Temporal dynamics of host molecular responses differentiate symptomatic and asymptomatic influenza a infection.

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.

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

There is great potential for host-based gene expression analysis to impact the early diagnosis of infectious diseases. In particular, the influenza pandemic of 2009 highlighted the challenges and limitations of traditional pathogen-based testing for suspected upper respiratory viral infection. We inoculated human volunteers with either influenza A (A/Brisbane/59/2007 (H1N1) or A/Wisconsin/67/2005 (H3N2)), and assayed the peripheral blood transcriptome every 8 hours for 7 days. Of 41 inoculated volunteers, 18 (44%) developed symptomatic infection. Using unbiased sparse latent factor regression analysis, we generated a gene signature (or factor) for symptomatic influenza capable of detecting 94% of infected cases. This gene signature is detectable as early as 29 hours post-exposure and achieves maximal accuracy on average 43 hours (p = 0.003, H1N1) and 38 hours (p-value = 0.005, H3N2) before peak clinical symptoms. In order to test the relevance of these findings in naturally acquired disease, a composite influenza A signature built from these challenge studies was applied to Emergency Department patients where it discriminates between swine-origin influenza A/H1N1 (2009) infected and non-infected individuals with 92% accuracy. The host genomic response to Influenza infection is robust and may provide the means for detection before typical clinical symptoms are apparent.

Influenza vaccination of household contacts of newborns: a hospital-based strategy to increase vaccination rates.

We implemented a hospital-based influenza vaccination program for household contacts of newborns. Among mothers not vaccinated prenatally, 44.7% were vaccinated through the program, as were 25.7% of fathers. A hospital-based program provided opportunities for vaccination of household contacts of newborns, thereby facilitating better adherence to national vaccination guidelines.

Impact of a Point-of-Care Rapid Influenza Test on Antibiotic Prescribing Patterns in Southern Sri Lanka

Background: Acute febrile respiratory illnesses, including influenza, account for a large proportion of ambulatory care visits worldwide. In the developed world, these encounters commonly result in unwarranted antibiotic prescriptions; data from more resource-limited settings are lacking. The purpose of this study was to describe the epidemiology of influenza among outpatients in southern Sri Lanka and to determine if access to rapid influenza test results was associated with decreased antibiotic prescriptions.

Methods: In this pretest- posttest study, consecutive patients presenting from March 2013- April 2014 to the Outpatient Department of the largest tertiary care hospital in southern Sri Lanka were surveyed for influenza-like illness (ILI). Patients meeting World Health Organization criteria for ILI-- acute onset of fever ≥38.0°C and cough in the prior 7 days--were enrolled. Consenting patients were administered a structured questionnaire, physical examination, and nasal/nasopharyngeal sampling. Rapid influenza A/B testing (Veritor System, Becton Dickinson) was performed on all patients, but test results were only released to patients and clinicians during the second phase of the study (December 2013- April 2014).

Results: We enrolled 397 patients with ILI, with 217 (54.7%) adults ≥12 years and 188 (47.4%) females. A total of 179 (45.8%) tested positive for influenza by rapid testing, with April- July 2013 and September- November 2013 being the periods with the highest proportion of ILI due to influenza. A total of 310 (78.1%) patients with ILI received a prescription for an antibiotic from their outpatient provider. The proportion of patients prescribed antibiotics decreased from 81.4% in the first phase to 66.3% in the second phase (p=.005); among rapid influenza-positive patients, antibiotic prescriptions decreased from 83.7% in the first phase to 56.3% in the second phase (p=.001). On multivariable analysis, having a positive rapid influenza test available to clinicians was associated with decreased antibiotic use (OR 0.20, 95% CI 0.05- 0.82).

Conclusions: Influenza virus accounted for almost 50% of acute febrile respiratory illness in this study, but most patients were prescribed antibiotics. Providing rapid influenza test results to clinicians was associated with fewer antibiotic prescriptions, but overall prescription of antibiotics remained high. In this developing country setting, a multi-faceted approach that includes improved access to rapid diagnostic tests may help decrease antibiotic use and combat antimicrobial resistance.

Neutralization activity in a geographically diverse East London cohort of human immunodeficiency virus type 1-infected patients: clade C infection results in a stronger and broader humoral immune response than clade B infection.

The array of human immunodeficiency virus (HIV) subtypes encountered in East London, an area long associated with migration, is unusually heterogeneous, reflecting the diverse geographical origins of the population. In this study it was shown that viral subtypes or clades infecting a sample of HIV type 1 (HIV-1)-positive individuals in East London reflect the global pandemic. The authors studied the humoral response in 210 treatment-naïve chronically HIV-1-infected (>1 year) adult subjects against a panel of 12 viruses from six different clades. Plasmas from individuals infected with clade C, but also plasmas from clade A, and to a lesser degree clade CRF02_AG and CRF01_AE, were significantly more potent at neutralizing the tested viruses compared with plasmas from individuals infected with clade B. The difference in humoral robustness between clade C- and B-infected patients was confirmed in titration studies with an extended panel of clade B and C viruses. These results support the approach to develop an HIV-1 vaccine that includes clade C or A envelope protein (Env) immunogens for the induction of a potent neutralizing humoral response.

Bayesian inference of the number of factors in gene-expression analysis: application to human virus challenge studies.

BACKGROUND: Nonparametric Bayesian techniques have been developed recently to extend the sophistication of factor models, allowing one to infer the number of appropriate factors from the observed data. We consider such techniques for sparse factor analysis, with application to gene-expression data from three virus challenge studies. Particular attention is placed on employing the Beta Process (BP), the Indian Buffet Process (IBP), and related sparseness-promoting techniques to infer a proper number of factors. The posterior density function on the model parameters is computed using Gibbs sampling and variational Bayesian (VB) analysis. RESULTS: Time-evolving gene-expression data are considered for respiratory syncytial virus (RSV), Rhino virus, and influenza, using blood samples from healthy human subjects. These data were acquired in three challenge studies, each executed after receiving institutional review board (IRB) approval from Duke University. Comparisons are made between several alternative means of per-forming nonparametric factor analysis on these data, with comparisons as well to sparse-PCA and Penalized Matrix Decomposition (PMD), closely related non-Bayesian approaches. CONCLUSIONS: Applying the Beta Process to the factor scores, or to the singular values of a pseudo-SVD construction, the proposed algorithms infer the number of factors in gene-expression data. For real data the "true" number of factors is unknown; in our simulations we consider a range of noise variances, and the proposed Bayesian models inferred the number of factors accurately relative to other methods in the literature, such as sparse-PCA and PMD. We have also identified a "pan-viral" factor of importance for each of the three viruses considered in this study. We have identified a set of genes associated with this pan-viral factor, of interest for early detection of such viruses based upon the host response, as quantified via gene-expression data.

A dimensionless number for understanding the evolutionary dynamics of antigenically variable RNA viruses.

Antigenically variable RNA viruses are significant contributors to the burden of infectious disease worldwide. One reason for their ubiquity is their ability to escape herd immunity through rapid antigenic evolution and thereby to reinfect previously infected hosts. However, the ways in which these viruses evolve antigenically are highly diverse. Some have only limited diversity in the long-run, with every emergence of a new antigenic variant coupled with a replacement of the older variant. Other viruses rapidly accumulate antigenic diversity over time. Others still exhibit dynamics that can be considered evolutionary intermediates between these two extremes. Here, we present a theoretical framework that aims to understand these differences in evolutionary patterns by considering a virus's epidemiological dynamics in a given host population. Our framework, based on a dimensionless number, probabilistically anticipates patterns of viral antigenic diversification and thereby quantifies a virus's evolutionary potential. It is therefore similar in spirit to the basic reproduction number, the well-known dimensionless number which quantifies a pathogen's reproductive potential. We further outline how our theoretical framework can be applied to empirical viral systems, using influenza A/H3N2 as a case study. We end with predictions of our framework and work that remains to be done to further integrate viral evolutionary dynamics with disease ecology.

The impact of host immune status on the within-host and population dynamics of antigenic immune escape.

Antigenically evolving pathogens such as influenza viruses are difficult to control owing to their ability to evade host immunity by producing immune escape variants. Experimental studies have repeatedly demonstrated that viral immune escape variants emerge more often from immunized hosts than from naive hosts. This empirical relationship between host immune status and within-host immune escape is not fully understood theoretically, nor has its impact on antigenic evolution at the population level been evaluated. Here, we show that this relationship can be understood as a trade-off between the probability that a new antigenic variant is produced and the level of viraemia it reaches within a host. Scaling up this intra-host level trade-off to a simple population level model, we obtain a distribution for variant persistence times that is consistent with influenza A/H3N2 antigenic variant data. At the within-host level, our results show that target cell limitation, or a functional equivalent, provides a parsimonious explanation for how host immune status drives the generation of immune escape mutants. At the population level, our analysis also offers an alternative explanation for the observed tempo of antigenic evolution, namely that the production rate of immune escape variants is driven by the accumulation of herd immunity. Overall, our results suggest that disease control strategies should be further assessed by considering the impact that increased immunity--through vaccination--has on the production of new antigenic variants.

Maternal Effects of Respiratory Syncytial Virus Infection during Pregnancy.

Given the illness and deaths caused by respiratory syncytial virus (RSV) infection during the first year of life, preventing infant RSV infections through maternal vaccination is intriguing. However, little is known about the extent and maternal effects of RSV infection during pregnancy. We describe 3 cases of maternal RSV infection diagnosed at a US center during winter 2014. Case-patient 1 (26 years old, week 33 of gestation) received a diagnosis of RSV infection and required mechanical ventilation. Case-patient 2 (27 years old, week 34 of gestation) received a diagnosis of infection with influenza A(H1N1) virus and RSV and required mechanical ventilation. Case-patient 3 (21 years old, week 32 of gestation) received a diagnosis of group A streptococcus pharyngitis and RSV infection and was monitored as an outpatient. Clarifying the effects of maternal RSV infection could yield valuable insights into potential maternal and fetal benefits of an effective RSV vaccination program.