From Immunity to Impact: Quantifying immune response dynamics to inform immune-mediated protection and immunotherapy strategies Restricted; Files Only
Saha, Ananya (Summer 2025)
Abstract
Immunological memory is the hallmark of adaptive immune response following immunization or clearance of acute infections; offering protection against reinfection, reducing severe disease if infected, and preventing onward transmission of infection. While immune memory’s protective roles are conceptually clear, quantifying them from data is difficult. Additionally, immune memory declines over time, diminishing its effectiveness. Understanding how to quantify the waning of immune memory and its protective effects is crucial for developing future immunization techniques.
This thesis aims to quantify broad patterns in the waning of immune memory and to characterize its protection against respiratory viruses. In the first part, we analyze a dataset with antibody titer measurements from individuals followed over 10-15 years, finding that a power law model best captures observed antibody waning patterns, outperforming the widely used exponential models. The power law model accurately describes the data, particularly when rate of waning declines over time, and suggests that immune memory may persist longer than previously thought. It also highlights the importance of time after immunization in measuring rate of waning. Accounting for time after immunization reduces previously estimated differences in immune waning rates after immunization with protein based vaccines vs live attenuated vaccines. Finally, the power law model enables rapid estimation of long-term (decades-long) waning using just 2–3 years of data.
In the second part of the thesis, we analyzed data from experimental transmission studies to assess the role of CD8 T cells in limiting respiratory virus transmission. Using a mouse model of sendai virus infection, we characterized how tissue-resident memory (TRM) T cells prevent infection and reduce onward transmission. Our results show that respiratory tract TRM cells shorten the transmission window compared to mice lacking T cell immunity. Presence of these cells also decrease infection probability and can provide long-lasting protection. Furthermore, we mapped between-host transmission probability to within-host infection burden, revealing that TRM cells may lower both infection burden and infectiousness.
Unlike acute infections, in chronic infections, that are characterized by the persistence of the infection causing pathogen, the CD8 T cells are exhausted. Immunotherapeutic approaches targeted to reverse the exhaustion state fail in a substantial fraction of treated individuals. We end the thesis with our perspective on using mathematical models to explore potential mechanisms of treatment failure.
The studies reported in the thesis have implications on designing next generation immunotherapeutics.
Table of Contents
1 Introduction 1
1.1 Immunotherapy against virus infections and other diseases . . . . . . . . . 1
1.2 Fascinating questions about the immune system dynamics pertinent to
effective immunotherapy design . . . . . . . . . . . . . . . . . . . . . . . 3
1.3 Humoral vs cellular immune responses . . . . . . . . . . . . . . . . . . . . 4
1.3.1 Different flavors of humoral immunity . . . . . . . . . . . . . . . . 7
1.3.2 Different flavors of cellular immunity . . . . . . . . . . . . . . . . 8
1.4 Waning of immunity, reinfections, and infectious disease epidemiology . . . 9
1.4.1 Durable humoral immunity is provided by long-lasting plasma cells 11
1.4.2 Quantifying waning of humoral immunity is challenging . . . . . . 12
1.5 Enhancing cellular immunity in respiratory virus vaccines . . . . . . . . . 13
1.5.1 CD8 T cell responses following respiratory virus infections and
vaccination . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
1.5.2 Quantifying cellular immunity’s role in preventing transmission in
laboratory setting is challenging . . . . . . . . . . . . . . . . . . . 16
1.6 Immunotherapeutic approaches to rejuvenate an exhausted immune response 17
1.7 Scope of this thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
2 Quantifying the Waning of Humoral Immunity 20
2.1 Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
2.2 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
2.3 Study Details . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
2.4 Method Details . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
2.5 Antibody waning follows a power-law model . . . . . . . . . . . . . . . . . 25
2.6 Quantifying antibody waning to different viruses and vaccines . . . . . . . 30
2.7 A power-law model predicts longer times to loss of protective immunity . . 33
2.8 The power-law allows estimation of rates of waning from short time-series . 36
2.9 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
3 Prevention of respiratory virus transmission by T cells 43
3.1 Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
3.2 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
3.3 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
3.3.1 Mice and viruses . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
3.3.2 In vivo imaging . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
3.3.3 Sendai virus plaque assay . . . . . . . . . . . . . . . . . . . . . . 47
3.3.4 Data analysis and statistics . . . . . . . . . . . . . . . . . . . . . . 48
3.4 CD8+ TRM cells limit the transmission window . . . . . . . . . . . . . . . 48
3.5 IFN𝛾 is required to limit transmission . . . . . . . . . . . . . . . . . . . . 52
3.6 TRM cells limit susceptibility to infection . . . . . . . . . . . . . . . . . . 56
3.7 CD8+ TRM cells can provide durable protection . . . . . . . . . . . . . . . 57
3.8 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
4 Quantifying T cell mediated prevention of respiratory virus transmission 63
4.1 Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
4.2 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
4.3 Materials and Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
4.3.1 Experimental transmission study details . . . . . . . . . . . . . . . 66
4.3.2 Data analysis and modelling of transmission experiments . . . . . . 68
4.4 Pre-existing cellular immunity reduces onward transmission probability by
reducing infection burden . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
4.5 Lower infection burdens do not fully explain lower onward transmission
probabilities from pre-immune mice . . . . . . . . . . . . . . . . . . . . . 75
4.6 Awithin host mathematical model with immune status-specific infectiousness
predict key patterns observed in the two experimental transmission studies . 82
4.7 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
5 Modeling T cell exhaustion in chronic infections 91
5.1 Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
5.2 CD8 T cell exhaustion in chronic viral infections and cancers: scope of
therapeutic interventions . . . . . . . . . . . . . . . . . . . . . . . . . . . 92
5.3 Mathematical models of T cell exhaustion help understanding treatment
outcomes in chronic infection settings . . . . . . . . . . . . . . . . . . . . 93
5.4 Limitations of the existing models: Observations that these models do not
explain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96
5.4.1 Can antigen withdrawal from chronic antigenic stimulation result in
long-term immune memory? . . . . . . . . . . . . . . . . . . . . . 97
5.4.2 Can reversal of T cell exhaustion be modelled using the existing
models? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98
5.5 Recent developments in understanding differentiation rules governing T cell
exhaustion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
5.6 Implications of heterogeneity in the exhausted T cell pool . . . . . . . . . . 101
5.6.1 Immune memory formation after chronic antigen clearance depends
on quantity and quality of survived cells . . . . . . . . . . . . . . . 101
5.6.2 Anti-PD1 treatment changes the exhausted T cell population dynamics102
5.7 Models of exhaustion incorporating heterogeneity . . . . . . . . . . . . . . 104
5.8 Concluding remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106
6 Conclusion 108
6.1 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108
6.2 Outstanding questions and future directions . . . . . . . . . . . . . . . . . 113
A Appendix 117
Bibliography 127
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