Collaborating Center for Reference and Research on Influenza at the Peter Doherty Institute for Infection and Immunity

Parkville, Australia

Collaborating Center for Reference and Research on Influenza at the Peter Doherty Institute for Infection and Immunity

Parkville, Australia
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PubMed | Centers for Disease Control and Prevention, World Health Organization, University of Wisconsin - Madison, UK National Institute for Medical Research and 6 more.
Type: Journal Article | Journal: Nature microbiology | Year: 2016

Influenza viruses mutate frequently, necessitating constant updates of vaccine viruses. To establish experimental approaches that may complement the current vaccine strain selection process, we selected antigenic variants from human H1N1 and H3N2 influenza virus libraries possessing random mutations in the globular head of the haemagglutinin protein (which includes the antigenic sites) by incubating them with human and/or ferret convalescent sera to human H1N1 and H3N2 viruses. We also selected antigenic escape variants from human viruses treated with convalescent sera and from mice that had been previously immunized against human influenza viruses. Our pilot studies with past influenza viruses identified escape mutants that were antigenically similar to variants that emerged in nature, establishing the feasibility of our approach. Our studies with contemporary human influenza viruses identified escape mutants before they caused an epidemic in 2014-2015. This approach may aid in the prediction of potential antigenic escape variants and the selection of future vaccine candidates before they become widespread in nature.


PubMed | Burnet Institute, Collaborating Center for Reference and Research on Influenza at the Peter Doherty Institute for Infection and Immunity, Monash University, Copenhagen University and University of Melbourne
Type: Journal Article | Journal: Journal of virology | Year: 2016

This study seeks to assess the ability of seasonal trivalent inactivated influenza vaccine (TIV) to induce nonneutralizing antibodies (Abs) with Fc-mediated functions in HIV-uninfected and HIV-infected subjects. Functional influenza-specific Ab responses were studied in 30 HIV-negative and 27 HIV-positive subjects immunized against seasonal influenza. All 57 subjects received the 2015 TIV. Fc-mediated antihemagglutinin (anti-HA) Ab activity was measured in plasma before and 4 weeks after vaccination using Fc-receptor-binding assays, NK cell activation assays, and phagocytosis assays. At baseline, the HIV-positive group had detectable but reduced functional Ab responses to both vaccine and nonvaccine influenza antigens. TIV enhanced Fc-mediated Ab responses in both HIV-positive and HIV-negative groups. A larger rise was generally observed in the HIV-positive group, such that there was no difference in functional Ab responses between the two groups after vaccination. The 2015 TIV enhanced functional influenza-specific Ab responses in both HIV-negative and HIV-positive subjects to a range of influenza HA proteins. The increase in functional Ab responses in the HIV-positive group supports recommendations to immunize this at-risk group.Infection with HIV is associated with increasing disease severity following influenza infections, and annual influenza vaccinations are recommended for this target group. However, HIV-infected individuals respond relatively poorly to vaccination compared to healthy individuals, particularly if immunodeficient. There is therefore a need to increase our understanding of immunity to influenza in the context of underlying HIV infection. While antibodies can mediate direct virus neutralization, interactions with cellular Fc receptors may be important for anti-influenza immunity in vivo by facilitating antibody-dependent cellular cytotoxicity (ADCC) and/or antibody-dependent phagocytosis (ADP). The ability of seasonal influenza vaccines to induce antibody responses with potent Fc-mediated antiviral activity is currently unclear. Probing the ADCC and ADP responses to influenza vaccination has provided important new information in the quest to improve immunity to influenza.


Cao P.,University of Melbourne | Yan A.W.C.,University of Melbourne | Heffernan J.M.,University of Melbourne | Heffernan J.M.,York University | And 11 more authors.
PLoS Computational Biology | Year: 2015

Influenza is an infectious disease that primarily attacks the respiratory system. Innate immunity provides both a very early defense to influenza virus invasion and an effective control of viral growth. Previous modelling studies of virus–innate immune response interactions have focused on infection with a single virus and, while improving our understanding of viral and immune dynamics, have been unable to effectively evaluate the relative feasibility of different hypothesised mechanisms of antiviral immunity. In recent experiments, we have applied consecutive exposures to different virus strains in a ferret model, and demonstrated that viruses differed in their ability to induce a state of temporary immunity or viral interference capable of modifying the infection kinetics of the subsequent exposure. These results imply that virus-induced early immune responses may be responsible for the observed viral hierarchy. Here we introduce and analyse a family of within-host models of re-infection viral kinetics which allow for different viruses to stimulate the innate immune response to different degrees. The proposed models differ in their hypothesised mechanisms of action of the non-specific innate immune response. We compare these alternative models in terms of their abilities to reproduce the re-exposure data. Our results show that 1) a model with viral control mediated solely by a virus-resistant state, as commonly considered in the literature, is not able to reproduce the observed viral hierarchy; 2) the synchronised and desynchronised behaviour of consecutive virus infections is highly dependent upon the interval between primary virus and challenge virus exposures and is consistent with virus-dependent stimulation of the innate immune response. Our study provides the first mechanistic explanation for the recently observed influenza viral hierarchies and demonstrates the importance of understanding the host response to multi-strain viral infections. Re-exposure experiments provide a new paradigm in which to study the immune response to influenza and its role in viral control. © 2015 Cao et al.


PubMed | Collaborating Center for Reference and Research on Influenza at the Peter Doherty Institute for Infection and Immunity, University of Melbourne and Monash Institute of Pharmaceutical Sciences
Type: Journal Article | Journal: PloS one | Year: 2016

Immunity to human influenza A virus (IAV) infection is only partially understood. Broadly non-neutralizing antibodies may assist in reducing disease but have not been well characterized.We measured internalization of opsonized, influenza protein-coated fluorescent beads and live IAV into a monocytic cell line to study antibody-dependent phagocytosis (ADP) against multiple influenza hemagglutinin (HA) subtypes. We analyzed influenza HA-specific ADP in healthy human donors, in preparations of intravenous immunoglobulin (IVIG), and following IAV infection of humans and macaques.We found that both sera from healthy adults and IVIG preparations had broad ADP to multiple seasonal HA proteins and weak cross-reactive ADP to non-circulating HA proteins. The ADP in experimentally influenza-infected macaque plasma and naturally influenza-infected human sera mediated phagocytosis of both homologous and heterologous IAVs. Further, the IAV phagocytosed in an antibody-mediated manner had reduced infectivity in vitro.We conclude that IAV infections in humans and macaques leads to the development of influenza-specific ADP that can clear IAV infection in vitro. Repeated exposure of humans to multiple IAV infections likely leads to the development of ADP that is cross-reactive to strains not previously encountered. Further analyses of the protective capacity of broadly reactive influenza-specific ADP is warranted.


PubMed | York University, Murdoch Childrens Research Institute, Collaborating Center for Reference and Research on Influenza at the Peter Doherty Institute for Infection and Immunity and University of Melbourne
Type: Journal Article | Journal: PLoS computational biology | Year: 2015

Influenza is an infectious disease that primarily attacks the respiratory system. Innate immunity provides both a very early defense to influenza virus invasion and an effective control of viral growth. Previous modelling studies of virus-innate immune response interactions have focused on infection with a single virus and, while improving our understanding of viral and immune dynamics, have been unable to effectively evaluate the relative feasibility of different hypothesised mechanisms of antiviral immunity. In recent experiments, we have applied consecutive exposures to different virus strains in a ferret model, and demonstrated that viruses differed in their ability to induce a state of temporary immunity or viral interference capable of modifying the infection kinetics of the subsequent exposure. These results imply that virus-induced early immune responses may be responsible for the observed viral hierarchy. Here we introduce and analyse a family of within-host models of re-infection viral kinetics which allow for different viruses to stimulate the innate immune response to different degrees. The proposed models differ in their hypothesised mechanisms of action of the non-specific innate immune response. We compare these alternative models in terms of their abilities to reproduce the re-exposure data. Our results show that 1) a model with viral control mediated solely by a virus-resistant state, as commonly considered in the literature, is not able to reproduce the observed viral hierarchy; 2) the synchronised and desynchronised behaviour of consecutive virus infections is highly dependent upon the interval between primary virus and challenge virus exposures and is consistent with virus-dependent stimulation of the innate immune response. Our study provides the first mechanistic explanation for the recently observed influenza viral hierarchies and demonstrates the importance of understanding the host response to multi-strain viral infections. Re-exposure experiments provide a new paradigm in which to study the immune response to influenza and its role in viral control.


PubMed | University of Hong Kong and Collaborating Center for Reference and Research on Influenza at the Peter Doherty Institute for Infection and Immunity
Type: Journal Article | Journal: Epidemiology and infection | Year: 2016

Influenza vaccination is the most practical means available for preventing influenza virus infection and is widely used in many countries. Because vaccine components and circulating strains frequently change, it is important to continually monitor vaccine effectiveness (VE). The test-negative design is frequently used to estimate VE. In this design, patients meeting the same clinical case definition are recruited and tested for influenza; those who test positive are the cases and those who test negative form the comparison group. When determining VE in these studies, the typical approach has been to use logistic regression, adjusting for potential confounders. Because vaccine coverage and influenza incidence change throughout the season, time is included among these confounders. While most studies use unconditional logistic regression, adjusting for time, an alternative approach is to use conditional logistic regression, matching on time. Here, we used simulation data to examine the potential for both regression approaches to permit accurate and robust estimates of VE. In situations where vaccine coverage changed during the influenza season, the conditional model and unconditional models adjusting for categorical week and using a spline function for week provided more accurate estimates. We illustrated the two approaches on data from a test-negative study of influenza VE against hospitalization in children in Hong Kong which resulted in the conditional logistic regression model providing the best fit to the data.

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