Utilizing population variation, vaccination, and systems biology to study human immunology

Abstract

The move toward precision medicine has highlighted the importance of understanding biological variability within and across individuals in the human population. In particular, given the prevalent involvement of the immune system in diverse pathologies, an important question is how much and what information about the state of the immune system is required to enable accurate prediction of future health and response to medical interventions. Towards addressing this question, recent studies using vaccination as a model perturbation and systems-biology approaches are beginning to provide a glimpse of how natural population variation together with multiplexed, high-throughput measurement and computational analysis can be used to uncover predictors of immune response quality in humans. Here I discuss recent developments in this emerging field, with emphasis on baseline correlates of vaccination responses, sources of immune-state variability, as well as relevant features of study design, data generation, and computational analysis.

Figure 1

(A) Illustration of the dynamical trajectory of two hypothetical parameters within two subjects (green and blue lines) before and after a perturbation. At any given moment (e.g., a snapshot measurement of the parameter in both subjects) before the perturbation (i.e., ‘baseline’), the total amount of observed variability can be attributed to inter-subject differences, temporal variations within subjects, and technical variation (or measurement noise – as indicated by the thickness of the shade). The measured variation in parameter 1 (left panel) is dominated by inter-subject variation (decomposition of variation is illustrated using a pie chart), and thus is an example of a temporally-stable parameter – in other words, inter-subject difference is well maintained regardless of when the measurement is made. Parameter 2 (right panel) exhibits lower subject-to-subject differences, but the fluctuations within subjects are much higher relative to parameter 1. Parameter 2 is an example of a temporally-unstable parameter. Both subjects responded to the perturbation by increasing the value of parameter 1, but the amount of increase relative to the baseline is different across the two subjects, thus showing a qualitatively-consistent change (i.e., a coherent change) that is quantitatively variable (i.e., a response variation). Parameter 2 also showed an increase in value after the perturbation but, owing to the amount of fluctuations within subjects, this coherent change is difficult to detect statistically. (B) Extensive subject-to-subject variability is essential for assessing correlation between two parameters. The top panel illustrates a scenario where two biologically associated parameters X and Y exhibit substantial subject-to-subject variability relative to measurement noise (indicated by error bars), which in turn enables robust detection of correlation between these two parameters (right scatter plot). The bottom illustrates an opposite scenario: the two parameters here are also biologically associated (i.e., they co-vary) but, because the amount of inter-subject variation is small relative to measurement noise, detection of correlation is impossible (right scatter plot).

Figure 2

Contributors to vaccination response variability. (A) Genetics, gender, and accumulated environment exposure since birth shape the pre-vaccination state of the immune system which, together with other intrinsic variables such as BMI, age, status of chronic infection (CMV/EBV), and initial antibody titers, can help to explain inter-subject variability in antibody titer responses, whose distribution across subjects is depicted as a gray histogram on the right. In addition to the known intrinsic correlates (highlighted in blue font), some of the pre- and post-vaccination correlates identified in recent studies are also indicated. (B) One approach for assessing contributors of titer response variation following vaccination. Intrinsic factors (e.g., age, gender) can contribute to variations in baseline immune states and post-vaccination responses. Baseline immune states can in turn contribute to response variations (Figure 1A), and all of these factors can together contribute to variation in titer responses. Correlates of titer responses can be analyzed following the ‘flow’ of time: the contributions of intrinsic variables are first analyzed, and their effects are then removed statistically from baseline and response parameters, which were then analyzed in a similar stepwise manner to assess their independent contributions to titer response variation. Figure design adopted from Figure 5A in [13]. Abbreviations: BMI, body mass index; CMV, cytomegalovirus; EBV, Epstein–Barr virus.