Discovering the mechanisms underlying serotonin (5-HT)2A and 5-HT2C receptor regulation following nicotine withdrawal in rats

We have previously demonstrated that nicotine withdrawal produces depression-like behavior and that serotonin (5-HT)2A/2C receptor ligands modulate that mood-like state. In the present study we aimed to identify the mechanisms (changes in radioligand binding, transcription or RNA-editing) related to such a behavioral outcome. Rats received vehicle or nicotine (0.4 mg/kg, s.c.) for 5 days in home cages. Brain 5-HT2A/2C receptors were analyzed on day 3 of nicotine withdrawal. Nicotine withdrawal increased [(3) H]ketanserin binding to 5-HT2A receptors in the ventral tegmental area and ventral dentate gyrus, yet decreased binding in the nucleus accumbens shell. Reduction in [(3) H]mesulergine binding to 5-HT2C receptors was seen in the ventral dentate gyrus. Profound decrease in the 5-HT2A receptor transcript level was noted in the hippocampus and ventral tegmental area. Out of five 5-HT2C receptor mRNA editing sites, deep sequencing data showed a reduction in editing at the E site and a trend toward reduction at the C site in the hippocampus. In the ventral tegmental area, a reduction for the frequency of CD 5-HT2C receptor transcript was seen. These results show that the reduction in the 5-HT2A receptor transcript level may be an auto-regulatory response to the increased receptor density in the hippocampus and ventral tegmental area during nicotine withdrawal, while decreased 5-HT2C receptor mRNA editing may explain the reduction in receptor labeling in the hippocampus. Serotonin (5-HT)2A/2C receptor ligands alleviate depression-like state in nicotine-withdrawn rats. Here, we show that the reduction in 5-HT2A receptor transcript level may be an auto-regulatory response to the increased receptor number in the hippocampus and ventral tegmental area during nicotine withdrawal, while attenuated 5-HT2C receptor mRNA editing in the hippocampus might explain reduced inverse agonist binding to 5-HT2C receptor and suggest a shift toward a population of more active receptors. 5-HT, serotonin; 5-HT2A R, 5-HT2A receptor; 5-HT2C R, 5-HT2C receptor

Ethanol deprivation and central 5-HT deficiency differentially affect the mRNA editing of the 5-HT(2C) receptor in the mouse brain

BACKGROUND: Serotonin (5-HT) 5-HT(2C) receptor mRNA editing (at five sites, A-E), implicated in neuropsychiatric disorders, including clinical depression, remains unexplored during alcohol abstinence-often accompanied by depressive symptoms. METHODS: We used deep sequencing to investigate 5-HT(2C) receptor editing in mice during early ethanol deprivation following prolonged alcohol exposure and mice lacking tryptophan hydroxylase (TPH)2, a key enzyme in central 5-HT production. We also examined Tph2 expression in ethanol-deprived animals using quantitative real-time PCR (qPCR). RESULTS: Cessation from chronic 10% ethanol exposure in a two-bottle choice paradigm enhanced immobility time and decreased latency in the forced swim test (FST), indicating a depression-like phenotype. In the hippocampus, ethanol-deprived "high ethanol-drinking" mice displayed reduced Tph2 expression, elevated 5-HT(2C) receptor editing efficiency, and decreased frequency of the D mRNA variant, encoding the less-edited INV protein isoform. Tph2(-/-) mice showed attenuated receptor editing in the hippocampus and elevated frequency of non-edited None and D variants. In the prefrontal cortex, Tph2 deficiency increased receptor mRNA editing at site D and reduced the frequency of AB transcript, predicting a reduction in the corresponding partially edited VNI isoform. CONCLUSIONS: Our findings reveal differential effects of 5-HT depletion and ethanol cessation on 5-HT(2C) receptor editing. Central 5-HT depletion attenuated editing in the prefrontal cortex and the hippocampus, whereas ethanol deprivation, coinciding with reduced Tph2 expression in the hippocampus, enhanced receptor editing efficiency specifically in this brain region. This study highlights the interplay between 5-HT synthesis, ethanol cessation, and 5-HT(2C) receptor editing, providing potential mechanism underlying increased ethanol consumption and deprivation

Generating Explainable and Effective Data Descriptors Using Relational Learning: Application to Cancer Biology

The key to success in machine learning is the use of effective data representations. The success of deep neural networks (DNNs) is based on their ability to utilize multiple neural network layers, and big data, to learn how to convert simple input representations into richer internal representations that are effective for learning. However, these internal representations are sub-symbolic and difficult to explain. In many scientific problems explainable models are required, and the input data is semantically complex and unsuitable for DNNs. This is true in the fundamental problem of understanding the mechanism of cancer drugs, which requires complex background knowledge about the functions of genes/proteins, their cells, and the molecular structure of the drugs. This background knowledge cannot be compactly expressed propositionally, and requires at least the expressive power of Datalog. Here we demonstrate the use of relational learning to generate new data descriptors in such semantically complex background knowledge. These new descriptors are effective: adding them to standard propositional learning methods significantly improves prediction accuracy. They are also explainable, and add to our understanding of cancer. Our approach can readily be expanded to include other complex forms of background knowledge, and combines the generality of relational learning with the efficiency of standard propositional learning

Epigenetic dynamics of monocyte-to-macrophage differentiation

Background Monocyte-to-macrophage differentiation involves major biochemical and structural changes. In order to elucidate the role of gene regulatory changes during this process, we used high-throughput sequencing to analyze the complete transcriptome and epigenome of human monocytes that were differentiated in vitro by addition of colony-stimulating factor 1 in serum-free medium. Results Numerous mRNAs and miRNAs were significantly up- or down-regulated. More than 100 discrete DNA regions, most often far away from transcription start sites, were rapidly demethylated by the ten eleven translocation enzymes, became nucleosome-free and gained histone marks indicative of active enhancers. These regions were unique for macrophages and associated with genes involved in the regulation of the actin cytoskeleton, phagocytosis and innate immune response. Conclusions In summary, we have discovered a phagocytic gene network that is repressed by DNA methylation in monocytes and rapidly de-repressed after the onset of macrophage differentiation

Epigenomic Profiling of Human CD4+ T Cells Supports a Linear Differentiation Model and Highlights Molecular Regulators of Memory Development

SummaryThe impact of epigenetics on the differentiation of memory T (Tmem) cells is poorly defined. We generated deep epigenomes comprising genome-wide profiles of DNA methylation, histone modifications, DNA accessibility, and coding and non-coding RNA expression in naive, central-, effector-, and terminally differentiated CD45RA+ CD4+ Tmem cells from blood and CD69+ Tmem cells from bone marrow (BM-Tmem). We observed a progressive and proliferation-associated global loss of DNA methylation in heterochromatic parts of the genome during Tmem cell differentiation. Furthermore, distinct gradually changing signatures in the epigenome and the transcriptome supported a linear model of memory development in circulating T cells, while tissue-resident BM-Tmem branched off with a unique epigenetic profile. Integrative analyses identified candidate master regulators of Tmem cell differentiation, including the transcription factor FOXP1. This study highlights the importance of epigenomic changes for Tmem cell biology and demonstrates the value of epigenetic data for the identification of lineage regulators