Arthur Menezes

Understanding the serological landscape of endemic and emergent coronaviruses is critical to interpreting early-pandemic immune responses and evaluating hy

Estimating the durability of immunity from vaccination is complicated by unreported revaccination and unobserved natural infection or reexposure, which could result in overestimation of protection longevity. We tested serial, cross-sectional serum samples from 2005 to 2015 (n = 2530) for immunoglobulin G (IgG) to examine measles seroprevalence, spatiotemporal patterns of titers across regions, and antibody dynamics among children aged 1-9 years who grew up during varying measles circulation in Madagascar under a 1-dose vaccination schedule. We found that measles seroprevalence generally decreased over this period. Furthermore, we conducted 2 nested serological surveys, analyzing 393 samples taken in 2005 (n = 158), a time point preceded by high levels of measles circulation, and 2015 (n = 235), a time point preceded by low levels of measles circulation. Among children alive during periods of limited measles circulation, we found lower measles seroprevalence in all age groups and lower antibody titers in children aged 7-9 years old. Notably, titers among children aged 7-9 dipped near the threshold of protection, highlighting the importance of additional measles vaccine doses. Our findings suggest vulnerabilities might emerge during periods of limited measles circulation for countries with a 1-dose schedule, due to both the buildup of susceptible individuals and waning titers.

The relative and synergistic contributions of genetics and environment to interindividual immune response variation remain unclear, despite implications in evolutionary biology and medicine. Here we quantify interactive effects of genotype and environment on immune traits by investigating C57BL/6, 129S1 and PWK/PhJ inbred mice, rewilded in an outdoor enclosure and infected with the parasite Trichuris muris. Whereas cellular composition was shaped by interactions between genotype and environment, cytokine response heterogeneity including IFNγ concentrations was primarily driven by genotype with consequence on worm burden. In addition, we show that other traits, such as expression of CD44, were explained mostly by genetics on T cells, whereas expression of CD44 on B cells was explained more by environment across all strains. Notably, genetic differences under laboratory conditions were decreased following rewilding. These results indicate that nonheritable influences interact with genetic factors to shape immune variation and parasite burden.

Environmental influences on immune phenotypes are well-documented, but our understanding of which elements of the environment affect immune systems, and how, remains vague. Behaviors, including socializing with others, are central to an individual’s interaction with its environment. We therefore tracked behavior of rewilded laboratory mice of three inbred strains in outdoor enclosures and examined contributions of behavior, including associations measured from spatiotemporal co-occurrences, to immune phenotypes. We found extensive variation in individual and social behavior among and within mouse strains upon rewilding. In addition, we found that the more associated two individuals were, the more similar their immune phenotypes were. Spatiotemporal association was particularly predictive of similar memory T and B cell profiles and was more influential than sibling relationships or shared infection status. These results highlight the importance of shared spatiotemporal activity patterns and/or social networks for immune phenotype and suggest potential immunological correlates of social life.

serosim is an open-source R package designed to aid inference from serological studies, by simulating data arising from user-specified vaccine and antibody kinetics processes using a random effects model. Serological data are used to assess population immunity by directly measuring individuals’ antibody titers. They uncover locations and/or populations which are susceptible and provide evidence of past infection or vaccination to help inform public health measures and surveillance. Both serological data and new analytical techniques used to interpret them are increasingly widespread. This creates a need for tools to simulate serological studies and the processes underlying observed titer values, as this will enable researchers to identify best practices for serological study design, and provide a standardized framework to evaluate the performance of different inference methods. serosim allows users to specify and adjust model inputs representing underlying processes responsible for generating the observed titer values like time-varying patterns of infection and vaccination, population demography, immunity and antibody kinetics, and serological sampling design in order to best represent the population and disease system(s) of interest. This package will be useful for planning sampling design of future serological studies, understanding determinants of observed serological data, and validating the accuracy and power of new statistical methods.

Environmental influences on immune phenotypes are well-documented, but our understanding of which elements of the environment affect immune systems, and how, remains vague. Behaviors, including socializing with others, are central to an individual’s interaction with its environment. We tracked behavior of rewilded laboratory mice of three inbred strains in outdoor enclosures and examined contributions of behavior, including social associations, to immune phenotypes. We found that the more associated two individuals were, the more similar their immune phenotypes were. Social association was particularly predictive of similar memory T and B cell profiles and was more influential than sibling relationships or worm infection status. These results highlight the importance of social networks for immune phenotype and reveal important immunological correlates of social life.

serosim is an open source R package designed to aid inference of serological surveys, by simulating data arising from user-specified vaccine and infection-generated antibody kinetics processes using a random effects model. Serological surveys are used to assess population immunity by directly measuring individuals’ antibody titers. They uncover locations and/or populations which are susceptible and provide evidence of past infection or vaccination to help inform public health measures and surveillance. Both serological surveys and new analytical techniques used to interpret them are increasingly widespread. This expansion creates a need for tools to simulate serological surveys and the processes underlying the observed titer values, as this will enable researchers to identify best practices for serological survey design, and provide a standardized framework to evaluate the performance of different inference methods. serosim allows users to specify and adjust model inputs representing underlying processes responsible for generating the observed titer values like time-varying patterns of infection and vaccination, population demography, immunity and antibody kinetics, and serological survey sampling design in order to best represent the population and disease system(s) of interest. This package will be useful for planning sampling design of future serological surveys, understanding determinants of observed serological data, and validating the accuracy and power of new statistical methods. Author Summary: Public health researchers use serological surveys to obtain serum samples from individuals and measure antibody levels against one or more pathogens. When paired with appropriate analytical methods, these surveys can be used to determine whether individuals have been previously infected with or vaccinated against those pathogens. However, there is currently a lack of tools to simulate realistic serological survey data from the processes determining these observed antibody levels. We developed serosim , an open source R package which enables users to simulate serological survey data matching their disease system(s) of interest. This package allows users to specify and modify model inputs responsible for generating an individual’s antibody level at various levels, from the within-host processes to the observation process. serosim will be useful for designing more informative serological surveys, better understanding the processes behind observed serological data, and assessing new serological survey analytical methods.

Diphtheria is a potentially devastating disease whose epidemiology remains poorly described in many settings, including Madagascar. Diphtheria vaccination is delivered in combination with pertussis and tetanus antigens and coverage of this vaccine is often used as a core measure of health system functioning. However, coverage is challenging to estimate due to the difficulty in translating numbers of doses delivered into numbers of children effectively immunised. Serology provides an alternative lens onto immunisation, but is complicated by challenges in discriminating between natural and vaccine-derived seropositivity. Here, we leverage known features of the serological profile of diphtheria to bound expectations for vaccine coverage for diphtheria, and further refine these using serology for pertussis. We measured diphtheria antibody titres in 185 children aged 6–11 months and 362 children aged 8–15 years and analysed them with pertussis antibody titres previously measured for each individual. Levels of diphtheria seronegativity varied among age groups (18.9% of children aged 6–11 months old and 11.3% of children aged 8–15 years old were seronegative) and also among the districts. We also find surprisingly elevated levels of individuals seropositive to diphtheria but not pertussis in the 6–11 month old age group suggesting that vaccination coverage or efficacy of the pertussis component of the DTP vaccine remains low or that natural infection of diphtheria may be playing a significant role in seropositivity in Madagascar.