Does vaccine efficacy depend on the time of day of delivery?

Background

Population studies on humans suggest that time of day that vaccines are given (time of day of vaccination, ToDV) can enhance the effectiveness of the immune response, translating to lower risk of infection or reducing the severity of symptoms [e.g. 1].  However, as highlighted by a recent meta-analysis of these studies [2], there is no consensus on when the best time is to vaccinate, nor for how much ToDV affects clinical and immune readouts. 

The body's circadian clocks generate endogenous, 24-hour cycles in physiological processes, including the immune system [3]. Immune responses, such as cytokine production and lymphocyte activities, exhibit daily oscillations that significantly affect the risk of acquiring infections, the severity of symptoms, and the subsequent spread of pathogens [3]. Thus, it would be surprising if the ToDV does not affect efficacy against pathogens, as it does for cancer [e.g. 4].

The lack of clarity from human studies is thought to be due to uncontrolled (or unmeasured) heterogeneity across individuals in the demography of participants, immune status prior to vaccination, self-selection of vaccination time, or confounding factors such as sleep, chronotype, and shiftwork. Furthermore, whether the type of vaccine and the type of pathogen it protects from (e.g. some parasites have evolved intrinsic rhythmicity in infectivity and virulence traits) also matter are unknown. 

Vaccination remains one of the most effective public health interventions, significantly reducing morbidity and mortality from infectious diseases. Optimising the ToDV could improve their efficacy, particularly for the immunocompromised, and maximise compliance by reducing the risk of side effects. 

Aims

We have identified that livestock represent an excellent model to explore how ToDV affects efficacy. For example, unlike humans, the daily activity rhythms of livestock are fairly well controlled, and flocks of sheep in particular, provide a large sample size with low heterogeneity in key factors such as age, sex, diet, and past infection history. Furthermore, readouts on immune responses, productivity/health and risk of breakthrough infections are readily collected and because sheep can be studied from juvenile to adulthood over a relatively short period, results are tractable within a PhD timeframe. Thus, the project aims to:

1. Survey farms (1% return from membership = 50 responses) that are existing partners of the Moredun Research Institute to ascertain vaccination schedule. Ask what farmers vaccinate against, the time of day they generally vaccinate (for primary and boosters), how repeatable their schedules are, and how long it takes to administer vaccines.
2. Analyse vaccination schedules to explore drivers of ToDV variation, including daylength, the age or reproductive state of animals, other seasonal drivers such as weather and presence of other infections.
3. Design protocols for sheep recruited from N>3 farms to be vaccinated in 2 or 3 stratified ToDV windows of 2-3h each that span the variation in schedules reported in (1). Recruit a further N>3 farms to record ToDV as part of their routine practice. Follow up to record breakthrough infections, health and productivity outcomes. This initial dataset will include multiple vacciations, including an inactivated pathogen vaccine (e.g. Footvax), and will incorporate readouts for cellular and humoral responses.
4. Test specific hypotheses and uncover underlying molecular and cellular mechanisms by which circadian rhythms affect vaccination using challenge experiments at the Moredun Research Institute. For example, testing the hypothesis that responses to vaccines which primarily elicit cellular responses are most likely to be sensitive to ToDV, via immune profiling.

Training outcomes

• Conceptual: immunology, infectious disease biology, pathology  
• Lab: immune profiling, pathogen detection, in vivo experiments with animals
• Compututional: testing and parameterizing circadian rhythmicity, multivariate statistical analysis of longitudinal data sets, meta-analyses
• Translational: ability to communicate with pharma industry, farmers, policy makers