Aging and Adaptation in Infectious Diseases III
Category: Application Area Application Area: Infectious Diseases
Date/Time: January 14, 2020 - January 17, 2020
Location: Santa Fe Institute (Noyce Conference Room)
Jean Carlson (UCSB)
Mercedes Pascual (Univ. Chicago)
Phil Arevalo (Univ. Chicago)
Sarah Cobey (Univ. Chicago)
Andrew P. Dobson (Princeton)
Katie Gostic (UCLA)
Andrea L. Graham (Princeton)
Qixin He (Univ. Chicago)
Eric Jones (UCSB)
Kangchon Kim (Univ. Chicago)
Katia Koelle (Emory Univ.)
Micaela Martinez (Columbia Univ.)
Pamela Martinez (Harvard)
Alan Perelson (LANL/SFI)
David Schneider (Stanford)
Jiming Sheng (UCLA)
Marcos Viera (Univ. Chicago)
Shenshen Wang (UCLA)
Our working group aims to explore the role of aging and adaptation in infectious diseases operating over multiple organizational and temporal scales. General areas include immune system dynamics and age, host-pathogen co-adaptation in chronic vs. acute infections, pathogen antigenic diversity and endemism, effects of age on infectious diseases in human and non-human hosts. Overarching themes include memory, (co)adaptation, diversity, feedback, robustness and fragility. We are interested in aging as increasing fragility to infection, and in complex biological time as related to individual variation in disease progression and recovery. We are also interested in aging of the pathogen in terms of its ability to persist and withstand intervention efforts, and how this robustness is in turn related to pathogen (antigenic) diversity. In all these areas, the dynamic acquisition and loss of information through the immune system plays a central role at the individual and population levels. The goal of this third working group is to reunite participants from the first and second meetings to update progress and develop our next set of objectives for collaborative research on the questions that emerged from our previous discussions. These questions include the interaction of the adaptive and innate immune system in the dynamics of infection, the role of early-childhood exposure (‘imprinting’) in later immune protection and in defining the temporal changes of the antigenic map, and the allometric scaling of the immune system dynamics with organism size.
Jean Carlson (UCSB), Eric Jones (UCSB) - Adaptive collaboration[edit source]
While participating in the Aging and Adaptation in Infectious Diseases working group, we refined our mathematical model of the immune system based on expert feedback from other participants. In particular, we discussed parameterizing our model based on existing experimental data of how human memory and naive cell populations change with age. We received several recommendations for relevant studies that we were unaware of before the meeting. Additionally, we discussed how our understanding of the immune model could be improved by a thorough bifurcation analysis, and in particular how this analysis might indicate sensitive parameters that can help quantify immune risk. We discussed how our model could be modified in future work to be applicable to influenza: in particular, influenza rapidly mutates and so considerations of cross-reactive antibodies need to be considered (which our model does not currently include). Future work could also focus on the coevolving feedbacks between our immune model and pathogen strains, and in particular how the evolutionary pressures of an adaptive immune response drive can drive the evolution of diversity of pathogens. Participants expressed interest in studying how chronic infections affect immune outcomes, focusing in particular on cytomegalovirus and its debilitating effect on a host's immune response. Lastly, we have entered into exciting new discussions with Chris Kempes and Andy Dobson regarding how immune system responses scale with host size, which might reveal how immune behaviors are conserved across species' size and age.
Katie Gostic (UCLA) Link to the source page[edit source]
Immunity to antigenically variable pathogens arises from an individual's history of exposures to multiple strains. The success of a new strain in turn depends on how strongly it is recognized by immune responses generated against previous strains. Traditionally, cross-reactivity between two strains is thought to depend on the similarity between their antigenic structures. Antigenic maps are a widely used visualization tool in which the distance between strains (represented as points in Euclidean space) provides a measure of their antigenic similarity, and potential for cross-reactivity.
However, the concept of a fixed antigenic distance between two strains implies that all hosts, regardless of their age and exposure history, would gain the same degree of cross-protection against strain B, given exposure to or vaccination with strain A. This contradicts a growing body of experimental and epidemiological evidence, showing that individuals with different exposure histories can exhibit vastly different levels of cross-protection against the same viral challenge.
We are developing an individual-based model which we will use to explore how differences in individual exposure history can cause hosts to perceive different antigenic distances between strains. We will use this model to explore how history-specific differences in immunity arise, and to what extent they cause immunity to differ from the predictions of existing maps, which assume a fixed distance between strains.
Reference Materials by Presenting Attendees[edit source]