Cerebral infection by the larvae of the cestode, Taenia solium (neurocysticercosis), is thought to be the leading cause of adult-acquired epilepsy worldwide. Despite this, little is known about the cellular mechanisms that underlie seizure development in this condition. In this talk I will present our recent data exploring multiple interactions between cestode larvae, neuroinflammatory processes and network excitability. We find that viable cestode larvae are able to strongly suppress microglial activation and inflammatory cytokine release with consequences for the modulation host neuroinflammatory responses and seizure development in vivo. At the same time, larvae produce and release glutamate, with acute excitatory effects on neuronal circuits. We hope that an improved understanding of epileptogenic mechanisms in neurocysticercosis will one day improve the management of this condition as well as other inflammatory causes of epilepsy.

Microglia are the immune cells of the brain, and play increasingly appreciated roles in synapse formation, brain plasticity, and cognition. A growing appreciation that the immune system involved in diseases like schizophrenia, epilepsy, and neurodegenerative diseases has led to renewed interest in how microglia regulate synaptic connectivity. Our group previously identified the IL-1 family cytokine Interleukin-33 (IL-33) as a novel regulator of microglial activation and function. I will discuss a mechanism by which microglia regulate synaptic plasticity and long-term memories by engulfing brain extracellular matrix (ECM) proteins. These studies raise the question of how these pathways may be altered or could be modified in the context of disease.

We have developed a robotic platform allowing us to monitor cytokines dynamics (including IL-2, IFN-g, TNF, IL-6) of immune cells in vitro, with unprecedented resolution. To understand the complex emerging dynamics, we use interpretable machine learning techniques to build a generative model of cytokine response. We discover that, surprisingly, immune activity is encoded into one global parameter, encoding ligand antigenic properties and to a less extent ligand quantity. Based on this we build a simple interpretable model which can fully explain the broad variability of cytokines dynamics. We validate our approach using different lines of cells and different ligands. Two processes are identified, connected to timing and intensity of cytokine response, which we successfully modulate using drugs or by changing conditions such as initial T cell numbers. Our work reveals a simple "cytokine code", which can be used to better understand immune response in different contexts including immunotherapy. More generally, it reveals how robotic platforms and machine learning can be leveraged to build and validate systems biology models.