcriptomic studies have not yielded homogenous results that can be harnessed into a reliable diagnostic tool. Here we utilized a multi-cohort analysis approach using machine-learning algorithms to identify blood gene signatures that can distinguish severe and cerebral malaria from moderate and non-cerebral cases. Using a Regularized Random Forest model, we identified 28-gene and 32-gene signatures that can reliably distinguish severe and cerebral malaria, respectively. We tested the specificity of both signatures against other common infectious diseases to ensure the signatures reliability and suitability as diagnostic markers. The severe and cerebral malaria gene-signatures were further integrated through k-top scoring pairs classifiers into ten and nine gene pairs that could distinguish severe and cerebral malaria, respectively. These signatures have various implications that can be utilized as blood diagnostic tools for malaria severity in endemic countries.

Gene Co-Expression Analysis Identified Preserved and Survival-Related Modules in Severe Blunt Trauma, Burns, Sepsis, and Systemic Inflammatory Response Syndrome

Background

Severe trauma and burns accompanied by sepsis are associated with high morbidity and mortality. Little is known about the transcriptional similarity between trauma, burns, sepsis, and systemic inflammatory response syndrome (SIRS). Uncovering key genes and molecular networks is critical to understanding the biology of disease. Conventional analysis of gene changes (fold change) analysis is difficult for time-serial data such as post-injury blood transcriptome.

Methods

Weighted gene co-expression network analysis (WGCNA) was applied to the trauma dataset to identify modules and hub genes. Module stability was tested by half sampling. Module preservations of burns, sepsis, and SIRS were calculated using trauma as reference. Module functional enrichment was analyzed in gProfiler server. Candidate drugs were screened using Connectivity Map based on hub genes. The modules were visualized in Cytoscape.

Results

Seventeen modules were identified. The modules were robust to the exclusion of half the sample. They were involved in lymphocyte and platelet activation, erythrocyte differentiation, cell cycle, translation, and interferon signaling. In addition, highly connected hub genes were identified in each module, such as GUCY1B1, BCL11B, HMMR, and CEACAM6. High BCL11B (M13) or low CEACAM6 (M27) expression indicates better survival prognosis in sepsis patients regardless of endotype class and age. Network preservation in burns, sepsis, and SIRS showed a general similarity between trauma and burns. M4, M5, M13, M16, M20, and M27 were significantly associated with injury time in trauma and burns. High M13 (T cell activation), low M15 (cell cycle), and low M27 (neutrophil activation) indicate better survival of sepsis patients, regardless of endotype class and age. Moreover, the modules can efficiently separate patients with different diseases. Some modules and hub genes have good prognostic performance in sepsis. Based on the hub genes of each module, six candidate drugs were screened.

Conclusion

This study first compared the gene co-expression modules in trauma, burns, sepsis, and SIRS. The identified modules are useful for disease prognosis and drug discovery. BCL11B and CEACAM6 may be promising biomarkers for sepsis risk assessment.

Blood transcriptional biomarkers of acute viral infection for detection of pre-symptomatic SARS-CoV-2 infection: a nested, case-control diagnostic accuracy study

BACKGROUND

We hypothesised that host-response biomarkers of viral infections might contribute to early identification of individuals infected with SARS-CoV-2, which is critical to breaking the chains of transmission. We aimed to evaluate the diagnostic accuracy of existing candidate whole-blood transcriptomic signatures for viral infection to predict positivity of nasopharyngeal SARS-CoV-2 PCR testing.

METHODS

We did a nested case-control diagnostic accuracy study among a prospective cohort of health-care workers (aged ≥18 years) at St Bartholomew's Hospital (London, UK) undergoing weekly blood and nasopharyngeal swab sampling for whole-blood RNA sequencing and SARS-CoV-2 PCR testing, when fit to attend work. We identified candidate blood transcriptomic signatures for viral infection through a systematic literature search. We searched MEDLINE for articles published between database inception and Oct 12, 2020, using comprehensive MeSH and keyword terms for "viral infection", "transcriptome", "biomarker", and "blood". We reconstructed signature scores in blood RNA sequencing data and evaluated their diagnostic accuracy for contemporaneous SARS-CoV-2 infection, compared with the gold standard of SARS-CoV-2 PCR testing, by quantifying the area under the receiver operating characteristic curve (AUROC), sensitivities, and specificities at a standardised Z score of at least 2 based on the distribution of signature scores in test-negative controls. We used pairwise DeLong tests compared with the most discriminating signature to identify the subset of best performing biomarkers. We evaluated associations between signature expression, viral load (using PCR cycle thresholds), and symptom status visually and using Spearman rank correlation. The primary outcome was the AUROC for discriminating between samples from participants who tested negative throughout the study (test-negative controls) and samples from participants with PCR-confirmed SARS-CoV-2 infection (test-positive participants) during their first week of PCR positivity.

FINDINGS

We identified 20 candidate blood transcriptomic signatures of viral infection from 18 studies and evaluated their accuracy among 169 blood RNA samples from 96 participants over 24 weeks. Participants were recruited between March 23 and March 31, 2020. 114 samples were from 41 participants with SARS-CoV-2 infection, and 55 samples were from 55 test-negative controls. The median age of participants was 36 years (IQR 27-47) and 69 (72%) of 96 were women. Signatures had little overlap of component genes, but were mostly correlated as components of type I interferon responses. A single blood transcript for IFI27 provided the highest accuracy for discriminating between test-negative controls and test-positive individuals at the time of their first positive SARS-CoV-2 PCR result, with AUROC of 0·95 (95% CI 0·91-0·99), sensitivity 0·84 (0·70-0·93), and specificity 0·95 (0·85-0·98) at a predefined threshold (Z score >2). The transcript performed equally well in individuals with and without symptoms. Three other candidate signatures (including two to 48 transcripts) had statistically equivalent discrimination to IFI27 (AUROCs 0·91-0·95).

INTERPRETATION

Our findings support further urgent evaluation and development of blood IFI27 transcripts as a biomarker for early phase SARS-CoV-2 infection for screening individuals at high risk of infection, such as contacts of index cases, to facilitate early case isolation and early use of antiviral treatments as they emerge.

FUNDING

Barts Charity, Wellcome Trust, and National Institute of Health Research.

Development of a 3-transcript host expression assay to differentiate between viral and bacterial infections in pigs

Indiscriminate use of antibiotics to treat infections that are of viral origin contributes to unnecessary use which potentially may induce resistance in commensal bacteria. To counteract this a number of host gene transcriptional studies have been conducted to identify genes that are differently expressed during bacterial and viral infections in humans, and thus could be used as a tool to base decisions on the use of antibiotics. In this paper, we aimed to evaluate the potential of a selection of genes that have been considered biomarkers in humans, to differentially diagnose bacterial from viral infections in the pig. First porcine PBMC were induced with six toll-like receptor (TLR) agonists (FliC, LPS, ODN 2216, Pam3CSK4, poly I:C, R848) to mimic host gene expression induced by bacterial or viral pathogens, or exposed to heat-killed Actinobacillus pleuropneumoniae or a split influenza virus. Genes that were differentially expressed between bacterial and viral inducers were further evaluated on clinical material comprising eleven healthy pigs, and six pigs infected with A. pleuropneumoniae. This comprised three virally upregulated genes (IFI44L, MxA, RSAD2) and four bacterially upregulated genes (IL-1β, IL-8, FAM89A, S100PBP). All six infected pigs could be differentially diagnosed to healthy pigs using a host gene transcription assay based on the geometric average of the bacterially induced genes IL-8 and S100PBP over that of the virally induced gene MxA.

Development and validation of a prediction model for invasive bacterial infections in febrile children at European Emergency Departments: MOFICHE, a prospective observational study

OBJECTIVES

To develop and cross-validate a multivariable clinical prediction model to identify invasive bacterial infections (IBI) and to identify patient groups who might benefit from new biomarkers.

DESIGN

Prospective observational study.

SETTING

12 emergency departments (EDs) in 8 European countries.

PATIENTS

Febrile children aged 0-18 years.

MAIN OUTCOME MEASURES

IBI, defined as bacteraemia, meningitis and bone/joint infection. We derived and cross-validated a model for IBI using variables from the Feverkidstool (clinical symptoms, C reactive protein), neurological signs, non-blanching rash and comorbidity. We assessed discrimination (area under the receiver operating curve) and diagnostic performance at different risk thresholds for IBI: sensitivity, specificity, negative and positive likelihood ratios (LRs).

RESULTS

Of 16 268 patients, 135 (0.8%) had an IBI. The discriminative ability of the model was 0.84 (95% CI 0.81 to 0.88) and 0.78 (95% CI 0.74 to 0.82) in pooled cross-validations. The model performed well for the rule-out threshold of 0.1% (sensitivity 0.97 (95% CI 0.93 to 0.99), negative LR 0.1 (95% CI 0.0 to 0.2) and for the rule-in threshold of 2.0% (specificity 0.94 (95% CI 0.94 to 0.95), positive LR 8.4 (95% CI 6.9 to 10.0)). The intermediate thresholds of 0.1%-2.0% performed poorly (ranges: sensitivity 0.59-0.93, negative LR 0.14-0.57, specificity 0.52-0.88, positive LR 1.9-4.8) and comprised 9784 patients (60%).

CONCLUSIONS

The rule-out threshold of this model has potential to reduce antibiotic treatment while the rule-in threshold could be used to target treatment in febrile children at the ED. In more than half of patients at intermediate risk, sensitive biomarkers could improve identification of IBI and potentially reduce unnecessary antibiotic prescriptions.

Future Biomarkers for Infection and Inflammation in Febrile Children

Febrile patients, suffering from an infection, inflammatory disease or autoimmunity may present with similar or overlapping clinical symptoms, which makes early diagnosis difficult. Therefore, biomarkers are needed to help physicians form a correct diagnosis and initiate the right treatment to improve patient outcomes following first presentation or admittance to hospital. Here, we review the landscape of novel biomarkers and approaches of biomarker discovery. We first discuss the use of current plasma parameters and whole blood biomarkers, including results obtained by RNA profiling and mass spectrometry, to discriminate between bacterial and viral infections. Next we expand upon the use of biomarkers to distinguish between infectious and non-infectious disease. Finally, we discuss the strengths as well as the potential pitfalls of current developments. We conclude that the use of combination tests, using either protein markers or transcriptomic analysis, have advanced considerably and should be further explored to improve current diagnostics regarding febrile infections and inflammation. If proven effective when combined, these biomarker signatures will greatly accelerate early and tailored treatment decisions.

Rapid and simple single-chamber nucleic acid detection system prepared through nature-inspired surface engineering

Background: Nucleic acid (NA)-based diagnostics enable a rapid response to various diseases, but current techniques often require multiple labor-intensive steps, which is a major obstacle to successful translation to a clinical setting.

Methods: We report on a surface-engineered single-chamber device for NA extraction and in situ amplification without sample transfer. Our system has two reaction sites: a NA extraction chamber whose surface is patterned with micropillars and a reaction chamber filled with reagents for in situ polymerase-based NA amplification. These two sites are integrated in a single microfluidic device; we applied plastic injection molding for cost-effective, mass-production of the designed device. The micropillars were chemically activated via a nature-inspired silica coating to possess a specific affinity to NA.

Results: As a proof-of-concept, a colorimetric pH indicator was coupled to the on-chip analysis of NA for the rapid and convenient detection of pathogens. The NA enrichment efficiency was dependent on the lysate incubation time, as diffusion controls the NA contact with the engineered surface. We could detect down to 1×10³ CFU by the naked eye within one hour of the total assay time.

Conclusion: We anticipate that the surface engineering technique for NA enrichment could be easily integrated as a part of various types of microfluidic chips for rapid and convenient nucleic acid-based diagnostics.

Identification of a Minimal 3-Transcript Signature to Differentiate Viral from Bacterial Infection from Best Genome-Wide Host RNA Biomarkers: A Multi-Cohort Analysis

The fight against the spread of antibiotic resistance is one of the most important challenges facing health systems worldwide. Given the limitations of current diagnostic methods, the development of fast and accurate tests for the diagnosis of viral and bacterial infections would improve patient management and treatment, as well as contribute to reducing antibiotic misuse in clinical settings. In this scenario, analysis of host transcriptomics constitutes a promising target to develop new diagnostic tests based on the host-specific response to infections. We carried out a multi-cohort meta-analysis of blood transcriptomic data available in public databases, including 11 different studies and 1209 samples from virus- (n = 695) and bacteria- (n = 514) infected patients. We applied a Parallel Regularized Regression Model Search (PReMS) on a set of previously reported genes that distinguished viral from bacterial infection to find a minimum gene expression bio-signature. This strategy allowed us to detect three genes, namely BAFT, ISG15 and DNMT1, that clearly differentiate groups of infection with high accuracy (training set: area under the curve (AUC) 0.86 (sensitivity: 0.81; specificity: 0.87); testing set: AUC 0.87 (sensitivity: 0.82; specificity: 0.86)). BAFT and ISG15 are involved in processes related to immune response, while DNMT1 is related to the preservation of methylation patterns, and its expression is modulated by pathogen infections. We successfully tested this three-transcript signature in the 11 independent studies, demonstrating its high performance under different scenarios. The main advantage of this three-gene signature is the low number of genes needed to differentiate both groups of patient categories.

Transcriptome-wide association study reveals two genes that influence mismatch negativity

Mismatch negativity (MMN) is a differential electrophysiological response measuring cortical adaptability to unpredictable stimuli. MMN is consistently attenuated in patients with psychosis. However, the genetics of MMN are uncharted, limiting the validation of MMN as a psychosis endophenotype. Here, we perform a transcriptome-wide association study of 728 individuals, which reveals 2 genes (FAM89A and ENGASE) whose expression in cortical tissues is associated with MMN. Enrichment analyses of neurodevelopmental expression signatures show that genes associated with MMN tend to be overexpressed in the frontal cortex during prenatal development but are significantly downregulated in adulthood. Endophenotype ranking value calculations comparing MMN and three other candidate psychosis endophenotypes (lateral ventricular volume and two auditory-verbal learning measures) find MMN to be considerably superior. These results yield promising insights into sensory processing in the cortex and endorse the notion of MMN as a psychosis endophenotype.

TIPICO X: report of the 10th interactive infectious disease workshop on infectious diseases and vaccines

TIPICO is an expert meeting and workshop that aims to provide the most recent evidence in the field of infectious diseases and vaccination. The 10th Interactive Infectious Disease TIPICO workshop took place in Santiago de Compostela, Spain, on November 21-22, 2019. Cutting-edge advances in vaccination against respiratory syncytial virus, Streptococcus pneumoniae, rotavirus, human papillomavirus, Neisseria meningitidis, influenza virus, and Salmonella Typhi were discussed. Furthermore, heterologous vaccine effects were updated, including the use of Bacillus Calmette-Guérin (BCG) vaccine as potential treatment for type 1 diabetes. Finally, the workshop also included presentations and discussion on emergent virus and zoonoses, vaccine resilience, building and sustaining confidence in vaccination, approaches to vaccine decision-making, pros and cons of compulsory vaccination, the latest advances in decoding infectious diseases by RNA gene signatures, and the application of big data approaches.

Host Transcriptomic Response Following Administration of Rotavirus Vaccine in Infants' Mimics Wild Type Infection

Background

Rotavirus (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.

Methods

We compared the transcriptome of children affected by community-acquired RV infection and children immunized with a live attenuated RV vaccine (RotaTeq®).

Results

RV 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.

Discussion

Our 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.

Differential Markers of Bacterial and Viral Infections in Children for Point-of-Care Testing

Children suffering from infectious diseases, both bacterial and viral, are often treated with empirical antibiotics. Keeping in mind both the menace of microorganisms and antibiotic toxicity, it is imperative to develop point-of-care testing (POCT) to discriminate bacterial from viral infections, and to define indications for antibiotic treatment. This article reviews potential protein biomarkers and host-derived gene expression signatures for differentiating between bacterial and viral infections in children, and focuses on emerging multiplex POCT devices for the simultaneous detection of sets of protein biomarkers or streamlined gene expression signatures that may provide rapid and cost-effective pathogen-discriminating tools.

Bacteria and viruses activate or inhibit different signaling pathways in the cells they infect, and further give rise to different host transcriptional signatures as well as to unique protein biomarkers.

Many of the newly evaluated protein biomarkers, especially in combination, have better discriminative value for distinguishing between bacterial and viral infections than the biomarkers that are currently used for examining infections in children.

The transcriptomes of children undergo remarkable changes when they are infected by different types of bacteria and viruses. Approaches based on host-derived DNA/RNA signatures can accurately discriminate bacterial from viral infections.

Emerging multiplex POCT techniques allow simultaneous testing of protein- or gene-based biomarkers in an outpatient setting.

Antibiotic utilization in hospitalized children under 2 years of age with influenza or respiratory syncytial virus infection - a comparative, retrospective analysis

BACKGROUND

Infections due to Respiratory Syncytial Virus (RSV) and Influenza virus (FLU) are leading causes of hospitalization in young children. Yet, there is little data on factors associated with antibiotic use in these patients.

METHODS

We conducted a retrospective, single-center study of all patients below 2 years of age hospitalized between 2014 and 2018. We compared children with RSV infection to children with FLU infection analyzing clinical characteristics and factors contributing to an increased rate of antimicrobial utilization.

RESULTS

RSV infection was diagnosed in 476/573 (83.1%), FLU in 95/573 (16.6%), and RSV-FLU-co-infection in 2/573 (0.3%) patients. Median age was lower for RSV compared to FLU (4 vs. 12 months; p < 0.0001). Children with RSV had longer hospitalization (5 vs. 4 days; p = 0.0023) and needed oxygen more frequently (314/476 vs. 23/95; p < 0.0001) than FLU patients. There was no significant difference in the overall antibiotic utilization between RSV and FLU patients (136/476 vs. 21/95; p = 0.2107). Logistic regression analyses revealed that septic appearance on admission (odds ratio [OR] 8.95, 95% confidence interval [CI] 1.5-54.1), acute otitis media (OR 4.5, 95% CI 2.1-9.4), a longer oxygen therapy (OR 1.40; 95% CI 1.13-1.74) and a higher C-reactive protein (CRP) (OR 1.7, 95% CI 1.5-2.0) were significantly associated with antibiotic use in both groups, but not age or pneumonia.

CONCLUSIONS

In our cohort, the rate of antibiotic utilization was comparable between RSV and FLU patients, while for both groups distinct clinical presentation and a high CRP value were associated with higher antibiotic use.

The Feasibility of Host Transcriptome Profiling as a Diagnostic Tool for Microbial Etiology in Childhood Cancer Patients with Febrile Neutropenia

Infection is a common and serious complication of cancer treatment in children that often presents as febrile neutropenia (FN). Gene-expression profiling techniques can reveal transcriptional signatures that discriminate between viral, bacterial and asymptomatic infections in otherwise healthy children. Here, we examined whether gene-expression profiling was feasible in children with FN who were undergoing cancer treatment. The blood transcriptome of the children (n = 63) was investigated at time of FN diagnosed as viral, bacterial, co-infection or unknown etiology, respectively, and compared to control samples derived from 12 of the patients following the FN episode. RNA sequencing was successful in 43 (68%) of the FN episodes. Only two genes were significantly differentially expressed in the bacterial versus the control group. Significantly up-regulated genes in patients with the other three etiologies versus the control group were enriched with cellular processes related to proliferation and cellular stress response, with no clear enrichment with innate responses to pathogens. Among the significantly down-regulated genes, a few clustered into pathways connected to responses to infection. In the present study of children during cancer treatment, the blood transcriptome was not suitable for determining the etiology of FN because of too few circulating immune cells for reliable gene expression analysis.

Associations Between IFI44L Gene Variants and Rates of Respiratory Tract Infections During Early Childhood

Background

Genetic heterogeneity in type I interferon (IFN)–related gene IFI44L may account for variable susceptibility to respiratory tract infections (RTIs) in children.

Methods

In 2 prospective, population-based birth cohorts, the STEPS Study and the FinnBrain Birth Cohort Study, IFI44L genotypes for rs273259 and rs1333969 were determined in relation to the development of RTIs until 1 or 2 years of age, respectively. At age 3 months, whole-blood transcriptional profiles were analyzed and nasal samples were tested for respiratory viruses in a subset of children.

Results

In the STEPS Study (n = 1135), IFI44L minor/minor gene variants were associated with lower rates of acute otitis media episodes (adjusted incidence rate ratio, 0.77 [95% confidence interval, .61–.96] for rs273259 and 0.74 [.55–.99] for rs1333969) and courses of antibiotics for RTIs (0.76 [.62–.95] and 0.73 [.56–.97], respectively. In the FinnBrain cohort (n = 971), IFI44L variants were associated with lower rates of RTIs and courses of antibiotics for RTIs. In respiratory virus–positive 3-month-old children, IFI44L gene variants were associated with decreased expression levels of IFI44L and several other IFN-related genes.

Conclusions

Variant forms of IFI44L gene were protective against early-childhood RTIs or acute otitis media, and they attenuated IFN pathway activation by respiratory viruses.

RNA-Seq Data-Mining Allows the Discovery of Two Long Non-Coding RNA Biomarkers of Viral Infection in Humans

There is a growing interest in unraveling gene expression mechanisms leading to viral host invasion and infection progression. Current findings reveal that long non-coding RNAs (lncRNAs) are implicated in the regulation of the immune system by influencing gene expression through a wide range of mechanisms. By mining whole-transcriptome shotgun sequencing (RNA-seq) data using machine learning approaches, we detected two lncRNAs (ENSG00000254680 and ENSG00000273149) that are downregulated in a wide range of viral infections and different cell types, including blood monocluclear cells, umbilical vein endothelial cells, and dermal fibroblasts. The efficiency of these two lncRNAs was positively validated in different viral phenotypic scenarios. These two lncRNAs showed a strong downregulation in virus-infected patients when compared to healthy control transcriptomes, indicating that these biomarkers are promising targets for infection diagnosis. To the best of our knowledge, this is the very first study using host lncRNAs biomarkers for the diagnosis of human viral infections.

A Meta-Analysis of Multiple Whole Blood Gene Expression Data Unveils a Diagnostic Host-Response Transcript Signature for Respiratory Syncytial Virus

Respiratory syncytial virus (RSV) is one of the major causes of acute lower respiratory tract infection worldwide. The absence of a commercial vaccine and the limited success of current therapeutic strategies against RSV make further research necessary. We used a multi-cohort analysis approach to investigate host transcriptomic biomarkers and shed further light on the molecular mechanism underlying RSV-host interactions. We meta-analyzed seven transcriptome microarray studies from the public Gene Expression Omnibus (GEO) repository containing a total of 922 samples, including RSV, healthy controls, coronaviruses, enteroviruses, influenzas, rhinoviruses, and coinfections, from both adult and pediatric patients. We identified > 1500 genes differentially expressed when comparing the transcriptomes of RSV-infected patients against healthy controls. Functional enrichment analysis showed several pathways significantly altered, including immunologic response mediated by RSV infection, pattern recognition receptors, cell cycle, and olfactory signaling. In addition, we identified a minimal 17-transcript host signature specific for RSV infection by comparing transcriptomic profiles against other respiratory viruses. These multi-genic signatures might help to investigate future drug targets against RSV infection.