Gene expression profiles in febrile children with defined viral and bacterial infection

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.

HHV-6B detection and host gene expression implicate HHV-6B as pulmonary pathogen after hematopoietic cell transplant

Limited understanding of the immunopathogenesis of human herpesvirus 6B (HHV-6B) has prevented its acceptance as a pulmonary pathogen after hematopoietic cell transplant (HCT). In this prospective multicenter study of patients undergoing bronchoalveolar lavage (BAL) for pneumonia after allogeneic HCT, we test blood and BAL fluid (BALF) for HHV-6B DNA and mRNA transcripts associated with lytic infection and perform RNA-seq on paired blood. Among 116 participants, HHV-6B DNA is detected in 37% of BALs, 49% of which also have HHV-6B mRNA detection. We establish HHV-6B DNA viral load thresholds in BALF that are highly predictive of HHV-6B mRNA detection and associated with increased risk for overall mortality and death from respiratory failure. Participants with HHV-6B DNA in BALF exhibit distinct host gene expression signatures, notable for enriched interferon signaling pathways in participants clinically diagnosed with idiopathic pneumonia. These data implicate HHV-6B as a pulmonary pathogen after allogeneic HCT.

Diagnosis of childhood febrile illness using a multi-class blood RNA molecular signature

BACKGROUND

Appropriate treatment and management of children presenting with fever depend on accurate and timely diagnosis, but current diagnostic tests lack sensitivity and specificity and are frequently too slow to inform initial treatment. As an alternative to pathogen detection, host gene expression signatures in blood have shown promise in discriminating several infectious and inflammatory diseases in a dichotomous manner. However, differential diagnosis requires simultaneous consideration of multiple diseases. Here, we show that diverse infectious and inflammatory diseases can be discriminated by the expression levels of a single panel of genes in blood.

METHODS

A multi-class supervised machine-learning approach, incorporating clinical consequence of misdiagnosis as a "cost" weighting, was applied to a whole-blood transcriptomic microarray dataset, incorporating 12 publicly available datasets, including 1,212 children with 18 infectious or inflammatory diseases. The transcriptional panel identified was further validated in a new RNA sequencing dataset comprising 411 febrile children.

FINDINGS

We identified 161 transcripts that classified patients into 18 disease categories, reflecting individual causative pathogen and specific disease, as well as reliable prediction of broad classes comprising bacterial infection, viral infection, malaria, tuberculosis, or inflammatory disease. The transcriptional panel was validated in an independent cohort and benchmarked against existing dichotomous RNA signatures.

CONCLUSIONS

Our data suggest that classification of febrile illness can be achieved with a single blood sample and opens the way for a new approach for clinical diagnosis.

FUNDING

European Union's Seventh Framework no. 279185; Horizon2020 no. 668303 PERFORM; Wellcome Trust (206508/Z/17/Z); Medical Research Foundation (MRF-160-0008-ELP-KAFO-C0801); NIHR Imperial BRC.

The Applicability of a 2-Transcript Signature to Identify Bacterial Infections in Children with Febrile Neutropenia

Febrile neutropenia is a common complication during chemotherapy in paediatric cancer care. In this setting, clinical features and current diagnostic tests do not reliably distinguish between bacterial and viral infections. Children with cancer (n = 63) presenting with fever and neutropenia were recruited for extensive microbiological and blood RNA sampling. RNA sequencing was successful in 43 cases of febrile neutropenia. These were classified as having probable bacterial infection (n = 17), probable viral infection (n = 13) and fever of unknown origin (n = 13) based on microbiological defined infections and CRP cut-off levels. RNA expression data with focus on the 2-transcript signature (FAM89A and IFI44L), earlier shown to identify bacterial infections with high specificity and sensitivity, was implemented as a disease risk score. The median disease risk score was higher in the probable bacterial infection group, -0.695 (max 2.795; min -5.478) compared to the probable viral infection group -3.327 (max 0.218; min -7.861), which in ROC analysis corresponded to a sensitivity of 0.88 and specificity of 0.54 with an AUC of 0.80. To further characterise the immune signature, analysis of significantly expressed genes and pathways was performed and upregulation of genes associated to antibacterial responses was present in the group classified as probable bacterial infection. Our results suggest that the 2-transcript signature may have a potential use as a diagnostic tool to identify bacterial infections in immunosuppressed children with febrile neutropenia.

Novel Host Response-Based Diagnostics to Differentiate the Etiology of Fever in Patients Presenting to the Emergency Department

Fever is a common presentation to urgent-care services and is linked to multiple disease processes. To rapidly determine the etiology of fever, improved diagnostic modalities are necessary. This prospective study of 100 hospitalized febrile patients included both positive (FP) and negative (FN) subjects in terms of infection status and 22 healthy controls (HC). We evaluated the performance of a novel PCR-based assay measuring five host mRNA transcripts directly from whole blood to differentiate infectious versus non-infectious febrile syndromes as compared to traditional pathogen-based microbiology results. The FP and FN groups observed a robust network structure with a significant correlation between the five genes. There were statistically significant associations between positive infection status and four of the five genes: IRF-9 (OR = 1.750, 95% CI = 1.16-2.638), ITGAM (OR = 1.533, 95% CI = 1.047-2.244), PSTPIP2 (OR = 2.191, 95% CI = 1.293-3.711), and RUNX1 (OR = 1.974, 95% CI = 1.069-3.646). We developed a classifier model to classify study participants based on these five genes and other variables of interest to assess the discriminatory power of the genes. The classifier model correctly classified more than 80% of the participants into their respective groups, i.e., FP or FN. The GeneXpert prototype holds promise for guiding rapid clinical decision-making, reducing healthcare costs, and improving outcomes in undifferentiated febrile patients presenting for urgent evaluation.

Immune signatures, testing, and management of febrile neutropenia in pediatric cancer patients

INTRODUCTION

Infectious complications, particularly invasive bacterial and fungal infections, are still a major cause of morbidity in pediatric cancer patients and are associated with significant mortality. Over the last few years, there has been much effort in defining risk groups to tailor antimicrobial therapy, and in establishing pediatric-specific guidelines for antimicrobial strategies.

AREAS COVERED

This review provides a critical overview of defining risk groups for infection, diagnostic work-up, antimicrobial prophylaxis, empirical therapy, and treatment of established infections.

EXPERT OPINION

To date, no generalizable risk prediction model has been established for pediatric cancer patients. There is growing interest in defining the impact of the individual genetic background on infectious complications. New diagnostic tools have been developed over the last few years, but they need to be validated in pediatric cancer patients. International, pediatric-specific guidelines for antimicrobial prophylaxis, empirical therapy, and treatment of established infections have recently been published and will harmonize antimicrobial strategies in the future.

Utility of a three-gene transcriptomic signature in the diagnosis of tuberculosis in a low-endemic hospital setting

BACKGROUND

Host transcriptomic blood signatures have demonstrated diagnostic potential for tuberculosis (TB), requiring further validation across different geographical settings. Discriminating TB from other diseases with similar clinical manifestations is crucial for the development of an accurate immunodiagnostic tool. In this exploratory cohort study, we evaluated the performance of potential blood-based transcriptomic signatures in distinguishing TB disease from non-TB lower respiratory tract infections in hospitalised patients in a TB low-endemic country.

METHOD

Quantitative real-time polymerase chain reaction qPCR) was used to evaluate 26 previously published genes in blood from 31 patients (14 TB and 17 lower respiratory tract infection cases) admitted to Oslo University Hospital in Norway. The diagnostic accuracies of differentially expressed genes were determined by receiver operating characteristic curves.

RESULTS

A significant difference (p < .01) in the age distribution was observed between patients with TB (mean age, 40 ± 15 years) and lower respiratory tract infection (mean age 59 ± 12 years). Following adjustment for age, ETV7, GBP1, GBP5, P2RY14 and BLK were significantly differentially expressed between patients with TB and those with LRI. A general discriminant analysis generated a three-gene signature (BAFT2, ETV7 and CD1C), which diagnosed TB with an area under the receiver operating characteristic curve (AUC) of 0.86 (95% CI, 0.69 - 1.00), sensitivity of 69.23% (95% CI, 38.57%-90.91%) and specificity of 94.12% (95% CI, 71.31%-99.85%).

CONCLUSION

The three-genes signature may have potential to improve diagnosis of TB in a hospitalised low-burden setting. However, the influence of confounding variables or covariates such as age requires further evaluation in larger studies.

Benchmarking transcriptional host response signatures for infection diagnosis

Identification of host transcriptional response signatures has emerged as a new paradigm for infection diagnosis. For clinical applications, signatures must robustly detect the pathogen of interest without cross-reacting with unintended conditions. To evaluate the performance of infectious disease signatures, we developed a framework that includes a compendium of 17,105 transcriptional profiles capturing infectious and non-infectious conditions and a standardized methodology to assess robustness and cross-reactivity. Applied to 30 published signatures of infection, the analysis showed that signatures were generally robust in detecting viral and bacterial infections in independent data. Asymptomatic and chronic infections were also detectable, albeit with decreased performance. However, many signatures were cross-reactive with unintended infections and aging. In general, we found robustness and cross-reactivity to be conflicting objectives, and we identified signature properties associated with this trade-off. The data compendium and evaluation framework developed here provide a foundation for the development of signatures for clinical application. A record of this paper's transparent peer review process is included in the supplemental information.

A robust host-response-based signature distinguishes bacterial and viral infections across diverse global populations

Limited sensitivity and specificity of current diagnostics lead to the erroneous prescription of antibiotics. Host-response-based diagnostics could address these challenges. However, using 4,200 samples across 69 blood transcriptome datasets from 20 countries from patients with bacterial or viral infections representing a broad spectrum of biological, clinical, and technical heterogeneity, we show current host-response-based gene signatures have lower accuracy to distinguish intracellular bacterial infections from viral infections than extracellular bacterial infections. Using these 69 datasets, we identify an 8-gene signature to distinguish intracellular or extracellular bacterial infections from viral infections with an area under the receiver operating characteristic curve (AUROC) > 0.91 (85.9% specificity and 90.2% sensitivity). In prospective cohorts from Nepal and Laos, the 8-gene classifier distinguished bacterial infections from viral infections with an AUROC of 0.94 (87.9% specificity and 91% sensitivity). The 8-gene signature meets the target product profile proposed by the World Health Organization and others for distinguishing bacterial and viral infections.

An automated DNA computing platform for rapid etiological diagnostics

Rapid and accurate classification of the etiology for acute respiratory illness not only helps establish timely therapeutic plans but also prevents inappropriate use of antibiotics. Host gene expression patterns in peripheral blood can discriminate bacterial from viral causes of acute respiratory infection (ARI) but suffer from long turnaround time, as well as high cost resulting from the measurement methods of microarrays and next-generation sequencing. Here, we developed an automated DNA computing-based platform that can implement an in silico trained classification model at the molecular level with seven different mRNA expression patterns for accurate diagnosis of ARI etiology in 4 hours. By integrating sample loading, marker amplification, classifier implementation, and results reporting into one platform, we obtained a diagnostic accuracy of 87% in 80 clinical samples without the aid of computer and laboratory technicians. This platform creates opportunities toward an accurate, rapid, low-cost, and automated diagnosis of disease etiology in emergency departments or point-of-care clinics.

Staphylococcus aureus induces a muted host response in human blood that blunts the recruitment of neutrophils

Staphylococcus aureus is an opportunistic pathogen and chief among bloodstream-infecting bacteria. S. aureus produces an array of human-specific virulence factors that may contribute to immune suppression. Here, we defined the response of primary human phagocytes following infection with S. aureus using RNA-sequencing (RNA-Seq). We found that the overall transcriptional response to S. aureus was weak both in the number of genes and in the magnitude of response. Using an ex vivo bacteremia model with fresh human blood, we uncovered that infection with S. aureus resulted in the down-regulation of genes related to innate immune response and cytokine and chemokine signaling. This muted transcriptional response was conserved across diverse S. aureus clones but absent in blood exposed to heat-killed S. aureus or blood infected with the less virulent staphylococcal species Staphylococcus epidermidis. Notably, this signature was also present in patients with S. aureus bacteremia. We identified the master regulator S. aureus exoprotein expression (SaeRS) and the SaeRS-regulated pore-forming toxins as key mediators of the transcriptional suppression. The S. aureus-mediated suppression of chemokine and cytokine transcription was reflected by circulating protein levels in the plasma. Wild-type S. aureus elicited a soluble milieu that was restrictive in the recruitment of human neutrophils compared with strains lacking saeRS. Thus, S. aureus blunts the inflammatory response resulting in impaired neutrophil recruitment, which could promote the survival of the pathogen during invasive infection.

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.

Myxovirus resistance protein A for discriminating between viral and bacterial lower respiratory tract infections in children - The TREND study

OBJECTIVE

Discriminating between viral and bacterial lower respiratory tract infection (LRTI) in children is challenging, leading to an excessive use of antibiotics. Myxovirus resistance protein A (MxA) is a promising biomarker for viral infections. The primary aim of the study was to assess differences in blood MxA levels between children with viral and bacterial LRTI. Secondary aims were to assess differences in blood MxA levels between children with viral LRTI and asymptomatic controls and to assess MxA levels in relation to different respiratory viruses.

METHODS

Children with LRTI were enrolled as cases at Sachs' Children and Youth Hospital, Stockholm, Sweden. Nasopharyngeal aspirates and blood samples for analysis of viral PCR, MxA and CRP were systematically collected from all study subjects in addition to standard laboratory/radiology assessment. Aetiology was defined according to an algorithm based on laboratory and radiological findings. Asymptomatic children with minor surgical disease were enrolled as controls.

RESULTS

MxA levels were higher in children with viral LRTI (n=242) as compared to both bacterial (n=5) LRTI (p<0.01, area under the curve (AUC) 0.90, 95% confidence interval (CI):0.81-0.99) and controls (AUC 0.92, 95% CI:0.88-0.95). In the subgroup of children with pneumonia diagnosis, a cut-off of MxA 430μg/l discriminated between viral (n=29) and bacterial (n=4) aetiology with 93% (95% CI: 78%-99%) sensitivity and 100% (95% CI: 51%-100%) specificity (AUC 0.98, 95% CI: 0.94-1.00). The highest MxA levels were seen in cases PCR positive for influenza (median MxA 1699μg/l, interquartile range (IQR): 732-2996) and respiratory syncytial virus (median MxA 1115μg/l, IQR: 679-2489).

CONCLUSION

MxA accurately discriminated between viral and bacterial aetiology in children with LRTI, particularly in the group of children with pneumonia diagnosis, but the number of children with bacterial LRTI was low.

Systematic comparison of published host gene expression signatures for bacterial/viral discrimination

Background

Measuring host gene expression is a promising diagnostic strategy to discriminate bacterial and viral infections. Multiple signatures of varying size, complexity, and target populations have been described. However, there is little information to indicate how the performance of various published signatures compare to one another.

Methods

This systematic comparison of host gene expression signatures evaluated the performance of 28 signatures, validating them in 4589 subjects from 51 publicly available datasets. Thirteen COVID-specific datasets with 1416 subjects were included in a separate analysis. Individual signature performance was evaluated using the area under the receiving operating characteristic curve (AUC) value. Overall signature performance was evaluated using median AUCs and accuracies.

Results

Signature performance varied widely, with median AUCs ranging from 0.55 to 0.96 for bacterial classification and 0.69–0.97 for viral classification. Signature size varied (1–398 genes), with smaller signatures generally performing more poorly (P < 0.04). Viral infection was easier to diagnose than bacterial infection (84% vs. 79% overall accuracy, respectively; P < .001). Host gene expression classifiers performed more poorly in some pediatric populations (3 months–1 year and 2–11 years) compared to the adult population for both bacterial infection (73% and 70% vs. 82%, respectively; P < .001) and viral infection (80% and 79% vs. 88%, respectively; P < .001). We did not observe classification differences based on illness severity as defined by ICU admission for bacterial or viral infections. The median AUC across all signatures for COVID-19 classification was 0.80 compared to 0.83 for viral classification in the same datasets.

Conclusions

In this systematic comparison of 28 host gene expression signatures, we observed differences based on a signature’s size and characteristics of the validation population, including age and infection type. However, populations used for signature discovery did not impact performance, underscoring the redundancy among many of these signatures. Furthermore, differential performance in specific populations may only be observable through this type of large-scale validation.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13073-022-01025-x.

Machine Learning Approaches for Discriminating Bacterial and Viral Targeted Human Proteins

Infectious diseases are one of the core biological complications for public health. It is important to recognize the pathogen-specific mechanisms to improve our understanding of infectious diseases. Differentiations between bacterial- and viral-targeted human proteins are important for improving both prognosis and treatment for the patient. Here, we introduce machine learning-based classifiers to discriminate between the two groups of human proteins. We used the sequence, network, and gene ontology features of human proteins. Among different classifiers and features, the deep neural network (DNN) classifier with amino acid composition (AAC), dipeptide composition (DC), and pseudo-amino acid composition (PAAC) (445 features) achieved the best area under the curve (AUC) value (0.939), F1-score (94.9%), and Matthews correlation coefficient (MCC) value (0.81). We found that each of the selected top 100 of the bacteria- and virus-targeted human proteins from a candidate pool of 1618 and 3916 proteins, respectively, were part of distinct enriched biological processes and pathways. Our proposed method will help to differentiate between the bacterial and viral infections based on the targeted human proteins on a global scale. Furthermore, identification of the crucial pathogen targets in the human proteome would help us to better understand the pathogen-specific infection strategies and develop novel therapeutics.

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.

Biomarkers for Diagnosing Febrile Illness in Immunocompromised Children: A Systematic Review of the Literature

OBJECTIVE

This study aims to assess the performance of biomarkers used for the prediction of bacterial, viral, and fungal infection in immunocompromised children upon presentation with fever.

METHODS

We performed a literature search using PubMed and MEDLINE and In-Process & Other Non-indexed Citations databases. Cohort and case-control studies assessing biomarkers for the prediction of bacterial, viral, or fungal infection in immunocompromised children vs. conventional microbiological investigations were eligible. Studies including adult patients were eligible if pediatric data were separately assessable. Data on definitions used for infections, fever, and neutropenia and predictive values were collected. Risk of bias was assessed with the Quality Assessment of Diagnostic Accuracy Studies-2 tool.

RESULTS

Fifty-two studies involving 13,939 febrile episodes in 7,059 children were included. In total, 92.2% were in cancer patients (n = 48), and 15.7% also included hematopoietic stem cell transplantation patients (n = 8). Forty-three biomarkers were investigated, of which 6 (CRP, PCT, IL-8, IL-6, IL-10, and TNFα) were significantly associated with bacterial infection at admission, studied in multiple studies, and provided predictive data. Literature on the prediction of viral and fungal infection was too limited. Eight studies compared C-reactive protein (CRP) and procalcitonin (PCT), with PCT demonstrating superiority in 5. IL-6, IL-8, and IL-10 were compared with CRP in six, four, and one study, respectively, with mixed results on diagnostic superiority. No clear superior biomarker comparing PCT vs. IL-6, IL-8, or IL-10 was identified.

DISCUSSION

There is great heterogeneity in the biomarkers studied and cutoff values and definitions used, thus complicating the analysis. Literature for immunocompromised children with non-malignant disease and for non-bacterial infection is sparse. Literature on novel diagnostics was not available. We illustrated the challenges of diagnosing fever adequately in this study population and the need for improved biomarkers and clinical decision-making tools.

Future Applications of Metagenomic Next-Generation Sequencing for Infectious Diseases Diagnostics

Metagenomic next-generation sequencing (mNGS) has the theoretical capacity to detect any microbe present in a host. mNGS also has the potential to infer a pathogen's phenotypic characteristics, including the ability to colonize humans, cause disease, and resist treatment. Concurrent host nucleic acid sequencing can assess the infected individual's physiological state, including characterization and appropriateness of the immune response. When the pathogen cannot be identified, host RNA sequencing may help infer the organism's nature. While the full promise of mNGS remains far from realization, the potential ability to identify all microbes in a complex clinical sample, assess each organism's virulence and antibiotic susceptibility traits, and simultaneously characterize the host's response to infection provide opportunities for mNGS to supplant existing technologies and become the primary method of infectious diseases diagnostics.

A comparison of host response strategies to distinguish bacterial and viral infection

OBJECTIVES

Compare three host response strategies to distinguish bacterial and viral etiologies of acute respiratory illness (ARI).

METHODS

In this observational cohort study, procalcitonin, a 3-protein panel (CRP, IP-10, TRAIL), and a host gene expression mRNA panel were measured in 286 subjects with ARI from four emergency departments. Multinomial logistic regression and leave-one-out cross validation were used to evaluate the protein and mRNA tests.

RESULTS

The mRNA panel performed better than alternative strategies to identify bacterial infection: AUC 0.93 vs. 0.83 for the protein panel and 0.84 for procalcitonin (P<0.02 for each comparison). This corresponded to a sensitivity and specificity of 92% and 83% for the mRNA panel, 81% and 73% for the protein panel, and 68% and 87% for procalcitonin, respectively. A model utilizing all three strategies was the same as mRNA alone. For the diagnosis of viral infection, the AUC was 0.93 for mRNA and 0.84 for the protein panel (p<0.05). This corresponded to a sensitivity and specificity of 89% and 82% for the mRNA panel, and 85% and 62% for the protein panel, respectively.

CONCLUSIONS

A gene expression signature was the most accurate host response strategy for classifying subjects with bacterial, viral, or non-infectious ARI.

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.

Host transcriptional signatures as predictive markers of infection in children

PURPOSE OF REVIEW

Analyses of the host transcriptional response to infection has proved to be an alternative diagnostic strategy to standard direct pathogen detection. This review summarizes the value of applying blood and mucosal transcriptome analyses for the diagnosis and management of children with viral and bacterial infections.

RECENT FINDINGS

Over the years, studies have validated the concept that RNA transcriptional profiles derived from children with infectious diseases carry a pathogen-specific biosignature that can be qualitatively and quantitively measured. These biosignatures can be translated into a biologically meaningful context to improve patient diagnosis, as seen in children with tuberculosis, rhinovirus infections, febrile infants and children with pneumonia; understand disease pathogenesis (i.e. congenital CMV) and objectively classify patients according to clinical severity (i.e. respiratory syncytial virus).

SUMMARY

The global assessment of host RNA transcriptional immune responses has improved our understanding of the host-pathogen interactions in the clinical setting. It has shown the potential to be used in clinical situations wherein our current diagnostic tools are inadequate, guiding the diagnosis and classification of children with infectious diseases.

Discriminating Bacterial and Viral Infection Using a Rapid Host Gene Expression Test

OBJECTIVES

Host gene expression signatures discriminate bacterial and viral infection but have not been translated to a clinical test platform. This study enrolled an independent cohort of patients to describe and validate a first-in-class host response bacterial/viral test.

DESIGN

Subjects were recruited from 2006 to 2016. Enrollment blood samples were collected in an RNA preservative and banked for later testing. The reference standard was an expert panel clinical adjudication, which was blinded to gene expression and procalcitonin results.

SETTING

Four U.S. emergency departments.

PATIENTS

Six-hundred twenty-three subjects with acute respiratory illness or suspected sepsis.

INTERVENTIONS

Forty-five-transcript signature measured on the BioFire FilmArray System (BioFire Diagnostics, Salt Lake City, UT) in ~45 minutes.

MEASUREMENTS AND MAIN RESULTS

Host response bacterial/viral test performance characteristics were evaluated in 623 participants (mean age 46 yr; 45% male) with bacterial infection, viral infection, coinfection, or noninfectious illness. Performance of the host response bacterial/viral test was compared with procalcitonin. The test provided independent probabilities of bacterial and viral infection in ~45 minutes. In the 213-subject training cohort, the host response bacterial/viral test had an area under the curve for bacterial infection of 0.90 (95% CI, 0.84-0.94) and 0.92 (95% CI, 0.87-0.95) for viral infection. Independent validation in 209 subjects revealed similar performance with an area under the curve of 0.85 (95% CI, 0.78-0.90) for bacterial infection and 0.91 (95% CI, 0.85-0.94) for viral infection. The test had 80.1% (95% CI, 73.7-85.4%) average weighted accuracy for bacterial infection and 86.8% (95% CI, 81.8-90.8%) for viral infection in this validation cohort. This was significantly better than 68.7% (95% CI, 62.4-75.4%) observed for procalcitonin (p < 0.001). An additional cohort of 201 subjects with indeterminate phenotypes (coinfection or microbiology-negative infections) revealed similar performance.

CONCLUSIONS

The host response bacterial/viral measured using the BioFire System rapidly and accurately discriminated bacterial and viral infection better than procalcitonin, which can help support more appropriate antibiotic use.

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.

FAM89A and IFI44L for distinguishing between viral and bacterial infections in children with febrile illness

IMPORTANCE

The current lack of reliable rapid tests for distinguishing between bacterial and viral infections has contributed to antibiotic misuse.

OBJECTIVE

This study aimed to develop a novel biomarker assay that integrates FAM89A and IFI44L measurements to assist in differentiating between bacterial and viral infections.

METHODS

This prospective study recruited children with febrile illness from two hospitals between July 1, 2018, and June 30, 2019. A panel of three experienced pediatricians performed reference standard diagnoses of all patients (i.e., bacterial or viral infection) using available clinical and laboratory data, including a 28-day follow-up assessment. Assay operators were blinded to the reference standard diagnoses. The expression levels of FAM89A and IFI44L were determined by quantitative real-time polymerase chain reaction assessment.

RESULTS

Of 133 potentially eligible patients with suspected bacterial or viral infection, 35 were excluded after the application of exclusion criteria. The resulting cohort included 98 patients: 59 with viral diagnoses and 39 with bacterial diagnoses. The areas under the curve (AUCs) of diagnoses using FAM89A and IFI44L were 0.694 [95% confidence interval (CI): 0.583-0.804] and 0.751 (95% CI: 0.651-0.851), respectively. The disease risk score (DRS) [log2(FAM89A expression) - log2(IFI44L expression)] signature achieved an improved area under the receiver operating characteristic curve (AUC, 0.825; 95% CI: 0.735-0.915), compared with the AUC generated from individual host RNA. A combination of the DRS and the C-reactive protein (CRP) level achieved an AUC of 0.896 (95% CI: 0.825-0.966). Optimal cutoffs for the DRS and CRP level were -3.18 and 19.80 mg/L, respectively.

INTERPRETATION

The DRS was significantly more accurate than the CRP level in distinguishing between bacterial and viral infections; the combination of these two parameters exhibited greater sensitivity and specificity. This study provides information that could be useful for the clinical application of FAM89A and IFI44L in terms of distinguishing between viral and bacterial infections.

A two-transcript biomarker of host classifier genes for discrimination of bacterial from viral infection in acute febrile illness: a multicentre discovery and validation study

BACKGROUND

Acute febrile illness is one of the main reasons for outpatient hospital visits worldwide. However, differential diagnosis between bacterial and viral causes is challenging and misdiagnosis can result in antimicrobial overuse and hinder prompt treatment. We aimed to build and validate a diagnostic model to discriminate bacterial from viral infection in acute febrile illness by evaluating the expression of potential classifier host genes.

METHODS

In this multicentre discovery and validation study, we included patients aged 14-85 years with acute febrile illness (fever for ≤14 days, axillary temperature of ≥38°C, and confirmed bacterial infection, viral infection, or non-infectious inflammatory disease), and healthy control participants (no significant medical history and no fever within the past 90 days) from four hospitals in Shandong province, China. Patients from the first hospital were divided into the screening, discovery, and internal validation groups, and patients from the three other hospitals comprised the external validation group. We measured expression of candidate genes in peripheral blood by RT-PCR, and patients for whom a successful RT-PCT result was recorded were included in the next-step analysis. For patients from the first hospital, those enrolled during the early phase of the study were assigned to the screening group, which was used to identify the optimal transcripts (IFI44L and PI3) for discrimination between bacterial and viral infections by screening four candidate genes (FAM89A, IFI44L, PI3, and ITGB2) by RT-PCR. The remaining patients were then randomly assigned (1:1) to discovery and internal validation groups by time of admission and blood drawing via the equidistant random sampling method. A logistic regression model integrating the mRNA levels of IFI44L and PI3 was built by use of the discovery group, and the diagnostic performance of the model was evaluated in the internal and external validation groups using area under the receiver operating curve (AUC), sensitivity, and specificity.

FINDINGS

Between March 1, 2018, and Aug 31, 2019, we assessed 1658 individuals for inclusion in the study. After exclusion of ineligible participants, 458 participants were enrolled (178 patients with acute febrile illness caused by bacterial infection, 212 with acute febrile illness caused by viral infection, 38 with non-infectious inflammatory diseases, and 30 healthy controls). The 390 patients with bacterial or viral infections were assigned to one of four groups: screening (n=64, 33 with bacterial infections and 31 with viral infections), discovery (n=124, 55 with bacterial infections and 69 with viral infections), internal validation (n=124, 55 with bacterial infections and 69 with viral infections), and external validation (n=78, 35 with bacterial infections and 43 with viral infections). Of the four candidate host genes (FAM89A, IFI44L, PI3, and ITGB2), IFI44L and PI3 showed the most discriminative expression pattern and were used to build the logistic regression model. We established the optimal cutoff of the bacterial infection likelihood score to be 0·547598. With the diagnostic result from the gold standard tests (culture and PCR) as the reference, the two-transcript classifier model had an AUC of 0·969 (95% CI 0·937-1·000), sensitivity of 0·891 (0·782-0·949), and specificity of 0·971 (0·900-0·992) to discriminate bacterial and viral infections in the internal validation group. The model showed similar results in the external validation group (AUC 0·986, 95% CI 0·968-1·000; sensitivity 0·857, 0·706-0·937; and specificity 0·954, 0·845-0·987).

INTERPRETATION

IFI44L and PI3 transcripts, measured by RT-PCR, are robust classifiers to discriminate bacterial from viral infection in acute febrile illness. This two-transcript biomarker has the potential to be transformed into a commercial panel and applied universally.

FUNDING

None.

Dual RNA-Seq Enables Full-Genome Assembly of Measles Virus and Characterization of Host-Pathogen Interactions

Measles virus (MeV) has a negative-sense 15 kb long RNA genome, which is generally conserved. Recent advances in high-throughput sequencing (HTS) and Dual RNA-seq allow the analysis of viral RNA genomes and the discovery of viral infection biomarkers, via the simultaneous characterization of the host transcriptome. However, these host-pathogen interactions remain largely unexplored in MeV infections. We performed untargeted Dual RNA-seq in 6 pharyngeal and 6 peripheral blood mononuclear cell (PBMCs) specimens from patients with MeV infection, as confirmed via routine real-time PCR testing. Following optimised DNase treatment of total nucleic acids, we used the pharyngeal samples to build poly-A-enriched NGS libraries. We reconstructed the viral genomes using the pharyngeal datasets and we further conducted differential expression, gene-ontology and pathways enrichment analysis to compare both the pharyngeal and the peripheral blood transcriptomes of the MeV-infected patients vs. control groups of healthy individuals. We obtained 6 MeV genotype-B3 full-genome sequences. We minutely analyzed the transcriptome of the MeV-infected pharyngeal epithelium, detecting all known viral infection biomarkers, but also revealing a functional cluster of local antiviral and inflammatory immune responses, which differ substantially from those observed in the PBMCs transcriptome. The application of Dual RNA-seq technologies in MeV-infected patients can potentially provide valuable information on the virus genome structure and the cellular innate immune responses and drive the discovery of new targets for antiviral therapy.

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.

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.

A host-based two-gene model for the identification of bacterial infection in general clinical settings

OBJECTIVES

In this study, we aimed to develop a simple gene model to identify bacterial infection, which can be implemented in general clinical settings.

METHODS

We used a clinically availablereal-time quantitative polymerase chain reaction platform to conduct focused gene expression assays on clinical blood samples. Samples were collected from 2 tertiary hospitals.

RESULTS

We found that the 8 candidate genes for bacterial infection were significantly dysregulated in bacterial infection and displayed good performance in group classification, whereas the 2 genes for viral infection displayed poor performance. A two-gene model (S100A12 and CD177) displayed 93.0% sensitivity and 93.7% specificity in the modeling stage. In the independent validation stage, 87.8% sensitivity and 96.6% specificity were achieved in one set of case-control groups, and 93.6% sensitivity and 97.1% specificity in another set.

CONCLUSIONS

We have validated the signature genes for bacterial infection and developed a two-gene model to identify bacterial infection in general clinical settings.

An OMICs based meta-analysis to support infection state stratification

MOTIVATION

A fundamental problem for disease treatment is that while antibiotics are a powerful counter to bacteria, they are ineffective against viruses. Often, bacterial and viral infections are confused due to their similar symptoms and lack of rapid diagnostics. With many clinicians relying primarily on symptoms for diagnosis, overuse and misuse of modern antibiotics are rife, contributing to the growing pool of antibiotic resistance. To ensure an individual receives optimal treatment given their disease state and to reduce over-prescription of antibiotics, the host response can in theory be measured quickly to distinguish between the two states. To establish a predictive biomarker panel of disease state (viral/bacterial/no-infection) we conducted a meta-analysis of human blood infection studies using Machine Learning (ML).

RESULTS

We focused on publicly available gene expression data from two widely used platforms, Affymetrix and Illumina microarrays as they represented a significant proportion of the available data. We were able to develop multi-class models with high accuracies with our best model predicting 93% of bacterial and 89% viral samples correctly. To compare the selected features in each of the different technologies, we reverse engineered the underlying molecular regulatory network and explored the neighbourhood of the selected features. The networks highlighted that although on the gene-level the models differed, they contained genes from the same areas of the network. Specifically, this convergence was to pathways including the Type I interferon Signalling Pathway, Chemotaxis, Apoptotic Processes, and Inflammatory/Innate Response.

AVAILABILITY

Data and code are available on the Gene Expression Omnibus and github.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Whole blood genome-wide transcriptome profiling and metagenomics next-generation sequencing in young infants with suspected sepsis in a low-and middle-income country: A study protocol

Conducting collaborative and comprehensive epidemiological research on neonatal sepsis in low- and middle-income countries (LMICs) is challenging due to a lack of diagnostic tests. This prospective study protocol aims to obtain epidemiological data on bacterial sepsis in newborns and young infants at Kamuzu Central Hospital in Lilongwe, Malawi. The main goal is to determine if the use of whole blood transcriptome host immune response signatures can help in the identification of infants who have sepsis of bacterial causes. The protocol includes a detailed clinical assessment with vital sign measurements, strict aseptic blood culture protocol with state-of-the-art microbial analyses and RNA-sequencing and metagenomics evaluations of host responses and pathogens, respectively. We also discuss the directions of a brief analysis plan for RNA sequencing data. This study will provide robust epidemiological data for sepsis in neonates and young infants in a setting where sepsis confers an inordinate burden of disease.

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.

Whole blood genome-wide transcriptome profiling and metagenomics next-generation sequencing in young infants with suspected sepsis in low-and middle-income countries: A study protocol

Conducting collaborative and comprehensive epidemiological research on neonatal sepsis in low- and middle-income countries (LMICs) is challenging due to a lack of diagnostic tests. This prospective study protocol aims to obtain epidemiological data on bacterial sepsis in newborns and young infants at Kamuzu Central Hospital in Lilongwe, Malawi. The main goal is to determine if the use of whole blood transcriptome host immune response signatures can help in the identification of infants who have sepsis of bacterial causes. The protocol includes a detailed clinical assessment with vital sign measurements, strict aseptic blood culture protocol with state-of-the-art microbial analyses and RNA-sequencing and metagenomics evaluations of host responses and pathogens, respectively. We also discuss the directions of a brief analysis plan for RNA sequencing data. This study will provide robust epidemiological data for sepsis in neonates and young infants in a setting where sepsis confers an inordinate burden of disease.

A blood-based host gene expression assay for early detection of respiratory viral infection: an index-cluster prospective cohort study

Background

Early and accurate identification of individuals with viral infections is crucial for clinical management and public health interventions. We aimed to assess the ability of transcriptomic biomarkers to identify naturally acquired respiratory viral infection before typical symptoms are present.

Methods

In this index-cluster study, we prospectively recruited a cohort of undergraduate students (aged 18–25 years) at Duke University (Durham, NC, USA) over a period of 5 academic years. To identify index cases, we monitored students for the entire academic year, for the presence and severity of eight symptoms of respiratory tract infection using a daily web-based survey, with symptoms rated on a scale of 0–4. Index cases were defined as individuals who reported a 6-point increase in cumulative daily symptom score. Suspected index cases were visited by study staff to confirm the presence of reported symptoms of illness and to collect biospecimen samples. We then identified clusters of close contacts of index cases (ie, individuals who lived in close proximity to index cases, close friends, and partners) who were presumed to be at increased risk of developing symptomatic respiratory tract infection while under observation. We monitored each close contact for 5 days for symptoms and viral shedding and measured transcriptomic responses at each timepoint each day using a blood-based 36-gene RT-PCR assay.

Findings

Between Sept 1, 2009, and April 10, 2015, we enrolled 1465 participants. Of 264 index cases with respiratory tract infection symptoms, 150 (57%) had a viral cause confirmed by RT-PCR. Of their 555 close contacts, 106 (19%) developed symptomatic respiratory tract infection with a proven viral cause during the observation window, of whom 60 (57%) had the same virus as their associated index case. Nine viruses were detected in total. The transcriptomic assay accurately predicted viral infection at the time of maximum symptom severity (mean area under the receiver operating characteristic curve [AUROC] 0·94 [95% CI 0·92–0·96]), as well as at 1 day (0·87 [95% CI 0·84–0·90]), 2 days (0·85 [0·82–0·88]), and 3 days (0·74 [0·71–0·77]) before peak illness, when symptoms were minimal or absent and 22 (62%) of 35 individuals, 25 (69%) of 36 individuals, and 24 (82%) of 29 individuals, respectively, had no detectable viral shedding.

Interpretation

Transcriptional biomarkers accurately predict and diagnose infection across diverse viral causes and stages of disease and thus might prove useful for guiding the administration of early effective therapy, quarantine decisions, and other clinical and public health interventions in the setting of endemic and pandemic infectious diseases.

Funding

US Defense Advanced Research Projects Agency.

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.

Distinguishing Kawasaki Disease from Febrile Infectious Disease Using Gene Pair Signatures

Kawasaki disease (KD) is an acute systemic vasculitis of childhood with prolonged fever, and the diagnosis of KD is mainly based on clinical criteria, which is prone to misdiagnosis with other febrile infectious (FI) diseases. Currently, there remain no effective molecular markers for KD diagnosis. In this study, we aimed to use a relative-expression-based method k-TSP and resampling framework to identify robust gene pair signatures to distinguish KD from bacterial and virus febrile infectious diseases. Our study pool consisted of 808 childhood patients from several studies and assigned to three groups, namely, the discovery set (n = 224), validation set-1 (n = 197), and validation set-2 (n = 387). We had identified 60 biologically relevant gene pairs and developed a top-ranked gene pair classifier (TRGP) using the first seven signatures, with the area under the receiver-operating characteristic curves (AUROC) of 0.947 (95% CI, 0.918-0.976), a sensitivity of 0.936 (95% CI, 0.872-0.987), and a specificity of 0.774 (95% CI, 0.705-0.836) in the discovery set. In the validation set-1, the TRGP classifier distinguished KD from FI with AUROC of 0.955 (95% CI, 0.919-0.991), a sensitivity of 0.959 (95% CI, 0.925-0.986), and a specificity of 0.863 (95% CI, 0.764-0.961). In the validation set-2, the predictive performance of classification was with an AUROC of 0.796 (95% CI, 0.747-0.845), a sensitivity of 0.797 (95% CI, 0.720-0.864), and a specificity of 0.661 (95% CI, 0.606-0.717). Our study reveals that gene pair signatures are robust across diverse studies and can be utilized as objective biomarkers to distinguish KD from FI, helping to develop a fast, simple, and effective molecular approach to improve the diagnosis of KD.

Global Transcriptome Analysis Identifies a Diagnostic Signature for Early Disseminated Lyme Disease and Its Resolution

A bioinformatics approach was employed to identify transcriptome alterations in the peripheral blood mononuclear cells of well-characterized human subjects who were diagnosed with early disseminated Lyme disease (LD) based on stringent microbiological and clinical criteria. Transcriptomes were assessed at the time of presentation and also at approximately 1 month (early convalescence) and 6 months (late convalescence) after initiation of an appropriate antibiotic regimen. Comparative transcriptomics identified 335 transcripts, representing 233 unique genes, with significant alterations of at least 2-fold expression in acute- or convalescent-phase blood samples from LD subjects relative to healthy donors. Acute-phase blood samples from LD subjects had the largest number of differentially expressed transcripts (187 induced, 54 repressed). This transcriptional profile, which was dominated by interferon-regulated genes, was sustained during early convalescence. 6 months after antibiotic treatment the transcriptome of LD subjects was indistinguishable from that of healthy controls based on two separate methods of analysis. Return of the LD expression profile to levels found in control subjects was concordant with disease outcome; 82% of subjects with LD experienced at least one symptom at the baseline visit compared to 43% at the early convalescence time point and only a single patient (9%) at the 6-month convalescence time point. Using the random forest machine learning algorithm, we developed an efficient computational framework to identify sets of 20 classifier genes that discriminated LD from other bacterial and viral infections. These novel LD biomarkers not only differentiated subjects with acute disseminated LD from healthy controls with 96% accuracy but also distinguished between subjects with acute and resolved (late convalescent) disease with 97% accuracy.IMPORTANCE Lyme disease (LD), caused by Borrelia burgdorferi, is the most common tick-borne infectious disease in the United States. We examined gene expression patterns in the blood of individuals with early disseminated LD at the time of diagnosis (acute) and also at approximately 1 month and 6 months following antibiotic treatment. A distinct acute LD profile was observed that was sustained during early convalescence (1 month) but returned to control levels 6 months after treatment. Using a computer learning algorithm, we identified sets of 20 classifier genes that discriminate LD from other bacterial and viral infections. In addition, these novel LD biomarkers are highly accurate in distinguishing patients with acute LD from healthy subjects and in discriminating between individuals with active and resolved infection. This computational approach offers the potential for more accurate diagnosis of early disseminated Lyme disease. It may also allow improved monitoring of treatment efficacy and disease resolution.

Companion and complementary diagnostics for infectious diseases

INTRODUCTION

Companion diagnostics (CDx) are important in oncology therapeutic decision-making, but specific regulatory-approved CDx for infectious disease treatment are officially lacking. While not approved as CDx, several ID diagnostics are used as CDx. The diagnostics community, manufacturers, and regulatory agencies have made major efforts to ensure that diagnostics for new antimicrobials are available at or near release of new agents.

AREAS COVERED

This review highlights the status of Complementary and companion diagnostic (c/CDx) in the infectious disease literature, with a focus on genotypic antimicrobial resistance testing against pathogens as a class of diagnostic tests.

EXPERT OPINION

CRISPR, sepsis markers, and narrow spectrum antimicrobials, in addition to current and emerging technologies, present opportunities for infectious disease c/CDx. Challenges include slow guideline revision, high costs for regulatory approval, lengthy buy in by agencies, discordant pharmaceutical/diagnostic partnerships, and higher treatment costs. The number of patients and available medications used to treat different infectious diseases is well suited to support competing diagnostic tests. However, newer approaches to treatment (for example, narrow spectrum antibiotics), may be well suited for a small number of patients, i.e. a niche market in support of a CDx. The current emphasis is rapid and point-of-care (POC) diagnostic platforms as well as changes in treatment.

Gene Expression Profiles in Children With Suspected Sepsis

STUDY OBJECTIVE

Sepsis recognition is a clinical challenge in children. We aim to determine whether peripheral blood gene expression profiles are associated with pathogen type and sepsis severity in children with suspected sepsis.

METHODS

This was a prospective pilot observational study in a tertiary pediatric emergency department with a convenience sample of children enrolled. Participants were older than 56 days and younger than 18 years, had suspected sepsis, and had not received broad-spectrum antibiotics in the previous 4 hours. Primary outcome was source pathogen, defined as confirmed bacterial source from sterile body fluid or confirmed viral source. Secondary outcome was sepsis severity, defined as maximum therapy required for shock reversal in the first 3 hospital days. We drew peripheral blood for ribonucleic acid isolation at the sepsis protocol activation, obtained gene expression measures with the GeneChip Human Gene 2.0 ST Array, and conducted differential expression analysis.

RESULTS

We collected ribonucleic acid samples from a convenience sample of 122 children with suspected sepsis and 12 healthy controls. We compared the 66 children (54%) with confirmed bacterial or viral infection and found 558 differentially expressed genes, many related to interferon signaling or viral immunity. We did not find statistically significant gene expression differences in patients according to sepsis severity.

CONCLUSION

The study demonstrates feasibility of evaluating gene expression profiling data in children evaluated for sepsis in the pediatric emergency department setting. Our results suggest that gene expression profiling may facilitate identification of source pathogen in children with suspected sepsis, which could ultimately lead to improved tailoring of sepsis treatment and antimicrobial stewardship.

Host Gene Expression in Nose and Blood for the Diagnosis of Viral Respiratory Infection

BACKGROUND

Recently there has been a growing interest in the potential for host transcriptomic analysis to augment the diagnosis of infectious diseases.

METHODS

We compared nasal and blood samples for evaluation of the host transcriptomic response in children with acute respiratory syncytial virus (RSV) infection, symptomatic non-RSV respiratory virus infection, asymptomatic rhinovirus infection, and virus-negative asymptomatic controls. We used nested leave-one-pair-out cross-validation and supervised principal components analysis to define small sets of genes whose expression patterns accurately classified subjects. We validated gene classification scores using an external data set.

RESULTS

Despite lower quality of nasal RNA, the number of genes detected by microarray in each sample type was equivalent. Nasal gene expression signal derived mainly from epithelial cells but also included a variable leukocyte contribution. The number of genes with increased expression in virus-infected children was comparable in nasal and blood samples, while nasal samples also had decreased expression of many genes associated with ciliary function and assembly. Nasal gene expression signatures were as good or better for discriminating between symptomatic, asymptomatic, and uninfected children.

CONCLSUSIONS

Our results support the use of nasal samples to augment pathogen-based tests to diagnose viral respiratory infection.

Rapid, Sample-to-Answer Host Gene Expression Test to Diagnose Viral Infection

Objective

Distinguishing bacterial, viral, or other etiologies of acute illness is diagnostically challenging with significant implications for appropriate antimicrobial use. Host gene expression offers a promising approach, although no clinically useful test has been developed yet to accomplish this. Here, Qvella's FAST HR (Richmond Hill, Ontario, Canada) process was developed to quantify previously identified host gene expression signatures in whole blood in <45 minutes.

Method

Whole blood was collected from 128 human subjects (mean age 47, range 18-88) with clinically adjudicated, microbiologically confirmed viral infection, bacterial infection, noninfectious illness, or healthy controls. Stabilized mRNA was released from cleaned and stabilized RNA-surfactant complexes using e-lysis, an electrical process providing a quantitative real-time reverse transcription polymerase chain reaction-ready sample. Threshold cycle values (C_T) for 10 host response targets were normalized to hypoxanthine phosphoribosyltransferase 1 expression, a control mRNA. The transcripts in the signature were specifically chosen to discriminate viral from nonviral infection (bacterial, noninfectious illness, or healthy). Classification accuracy was determined using cross-validated sparse logistic regression.

Results

Reproducibility of mRNA quantification was within 1 cycle as compared to the difference seen between subjects with viral versus nonviral infection (up to 5.0 normalized C_T difference). Classification of 128 subjects into viral or nonviral etiologies demonstrated 90.6% overall accuracy compared to 82.0% for procalcitonin (P = .06). FAST HR achieved rapid and accurate measurement of the host response to viral infection in less than 45 minutes.

Conclusions

These results demonstrate the ability to translate host gene expression signatures to clinical platforms for use in patients with suspected infection.

Clinical Trials Registration

NCT00258869.

Validation of a host response test to distinguish bacterial and viral respiratory infection

BACKGROUND

Distinguishing bacterial and viral respiratory infections is challenging. Novel diagnostics based on differential host gene expression patterns are promising but have not been translated to a clinical platform nor extensively tested. Here, we validate a microarray-derived host response signature and explore performance in microbiology-negative and coinfection cases.

METHODS

Subjects with acute respiratory illness were enrolled in participating emergency departments. Reference standard was an adjudicated diagnosis of bacterial infection, viral infection, both, or neither. An 87-transcript signature for distinguishing bacterial, viral, and noninfectious illness was measured from peripheral blood using RT-PCR. Performance characteristics were evaluated in subjects with confirmed bacterial, viral, or noninfectious illness. Subjects with bacterial-viral coinfection and microbiologically-negative suspected bacterial infection were also evaluated. Performance was compared to procalcitonin.

FINDINGS

151 subjects with microbiologically confirmed, single-etiology illness were tested, yielding AUROCs 0•85-0•89 for bacterial, viral, and noninfectious illness. Accuracy was similar to procalcitonin (88% vs 83%, p = 0•23) for bacterial vs. non-bacterial infection. Whereas procalcitonin cannot distinguish viral from non-infectious illness, the RT-PCR test had 81% accuracy in making this determination. Bacterial-viral coinfection was subdivided. Among 19 subjects with bacterial superinfection, the RT-PCR test identified 95% as bacterial, compared to 68% with procalcitonin (p = 0•13). Among 12 subjects with bacterial infection superimposed on chronic viral infection, the RT-PCR test identified 83% as bacterial, identical to procalcitonin. 39 subjects had suspected bacterial infection; the RT-PCR test identified bacterial infection more frequently than procalcitonin (82% vs 64%, p = 0•02).

INTERPRETATION

The RT-PCR test offered similar diagnostic performance to procalcitonin in some subgroups but offered better discrimination in others such as viral vs. non-infectious illness and bacterial/viral coinfection. Gene expression-based tests could impact decision-making for acute respiratory illness as well as a growing number of other infectious and non-infectious diseases.

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

BACKGROUND

Meningitis can be caused by several viruses and bacteria. Identifying the causative pathogen as quickly as possible is crucial to initiate the most optimal therapy, as acute bacterial meningitis is associated with a significant morbidity and mortality. Bacterial meningitis requires antibiotics, as opposed to enteroviral meningitis, which only requires supportive therapy. Clinical presentation is usually not sufficient to differentiate between viral and bacterial meningitis, thereby necessitating cerebrospinal fluid (CSF) analysis by PCR and/or time-consuming bacterial cultures. However, collecting CSF in children is not always feasible and a rather invasive procedure.

METHODS

In 12 Belgian hospitals, we obtained acute blood samples from children with signs of meningitis (49 viral and 7 bacterial cases) (aged between 3 months and 16 years). After pathogen confirmation on CSF, the patient was asked to give a convalescent sample after recovery. 3' mRNA sequencing was performed to determine differentially expressed genes (DEGs) to create a host transcriptomic profile.

RESULTS

Enteroviral meningitis cases displayed the largest upregulated fold change enrichment in type I interferon production, response and signaling pathways. Patients with bacterial meningitis showed a significant upregulation of genes related to macrophage and neutrophil activation. We found several significantly DEGs between enteroviral and bacterial meningitis. Random forest classification showed that we were able to differentiate enteroviral from bacterial meningitis with an AUC of 0.982 on held-out samples.

CONCLUSIONS

Enteroviral meningitis has an innate immunity signature with type 1 interferons as key players. Our classifier, based on blood host transcriptomic profiles of different meningitis cases, is a possible strong alternative for diagnosing enteroviral meningitis.

Viral Mimetic-Induced Inflammation Abolishes Q-Pathway, but Not S-Pathway, Respiratory Motor Plasticity in Adult Rats

Inflammation arises from diverse stimuli eliciting distinct inflammatory profiles, yet little is known about the effects of different inflammatory stimuli on respiratory motor plasticity. Respiratory motor plasticity is a key feature of the neural control of breathing and commonly studied in the form of phrenic long-term facilitation (pLTF). At least two distinct pathways can evoke pLTF with differential sensitivities to bacterial-induced inflammation. The Q-pathway is abolished by bacterial-induced inflammation, while the S-pathway is inflammation-resistant. Since viral-induced inflammation is common and elicits distinct temporal inflammatory gene profiles compared to bacterial inflammation, we tested the hypothesis that inflammation induced by a viral mimetic (polyinosinic:polycytidylic acid, polyIC) would abolish Q-pathway-evoked pLTF, but not S-pathway-evoked pLTF. Further, we hypothesized Q-pathway impairment would occur later relative to bacterial-induced inflammation. PolyIC (750 μg/kg, i.p.) transiently increased inflammatory genes in the cervical spinal cord (3 h), but did not alter medullary and splenic inflammatory gene expression, suggesting region specific inflammation after polyIC. Dose-response experiments revealed 750 μg/kg polyIC (i.p.) was sufficient to abolish Q-pathway-evoked pLTF at 24 h (17 ± 15% change from baseline, n = 5, p > 0.05). However, polyIC (750 μg/kg, i.p.) at 3 h was not sufficient to abolish Q-pathway-evoked pLTF (67 ± 21%, n = 5, p < 0.0001), suggesting a unique temporal impairment of pLTF after viral-mimetic-induced systemic inflammation. A non-steroidal anti-inflammatory (ketoprofen, 12.5 mg/kg, i.p., 3 h) restored Q-pathway-evoked pLTF (64 ± 24%, n = 5, p < 0.0001), confirming the role of inflammatory signaling in pLTF impairment. On the contrary, S-pathway-evoked pLTF was unaffected by polyIC-induced inflammation (750 μg/kg, i.p., 24 h; 72 ± 25%, n = 5, p < 0.0001) and was not different from saline controls (65 ± 32%, n = 4, p = 0.6291). Thus, the inflammatory-impairment of Q-pathway-evoked pLTF is generalizable between distinct inflammatory stimuli, but differs temporally. On the contrary, S-pathway-evoked pLTF is inflammation-resistant. Therefore, in situations where respiratory motor plasticity may be used as a tool to improve motor function, strategies targeting S-pathway-evoked plasticity may facilitate therapeutic outcomes.

Transcriptional blood signatures for active and amphotericin B treated visceral leishmaniasis in India

Amphotericin B provides improved therapy for visceral leishmaniasis (VL) caused by Leishmania donovani, with single dose liposomal-encapsulated Ambisome providing the best cure rates. The VL elimination program aims to reduce the incidence rate in the Indian subcontinent to <1/10,000 population/year. Ability to predict which asymptomatic individuals (e.g. anti-leishmanial IgG and/or Leishmania-specific modified Quantiferon positive) will progress to clinical VL would help in monitoring disease outbreaks. Here we examined whole blood transcriptional profiles associated with asymptomatic infection, active disease, and in treated cases. Two independent microarray experiments were performed, with analysis focussed primarily on differentially expressed genes (DEGs) concordant across both experiments. No DEGs were identified for IgG or Quantiferon positive asymptomatic groups compared to negative healthy endemic controls. We therefore concentrated on comparing concordant DEGs from active cases with all healthy controls, and in examining differences in the transcriptome following different regimens of drug treatment. In these comparisons 6 major themes emerged: (i) expression of genes and enrichment of gene sets associated with erythrocyte function in active cases; (ii) strong evidence for enrichment of gene sets involved in cell cycle in comparing active cases with healthy controls; (iii) identification of IFNG encoding interferon-γ as the major hub gene in concordant gene expression patterns across experiments comparing active cases with healthy controls or with treated cases; (iv) enrichment for interleukin signalling (IL-1/3/4/6/7/8) and a prominent role for CXCL10/9/11 and chemokine signalling pathways in comparing active cases with treated cases; (v) the novel identification of Aryl Hydrocarbon Receptor signalling as a significant canonical pathway when comparing active cases with healthy controls or with treated cases; and (vi) global expression profiling support for more effective cure at day 30 post-treatment with a single dose of liposomal encapsulated amphotericin B compared to multi-dose non-liposomal amphotericin B treatment over 30 days. (296 words; 300 words allowed).

Genomic Circuitry Underlying Immunological Response to Pediatric Acute Respiratory Infection

Acute respiratory tract viral infections (ARTIs) cause significant morbidity and mortality. CD8 T cells are fundamental to host responses, but transcriptional alterations underlying anti-viral mechanisms and links to clinical characteristics remain unclear. CD8 T cell transcriptional circuitry in acutely ill pediatric patients with influenza-like illness was distinct for different viral pathogens. Although changes included expected upregulation of interferon-stimulated genes (ISGs), transcriptional downregulation was prominent upon exposure to innate immune signals in early IFV infection. Network analysis linked changes to severity of infection, asthma, sex, and age. An influenza pediatric signature (IPS) distinguished acute influenza from other ARTIs and outperformed other influenza prediction gene lists. The IPS allowed a deeper investigation of the connection between transcriptional alterations and clinical characteristics of acute illness, including age-based differences in circuits connecting the STAT1/2 pathway to ISGs. A CD8 T cell-focused systems immunology approach in pediatrics identified age-based alterations in ARTI host response pathways.

Shared Molecular Signatures Across Neurodegenerative Diseases and Herpes Virus Infections Highlights Potential Mechanisms for Maladaptive Innate Immune Responses

Growing evidence suggests that peripheral factors to the brain driving neuro-inflammation could affect Alzheimer’s Disease (AD) and Parkinson’s Disease (PD) severity. Herpes simplex virus type 1 (HSV1) infection has been associated with AD while other related viruses, including cytomegalovirus (CMV), Epstein-Bar virus and human herpesvirus 6 (HHV6), are known to infect neurons. Here we compare gene expression profiles between AD or PD patients to those afflicted with herpes viral infections as to discover novel potential neuro-inflammation pathways. We found multiple significant differentially expressed genes (DEGs) shared between AD/PD and viral infections including SESN3 which has a genetic association for increased AD risk. Pathway enrichment analysis revealed viruses shared Oxidative Stress Defense System and LRRK2 pathways with AD and PD, respectively. We further processed our data to identify novel target and drug-repurposing opportunities including anti-inflammatory therapy, immune-modulators and cholinesterase inhibitors which could lead to new therapeutics paradigms for these neurodegenerative diseases.

Investigating Pneumonia Etiology Among Refugees and the Lebanese population (PEARL): A study protocol

Background: Community-acquired pneumonia (CAP), a leading cause of mortality, mainly affects children in developing countries. The harsh circumstances experienced by refugees include various factors associated with respiratory pathogen transmission, and clinical progression of CAP. Consequently, the etiology of CAP in humanitarian crisis situations may differ to that of settled populations, which would impact appropriate case management. Therefore, the Pneumonia Etiology Among Refugees and the Lebanese population (PEARL) study was initiated with the objective of identifying the causal pathogenic microorganisms in the respiratory tract of children and adults from both the refugee and host country population presenting with signs of CAP during a humanitarian crisis.

Methods: PEARL, a prospective, multicentric, case-control study, will be conducted at four primary healthcare facilities in Tripoli and the Bekaa valley over 15 months (including two high-transmission seasons/winters). Sociodemographic and medical data, and biological samples will be collected from at least 600 CAP cases and 600 controls. Nasopharyngeal swabs, sputum, urine and blood samples will be analyzed at five clinical pathology laboratories in Lebanon to identify the bacterial and viral etiological agents of CAP. Transcriptomic profiling of host leukocytes will be performed.

Conclusions: PEARL is an original observational study that will provide important new information on the etiology of pneumonia among refugees, which may improve case management, help design antimicrobial stewardship interventions, and reduce morbidity and mortality due to CAP in a humanitarian crisis.

Emerging Analytical Techniques for Rapid Pathogen Identification and Susceptibility Testing

In the face of looming threats from multi-drug resistant microorganisms, there is a growing need for technologies that will enable rapid identification and drug susceptibility profiling of these pathogens in health care settings. In particular, recent progress in microfluidics and nucleic acid amplification is pushing the boundaries of timescale for diagnosing bacterial infections. With a diverse range of techniques and parallel developments in the field of analytical chemistry, an integrative perspective is needed to understand the significance of these developments. This review examines the scope of new developments in assay technologies grouped by key enabling domains of research. First, we examine recent development in nucleic acid amplification assays for rapid identification and drug susceptibility testing in bacterial infections. Next, we examine advances in microfluidics that facilitate acceleration of diagnostic assays via integration and scale. Lastly, recentdevelopments in biosensor technologies are reviewed. We conclude this review with perspectives on the use of emerging concepts to develop paradigm-changing assays.