Stress-induced despair behavior develops independently of the Ahr-RORγt axis in CD4 + cells

Stress-induced mucin 13 reductions drive intestinal microbiome shifts and despair behaviors

Depression is a prevalent psychological condition with limited treatment options. While its etiology is multifactorial, both chronic stress and changes in microbiome composition are associated with disease pathology. Stress is known to induce microbiome dysbiosis, defined here as a change in microbial composition associated with a pathological condition. This state of dysbiosis is known to feedback on depressive symptoms. While studies have demonstrated that targeted restoration of the microbiome can alleviate depressive-like symptoms in mice, translating these findings to human patients has proven challenging due to the complexity of the human microbiome. As such, there is an urgent need to identify factors upstream of microbial dysbiosis. Here we investigate the role of mucin 13 as an upstream mediator of microbiome composition changes in the context of stress. Using a model of chronic stress, we show that the glycocalyx protein, mucin 13, is selectively reduced after psychological stress exposure. We further demonstrate that the reduction of Muc13 is mediated by the Hnf4 transcription factor family. Finally, we determine that deleting Muc13 is sufficient to drive microbiome shifts and despair behaviors. These findings shed light on the mechanisms behind stress-induced microbial changes and reveal a novel regulator of mucin 13 expression.

Using deep learning to study emotional behavior in rodent models

Quantifying emotional aspects of animal behavior (e.g., anxiety, social interactions, reward, and stress responses) is a major focus of neuroscience research. Because manual scoring of emotion-related behaviors is time-consuming and subjective, classical methods rely on easily quantified measures such as lever pressing or time spent in different zones of an apparatus (e.g., open vs. closed arms of an elevated plus maze). Recent advancements have made it easier to extract pose information from videos, and multiple approaches for extracting nuanced information about behavioral states from pose estimation data have been proposed. These include supervised, unsupervised, and self-supervised approaches, employing a variety of different model types. Representations of behavioral states derived from these methods can be correlated with recordings of neural activity to increase the scope of connections that can be drawn between the brain and behavior. In this mini review, we will discuss how deep learning techniques can be used in behavioral experiments and how different model architectures and training paradigms influence the type of representation that can be obtained.