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Novel correlates of protection against pandemic H1N1 influenza A virus infection

Nature Medicine volume 25, pages 962–967 (2019)Cite this article

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Abstract

Influenza viruses remain a severe threat to human health, causing up to 650,000 deaths annually1,2. Seasonal influenza virus vaccines can prevent infection, but are rendered ineffective by antigenic drift. To provide improved protection from infection, novel influenza virus vaccines that target the conserved epitopes of influenza viruses, specifically those in the hemagglutinin stalk and neuraminidase, are currently being developed3. Antibodies against the hemagglutinin stalk confer protection in animal studies4,5,6. However, no data exist on natural infections in humans, and these antibodies do not show activity in the hemagglutination inhibition assay, the hemagglutination inhibition titer being the current correlate of protection against influenza virus infection7,8,9. While previous studies have investigated the protective effect of cellular immune responses and neuraminidase-inhibiting antibodies, additional serological correlates of protection from infection could aid the development of broadly protective or universal influenza virus vaccines10,11,12,13. To address this gap, we performed a household transmission study to identify alternative correlates of protection from infection and disease in naturally exposed individuals. Using this study, we determined 50% protective titers and levels for hemagglutination inhibition, full-length hemagglutinin, neuraminidase and hemagglutinin stalk-specific antibodies. Further, we found that hemagglutinin stalk antibodies independently correlated with protection from influenza virus infection.

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Fig. 1: Study overview and antibody levels in relation to rates of infection.
Fig. 2: Preexisting antibody levels based on influenza outcomes.
Fig. 3: Protective effects associated with a fourfold increase in antibody level.
Fig. 4: Non-responder subpopulation.

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Data availability

The de-identified data sets used for the study are available on ImmPort (study no. SDY1436). Identifying data required to replicate the study analyses, such as exact age, are available by request as required by the institutional review board-approved protocol for the Nicaragua Household Influenza Transmission Study.

Code availability

Code to understand and assess the conclusions of this research is available via ImmPort (study no. SDY1436).

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Acknowledgements

The authors thank past and present members of the study team based at the Health Center Sócrates Flores Vivas, the National Virology Laboratory at the Centro Nacional de Diagnóstico y Referencia, and the Sustainable Sciences Institute in Nicaragua for their dedication and high-quality work. We are grateful to the study participants and their families. Lastly, we thank F. Busto Carrillo for assistance in generating the figures. This work was supported by the National Institute for Allergy and Infectious Diseases (award no. R01 AI120997 to A.G. and contract nos. HHSN272201400008C to F.K. and HHSN272201400006C to A.G.). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Author information

Author notes

  1. These authors contributed equally: S. Ng, R. Nachbagauer.

  2. These authors jointly supervised this work: F. Krammer, A. Gordon.

Authors and Affiliations

  1. Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA

    Sophia Ng, Mayuri Patel & Aubree Gordon

  2. St. Jude Center of Excellence for Influenza Research and Surveillance, Memphis, TN, USA

    Sophia Ng, Mayuri Patel & Aubree Gordon

  3. Centers of Excellence for Influenza Research and Surveillance, Bethesda, MD, USA

    Sophia Ng, Raffael Nachbagauer, Daniel Stadlbauer, Mayuri Patel, Arvind Rajabhathor, Fatima Amanat, Florian Krammer & Aubree Gordon

  4. Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Raffael Nachbagauer, Daniel Stadlbauer, Arvind Rajabhathor, Andrea F. Guglia, Fatima Amanat & Florian Krammer

  5. Center for Research on Influenza Pathogenesis, New York, NY, USA

    Raffael Nachbagauer, Daniel Stadlbauer, Arvind Rajabhathor, Fatima Amanat & Florian Krammer

  6. Laboratorio Nacional de Virología, Centro Nacional de Diagnóstico y Referencia, Ministry of Health, Managua, Nicaragua

    Angel Balmaseda & Roger Lopez

  7. Sustainable Sciences Institute, Managua, Nicaragua

    Angel Balmaseda, Sergio Ojeda, Roger Lopez, Nery Sanchez, Lionel Gresh & Guillermina Kuan

  8. Centro de Salud Sócrates Flores Vivas, Ministry of Health, Managua, Nicaragua

    Guillermina Kuan

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  1. Sophia Ng
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Contributions

A.G., F.K., S.N. and R.N. designed the study. A.G., G.K., L.G., A.B., S.O., R.L. and N.S. collected the data. M.P., D.S., A.R., R.L., A.F.G. and F.A. generated the laboratory data. S.N., R.N. and A.G. analyzed the data. S.N., RN, A.G. and F.K. interpreted the data. All authors critically reviewed the paper and approved of the final version of the paper for submission.

Corresponding authors

Correspondence to Florian Krammer or Aubree Gordon.

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Competing interests

The Icahn School of Medicine at Mount Sinai has filed patent applications regarding influenza virus vaccines. The Krammer laboratory receives funding for universal influenza virus vaccine projects from the Department of Defense, PATH, the Bill and Melinda Gates Foundation and GlaxoSmithKline.

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Extended data

Extended Data Fig. 1 Participant follow-up timeline.

Participant sample collection timeline with the number of samples collected from unique individuals (n = 300 individuals). Day ranges are represented as quintiles.

Extended Data Fig. 2 Preexisting antibodies and corresponding SARs in 2015 (n = 198 individuals).

a, PCR-confirmed infection. b, Symptomatic influenza. Note that the geometric mean baseline hemagglutination inhibition titer for this year was 1:10. The gray tags indicate a 50% protection level and the black tags indicate an 80% protection level. The gray bars show the proportion of household contacts having a certain level of preexisting antibody levels. The bars group individuals between the antibody levels covered by the bars on the x axis (for example, the left-most bar includes all individuals with antibody levels <10, followed by 10 but less than 40, etc.). The red lines fit the antibody level-specific SAR based on the observed rates, which are indicated as cyan points. The attack rate was calculated by dividing the number of infected contacts who had a specific baseline antibody level by the total number of contacts who had the same level of antibodies. The shaded area represents the 95% CIs.

Extended Data Fig. 3 Preexisting antibodies and corresponding SARs in 2013 (n = 102 individuals).

a, PCR-confirmed infection. b, Symptomatic influenza. Note that the geometric mean baseline hemagglutination inhibition titer for this year was 1:34. The gray tags indicate a 50% protection level and the black tags indicate an 80% protection level. The gray bars show the proportion of household contacts having a certain level of preexisting antibody levels. The bars group individuals between the antibody levels covered by the bars on the x axis (for example, the left-most bar includes all individuals with antibody levels <10, followed by 10 but less than 40, etc.). The red lines show the sigmoid function fitted to the observed antibody level-specific SARs, which are indicated as cyan points. The attack rate was calculated by dividing the number of infected contacts who had a specific baseline antibody level by the total number of contacts who had the same level of antibodies. The shaded area represents the 95% CIs for the predicted antibody level-specific SAR.

Extended Data Fig. 4 Influenza outcome-specific distribution of preexisting antibodies.

a, 2015 influenza A (H1N1) pandemic epidemic. b, 2013 influenza A (H1N1) pandemic epidemic. Antibody levels for each individual, and the median and interquartile range, are shown. The y axis indicates the antibody levels. Individuals were separated by PCR-positivity status (blue dots) and by symptomatic influenza (green dots). Individual antibody titer data points were compared between disease outcome groups using a two-tailed Wilcoxon rank-sum test. Analyses were performed combined (all ages; 2013: n = 102 individuals; 2015: n = 198 individuals) as well as separately for children (0–14 years old; 2013: n = 38 individuals; 2015: n = 64 individuals) and adults (15–85 years old; 2013: n = 63 individuals; 2015: n = 135 individuals). See Supplementary Tables 5 and 6 for the FDR analyses. Age groups and outcomes were prespecified before the analyses.

Extended Data Fig. 5 Protective effects associated with a fourfold increase in antibody level among children.

Results are shown for three different sets of assays. a, PCR-confirmed infection. b, Symptomatic influenza (n = 101 individuals). Assay set 1 combines hemagglutination inhibition, hemagglutinin stalk and neuraminidase ELISAs. Assay set 2 combines full-length hemagglutinin and neuraminidase ELISAs. Assay set 3 combines hemagglutination inhibition and neuraminidase ELISAs. Adjusted ORs for the single-assay model are shown as green squares and the multi-assay model as orange circles. The black lines denote the 95% CIs.

Extended Data Fig. 6 Protective effects associated with a fourfold increase in antibody level among adults.

Results are shown for three different sets of assays. a, PCR-confirmed infection. b, Symptomatic influenza (n = 199 individuals). Assay set 1 combines hemagglutination inhibition, hemagglutinin stalk and neuraminidase ELISAs. Assay set 2 combines full-length hemagglutinin and neuraminidase ELISAs. Assay set 3 combines hemagglutination inhibition and neuraminidase ELISAs. Adjusted ORs for the single-assay model are shown as green squares and the multi-assay model as orange circles. The black lines denote the 95% CIs.

Extended Data Fig. 7 PPVs and NPVs of the best serology testing strategy identified by decision tree analyses.

True positive cases were individuals who had PCR-confirmed influenza virus infection. True negative cases were individuals who had neither a positive PCR nor a fourfold rise in antibody serology tests. The model also suggested optimal cutoff points to use when defining seroconversion.

Extended Data Fig. 8 Sensitivity and specificity of hemagglutination inhibition and ELISA in detecting PCR-confirmed infections.

Curves are plotted as solid lines for sensitivity (Sn) in blue and specificity (Sp) in green. The shaded areas indicate the 95% CIs. The x axes show the fold induction for the respective assay. Analyses were performed combined (all ages, n = 300) as well as separately for children (0–14 years old, n = 101) and adults (15–85 years old, n = 199).

Extended Data Fig. 9 Antibody titer correlations.

a–c, Correlation analyses for antibody titers were performed combined (all ages, n = 300 individuals) (a) as well as separately for children (0–14 years old, n = 101 individuals) (b) and adults (15–85 years old, n = 199 individuals) (c). Pearson’s r is plotted in each figure.

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Ng, S., Nachbagauer, R., Balmaseda, A. et al. Novel correlates of protection against pandemic H1N1 influenza A virus infection. Nat Med 25, 962–967 (2019). https://doi.org/10.1038/s41591-019-0463-x

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