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    • The mathematical models used here are

    2018-11-09

    The mathematical models used here are simplistic representations of modeled epidemics and as such their projections, although comparable with the results of other modeling studies in similar populations (Gray et al., 2011; Hontelez et al., 2011; Andersson et al., 2011), shouldn\'t be perceived as comprehensive prediction of the epidemic dynamics over the next 20–30years of HIV infection in SF or SA. Other simplifying assumptions, integrated in the model may also affect the epidemic projections. For instance, ART of the infected individuals was not modeled separately. It was incorporated indirectly in the model by reducing the average transmission probability per act and increasing the survival time on HIV. These parameters (among other) were selected to represent the observed population and epidemic dynamics. We have focused on modeling stable epidemics, which resemble the epidemics in South Africa and San Francisco. However, alternative scenarios exploring epidemics with decreasing HIV prevalence and incidence showed similar to our main results (Figs. S3 and S4).
    Conclusions
    Role of Funding Source Funding for this study was provided by NIH NIAID5UM1AI068614-09.
    Acknowledgments
    Introduction The human B cell immunoglobulin repertoire has the theoretical potential to include up to 1011 unique variants (Glanville et al., 2009); such diversity is key to confer immunity against the variety of order EMD638683 that may be encountered during a lifetime. Upon antigen recognition, cognate B cells become activated, proliferate, and differentiate to produce short-lived antibody-secreting plasma cells (PCs) important in the initial response, and long-lived PCs and memory cells that contribute to sustained immunity. The use of vaccines as a controlled model system to study B cell responses is an important approach in human immunology (Lambert et al., 2005). B cell responses to vaccination are conventionally assessed by global measures of the quantity and function of specific antibody in the blood, but these provide little insight into the characteristics of the B cells responsible for producing the antibody, or their kinetics over time. Understanding which B cells produce specific antibody, and their kinetics in response to antigen encounter has application in the field of vaccine development and evaluation, and also in assessing response to infection (Galson et al., 2014). Antibody specificity is largely determined by the immunoglobulin heavy chain gene sequence used by each individual B cell (Xu and Davis, 2000). Recent improvements in throughput, and decreases in cost of next-generation sequencing make it feasible to use this technology to characterize the immunoglobulin heavy chain repertoire in large numbers of samples. This approach has already yielded clinical applications in the monitoring of minimal disease residue in B cell lymphoma patients (Boyd et al., 2009), and the rapid identification of monoclonal antibody sequences (Reddy et al., 2010), and has shown promise for increasing understanding of autoimmune conditions (Palanichamy et al., 2014) and in disease diagnostics (Parameswaran et al., 2013). High-throughput immunoglobulin sequencing is now also being applied to vaccine studies (Ademokun et al., 2011; Jiang et al., 2013; Laserson et al., 2014; Trück et al., 2015; Vollmers et al., 2013; Wang et al., 2014), and it appears possible to detect vaccine-induced perturbations in the total repertoire that relate to the functional B cell response (Jackson et al., 2014; Lavinder et al., 2014). Of interest, despite immunoglobulin repertoire diversity, there appears to be a degree of sequence convergence across individuals for a given antigen. Convergence has been seen seven days following vaccination with simple polysaccharide antigens (Trück et al., 2015), and more complex influenza antigens (Jackson et al., 2014), as well as following dengue infection (Parameswaran et al., 2013). It remains unclear how long perturbations in the immunoglobulin repertoire can be detected following vaccination, and whether such perturbations in the global repertoire can be used to identify the specific immunoglobulin sequences of the B cells generating the response. The ability to de novo enrich for immunoglobulin sequences with certain specificities from the total repertoire is key for understanding specific B cell responses to vaccination, and uncovering the clinical potential of this technology.