Gene expression signatures diagnose influenza and other symptomatic respiratory viral infections in humans

Blood RNA Biomarkers Identify Bacterial and Biofilm Coinfections in COVID-19 Intensive Care Patients

Purpose: Secondary opportunistic coinfections are a significant contributor to morbidity and mortality in intensive care unit (ICU) patients, but can be difficult to identify. Presently, new blood RNA biomarkers were tested in ICU patients to diagnose viral, bacterial, and biofilm coinfections. Methods: COVID-19 ICU patients had whole blood drawn in RNA preservative and stored at -80°C. Controls and subclinical infections were also studied. Droplet digital polymerase chain reaction (ddPCR) quantified 6 RNA biomarkers of host neutrophil activation to bacterial (DEFA1), biofilm (alkaline phosphatase [ALPL], IL8RB/CXCR2), and viral infections (IFI27, RSAD2). Viral titer in blood was measured by ddPCR for SARS-CoV2 (SCV2). Results: RNA biomarkers were elevated in ICU patients relative to controls. DEFA1 and ALPL RNA were significantly higher in severe versus incidental/moderate cases. SOFA score was correlated with white blood cell count (0.42), platelet count (-0.41), creatinine (0.38), and lactate dehydrogenase (0.31). ALPL RNA (0.59) showed the best correlation with SOFA score. IFI27 (0.52) and RSAD2 (0.38) were positively correlated with SCV2 viral titer. Overall, 57.8% of COVID-19 patients had a positive RNA biomarker for bacterial or biofilm infection. Conclusions: RNA biomarkers of host neutrophil activation indicate the presence of bacterial and biofilm coinfections in most COVID-19 patients. Recognizing coinfections may help to guide the treatment of ICU patients.

homeRNA self-blood collection enables high-frequency temporal profiling of presymptomatic 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 Tasso-SST based self-blood collection and stabilization tool (homeRNA) to profile detailed kinetics of the presymptomatic 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 presymptomatic to convalescence 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 785 immune genes. Generalized additive mixed models (GAMM) were used to identify temporally dynamic genes from the longitudinal samples and linear mixed models (LMM) were used to identify baseline differences between exposed-infected (n = 8), exposed-uninfected (n = 4), and uninfected (n = 13) participant groups.

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, of which five were still presymptomatic or pre-shedding, enabling us to profile detailed expression kinetics of the earliest blood transcriptional response to contemporaneous variants of concern. 51% of the genes assessed were found to be temporally dynamic during COVID-19 infection. During the pre-shedding phase, a robust but transient response consisting of genes involved in cell migration, stress response, and T cell activation were observed. 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 and symptom burden. Finally, elevated baseline expression of antimicrobial peptides were observed in exposed-uninfected individuals.

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.

Funding

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

Host response to influenza infections in human blood: association of influenza severity with host genetics and transcriptomic response

Introduction

Influenza virus infections are a major global health problem. Influenza can result in mild/moderate disease or progress to more severe disease, leading to high morbidity and mortality. Severity is thought to be primarily driven by immunopathology, but predicting which individuals are at a higher risk of being hospitalized warrants investigation into host genetics and the molecular signatures of the host response during influenza infections.

Methods

Here, we performed transcriptome and genotype analysis in healthy controls and patients exhibiting mild/moderate or severe influenza (ICU patients). A unique aspect of our study was the genotyping of all participants, which allowed us to assign ethnicities based on genetic variation and assess whether the variation was correlated with expression levels.

Results

We identified 169 differentially expressed genes and related molecular pathways between patients in the ICU and those who were not in the ICU. The transcriptome/genotype association analysis identified 871 genes associated to a genetic variant and 39 genes distinct between African-Americans and Caucasians. We also investigated the effects of age and sex and found only a few discernible gene effects in our cohort.

Discussion

Together, our results highlight select risk factors that may contribute to an increased risk of ICU admission for influenza-infected patients. This should help to develop better diagnostic tools based on molecular signatures, in addition to a better understanding of the biological processes in the host response to influenza.

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.

Detecting tipping points of complex diseases by network information entropy

The progression of complex diseases often involves abrupt and non-linear changes characterized by sudden shifts that trigger critical transformations. Identifying these critical states or tipping points is crucial for understanding disease progression and developing effective interventions. To address this challenge, we have developed a model-free method named Network Information Entropy of Edges (NIEE). Leveraging dynamic network biomarkers, sample-specific networks, and information entropy theories, NIEE can detect critical states or tipping points in diverse data types, including bulk, single-sample expression data. By applying NIEE to real disease datasets, we successfully identified critical predisease stages and tipping points before disease onset. Our findings underscore NIEE's potential to enhance comprehension of complex disease development.

The Immune Response of OAS1, IRF9, and IFI6 Genes in the Pathogenesis of COVID-19

COVID-19 is characterized by a wide range of clinical manifestations, where aging, underlying diseases, and genetic background are related to worse outcomes. In the present study, the differential expression of seven genes related to immunity, IRF9, CCL5, IFI6, TGFB1, IL1B, OAS1, and TFRC, was analyzed in individuals with COVID-19 diagnoses of different disease severities. Two-step RT-qPCR was performed to determine the relative gene expression in whole-blood samples from 160 individuals. The expression of OAS1 (p < 0.05) and IFI6 (p < 0.05) was higher in moderate hospitalized cases than in severe ones. Increased gene expression of OAS1 (OR = 0.64, CI = 0.52-0.79; p = 0.001), IRF9 (OR = 0.581, CI = 0.43-0.79; p = 0.001), and IFI6 (OR = 0.544, CI = 0.39-0.69; p < 0.001) was associated with a lower risk of requiring IMV. Moreover, TGFB1 (OR = 0.646, CI = 0.50-0.83; p = 0.001), CCL5 (OR = 0.57, CI = 0.39-0.83; p = 0.003), IRF9 (OR = 0.80, CI = 0.653-0.979; p = 0.03), and IFI6 (OR = 0.827, CI = 0.69-0.991; p = 0.039) expression was associated with patient survival. In conclusion, the relevance of OAS1, IRF9, and IFI6 in controlling the viral infection was confirmed.

Nasopharyngeal metatranscriptomics reveals host-pathogen signatures of pediatric sinusitis

Acute sinusitis (AS) is the fifth leading cause of antibiotic prescriptions in children. Distinguishing bacterial AS from common viral upper respiratory infections in children is crucial to prevent unnecessary antibiotic use but is challenging with current diagnostic methods. Despite its speed and cost, untargeted RNA sequencing of clinical samples from children with suspected AS has the potential to overcome several limitations of other methods. However, the utility of sequencing-based approaches in analysis of AS has not been fully explored. Here, we performed RNA-seq of nasopharyngeal samples from 221 children with clinically diagnosed AS to characterize their pathogen and host-response profiles. Results from RNA-seq were compared with those obtained using culture for three common bacterial pathogens and qRT-PCR for 12 respiratory viruses. Metatranscriptomic pathogen detection showed high concordance with culture or qRT-PCR, showing 87%/81% sensitivity (sens) / specificity (spec) for detecting bacteria, and 86%/92% (sens/spec) for viruses, respectively. We also detected an additional 22 pathogens not tested for in the clinical panel, and identified plausible pathogens in 11/19 (58%) of cases where no organism was detected by culture or qRT-PCR. We assembled genomes of 205 viruses across the samples including novel strains of coronaviruses, respiratory syncytial virus (RSV), and enterovirus D68. By analyzing host gene expression, we identified host-response signatures that distinguished bacterial and viral infections and correlated with pathogen abundance. Ultimately, our study demonstrates the potential of untargeted metatranscriptomics for in depth analysis of the etiology of AS, comprehensive host-response profiling, and using these together to work towards optimized patient care.

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.

Systems immunology of transcriptional responses to viral infection identifies conserved antiviral pathways across macaques and humans

Viral pandemics and epidemics pose a significant global threat. While macaque models of viral disease are routinely used, it remains unclear how conserved antiviral responses are between macaques and humans. Therefore, we conducted a cross-species analysis of transcriptomic data from over 6,088 blood samples from macaques and humans infected with one of 31 viruses. Our findings demonstrate that irrespective of primate or viral species, there are conserved antiviral responses that are consistent across infection phase (acute, chronic, or latent) and viral genome type (DNA or RNA viruses). Leveraging longitudinal data from experimental challenges, we identify virus-specific response kinetics such as host responses to Coronaviridae and Orthomyxoviridae infections peaking 1-3 days earlier than responses to Filoviridae and Arenaviridae viral infections. Our results underscore macaque studies as a powerful tool for understanding viral pathogenesis and immune responses that translate to humans, with implications for viral therapeutic development and pandemic preparedness.

Enabling personalised disease diagnosis by combining a patient's time-specific gene expression profile with a biomedical knowledge base

BACKGROUND

Recent developments in the domain of biomedical knowledge bases (KBs) open up new ways to exploit biomedical knowledge that is available in the form of KBs. Significant work has been done in the direction of biomedical KB creation and KB completion, specifically, those having gene-disease associations and other related entities. However, the use of such biomedical KBs in combination with patients' temporal clinical data still largely remains unexplored, but has the potential to immensely benefit medical diagnostic decision support systems.

RESULTS

We propose two new algorithms, LOADDx and SCADDx, to combine a patient's gene expression data with gene-disease association and other related information available in the form of a KB, to assist personalized disease diagnosis. We have tested both of the algorithms on two KBs and on four real-world gene expression datasets of respiratory viral infection caused by Influenza-like viruses of 19 subtypes. We also compare the performance of proposed algorithms with that of five existing state-of-the-art machine learning algorithms (k-NN, Random Forest, XGBoost, Linear SVM, and SVM with RBF Kernel) using two validation approaches: LOOCV and a single internal validation set. Both SCADDx and LOADDx outperform the existing algorithms when evaluated with both validation approaches. SCADDx is able to detect infections with up to 100% accuracy in the cases of Datasets 2 and 3. Overall, SCADDx and LOADDx are able to detect an infection within 72 h of infection with 91.38% and 92.66% average accuracy respectively considering all four datasets, whereas XGBoost, which performed best among the existing machine learning algorithms, can detect the infection with only 86.43% accuracy on an average.

CONCLUSIONS

We demonstrate how our novel idea of using the most and least differentially expressed genes in combination with a KB can enable identification of the diseases that a patient is most likely to have at a particular time, from a KB with thousands of diseases. Moreover, the proposed algorithms can provide a short ranked list of the most likely diseases for each patient along with their most affected genes, and other entities linked with them in the KB, which can support health care professionals in their decision-making.

Nasopharyngeal and Peripheral Blood Type II Interferon Signature Evaluation in Infants during Respiratory Syncytial Virus Infection

Background and Objectives: In this study, we applied one-step real time rt-PCR technology type II INF signature to blood and nasopharyngeal (NPS) swabs of acute early recovery children < 1 years hospitalized for bronchiolitis with laboratory-confirmed RSV infection. Materials and Methods: A prospective observational case-control study was conducted in 2021-2022. The study took place in Children Hospital "Regina Margherita", Torino Italy. The study included 66 infants, of which 30 patients were hospitalized for bronchiolitis due to RSV infection and 36 age-matched controls. Inclusion criteria included a positive RSV test for infants with bronchiolitis. We collected peripheral blood and nasopharyngeal swabs for relative quantification of type II Interferon signature by One-Step Multiplex PCR real time. Results: IFN levels were downregulated in the peripheral blood of bronchiolitis patients; these data were not confirmed in the nasopharyngeal swab. There was no correlation between NPS and the type II IFN score in peripheral blood. Conclusions: our study shows for the first time that type II IFN score was significant reduced in peripheral blood of infants with bronchiolitis by RSV compared to age-matched healthy controls; in the NPS swab this resulted downregulation was not statistically significant and the type II IFN score in the NPS swab can be used as marker of resolution of infection or improvement of clinical conditions.

Effects of Contagious Respiratory Pathogens on Breath Biomarkers

Due to their immediate exhalation after generation at the cellular/microbiome levels, exhaled volatile organic compounds (VOCs) may provide real-time information on pathophysiological mechanisms and the host response to infection. In recent years, the metabolic profiling of the most frequent respiratory infections has gained interest as it holds potential for the early, non-invasive detection of pathogens and the monitoring of disease progression and the response to therapy. Using previously unpublished data, randomly selected individuals from a COVID-19 test center were included in the study. Based on multiplex PCR results (non-SARS-CoV-2 respiratory pathogens), the breath profiles of 479 subjects with the presence or absence of flu-like symptoms were obtained using proton-transfer-reaction time-of-flight mass spectrometry. Among 223 individuals, one respiratory pathogen was detected in 171 cases, and more than one pathogen in 52 cases. A total of 256 subjects had negative PCR test results and had no symptoms. The exhaled VOC profiles were affected by the presence of Haemophilus influenzae, Streptococcus pneumoniae, and Rhinovirus. The endogenous ketone, short-chain fatty acid, organosulfur, aldehyde, and terpene concentrations changed, but only a few compounds exhibited concentration changes above inter-individual physiological variations. Based on the VOC origins, the observed concentration changes may be attributed to oxidative stress and antioxidative defense, energy metabolism, systemic microbial immune homeostasis, and inflammation. In contrast to previous studies with pre-selected patient groups, the results of this study demonstrate the broad inter-individual variations in VOC profiles in real-life screening conditions. As no unique infection markers exist, only concentration changes clearly above the mentioned variations can be regarded as indicative of infection or colonization.

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.

The spike-specific TCRβ repertoire shows distinct features in unvaccinated or vaccinated patients with SARS-CoV-2 infection

BACKGROUND

The evolving variants of SARS-CoV-2 may escape immunity from prior infections or vaccinations. It's vital to understand how immunity adapts to these changes. Both infection and mRNA vaccination induce T cells that target the Spike protein. These T cells can recognize multiple variants, such as Delta and Omicron, even if neutralizing antibodies are weakened. However, the degree of recognition can vary among people, affecting vaccine efficacy. Previous studies demonstrated the capability of T-cell receptor (TCR) repertoire analysis to identify conserved and immunodominant peptides with cross-reactive potential among variant of concerns. However, there is a need to extend the analysis of the TCR repertoire to different clinical scenarios. The aim of this study was to examine the Spike-specific TCR repertoire profiles in natural infections and those with combined natural and vaccine immunity.

METHODS

A T-cell enrichment approach and bioinformatic tools were used to investigate the Spike-specific TCRβ repertoire in peripheral blood mononuclear cells of previously vaccinated (n = 8) or unvaccinated (n = 6) COVID-19 patients.

RESULTS

Diversity and clonality of the TCRβ repertoire showed no significant differences between vaccinated and unvaccinated groups. When comparing the TCRβ data to public databases, 692 unique TCRβ sequences linked to S epitopes were found in the vaccinated group and 670 in the unvaccinated group. TCRβ clonotypes related to spike regions S135-177, S264-276, S319-350, and S448-472 appear notably more prevalent in the vaccinated group. In contrast, the S673-699 epitope, believed to have super antigenic properties, is observed more frequently in the unvaccinated group. In-silico analyses suggest that mutations in epitopes, relative to the main SARS-CoV-2 variants of concern, don't hinder their cross-reactive recognition by associated TCRβ clonotypes.

CONCLUSIONS

Our findings reveal distinct TCRβ signatures in vaccinated and unvaccinated individuals with COVID-19. These differences might be associated with disease severity and could influence clinical outcomes.

TRIAL REGISTRATION

FESR/FSE 2014-2020 DDRC n. 585, Action 10.5.12, noCOVID19@UMG.

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.

Controlled Human Infection Challenge Studies with RSV

Despite considerable momentum in the development of RSV vaccines and therapeutics, there remain substantial barriers to the development and licensing of effective agents, particularly in high-risk populations. The unique immunobiology of RSV and lack of clear protective immunological correlates has held back RSV vaccine development, which, therefore, depends on large and costly clinical trials to demonstrate efficacy. Studies involving the deliberate infection of human volunteers offer an intermediate step between pre-clinical and large-scale studies of natural infection. Human challenge has been used to demonstrate the potential efficacy of vaccines and antivirals while improving our understanding of the protective immunity against RSV infection. Early RSV human infection challenge studies determined the role of routes of administration and size of inoculum on the disease. However, inherent limitations, the use of highly attenuated/laboratory-adapted RSV strains and the continued evolutionary adaptation of RSV limits extrapolation of results to present-day vaccine testing. With advances in technology, it is now possible to perform more detailed investigations of human mucosal immunity against RSV in experimentally infected adults and, more recently, older adults to optimise the design of vaccines and novel therapies. These studies identified defects in RSV-induced humoral and CD8+ T cell immunity that may partly explain susceptibility to recurrent RSV infection. We discuss the insights from human infection challenge models, ethical and logistical considerations, potential benefits, and role in streamlining and accelerating novel antivirals and vaccines against RSV. Finally, we consider how human challenges might be extended to include relevant at-risk populations.

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.

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.

Virus-specific and shared gene expression signatures in immune cells after vaccination in response to influenza and vaccinia stimulation

Background

In the vaccine era, individuals receive multiple vaccines in their lifetime. Host gene expression in response to antigenic stimulation is usually virus-specific; however, identifying shared pathways of host response across a wide spectrum of vaccine pathogens can shed light on the molecular mechanisms/components which can be targeted for the development of broad/universal therapeutics and vaccines.

Method

We isolated PBMCs, monocytes, B cells, and CD8+ T cells from the peripheral blood of healthy donors, who received both seasonal influenza vaccine (within <1 year) and smallpox vaccine (within 1 - 4 years). Each of the purified cell populations was stimulated with either influenza virus or vaccinia virus. Differentially expressed genes (DEGs) relative to unstimulated controls were identified for each in vitro viral infection, as well as for both viral infections (shared DEGs). Pathway enrichment analysis was performed to associate identified DEGs with KEGG/biological pathways.

Results

We identified 2,906, 3,888, 681, and 446 DEGs in PBMCs, monocytes, B cells, and CD8+ T cells, respectively, in response to influenza stimulation. Meanwhile, 97, 120, 20, and 10 DEGs were identified as gene signatures in PBMCs, monocytes, B cells, and CD8+ T cells, respectively, upon vaccinia stimulation. The majority of DEGs identified in PBMCs were also found in monocytes after either viral stimulation. Of the virus-specific DEGs, 55, 63, and 9 DEGs occurred in common in PBMCs, monocytes, and B cells, respectively, while no DEGs were shared in infected CD8+ T cells after influenza and vaccinia. Gene set enrichment analysis demonstrated that these shared DEGs were over-represented in innate signaling pathways, including cytokine-cytokine receptor interaction, viral protein interaction with cytokine and cytokine receptor, Toll-like receptor signaling, RIG-I-like receptor signaling pathways, cytosolic DNA-sensing pathways, and natural killer cell mediated cytotoxicity.

Conclusion

Our results provide insights into virus-host interactions in different immune cells, as well as host defense mechanisms against viral stimulation. Our data also highlights the role of monocytes as a major cell population driving gene expression in ex vivo PBMCs in response to viral stimulation. The immune response signaling pathways identified in this study may provide specific targets for the development of novel virus-specific therapeutics and improved vaccines for vaccinia and influenza. Although influenza and vaccinia viruses have been selected in this study as pathogen models, this approach could be applicable to other pathogens.

Rapid, Point-of-Care Host-Based Gene Expression Diagnostics Using Giant Magnetoresistive Biosensors

Host-based gene expression analysis is a promising tool for a broad range of clinical applications, including rapid infectious disease diagnostics and real-time disease monitoring. However, the complex instrumentation requirements and slow turnaround-times associated with traditional gene expression analysis methods have hampered their widespread adoption at the point-of-care (POC). To overcome these challenges, we have developed an automated and portable platform that utilizes polymerase chain reaction (PCR) and giant magnetoresistive (GMR) biosensors to perform rapid multiplexed, targeted gene expression analysis at the POC. As proof-of-concept, we utilized our platform to amplify and measure the expression of four genes (HERC5, HERC6, IFI27, and IFIH1) that were previously shown to be upregulated in hosts infected with influenza viruses. The compact instrument conducted highly automated PCR amplification and GMR detection to measure the expression of the four genes in multiplex, then utilized Bluetooth communication to relay results to users on a smartphone application. To validate the platform, we tested 20 cDNA samples from symptomatic patients that had been previously diagnosed as either influenza-positive or influenza-negative using a RT-PCR virology panel. A non-parametric Mann-Whitney test revealed that day 0 (day of symptom onset) gene expression was significantly different between the two groups (p < 0.0001, n = 20). Hence, we preliminarily demonstrated that our platform could accurately discriminate between symptomatic influenza and non-influenza populations based on host gene expression in ∼30 min. This study not only establishes the potential clinical utility of our proposed assay and device for influenza diagnostics but it also paves the way for broadscale and decentralized implementation of host-based gene expression diagnostics at the POC.

Integrated host/microbe metagenomics enables accurate lower respiratory tract infection diagnosis in critically ill children

BACKGROUNDLower respiratory tract infection (LRTI) is a leading cause of death in children worldwide. LRTI diagnosis is challenging because noninfectious respiratory illnesses appear clinically similar and because existing microbiologic tests are often falsely negative or detect incidentally carried microbes, resulting in antimicrobial overuse and adverse outcomes. Lower airway metagenomics has the potential to detect host and microbial signatures of LRTI. Whether it can be applied at scale and in a pediatric population to enable improved diagnosis and treatment remains unclear.METHODSWe used tracheal aspirate RNA-Seq to profile host gene expression and respiratory microbiota in 261 children with acute respiratory failure. We developed a gene expression classifier for LRTI by training on patients with an established diagnosis of LRTI (n = 117) or of noninfectious respiratory failure (n = 50). We then developed a classifier that integrates the host LRTI probability, abundance of respiratory viruses, and dominance in the lung microbiome of bacteria/fungi considered pathogenic by a rules-based algorithm.RESULTSThe host classifier achieved a median AUC of 0.967 by cross-validation, driven by activation markers of T cells, alveolar macrophages, and the interferon response. The integrated classifier achieved a median AUC of 0.986 and increased the confidence of patient classifications. When applied to patients with an uncertain diagnosis (n = 94), the integrated classifier indicated LRTI in 52% of cases and nominated likely causal pathogens in 98% of those.CONCLUSIONLower airway metagenomics enables accurate LRTI diagnosis and pathogen identification in a heterogeneous cohort of critically ill children through integration of host, pathogen, and microbiome features.FUNDINGSupport for this study was provided by the Eunice Kennedy Shriver National Institute of Child Health and Human Development and the National Heart, Lung, and Blood Institute (UG1HD083171, 1R01HL124103, UG1HD049983, UG01HD049934, UG1HD083170, UG1HD050096, UG1HD63108, UG1HD083116, UG1HD083166, UG1HD049981, K23HL138461, and 5R01HL155418) as well as by the Chan Zuckerberg Biohub.

Using machine learning to determine the time of exposure to infection by a respiratory pathogen

Given an infected host, estimating the time that has elapsed since initial exposure to the pathogen is an important problem in public health. In this paper we use longitudinal gene expression data from human challenge studies of viral respiratory illnesses for building predictive models to estimate the time elapsed since onset of respiratory infection. We apply sparsity driven machine learning to this time-stamped gene expression data to model the time of exposure by a pathogen and subsequent infection accompanied by the onset of the host immune response. These predictive models exploit the fact that the host gene expression profile evolves in time and its characteristic temporal signature can be effectively modeled using a small number of features. Predicting the time of exposure to infection to be in first 48 h after exposure produces BSR in the range of 80-90% on sequestered test data. A variety of machine learning experiments provide evidence that models developed on one virus can be used to predict exposure time for other viruses, e.g., H1N1, H3N2, and HRV. The interferon [Formula: see text] signaling pathway appears to play a central role in keeping time from onset of infection. Successful prediction of the time of exposure to a pathogen has potential ramifications for patient treatment and contact tracing.

Susceptibility identification for seasonal influenza A/H3N2 based on baseline blood transcriptome

Introduction

Influenza susceptibility difference is a widely existing trait that has great practical significance for the accurate prevention and control of influenza.

Methods

Here, we focused on the human susceptibility to the seasonal influenza A/H3N2 of healthy adults at baseline level. Whole blood expression data for influenza A/H3N2 susceptibility from GEO were collected firstly (30 symptomatic and 19 asymptomatic). Then to explore the differences at baseline, a suite of systems biology approaches - the differential expression analysis, co-expression network analysis, and immune cell frequencies analysis were utilized.

Results

We found the baseline condition, especially immune condition between symptomatic and asymptomatic, was different. Co-expression module that is positively related to asymptomatic is also related to immune cell type of naïve B cell. Function enrichment analysis showed significantly correlation with "B cell receptor signaling pathway", "immune response-activating cell surface receptor signaling pathway" and so on. Also, modules that are positively related to symptomatic are also correlated to immune cell type of neutrophils, with function enrichment analysis showing significantly correlations with "response to bacterium", "inflammatory response", "cAMP-dependent protein kinase complex" and so on. Responses of symptomatic and asymptomatic hosts after virus exposure show differences on resisting the virus, with more effective frontline defense for asymptomatic hosts. A prediction model was also built based on only baseline transcription information to differentiate symptomatic and asymptomatic population with accuracy of 0.79.

Discussion

The results not only improve our understanding of the immune system and influenza susceptibility, but also provide a new direction for precise and targeted prevention and therapy of influenza.

Transcriptional responses define dysregulated immune activation in Hepatitis C (HCV)-naïve recipients of HCV-infected donor kidneys

Renal transplantation from hepatitis C (HCV) nucleic acid amplification test-positive (NAAT-positive) donors to uninfected recipients has greatly increased the organ donation pool. However, there is concern for adverse outcomes in these recipients due to dysregulated immunologic activation secondary to active inflammation from acute viremia at the time of transplantation. This includes increased rates of cytomegalovirus (CMV) DNAemia and allograft rejection. In this study, we evaluate transcriptional responses in circulating leukocytes to define the character, timing, and resolution of this immune dysregulation and assess for biomarkers of adverse outcomes in transplant patients. We enrolled 67 renal transplant recipients (30 controls, 37 HCV recipients) and performed RNA sequencing on serial samples from one, 3-, and 6-months post-transplant. CMV DNAemia and allograft rejection outcomes were measured. Least absolute shrinkage and selection operator was utilized to develop gene expression classifiers predictive of clinical outcomes. Acute HCV incited a marked transcriptomic response in circulating leukocytes of renal transplant recipients in the acute post-transplant setting, despite the presence of immunosuppression, with 109 genes significantly differentially expressed compared to controls. These HCV infection-associated genes were reflective of antiviral immune pathways and generally resolved by the 3-month timepoint after sustained viral response (SVR) for HCV. Differential gene expression was also noted from patients who developed CMV DNAemia or allograft rejection compared to those who did not, although transcriptomic classifiers could not accurately predict these outcomes, likely due to sample size and variable time-to-event. Acute HCV infection incites evidence of immune activation and canonical antiviral responses in the human host even in the presence of systemic immunosuppression. After treatment of HCV with antiviral therapy and subsequent aviremia, this immune activation resolves. Changes in gene expression patterns in circulating leukocytes are associated with some clinical outcomes, although larger studies are needed to develop accurate predictive classifiers of these events.

Pre-exposure cognitive performance variability is associated with severity of respiratory infection

Using data from a longitudinal viral challenge study, we find that the post-exposure viral shedding and symptom severity are associated with a novel measure of pre-exposure cognitive performance variability (CPV), defined before viral exposure occurs. Each individual's CPV score is computed from data collected from a repeated NeuroCognitive Performance Test (NCPT) over a 3 day pre-exposure period. Of the 18 NCPT measures reported by the tests, 6 contribute materially to the CPV score, prospectively differentiating the high from the low shedders. Among these 6 are the 4 clinical measures digSym-time, digSym-correct, trail-time, and reaction-time, commonly used for assessing cognitive executive functioning. CPV is found to be correlated with stress and also with several genes previously reported to be associated with cognitive development and dysfunction. A perturbation study over the number and timing of NCPT sessions indicates that as few as 5 sessions is sufficient to maintain high association between the CPV score and viral shedding, as long as the timing of these sessions is balanced over the three pre-exposure days. Our results suggest that variations in cognitive function are closely related to immunity and susceptibility to severe infection. Further studying these relationships may help us better understand the links between neurocognitive and neuroimmune systems which is timely in this COVID-19 pandemic era.

Benchmarking transcriptional host response signatures for infection diagnosis

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

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

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

C1 esterase inhibitor-mediated immunosuppression in COVID-19: Friend or foe?

From asymptomatic to severe, SARS-CoV-2, causative agent of COVID-19, elicits varying disease severities. Moreover, understanding innate and adaptive immune responses to SARS-CoV-2 is imperative since variants such as Omicron negatively impact adaptive antibody neutralization. Severe COVID-19 is, in part, associated with aberrant activation of complement and Factor XII (FXIIa), initiator of contact system activation. Paradoxically, a protein that inhibits the three known pathways of complement activation and FXIIa, C1 esterase inhibitor (C1-INH), is increased in COVID-19 patient plasma and is associated with disease severity. Here we review the role of C1-INH in the regulation of innate and adaptive immune responses. Additionally, we contextualize regulation of C1-INH and SERPING1, the gene encoding C1-INH, by other pathogens and SARS viruses and propose that viral proteins bind to C1-INH to inhibit its function in severe COVID-19. Finally, we review the current clinical trials and published results of exogenous C1-INH treatment in COVID-19 patients.

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.

Species-specific transcriptomic changes upon respiratory syncytial virus infection in cotton rats

The cotton rat (Sigmodon) is the gold standard pre-clinical small animal model for respiratory viral pathogens, especially for respiratory syncytial virus (RSV). However, without a reference genome or a published transcriptome, studies requiring gene expression analysis in cotton rats are severely limited. The aims of this study were to generate a comprehensive transcriptome from multiple tissues of two species of cotton rats that are commonly used as animal models (Sigmodon fulviventer and Sigmodon hispidus), and to compare and contrast gene expression changes and immune responses to RSV infection between the two species. Transcriptomes were assembled from lung, spleen, kidney, heart, and intestines for each species with a contig N50 > 1600. Annotation of contigs generated nearly 120,000 gene annotations for each species. The transcriptomes of S. fulviventer and S. hispidus were then used to assess immune response to RSV infection. We identified 238 unique genes that are significantly differentially expressed, including several genes implicated in RSV infection (e.g., Mx2, I27L2, LY6E, Viperin, Keratin 6A, ISG15, CXCL10, CXCL11, IRF9) as well as novel genes that have not previously described in RSV research (LG3BP, SYWC, ABEC1, IIGP1, CREB1). This study presents two comprehensive transcriptome references as resources for future gene expression analysis studies in the cotton rat model, as well as provides gene sequences for mechanistic characterization of molecular pathways. Overall, our results provide generalizable insights into the effect of host genetics on host-virus interactions, as well as identify new host therapeutic targets for RSV treatment and prevention.

Comparative Upper Respiratory Tract Transcriptomic Profiling Reveals a Potential Role of Early Activation of Interferon Pathway in Severe COVID-19

Infection with SARS-CoV-2 results in Coronavirus disease 2019 (COVID-19) is known to cause mild to acute respiratory infection and sometimes progress towards respiratory failure and death. The mechanisms driving the progression of the disease and accumulation of high viral load in the lungs without initial symptoms remain elusive. In this study, we evaluated the upper respiratory tract host transcriptional response in COVID-19 patients with mild to severe symptoms and compared it with the control COVID-19 negative group using RNA-sequencing (RNA-Seq). Our results reveal an upregulated early type I interferon response in severe COVID-19 patients as compared to mild or negative COVID-19 patients. Moreover, severely symptomatic patients have pronounced induction of interferon stimulated genes (ISGs), particularly the oligoadenylate synthetase (OAS) family of genes. Our results are in concurrence with other studies depicting the early induction of IFN-I response in severe COVID-19 patients, providing novel insights about the ISGs involved.

Upregulation of interleukin-19 in saliva of patients with COVID-19

Cytokines are major players in orchestrating inflammation, disease pathogenesis and severity during COVID-19 disease. However, the role of IL-19 in COVID-19 pathogenesis remains elusive. Herein, through the analysis of transcriptomic datasets of SARS-CoV-2 infected lung cells, nasopharyngeal swabs, and lung autopsies of COVID-19 patients, we report that expression levels of IL-19 and its receptor, IL-20R2, were upregulated following SARS-CoV-2 infection. Of 202 adult COVID-19 patients, IL-19 protein level was significantly higher in blood and saliva of asymptomatic patients compared to healthy controls when adjusted for patients’ demographics (P < 0.001). Interestingly, high saliva IL-19 level was also associated with COVID-19 severity (P < 0.0001), need for mechanical ventilation (P = 0.002), and/or death (P = 0.010) within 29 days of admission, after adjusting for patients’ demographics, diabetes mellitus comorbidity, and COVID-19 serum markers of severity such as D-dimer, C-reactive protein, and ferritin. Moreover, patients who received interferon beta during their hospital stay had lower plasma IL-19 concentrations (24 pg mL⁻¹) than those who received tocilizumab (39.2 pg mL⁻¹) or corticosteroids (42.5 pg mL⁻¹). Our findings indicate that high saliva IL-19 level was associated with COVID-19 infectivity and disease severity.

The Effect of Tropical Temperatures on the Quality of RNA Extracted from Stabilized Whole-Blood Samples

Whole-blood-derived transcriptional profiling is widely used in biomarker discovery, immunological research, and therapeutic development. Traditional molecular and high-throughput transcriptomic platforms, including molecular assays with quantitative PCR (qPCR) and RNA-sequencing (RNA-seq), are dependent upon high-quality and intact RNA. However, collecting high-quality RNA from field studies in remote tropical locations can be challenging due to resource restrictions and logistics of post-collection processing. The current study tested the relative performance of the two most widely used whole-blood RNA collection systems, PAXgene^® and Tempus™, in optimal laboratory conditions as well as suboptimal conditions in tropical field sites, including the effects of extended storage times and high storage temperatures. We found that Tempus™ tubes maintained a slightly higher RNA quantity and integrity relative to PAXgene^® tubes at suboptimal tropical conditions. Both PAXgene^® and Tempus™ tubes gave similar RNA purity (A260/A280). Additionally, Tempus^™ tubes preferentially maintained the stability of mRNA transcripts for two reference genes tested, Succinate dehydrogenase complex, subunit A (SDHA) and TATA-box-binding protein (TBP), even when RNA quality decreased with storage length and temperature. Both tube types preserved the rRNA transcript 18S ribosomal RNA (18S) equally. Our results suggest that Tempus^™ blood RNA collection tubes are preferable to PAXgene^® for whole-blood collection in suboptimal tropical conditions for RNA-based studies in resource-limited settings.

A diagnostic classifier for gene expression-based identification of early Lyme disease

Background

Lyme disease is a tick-borne illness that causes an estimated 476,000 infections annually in the United States. New diagnostic tests are urgently needed, as existing antibody-based assays lack sufficient sensitivity and specificity.

Methods

Here we perform transcriptome profiling by RNA sequencing (RNA-Seq), targeted RNA-Seq, and/or machine learning-based classification of 263 peripheral blood mononuclear cell samples from 218 subjects, including 94 early Lyme disease patients, 48 uninfected control subjects, and 57 patients with other infections (influenza, bacteremia, or tuberculosis). Differentially expressed genes among the 25,278 in the reference database are selected based on ≥1.5-fold change, ≤0.05 p value, and ≤0.001 false-discovery rate cutoffs. After gene selection using a k-nearest neighbor algorithm, the comparative performance of ten different classifier models is evaluated using machine learning.

Results

We identify a 31-gene Lyme disease classifier (LDC) panel that can discriminate between early Lyme patients and controls, with 23 genes (74.2%) that have previously been described in association with clinical investigations of Lyme disease patients or in vitro cell culture and rodent studies of Borrelia burgdorferi infection. Evaluation of the LDC using an independent test set of samples from 63 subjects yields an overall sensitivity of 90.0%, specificity of 100%, and accuracy of 95.2%. The LDC test is positive in 85.7% of seronegative patients and found to persist for ≥3 weeks in 9 of 12 (75%) patients.

Conclusions

These results highlight the potential clinical utility of a gene expression classifier for diagnosis of early Lyme disease, including in patients negative by conventional serologic testing.

Proteomic profiling of serum small extracellular vesicles reveals immune signatures of children with pneumonia

BACKGROUND

Pneumonia is the leading cause of death in young children globally. However, the underlying pathological mechanism of pediatric pneumonia remains unclear. In infection disease contexts, small extracellular vesicles (sEVs) have been shown to be a useful source of markers for pathogenesis and immune response. We hypothesized that functional molecules such as protein harbored by sEVs would provide mechanistic insights into the immune response in children with pneumonia.

METHODS

We isolated sEVs from serum collected from children with and without pneumonia, performed proteomic analysis of the sEVs with label-free mass spectrometry, and then conducted functional enrichment analysis of proteomic data.

RESULTS

We identified fifteen differentially expressed proteins and ten unique proteins in children with pneumonia as compared to healthy children. In the pneumonia group, immune-related processes and pathways were positively enriched as upregulated proteins were involved in neutrophil activation, complement regulation, defense against bacteria, humoral immune response and regulation of immune effector processes However, pathways associated with tissue development and extracellular matrix remodeling were negatively enriched, as downregulated proteins were linked to extracellular matrix structure and cell adhesions.

CONCLUSIONS

Our findings provided insights into host responses to pathogen infection, which has contributed to understanding the pathogenesis of children with pneumonia. Furthermore, our studies suggested that serum sEVs proteins could be considered a potential source of biomarkers for diagnosing pediatric pneumonia.

Identifying Methylation Signatures and Rules for COVID-19 With Machine Learning Methods

The occurrence of coronavirus disease 2019 (COVID-19) has become a serious challenge to global public health. Definitive and effective treatments for COVID-19 are still lacking, and targeted antiviral drugs are not available. In addition, viruses can regulate host innate immunity and antiviral processes through the epigenome to promote viral self-replication and disease progression. In this study, we first analyzed the methylation dataset of COVID-19 using the Monte Carlo feature selection method to obtain a feature list. This feature list was subjected to the incremental feature selection method combined with a decision tree algorithm to extract key biomarkers, build effective classification models and classification rules that can remarkably distinguish patients with or without COVID-19. EPSTI1, NACAP1, SHROOM3, C19ORF35, and MX1 as the essential features play important roles in the infection and immune response to novel coronavirus. The six significant rules extracted from the optimal classifier quantitatively explained the expression pattern of COVID-19. Therefore, these findings validated that our method can distinguish COVID-19 at the methylation level and provide guidance for the diagnosis and treatment of COVID-19.

Distinguishing viruses responsible for influenza-like illness

The many respiratory viruses that cause influenza-like illness (ILI) are reported and tracked as one entity, defined by the CDC as a group of symptoms that include a fever of 100 degrees Fahrenheit, a cough, and/or a sore throat. In the United States alone, ILI impacts 9-49 million people every year. While tracking ILI as a single clinical syndrome is informative in many respects, the underlying viruses differ in parameters and outbreak properties. Most existing models treat either a single respiratory virus or ILI as a whole. However, there is a need for models capable of comparing several individual viruses that cause respiratory illness, including ILI. To address this need, here we present a flexible model and simulations of epidemics for influenza, RSV, rhinovirus, seasonal coronavirus, adenovirus, and SARS/MERS, parameterized by a systematic literature review and accompanied by a global sensitivity analysis. We find that for these biological causes of ILI, their parameter values, timing, prevalence, and proportional contributions differ substantially. These results demonstrate that distinguishing the viruses that cause ILI will be an important aspect of future work on diagnostics, mitigation, modeling, and preparation for future pandemics.

Transcriptional Profiles Elucidate Differential Host Responses to Infection with Cryptococcus neoformans and Cryptococcus gattii

Many aspects of the host response to invasive cryptococcal infections remain poorly understood. In order to explore the pathobiology of infection with common clinical strains, we infected BALB/cJ mice with Cryptococcus neoformans, Cryptococcus gattii, or sham control, and assayed host transcriptomic responses in peripheral blood. Infection with C. neoformans resulted in markedly greater fungal burden in the CNS than C. gattii, as well as slightly higher fungal burden in the lungs. A total of 389 genes were significantly differentially expressed in response to C. neoformans infection, which mainly clustered into pathways driving immune function, including complement activation and TH2-skewed immune responses. C. neoformans infection demonstrated dramatic up-regulation of complement-driven genes and greater up-regulation of alternatively activated macrophage activity than seen with C gattii. A 27-gene classifier was built, capable of distinguishing cryptococcal infection from animals with bacterial infection due to Staphylococcus aureus with 94% sensitivity and 89% specificity. Top genes from the murine classifiers were also differentially expressed in human PBMCs following infection, suggesting cross-species relevance of these findings. The host response, as manifested in transcriptional profiles, informs our understanding of the pathophysiology of cryptococcal infection and demonstrates promise for contributing to development of novel diagnostic approaches.

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

Background

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

Methods

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

Results

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

Conclusions

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

Supplementary Information

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

DeepFlu: a deep learning approach for forecasting symptomatic influenza A infection based on pre-exposure gene expression

BACKGROUND AND OBJECTIVE

Not everyone gets sick after an exposure to influenza A viruses (IAV). Although KLRD1 has been identified as a potential biomarker for influenza susceptibility, it remains unclear whether forecasting symptomatic flu infection based on pre-exposure host gene expression might be possible.

METHOD

To examine this hypothesis, we developed DeepFlu using the state-of-the-art deep learning approach on the human gene expression data infected with IAV subtype H1N1 or H3N2 viruses to forecast who would catch the flu prior to an exposure to IAV.

RESULTS

The results indicated that such forecast is possible and, in other words, gene expression could reflect the strength of host immunity. In the leave-one-person-out cross-validation, DeepFlu based on deep neural network outperformed the models using convolutional neural network, random forest, or support vector machine, achieving 70.0% accuracy, 0.787 AUROC, and 0.758 AUPR for H1N1 and 73.8% accuracy, 0.847 AUROC, and 0.901 AUPR for H3N2. In the external validation, DeepFlu also reached 71.4% accuracy, 0.700 AUROC, and 0.723 AUPR for H1N1 and 73.5% accuracy, 0.732 AUROC, and 0.749 AUPR for H3N2, surpassing the KLRD1 biomarker. In addition, DeepFlu which was trained only by pre-exposure data worked the best than by other time spans and mixed training data of H1N1 and H3N2 did not necessarily enhance prediction. DeepFlu is available at https://github.com/ntou-compbio/DeepFlu.

CONCLUSIONS

DeepFlu is a prognostic tool that can moderately recognize individuals susceptible to the flu and may help prevent the spread of IAV.

Experimental rhinovirus infection induces an antiviral response in circulating B cells which is dysregulated in patients with asthma

BACKGROUND

Rhinoviruses are the predominant cause of respiratory viral infections and are strongly associated with asthma exacerbations. While humoral immunity plays an important role during virus infections, cellular aspects of this response are less well understood. Here, we investigated the antiviral response of circulating B cells upon experimental rhinovirus infection in healthy individuals and asthma patients.

METHODS

We purified B cells from experimentally infected healthy individuals and patients with asthma and subjected them to total RNA-sequencing. Rhinovirus-derived RNA was measured in isolated B cells using a highly sensitive PCR. B cells were stimulated with rhinovirus in vitro to further study gene expression, expression of antiviral proteins and B-cell differentiation in response rhinovirus stimulation. Protein expression of pro-inflammatory cytokines in response to rhinovirus was assessed using a proximity extension assay.

RESULTS

B cells isolated from experimentally infected subjects exhibited an antiviral gene profile linked to IFN-alpha, carried viral RNA in vivo and were transiently infected by rhinovirus in vitro. B cells rapidly differentiated into plasmablasts upon rhinovirus stimulation. While B cells lacked expression of interferons in response to rhinovirus exposure, co-stimulation with rhinovirus and IFN-alpha upregulated pro-inflammatory cytokine expression suggesting a potential new function of B cells during virus infections. Asthma patients showed extensive upregulation and dysregulation of antiviral gene expression.

CONCLUSION

These findings add to the understanding of systemic effects of rhinovirus infections on B-cell responses in the periphery, show potential dysregulation in patients with asthma and might also have implications during infection with other respiratory viruses.

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.

The potential of using blood circular RNA as liquid biopsy biomarker for human diseases

Circular RNA (circRNA) is a novel class of single-stranded RNAs with a closed loop structure. The majority of circRNAs are formed by a back-splicing process in pre-mRNA splicing. Their expression is dynamically regulated and shows spatiotemporal patterns among cell types, tissues and developmental stages. CircRNAs have important biological functions in many physiological processes, and their aberrant expression is implicated in many human diseases. Due to their high stability, circRNAs are becoming promising biomarkers in many human diseases, such as cardiovascular diseases, autoimmune diseases and human cancers. In this review, we focus on the translational potential of using human blood circRNAs as liquid biopsy biomarkers for human diseases. We highlight their abundant expression, essential biological functions and significant correlations to human diseases in various components of peripheral blood, including whole blood, blood cells and extracellular vesicles. In addition, we summarize the current knowledge of blood circRNA biomarkers for disease diagnosis or prognosis.

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.

Differential Analysis and Putative Roles of Genes, Cytokines and Apoptotic Proteins in Blood Samples of Patients with Respiratory Viral Infections: A Single Center Study

Insights into the molecular pathogenesis of respiratory viral infections were investigated using serum and peripheral blood from patients with clinical syndromes. Signatures of expression of cytokines, genes and apoptotic proteins that discriminate symptomatic individuals from healthy individuals were determined among 21 patients. In symptomatic patients, significant upregulation of IL-1β, IL-2, IL-4, IL-6, IL-8, IL-12, IL-15, TNF-a and IFN-g (P<0.05) was noted, while IL-10 was significantly downregulated (P<0.05). This is accompanied by either up or down-regulation of various pro-apoptotic and anti-apoptotic markers, suggesting a protective role of immune responses against viral infection and the capacity of viruses to subvert host cell apoptosis. Gene expression analysis for both T and B cells were categorized according to their functional status of activation, proliferation, and differentiation. Of note, genes SH2D1A and TCL1A were upregulated only in rhinovirus samples, while PSMB7, CD4, CD8A, HLA-DMA, HLA-DRA and CD69 were upregulated in samples of Flu A and RSV but were not significant in samples of rhinovirus as compared to healthy individuals. These results demonstrated Flu A and RSV elicit different alterations in human peripheral blood gene expression as compared to rhinovirus. Overall, despite the small number of study subjects, the current study for the first time has recognized signature genes, cytokines and proteins that are used by some respiratory viruses that may serve as candidates for rapid diagnosis as well as targets for therapeutic interventions.

Dynamic changes in immune gene co-expression networks predict development of type 1 diabetes

Significant progress has been made in elucidating genetic risk factors influencing Type 1 diabetes (T1D); however, features other than genetic variants that initiate and/or accelerate islet autoimmunity that lead to the development of clinical T1D remain largely unknown. We hypothesized that genetic and environmental risk factors can both contribute to T1D through dynamic alterations of molecular interactions in physiologic networks. To test this hypothesis, we utilized longitudinal blood transcriptomic profiles in The Environmental Determinants of Diabetes in the Young (TEDDY) study to generate gene co-expression networks. In network modules that contain immune response genes associated with T1D, we observed highly dynamic differences in module connectivity in the 600 days (~ 2 years) preceding clinical diagnosis of T1D. Our results suggest that gene co-expression is highly plastic and that connectivity differences in T1D-associated immune system genes influence the timing and development of clinical disease.

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

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

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.

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.