Timothy Veldman

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.

Loss of hPot1 function leads to telomere instability and a cut-like phenotype.

The human telomere binding protein hPot1 binds to the most distal single-stranded extension of telomeric DNA in vitro, and probably in vivo, as well as associating with the double-stranded telomeric DNA binding proteins TRF1 and TRF2 through the bridging proteins PTOP (also known as PIP1 or TINT1) and TIN2. Disrupting either the DNA binding activity of hPot1 or its association with PTOP results in elongated telomeres, suggesting a role for hPot1 in telomere length regulation. However, mutations to POT1 and Cdc13p, the fission and budding yeast genes encoding the structural orthologs of this protein, leads to telomere instability and cell death. Thus, it is possible that the hPot1 protein may also serve to cap and protect telomeres in humans. Indeed, we now find that knocking down the expression of hPot1 in human cells causes apoptosis or senescence, as well as an increase in telomere associations and anaphase bridges, telltale signs of telomere instability. In addition, knockdown cells also displayed chromatin bridges between interphase cells, reminiscent of the cut phenotype that was first described in fission yeast and in which cytokinesis progresses despite a failure of chromatid separation. However, unlike the yeast cut phenotypes, we suggest that the cut-like phenotype observed in hPot1 knockdown cells is a consequence of the fusion of chromosome ends and that this fusion impedes proper chromosomal segregation. We conclude that hPot1 protects chromosome ends from illegitimate recombination, catastrophic chromosome instability, and abnormal chromosome segregation.

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.

A Host-Based RT-PCR Gene Expression Signature to Identify Acute Respiratory Viral Infection

To improve the diagnosis of respiratory viral infection, a multiplex RT-PCR assay based on the host response was derived from experimentally infected subjects and validated in patients with febrile illness. Diagnosing the Cause of Coughs and Sneezes Diagnosis of viral respiratory infections remains a challenge. Early differentiation between a viral and bacterial etiology of respiratory symptoms would help to direct therapy more appropriately and prevent overuse of antibiotics. Measuring the host immune response to infection is an alternative to pathogen-based diagnostic testing and may improve diagnostic accuracy. Now, Zaas et al. have developed a reverse transcription polymerase chain reaction (RT-PCR) assay for blood RNA that can classify respiratory viral infections based on the host immune response. They developed their assay using two groups of individuals experimentally infected with either influenza A H3N2/Wisconsin or influenza A H1N1/Brisbane. They then validated their RT-PCR diagnostic in a sample of adults presenting to the emergency department with fever, who had microbiologically confirmed viral or bacterial illness. The sensitivity of the RT-PCR assay was 89% [95% confidence interval (CI), 72 to 98%], and the specificity was 94% (95% CI, 86 to 99%). These data establish an important “proof of concept” that host expression of a relatively small set of genes measured by RT-PCR can be used to classify viral respiratory illness in unselected individuals presenting at an emergency department for evaluation of fever. The development of this new assay and its validation in an independent “real-world” patient population is an important step on the translational pathway to establishing this platform for diagnostic testing in the clinic. Improved ways to diagnose acute respiratory viral infections could decrease inappropriate antibacterial use and serve as a vital triage mechanism in the event of a potential viral pandemic. Measurement of the host response to infection is an alternative to pathogen-based diagnostic testing and may improve diagnostic accuracy. We have developed a host-based assay with a reverse transcription polymerase chain reaction (RT-PCR) TaqMan low-density array (TLDA) platform for classifying respiratory viral infection. We developed the assay using two cohorts experimentally infected with influenza A H3N2/Wisconsin or influenza A H1N1/Brisbane, and validated the assay in a sample of adults presenting to the emergency department with fever (n = 102) and in healthy volunteers (n = 41). Peripheral blood RNA samples were obtained from individuals who underwent experimental viral challenge or who presented to the emergency department and had microbiologically proven viral respiratory infection or systemic bacterial infection. The selected gene set on the RT-PCR TLDA assay classified participants with experimentally induced influenza H3N2 and H1N1 infection with 100 and 87% accuracy, respectively. We validated this host gene expression signature in a cohort of 102 individuals arriving at the emergency department. The sensitivity of the RT-PCR test was 89% [95% confidence interval (CI), 72 to 98%], and the specificity was 94% (95% CI, 86 to 99%). These results show that RT-PCR–based detection of a host gene expression signature can classify individuals with respiratory viral infection and sets the stage for prospective evaluation of this diagnostic approach in a clinical setting.

Comparing influenza and RSV viral and disease dynamics in experimentally infected adults predicts clinical effectiveness of RSV antivirals.

BACKGROUND Antivirals reduce influenza viral replication and illness measures, particularly if initiated early, within 48 h of symptom onset. Whether experimental antivirals that reduce respiratory syncytial virus (RSV) load would also reduce disease is unknown. This study compares viral and disease dynamics in humans experimentally infected with influenza or RSV. METHODS Clinical strains of RSV-A and influenza A were inoculated intranasally into 20 and 17 healthy volunteers, respectively, on day 0. Symptom scores and nasal washes were performed twice daily, and daily mucus weights were collected. Viral loads in nasal washes were quantified by culture (plaque assay in HEp-2 cells for RSV and by end point dilution in Madin-Darby canine kidney cells for influenza). RESULTS After influenza inoculation, influenza viral load and illness markers increased simultaneously until day 2. Within individual subjects, peak influenza load occurred 0.4 days (95% CI -0.4, 1.3) before peak symptoms. Influenza viral load and disease declined thereafter. After RSV inoculation, a longer incubation period occurred prior to viral detection and symptom onset. RSV load and disease increased together until day 5. Within individual subjects, peak RSV loads occurred 0.2 days (95% CI -0.7, 1.05) before peak symptoms, after which both illness measures and viral load declined together. CONCLUSIONS Viral and disease dynamics in experimental human infections suggest that reducing RSV load, if timed similarly to clinically-effective influenza antivirals, might be expected to have a similar or greater window of opportunity for reducing clinical RSV disease.

Longitudinal analysis of leukocyte differentials in peripheral blood of patients with acute respiratory viral infections

BACKGROUND Leukocyte counts and differentials are commonly acquired in patients with suspected respiratory viral infections and may contribute diagnostic information. However, most published work is limited to a single timepoint at initial presentation to a medical provider, which may correspond to widely varying points in the course of disease. OBJECTIVES To examine the temporal development and time-dependent utility of routine leukocyte differentials in the diagnosis of respiratory viral infections. STUDY DESIGN We analyzed data from recent experimental human challenges with influenza A/H3N2, human rhinovirus (HRV), and respiratory syncytial virus (RSV). Routine clinical lab cell counts and differentials were measured daily from the time period immediately prior to inoculation through the eventual resolution of symptomatic disease. RESULTS Approximately 50% of challenged individuals developed symptoms and viral shedding consistent with clinical disease. Subpopulations of WBC showed marked differences between symptomatic and asymptomatic individuals over time, but these changes were much more profound and consistent in influenza infection. Influenza-infected subjects develop both relative lymphopenia and relative monocytosis, both of which closely mirror symptom development in time. A lymphocyte:monocyte ratio of <2 correctly classifies 100% of influenza (but not RSV or HRV) infected subjects at the time of maximal symptoms. CONCLUSIONS Leukocyte differentials may suggest a viral etiology in patients with upper respiratory infection, but are not sufficient to allow differentiation between common viruses. Timing of data acquisition relative to the disease course is a key component in determining the utility of these tests.

Differential evolution of peripheral cytokine levels in symptomatic and asymptomatic responses to experimental influenza virus challenge.

Exposure to influenza virus triggers a complex cascade of events in the human host. In order to understand more clearly the evolution of this intricate response over time, human volunteers were inoculated with influenza A/Wisconsin/67/2005 (H3N2), and then had serial peripheral blood samples drawn and tested for the presence of 25 major human cytokines. Nine of 17 (53%) inoculated subjects developed symptomatic influenza infection. Individuals who will go on to become symptomatic demonstrate increased circulating levels of interleukin (IL)‐6, IL‐8, IL‐15, monocyte chemotactic protein (MCP)‐1 and interferon (IFN) gamma‐induced protein (IP)‐10 as early as 12–29 h post‐inoculation (during the presymptomatic phase), whereas challenged patients who remain asymptomatic do not. Overall, the immunological pathways of leucocyte recruitment, Toll‐like receptor (TLR)‐signalling, innate anti‐viral immunity and fever production are all over‐represented in symptomatic individuals very early in disease, but are also dynamic and evolve continuously over time. Comparison with simultaneous peripheral blood genomics demonstrates that some inflammatory mediators (MCP‐1, IP‐10, IL‐15) are being expressed actively in circulating cells, while others (IL‐6, IL‐8, IFN‐α and IFN‐γ) are probable effectors produced locally at the site of infection. Interestingly, asymptomatic exposed subjects are not quiescent either immunologically or genomically, but instead exhibit early and persistent down‐regulation of important inflammatory mediators in the periphery. The host inflammatory response to influenza infection is variable but robust, and evolves over time. These results offer critical insight into pathways driving influenza‐related symptomatology and offer the potential to contribute to early detection and differentiation of infected hosts.

A Genomic Signature of Influenza Infection Shows Potential for Presymptomatic Detection, Guiding Early Therapy, and Monitoring Clinical Responses

Early, presymptomatic intervention with oseltamivir (corresponding to the onset of a published host-based genomic signature of influenza infection) resulted in decreased overall influenza symptoms (aggregate symptom scores of 23.5 vs 46.3), more rapid resolution of clinical disease (20 hours earlier), reduced viral shedding (total median tissue culture infectious dose [TCID50] 7.4 vs 9.7), and significantly reduced expression of several inflammatory cytokines (interferon-γ, tumor necrosis factor-α, interleukin-6, and others). The host genomic response to influenza infection is robust and may provide the means for early detection, more timely therapeutic interventions, a meaningful reduction in clinical disease, and an effective molecular means to track response to therapy.

Nasopharyngeal Protein Biomarkers of Acute Respiratory Virus Infection

Infection of respiratory mucosa with viral pathogens triggers complex immunologic events in the affected host. We sought to characterize this response through proteomic analysis of nasopharyngeal lavage in human subjects experimentally challenged with influenza A/H3N2 or human rhinovirus, and to develop targeted assays measuring peptides involved in this host response allowing classification of acute respiratory virus infection. Unbiased proteomic discovery analysis identified 3285 peptides corresponding to 438 unique proteins, and revealed that infection with H3N2 induces significant alterations in protein expression. These include proteins involved in acute inflammatory response, innate immune response, and the complement cascade. These data provide insights into the nature of the biological response to viral infection of the upper respiratory tract, and the proteins that are dysregulated by viral infection form the basis of signature that accurately classifies the infected state. Verification of this signature using targeted mass spectrometry in independent cohorts of subjects challenged with influenza or rhinovirus demonstrates that it performs with high accuracy (0.8623 AUROC, 75% TPR, 97.46% TNR). With further development as a clinical diagnostic, this signature may have utility in rapid screening for emerging infections, avoidance of inappropriate antibacterial therapy, and more rapid implementation of appropriate therapeutic and public health strategies.

Bayesian modeling of temporal properties of infectious disease in a college student population

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.