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Lebel, Y.

Publications and source records attributed to Lebel, Y..

3 recordsLinked to original sources

Excitability as a Design Principle in the Immune System

Growing datasets and mechanistic detail in immunology have outpaced the development of unifying concepts. Such concepts are required to explain the primary goals of immune circuits - strong response to pathogens, tolerance to self, and prevention of collateral damage. A principle that achieves these goals across diverse immune circuits could unify our understanding of the immune system. Here, we propose that excitability, a concept from dynamical systems, serves this role. We screen thousands of circuits to identify those that generate excitable dynamics, and find a single robust design. We scan the human immune network to find this circuit architecture in dozens of innate and adaptive subsystems. We provide evidence for excitability in data on longitudinal responses to SARS-CoV-2. Similar motifs underlie T cell activation, autoimmune flares, and tumor immune responses. This conserved motif provides therapeutic targets and suggests that excitability is a core design principle of immunity, bridging molecular and cellular levels.

immunology↗

An immunologist, ecologist and clinician walk into Plato's cave to discuss infections

If the immune system is an interconnected network, then the evolution of an archetype that is ideal for fighting one pathogen should result in tradeoffs decreasing its ability to fight others. How many archetypes are there in an immune system? We infected diverse mice with Plasmodium chabaudi, and identified five distinct archetypes of responses based on the hosts position in microbial load, immune activity, and host damage space. To better understand the nature of these archetypes, we developed a mathematical model of a generalized host-pathogen system. This model explains the number, and distribution of archetypes across a population of diverse hosts. Mice resilient to P. chabaudi exhibited poor outcomes when challenged with influenza, SARS-CoV-1, or Mycobacterium tuberculosis, and vice versa, supporting our tradeoff hypothesis.

systems biology↗

Excitable dynamics of flares and relapses in autoimmune diseases

Many autoimmune diseases show flares in which symptoms erupt and then decline. A prominent example is multiple sclerosis (MS) in its relapsing-remitting phase. Mathematical models attempting to capture the flares in multiple sclerosis have often been oscillatory in nature, assuming a regular pattern of symptom flare-ups and remissions. However, this fails to account for the non-periodic nature of flares, which can appear at seemingly random intervals. Here we propose that flares resemble excitable dynamics triggered by stochastic events and show that a minimal mathematical model of autoimmune cells and inhibitory regulatory cells can provide such excitability. In our model, autoimmune response releases antigens that cause autoimmune cells to expand in a positive feedback loop, while regulatory cells inhibit the autoimmune cells in a negative feedback loop. The model can quantitatively explain the decline of MS relapses during pregnancy and their postpartum surge based on lymphocyte dynamics, as well as the decline in MS relapses with age. The model also points to potential therapeutic targets and predicts that even small modulation of regulatory T cell production, removal or activity can have a large effect on relapse rate. Excitable dynamics may underlie flares and relapses found across autoimmune diseases, thus providing an understanding that may help improve treatment strategies.

systems biology↗