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The reinforcing and rewarding properties of cocaine are attributed to its ability to increase dopaminergic transmission in nucleus accumbens (NAc). This action reinforces drug taking and seeking and leads to potent and long-lasting associations between the rewarding effects of the drug and the cues associated with its availability. The inability to extinguish these associations is a key factor contributing to relapse. Dopamine produces these effects by controlling the activity of two subpopulations of NAc medium spiny neurons (MSNs) that are defined by their predominant expression of either dopamine D1 or D2 receptors. Previous work has demonstrated that optogenetically stimulating D1 MSNs promotes reward, whereas stimulating D2 MSNs produces aversion. However, we still lack a clear understanding of how the endogenous activity of these cell types is affected by cocaine and encodes information that drives drug-associated behaviors. Using fiber photometry calcium imaging we define D1 MSNs as the specific population of cells in NAc that encodes information about drug associations and elucidate the temporal profile with which D1 activity is increased to drive drug seeking in response to contextual cues. Chronic cocaine exposure dysregulates these D1 signals to both prevent extinction and facilitate reinstatement of drug seeking to drive relapse. Directly manipulating these D1 signals using designer receptors exclusively activated by designer drugs prevents contextual associations. Together, these data elucidate the responses of D1- and D2-type MSNs in NAc to acute cocaine and during the formation of context–reward associations and define how prior cocaine exposure selectively dysregulates D1 signaling to drive relapse.

Understanding the transcriptional changes that are engaged in stress resilience may reveal novel antidepressant targets. Here we use gene co-expression analysis of RNA-sequencing data from brains of resilient mice to identify a gene network that is unique to resilience. Zfp189, which encodes a previously unstudied zinc finger protein, is the highest-ranked key driver gene in the network, and overexpression of Zfp189 in prefrontal cortical neurons preferentially activates this network and promotes behavioral resilience. The transcription factor CREB is a predicted upstream regulator of this network and binds to the Zfp189 promoter. To probe CREB–Zfp189 interactions, we employ CRISPR-mediated locus-specific transcriptional reprogramming to direct CREB or G9a (a repressive histone methyltransferase) to the Zfp189 promoter in prefrontal cortex neurons. Induction of Zfp189 with site-specific CREB is pro-resilient, whereas suppressing Zfp189 expression with G9a increases susceptibility. These findings reveal an essential role for Zfp189 and CREB–Zfp189 interactions in mediating a central transcriptional network of resilience.

Stress resilience involves numerous brain-wide transcriptional changes. Determining the organization and orchestration of these transcriptional events may reveal novel antidepressant targets, but this remains unexplored. Here, we characterize the resilient transcriptome with co-expression analysis and identify a single transcriptionally-active uniquely-resilient gene network. Zfp189, a previously unstudied zinc finger protein, is the top network key driver and its overexpression in prefrontal cortical (PFC) neurons preferentially activates this network, alters neuronal activity and promotes behavioral resilience. CREB, which binds Zfp189, is the top upstream regulator of this network. To probe CREB-Zfp189 interactions as a network regulatory mechanism, we employ CRISPR-mediated locus-specific transcriptional reprogramming to direct CREB selectively to the Zfp189 promoter. This single molecular interaction in PFC neurons recapitulates the pro-resilient Zfp189-dependent downstream effects on gene network activity, electrophysiology and behavior. These findings reveal an essential role for Zfp189 and a CREB-Zfp189 regulatory axis in mediating a central transcriptional network of resilience. Footnotes * Supplemental Figures were not appearing correctly.

The role of somatostatin interneurons in nucleus accumbens (NAc), a key brain reward region, remains poorly understood due to the fact that these cells account for < 1% of NAc neurons. Here, we use optogenetics, electrophysiology, and RNA-sequencing to characterize the transcriptome and functioning of NAc somatostatin interneurons after repeated exposure to cocaine. We find that the activity of somatostatin interneurons regulates behavioral responses to cocaine, with repeated cocaine reducing the excitability of these neurons. Repeated cocaine also induces transcriptome-wide changes in gene expression within NAc somatostatin interneurons. We identify the JUND transcription factor as a key regulator of cocaine action and confirmed, by use of viral-mediated gene transfer, that JUND activity in somatostatin interneurons influences behavioral responses to cocaine. Our results identify alterations in NAc induced by cocaine in a sparse population of somatostatin interneurons, and illustrate the value of studying brain diseases using cell type-specific whole transcriptome RNA-sequencing. While making up a small percentage of neurons in the nucleus accumbens, somatostatin interneurons may have important function in dopamine- and addiction-related behavior. Here, Ribeiro and colleagues show that somatostatin interneurons regulate behavioral responses to cocaine with physiological and transcriptomic changes.

Diabetes mellitus commonly known as diabetes, is a group of metabolic disorders characterized by a high blood sugar level over a prolonged period of time. Symptoms include frequent urination, increased thirst, and increased appetite. If left untreated it can cause additional diseases, even death. The Qatar Metabolomics Study is a Type-2 diabetes study conducted in2012 on 374 participants aged 17-81 whose plasma, urine, and saliva samples were analyzed using untargeted liquid chromatography mass spectrometry assay from Metabolon. The data was preprocessed with missing values treated, normalized, transformed, and scaled. Using regularized regression and tree based supervised machine learning algorithms, we tested if the assay’s measurements had any statistical properties to accurately classify a binary diabetes variable. Our analysis created 10 independent class balanced test sets and trained on the hold out set per every iteration. The set of coefficients for every linear model were averaged together into the average model. While using variable gain, frequency, and cover average to parameterize the gradient boosted decision tree. A principal component analysis was calculated to evaluate if the dimensionality of the dataset could be linearly reduced. Lastly, we attempted to infer a network via a graphical gaussian model using covariance shrinkage methods. The results were a set of features that statistically and biologically describe the metabolome for a cohort of diabetic participants living in Qatar.