tetano
Editor, Senior Moderator
PLoS Biol. 2018 Aug 20;16(8):e2004974. doi: 10.1371/journal.pbio.2004974. [Epub ahead of print]
[h=1]Timescales of influenza A/H3N2 antibody dynamics.[/h] Kucharski AJ[SUP]1[/SUP], Lessler J[SUP]2[/SUP], Cummings DAT[SUP]3[/SUP], Riley S[SUP]4[/SUP].
[h=3]Author information[/h]
[h=3]Abstract[/h] Human immunity influences the evolution and impact of influenza strains. Because individuals are infected with multiple influenza strains during their lifetime, and each virus can generate a cross-reactive antibody response, it is challenging to quantify the processes that shape observed immune responses or to reliably detect recent infection from serological samples. Using a Bayesian model of antibody dynamics at multiple timescales, we explain complex cross-reactive antibody landscapes by inferring participants' histories of infection with serological data from cross-sectional and longitudinal studies of influenza A/H3N2 in southern China and Vietnam. We find that individual-level influenza antibody profiles can be explained by a short-lived, broadly cross-reactive response that decays within a year to leave a smaller long-term response acting against a narrower range of strains. We also demonstrate that accounting for dynamic immune responses alongside infection history can provide a more accurate alternative to traditional definitions of seroconversion for the estimation of infection attack rates. Our work provides a general model for quantifying aspects of influenza immunity acting at multiple timescales based on contemporary serological data and suggests a two-armed immune response to influenza infection consistent with competitive dynamics between B cell populations. This approach to analysing multiple timescales for antigenic responses could also be applied to other multistrain pathogens such as dengue and related flaviviruses.
PMID: 30125272 DOI: 10.1371/journal.pbio.2004974
Free full text
[h=1]Timescales of influenza A/H3N2 antibody dynamics.[/h] Kucharski AJ[SUP]1[/SUP], Lessler J[SUP]2[/SUP], Cummings DAT[SUP]3[/SUP], Riley S[SUP]4[/SUP].
[h=3]Author information[/h]
[h=3]Abstract[/h] Human immunity influences the evolution and impact of influenza strains. Because individuals are infected with multiple influenza strains during their lifetime, and each virus can generate a cross-reactive antibody response, it is challenging to quantify the processes that shape observed immune responses or to reliably detect recent infection from serological samples. Using a Bayesian model of antibody dynamics at multiple timescales, we explain complex cross-reactive antibody landscapes by inferring participants' histories of infection with serological data from cross-sectional and longitudinal studies of influenza A/H3N2 in southern China and Vietnam. We find that individual-level influenza antibody profiles can be explained by a short-lived, broadly cross-reactive response that decays within a year to leave a smaller long-term response acting against a narrower range of strains. We also demonstrate that accounting for dynamic immune responses alongside infection history can provide a more accurate alternative to traditional definitions of seroconversion for the estimation of infection attack rates. Our work provides a general model for quantifying aspects of influenza immunity acting at multiple timescales based on contemporary serological data and suggests a two-armed immune response to influenza infection consistent with competitive dynamics between B cell populations. This approach to analysing multiple timescales for antigenic responses could also be applied to other multistrain pathogens such as dengue and related flaviviruses.
PMID: 30125272 DOI: 10.1371/journal.pbio.2004974
Free full text