Donner, C. | bioRxiv Science

bioRxiv2021

24-Nor-Ursodeoxycholic acid reshapes immunometabolism in CD8+ T cells and alleviates hepatic inflammation

Background & Aims24-NorUrsodeoxycholic acid (NorUDCA) is novel therapy for immune-mediated liver diseases such as primary sclerosing cholangitis (PSC) where dysregulated T cells including CD8+ T cells cause liver immunopathology. We hypothesized that NorUDCA may directly modulate CD8+ T cell effector function thus contributing to its therapeutic efficacy independent of anti-cholestatic effects. MethodsNorUDCA effects on CD8+ T cell function in vivo were investigated in a hepatic injury model system induced by excessive CD8+ T cell immune response upon non-cytolytic lymphocytic choriomeningitis virus (LCMV) infection. Mechanistic studies included molecular and biochemical approaches, flow cytometry and metabolic assays in mouse CD8+ T cells in vitro. Mass spectrometry (MS) was used to identify potential targets modulated by NorUDCA in CD8+ T cells. NorUDCA signaling effects observed in murine systems were validated in peripheral T cells from healthy volunteers and PSC patients. ResultsIn vivo NorUDCA ameliorated hepatic injury and systemic inflammation upon LCMV infection. Mechanistically, NorUDCA demonstrated a strong immunomodulatory efficacy in CD8+ T cells affecting lymphoblastogenesis, mTORC1 signaling and glycolysis of CD8+ T cells. With MS, we identified that NorUDCA regulates CD8+ T cells via targeting mTORC1. NorUDCAs impact on mTORC1 signaling was further confirmed in circulating human CD8+ T cells. ConclusionsNorUDCA possesses a yet-unrecognized direct modulatory potency on CD8+ T cells and attenuates excessive CD8+ T cell hepatic immunopathology. These findings may be relevant for treatment of immune-mediated liver diseases such as PSC and beyond.

molecular biology↗

bioRxiv2019

Inferring the collective dynamics of neuronal populations from single-trial spike trains using mechanistic models

Multi-neuronal spike-train data recorded in vivo often exhibit rich dynamics as well as considerable variability across cells and repetitions of identical experimental conditions (trials). Efforts to characterize and predict the population dynamics and the contributions of individual neurons require model-based tools. Abstract statistical models allow for principled parameter estimation and model selection, but possess only limited interpretive power because they typically do not incorporate prior biophysical constraints. Here we present a statistically principled approach based on a population of doubly-stochastic integrate-and-fire neurons, taking into account basic biophysics. This model class comprises an idealized description for the dynamics of the neuronal membrane voltage in response to fast independent and slower shared input fluctuations. To efficiently estimate the model parameters and compare different model variants we compute the likelihood of observed single-trail spike trains by leveraging analytical methods for spiking neuron models combined with inference techniques for hidden Markov models. This allows us to reconstruct the shared input variations, classify their dynamics, obtain precise spike rate estimates, and quantify how individual neurons couple to the low-dimensional overall population dynamics, all from a single trial. Extensive evaluations based on simulated data show that our method correctly identifies the dynamics of the shared input process and accurately estimates the model parameters. Validations on ground truth recordings of neurons in vitro demonstrate that our approach successfully reconstructs the dynamics of hidden inputs and yields improved fits compared to a typical phenomenological model. Finally, we apply the method to a neuronal population recorded in vivo, for which we assess the contributions of individual neurons to the overall spiking dynamics. Altogether, our work provides statistical inference tools for a class of reasonably constrained, mechanistic models and demonstrates the benefits of this approach to analyze measured spike train data.

neuroscience↗