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

A T-Cell-Derived 3-Gene Signature Distinguishes SARS-CoV-2 from Common Respiratory Viruses

Research on the host responses to respiratory viruses could help develop effective interventions and therapies against the current and future pandemics from the host perspective. To explore the pathogenesis that distinguishes SARS-CoV-2 infections from other respiratory viruses, we performed a multi-cohort analysis with integrated bioinformatics and machine learning. We collected 3730 blood samples from both asymptomatic and symptomatic individuals infected with SARS-CoV-2, seasonal human coronavirus (sHCoVs), influenza virus (IFV), respiratory syncytial virus (RSV), or human rhinovirus (HRV) across 15 cohorts. First, we identified an enhanced cellular immune response but limited interferon activities in SARS-CoV-2 infection, especially in asymptomatic cases. Second, we identified a SARS-CoV-2-specific 3-gene signature (CLSPN, RBBP6, CCDC91) that was predominantly expressed by T cells, could distinguish SARS-CoV-2 infection, including Omicron, from other common respiratory viruses regardless of symptoms, and was predictive of SARS-CoV-2 infection before detectable viral RNA on RT-PCR testing in a longitude follow-up study. Thereafter, a user-friendly online tool, based on datasets collected here, was developed for querying a gene of interest across multiple viral infections. Our results not only identify a unique host response to the viral pathogenesis in SARS-CoV-2 but also provide insights into developing effective tools against viral pandemics from the host perspective.

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.

homeRNA self-blood collection enables high-frequency temporal profiling of pre-symptomatic host immune kinetics to respiratory viral infection: a prospective cohort study

Background

Early host immunity to acute respiratory infections (ARIs) is heterogenous, dynamic, and critical to an individual's infection outcome. Due to limitations in sampling frequency/timepoints, kinetics of early immune dynamics in natural human infections remain poorly understood. In this nationwide prospective cohort study, we leveraged a self-blood collection tool (homeRNA) to profile detailed kinetics of the pre-symptomatic to convalescence host immunity to contemporaneous respiratory pathogens.

Methods

We enrolled non-symptomatic adults with recent exposure to ARIs who subsequently tested negative (exposed-uninfected) or positive for respiratory pathogens. Participants self-collected blood and nasal swabs daily for seven consecutive days followed by weekly blood collection for up to seven additional weeks. Symptom burden was assessed during each collection. Nasal swabs were tested for SARS-CoV-2 and common respiratory pathogens. 92 longitudinal blood samples spanning the pre-shedding to post-acute phase of eight SARS-CoV-2-infected participants and 40 interval-matched samples from four exposed-uninfected participants were subjected to high-frequency longitudinal profiling of 773 host immune genes.

Findings

Between June 2021 - April 2022, 68 participants across 26 U.S. states completed the study and self-collected a total of 691 and 466 longitudinal blood and nasal swab samples along with 688 symptom surveys. SARS-CoV-2 was detected in 17 out of 22 individuals with study-confirmed respiratory infection. With rapid dissemination of home self-collection kits, two and four COVID-19+ participants started collection prior to viral shedding and symptom onset, respectively, enabling us to profile detailed expression kinetics of the earliest blood transcriptional response to contemporaneous variants of concern. In pre-shedding samples, we observed transient but robust expression of T-cell response signatures, transcription factor complexes, prostaglandin biosynthesis genes, pyrogenic cytokines, and cytotoxic granule genes. This is followed by a rapid induction of many interferon-stimulated genes (ISGs), concurrent to onset of viral shedding and increase in nasal viral load. Finally, we observed increased expression of host defense peptides (HDPs) in exposed-uninfected individuals over the 4-week observational window.

Interpretation

We demonstrated that unsupervised self-collection and stabilization of capillary blood can be applied to natural infection studies to characterize detailed early host immune kinetics at a temporal resolution comparable to that of human challenge studies. The remote (decentralized) study framework enables conduct of large-scale population-wide longitudinal mechanistic studies. Expression of cytotoxic/T-cell signatures in pre-shedding samples preceding expansion of innate ISGs suggests a potential role for T-cell mediated pathogen control during early infection. Elevated expression of HDPs in exposed-uninfected individuals warrants further validation studies to assess their potential role in protective immunity during pathogen exposure.

Funding

This study was funded by R35GM128648 to ABT for in-lab developments of homeRNA, Packard Fellowship from the David and Lucile Packard Foundation to ABT, and R01AI153087 to AW.

Virome Data Explorer: A web resource to longitudinally explore respiratory viral infections, their interactions with other pathogens and host transcriptomic changes in over 100 people

Viral respiratory infections are an important public health concern due to their prevalence, transmissibility, and potential to cause serious disease. Disease severity is the product of several factors beyond the presence of the infectious agent, including specific host immune responses, host genetic makeup, and bacterial coinfections. To understand these interactions within natural infections, we designed a longitudinal cohort study actively surveilling respiratory viruses over the course of 19 months (2016 to 2018) in a diverse cohort in New York City. We integrated the molecular characterization of 800+ nasopharyngeal samples with clinical data from 104 participants. Transcriptomic data enabled the identification of respiratory pathogens in nasopharyngeal samples, the characterization of markers of immune response, the identification of signatures associated with symptom severity, individual viruses, and bacterial coinfections. Specific results include a rapid restoration of baseline conditions after infection, significant transcriptomic differences between symptomatic and asymptomatic infections, and qualitatively similar responses across different viruses. We created an interactive computational resource (Virome Data Explorer) to facilitate access to the data and visualization of analytical results.

Human mRNA in saliva can correctly identify individuals harboring acute infection

IMPORTANCE

There are a variety of clinical and laboratory criteria available to clinicians in controlled healthcare settings to help them identify whether an infectious disease is present. However, in situations such as a new epidemic caused by an unknown infectious agent, in health screening contexts performed within communities and outside of healthcare facilities or in battlefield or potential biowarfare situations, this gets more difficult. Pathogen-agnostic methods for rapid screening and triage of large numbers of people for infection status are needed, in particular methods that might work on an easily accessible biospecimen like saliva. Here, we identify a small, core set of approximately 70 human genes whose transcripts serve as saliva-based biomarkers of infection in the human body, in a way that is agnostic to the specific pathogen causing infection.

Host-response transcriptional biomarkers accurately discriminate bacterial and viral infections of global relevance

Diagnostic limitations challenge management of clinically indistinguishable acute infectious illness globally. Gene expression classification models show great promise distinguishing causes of fever. We generated transcriptional data for a 294-participant (USA, Sri Lanka) discovery cohort with adjudicated viral or bacterial infections of diverse etiology or non-infectious disease mimics. We then derived and cross-validated gene expression classifiers including: 1) a single model to distinguish bacterial vs. viral (Global Fever-Bacterial/Viral [GF-B/V]) and 2) a two-model system to discriminate bacterial and viral in the context of noninfection (Global Fever-Bacterial/Viral/Non-infectious [GF-B/V/N]). We then translated to a multiplex RT-PCR assay and independent validation involved 101 participants (USA, Sri Lanka, Australia, Cambodia, Tanzania). The GF-B/V model discriminated bacterial from viral infection in the discovery cohort an area under the receiver operator curve (AUROC) of 0.93. Validation in an independent cohort demonstrated the GF-B/V model had an AUROC of 0.84 (95% CI 0.76-0.90) with overall accuracy of 81.6% (95% CI 72.7-88.5). Performance did not vary with age, demographics, or site. Host transcriptional response diagnostics distinguish bacterial and viral illness across global sites with diverse endemic pathogens.

A comprehensive analysis of gene expression profiling data in COVID-19 patients for discovery of specific and differential blood biomarker signatures

COVID-19 is a newly recognized illness with a predominantly respiratory presentation. Although initial analyses have identified groups of candidate gene biomarkers for the diagnosis of COVID-19, they have yet to identify clinically applicable biomarkers, so we need disease-specific diagnostic biomarkers in biofluid and differential diagnosis in comparison with other infectious diseases. This can further increase knowledge of pathogenesis and help guide treatment. Eight transcriptomic profiles of COVID-19 infected versus control samples from peripheral blood (PB), lung tissue, nasopharyngeal swab and bronchoalveolar lavage fluid (BALF) were considered. In order to find COVID-19 potential Specific Blood Differentially expressed genes (SpeBDs), we implemented a strategy based on finding shared pathways of peripheral blood and the most involved tissues in COVID-19 patients. This step was performed to filter blood DEGs with a role in the shared pathways. Furthermore, nine datasets of the three types of Influenza (H1N1, H3N2, and B) were used for the second step. Potential Differential Blood DEGs of COVID-19 versus Influenza (DifBDs) were found by extracting DEGs involved in only enriched pathways by SpeBDs and not by Influenza DEGs. Then in the third step, a machine learning method (a wrapper feature selection approach supervised by four classifiers of k-NN, Random Forest, SVM, Naïve Bayes) was utilized to narrow down the number of SpeBDs and DifBDs and find the most predictive combination of them to select COVID-19 potential Specific Blood Biomarker Signatures (SpeBBSs) and COVID-19 versus influenza Differential Blood Biomarker Signatures (DifBBSs), respectively. After that, models based on SpeBBSs and DifBBSs and the corresponding algorithms were built to assess their performance on an external dataset. Among all the extracted DEGs from the PB dataset (from common PB pathways with BALF, Lung and Swab), 108 unique SpeBD were obtained. Feature selection using Random Forest outperformed its counterparts and selected IGKC, IGLV3-16 and SRP9 among SpeBDs as SpeBBSs. Validation of the constructed model based on these genes and Random Forest on an external dataset resulted in 93.09% Accuracy. Eighty-three pathways enriched by SpeBDs and not by any of the influenza strains were identified, including 87 DifBDs. Using feature selection by Naive Bayes classifier on DifBDs, FMNL2, IGHV3-23, IGLV2-11 and RPL31 were selected as the most predictable DifBBSs. The constructed model based on these genes and Naive Bayes on an external dataset was validated with 87.2% accuracy. Our study identified several candidate blood biomarkers for a potential specific and differential diagnosis of COVID-19. The proposed biomarkers could be valuable targets for practical investigations to validate their potential.

Blood RNA alternative splicing events as diagnostic biomarkers for infectious disease

Assays detecting blood transcriptome changes are studied for infectious disease diagnosis. Blood-based RNA alternative splicing (AS) events, which have not been well characterized in pathogen infection, have potential normalization and assay platform stability advantages over gene expression for diagnosis. Here, we present a computational framework for developing AS diagnostic biomarkers. Leveraging a large prospective cohort of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and whole-blood RNA sequencing (RNA-seq) data, we identify a major functional AS program switch upon viral infection. Using an independent cohort, we demonstrate the improved accuracy of AS biomarkers for SARS-CoV-2 diagnosis compared with six reported transcriptome signatures. We then optimize a subset of AS-based biomarkers to develop microfluidic PCR diagnostic assays. This assay achieves nearly perfect test accuracy (61/62 = 98.4%) using a naive principal component classifier, significantly more accurate than a gene expression PCR assay in the same cohort. Therefore, our RNA splicing computational framework enables a promising avenue for host-response diagnosis of infection.

Deep Learning of Dual Plasma Fingerprints for High-Performance Infection Classification

Infection classification is the key for choosing the proper treatment plans. Early determination of the causative agents is critical for disease control. Host responses analysis can detect variform and sensitive host inflammatory responses to ascertain the presence and type of the infection. However, traditional host-derived inflammatory indicators are insufficient for clinical infection classification. Fingerprints-based omic analysis has attracted increasing attention globally for analyzing the complex host systemic immune response. A single type of fingerprints is not applicable for infection classification (area under curve (AUC) of 0.550-0.617). Herein, an infection classification platform based on deep learning of dual plasma fingerprints (DPFs-DL) is developed. The DPFs with high reproducibility (coefficient of variation <15%) are obtained at low sample consumption (550 nL native plasma) using inorganic nanoparticle and organic matrix assisted laser desorption/ionization mass spectrometry. A classifier (DPFs-DL) for viral versus bacterial infection discrimination (AUC of 0.775) and coronavirus disease 2019 (COVID-2019) diagnosis (AUC of 0.917) is also built. Furthermore, a metabolic biomarker panel of two differentially regulated metabolites, which may serve as potential biomarkers for COVID-19 management (AUC of 0.677-0.883), is constructed. This study will contribute to the development of precision clinical care for infectious diseases.

Current status of silica-based nanoparticles as therapeutics and its potential as therapies against viruses

In the past decade, interest in nanoparticles for clinical indications has been steadily gaining traction. Most recently, Lipid Nanoparticles (LNP) have been used successfully to construct the SARS-CoV-2 mRNA vaccines for rapid pandemic response. Similarly, silica is another nanomaterial which holds much potential to create nanomedicines against pathogens of interest. One major advantage of silica-based nanoparticles is its crystalline and highly ordered structure, which can be specifically tuned to achieve the desired properties needed for clinical applications. Increasingly, clinical research has shown the potential of silica nanoparticles not only as an antiviral, but also its ability as a delivery system for antiviral small molecules and vaccines against viruses. Silica has an excellent biosafety profile and has been tested in several early phase clinical trials since 2012, demonstrating good tolerability and minimal reported side effects. In this review, we discuss the clinical development of silica nanoparticles to date and identify the gaps and potential pitfalls in its path to clinical translation.

Longitudinal home self-collection of capillary blood using homeRNA correlates interferon and innate viral defense pathways with SARS-CoV-2 viral clearance

Blood transcriptional profiling is a powerful tool to evaluate immune responses to infection; however, blood collection via traditional phlebotomy remains a barrier to precise characterization of the immune response in dynamic infections (e.g., respiratory viruses). Here we present an at-home self-collection methodology, homeRNA, to study the host transcriptional response during acute SARS-CoV-2 infections. This method uniquely enables high frequency measurement of the host immune kinetics in non-hospitalized adults during the acute and most dynamic stage of their infection. COVID-19+ and healthy participants self-collected blood every other day for two weeks with daily nasal swabs and symptom surveys to track viral load kinetics and symptom burden, respectively. While healthy uninfected participants showed remarkably stable immune kinetics with no significant dynamic genes, COVID-19+ participants, on the contrary, depicted a robust response with over 418 dynamic genes associated with interferon and innate viral defense pathways. When stratified by vaccination status, we detected distinct response signatures between unvaccinated and breakthrough (vaccinated) infection subgroups; unvaccinated individuals portrayed a response repertoire characterized by higher innate antiviral responses, interferon signaling, and cytotoxic lymphocyte responses while breakthrough infections portrayed lower levels of interferon signaling and enhanced early cell-mediated response. Leveraging cross-platform longitudinal sampling (nasal swabs and blood), we observed that IFI27, a key viral response gene, tracked closely with SARS-CoV-2 viral clearance in individual participants. Taken together, these results demonstrate that at-home sampling can capture key host antiviral responses and facilitate frequent longitudinal sampling to detect transient host immune kinetics during dynamic immune states.

Selection of Ideal Reference Genes for Gene Expression Analysis in COVID-19 and Mucormycosis

Selection of reference genes during real-time quantitative PCR (qRT-PCR) is critical to determine accurate and reliable mRNA expression. Nonetheless, not a single study has investigated the expression stability of candidate reference genes to determine their suitability as internal controls in SARS-CoV-2 infection or COVID-19-associated mucormycosis (CAM). Using qRT-PCR, we determined expression stability of the nine most commonly used housekeeping genes, namely, TATA-box binding protein (TBP), cyclophilin (CypA), β-2-microglobulin (B2M), 18S rRNA (18S), peroxisome proliferator-activated receptor gamma (PPARG) coactivator 1 alpha (PGC-1α), glucuronidase beta (GUSB), hypoxanthine phosphoribosyltransferase 1 (HPRT-1), β-ACTIN, and glyceraldehyde-3-phosphate dehydrogenase (GAPDH) in patients with COVID-19 of various severities (asymptomatic, mild, moderate, and severe) and those with CAM. We used statistical algorithms (delta-CT [threshold cycle], NormFinder, BestKeeper, GeNorm, and RefFinder) to select the most appropriate reference gene and observed that clinical severity profoundly influences expression stability of reference genes. CypA demonstrated the most consistent expression irrespective of disease severity and emerged as the most suitable reference gene in COVID-19 and CAM. Incidentally, GAPDH, the most commonly used reference gene, showed the maximum variations in expression and emerged as the least suitable. Next, we determined expression of nuclear factor erythroid 2-related factor 2 (NRF2), interleukin-6 (IL-6), and IL-15 using CypA and GAPDH as internal controls and show that CypA-normalized expression matches well with the RNA sequencing-based expression of these genes. Further, IL-6 expression correlated well with the plasma levels of IL-6 and C-reactive protein, a marker of inflammation. In conclusion, GAPDH emerged as the least suitable and CypA as the most suitable reference gene in COVID-19 and CAM. The results highlight the expression variability of housekeeping genes due to disease severity and provide a strong rationale for identification of appropriate reference genes in other chronic conditions as well. IMPORTANCE Gene expression studies are critical to develop new diagnostics, therapeutics, and prognostic modalities. However, accurate determination of expression requires data normalization with a reference gene, whose expression does not vary across different disease stages. Misidentification of a reference gene can produce inaccurate results. Unfortunately, despite the global impact of COVID-19 and an urgent unmet need for better treatment, not a single study has investigated the expression stability of housekeeping genes across the disease spectrum to determine their suitability as internal controls. Our study identifies CypA and then TBP as the two most suitable reference genes for COVID-19 and CAM. Further, GAPDH, the most commonly used reference gene in COVID-19 studies, turned out to be the least suitable. This work fills an important gap in the field and promises to facilitate determination of an accurate expression of genes to catalyze development of novel molecular diagnostics and therapeutics for improved patient care.

Rhinolophus sinicus virome revealed multiple novel mosquito-borne zoonotic viruses

To exploit the Rhinolophus sinicus–specific virome, 29 Rhinolophus sinicus were gathered in Lincang, China. Enriched viral sequences of 22 virus families were acquired by metavirome techniques. Hereby, the part of virome in Rhinolophus sinicus, including Chikungunya virus (CHIKV), Getah virus, and Japanese encephalitis virus (JEV) were validated by PCR. Five CHIKV viral sequences were amplified, among which CHIKV-China/B2016C-1 shared the highest homology to CHIKV isolated from Italy in 2007, with the genotype as African ECS. Eight JEV viral sequences were amplified, of which JEV-China/B2016E-1 shared the highest homology with at least 91.3% nt identity with the JEV sequence found in South Korea in 1988 and was classified as genotype III. Notably, JEV was isolated for the first time in Rhinolophus sinicus. The newly isolated JEV-China/B2016-1 could increase infectivity while passaging in Vero cells from BHK-21 cells. Overall, the research sheds insight into the diversity and viral susceptibility dynamics of the virome in Rhinolophus sinicus and reveals new light on the ecology of other important viral hosts.

Metavirome Analysis of Culex tritaeniorhynchus Reveals Novel Japanese Encephalitis Virus and Chikungunya Virus

To explore the Culex tritaeniorhynchuses–specific virome, 6400 C. tritaeniorhynchuses were collected in Honghe autonomous prefecture, China. Abundant virus sequences were obtained from 28 viral families using metavirome sequencing. Herein, several viruses in C. tritaeniorhynchuses virome were verified using the PCR technique, which covers Japanese encephalitis virus (JEV), Getah virus, and even Chikungunya virus (CHIKV). Seven JEV gene sequences were amplified successfully, of which JEV-China/CT2016E-1 shared the highest homology with the known JEV sequence isolated in Korea, 1946, with at least 96.1% nucleotide (nt) identity, which belonged to genotype III. Nine CHIKV gene sequences were amplified, which shared the highest with at least 93.0% nt identity with CHIKV from Thailand isolated in 2007, which was assigned to genotype Asian. Remarkably, CHIKV was isolated from C. tritaeniorhynchus in China for the first time. It was initially confirmed that the isolated virus CHIKV-China/CT2016-1 may increase infectivity after passaging in Vero cells from BHK-21 cells. Collectively, our study reveals the diversity, properties, and potential virus susceptibility dynamics of the C. tritaeniorhynchus virome and sheds new perspectives on the viral ecology in other important biological vectors.

Wearable sensor-based detection of influenza in presymptomatic and asymptomatic individuals

BACKGROUND

The COVID-19 pandemic highlighted the need for early detection of viral infections in symptomatic and asymptomatic individuals to allow for timely clinical management and public health interventions.

METHODS

Twenty healthy adults were challenged with an influenza A (H3N2) virus and prospectively monitored from 7 days before through 10 days after inoculation, using wearable electrocardiogram and physical activity sensors (Clinical Trial: NCT04204493; https://clinicaltrials.gov/ct2/show/NCT04204993). This framework allowed for responses to be accurately referenced to the infection event. For each participant, we trained a semi-supervised multivariable anomaly detection model on data acquired before inoculation and used it to classify the post-inoculation dataset.

RESULTS

Inoculation with this challenge virus was well-tolerated with an infection rate of 85%. With the model classification threshold set so that no alarms were recorded in the 170 healthy days recorded, the algorithm correctly identified 16 of 17 (94%) positive presymptomatic and asymptomatic individuals, on average 58 hours post inoculation and 23 hrs before the symptom onset.

CONCLUSION

The data processing and modeling methodology show promise for the early detection of respiratory illness. The detection algorithm is compatible with data collected from smartwatches using optical techniques but needs to be validated in large heterogeneous cohorts in normal living conditions.

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

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

The Potential of Molecular Indicators of Plant Virus Infection: Are Plants Able to Tell Us They Are Infected?

To our knowledge, there are no reports that demonstrate the use of host molecular markers for the purpose of detecting generic plant virus infection. Two approaches involving molecular indicators of virus infection in the model plant Arabidopsis thaliana were examined: the accumulation of small RNAs (sRNAs) using a microfluidics-based method (Bioanalyzer); and the transcript accumulation of virus-response related host plant genes, suppressor of gene silencing 3 (AtSGS3) and calcium-dependent protein kinase 3 (AtCPK3) by reverse transcriptase-quantitative PCR (RT-qPCR). The microfluidics approach using sRNA chips has previously demonstrated good linearity and good reproducibility, both within and between chips. Good limits of detection have been demonstrated from two-fold 10-point serial dilution regression to 0.1 ng of RNA. The ratio of small RNA (sRNA) to ribosomal RNA (rRNA), as a proportion of averaged mock-inoculation, correlated with known virus infection to a high degree of certainty. AtSGS3 transcript decreased between 14- and 28-days post inoculation (dpi) for all viruses investigated, while AtCPK3 transcript increased between 14 and 28 dpi for all viruses. A combination of these two molecular approaches may be useful for assessment of virus-infection of samples without the need for diagnosis of specific virus infection.

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.

TIPICO XI: report of the first series and podcast on infectious diseases and vaccines (aTIPICO)

TIPiCO is an annual expert meeting and workshop on infectious diseases and vaccination. The edition of 2020 changed its name and format to aTIPiCO, the first series and podcasts on infectious diseases and vaccines. A total of 13 prestigious experts from different countries participated in this edition launched on the 26 November 2020. The state of the art of coronavirus disease-2019 (COVID-19) and the responsible pathogen, the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), and the options to tackle the pandemic situation were discussed in light of the knowledge in November 2020. Despite COVID-19, the status of other infectious diseases, including influenza infections, respiratory syncytial virus disease, human papillomavirus infection, measles, pertussis, tuberculosis, meningococcal disease, and pneumococcal disease, were also addressed. The essential lessons that can be learned from these diseases and their vaccines to use in the COVID-19 pandemic were also commented with the experts.

The Host Response to Viral Infections Reveals Common and Virus-Specific Signatures in the Peripheral Blood

Viruses cause a wide spectrum of clinical disease, the majority being acute respiratory infections (ARI). In most cases, ARI symptoms are similar for different viruses although severity can be variable. The objective of this study was to understand the shared and unique elements of the host transcriptional response to different viral pathogens. We identified 162 subjects in the US and Sri Lanka with infections due to influenza, enterovirus/rhinovirus, human metapneumovirus, dengue virus, cytomegalovirus, Epstein Barr Virus, or adenovirus. Our dataset allowed us to identify common pathways at the molecular level as well as virus-specific differences in the host immune response. Conserved elements of the host response to these viral infections highlighted the importance of interferon pathway activation. However, the magnitude of the responses varied between pathogens. We also identified virus-specific responses to influenza, enterovirus/rhinovirus, and dengue infections. Influenza-specific differentially expressed genes (DEG) revealed up-regulation of pathways related to viral defense and down-regulation of pathways related to T cell and neutrophil responses. Functional analysis of entero/rhinovirus-specific DEGs revealed up-regulation of pathways for neutrophil activation, negative regulation of immune response, and p38MAPK cascade and down-regulation of virus defenses and complement activation. Functional analysis of dengue-specific up-regulated DEGs showed enrichment of pathways for DNA replication and cell division whereas down-regulated DEGs were mainly associated with erythrocyte and myeloid cell homeostasis, reactive oxygen and peroxide metabolic processes. In conclusion, our study will contribute to a better understanding of molecular mechanisms to viral infections in humans and the identification of biomarkers to distinguish different types of viral infections.

Early Warning Diagnostics for Emerging Infectious Diseases in Developing into Late-Stage Pandemics

The spread of infectious diseases due to travel and trade can be seen throughout history, whether from early settlers or traveling businessmen. Increased globalization has allowed infectious diseases to quickly spread to different parts of the world and cause widespread infection. Posthoc analysis of more recent outbreaks-SARS, MERS, swine flu, and COVID-19-has demonstrated that the causative viruses were circulating through populations for days or weeks before they were first detected, allowing disease to spread before quarantines, contact tracing, and travel restrictions could be implemented. Earlier detection of future novel pathogens could decrease the time before countermeasures are enacted. In this Account, we examined a variety of novel technologies from the past 10 years that may allow for earlier detection of infectious diseases. We have arranged these technologies chronologically from pre-human predictive technologies to population-level screening tools. The earliest detection methods utilize artificial intelligence to analyze factors such as climate variation and zoonotic spillover as well as specific species and geographies to identify where the infection risk is high. Artificial intelligence can also be used to monitor health records, social media, and various publicly available data to identify disease outbreaks faster than traditional epidemiology. Secondary to predictive measures is monitoring infection in specific sentinel animal species, where domestic animals or wildlife are indicators of potential disease hotspots. These hotspots inform public health officials about geographic areas where infection risk in humans is high. Further along the timeline, once the disease has begun to infect humans, wastewater epidemiology can be used for unbiased sampling of large populations. This method has already been shown to precede spikes in COVID-19 diagnoses by 1 to 2 weeks. As total infections increase in humans, bioaerosol sampling in high-traffic areas can be used for disease monitoring, such as within an airport. Finally, as disease spreads more quickly between humans, rapid diagnostic technologies such as lateral flow assays and nucleic acid amplification become very important. Minimally invasive point-of-care methods can allow for quick adoption and use within a population. These individual diagnostic methods then transfer to higher-throughput methods for more intensive population screening as an infection spreads. There are many promising early warning technologies being developed. However, no single technology listed herein will prevent every future outbreak. A combination of technologies from across our infection timeline would offer the most benefit in preventing future widespread disease outbreaks and pandemics.

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.

Discovery and validation of a three-gene signature to distinguish COVID-19 and other viral infections in emergency infectious disease presentations: a case-control and observational cohort study

Background

Emergency admissions for infection often lack initial diagnostic certainty. COVID-19 has highlighted a need for novel diagnostic approaches to indicate likelihood of viral infection in a pandemic setting. We aimed to derive and validate a blood transcriptional signature to detect viral infections, including COVID-19, among adults with suspected infection who presented to the emergency department.

Methods

Individuals (aged ≥18 years) presenting with suspected infection to an emergency department at a major teaching hospital in the UK were prospectively recruited as part of the Bioresource in Adult Infectious Diseases (BioAID) discovery cohort. Whole-blood RNA sequencing was done on samples from participants with subsequently confirmed viral, bacterial, or no infection diagnoses. Differentially expressed host genes that met additional filtering criteria were subjected to feature selection to derive the most parsimonious discriminating signature. We validated the signature via RT-qPCR in a prospective validation cohort of participants who presented to an emergency department with undifferentiated fever, and a second case-control validation cohort of emergency department participants with PCR-positive COVID-19 or bacterial infection. We assessed signature performance by calculating the area under receiver operating characteristic curves (AUROCs), sensitivities, and specificities.

Findings

A three-gene transcript signature, comprising HERC6, IGF1R, and NAGK, was derived from the discovery cohort of 56 participants with bacterial infections and 27 with viral infections. In the validation cohort of 200 participants, the signature differentiated bacterial from viral infections with an AUROC of 0·976 (95% CI 0·919−1·000), sensitivity of 97·3% (85·8−99·9), and specificity of 100% (63·1−100). The AUROC for C-reactive protein (CRP) was 0·833 (0·694−0·944) and for leukocyte count was 0·938 (0·840−0·986). The signature achieved higher net benefit in decision curve analysis than either CRP or leukocyte count for discriminating viral infections from all other infections. In the second validation analysis, which included SARS-CoV-2-positive participants, the signature discriminated 35 bacterial infections from 34 SARS-CoV-2-positive COVID-19 infections with AUROC of 0·953 (0·893−0·992), sensitivity 88·6%, and specificity of 94·1%.

Interpretation

This novel three-gene signature discriminates viral infections, including COVID-19, from other emergency infection presentations in adults, outperforming both leukocyte count and CRP, thus potentially providing substantial clinical utility in managing acute presentations with infection.

Funding

National Institute for Health Research, Medical Research Council, Wellcome Trust, and EU-FP7.

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.

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.