Substance use disorder (SUD) is a chronic, relapsing disease with a highly multifaceted pathology that includes (but is not limited to) sensitivity to drug-associated cues, negative affect, and motivation to maintain drug consumption. SUDs are highly prevalent, with 35 million people meeting criteria for SUD. While drug use and addiction are highly studied, most investigations of SUDs examine drug use in isolation, rather than in the more prevalent context of comorbid substance histories. Indeed, 11.3% of individuals diagnosed with a SUD have concurrent alcohol and illicit drug use disorders. Furthermore, having a SUD with one substance increases susceptibility to developing dependence on additional substances. For example, the increased risk of developing heroin dependence is twofold for alcohol misusers, threefold for cannabis users, 15fold for cocaine users, and 40-fold for prescription misusers. Given the prevalence and risk associated with polysubstance use and current public health crises, examining these disorders through the lens of co-use is essential for translatability and improved treatment efficacy. The escalating economic and social costs and continued rise in drug use has spurred interest in developing preclinical models that effectively model this phenomenon. Here, we review the current state of the field in understanding the behavioral and neural circuitry in the context of co-use with common pairings of alcohol, nicotine, cannabis, and other addictive substances. Moreover, we outline key considerations when developing polysubstance models, including challenges to developing preclinical models to provide insights and improve treatment outcomes.

Summary Obesity is associated with physical inactivity, which exacerbates the health consequences of weight gain. However, the mechanisms that mediate this association are unknown. We hypothesized that deficits in dopamine signaling contribute to physical inactivity in obesity. To investigate this, we quantified multiple aspects of dopamine signaling in lean and obese mice. We found that D2-type receptor (D2R) binding in the striatum, but not D1-type receptor binding or dopamine levels, was reduced in obese mice. Genetically removing D2Rs from striatal medium spiny neurons was sufficient to reduce motor activity in lean mice, while restoring Gi signaling in these neurons increased activity in obese mice. Surprisingly, while mice with low D2Rs were less active, they were not more vulnerable to diet-induced weight gain than control mice. We conclude that deficits in striatal D2R signaling contribute to physical inactivity in obesity, but inactivity is more a consequence than a cause of obesity.

Background Measuring food intake in rodents is a conceptually simple yet labor-intensive and temporally-imprecise task. Most commonly, food is weighed manually, with an interval of hours or days between measurements. Commercial feeding monitors are excellent, but are costly and require specialized caging and equipment. New method We have developed the Feeding Experimentation Device (FED): a low-cost, open-source, home cage-compatible feeding system. FED utilizes an Arduino microcontroller and open-source software and hardware. FED dispenses a single food pellet into a food well where it is monitored by an infrared beam. When the mouse removes the pellet, FED logs the timestamp to a secure digital (SD) card and dispenses a new pellet into the well. Post-hoc analyses of pellet retrieval timestamps reveal high-resolution details about feeding behavior. Results FED is capable of accurately measuring food intake, identifying discrete trends during light and dark-cycle feeding. Additionally, we show the utility of FED for measuring increases in feeding resulting from optogenetic stimulation of agouti-related peptide neurons in the arcuate nucleus of the hypothalamus. Comparison to existing methods With a cost of ~$350 per device, FED is >10x cheaper than commercially available feeding systems. FED is also self-contained, battery powered, and designed to be placed in standard colony rack cages, allowing for monitoring of true home cage feeding behavior. Moreover, FED is highly adaptable and can be synchronized with emerging techniques in neuroscience, such as optogenetics, as we demonstrate here. Conclusions FED allows for accurate, precise monitoring of feeding behavior in a home cage setting.