Antibody interference governs immune evasion in influenza

One of the most fascinating properties of pathogens is the complexity of the mechanisms they employ to gain an upper hand in their constant battles with the host’s immune system. In previous work (Ndifon et al. Proc Natl Acad Sci USA 2009), we discovered an immune evasion mechanism used by influenza viruses that relies, paradoxically, on the immune system’s own weapons — i.e. antibodies. Specifically, the dominant way in which antibodies neutralize the influenza virus is by binding to it and blocking its ability to infect cells. After binding to the virus some antibodies do neutralize the virus, whereas other antibodies fail to do so. Importantly, we found that these non-neutralizing antibodies prevent the neutralizing antibodies from binding to the virus, thereby enabling immune evasion. The virus exploits this fact by increasing the probability that the non-neutralizing antibodies will bind while reducing the probability that the neutralizing antibodies will bind through genetic mutation. These insights led to a novel proposal on how to design improved influenza vaccines.

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Chromatin conformation governs differential gene segment use in T cells

T cells orchestrate adaptive immunity against pathogens by sensing the pathogens’ presence using their T cell receptors (TCRs). The structural elements of the TCR used for sensing pathogens include certain gene segments, which are differentially incorporated into the TCR in a process involving combinatorial pairing of different gene segments. In previous work (Ndifon et al. Proc Natl Acad Sci USA 2012), we discovered that this differential use of the gene segments is governed by the conformation of the region of chromatin in which the segments are embedded. Specifically, chromatin bends in ways that bring certain segments closer to each other — thereby facilitating their pairing — while keeping other segments farther away from each other. We used a physical model of chromatin conformation to calculate the expected segment pairing probabilities and then showed that they predict accurately the segment usage in mice. Interestingly, we showed that the same model can be used prospectively to predict accurately the segment usage in humans.

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The Hayflick limit governs the collapse of T cell diversity in the elderly

One of the most dramatic changes that happen in the human body during aging is the decline of T cell diversity, which in many individuals collapses after the age of about 70 years. This is accompanied by an increased susceptibility to a variety of diseases. What is the organizing principle of this diversity collapse? Can it be prevented? A previous study (Johnson et al. Proc Natl Acad Sci USA 2012) proposed that random mutations occurring in T cells are to blame. Because such mutations are essentially impossible to prevent, according to this previous explanation T cell diversity cannot be prevented from
collapsing in old age.

In contrast, we have proposed (Ndifon & Dushoff J Immunol 2016) a very different explanation based on the
empirical facts that: 1) the homeostatic maintenance of T cells involves their constant proliferation (or division), and 2) T cells can divide for only a limited number of times called the Hayflick limit. T cells that reach the Hayflick limit tend to be eliminated by phagocytosis and other mechanisms. This causes the remaining T cells to divide to fill up the homeostatic space vacated by the eliminated cells, thus pushing them closer to the Hayflick limit. Using mathematical modeling and numerical simulations based on experimental data, we showed that on average this dynamic induces the abrupt collapse of T cell
diversity after 70 years of age. Importantly, we also predicted that thymic rejuvenation therapy will prevent and, if necessary, undo the collapse of T cell diversity by replacing the “old” cells that have reached the
Hayflick limit with much younger cells.

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Immune suppression by regulatory T cells governs the original antigenic sin

An important dogma in the field of immunology holds that after an immunologically competent person is exposed to a given virus variant (say variant A1) she develops an antibody response that is protective against subsequent infections by the same variant. This is generally true, and it is an important basis of the protection that vaccines confer against viruses.

However, when exposure to A1 is preceded by exposure to another variant (say A0) the antibody response to A1 can be sub-optimal while the response to A0 can be supra-optimal, dependent on the similarity between A0 and A1. This phenomenon was discovered more than 70 years ago and is called the
original antigenic sin (OAS). Interestingly, OAS can be prevented if adjuvants are administered at the time of either V0 or V1 exposure (to be effective, the adjuvants administered during V0 exposure must be strong, much more so than those given during V1 exposure).

In previous work (Ndifon J Roy Soc Interf 2015), we provided a unifying mechanistic explanation for
all these observations. Firstly, we showed that OAS occurs when T regulatory cells induced by A0 increase the threshold dose of A1 presented by dendritic cells required for the activation of cognate naive B cells and the production of antibodies. Secondly, we showed that adjuvants allow this threshold dose to be exceeded by enhancing the loading of V1 by dendritic cells, and that, compared to weaker adjuvants, stronger adjuvants are more effective when given during exposure to V0.

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Mathematical modelling reveals what the HI assay measures

The empirical facts on which much of our understanding of immunity rests are generated using tools whose outputs are often incompletely understood. This is particularly true for the HI assay, which has been used since the 1940s to measure serum antibodies reactive with influenza viruses and other pathogens. The HI assay’s outputs (titers) have historically been interpreted based on the intuition that the higher the titer obtained in the reaction between one pathogen variant with antibodies raised against another variant, the more closely related are both variants. Because there were previously no deep insights about what the titer actually measures, it was not possible to assess rigorously the validity of this intuition.

In previous work (Ndifon Infl Other Resp Viruses 2011), we developed a mathematical model that yielded the first explicit equation linking the titer to its underlying physical parameters. Using this model, we showed that the long-standing, intuitive interpretation of titers mentioned above is flawed, because the titer depends strongly on the reactivity of the tested pathogen variant with the red blood cells used in the HI assay. We showed mathematically how to eliminate this dependence of titers on red blood cell reactivity. This mathematical result was later validated experimentally by scientists based at the University of Pennsylvania (Li et al. J Virol 2013).

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