Exposure to influenza viruses is necessary, but not sufficient, for healthy human hosts to develop symptomatic illness. The host response is an important determinant of disease progression. In order to delineate host molecular responses that differentiate symptomatic and asymptomatic Influenza A infection, we inoculated 17 healthy adults with live influenza (H3N2/Wisconsin) and examined changes in host peripheral blood gene expression at 16 timepoints over 132 hours. Here we present distinct transcriptional dynamics of host responses unique to asymptomatic and symptomatic infections. We show that symptomatic hosts invoke, simultaneously, multiple pattern recognition receptors-mediated antiviral and inflammatory responses that may relate to virus-induced oxidative stress. In contrast, asymptomatic subjects tightly regulate these responses and exhibit elevated expression of genes that function in antioxidant responses and cell-mediated responses. We reveal an ab initio molecular signature that strongly correlates to symptomatic clinical disease and biomarkers whose expression patterns best discriminate early from late phases of infection. Our results establish a temporal pattern of host molecular responses that differentiates symptomatic from asymptomatic infections and reveals an asymptomatic host-unique non-passive response signature, suggesting novel putative molecular targets for both prognostic assessment and ameliorative therapeutic intervention in seasonal and pandemic influenza.
In tropical and subtropical settings, the epidemiology of viral acute respiratory tract infections varies widely between countries. We determined the etiology, seasonality, and clinical presentation of viral acute respiratory tract infections among outpatients in southern Sri Lanka. From March 2013 to January 2015, we enrolled outpatients presenting with influenza-like illness (ILI). Nasal/nasopharyngeal samples were tested in duplicate using antigen-based rapid influenza testing and multiplex polymerase chain reaction (PCR) for respiratory viruses. Monthly proportion positive was calculated for each virus. Bivariable and multivariable logistic regression were used to identify associations between sociodemographic/clinical information and viral detection. Of 571 subjects, most (470, 82.3%) were ≥ 5 years of age and 53.1% were male. A respiratory virus was detected by PCR in 63.6% ( = 363). Common viral etiologies included influenza (223, 39%), human enterovirus/rhinovirus (HEV/HRV, 14.5%), respiratory syncytial virus (RSV, 4.2%), and human metapneumovirus (hMPV, 3.9%). Both ILI and influenza showed clear seasonal variation, with peaks from March to June each year. RSV and hMPV activity peaked from May to July, whereas HEV/HRV was seen year-round. Patients with respiratory viruses detected were more likely to report pain with breathing (odds ratio [OR] = 2.60, = 0.003), anorexia (OR = 2.29, < 0.001), and fatigue (OR = 2.00, = 0.002) compared with patients with no respiratory viruses detected. ILI showed clear seasonal variation in southern Sri Lanka, with most activity during March to June; peak activity was largely due to influenza. Targeted infection prevention activities such as influenza vaccination in January-February may have a large public health impact in this region.
Healthcare settings have played a major role in propagation of Ebola virus (EBOV) outbreaks. Healthcare workers (HCWs) have elevated risk of contact with EBOV-infected patients, particularly if safety precautions are not rigorously practiced. We conducted a serosurvey to determine seroprevalence against multiple EBOV antigens among HCWs of Boende Health Zone, Democratic Republic of the Congo, the site of a 2014 EBOV outbreak. Interviews and specimens were collected from 565 consenting HCWs. Overall, 234 (41.4%) of enrolled HCWs were reactive to at least 1 EBOV protein: 159 (28.1%) were seroreactive for anti-glycoprotein immunoglobulin G (IgG), 89 (15.8%) were seroreactive for anti-nucleoprotein IgG, and 54 (9.5%) were VP40 positive. Additionally, sera from 16 (2.8%) HCWs demonstrated neutralization capacity. These data demonstrate that a significant proportion of HCWs have the ability to neutralize virus, despite never having developed Ebola virus disease symptoms, highlighting an important and poorly documented aspect of EBOV infection and progression.
A Bayesian statistical model is developed for analysis of the time-evolving properties of infectious disease, with a particular focus on viruses. The model employs a latent semi-Markovian state process, and the state-transition statistics are driven by three terms: (i) a general time-evolving trend of the overall population, (ii) a semi-periodic term that accounts for effects caused by the days of the week, and (iii) a regression term that relates the probability of infection to covariates (here, specifically, to the Google Flu Trends data). Computations are performed using Markov Chain Monte Carlo sampling. Results are presented using a novel data set: daily self-reported symptom scores from hundreds of Duke University undergraduate students, collected over three academic years. The illnesses associated with these students are (imperfectly) labeled using real-time (RT) polymerase chain reaction (PCR) testing for several viruses, and gene-expression data were also analyzed. The statistical analysis is performed on the daily, self-reported symptom scores, and the RT PCR and gene-expression data are employed for analysis and interpretation of the model results.
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