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Sepsis causes significant mortality. However, most patients who die of sepsis do not present with severe infection, hampering efforts to deliver early, aggressive therapy. It is also known that the host gene expression response to infection precedes clinical illness. This study seeks to develop transcriptomic models to predict progression to sepsis or shock within 72 hours of hospitalization and to validate previously identified transcriptomic signatures in the prediction of 28-day mortality.Retrospective differential gene expression analysis and predictive modeling using RNA sequencing data.Two hundred seventy-seven patients enrolled at four large academic medical centers; all with clinically adjudicated infection were considered for inclusion in this study.Sepsis progression was defined as an increase in Sepsis 3 category within 72 hours. Transcriptomic data were generated using RNAseq of whole blood. Least absolute shrinkage and selection operator modeling was used to identify predictive signatures for various measures of disease progression. Four previously identified gene signatures were tested for their ability to predict 28-day mortality. There were no significant differentially expressed genes in 136 subjects with worsened Sepsis 3 category compared with 141 nonprogressor controls. There were 1,178 differentially expressed genes identified when sepsis progression was defined as ICU admission or 28-day mortality. A model based on these genes predicted progression with an area under the curve of 0.71. Validation of previously identified gene signatures to predict sepsis mortality revealed area under the receiver operating characteristic values of 0.70-0.75 and no significant difference between signatures.Host gene expression was unable to predict sepsis progression when defined by an increase in Sepsis-3 category, suggesting this definition is not a useful framework for transcriptomic prediction methods. However, there was a differential response when progression was defined as ICU admission or death. Validation of previously described signatures predicted 28-day mortality with insufficient accuracy to offer meaningful clinical utility.

Lower respiratory tract infections are frequently treated with antibiotics, despite a viral cause in many cases. It remains unknown whether low procalcitonin concentrations can identify patients with lower respiratory tract infection who are unlikely to benefit from antibiotics. We aimed to compare the efficacy and safety of azithromycin versus placebo to treat lower respiratory tract infections in patients with low procalcitonin.We conducted a randomised, placebo-controlled, double-blind, non-inferiority trial at five health centres in the USA. Adults aged 18 years or older with clinically suspected non-pneumonia lower respiratory tract infection and symptom duration from 24 h to 28 days were eligible for enrolment. Participants with a procalcitonin concentration of 0·25 ng/mL or less were randomly assigned (1:1), in blocks of four with stratification by site, to receive over-encapsulated oral azithromycin 250 mg or matching placebo (two capsules on day 1 followed by one capsule daily for 4 days). Participants, non-study clinical providers, investigators, and study coordinators were masked to treatment allocation. The primary outcome was efficacy of azithromycin versus placebo in terms of clinical improvement at day 5 in the intention-to-treat population. The non-inferiority margin was -12·5%. Solicited adverse events (abdominal pain, vomiting, diarrhoea, allergic reaction, or yeast infections) were recorded as a secondary outcome. This trial is registered with ClinicalTrials.gov, NCT03341273.Between Dec 8, 2017, and March 9, 2020, 691 patients were assessed for eligibility and 499 were enrolled and randomly assigned to receive azithromycin (n=249) or placebo (n=250). Clinical improvement at day 5 was observed in 148 (63%, 95% CI 54 to 71) of 238 participants with full data in the placebo group and 155 (69%, 61 to 77) of 227 participants with full data in the azithromycin group in the intention-to-treat analysis (between-group difference -6%, 95% CI -15 to 2). The 95% CI for the difference did not meet the non-inferiority margin. Solicited adverse events and the severity of solicited adverse events were not significantly different between groups at day 5, except for increased abdominal pain associated with azithromycin (47 [23%, 95% CI 18 to 29] of 204 participants) compared with placebo (35 [16%, 12 to 21] of 221; between-group difference -7% [95% CI -15 to 0]; p=0·066).Placebo was not non-inferior to azithromycin in terms of clinical improvement at day 5 in adults with lower respiratory tract infection and a low procalcitonin concentration. After accounting for both the rates of clinical improvement and solicited adverse events at day 5, it is unclear whether antibiotics are indicated for patients with lower respiratory tract infection and a low procalcitonin concentration.National Institute of Allergy and Infectious Diseases, bioMérieux.

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. This framework allowed for responses to be accurately referenced to the infection event. For each participant, we trained a semisupervised multivariable anomaly detection model on data acquired before inoculation and used it to classify the postinoculation 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 postinoculation and 23 hours before the symptom onset. Conclusions 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. Clinical Trials Registration. NCT04204493.

Background We evaluated the associations between baseline influenza virus–specific hemagglutination inhibition (HAI) and microneutralization (MN) titers and subsequent symptomatic influenza virus infection in a controlled human infection study. Methods We inoculated unvaccinated healthy adults aged 18–49 years with an influenza A/California/04/2009/H1N1pdm-like virus (NCT04044352). We collected serial safety labs, serum for HAI and MN, and nasopharyngeal swabs for reverse-transcription polymerase chain reaction (RT-PCR) testing. Analyses used the putative seroprotective titer of ≥40 for HAI and MN. The primary clinical outcome was mild-to-moderate influenza disease (MMID), defined as ≥1 postchallenge positive qualitative RT-PCR test with a qualifying symptom/clinical finding. Results Of 76 participants given influenza virus challenge, 54 (71.1%) experienced MMID. Clinical illness was generally very mild. MMID attack rates among participants with baseline titers ≥40 by HAI and MN were 64.9% and 67.9%, respectively, while MMID attack rates among participants with baseline titers Conclusions We achieved a 71.1% attack rate of MMID. High baseline HAI and MN were associated with protection from illness. Clinical Trials Registration. NCT04044352.

During pandemics, individuals exhibit differences in risk and clinical outcomes. Here, we developed single-cell high-throughput human

Background Pneumonia is the most common infectious cause of morbidity and excess mortality complicating hematopoietic cell transplantation (HCT) and treatment of hematologic malignancy. Standard bronchoscopic and noninvasive microbiologic testing identify causative pathogens in less than half of cases. The Karius Test, a plasma next-generation sequencing assay of microbial cell-free DNA, may improve diagnostic yield in these patients. Methods Patients with active hematologic malignancy or recent HCT undergoing bronchoscopy for suspected pneumonia were prospectively enrolled in this observational study conducted at 10 United States medical centers. A panel of expert clinicians blinded to Karius Test results reviewed a standardized panel of microbiologic and molecular testing from bronchoalveolar lavage and blood samples for bacterial and fungal testing, nasopharyngeal swab for respiratory viral testing, imaging results, clinical documentation, and any additional microbiologic or molecular testing collected per usual standard of care to adjudicate a probable cause of pneumonia. The panel then adjudicated whether a probable cause of pneumonia or other clinically relevant infection was identified by the Karius Test. Results Between January 3, 2020 and February 4, 2022, 257 patients were enrolled. A planned interim analysis of the first 69 sequentially enrolled patients in the per protocol population was conducted. An adjudicated probable cause of pneumonia was identified by standard care in 18/69 (26%) patients. The Karius Test identified an adjudicated probable cause of pneumonia in 10/51 (20%) patients when no cause of pneumonia was identified by standard care testing. The combination of standard care and the Karius Test together identified a probable cause of pneumonia in 28/69 (41%) patients. At least one additional pathogen adjudicated as a probable cause of pneumonia was identified by the Karius Test in 6/18 (33%) of patients with positive standard care testing. Conclusion The Karius Test notably increased the probability of identifying a pathogenic cause of pneumonia among immunocompromised patients undergoing bronchoscopy. The additive diagnostic value of the Karius Test may significantly enhance management of this common condition. Disclosures Roy F. Chemaly, MD/MPH, Karius: Advisor/Consultant|Karius: Grant/Research Support Radha Duttagupta, PhD, Karius Inc: Stocks/Bonds Sanjeet S. Dadwal, MD, FACP, FIDSA, AlloVir: Advisor/Consultant|AlloVir: Grant/Research Support|Ansun Biopharma: Grant/Research Support|Aseptiscope: Advisor/Consultant|Aseptiscope: Stocks/Bonds|Astellas: Speaker's Bureau|Cidara: Advisor/Consultant|Gilead: Grant/Research Support|Karius: Grant/Research Support|Merck: Advisor/Consultant|Merck: Grant/Research Support|Merck: Speaker's Bureau|Takeda: Speaker's Bureau Joshua A. Hill, MD, Allovir: Advisor/Consultant|Allovir: Grant/Research Support|Covance/CSL: Advisor/Consultant|CRISPR: Advisor/Consultant|Deverra: Grant/Research Support|Gilead: Grant/Research Support|Karius: Advisor/Consultant|Karius: Grant/Research Support|Merck: Grant/Research Support|Octapharma: Advisor/Consultant|OptumHealth: Advisor/Consultant|Oxford Immunotec: Grant/Research Support|Pfizer: Advisor/Consultant|Symbio: Advisor/Consultant|Takeda: Advisor/Consultant Ghady Haidar, MD, Karius, Allovir, and AstraZeneca: Grant/Research Support Alfred Luk, MD, Karius: Grant/Research Support Jamie Todd, MD, Altavant Sciences: Advisor/Consultant|AstraZeneca: Grant/Research Support|Boehringer Ingelheim: Grant/Research Support|CareDx: Grant/Research Support|Cellarity: Advisor/Consultant|Natera: Advisor/Consultant Genovefa Papanicolaou, MD, AlloVir: Board Member|AlloVir: Serve as member of DSMC|Amplyx: Board Member|Amplyx: Serve as member of DSMC|Astellas: Advisor/Consultant|Cidara: Advisor/Consultant|CSL Behring: Advisor/Consultant|Merck: Advisor/Consultant|Merck: Grant/Research Support|Merck: Investigator for Merck|MSD: Advisor/Consultant|Octapharma: Advisor/Consultant|Octapharma: Board Member|Octapharma: Serve as EAC member|Partners RX: Advisor/Consultant|SymBio: Advisor/Consultant|Takeda: Advisor/Consultant|Takeda: Grant/Research Support|Takeda: Investigator for Takeda|Vera: Board Member|Vera: Serve as member of DSMC Elena Nemirovich-Danchenko, MD PhD, Karius: Stocks/Bonds Mona Mughar, BS, Karius: Stocks/Bonds Sudeb Dalai, MD, Karius: Stocks/Bonds Sudeb Dalai, MD, Karius: Stocks/Bonds Yuen Cho, MS, CLS(CA-DPH), Karius: Stocks/Bonds Asim A. Ahmed, MD, Karius: Employee|Karius: Stocks/Bonds Desiree Hollemon, MSN, MPH, Karius: Stocks/Bonds David K. Hong, MD, Janssen Pharmaceutical Companies of Johnson & Johnson: Employee|Vir Biotechnology: Employee|Vir Biotechnology: Stocks/Bonds Marla Lay Vaughn, BS, MT(ASCP), Karius: Employee|Karius: Stocks/Bonds Tim Blauwkamp, PhD, Karius: Board Member|Karius: Ownership Interest Zivjena Vucetic, MD, Karius: Stocks/Bonds Rina Romano, BS, Karius Inc: Stocks/Bonds|Karius Inc: Stocks/Bonds Rina Romano, BS, Karius Inc: Stocks/Bonds|Karius Inc: Stocks/Bonds Rina Romano, BS, Karius Inc: Stocks/Bonds|Karius Inc: Stocks/Bonds Vance G. Fowler, Jr, MD, MHS, Affinergy: Grant/Research Support|Affinergy: Honoraria|Affinium: Honoraria|Amphliphi Biosciences: Honoraria|ArcBio: Stocks/Bonds|Basilea: Grant/Research Support|Basilea: Honoraria|Bayer: Honoraria|C3J: Honoraria|Cerexa/Forest/Actavis/Allergan: Grant/Research Support|Contrafect: Grant/Research Support|Contrafect: Honoraria|Cubist/Merck: Grant/Research Support|Debiopharm: Grant/Research Support|Deep Blue: Grant/Research Support|Destiny: Honoraria|Genentech: Grant/Research Support|Genentech: Honoraria|Integrated Biotherapeutics: Honoraria|Janssen: Grant/Research Support|Janssen: Honoraria|Karius: Grant/Research Support|Medicines Co.: Honoraria|MedImmune: Grant/Research Support|MedImmune: Honoraria|NIH: Grant/Research Support|Novartis: Grant/Research Support|Novartis: Honoraria|Pfizer: Grant/Research Support|Regeneron: Grant/Research Support|Regeneron: Honoraria|Sepsis diagnostics: Sepsis diagnostics patent pending|UpToDate: Royalties|Valanbio: Stocks/Bonds Thomas L. Holland, MD, Aridis: Advisor/Consultant|Lysovant: Advisor/Consultant.

During pandemics, individuals exhibit differences in risk and clinical outcomes. Here, we developed single-cell high-throughput human in vitro susceptibility testing (scHi-HOST), a method for rapidly identifying genetic variants that confer resistance and susceptibility. We applied this method to influenza A virus (IAV), the cause of four pandemics since the start of the 20th century. scHi-HOST leverages single-cell RNA sequencing (scRNA-seq) to simultaneously assign genetic identity to cells in mixed infections of cell lines of European, African, and Asian origin, reveal associated genetic variants for viral burden, and identify expression quantitative trait loci. Integration of scHi-HOST with human challenge and experimental validation demonstrated that a missense variant in endoplasmic reticulum aminopeptidase 1 (ERAP1; rs27895) increased IAV burden in cells and human volunteers. rs27895 exhibits population differentiation, likely contributing to greater permissivity of cells from African populations to IAV. scHi-HOST is a broadly applicable method and resource for decoding infectious-disease genetics.

BackgroundInfluenza controlled human infection model (CHIM) studies can advance development of vaccines and therapeutics. Our objective was to evaluate the associations between baseline challenge virus-specific hemagglutination inhibition (HAI) and microneutralization (MN) titers and subsequent symptomatic influenza virus infection.MethodsWe enrolled healthy adults aged 18 through 49 years in a multisite CHIM study using influenza A/Bethesda/MM2/H1N1, an A/California/04/2009/H1N1pdm-like virus (NCT04044352). We excluded persons vaccinated against influenza within the previous six months. We collected serial safety labs, serum for HAI and MN, and nasopharyngeal swabs for RT-PCR testing. Analyses used the putative seroprotective titer of ≥40 for HAI and MN. The primary clinical outcome was mild-to-moderate influenza disease (MMID), defined as ≥1 post-challenge positive qualitative RT-PCR test with a qualifying symptom/clinical finding.FindingsOf 76 participants given influenza virus challenge, 54 (71.1%) experienced MMID. Clinical illness post-challenge was generally very mild. MMID attack rates among participants with baseline titers ≥40 by HAI and MN were 64.9% and 67.9%, respectively, while MMID attack rates among participants with baseline titers InterpretationIn a multi-site influenza CHIM study, we assured the safety of our participants and achieved a 71.1% attack rate of MMID. High baseline HAI and MN were associated with protection from illness.

BackgroundCandidemia 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 toCandidainfections in human patients remains poorly defined.MethodsIn order to better define the host response toCandidainfection at the transcriptional level, we performed RNA sequencing on serial peripheral blood samples from 48 hospitalized patients with blood cultures positive forCandidaspecies 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.ResultsCandidemia 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).ConclusionsTranscriptional analysis of circulating leukocytes in patients with acuteCandidainfections 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.

The identification of a COVID-19 host response signature in blood can increase the understanding of SARS-CoV-2 pathogenesis and improve diagnostic tools. Applying a multi-objective optimization framework to both massive public and new multi-omics data, we identified a COVID-19 signature regulated at both transcriptional and epigenetic levels. We validated the signature's robustness in multiple independent COVID-19 cohorts. Using public data from 8,630 subjects and 53 conditions, we demonstrated no cross-reactivity with other viral and bacterial infections, COVID-19 comorbidities, or confounders. In contrast, previously reported COVID-19 signatures were associated with significant cross-reactivity. The signature's interpretation, based on cell-type deconvolution and single-cell data analysis, revealed prominent yet complementary roles for plasmablasts and memory T cells. Although the signal from plasmablasts mediated COVID-19 detection, the signal from memory T cells controlled against cross-reactivity with other viral infections. This framework identified a robust, interpretable COVID-19 signature and is broadly applicable in other disease contexts. A record of this paper's transparent peer review process is included in the supplemental information.

Several cases of symptomatic reinfection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) after full recovery from a prior episode have been reported. As reinfection has become an increasingly common phenomenon, an improved understanding of the risk factors for reinfection and the character and duration of the serological responses to infection and vaccination is critical for managing the coronavirus disease 2019 (COVID-19) pandemic.We described four cases of SARS-CoV-2 reinfection in individuals representing a spectrum of healthy and immunocompromised states, including (1) a healthy 41-year-old pediatrician, (2) an immunocompromised 31-year-old with granulomatosis with polyangiitis, (3) a healthy 26-year-old pregnant woman, and (4) a 50-year-old with hypertension and hyperlipidemia. We performed confirmatory quantitative reverse transcription-polymerase chain reaction and qualitative immunoglobulin M and quantitative IgG testing on all available patient samples to confirm the presence of infection and serological response to infection.Our analysis showed that patients 1 and 2, a healthy and an immunocompromised patient, both failed to mount a robust serologic response to the initial infection. In contrast, patients 3 and 4, with minimal comorbid disease, both mounted a strong serological response to their initial infection, but were still susceptible to reinfection.Repeat episodes of COVID-19 are capable of occurring in patients regardless of the presence of known risk factors for infection or level of serological response to infection, although this did not trigger critical illness in any instance.

Background Host gene expression has emerged as a complementary strategy to pathogen detection tests for the discrimination of bacterial and viral infection. The impact of immunocompromise on host-response tests remains unknown. We evaluated a host-response test discriminating bacterial, viral, and noninfectious conditions in immunocompromised subjects. Methods An 81-gene signature was measured using real-time–polymerase chain reaction in subjects with immunocompromise (chemotherapy, solid-organ transplant, immunomodulatory agents, AIDS) with bacterial infection, viral infection, or noninfectious illness. A regularized logistic regression model trained in immunocompetent subjects was used to estimate the likelihood of each class in immunocompromised subjects. Results Accuracy in the 136-subject immunocompetent training cohort was 84.6% for bacterial versus nonbacterial discrimination and 80.8% for viral versus nonviral discrimination. Model validation in 134 immunocompromised subjects showed overall accuracy of 73.9% for bacterial infection (P = .04 relative to immunocompetent subjects) and 75.4% for viral infection (P = .30). A scheme reporting results by quartile improved test utility. The highest probability quartile ruled-in bacterial and viral infection with 91.4% and 84.0% specificity, respectively. The lowest probability quartile ruled-out infection with 90.1% and 96.4% sensitivity for bacterial and viral infection, respectively. Performance was independent of the type or number of immunocompromising conditions. Conclusions A host gene expression test discriminated bacterial, viral, and noninfectious etiologies at a lower overall accuracy in immunocompromised patients compared with immunocompetent patients, although this difference was only significant for bacterial infection classification. With modified interpretive criteria, a host-response strategy may offer clinically useful diagnostic information for patients with immunocompromise.

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.

Bacterial and viral causes of acute respiratory illness (ARI) are difficult to clinically distinguish, resulting in the inappropriate use of antibacterial therapy. The use of a host gene expression-based test that is able to discriminate bacterial from viral infection in less than 1 hour may improve care and antimicrobial stewardship.To validate the host response bacterial/viral (HR-B/V) test and assess its ability to accurately differentiate bacterial from viral infection among patients with ARI.This prospective multicenter diagnostic study enrolled 755 children and adults with febrile ARI of 7 or fewer days' duration from 10 US emergency departments. Participants were enrolled from October 3, 2014, to September 1, 2019, followed by additional enrollment of patients with COVID-19 from March 20 to December 3, 2020. Clinical adjudication of enrolled participants identified 616 individuals as having bacterial or viral infection. The primary analysis cohort included 334 participants with high-confidence reference adjudications (based on adjudicator concordance and the presence of an identified pathogen confirmed by microbiological testing). A secondary analysis of the entire cohort of 616 participants included cases with low-confidence reference adjudications (based on adjudicator discordance or the absence of an identified pathogen in microbiological testing). Thirty-three participants with COVID-19 were included post hoc.The HR-B/V test quantified the expression of 45 host messenger RNAs in approximately 45 minutes to derive a probability of bacterial infection.Performance characteristics for the HR-B/V test compared with clinical adjudication were reported as either bacterial or viral infection or categorized into 4 likelihood groups (viral very likely [probability score0.19], viral likely [probability score of 0.19-0.40], bacterial likely [probability score of 0.41-0.73], and bacterial very likely [probability score0.73]) and compared with procalcitonin measurement.Among 755 enrolled participants, the median age was 26 years (IQR, 16-52 years); 360 participants (47.7%) were female, and 395 (52.3%) were male. A total of 13 participants (1.7%) were American Indian, 13 (1.7%) were Asian, 368 (48.7%) were Black, 131 (17.4%) were Hispanic, 3 (0.4%) were Native Hawaiian or Pacific Islander, 297 (39.3%) were White, and 60 (7.9%) were of unspecified race and/or ethnicity. In the primary analysis involving 334 participants, the HR-B/V test had sensitivity of 89.8% (95% CI, 77.8%-96.2%), specificity of 82.1% (95% CI, 77.4%-86.6%), and a negative predictive value (NPV) of 97.9% (95% CI, 95.3%-99.1%) for bacterial infection. In comparison, the sensitivity of procalcitonin measurement was 28.6% (95% CI, 16.2%-40.9%; P .001), the specificity was 87.0% (95% CI, 82.7%-90.7%; P = .006), and the NPV was 87.6% (95% CI, 85.5%-89.5%; P .001). When stratified into likelihood groups, the HR-B/V test had an NPV of 98.9% (95% CI, 96.1%-100%) for bacterial infection in the viral very likely group and a positive predictive value of 63.4% (95% CI, 47.2%-77.9%) for bacterial infection in the bacterial very likely group. The HR-B/V test correctly identified 30 of 33 participants (90.9%) with acute COVID-19 as having a viral infection.In this study, the HR-B/V test accurately discriminated bacterial from viral infection among patients with febrile ARI and was superior to procalcitonin measurement. The findings suggest that an accurate point-of-need host response test with high NPV may offer an opportunity to improve antibiotic stewardship and patient outcomes.

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 post-infection. 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 2,718 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.

SummaryDiversity in the human genome is one factor that confers resistance and susceptibility to infectious diseases. This is observed most dramatically during pandemics, where individuals exhibit large differences in risk and clinical outcomes against a pathogen infecting large portions of the world’s populations. Here, we developed scHi-HOST (single cell High-throughput Human in vitrO Susceptibility Testing), a method for rapidly identifying genetic variants that confer resistance and susceptibility to pathogens. scHi-HOST leverages scRNA-seq (single-cell RNA-sequencing) to simultaneously assign genetic identity to individual cells in mixed infections of cell lines of European, African, and Asian origin, reveal associated genetic variants for viral entry and replication, and identify expression quantitative trait loci (eQTLs). Applying scHi-HOST to influenza A virus (IAV), we identified eQTLs at baseline and in genes that are induced by IAV infection. Integration of scHi-HOST with a human IAV challenge study (Prometheus) revealed that a missense variant inERAP1(Endoplasmic reticulum aminopeptidase 1;rs27895) was associated with IAV burden in cells and human volunteers. Functional studies using RNA interference, ERAP1 inhibitor, and overexpression of alternative alleles demonstrated that ERAP1 is exploited by IAV to promote infection. Specifically, the nonsynonymous substitution, which results in a glycine to aspartate substitution at ERAP1 residue 348, would disrupt the substrate binding pocket of ERAP1, likely resulting in a significantly altered preference for substrates, poorer catalytic efficiency, or both. Finally, rs27895 exhibits substantial population differentiation, with the higher frequency of the minor T allele in two African populations likely contributing to the greater permissivity of cells from these populations to IAV infection. scHi-HOST is an important resource for understanding susceptibility to influenza and is a broadly applicable method for decoding human genetics of infectious disease.

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 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.

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.

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.

Background: Distinguishing bacterial and viral respiratory infections is challenging. Novel diagnostics based on differential host gene expression patterns are promising but have not been translated to a clinical platform nor extensively tested. Here, we validate a microarray-derived host response signature and explore performance in microbiology-negative and coinfection cases. Methods: Subjects with acute respiratory illness were enrolled in participating emergency departments. Reference standard was an adjudicated diagnosis of bacterial infection, viral infection, both, or neither. An 87-transcript signature for distinguishing bacterial, viral, and noninfectious illness was measured from peripheral blood using RT-PCR. Performance characteristics were evaluated in subjects with confirmed bacterial, viral, or noninfectious illness. Subjects with bacterial-viral coinfection and microbiologically-negative suspected bacterial infection were also evaluated. Performance was compared to procalcitonin. Findings: 151 subjects with microbiologically confirmed, single-etiology illness were tested, yielding AUROCs 0•85–0•89 for bacterial, viral, and noninfectious illness. Accuracy was similar to procalcitonin (88% vs 83%, p = 0•23) for bacterial vs. non-bacterial infection. Whereas procalcitonin cannot distinguish viral from non-infectious illness, the RT-PCR test had 81% accuracy in making this determination. Bacterial-viral coinfection was subdivided. Among 19 subjects with bacterial superinfection, the RT-PCR test identified 95% as bacterial, compared to 68% with procalcitonin (p = 0•13). Among 12 subjects with bacterial infection superimposed on chronic viral infection, the RT-PCR test identified 83% as bacterial, identical to procalcitonin. 39 subjects had suspected bacterial infection; the RT-PCR test identified bacterial infection more frequently than procalcitonin (82% vs 64%, p = 0•02). Interpretation: The RT-PCR test offered similar diagnostic performance to procalcitonin in some subgroups but offered better discrimination in others such as viral vs. non-infectious illness and bacterial/viral coinfection. Gene expression-based tests could impact decision-making for acute respiratory illness as well as a growing number of other infectious and non-infectious diseases. Keywords: Biomarkers, Gene expression, Respiratory tract infections, Coinfection, Diagnosis, Precision medicine

Background Approaches for quantifying physical resilience in older adults have not been described. Methods We apply two conceptual approaches to defining physical resilience to existing longitudinal data sets in which outcomes are measured after an acute physical stressor. A “recovery phenotype” approach uses statistical methods to describe how quickly and completely a patient recovers. Statistical methods using a recovery phenotype approach can consider multiple outcomes simultaneously in a composite score (eg, factor analysis and principal components analysis) or identify groups of patients with similar recovery trajectories across multiple outcomes (eg, latent class profile analysis). An “expected recovery differential” approach quantifies how patients’ actual outcomes are compared to their predicted outcome based on a population-derived model and their individual clinical characteristics at the time of the stressor. Results Application of the approaches identified different participants as being the most or least physically resilient. In the viral respiratory cohort (n = 186) weighted kappa for agreement across resilience quartiles was 0.37 (0.27–0.47). The expected recovery differential approach identified a group with more comorbidities and lower baseline function as highly resilient. In the hip fracture cohort (n = 541), comparison of the expected recovery differentials across 10 outcome measures within individuals provided preliminary support for the hypothesis that there is a latent resilience trait at the whole-person level. Conclusions We posit that recovery phenotypes may be useful in clinical applications such as prediction models because they summarize the observed outcomes across multiple measures. Expected recovery differentials offer insight into mechanisms behind physical resilience not captured by age and other comorbidities.

As SARS-CoV-2 continues to spread globally, questions have emerged regarding the strength and durability of immune responses in specific populations. In this study, we evaluated humoral immune responses in 69 children and adolescents with asymptomatic or mild symptomatic SARS-CoV-2 infection. We detected robust IgM, IgG, and IgA antibody responses to a broad array of SARS-CoV-2 antigens at the time of acute infection and 2 and 4 months after acute infection in all participants. Notably, these antibody responses were associated with virus neutralizing activity that was still detectable 4 months after acute infection in 94% of children. Moreover, antibody responses and neutralizing activity in sera from children and adolescents were comparable or superior to those observed in sera from 24 adults with mild symptomatic infection. Taken together, these findings indicate children and adolescents with mild or asymptomatic SARS-CoV-2 infection generate robust and durable humoral immune responses that are likely to protect from reinfection.

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.

Background While genome-wide associations studies (GWAS) have successfully elucidated the genetic architecture of complex human traits and diseases, understanding mechanisms that lead from genetic variation to pathophysiology remains an important challenge. Methods are needed to systematically bridge this crucial gap to facilitate experimental testing of hypotheses and translation to clinical utility. Results Here, we leveraged cross-phenotype associations to identify traits with shared genetic architecture, using linkage disequilibrium (LD) information to accurately capture shared SNPs by proxy, and calculate significance of enrichment. This shared genetic architecture was examined across differing biological scales through incorporating data from catalogs of clinical, cellular, and molecular GWAS. We have created an interactive web database (interactive Cross-Phenotype Analysis of GWAS database (iCPAGdb)) to facilitate exploration and allow rapid analysis of user-uploaded GWAS summary statistics. This database revealed well-known relationships among phenotypes, as well as the generation of novel hypotheses to explain the pathophysiology of common diseases. Application of iCPAGdb to a recent GWAS of severe COVID-19 demonstrated unexpected overlap of GWAS signals between COVID-19 and human diseases, including with idiopathic pulmonary fibrosis driven by the DPP9 locus. Transcriptomics from peripheral blood of COVID-19 patients demonstrated that DPP9 was induced in SARS-CoV-2 compared to healthy controls or those with bacterial infection. Further investigation of cross-phenotype SNPs associated with both severe COVID-19 and other human traits demonstrated colocalization of the GWAS signal at the ABO locus with plasma protein levels of a reported receptor of SARS-CoV-2, CD209 (DC-SIGN). This finding points to a possible mechanism whereby glycosylation of CD209 by ABO may regulate COVID-19 disease severity. Conclusions Thus, connecting genetically related traits across phenotypic scales links human diseases to molecular and cellular measurements that can reveal mechanisms and lead to novel biomarkers and therapeutic approaches. The iCPAGdb web portal is accessible at http://cpag.oit.duke.edu and the software code at https://github.com/tbalmat/iCPAGdb.

SARS-CoV-2 infection triggers highly variable host responses and causes varying degrees of illness in humans. We sought to harness the peripheral blood mononuclear cell (PBMC) response over the course of illness to provide insight into COVID-19 physiology. We analyzed PBMCs from subjects with variable symptom severity at different stages of clinical illness before and after IgG seroconversion to SARS-CoV-2. Prior to seroconversion, PBMC transcriptomes did not distinguish symptom severity. In contrast, changes in chromatin accessibility were associated with symptom severity. Furthermore, single-cell analyses revealed evolution of the chromatin accessibility landscape and transcription factor motif occupancy for individual PBMC cell types. The most extensive remodeling occurred in CD14+ monocytes where sub-populations with distinct chromatin accessibility profiles were associated with disease severity. Our findings indicate that pre-seroconversion chromatin remodeling in certain innate immune populations is associated with divergence in symptom severity, and the identified transcription factors, regulatory elements, and downstream pathways provide potential prognostic markers for COVID-19 subjects.One sentence summaryChromatin accessibility in immune cells from COVID-19 subjects is remodeled prior to seroconversion to reflect disease severity.

Sexual dimorphisms in immune responses contribute to coronavirus disease 2019 (COVID-19) outcomes, yet the mechanisms governing this disparity remain incompletely understood. We carried out sex-balanced sampling of peripheral blood mononuclear cells from confirmed COVID-19 inpatients and outpatients, uninfected close contacts, and healthy controls for 36-color flow cytometry and single cell RNA-sequencing. Our results revealed a pronounced reduction of circulating mucosal associated invariant T (MAIT) cells in infected females. Integration of published COVID-19 airway tissue datasets implicate that this reduction represented a major wave of MAIT cell extravasation during early infection in females. Moreover, female MAIT cells possessed an immunologically active gene signature, whereas male counterparts were pro-apoptotic. Collectively, our findings uncover a female-specific protective MAIT profile, potentially shedding light on reduced COVID-19 susceptibility in females.

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.

Background Pathogen-based diagnostics for acute respiratory infection (ARI) have limited ability to detect etiology of illness. We previously showed that peripheral blood-based host gene expression classifiers accurately identify bacterial and viral ARI in cohorts of European and African descent. We determined classifier performance in a South Asian cohort. Methods Patients ≥15 years with fever and respiratory symptoms were enrolled in Sri Lanka. Comprehensive pathogen-based testing was performed. Peripheral blood ribonucleic acid was sequenced and previously developed signatures were applied: a pan-viral classifier (viral vs nonviral) and an ARI classifier (bacterial vs viral vs noninfectious). Results Ribonucleic acid sequencing was performed in 79 subjects: 58 viral infections (36 influenza, 22 dengue) and 21 bacterial infections (10 leptospirosis, 11 scrub typhus). The pan-viral classifier had an overall classification accuracy of 95%. The ARI classifier had an overall classification accuracy of 94%, with sensitivity and specificity of 91% and 95%, respectively, for bacterial infection. The sensitivity and specificity of C-reactive protein (>10 mg/L) and procalcitonin (>0.25 ng/mL) for bacterial infection were 100% and 34%, and 100% and 41%, respectively. Conclusions Previously derived gene expression classifiers had high predictive accuracy at distinguishing viral and bacterial infection in South Asian patients with ARI caused by typical and atypical pathogens., Host biomarkers can identify bacterial versus viral acute respiratory infection (ARI). Previously, we derived host gene expression classifiers that accurately identify bacterial versus viral ARI. Here, we show that our classifiers had high predictive accuracy in a South Asian cohort.

The response to respiratory viruses varies substantially between individuals, and there are currently no known molecular predictors from the early stages of infection. Here we conduct a community-based analysis to determine whether pre- or early post-exposure molecular factors could predict physiologic responses to viral exposure. Using peripheral blood gene expression profiles collected from healthy subjects prior to exposure to one of four respiratory viruses (H1N1, H3N2, Rhinovirus, and RSV), as well as up to 24 h following exposure, we find that it is possible to construct models predictive of symptomatic response using profiles even prior to viral exposure. Analysis of predictive gene features reveal little overlap among models; however, in aggregate, these genes are enriched for common pathways. Heme metabolism, the most significantly enriched pathway, is associated with a higher risk of developing symptoms following viral exposure. This study demonstrates that pre-exposure molecular predictors can be identified and improves our understanding of the mechanisms of response to respiratory viruses., The response to respiratory virus exposure can currently not be predicted by pre- or early post-exposure molecular signatures. Here, the authors conduct a community-based analysis of blood gene expression from healthy individuals exposed to respiratory viruses and provide predictive models and biological insight into the physiological response.

Key Points Question Can noninvasive, wrist-worn wearable devices detect acute viral respiratory infection and predict infection severity before symptom onset? Findings In a cohort study of 31 participants inoculated with H1N1 and 18 participants with rhinovirus, infection detection and severity prediction models trained using data on wearable devices were able to distinguish between infection and noninfection with 92% accuracy for H1N1 and 88% accuracy for rhinovirus and were able to distinguish between mild and moderate infection 24 hours prior to symptom onset with 90% accuracy for H1N1 and 89% accuracy for rhinovirus. Meaning This study suggests that the use of wearable devices to identify individuals with presymptomatic acute viral respiratory infection is feasible; because wearable devices are common in the general population, using them for infection screening may help limit the spread of contagion., 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., This cohort study evaluates the feasibility of using noninvasive, wrist-worn wearable devices to detect presymptomatic viral infection after exposure and predict infection severity in patients exposed to H1N1 influenza or human rhinovirus.

Background Sexual dimorphisms in immune responses contribute to coronavirus disease 2019 (COVID-19) outcomes, but the mechanisms governing this disparity remain incompletely understood. Methods We carried out sex-balanced sampling of peripheral blood mononuclear cells from hospitalized and non-hospitalized individuals with confirmed COVID-19, uninfected close contacts, and healthy control individuals for 36-color flow cytometry and single-cell RNA sequencing. Findings Our results revealed a pronounced reduction of circulating mucosal-associated invariant T (MAIT) cells in infected females. Integration of published COVID-19 airway tissue datasets suggests that this reduction represented a major wave of MAIT cell extravasation during early infection in females. Moreover, MAIT cells from females possessed an immunologically active gene signature, whereas cells from males were pro-apoptotic. Conclusions Our findings uncover a female-specific protective MAIT cell profile, potentially shedding light on reduced COVID-19 susceptibility in females. Funding This work was supported by NIH/NIAID (U01AI066569 and UM1AI104681), the Defense Advanced Projects Agency (DARPA; N66001-09-C-2082 and HR0011-17-2-0069), the Veterans Affairs Health System, and Virology Quality Assurance (VQA; 75N93019C00015). The content is solely the responsibility of the authors and does not necessarily represent the official view of the National Institutes of Health. COVID-19 samples were processed under Biosafety level 2 (BSL-2) with aerosol management enhancement or BSL-3 in the Duke Regional Biocontainment Laboratory, which received partial support for construction from NIH/NIAID (UC6AI058607)., Graphical abstract, Context and significance Why are women twice as less likely than men to experience severe COVID-19? Answering this question may help us more completely understand the immune defenses that counter SARS-CoV-2. This study from researchers at Duke University suggests that a type of white blood cells, mucosal-associated invariant T (MAIT) cells, is superior in women with COVID-19. The potential importance of the finding is that these highly specialized white blood cells have been shown to contribute critically to immune defenses in other viral and bacterial infections. These findings may shed light on the underlying reasons for reduced COVID-19 susceptibility in women and highlights the potential role of MAIT cells in countering SARS-CoV-2., Responses in COVID-19 patients are distinct across sexes. Yu et al. show that, compared with male patients, female COVID-19 patients exhibit evidence of a major recruitment wave of circulating mucosal-associated invariant T (MAIT) cells into the airways and demonstrate that these cells display an immunologically active phenotype.

Background Difficulty distinguishing bacterial and viral infections contributes to excess antibiotic use. A host response strategy overcomes many limitations of pathogen-based tests, but depends on a functional immune system. This approach may therefore be limited in immunocompromised (IC) hosts. Here, we evaluated a host response test in IC subjects, which has not been extensively studied in this manner. Methods An 81-gene signature was measured using qRT-PCR in previously enrolled IC subjects (chemotherapy, solid organ transplant, immunomodulatory agents, AIDS) with confirmed bacterial infection, viral infection, or non-infectious illness (NI). A regularized logistic regression model estimated the likelihood of bacterial, viral, and noninfectious classes. Clinical adjudication was the reference standard. Results A host gene expression model trained in a cohort of 136 immunocompetent subjects (43 bacterial, 41 viral, and 52 NI) had an overall accuracy of 84.6% for the diagnosis of bacterial vs. non-bacterial infection and 80.8% for viral vs. non-viral infection. The model was validated in an independent cohort of 134 IC subjects (64 bacterial, 28 viral, 42 NI). The overall accuracy was 73.9% for bacterial infection (p=0.03 vs. training cohort) and 75.4% for viral infection (p=0.27). Test utility could be improved by reporting probability ranges. For example, results divided into probability quartiles would allow the highest quartile to be used to rule in infection and the lowest to rule out infection. For IC subjects in the lowest quartile, the test had 90.1% and 96.4% sensitivity for bacterial and viral infection, respectively. For the highest quartile, the test had 91.4% and 84.0% specificity for bacterial and viral infection, respectively. The type or number of immunocompromising conditions did not impact performance. Illness Etiology Probabilities Conclusion A host gene expression test discriminated bacterial, viral, and non-infectious etiologies at a lower overall accuracy in IC patients compared to immunocompetent patients, though this difference was only significant for bacterial vs non-bacterial disease. With modified interpretive criteria, a host response strategy may offer clinically useful and complementary diagnostic information for IC patients. Disclosures Thomas W. Burke, PhD, Predigen, Inc (Consultant) Geoffrey S. Ginsburg, MD PhD, Predigen, Inc (Shareholder, Other Financial or Material Support) Christopher W. Woods, MD, MPH, FIDSA, Predigen, Inc (Shareholder, Other Financial or Material Support) Ephraim L. Tsalik, MD, MHS, PhD, FIDSA, Predigen, Inc (Scientific Research Study Investigator, Shareholder, Other Financial or Material Support)

Knowledge of influenza virus evolution at the point of transmission and at the intrahost level remains limited, particularly for human hosts. Here, we analyze a unique viral data set of next-generation sequencing (NGS) samples generated from a human influenza challenge study wherein 17 healthy subjects were inoculated with cell- and egg-passaged virus. Nasal wash samples collected from 7 of these subjects were successfully deep sequenced. From these, we characterized changes in the subjects' viral populations during infection and identified differences between the virus in these samples and the viral stock used to inoculate the subjects. We first calculated pairwise genetic distances between the subjects' nasal wash samples, the viral stock, and the influenza virus A/Wisconsin/67/2005 (H3N2) reference strain used to generate the stock virus. These distances revealed that considerable viral evolution occurred at various points in the human challenge study. Further quantitative analyses indicated that (i) the viral stock contained genetic variants that originated and likely were selected for during the passaging process, (ii) direct intranasal inoculation with the viral stock resulted in a selective bottleneck that reduced nonsynonymous genetic diversity in the viral hemagglutinin and nucleoprotein, and (iii) intrahost viral evolution continued over the course of infection. These intrahost evolutionary dynamics were dominated by purifying selection. Our findings indicate that rapid viral evolution can occur during acute influenza infection in otherwise healthy human hosts when the founding population size of the virus is large, as is the case with direct intranasal inoculation. IMPORTANCE Influenza viruses circulating among humans are known to rapidly evolve over time. However, little is known about how influenza virus evolves across single transmission events and over the course of a single infection. To address these issues, we analyze influenza virus sequences from a human challenge experiment that initiated infection with a cell- and egg-passaged viral stock, which appeared to have adapted during its preparation. We find that the subjects' viral populations differ genetically from the viral stock, with subjects' viral populations having lower representation of the amino-acid-changing variants that arose during viral preparation. We also find that most of the viral evolution occurring over single infections is characterized by further decreases in the frequencies of these amino-acid-changing variants and that only limited intrahost genetic diversification through new mutations is apparent. Our findings indicate that influenza virus populations can undergo rapid genetic changes during acute human infections.

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 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 (CT) 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 CT 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.

Summary 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.