A host transcriptional signature for presymptomatic detection of infection in humans exposed to influenza H1N1 or H3N2

Defining the balance between optimal immunity and immunopathology in influenza virus infection

Influenza A viruses remain a global threat to human health, with continued pandemic potential. In this Review, we discuss our current understanding of the optimal immune responses that drive recovery from influenza virus infection, highlighting the fine balance between protective immune mechanisms and detrimental immunopathology. We describe the contribution of innate and adaptive immune cells, inflammatory modulators and antibodies to influenza virus-specific immunity, inflammation and immunopathology. We highlight recent human influenza virus challenge studies that advance our understanding of susceptibility to influenza and determinants of symptomatic disease. We also describe studies of influenza virus-specific immunity in high-risk groups following infection and vaccination that inform the design of future vaccines to promote optimal antiviral immunity, particularly in vulnerable populations. Finally, we draw on lessons from the COVID-19 pandemic to refocus our attention to the ever-changing, highly mutable influenza A virus, predicted to cause future global pandemics.

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.

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.

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.

Assessing the Influence of Climate Change and Environmental Factors on the Top Tick-Borne Diseases in the United States: A Systematic Review

In the United States (US), tick-borne diseases (TBDs) have more than doubled in the past fifteen years and are a major contributor to the overall burden of vector-borne diseases. The most common TBDs in the US-Lyme disease, rickettsioses (including Rocky Mountain spotted fever), and anaplasmosis-have gradually shifted in recent years, resulting in increased morbidity and mortality. In this systematic review, we examined climate change and other environmental factors that have influenced the epidemiology of these TBDs in the US while highlighting the opportunities for a One Health approach to mitigating their impact. We searched Medline Plus, PUBMED, and Google Scholar for studies focused on these three TBDs in the US from January 2018 to August 2023. Data selection and extraction were completed using Covidence, and the risk of bias was assessed with the ROBINS-I tool. The review included 84 papers covering multiple states across the US. We found that climate, seasonality and temporality, and land use are important environmental factors that impact the epidemiology and patterns of TBDs. The emerging trends, influenced by environmental factors, emphasize the need for region-specific research to aid in the prediction and prevention of TBDs.

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.

Reconstructing disease dynamics for mechanistic insights and clinical benefit

Diseases change over time, both phenotypically and in their underlying molecular processes. Though understanding disease progression dynamics is critical for diagnostics and treatment, capturing these dynamics is difficult due to their complexity and the high heterogeneity in disease development between individuals. We present TimeAx, an algorithm which builds a comparative framework for capturing disease dynamics using high-dimensional, short time-series data. We demonstrate the utility of TimeAx by studying disease progression dynamics for multiple diseases and data types. Notably, for urothelial bladder cancer tumorigenesis, we identify a stromal pro-invasion point on the disease progression axis, characterized by massive immune cell infiltration to the tumor microenvironment and increased mortality. Moreover, the continuous TimeAx model differentiates between early and late tumors within the same tumor subtype, uncovering molecular transitions and potential targetable pathways. Overall, we present a powerful approach for studying disease progression dynamics-providing improved molecular interpretability and clinical benefits for patient stratification and outcome prediction.

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.

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.

Detecting early-warning signals for influenza by dysregulated dynamic network biomarkers

As a dynamical system, complex disease always has a sudden state transition at the tipping point, which is the result of the long-term accumulation of abnormal regulations. This paper proposes a novel approach to detect the early-warning signals of influenza A (H3N2 and H1N1) outbreaks by dysregulated dynamic network biomarkers (dysregulated DNBs) for individuals. The results of cross-validation show that our approach can detect early-warning signals before the symptom appears successfully. Unlike the traditional DNBs, our dysregulated DNBs are anchored and very few, which is essential for disease early diagnosis in clinical practice. Moreover, the genes of dysregulated DNBs are significantly enriched in the influenza-related pathways. The source code of this paper can be freely downloaded from https://github.com/YanhaoHuo/dysregulated-DNBs.git.

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.

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.

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.

Lyme disease is a bacterial infection spread by ticks and there are nearly half a million cases a year in the United States. However, the disease is difficult to diagnose and existing laboratory tests have limited accuracy. Here, we develop a new genetic test, described as a Lyme disease classifier (LDC), for diagnosing early Lyme disease from blood samples by assessing the patient’s response to the infection. We find that the LDC can identify early Lyme disease patients (those presenting with symptoms within weeks of a tick bite) accurately, even before standard laboratory tests turn positive. In the future, the LDC may be clinically useful as a test for Lyme disease to diagnose patients earlier in the course of their illness, thus guiding more timely and effective treatment for the infection.

Servellita et al. develop a machine learning-based classifier to diagnose Lyme disease using gene expression data. The classifier achieves high sensitivity for early infections, even prior to positivity on antibody testing.

Controlled Human Infection Models To Accelerate Vaccine Development

The timelines for developing vaccines against infectious diseases are lengthy, and often vaccines that reach the stage of large phase 3 field trials fail to provide the desired level of protective efficacy. The application of controlled human challenge models of infection and disease at the appropriate stages of development could accelerate development of candidate vaccines and, in fact, has done so successfully in some limited cases. Human challenge models could potentially be used to gather critical information on pathogenesis, inform strain selection for vaccines, explore cross-protective immunity, identify immune correlates of protection and mechanisms of protection induced by infection or evoked by candidate vaccines, guide decisions on appropriate trial endpoints, and evaluate vaccine efficacy. We prepared this report to motivate fellow scientists to exploit the potential capacity of controlled human challenge experiments to advance vaccine development. In this review, we considered available challenge models for 17 infectious diseases in the context of the public health importance of each disease, the diversity and pathogenesis of the causative organisms, the vaccine candidates under development, and each model's capacity to evaluate them and identify correlates of protective immunity. Our broad assessment indicated that human challenge models have not yet reached their full potential to support the development of vaccines against infectious diseases. On the basis of our review, however, we believe that describing an ideal challenge model is possible, as is further developing existing and future challenge models.

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.

Advances in diagnostic tools for respiratory tract infections. From tuberculosis to COVID19: changing paradigms?

Respiratory tract infections (RTI) are one of the commonest reasons for seeking healthcare, but are amongst the most challenging diseases in terms of clinical decision making. Proper and timely diagnosis is critical in order to optimize management and prevent further emergence of antimicrobial resistance by misuse, or overuse of antibiotics. Diagnostic tools for RTI include those involving syndromic and etiological diagnosis: from clinical and radiological features to laboratory methods targeting both pathogen detection and host biomarkers, as well as their combinations in terms of clinical algorithms. They also include tools for predicting severity and monitoring treatment response. Unprecedented milestones have been achieved in the context of the COVID-19 pandemic, involving the most recent applications of diagnostic technologies both at genotypic and phenotypic level, which have changed paradigms in infectious respiratory diseases in terms of why, how and where diagnostics are performed. The aim of this review is to discuss advances in diagnostic tools that impact clinical decision making, surveillance and follow-up of RTI and tuberculosis. If properly harnessed, recent advances in diagnostic technologies, including omics and digital transformation emerge as an unprecedented opportunity to tackle ongoing and future epidemics while handling antimicrobial resistance from a One Health perspective.

Ultrasound Neuromodulation of the Spleen Has Time-Dependent Anti-Inflammatory Effect in a Pneumonia Model

Interfaces between the nervous and immune systems have been shown essential for the coordination and regulation of immune responses. Non-invasive ultrasound stimulation targeted to the spleen has recently been shown capable of activating one such interface, the splenic cholinergic anti-inflammatory pathway (CAP). Over the past decade, CAP and other neuroimmune pathways have been activated using implanted nerve stimulators and tested to prevent cytokine release and inflammation. However, CAP studies have typically been performed in models of severe, systemic (e.g., endotoxemia) or chronic inflammation (e.g., collagen-induced arthritis or DSS-induced colitis). Herein, we examined the effects of activation of the splenic CAP with ultrasound in a model of local bacterial infection by lung instillation of 10⁵ CFU of Streptococcus pneumoniae. We demonstrate a time-dependent effect of CAP activation on the cytokine response assay during infection progression. CAP activation-induced cytokine suppression is absent at intermediate times post-infection (16 hours following inoculation), but present during the early (4 hours) and later phases (48 hours). These results indicate that cytokine inhibition associated with splenic CAP activation is not observed at all timepoints following bacterial infection and highlights the importance of further studying neuroimmune interfaces within the context of different immune system and inflammatory states.

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

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

Immunopathological signatures in multisystem inflammatory syndrome in children and pediatric COVID-19

Pediatric Coronavirus Disease 2019 (pCOVID-19) is rarely severe; however, a minority of children infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) might develop multisystem inflammatory syndrome in children (MIS-C), with substantial morbidity. In this longitudinal multi-institutional study, we applied multi-omics (analysis of soluble biomarkers, proteomics, single-cell gene expression and immune repertoire analysis) to profile children with COVID-19 (n = 110) and MIS-C (n = 76), along with pediatric healthy controls (pHCs; n = 76). pCOVID-19 was characterized by robust type I interferon (IFN) responses, whereas prominent type II IFN-dependent and NF-κB-dependent signatures, matrisome activation and increased levels of circulating spike protein were detected in MIS-C, with no correlation with SARS-CoV-2 PCR status around the time of admission. Transient expansion of TRBV11-2 T cell clonotypes in MIS-C was associated with signatures of inflammation and T cell activation. The association of MIS-C with the combination of HLA A*02, B*35 and C*04 alleles suggests genetic susceptibility. MIS-C B cells showed higher mutation load than pCOVID-19 and pHC. These results identify distinct immunopathological signatures in pCOVID-19 and MIS-C that might help better define the pathophysiology of these disorders and guide therapy.

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.

COVID-19 Biomarkers Recognition & Classification Using Intelligent Systems

Background:

SARS-CoV-2 has paralyzed mankind due to its high transmissibility and its associated mortality, causing millions of infections and deaths worldwide. The search for gene expression biomarkers from the host transcriptional response to infection may help understand the underlying mechanisms by which the virus causes COVID-19. This research proposes a smart methodology integrating different RNA-Seq datasets from SARS-CoV-2, other respiratory diseases, and healthy patients.

Methods:

The proposed pipeline exploits the functionality of the ‘KnowSeq’ R/Bioc package, integrating different data sources and attaining a significantly larger gene expression dataset, thus endowing the results with higher statistical significance and robustness in comparison with previous studies in the literature. A detailed preprocessing step was carried out to homogenize the samples and build a clinical decision system for SARS-CoV-2. It uses machine learning techniques such as feature selection algorithm and supervised classification system. This clinical decision system uses the most differentially expressed genes among different diseases (including SARS-Cov-2) to develop a four-class classifier.

Results:

The multiclass classifier designed can discern SARS-CoV-2 samples, reaching an accuracy equal to 91.5%, a mean F1-Score equal to 88.5%, and a SARS-CoV-2 AUC equal to 94% by using only 15 genes as predictors. A biological interpretation of the gene signature extracted reveals relations with processes involved in viral responses.

Conclusion:

This work proposes a COVID-19 gene signature composed of 15 genes, selected after applying the feature selection ‘minimum Redundancy Maximum Relevance’ algorithm. The integration among several RNA-Seq datasets was a success, allowing for a considerable large number of samples and therefore providing greater statistical significance to the results than previous studies. Biological interpretation of the selected genes was also provided.

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.

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

Background

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

Methods

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

Results

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

Conclusions

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

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.

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.

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.

Detecting Pathogen Exposure During the Non-symptomatic Incubation Period Using Physiological Data: Proof of Concept in Non-human Primates

Background and Objectives: Early warning of bacterial and viral infection, prior to the development of overt clinical symptoms, allows not only for improved patient care and outcomes but also enables faster implementation of public health measures (patient isolation and contact tracing). Our primary objectives in this effort are 3-fold. First, we seek to determine the upper limits of early warning detection through physiological measurements. Second, we investigate whether the detected physiological response is specific to the pathogen. Third, we explore the feasibility of extending early warning detection with wearable devices.

Research Methods: For the first objective, we developed a supervised random forest algorithm to detect pathogen exposure in the asymptomatic period prior to overt symptoms (fever). We used high-resolution physiological telemetry data (aortic blood pressure, intrathoracic pressure, electrocardiograms, and core temperature) from non-human primate animal models exposed to two viral pathogens: Ebola and Marburg (N = 20). Second, to determine reusability across different pathogens, we evaluated our algorithm against three independent physiological datasets from non-human primate models (N = 13) exposed to three different pathogens: Lassa and Nipah viruses and Y. pestis. For the third objective, we evaluated performance degradation when the algorithm was restricted to features derived from electrocardiogram (ECG) waveforms to emulate data from a non-invasive wearable device.

Results: First, our cross-validated random forest classifier provides a mean early warning of 51 ± 12 h, with an area under the receiver-operating characteristic curve (AUC) of 0.93 ± 0.01. Second, our algorithm achieved comparable performance when applied to datasets from different pathogen exposures – a mean early warning of 51 ± 14 h and AUC of 0.95 ± 0.01. Last, with a degraded feature set derived solely from ECG, we observed minimal degradation – a mean early warning of 46 ± 14 h and AUC of 0.91 ± 0.001.

Conclusion: Under controlled experimental conditions, physiological measurements can provide over 2 days of early warning with high AUC. Deviations in physiological signals following exposure to a pathogen are due to the underlying host’s immunological response and are not specific to the pathogen. Pre-symptomatic detection is strong even when features are limited to ECG-derivatives, suggesting that this approach may translate to non-invasive wearable devices.

Assessment of the Feasibility of Using Noninvasive Wearable Biometric Monitoring Sensors to Detect Influenza and the Common Cold Before Symptom Onset

IMPORTANCE

Currently, there are no presymptomatic screening methods to identify individuals infected with a respiratory virus to prevent disease spread and to predict their trajectory for resource allocation.

OBJECTIVE

To evaluate the feasibility of using noninvasive, wrist-worn wearable biometric monitoring sensors to detect presymptomatic viral infection after exposure and predict infection severity in patients exposed to H1N1 influenza or human rhinovirus.

DESIGN, SETTING, AND PARTICIPANTS

The cohort H1N1 viral challenge study was conducted during 2018; data were collected from September 11, 2017, to May 4, 2018. The cohort rhinovirus challenge study was conducted during 2015; data were collected from September 14 to 21, 2015. A total of 39 adult participants were recruited for the H1N1 challenge study, and 24 adult participants were recruited for the rhinovirus challenge study. Exclusion criteria for both challenges included chronic respiratory illness and high levels of serum antibodies. Participants in the H1N1 challenge study were isolated in a clinic for a minimum of 8 days after inoculation. The rhinovirus challenge took place on a college campus, and participants were not isolated.

EXPOSURES

Participants in the H1N1 challenge study were inoculated via intranasal drops of diluted influenza A/California/03/09 (H1N1) virus with a mean count of 106 using the median tissue culture infectious dose (TCID50) assay. Participants in the rhinovirus challenge study were inoculated via intranasal drops of diluted human rhinovirus strain type 16 with a count of 100 using the TCID50 assay.

MAIN OUTCOMES AND MEASURES

The primary outcome measures included cross-validated performance metrics of random forest models to screen for presymptomatic infection and predict infection severity, including accuracy, precision, sensitivity, specificity, F1 score, and area under the receiver operating characteristic curve (AUC).

RESULTS

A total of 31 participants with H1N1 (24 men [77.4%]; mean [SD] age, 34.7 [12.3] years) and 18 participants with rhinovirus (11 men [61.1%]; mean [SD] age, 21.7 [3.1] years) were included in the analysis after data preprocessing. Separate H1N1 and rhinovirus detection models, using only data on wearble devices as input, were able to distinguish between infection and noninfection with accuracies of up to 92% for H1N1 (90% precision, 90% sensitivity, 93% specificity, and 90% F1 score, 0.85 [95% CI, 0.70-1.00] AUC) and 88% for rhinovirus (100% precision, 78% sensitivity, 100% specificity, 88% F1 score, and 0.96 [95% CI, 0.85-1.00] AUC). The infection severity prediction model was able to distinguish between mild and moderate infection 24 hours prior to symptom onset with an accuracy of 90% for H1N1 (88% precision, 88% sensitivity, 92% specificity, 88% F1 score, and 0.88 [95% CI, 0.72-1.00] AUC) and 89% for rhinovirus (100% precision, 75% sensitivity, 100% specificity, 86% F1 score, and 0.95 [95% CI, 0.79-1.00] AUC).

CONCLUSIONS AND RELEVANCE

This cohort study suggests that the use of a noninvasive, wrist-worn wearable device to predict an individual's response to viral exposure prior to symptoms is feasible. Harnessing this technology would support early interventions to limit presymptomatic spread of viral respiratory infections, which is timely in the era of COVID-19.

The host transcriptional response to Candidemia is dominated by neutrophil activation and heme biosynthesis and supports novel diagnostic approaches

BACKGROUND

Candidemia is one of the most common nosocomial bloodstream infections in the United States, causing significant morbidity and mortality in hospitalized patients, but the breadth of the host response to Candida infections in human patients remains poorly defined.

METHODS

In order to better define the host response to Candida infection at the transcriptional level, we performed RNA sequencing on serial peripheral blood samples from 48 hospitalized patients with blood cultures positive for Candida species and compared them to patients with other acute viral, bacterial, and non-infectious illnesses. Regularized multinomial regression was utilized to develop pathogen class-specific gene expression classifiers.

RESULTS

Candidemia triggers a unique, robust, and conserved transcriptomic response in human hosts with 1641 genes differentially upregulated compared to healthy controls. Many of these genes corresponded to components of the immune response to fungal infection, heavily weighted toward neutrophil activation, heme biosynthesis, and T cell signaling. We developed pathogen class-specific classifiers from these unique signals capable of identifying and differentiating candidemia, viral, or bacterial infection across a variety of hosts with a high degree of accuracy (auROC 0.98 for candidemia, 0.99 for viral and bacterial infection). This classifier was validated on two separate human cohorts (auROC 0.88 for viral infection and 0.87 for bacterial infection in one cohort; auROC 0.97 in another cohort) and an in vitro model (auROC 0.94 for fungal infection, 0.96 for bacterial, and 0.90 for viral infection).

CONCLUSIONS

Transcriptional analysis of circulating leukocytes in patients with acute Candida infections defines novel aspects of the breadth of the human immune response during candidemia and suggests promising diagnostic approaches for simultaneously differentiating multiple types of clinical illnesses in at-risk, acutely ill patients.

Systems Biological Analysis of Immune Response to Influenza Vaccination

The last decade has witnessed tremendous progress in immunology and vaccinology, owing to several scientific and technological breakthroughs. Systems vaccinology is a field that has emerged at the forefront of vaccine research and development and provides a unique way to probe immune responses to vaccination in humans. The goals of systems vaccinology are to use systems-based approaches to define signatures that can be used to predict vaccine immunogenicity and efficacy and to delineate the molecular mechanisms driving protective immunity. The application of systems biological approaches in influenza vaccination studies has enabled the discovery of early signatures that predict immunogenicity to vaccination and yielded novel mechanistic insights about vaccine-induced immunity. Here we review the contributions of systems vaccinology to influenza vaccine development and critically examine the potential of systems vaccinology toward enabling the development of a universal influenza vaccine that provides robust and durable immunity against diverse influenza viruses.

Highly multiplexed oligonucleotide probe-ligation testing enables efficient extraction-free SARS-CoV-2 detection and viral genotyping

There is an urgent and unprecedented need for sensitive and high-throughput molecular diagnostic tests to combat the SARS-CoV-2 pandemic. Here we present a generalized version of the RNA-mediated oligonucleotide Annealing Selection and Ligation with next generation DNA sequencing (RASL-seq) assay, called "capture RASL-seq" (cRASL-seq), which enables highly sensitive (down to ~1-100 pfu/ml or cfu/ml) and highly multiplexed (up to ~10,000 target sequences) detection of pathogens. Importantly, cRASL-seq analysis of COVID-19 patient nasopharyngeal (NP) swab specimens does not involve nucleic acid purification or reverse transcription, steps that have introduced supply bottlenecks into standard assay workflows. Our simplified protocol additionally enables the direct and efficient genotyping of selected, informative SARS-CoV-2 polymorphisms across the entire genome, which can be used for enhanced characterization of transmission chains at population scale and detection of viral clades with higher or lower virulence. Given its extremely low per-sample cost, simple and automatable protocol and analytics, probe panel modularity, and massive scalability, we propose that cRASL-seq testing is a powerful new technology with the potential to help mitigate the current pandemic and prevent similar public health crises.

Time-resolved systems immunology reveals a late juncture linked to fatal COVID-19

COVID-19 exhibits extensive patient-to-patient heterogeneity. To link immune response variation to disease severity and outcome over time, we longitudinally assessed circulating proteins as well as 188 surface protein markers, transcriptome, and T cell receptor sequence simultaneously in single peripheral immune cells from COVID-19 patients. Conditional-independence network analysis revealed primary correlates of disease severity, including gene expression signatures of apoptosis in plasmacytoid dendritic cells and attenuated inflammation but increased fatty acid metabolism in CD56dimCD16hi NK cells linked positively to circulating interleukin (IL)-15. CD8+ T cell activation was apparent without signs of exhaustion. Although cellular inflammation was depressed in severe patients early after hospitalization, it became elevated by days 17-23 post symptom onset, suggestive of a late wave of inflammatory responses. Furthermore, circulating protein trajectories at this time were divergent between and predictive of recovery versus fatal outcomes. Our findings stress the importance of timing in the analysis, clinical monitoring, and therapeutic intervention of COVID-19.

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

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

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

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

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

Background

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

Methods

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

Findings

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

Interpretation

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

Funding

US Defense Advanced Research Projects Agency.

Predicting viral exposure response from modeling the changes of co-expression networks using time series gene expression data

BACKGROUND

Deciphering the relationship between clinical responses and gene expression profiles may shed light on the mechanisms underlying diseases. Most existing literature has focused on exploring such relationship from cross-sectional gene expression data. It is likely that the dynamic nature of time-series gene expression data is more informative in predicting clinical response and revealing the physiological process of disease development. However, it remains challenging to extract useful dynamic information from time-series gene expression data.

RESULTS

We propose a statistical framework built on considering co-expression network changes across time from time series gene expression data. It first detects change point for co-expression networks and then employs a Bayesian multiple kernel learning method to predict exposure response. There are two main novelties in our method: the use of change point detection to characterize the co-expression network dynamics, and the use of kernel function to measure the similarity between subjects. Our algorithm allows exposure response prediction using dynamic network information across a collection of informative gene sets. Through parameter estimations, our model has clear biological interpretations. The performance of our method on the simulated data under different scenarios demonstrates that the proposed algorithm has better explanatory power and classification accuracy than commonly used machine learning algorithms. The application of our method to time series gene expression profiles measured in peripheral blood from a group of subjects with respiratory viral exposure shows that our method can predict exposure response at early stage (within 24 h) and the informative gene sets are enriched for pathways related to respiratory and influenza virus infection.

CONCLUSIONS

The biological hypothesis in this paper is that the dynamic changes of the biological system are related to the clinical response. Our results suggest that when the relationship between the clinical response and a single gene or a gene set is not significant, we may benefit from studying the relationships among genes in gene sets that may lead to novel biological insights.

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.

Metagenomics for neurological infections - expanding our imagination

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

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

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

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

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

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

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

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

Immune and Metabolic Signatures of COVID-19 Revealed by Transcriptomics Data Reuse

The current pandemic of coronavirus disease 19 (COVID-19) has affected millions of individuals and caused thousands of deaths worldwide. The pathophysiology of the disease is complex and mostly unknown. Therefore, identifying the molecular mechanisms that promote progression of the disease is critical to overcome this pandemic. To address such issues, recent studies have reported transcriptomic profiles of cells, tissues and fluids from COVID-19 patients that mainly demonstrated activation of humoral immunity, dysregulated type I and III interferon expression, intense innate immune responses and inflammatory signaling. Here, we provide novel perspectives on the pathophysiology of COVID-19 using robust functional approaches to analyze public transcriptome datasets. In addition, we compared the transcriptional signature of COVID-19 patients with individuals infected with SARS-CoV-1 and Influenza A (IAV) viruses. We identified a core transcriptional signature induced by the respiratory viruses in peripheral leukocytes, whereas the absence of significant type I interferon/antiviral responses characterized SARS-CoV-2 infection. We also identified the higher expression of genes involved in metabolic pathways including heme biosynthesis, oxidative phosphorylation and tryptophan metabolism. A BTM-driven meta-analysis of bronchoalveolar lavage fluid (BALF) from COVID-19 patients showed significant enrichment for neutrophils and chemokines, which were also significant in data from lung tissue of one deceased COVID-19 patient. Importantly, our results indicate higher expression of genes related to oxidative phosphorylation both in peripheral mononuclear leukocytes and BALF, suggesting a critical role for mitochondrial activity during SARS-CoV-2 infection. Collectively, these data point for immunopathological features and targets that can be therapeutically exploited to control COVID-19.

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

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

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

Highly multiplexed oligonucleotide probe-ligation testing enables efficient extraction-free SARS-CoV-2 detection and viral genotyping

The emergence of SARS-CoV-2 has caused the current COVID-19 pandemic with catastrophic societal impact. Because many individuals shed virus for days before symptom onset, and many show mild or no symptoms, an emergent and unprecedented need exists for development and deployment of sensitive and high throughput molecular diagnostic tests. RNA-mediated oligonucleotide Annealing Selection and Ligation with next generation DNA sequencing (RASL-seq) is a highly multiplexed technology for targeted analysis of polyadenylated mRNA, which incorporates sample barcoding for massively parallel analyses. Here we present a more generalized method, capture RASL-seq ("cRASL-seq"), which enables analysis of any targeted pathogen- (and/or host-) associated RNA molecules. cRASL-seq enables highly sensitive (down to ~1-100 pfu/ml or cfu/ml) and highly multiplexed (up to ~10,000 target sequences) detection of pathogens. Importantly, cRASL-seq analysis of COVID-19 patient nasopharyngeal (NP) swab specimens does not involve nucleic acid extraction or reverse transcription, steps that have caused testing bottlenecks associated with other assays. Our simplified workflow additionally enables the direct and efficient genotyping of selected, informative SARS-CoV-2 polymorphisms across the entire genome, which can be used for enhanced characterization of transmission chains at population scale and detection of viral clades with higher or lower virulence. Given its extremely low per-sample cost, simple and automatable protocol and analytics, probe panel modularity, and massive scalability, we propose that cRASL-seq testing is a powerful new surveillance technology with the potential to help mitigate the current pandemic and prevent similar public health crises.

A transcriptional signature accurately identifies Aspergillus Infection across healthy and immunosuppressed states

Invasive aspergillosis (IA) is a major cause of critical illness in immunocompromised (IC) patients. However, current fungal tests are limited. Disease-specific gene expression patterns in circulating host cells show promise as novel diagnostics, however it is unknown whether such a 'signature' exists for IA and the effect of iatrogenic immunosuppression on any such biomarkers. Male BALB/c mice were separated into 6 experimental groups based on Aspergillus fumigatus inhalational exposure and IC status (no immunosuppression, cyclophosphamide, and corticosteroids). Mice were sacrificed 4 days postinfection. Whole blood was assayed for transcriptomic responses in peripheral white blood cells via microarray. An elastic net regularized logistic regression was employed to develop classifiers of IA based on gene expression. Aspergillus infection triggers a powerful response in non-IC hosts with 2718 genes differentially expressed between IA and controls. We generated a 146-gene classifier able to discriminate between non-IC infected and uninfected mice with an AUC of 1. However, immunosuppressive medications exhibited a confounding effect on this transcriptomic classifier. After controlling for the genomic effects of immunosuppression, we were able to generate a 187-gene classifier with an AUC of 0.92 in the absence of immunosuppression, 1 with cyclophosphamide, and 0.9 with steroids. The host transcriptomic response to IA is robust and conserved. Pharmacologic perturbation of the host immune response has powerful effects on classifier performance and must be considered when developing such novel diagnostics. When appropriately designed, host-derived peripheral blood transcriptomic responses demonstrate the ability to accurately diagnose Aspergillus infection, even in the presence of immunosuppression.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLSUSIONS

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

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

Objective

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

Method

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

Results

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

Conclusions

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

Clinical Trials Registration

NCT00258869.

A big data pipeline: Identifying dynamic gene regulatory networks from time-course Gene Expression Omnibus data with applications to influenza infection

A biological host response to an external stimulus or intervention such as a disease or infection is a dynamic process, which is regulated by an intricate network of many genes and their products. Understanding the dynamics of this gene regulatory network allows us to infer the mechanisms involved in a host response to an external stimulus, and hence aids the discovery of biomarkers of phenotype and biological function. In this article, we propose a modeling/analysis pipeline for dynamic gene expression data, called Pipeline4DGEData, which consists of a series of statistical modeling techniques to construct dynamic gene regulatory networks from the large volumes of high-dimensional time-course gene expression data that are freely available in the Gene Expression Omnibus repository. This pipeline has a consistent and scalable structure that allows it to simultaneously analyze a large number of time-course gene expression data sets, and then integrate the results across different studies. We apply the proposed pipeline to influenza infection data from nine studies and demonstrate that interesting biological findings can be discovered with its implementation.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Neutrophils-related host factors associated with severe disease and fatality in patients with influenza infection

Severe influenza infection has no effective treatment available. One of the key barriers to developing host-directed therapy is a lack of reliable prognostic factors needed to guide such therapy. Here, we use a network analysis approach to identify host factors associated with severe influenza and fatal outcome. In influenza patients with moderate-to-severe diseases, we uncover a complex landscape of immunological pathways, with the main changes occurring in pathways related to circulating neutrophils. Patients with severe disease display excessive neutrophil extracellular traps formation, neutrophil-inflammation and delayed apoptosis, all of which have been associated with fatal outcome in animal models. Excessive neutrophil activation correlates with worsening oxygenation impairment and predicted fatal outcome (AUROC 0.817-0.898). These findings provide new evidence that neutrophil-dominated host response is associated with poor outcomes. Measuring neutrophil-related changes may improve risk stratification and patient selection, a critical first step in developing host-directed immune therapy.