A host-based RT-PCR gene expression signature to identify acute respiratory viral infection

Review: Diagnostic Potential for Collaborative Pharyngitis Biomarkers

Pharyngitis is an inflammatory condition of the pharynx and/or tonsils commonly seen in both children and adults. Viruses and bacteria represent the most common encountered etiologic agents-yeast/fungi and parasites are infrequently implicated. Some of these are predominantly observed in unique populations (eg, immunocompromised or unvaccinated individuals). This manuscript (part 3 of 3) summarizes the current state of biomarker diagnostic testing and highlights the expanding role they will likely play in the expedited diagnosis and management of patients with acute pharyngitis. Biomarkers, in conjunction with rapid antigen and/or nucleic acid amplification testing, will likely become the standard of care to accurately diagnose the etiologic agent(s) of pharyngitis. This novel testing paradigm has the potential to guide appropriate patient management and antibiotic stewardship by accurately determining if the cause of pharyngitis is due to a viral or bacterial etiology.

Application of a Machine Learning-Based Classification Approach for Developing Host Protein Diagnostic Models for Infectious Disease

In recent years, infectious disease diagnosis has increasingly turned to host-centered approaches as a complement to pathogen-directed ones. The former, however, typically requires the interpretation of complex multiple biomarker datasets to arrive at an informative diagnostic outcome. This report describes a machine learning (ML)-based classification workflow that is intended as a template for researchers seeking to apply ML approaches for developing host-based infectious disease biomarker classifiers. As an example, we built a classification model that could accurately distinguish between three disease etiology classes: bacterial, viral, and normal in human sera using host protein biomarkers of known diagnostic utility. After collecting protein data from known disease samples, we trained a series of increasingly complex Auto-ML models until arriving at an optimized classifier that could differentiate viral, bacterial, and non-disease samples. Even when limited to a relatively small training set size, the model had robust diagnostic characteristics and performed well when faced with a blinded sample set. We present here a flexible approach for applying an Auto-ML-based workflow for the identification of host biomarker classifiers with diagnostic utility for infectious disease, and which can readily be adapted for multiple biomarker classes and disease states.

Implementation of a Prospective Index-Cluster Sampling Strategy for the Detection of Presymptomatic Viral Respiratory Infection in Undergraduate Students

Background

Index-cluster studies may help characterize the spread of communicable infections in the presymptomatic state. We describe a prospective index-cluster sampling strategy (ICSS) to detect presymptomatic respiratory viral illness and its implementation in a college population.

Methods

We enrolled an annual cohort of first-year undergraduates who completed daily electronic symptom diaries to identify index cases (ICs) with respiratory illness. Investigators then selected 5-10 potentially exposed, asymptomatic close contacts (CCs) who were geographically co-located to follow for infections. Symptoms and nasopharyngeal samples were collected for 5 days. Logistic regression model-based predictions for proportions of self-reported illness were compared graphically for the whole cohort sampling group and the CC group.

Results

We enrolled 1379 participants between 2009 and 2015, including 288 ICs and 882 CCs. The median number of CCs per IC was 6 (interquartile range, 3-8). Among the 882 CCs, 111 (13%) developed acute respiratory illnesses. Viral etiology testing in 246 ICs (85%) and 719 CCs (82%) identified a pathogen in 57% of ICs and 15% of CCs. Among those with detectable virus, rhinovirus was the most common (IC: 18%; CC: 6%) followed by coxsackievirus/echovirus (IC: 11%; CC: 4%). Among 106 CCs with a detected virus, only 18% had the same virus as their associated IC. Graphically, CCs did not have a higher frequency of self-reported illness relative to the whole cohort sampling group.

Conclusions

Establishing clusters by geographic proximity did not enrich for cases of viral transmission, suggesting that ICSS may be a less effective strategy to detect spread of respiratory infection.

Priorities and Progress in Diagnostic Research by the Antibacterial Resistance Leadership Group

The advancement of infectious disease diagnostics, along with studies devoted to infections caused by gram-negative and gram-positive bacteria, is a top scientific priority of the Antibacterial Resistance Leadership Group (ARLG). Diagnostic tests for infectious diseases are rapidly evolving and improving. However, the availability of rapid tests designed to determine antibacterial resistance or susceptibility directly in clinical specimens remains limited, especially for gram-negative organisms. Additionally, the clinical impact of many new tests, including an understanding of how best to use them to inform optimal antibiotic prescribing, remains to be defined. This review summarizes the recent work of the ARLG toward addressing these unmet needs in the diagnostics field and describes future directions for clinical research aimed at curbing the threat of antibiotic-resistant bacterial 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.

Host Transcriptional Signatures Predict Etiology in Community-Acquired Pneumonia: Potential Antibiotic Stewardship Tools

Background:

Current approaches for pathogen identification in community-acquired pneumonia (CAP) remain suboptimal, leaving most patients without a microbiological diagnosis. If better diagnostic tools were available for differentiating between viral and bacterial CAP, unnecessary antibacterial therapy could be avoided in viral CAP patients.

Methods:

In 156 adults hospitalized with CAP classified to have bacterial, viral, or mixed viral-bacterial infection based on microbiological testing or both microbiological testing and procalcitonin (PCT) levels, we aimed to identify discriminatory host transcriptional signatures in peripheral blood samples acquired at hospital admission, by applying Dual-color-Reverse-Transcriptase-Multiplex-Ligation-dependent-Probe-Amplification (dc-RT MLPA).

Results:

In patients classified by microbiological testing, a 9-transcript signature showed high accuracy for discriminating bacterial from viral CAP (AUC 0.91, 95% CI 0.85-0.96), while a 10-transcript signature similarly discriminated mixed viral-bacterial from viral CAP (AUC 0.91, 95% CI 0.86-0.96). In patients classified by both microbiological testing and PCT levels, a 13-transcript signature showed excellent accuracy for discriminating bacterial from viral CAP (AUC 1.00, 95% CI 1.00-1.00), while a 7-transcript signature similarly discriminated mixed viral-bacterial from viral CAP (AUC 0.93, 95% CI 0.87-0.98).

Conclusion:

Our findings support host transcriptional signatures in peripheral blood samples as a potential tool for guiding clinical decision-making and antibiotic stewardship in CAP.

Detecting the critical states during disease development based on temporal network flow entropy

Complex diseases progression can be generally divided into three states, which are normal state, predisease/critical state and disease state. The sudden deterioration of diseases can be viewed as a bifurcation or a critical transition. Therefore, hunting for the tipping point or critical state is of great importance to prevent the disease deterioration. However, it is still a challenging task to detect the critical states of complex diseases with high-dimensional data, especially based on an individual. In this study, we develop a new method based on network fluctuation of molecules, temporal network flow entropy (TNFE) or temporal differential network flow entropy, to detect the critical states of complex diseases on the basis of each individual. By applying this method to a simulated dataset and six real diseases, including respiratory viral infections and tumors with four time-course and two stage-course high-dimensional omics datasets, the critical states before deterioration were detected and their dynamic network biomarkers were identified successfully. The results on the simulated dataset indicate that the TNFE method is robust under different noise strengths, and is also superior to the existing methods on detecting the critical states. Moreover, the analysis on the real datasets demonstrated the effectiveness of TNFE for providing early-warning signals on various diseases. In addition, we also predicted disease deterioration risk and identified drug targets for cancers based on stage-wise data.

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.

Comparing the Diagnostic Accuracy of Clinician Judgment to a Novel Host Response Diagnostic for Acute Respiratory Illness

Background

Difficulty discriminating bacterial from viral infections drives antibacterial misuse. Host gene expression tests discriminate bacterial and viral etiologies, but their clinical utility has not been evaluated.

Methods

Host gene expression and procalcitonin levels were measured in 582 emergency department participants with suspected infection. We also recorded clinician diagnosis and clinician-recommended treatment. These 4 diagnostic strategies were compared with clinical adjudication as the reference. To estimate the clinical impact of host gene expression, we calculated the change in overall Net Benefit (∆NB; the difference in Net Benefit comparing 1 diagnostic strategy with a reference) across a range of prevalence estimates while factoring in the clinical significance of false-positive and -negative errors.

Results

Gene expression correctly classified bacterial, viral, or noninfectious illness in 74.1% of subjects, similar to the other strategies. Clinical diagnosis and clinician-recommended treatment revealed a bias toward overdiagnosis of bacterial infection resulting in high sensitivity (92.6% and 94.5%, respectively) but poor specificity (67.2% and 58.8%, respectively), resulting in a 33.3% rate of inappropriate antibacterial use. Gene expression offered a more balanced sensitivity (79.0%) and specificity (80.7%), which corresponded to a statistically significant improvement in average weighted accuracy (79.9% vs 71.5% for procalcitonin and 76.3% for clinician-recommended treatment; P<.0001 for both). Consequently, host gene expression had greater Net Benefit in diagnosing bacterial infection than clinician-recommended treatment (∆NB=6.4%) and procalcitonin (∆NB=17.4%).

Conclusions

Host gene expression-based tests to distinguish bacterial and viral infection can facilitate appropriate treatment, improving patient outcomes and mitigating the antibacterial resistance crisis.

A host transcriptomic signature for identification of respiratory viral infections in the community

BACKGROUND

Fever-7 is a test evaluating host mRNA expression levels of IFI27, JUP, LAX, HK3, TNIP1, GPAA1 and CTSB in blood able to detect viral infections. This test has been validated mostly in hospital settings. Here we have evaluated Fever-7 to identify the presence of respiratory viral infections in a Community Health Center.

METHODS

A prospective study was conducted in the "Servicio de Urgencias de Atención Primaria" in Salamanca, Spain. Patients with clinical signs of respiratory infection and at least one point in the National Early Warning Score were recruited. Fever-7 mRNAs were profiled on a Nanostring nCounter® SPRINT instrument from blood collected upon patient enrolment. Viral diagnosis was performed on nasopharyngeal aspirates (NPAs) using the Biofire-RP2 panel.

RESULTS

A respiratory virus was detected in the NPAs of 66 of the 100 patients enrolled. Median National Early Warning Score was 7 in the group with no virus detected and 6.5 in the group with a respiratory viral infection (P > .05). The Fever-7 score yielded an overall AUC of 0.81 to predict a positive viral syndromic test. The optimal operating point for the Fever-7 score yielded a sensitivity of 82% with a specificity of 71%. Multivariate analysis showed that Fever-7 was a robust marker of viral infection independently of age, sex, major comorbidities and disease severity at presentation (OR [CI95%], 3.73 [2.14-6.51], P < .001).

CONCLUSIONS

Fever-7 is a promising host immune mRNA signature for the early identification of a respiratory viral infection in the community.

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.

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.

Machine Learning and Precision Medicine in Emergency Medicine: The Basics

As machine learning (ML) and precision medicine become more readily available and used in practice, emergency physicians must understand the potential advantages and limitations of the technology. This narrative review focuses on the key components of machine learning, artificial intelligence, and precision medicine in emergency medicine (EM). Based on the content expertise, we identified articles from EM literature. The authors provided a narrative summary of each piece of literature. Next, the authors provided an introduction of the concepts of ML, artificial intelligence as an extension of ML, and precision medicine. This was followed by concrete examples of their applications in practice and research. Subsequently, we shared our thoughts on how to consume the existing research in these subjects and conduct high-quality research for academic emergency medicine. We foresee that the EM community will continue to adapt machine learning, artificial intelligence, and precision medicine in research and practice. We described several key components using our expertise.

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.

Dysregulated transcriptional responses to SARS-CoV-2 in the periphery

SARS-CoV-2 infection has been shown to trigger a wide spectrum of immune responses and clinical manifestations in human hosts. Here, we sought to elucidate novel aspects of the host response to SARS-CoV-2 infection through RNA sequencing of peripheral blood samples from 46 subjects with COVID-19 and directly comparing them to subjects with seasonal coronavirus, influenza, bacterial pneumonia, and healthy controls. Early SARS-CoV-2 infection triggers a powerful transcriptomic response in peripheral blood with conserved components that are heavily interferon-driven but also marked by indicators of early B-cell activation and antibody production. Interferon responses during SARS-CoV-2 infection demonstrate unique patterns of dysregulated expression compared to other infectious and healthy states. Heterogeneous activation of coagulation and fibrinolytic pathways are present in early COVID-19, as are IL1 and JAK/STAT signaling pathways, which persist into late disease. Classifiers based on differentially expressed genes accurately distinguished SARS-CoV-2 infection from other acute illnesses (auROC 0.95 [95% CI 0.92-0.98]). The transcriptome in peripheral blood reveals both diverse and conserved components of the immune response in COVID-19 and provides for potential biomarker-based approaches to diagnosis.

A diagnostic host response biosignature for COVID-19 from RNA profiling of nasal swabs and blood

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes coronavirus disease-19 (COVID-19), has emerged as the cause of a global pandemic. We used RNA sequencing to analyze 286 nasopharyngeal (NP) swab and 53 whole-blood (WB) samples from 333 patients with COVID-19 and controls. Overall, a muted immune response was observed in COVID-19 relative to other infections (influenza, other seasonal coronaviruses, and bacterial sepsis), with paradoxical down-regulation of several key differentially expressed genes. Hospitalized patients and outpatients exhibited up-regulation of interferon-associated pathways, although heightened and more robust inflammatory responses were observed in hospitalized patients with more clinically severe illness. Two-layer machine learning-based host classifiers consisting of complete (>1000 genes), medium (<100), and small (<20) gene biomarker panels identified COVID-19 disease with 85.1-86.5% accuracy when benchmarked using an independent test set. SARS-CoV-2 infection has a distinct biosignature that differs between NP swabs and WB and can be leveraged for COVID-19 diagnosis.

Respiratory viral sepsis: epidemiology, pathophysiology, diagnosis and treatment

According to the Third International Consensus Definition for Sepsis and Septic Shock, sepsis is a life-threatening organ dysfunction resulting from dysregulated host responses to infection. Epidemiological data about sepsis from the 2017 Global Burden of Diseases, Injuries and Risk Factor Study showed that the global burden of sepsis was greater than previously estimated. Bacteria have been shown to be the predominant pathogen of sepsis among patients with pathogens detected, while sepsis caused by viruses is underdiagnosed worldwide. The coronavirus disease that emerged in 2019 in China and now in many other countries has brought viral sepsis back into the vision of physicians and researchers worldwide. Although the current understanding of the pathophysiology of sepsis has improved, the differences between viral and bacterial sepsis at the level of pathophysiology are not well understood. Diagnosis methods that can broadly differentiate between bacterial and viral sepsis at the initial stage after the development of sepsis are limited. New treatments that can be applied at clinics for sepsis are scarce and this situation is not consistent with the growing understanding of pathophysiology. This review aims to give a brief summary of current knowledge of the epidemiology, pathophysiology, diagnosis and treatment of viral sepsis.

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 preventive strategy for pandemics in the elderly is to collect in advance samples & data to counteract chronic inflammation (inflammaging)

Fighting the current COVID-19 pandemic, we must not forget to prepare for the next. Since elderly and frail people are at high risk, we wish to predict their vulnerability, and intervene if possible. For example, it would take little effort to take additional swabs or dried blood spots. Such minimally-invasive sampling, exemplified here during screening for potential COVID-19 infection, can yield the data to discover biomarkers to better handle this and the next respiratory disease pandemic. Longitudinal outcome data can then be combined with other epidemics and old-age health data, to discover the best biomarkers to predict (i) coping with infection & inflammation and thus hospitalization or intensive care, (ii) long-term health challenges, e.g. deterioration of lung function after intensive care, and (iii) treatment & vaccination response. Further, there are universal triggers of old-age morbidity & mortality, and the elimination of senescent cells improved health in pilot studies in idiopathic lung fibrosis & osteoarthritis patients alike. Biomarker studies are needed to test the hypothesis that resilience of the elderly during a pandemic can be improved by countering chronic inflammation and/or removing senescent cells. Our review suggests that more samples should be taken and saved systematically, following minimum standards, and data be made available, to maximize healthspan & minimize frailty, leading to savings in health care, gains in quality of life, and preparing us better for the next pandemic, all at the same time.

Dysregulated transcriptional responses to SARS-CoV-2 in the periphery support novel diagnostic approaches

In order to elucidate novel aspects of the host response to SARS-CoV-2 we performed RNA sequencing on peripheral blood samples across 77 timepoints from 46 subjects with COVID-19 and compared them to subjects with seasonal coronavirus, influenza, bacterial pneumonia, and healthy controls. Early SARS-CoV-2 infection triggers a conserved transcriptomic response in peripheral blood that is heavily interferon-driven but also marked by indicators of early B-cell activation and antibody production. Interferon responses during SARS-CoV-2 infection demonstrate unique patterns of dysregulated expression compared to other infectious and healthy states. Heterogeneous activation of coagulation and fibrinolytic pathways are present in early COVID-19, as are IL1 and JAK/STAT signaling pathways, that persist into late disease. Classifiers based on differentially expressed genes accurately distinguished SARS-CoV-2 infection from other acute illnesses (auROC 0.95). The transcriptome in peripheral blood reveals unique aspects of the immune response in COVID-19 and provides for novel biomarker-based approaches to diagnosis.

Minimal transmission in an influenza A (H3N2) human challenge-transmission model within a controlled exposure environment

Uncertainty about the importance of influenza transmission by airborne droplet nuclei generates controversy for infection control. Human challenge-transmission studies have been supported as the most promising approach to fill this knowledge gap. Healthy, seronegative volunteer ‘Donors’ (n = 52) were randomly selected for intranasal challenge with influenza A/Wisconsin/67/2005 (H3N2). ‘Recipients’ randomized to Intervention (IR, n = 40) or Control (CR, n = 35) groups were exposed to Donors for four days. IRs wore face shields and hand sanitized frequently to limit large droplet and contact transmission. One transmitted infection was confirmed by serology in a CR, yielding a secondary attack rate of 2.9% among CR, 0% in IR (p = 0.47 for group difference), and 1.3% overall, significantly less than 16% (p<0.001) expected based on a proof-of-concept study secondary attack rate and considering that there were twice as many Donors and days of exposure. The main difference between these studies was mechanical building ventilation in the follow-on study, suggesting a possible role for aerosols.

Understanding the relative importance of influenza modes of transmission informs strategic use of preventive measures to reduce influenza risk in high-risk settings such as hospitals and is important for pandemic preparedness. Given the increasing evidence from epidemiological modelling, exhaled viral aerosol, and aerobiological survival studies supporting a role for airborne transmission and the potential benefit of respirators (and other precautions designed to prevent inhalation of aerosols) versus surgical masks (mainly effective for reducing exposure to large droplets) to protect healthcare workers, more studies are needed to evaluate the extent of risk posed airborne versus contact and large droplet spray transmission modes. New human challenge-transmission studies should be carefully designed to overcome limitations encountered in the current study. The low secondary attack rate reported herein also suggests that the current challenge-transmission model may no longer be a more promising approach to resolving questions about transmission modes than community-based studies employing environmental monitoring and newer, state-of-the-art deep sequencing-based molecular epidemiological methods.

Metagenomics for neurological infections - expanding our imagination

Over the past two decades, the diagnosis rate for patients with encephalitis has remained poor despite advances in pathogen-specific testing such as PCR and antigen assays. Metagenomic next-generation sequencing (mNGS) of RNA and DNA extracted from cerebrospinal fluid and brain tissue now offers another strategy for diagnosing neurological infections. Given that mNGS simultaneously assays for a wide range of infectious agents in an unbiased manner, it can identify pathogens that were not part of a neurologist’s initial differential diagnosis either because of the rarity of the infection, because the microorganism has not been previously associated with a clinical phenotype or because it is a newly discovered organism. This Review discusses the technical advantages and pitfalls of cerebrospinal fluid mNGS in the context of patients with neuroinflammatory syndromes, including encephalitis, meningitis and myelitis. We also speculate on how mNGS testing potentially fits into current diagnostic testing algorithms given data on mNGS test performance, cost and turnaround time. Finally, the Review highlights future directions for mNGS technology and other hypothesis-free testing methodologies that are in development.

This Review discusses the advantages and pitfalls of metagenomic next-generation sequencing (mNGS) in patients with encephalitis, meningitis and myelitis. The authors outline data on mNGS test performance, cost and turnaround time and highlight future directions for mNGS technology.

Meningoencephalitis remains a challenging diagnosis owing to the multitude of possible infectious and autoimmune causes.

Meningoencephalitis is associated with a high rate of morbidity and mortality and requires prompt diagnosis and treatment.

Metagenomic next-generation sequencing (mNGS) is now a clinically validated test for neuroinfectious diseases that can aid clinicians with a timely diagnosis.

mNGS can improve the detection of pathogens that were missed by clinicians or on standard direct testing.

mNGS does not perform well when indirect tests are required to make the diagnosis (for example, serology), when infections are compartmentalized and for certain low abundance pathogens.

The clinical context of the case is required when interpreting the results of mNGS.

Previremic Identification of Ebola or Marburg Virus Infection Using Integrated Host-Transcriptome and Viral Genome Detection

Current molecular tests that identify infection with high-consequence viruses such as Ebola virus and Marburg virus are based on the detection of virus material in the blood. These viruses do not undergo significant early replication in the blood and, instead, replicate in organs such as the liver and spleen. Thus, virus begins to accumulate in the blood only after significant replication has already occurred in those organs, making viremia an indicator of infection only after initial stages have become established. Here, we show that a multianalyte assay can correctly identify the infectious agent in nonhuman primates (NHPs) prior to viremia through tracking host infection response transcripts. This illustrates that a single-tube, sample-to-answer format assay could be used to advance the time at which the type of infection can be determined and thereby improve outcomes.

Outbreaks of filoviruses, such as those caused by the Ebola (EBOV) and Marburg (MARV) virus, are difficult to detect and control. The initial clinical symptoms of these diseases are nonspecific and can mimic other endemic pathogens. This makes confident diagnosis based on clinical symptoms alone impossible. Molecular diagnostics for these diseases that rely on the detection of viral RNA in the blood are only effective after significant disease progression. As an approach to identify these infections earlier in the disease course, we tested the effectiveness of viral RNA detection combined with an assessment of sentinel host mRNAs that are upregulated following filovirus infection. RNAseq analysis of EBOV-infected nonhuman primates identified host RNAs that are upregulated at early stages of infection. NanoString probes that recognized these host-response RNAs were combined with probes that recognized viral RNA and were used to classify viral infection both prior to viremia and postviremia. This approach was highly successful at identifying samples from nonhuman primate subjects and correctly distinguished the causative agent in a previremic stage in 10 EBOV and 5 MARV samples. This work suggests that unified host response/viral fingerprint assays can enable diagnosis of disease earlier than testing for viral nucleic acid alone, which could decrease transmission events and increase therapeutic effectiveness.

Average Weighted Accuracy: Pragmatic Analysis for a Rapid Diagnostics in Categorizing Acute Lung Infections (RADICAL) Study

Patient management relies on diagnostic information to identify appropriate treatment. Standard evaluations of diagnostic tests consist of estimating sensitivity, specificity, positive/negative predictive values, likelihood ratios, and accuracy. Although useful, these metrics do not convey the tests' clinical value, which is critical to informing decision-making. Full appreciation of the clinical impact of a diagnostic test requires analyses that integrate sensitivity and specificity, account for the disease prevalence within the population of test application, and account for the relative importance of specificity vs sensitivity by considering the clinical implications of false-positive and false-negative results. We developed average weighted accuracy (AWA), representing a pragmatic metric of diagnostic yield or global utility of a diagnostic test. AWA can be used to compare test alternatives, even across different studies. We apply the AWA methodology to evaluate a new diagnostic test developed in the Rapid Diagnostics in Categorizing Acute Lung Infections (RADICAL) study.

Previously Derived Host Gene Expression Classifiers Identify Bacterial and Viral Etiologies of Acute Febrile Respiratory Illness in a South Asian Population

Background

Pathogen-based diagnostics for acute respiratory infection (ARI) have limited ability to detect etiology of illness. We previously showed that peripheral blood-based host gene expression classifiers accurately identify bacterial and viral ARI in cohorts of European and African descent. We determined classifier performance in a South Asian cohort.

Methods

Patients ≥15 years with fever and respiratory symptoms were enrolled in Sri Lanka. Comprehensive pathogen-based testing was performed. Peripheral blood ribonucleic acid was sequenced and previously developed signatures were applied: a pan-viral classifier (viral vs nonviral) and an ARI classifier (bacterial vs viral vs noninfectious).

Results

Ribonucleic acid sequencing was performed in 79 subjects: 58 viral infections (36 influenza, 22 dengue) and 21 bacterial infections (10 leptospirosis, 11 scrub typhus). The pan-viral classifier had an overall classification accuracy of 95%. The ARI classifier had an overall classification accuracy of 94%, with sensitivity and specificity of 91% and 95%, respectively, for bacterial infection. The sensitivity and specificity of C-reactive protein (>10 mg/L) and procalcitonin (>0.25 ng/mL) for bacterial infection were 100% and 34%, and 100% and 41%, respectively.

Conclusions

Previously derived gene expression classifiers had high predictive accuracy at distinguishing viral and bacterial infection in South Asian patients with ARI caused by typical and atypical pathogens.

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.

Biosensors for detecting viral and bacterial infections using host biomarkers: a review

A schematic diagram showing multiple modes of biosensing platforms for the diagnosis of bacterial or viral infections.

Challenges in the diagnosis of paediatric pneumonia in intervention field trials: recommendations from a pneumonia field trial working group

Pneumonia is a leading killer of children younger than 5 years despite high vaccination coverage, improved nutrition, and widespread implementation of the Integrated Management of Childhood Illnesses algorithm. Assessing the effect of interventions on childhood pneumonia is challenging because the choice of case definition and surveillance approach can affect the identification of pneumonia substantially. In anticipation of an intervention trial aimed to reduce childhood pneumonia by lowering household air pollution, we created a working group to provide recommendations regarding study design and implementation. We suggest to, first, select a standard case definition that combines acute (≤14 days) respiratory symptoms and signs and general danger signs with ancillary tests (such as chest imaging and pulse oximetry) to improve pneumonia identification; second, to prioritise active hospital-based pneumonia surveillance over passive case finding or home-based surveillance to reduce the risk of non-differential misclassification of pneumonia and, as a result, a reduced effect size in a randomised trial; and, lastly, to consider longitudinal follow-up of children younger than 1 year, as this age group has the highest incidence of severe pneumonia.

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.

Antiviral Response in the Nasopharynx Identifies Patients With Respiratory Virus Infection

Background

Despite the high burden of respiratory infection and the importance of early and accurate diagnosis, there is no simple diagnostic test to rule in viral infection as a cause of respiratory symptoms.

Methods

We performed RNA sequencing on human nasal epithelial cells following stimulation of the intracellular viral recognition receptor RIG-I. Next, we evaluated whether measuring identified host mRNAs and proteins from patient nasopharyngeal swabs could predict the presence of a respiratory virus in the sample.

Results

Our first study showed that a signature of 3 mRNAs, CXCL10, IFIT2, and OASL, predicted respiratory virus detection with an accuracy of 97% (95% confidence interval [CI], 0.9–1.0), and identified proteins correlating with virus detection. In a second study, elevated CXCL11 or CXCL10 protein levels identified samples containing respiratory viruses, including viruses not on the initial test panel. Overall area under the curve (AUC) values were: CXCL11 AUC = 0.901 (95% CI, 0.86–0.94); CXCL10 AUC = 0.85 (95% CI, 0.80–0.91).

Conclusions

Host antiviral mRNAs and single host proteins detectable using nasopharyngeal swabs accurately predict the presence of viral infection. This approach holds promise for developing rapid, cost-effective tests to improve management of patients with respiratory illnesses.

Personalizing the Management of Pneumonia

Pneumonia is a highly prevalent disease with considerable morbidity and mortality. However, diagnosis and therapy still rely on antiquated methods, leading to the vast overuse of antimicrobials, which carries risks for both society and the individual. Furthermore, outcomes in severe pneumonia remain poor. Genomic techniques have the potential to transform the management of pneumonia through deep characterization of pathogens as well as the host response to infection. This characterization will enable the delivery of selective antimicrobials and immunomodulatory therapy that will help to offset the disorder associated with overexuberant immune responses.

A host gene expression approach for identifying triggers of asthma exacerbations

Rationale

Asthma exacerbations often occur due to infectious triggers, but determining whether infection is present and whether it is bacterial or viral remains clinically challenging. A diagnostic strategy that clarifies these uncertainties could enable personalized asthma treatment and mitigate antibiotic overuse.

Objectives

To explore the performance of validated peripheral blood gene expression signatures in discriminating bacterial, viral, and noninfectious triggers in subjects with asthma exacerbations.

Methods

Subjects with suspected asthma exacerbations of various etiologies were retrospectively selected for peripheral blood gene expression analysis from a pool of subjects previously enrolled in emergency departments with acute respiratory illness. RT-PCR quantified 87 gene targets, selected from microarray-based studies, followed by logistic regression modeling to define bacterial, viral, or noninfectious class. The model-predicted class was compared to clinical adjudication and procalcitonin.

Results

Of 46 subjects enrolled, 7 were clinically adjudicated as bacterial, 18 as viral, and 21 as noninfectious. Model prediction was congruent with clinical adjudication in 15/18 viral and 13/21 noninfectious cases, but only 1/7 bacterial cases. None of the adjudicated bacterial cases had confirmatory microbiology; the precise etiology in this group was uncertain. Procalcitonin classified only one subject in the cohort as bacterial. 47.8% of subjects received antibiotics.

Conclusions

Our model classified asthma exacerbations by the underlying bacterial, viral, and noninfectious host response. Compared to clinical adjudication, the majority of discordances occurred in the bacterial group, due to either imperfect adjudication or model misclassification. Bacterial infection was identified infrequently by all classification schemes, but nearly half of subjects were prescribed antibiotics. A gene expression-based approach may offer useful diagnostic information in this population and guide appropriate antibiotic use.

Enhanced Signal Amplification in a Toll-like Receptor-4 Biosensor Utilizing Ferrocene-Terminated Mixed Monolayers

A major challenge in effectively treating infections is to provide timely diagnosis of a bacterial or viral agent. Current cell culture methods require >24 h to identify the cause of infection. The Toll-like Receptor (TLR) family of proteins can identify classes of pathogens and has been shown to work well in an impedance-based biosensor, where the protein is attached to an electrode via a self-assembled monolayer (SAM). While the sensitivity of these sensors has been good, they contain a high resistance (>1 kΩ) SAM, generating relatively small signals and requiring longer data collection, which is ill-suited to implementation outside of a laboratory. Here, we describe a novel approach to increase the signal magnitude and decrease the measurement time of a TLR-4 biosensor by inserting a redox-active ferrocenyl-terminated alkanethiol into a mixed SAM containing hydroxyl- and carboxyl-terminated alkanethiols. The SAM formation and modification was confirmed via contact angle and X-ray photoelectron spectroscopy measurements, with TLR-4 immobilization demonstrated through a modified immunosorbent assay. It is shown that these TLR-4 biosensors respond selectively to their intended target, Gram-negative bacteria at levels between 1 and 10⁵ lysed cells/mL, while remaining insensitive to Gram-positive bacteria or viral particles at up to 10⁵ particles/mL. Furthermore, the signal enhancement due to the addition of ferrocene decreased the measurement time to less than 1 min and has enabled this sensor to be used with an inexpensive, portable, hand-held potentiostat that could be easily implemented in field settings.

A miRNA Host Response Signature Accurately Discriminates Acute Respiratory Infection Etiologies

Background: Acute respiratory infections (ARIs) are the leading indication for antibacterial prescriptions despite a viral etiology in the majority of cases. The lack of available diagnostics to discriminate viral and bacterial etiologies contributes to this discordance. Recent efforts have focused on the host response as a source for novel diagnostic targets although none have explored the ability of host-derived microRNAs (miRNA) to discriminate between these etiologies. Methods: In this study, we compared host-derived miRNAs and mRNAs from human H3N2 influenza challenge subjects to those from patients with Streptococcus pneumoniae pneumonia. Sparse logistic regression models were used to generate miRNA signatures diagnostic of ARI etiologies. Generalized linear modeling of mRNAs to identify differentially expressed (DE) genes allowed analysis of potential miRNA:mRNA relationships. High likelihood miRNA:mRNA interactions were examined using binding target prediction and negative correlation to further explore potential changes in pathway regulation in response to infection. Results: The resultant miRNA signatures were highly accurate in discriminating ARI etiologies. Mean accuracy was 100% [88.8-100; 95% Confidence Interval (CI)] in discriminating the healthy state from S. pneumoniae pneumonia and 91.3% (72.0-98.9; 95% CI) in discriminating S. pneumoniae pneumonia from influenza infection. Subsequent differential mRNA gene expression analysis revealed alterations in regulatory networks consistent with known biology including immune cell activation and host response to viral infection. Negative correlation network analysis of miRNA:mRNA interactions revealed connections to pathways with known immunobiology such as interferon regulation and MAP kinase signaling. Conclusion: We have developed novel human host-response miRNA signatures for bacterial and viral ARI etiologies. miRNA host response signatures reveal accurate discrimination between S. pneumoniae pneumonia and influenza etiologies for ARI and integrated analyses of the host-pathogen interface are consistent with expected biology. These results highlight the differential miRNA host response to bacterial and viral etiologies of ARI, offering new opportunities to distinguish these entities.

A crowdsourced analysis to identify ab initio molecular signatures predictive of susceptibility to viral infection

The response to respiratory viruses varies substantially between individuals, and there are currently no known molecular predictors from the early stages of infection. Here we conduct a community-based analysis to determine whether pre- or early post-exposure molecular factors could predict physiologic responses to viral exposure. Using peripheral blood gene expression profiles collected from healthy subjects prior to exposure to one of four respiratory viruses (H1N1, H3N2, Rhinovirus, and RSV), as well as up to 24 h following exposure, we find that it is possible to construct models predictive of symptomatic response using profiles even prior to viral exposure. Analysis of predictive gene features reveal little overlap among models; however, in aggregate, these genes are enriched for common pathways. Heme metabolism, the most significantly enriched pathway, is associated with a higher risk of developing symptoms following viral exposure. This study demonstrates that pre-exposure molecular predictors can be identified and improves our understanding of the mechanisms of response to respiratory viruses.

Fecal Host Transcriptomics for Non-Invasive Human Mucosal Immune Profiling: Proof of Concept in Clostridium Difficile Infection

Background:

Host factors play an important role in pathogenesis and disease outcome in Clostridium difficile infection (CDI), and characterization of these responses could uncover potential host biomarkers to complement existing microbe-based diagnostics.

Methods:

We extracted RNA from fecal samples of patients with CDI and profiled human mRNA using amplicon-based next-generation sequencing (NGS). We compared the fecal host mRNA transcript expression profiles of patients with CDI to controls with non-CDI diarrhea.

Results:

We found that the ratio of human actin gamma 1 (ACTG1) to 16S ribosomal RNA (rRNA) was highly correlated with NGS quality as measured by percentage of reads on target. Patients with CDI could be differentiated from those with non-CDI diarrhea based on their fecal mRNA expression profiles using principal component analysis. Among the most differentially expressed genes were ones related to immune response (IL23A, IL34) and actin-cytoskeleton function (TNNT1, MYL4, SMTN, MYBPC3, all adjusted P-values < 1 x 10^-3).

Conclusions:

In this proof-of-concept study, we used host fecal transcriptomics for non-invasive profiling of the mucosal immune response in CDI. We identified differentially expressed genes with biological plausibility based on animal and cell culture models. This demonstrates the potential of fecal transcriptomics to uncover host-based biomarkers for enteric infections.

Heterotypic Infection and Spread of Rhinovirus A, B, and C among Childcare Attendees

Background

Despite the frequency of human rhinovirus (HRV), data describing the molecular epidemiology of HRV in the community are limited. Childcare centers are optimal settings to characterize heterotypic HRV cocirculation.

Methods

HRV specimens were prospectively obtained from a cohort of childcare attendees at enrollment and weekly during respiratory illness. The 5' noncoding region sequences were used to determine HRV species (A, B, C) and genotypes.

Results

Among 225 children followed, sequence data were available for 92 HRV infections: HRV-A (n = 80; 59%) was most common, followed by HRV-C (n = 52, 39%), and HRV-B (n = 3, 2%). Forty-one genotypes were identified and cocirculation was common. Frequent spread between classrooms occurred with 2 HRV-A genotypes. Repeated detections within single illnesses were a combination of persistent (n = 7) and distinct (n = 7) genotypes. Prevalence of HRV among asymptomatic children was 41%. HRV-C was clinically similar to HRV-A and HRV-B.

Conclusions

HRV epidemiology in childcare consists of heterotypic cocirculation of genotypes with periodic spread within and among classrooms. Based on our finding of multiple genotypes evident during the course of single illnesses, the use of sequence-based HRV type determination is critical in longitudinal studies of HRV epidemiology and transmission.

Volatile fingerprinting of Pseudomonas aeruginosa and respiratory syncytial virus infection in an in vitro cystic fibrosis co-infection model

Volatile molecules in exhaled breath represent potential biomarkers in the setting of infectious diseases, particularly those affecting the respiratory tract. In particular, Pseudomonas aeruginosa is a critically important respiratory pathogen in specific subsets of the population, such as those with cystic fibrosis (CF). Infections caused by P. aeruginosa can be particularly problematic when co-infection with respiratory syncytial virus (RSV) occurs, as this is correlated with the establishment of chronic P. aeruginosa infection. In the present study, we evaluate the volatile metabolites produced by P. aeruginosa (PAO1)-infected, RSV-infected, co-infected, or uninfected CF bronchial epithelial (CFBE) cells, in vitro. We identified a volatile metabolic signature that could discriminate between P. aeruginosa-infected and non-P. aeruginosa-infected CFBE with an area under the receiver operating characteristic curve (AUROC) of 0.850, using the machine learning algorithm random forest (RF). Although we could not discriminate between RSV-infected and non-RSV-infected CFBE (AUROC = 0.431), we note that sample classification probabilities for RSV-infected cell, generated using RF, were between those of uninfected CFBE and P. aeruginosa-infected CFBE, suggesting that RSV infection may result in a volatile metabolic profile that shares attributes with both of these groups. To more precisely elucidate the biological origins of the volatile metabolites that were discriminatory between P. aeruginosa-infected and non-P. aeruginosa-infected CFBE, we measured the volatile metabolites produced by P. aeruginosa grown in the absence of CFBE. Our findings suggest that the discriminatory metabolites produced likely result from the interaction of P. aeruginosa with the CFBE cells, rather than the metabolism of media components by the bacterium. Taken together, our findings support the notion that P. aeruginosa interacting with CFBE yields a particular volatile metabolic signature. Such a signature may have clinical utility in the monitoring of individuals with CF.

Whole-Genome mRNA Gene Expression Differs Between Patients With and Without Delirium

OBJECTIVE

To identify differences in gene expression between patients with in-hospital delirium from a known etiology (urinary tract infection [UTI]) and patients with delirium from an unknown etiology, as well as from nondelirious patients.

METHODS

Thirty patients with delirium (8 with UTI) and 21 nondelirious patients (11 with UTI) were included in this prospective case-control study. Transcriptomic profiles from messenger RNA sequencing of peripheral blood were analyzed for gene expression and disease-specific pathway enrichment patterns, correcting for systemic inflammatory response syndrome. Genes and pathways with significant differential activity based on Fisher exact test ( P < .05, |Z score| >2) are reported.

RESULTS

Patients with delirium with UTI, compared to patients with delirium without UTI, exhibited significant activation of interferon signaling, upstream cytokines, and transcription regulators, as well as significant inhibition of actin cytoskeleton, integrin, paxillin, glioma invasiveness signaling, and upstream growth factors. All patients with delirium, compared to nondelirious patients, had significant complement system activation. Among patients with delirium without UTI, compared to nondelirious patients without UTI, there was significant activation of elF4 and p7056 K signaling.

CONCLUSIONS

Differences exist in gene expression between delirious patients due to UTI presence, as well as due to the presence of delirium alone. Transcriptional profiling may help develop etiology-specific biomarkers for patients with delirium.

Volatile fingerprinting of human respiratory viruses from cell culture

Volatile metabolites are currently under investigation as potential biomarkers for the detection and identification of pathogenic microorganisms, including bacteria, fungi, and viruses. Unlike bacteria and fungi, which produce distinct volatile metabolic signatures associated with innate differences in both primary and secondary metabolic processes, viruses are wholly reliant on the metabolic machinery of infected cells for replication and propagation. In the present study, the ability of volatile metabolites to discriminate between respiratory cells infected and uninfected with virus, in vitro, was investigated. Two important respiratory viruses, namely respiratory syncytial virus (RSV) and influenza A virus (IAV), were evaluated. Data were analyzed using three different machine learning algorithms (random forest (RF), linear support vector machines (linear SVM), and partial least squares-discriminant analysis (PLS-DA)), with volatile metabolites identified from a training set used to predict sample classifications in a validation set. The discriminatory performances of RF, linear SVM, and PLS-DA were comparable for the comparison of IAV-infected versus uninfected cells, with area under the receiver operating characteristic curves (AUROCs) between 0.78 and 0.82, while RF and linear SVM demonstrated superior performance in the classification of RSV-infected versus uninfected cells (AUROCs between 0.80 and 0.84) relative to PLS-DA (0.61). A subset of discriminatory features were assigned putative compound identifications, with an overabundance of hydrocarbons observed in both RSV- and IAV-infected cell cultures relative to uninfected controls. This finding is consistent with increased oxidative stress, a process associated with viral infection of respiratory cells.

Bacterial pathogens were detected from human exhaled breath using a novel protocol

It is generally believed that influenza outbreak is associated with breath-borne transmission of viruses, however relevant evidence is little for that of respiratory bacterial infections. On another front, point-of-care infection diagnostic methods at the bedside are significantly lacking. Here, we used a newly developed protocol of integrating an exhaled breath condensate (EBC) collection device (PKU BioScreen) and Loop Mediated Isothermal Amplification (LAMP) to investigate what bacterial pathogens can be directly exhaled out from humans. Exhaled breath condensates were collected from human subjects with respiratory infection symptoms at Peking University 3rd hospital using the BioScreen. The screened bacterial pathogens included Streptococcus pneumoniae, Staphylococcus aureus, Methicillin-resistant Stphylococcus aureus (MRSA), Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa, Acinetobacter baumannii, Stenotrophomonas maltophilia, Haemophilus influenzae, Legionella pneumophila, Mycoplasma Pneumonia, Chlamydia pneumonia, and Mycobacterium tuberculosis. The results were further compared and validated using throat swabs from the same patients by a PCR method. Here, human bacterial pathogens such as H. influenzae, P. aeruginosa, E. coli, S. aureus and MRSA were detected in exhaled breath using the developed protocol that integrates the EBC collection and LAMP. For the patients recruited from the hospital, seven types of pathogens were detected from 36.5% of them, and for the remaining subjects none of those screened bacterial pathogens was detected. Importantly, some super resistant bacteria such as MRSA were detected from the exhaled breath, suggesting that breathing might be also an important bacterial transmission route. Results from throat swabs showed that 36.2% of the subjects were found to be infected with H. influenzae, P. aeruginosa, E. coli, S. maltophilia, S. aureus and MRSA. For the EBC samples, 33.3% were found to be infected with MRSA, E. coli and P. aeruginosa. Depending on the initial pathogen load in the sample, the entire protocol (EBC-LAMP) only takes 20-60 min to complete for a respiratory infection diagnosis. For different detection methods and pathogens, the agreements between the EBC and throat swabs from the same patients were found to range from 35% to 65%. Here, we have detected several bacterial pathogens including MRSA from exhaled breath, and the developed protocol could be very useful for the bedside pathogen screening particularly in remote areas where resources are significantly limited or prohibited.

Advancing Diagnostics to Address Antibacterial Resistance: The Diagnostics and Devices Committee of the Antibacterial Resistance Leadership Group

Diagnostics are a cornerstone of the practice of infectious diseases. However, various limitations frequently lead to unmet clinical needs. In most other domains, diagnostics focus on narrowly defined questions, provide readily interpretable answers, and use true gold standards for development. In contrast, infectious diseases diagnostics must contend with scores of potential pathogens, dozens of clinical syndromes, emerging pathogens, rapid evolution of existing pathogens and their associated resistance mechanisms, and the absence of gold standards in many situations. In spite of these challenges, the importance and value of diagnostics cannot be underestimated. Therefore, the Antibacterial Resistance Leadership Group has identified diagnostics as 1 of 4 major areas of emphasis. Herein, we provide an overview of that development, highlighting several examples where innovation in study design, content, and execution is advancing the field of infectious diseases diagnostics.

Robust classification of bacterial and viral infections via integrated host gene expression diagnostics

Improved diagnostics for acute infections could decrease morbidity and mortality by increasing early antibiotics for patients with bacterial infections and reducing unnecessary antibiotics for patients without bacterial infections. Several groups have used gene expression microarrays to build classifiers for acute infections, but these have been hampered by the size of the gene sets, use of overfit models, or lack of independent validation. We used multicohort analysis to derive a set of seven genes for robust discrimination of bacterial and viral infections, which we then validated in 30 independent cohorts. We next used our previously published 11-gene Sepsis MetaScore together with the new bacterial/viral classifier to build an integrated antibiotics decision model. In a pooled analysis of 1057 samples from 20 cohorts (excluding infants), the integrated antibiotics decision model had a sensitivity and specificity for bacterial infections of 94.0 and 59.8%, respectively (negative likelihood ratio, 0.10). Prospective clinical validation will be needed before these findings are implemented for patient care.

Validation of a Novel Assay to Distinguish Bacterial and Viral Infections

BACKGROUND

Reliably distinguishing bacterial from viral infections is often challenging, leading to antibiotic misuse. A novel assay that integrates measurements of blood-borne host-proteins (tumor necrosis factor-related apoptosis-inducing ligand, interferon γ-induced protein-10, and C-reactive protein [CRP]) was developed to assist in differentiation between bacterial and viral disease.

METHODS

We performed double-blind, multicenter assay evaluation using serum remnants collected at 5 pediatric emergency departments and 2 wards from children ≥3 months to ≤18 years without (n = 68) and with (n = 529) suspicion of acute infection. Infectious cohort inclusion criteria were fever ≥38°C and symptom duration ≤7 days. The reference standard diagnosis was based on predetermined criteria plus adjudication by experts blinded to assay results. Assay performers were blinded to the reference standard. Assay cutoffs were predefined.

RESULTS

Of 529 potentially eligible patients with suspected acute infection, 100 did not fulfill infectious inclusion criteria and 68 had insufficient serum. The resulting cohort included 361 patients, with 239 viral, 68 bacterial, and 54 indeterminate reference standard diagnoses. The assay distinguished between bacterial and viral patients with 93.8% sensitivity (95% confidence interval: 87.8%-99.8%) and 89.8% specificity (85.6%-94.0%); 11.7% had an equivocal assay outcome. The assay outperformed CRP (cutoff 40 mg/L; sensitivity 88.2% [80.4%-96.1%], specificity 73.2% [67.6%-78.9%]) and procalcitonin testing (cutoff 0.5 ng/mL; sensitivity 63.1% [51.0%-75.1%], specificity 82.3% [77.1%-87.5%]).

CONCLUSIONS

Double-blinded evaluation confirmed high assay performance in febrile children. Assay was significantly more accurate than CRP, procalcitonin, and routine laboratory parameters. Additional studies are warranted to support its potential to improve antimicrobial treatment decisions.

Reducing Uncertainty for Acute Febrile Illness in Resource-Limited Settings: The Current Diagnostic Landscape

AbstractDiagnosing the cause of acute febrile illness in resource-limited settings is important-to give the correct antimicrobials to patients who need them, to prevent unnecessary antimicrobial use, to detect emerging infectious diseases early, and to guide vaccine deployment. A variety of approaches are yielding more rapid and accurate tests that can detect more pathogens in a wider variety of settings. After decades of slow progress in diagnostics for acute febrile illness in resource-limited settings, a wave of converging advancements will enable clinicians in resource-limited settings to reduce uncertainty for the diagnosis of acute febrile illness.

Transcriptomic Biomarkers to Discriminate Bacterial from Nonbacterial Infection in Adults Hospitalized with Respiratory Illness

Lower respiratory tract infection (LRTI) commonly causes hospitalization in adults. Because bacterial diagnostic tests are not accurate, antibiotics are frequently prescribed. Peripheral blood gene expression to identify subjects with bacterial infection is a promising strategy. We evaluated whole blood profiling using RNASeq to discriminate infectious agents in adults with microbiologically defined LRTI. Hospitalized adults with LRTI symptoms were recruited. Clinical data and blood was collected, and comprehensive microbiologic testing performed. Gene expression was measured using RNASeq and qPCR. Genes discriminatory for bacterial infection were identified using the Bonferroni-corrected Wilcoxon test. Constrained logistic models to predict bacterial infection were fit using screened LASSO. We enrolled 94 subjects who were microbiologically classified; 53 as “non-bacterial” and 41 as “bacterial”. RNAseq and qPCR confirmed significant differences in mean expression for 10 genes previously identified as discriminatory for bacterial LRTI. A novel dimension reduction strategy selected three pathways (lymphocyte, α-linoleic acid metabolism, IGF regulation) including eleven genes as optimal markers for discriminating bacterial infection (naïve AUC = 0.94; nested CV-AUC = 0.86). Using these genes, we constructed a classifier for bacterial LRTI with 90% (79% CV) sensitivity and 83% (76% CV) specificity. This novel, pathway-based gene set displays promise as a method to distinguish bacterial from nonbacterial LRTI.

Antimicrobial Stewardship Approaches in the Intensive Care Unit

Antimicrobial stewardship programs aim to monitor, improve, and measure responsible antibiotic use. The intensive care unit (ICU), with its critically ill patients and prevalence of multiple drug-resistant pathogens, presents unique challenges. This article reviews approaches to stewardship with application to the ICU, including the value of diagnostics, principles of empirical and definitive therapy, and measures of effectiveness. There is good evidence that antimicrobial stewardship results in more appropriate antimicrobial use, shorter therapy durations, and lower resistance rates. Data demonstrating hard clinical outcomes, such as adverse events and mortality, are more limited but encouraging; further studies are needed.