Comprehensive risk factor predictions for 3-year survival among HIV-associated and disseminated cryptococcosis involving lungs and central nervous system.

BACKGROUND: The global mortality rate resulting from HIV-associated cryptococcal disease is remarkably elevated, particularly in severe cases with dissemination to the lungs and central nervous system (CNS). Regrettably, there is a dearth of predictive analysis regarding long-term survival, and few studies have conducted longitudinal follow-up assessments for comparing anti-HIV and antifungal treatments. METHODS: A cohort of 83 patients with HIV-related disseminated cryptococcosis involving the lung and CNS was studied for 3 years to examine survival. Comparative analysis of clinical and immunological parameters was performed between deceased and surviving individuals. Subsequently, multivariate Cox regression models were utilized to validate mortality predictions at 12, 24, and 36 months. RESULTS: Observed plasma cytokine levels before treatment were significantly lower for IL-1RA (p < 0.001) and MCP-1 (p < 0.05) when in the survivor group. Incorporating plasma levels of IL-1RA, IL-6, and high-risk CURB-65 score demonstrated the highest area under curve (AUC) value (0.96) for predicting 1-year mortality. For 1-, 2- and 3-year predictions, the single-factor model with IL-1RA demonstrated superior performance compared to all multiple-variate models (AUC = 0.95/0.78/0.78). CONCLUSIONS: IL-1RA is a biomarker for predicting 3-year survival. Further investigations to explore the pathogenetic role of IL-1RA in HIV-associated disseminated cryptococcosis and as a potential therapeutic target are warranted.

GDF15 promotes simultaneous astrocyte remodeling and tight junction strengthening at the blood-brain barrier.

Perivascular astrocyte processes (PAP) surround cerebral endothelial cells (ECs) and modulate the strengthening of tight junctions to influence blood-brain barrier (BBB) permeability. Morphologically altered astrocytes may affect barrier properties and trigger the onset of brain pathologies. However, astrocyte-dependent mediators of these events remain poorly studied. Here, we show a pharmacologically driven elevated expression and release of growth/differentiation factor 15 (GDF15) in rat primary astrocytes and cerebral PAP. GDF15 has been shown to possess trophic properties for motor neurons, prompting us to hypothesize similar effects on astrocytes. Indeed, its increased expression and release occurred simultaneously to morphological changes of astrocytes in vitro and PAP, suggesting modulatory effects of GDF15 on these cells, but also neighboring EC. Administration of recombinant GDF15 was sufficient to promote astrocyte remodeling and enhance barrier properties between ECs in vitro, whereas its pharmacogenetic abrogation prevented these effects. We validated our findings in male high anxiety-related behavior rats, an animal model of depressive-like behavior, with shrunk PAP associated with reduced expression of the junctional protein claudin-5, which were both restored by a pharmacologically induced increase in GDF15 expression. Thus, we identified GDF15 as an astrocyte-derived trigger of astrocyte process remodeling linked to enhanced tight junction strengthening at the BBB.

Genome-wide association study of restless legs syndrome identifies common variants in three genomic regions.

Restless legs syndrome (RLS) is a frequent neurological disorder characterized by an imperative urge to move the legs during night, unpleasant sensation in the lower limbs, disturbed sleep and increased cardiovascular morbidity. In a genome-wide association study we found highly significant associations between RLS and intronic variants in the homeobox gene MEIS1, the BTBD9 gene encoding a BTB(POZ) domain as well as variants in a third locus containing the genes encoding mitogen-activated protein kinase MAP2K5 and the transcription factor LBXCOR1 on chromosomes 2p, 6p and 15q, respectively. Two independent replications confirmed these association signals. Each genetic variant was associated with a more than 50% increase in risk for RLS, with the combined allelic variants conferring more than half of the risk. MEIS1 has been implicated in limb development, raising the possibility that RLS has components of a developmental disorder.

Fingolimod induces neuroprotective factors in human astrocytes.

BACKGROUND: Fingolimod (FTY720) is the first sphingosine-1-phosphate (S1P) receptor modulator approved for the treatment of multiple sclerosis. The phosphorylated active metabolite FTY720-phosphate (FTY-P) interferes with lymphocyte trafficking. In addition, it accumulates in the CNS and reduces brain atrophy in multiple sclerosis (MS), and neuroprotective effects are hypothesized. METHODS: Human primary astrocytes as well as human astrocytoma cells were stimulated with FTY-P or S1P. We analyzed gene expression by a genome-wide microarray and validated induced candidate genes by quantitative PCR (qPCR) and ELISA. To identify the S1P-receptor subtypes involved, we applied a membrane-impermeable S1P analog (dihydro-S1P), receptor subtype specific agonists and antagonists, as well as RNAi silencing. RESULTS: FTY-P induced leukemia inhibitory factor (LIF), interleukin 11 (IL11), and heparin-binding EGF-like growth factor (HBEGF) mRNA, as well as secretion of LIF and IL11 protein. In order to mimic an inflammatory milieu as observed in active MS lesions, we combined FTY-P application with tumor necrosis factor (TNF). In the presence of this key inflammatory cytokine, FTY-P synergistically induced LIF, HBEGF, and IL11 mRNA, as well as secretion of LIF and IL11 protein. TNF itself induced inflammatory, B-cell promoting, and antiviral factors (CXCL10, BAFF, MX1, and OAS2). Their induction was blocked by FTY-P. After continuous exposure of cells to FTY-P or S1P for up to 7 days, the extent of induction of neurotrophic factors and the suppression of TNF-induced inflammatory genes declined but was still detectable. The induction of neurotrophic factors was mediated via surface S1P receptors 1 (S1PR1) and 3 (S1PR3). CONCLUSIONS: We identified effects of FTY-P on astrocytes, namely induction of neurotrophic mediators (LIF, HBEGF, and IL11) and inhibition of TNF-induced inflammatory genes (CXCL10, BAFF, MX1, and OAS2). This supports the view that a part of the effects of fingolimod may be mediated via astrocytes.

Extracellular vesicle-mediated trafficking of molecular cues during human brain development.

Cellular crosstalk is an essential process influenced by numerous factors, including secreted vesicles that transfer nucleic acids, lipids, and proteins between cells. Extracellular vesicles (EVs) have been the center of many studies focusing on neurodegenerative disorders, but whether EVs display cell-type-specific features for cellular crosstalk during neurodevelopment is unknown. Here, using human-induced pluripotent stem cell-derived cerebral organoids, neural progenitors, neurons, and astrocytes, we identify heterogeneity in EV protein content and dynamics in a cell-type-specific and time-dependent manner. Our results support the trafficking of key molecules via EVs in neurodevelopment, such as the transcription factor YAP1, and their localization to differing cell compartments depending on the EV recipient cell type. This study sheds new light on the biology of EVs during human brain development.

GLIDE: GPU-based linear regression for detection of epistasis.

Due to recent advances in genotyping technologies, mapping phenotypes to single loci in the genome has become a standard technique in statistical genetics. However, one-locus mapping fails to explain much of the phenotypic variance in complex traits. Here, we present GLIDE, which maps phenotypes to pairs of genetic loci and systematically searches for the epistatic interactions expected to reveal part of this missing heritability. GLIDE makes use of the computational power of consumer-grade graphics cards to detect such interactions via linear regression. This enabled us to conduct a systematic two-locus mapping study on seven disease data sets from the Wellcome Trust Case Control Consortium and on in-house hippocampal volume data in 6 h per data set, while current single CPU-based approaches require more than a year's time to complete the same task.

Polymorphisms in FKBP5 are associated with increased recurrence of depressive episodes and rapid response to antidepressant treatment.

The stress hormone-regulating hypothalamic-pituitary-adrenal (HPA) axis has been implicated in the causality as well as the treatment of depression. To investigate a possible association between genes regulating the HPA axis and response to antidepressants and susceptibility for depression, we genotyped single-nucleotide polymorphisms in eight of these genes in depressed individuals and matched controls. We found significant associations of response to antidepressants and the recurrence of depressive episodes with single-nucleotide polymorphisms in FKBP5, a glucocorticoid receptor-regulating cochaperone of hsp-90, in two independent samples. These single-nucleotide polymorphisms were also associated with increased intracellular FKBP5 protein expression, which triggers adaptive changes in glucocorticoid receptor and, thereby, HPA-axis regulation. Individuals carrying the associated genotypes had less HPA-axis hyperactivity during the depressive episode. We propose that the FKBP5 variant-dependent alterations in HPA-axis regulation could be related to the faster response to antidepressant drug treatment and the increased recurrence of depressive episodes observed in this subgroup of depressed individuals. These findings support a central role of genes regulating the HPA axis in the causality of depression and the mechanism of action of antidepressant drugs.

Automatically annotated motion tracking identifies a distinct social behavioral profile following chronic social defeat stress.

Severe stress exposure increases the risk of stress-related disorders such as major depressive disorder (MDD). An essential characteristic of MDD is the impairment of social functioning and lack of social motivation. Chronic social defeat stress is an established animal model for MDD research, which induces a cascade of physiological and behavioral changes. Current markerless pose estimation tools allow for more complex and naturalistic behavioral tests. Here, we introduce the open-source tool DeepOF to investigate the individual and social behavioral profile in mice by providing supervised and unsupervised pipelines using DeepLabCut-annotated pose estimation data. Applying this tool to chronic social defeat in male mice, the DeepOF supervised and unsupervised pipelines detect a distinct stress-induced social behavioral pattern, which was particularly observed at the beginning of a novel social encounter and fades with time due to habituation. In addition, while the classical social avoidance task does identify the stress-induced social behavioral differences, both DeepOF behavioral pipelines provide a clearer and more detailed profile. Moreover, DeepOF aims to facilitate reproducibility and unification of behavioral classification by providing an open-source tool, which can advance the study of rodent individual and social behavior, thereby enabling biological insights and, for example, subsequent drug development for psychiatric disorders.

Polymorphisms in the drug transporter gene ABCB1 predict antidepressant treatment response in depression.

The clinical efficacy of a systemically administered drug acting on the central nervous system depends on its ability to pass the blood-brain barrier, which is regulated by transporter molecules such as ABCB1 (MDR1). Here we report that polymorphisms in the ABCB1 gene predict the response to antidepressant treatment in those depressed patients receiving drugs that have been identified as substrates of ABCB1 using abcb1ab double-knockout mice. Our results indicate that the combined consideration of both the medication's capacity to act as an ABCB1-transporter substrate and the patient's ABCB1 genotype are strong predictors for achieving a remission. This finding can be viewed as a further step into personalized antidepressant treatment.

The endocrine stress response is linked to one specific locus on chromosome 3 in a mouse model based on extremes in trait anxiety.

BACKGROUND: The hypothalamic-pituitary-adrenal (HPA) axis is essential to control physiological stress responses in mammals. Its dysfunction is related to several mental disorders, including anxiety and depression. The aim of this study was to identify genetic loci underlying the endocrine regulation of the HPA axis. METHOD: High (HAB) and low (LAB) anxiety-related behaviour mice were established by selective inbreeding of outbred CD-1 mice to model extremes in trait anxiety. Additionally, HAB vs. LAB mice exhibit comorbid characteristics including a differential corticosterone response upon stress exposure. We crossbred HAB and LAB lines to create F1 and F2 offspring. To identify the contribution of the endocrine phenotypes to the total phenotypic variance, we examined multiple behavioural paradigms together with corticosterone secretion-based phenotypes in F2 mice by principal component analysis. Further, to pinpoint the genomic loci of the quantitative trait of the HPA axis stress response, we conducted genome-wide multipoint oligogenic linkage analyses based on Bayesian Markov chain Monte Carlo approach as well as parametric linkage in three-generation pedigrees, followed by a two-dimensional scan for epistasis and association analysis in freely segregating F2 mice using 267 single-nucleotide polymorphisms (SNPs), which were identified to consistently differ between HAB and LAB mice as genetic markers. RESULTS: HPA axis reactivity measurements and behavioural phenotypes were represented by independent principal components and demonstrated no correlation. Based on this finding, we identified one single quantitative trait locus (QTL) on chromosome 3 showing a very strong evidence for linkage (2ln (L-score) > 10, LOD > 23) and significant association (lowest Bonferroni adjusted p < 10-28) to the neuroendocrine stress response. The location of the linkage peak was estimated at 42.3 cM (95% confidence interval: 41.3 - 43.3 cM) and was shown to be in epistasis (p-adjusted < 0.004) with the locus at 35.3 cM on the same chromosome. The QTL harbours genes involved in steroid synthesis and cardiovascular effects. CONCLUSION: The very prominent effect on stress-induced corticosterone secretion of the genomic locus on chromosome 3 and its involvement in epistasis highlights the critical role of this specific locus in the regulation of the HPA axis.

Epistasis detection on quantitative phenotypes by exhaustive enumeration using GPUs.

MOTIVATION: In recent years, numerous genome-wide association studies have been conducted to identify genetic makeup that explains phenotypic differences observed in human population. Analytical tests on single loci are readily available and embedded in common genome analysis software toolset. The search for significant epistasis (gene-gene interactions) still poses as a computational challenge for modern day computing systems, due to the large number of hypotheses that have to be tested. RESULTS: In this article, we present an approach to epistasis detection by exhaustive testing of all possible SNP pairs. The search strategy based on the Hilbert-Schmidt Independence Criterion can help delineate various forms of statistical dependence between the genetic markers and the phenotype. The actual implementation of this search is done on the highly parallelized architecture available on graphics processing units rendering the completion of the full search feasible within a day. AVAILABILITY: The program is available at http://www.mpipsykl.mpg.de/epigpuhsic/. CONTACT: tony@mpipsykl.mpg.de.

Chromosomal Region 11p14.1 is Associated with Pharmacokinetics and Pharmacodynamics of Bisoprolol.

Purpose: Bisoprolol is a widely used beta-blocker in patients with cardiovascular diseases. As with other beta-blockers, there is variability in response to bisoprolol, but the underlying reasons for this have not been clearly elucidated. Our aim was to investigate genetic factors that affect bisoprolol pharmacokinetics (PK) and pharmacodynamics (PD), and potentially the clinical outcomes. Patients and Methods: Patients with non-ST elevation acute coronary syndrome were recruited prospectively on admission to hospital and followed up for up to 2 years. Patients from this cohort who were on treatment with bisoprolol, at any dose, had bisoprolol adherence data and a plasma sample, one month after discharge from index hospitalisation were included in the study. Individual bisoprolol clearance values were estimated using population pharmacokinetic modeling. Genome-wide association analysis after genotyping was undertaken using an Illumina HumanOmniExpressExome-8 v1.0 BeadChip array, while CYP2D6 copy number variations were determined by PCR techniques and phenotypes for CYP2D6 and CYP3A were inferred from the genotype. GWAS significant SNPs were analysed for heart rate response to bisoprolol in an independent cohort of hypertensive subjects. Results: Six hundred twenty-two patients on bisoprolol underwent both PK and genome wide analysis. The mean (IQR) of the estimated clearance in this population was 13.6 (10.0-18.0) L/h. Bisoprolol clearance was associated with rs11029955 (p=7.17×10-9) mapped to the region of coiled-coil domain containing 34 region (CCDC34) on chromosome 11, and with rs116702638 (p=2.54×10-8). Each copy of the minor allele of rs11029955 was associated with 2.2 L/h increase in clearance. In an independent cohort of hypertensive subjects, rs11029955 was associated with 24-hour heart rate response to 4-week treatment with bisoprolol (p= 9.3×10-5), but not with rs116702638. Conclusion: A novel locus on the chromosomal region 11p14.1 was associated with bisoprolol clearance in a real-world cohort of patients and was validated in independent cohort with a pharmacodynamic association.

DiffBrainNet: Differential analyses add new insights into the response to glucocorticoids at the level of genes, networks and brain regions.

Genome-wide gene expression analyses are invaluable tools for studying biological and disease processes, allowing a hypothesis-free comparison of expression profiles. Traditionally, transcriptomic analysis has focused on gene-level effects found by differential expression. In recent years, network analysis has emerged as an important additional level of investigation, providing information on molecular connectivity, especially for diseases associated with a large number of linked effects of smaller magnitude, like neuropsychiatric disorders. Here, we describe how combined differential expression and prior-knowledge-based differential network analysis can be used to explore complex datasets. As an example, we analyze the transcriptional responses following administration of the glucocorticoid/stress receptor agonist dexamethasone in 8 mouse brain regions important for stress processing. By applying a combination of differential network- and expression-analyses, we find that these explain distinct but complementary biological mechanisms of the glucocorticoid responses. Additionally, network analysis identifies new differentially connected partners of risk genes and can be used to generate hypotheses on molecular pathways affected. With DiffBrainNet (http://diffbrainnet.psych.mpg.de), we provide an analysis framework and a publicly available resource for the study of the transcriptional landscape of the mouse brain which can identify molecular pathways important for basic functioning and response to glucocorticoids in a brain-region specific manner.

Systematic Review of Functional MRI Applications for Psychiatric Disease Subtyping.

Background: Psychiatric disorders have been historically classified using symptom information alone. Recently, there has been a dramatic increase in research interest not only in identifying the mechanisms underlying defined pathologies but also in redefining their etiology. This is particularly relevant for the field of personalized medicine, which searches for data-driven approaches to improve diagnosis, prognosis, and treatment selection for individual patients. Methods: This review aims to provide a high-level overview of the rapidly growing field of functional magnetic resonance imaging (fMRI) from the perspective of unsupervised machine learning applications for disease subtyping. Following the PRISMA guidelines for protocol reproducibility, we searched the PubMed database for articles describing functional MRI applications used to obtain, interpret, or validate psychiatric disease subtypes. We also employed the active learning framework ASReview to prioritize publications in a machine learning-guided way. Results: From the 20 studies that met the inclusion criteria, five used functional MRI data to interpret symptom-derived disease clusters, four used it to interpret clusters derived from biomarker data other than fMRI itself, and 11 applied clustering techniques involving fMRI directly. Major depression disorder and schizophrenia were the two most frequently studied pathologies (35% and 30% of the retrieved studies, respectively), followed by ADHD (15%), psychosis as a whole (10%), autism disorder (5%), and the consequences of early exposure to violence (5%). Conclusions: The increased interest in personalized medicine and data-driven disease subtyping also extends to psychiatric disorders. However, to date, this subfield is at an incipient exploratory stage, and all retrieved studies were mostly proofs of principle where further validation and increased sample sizes are craved for. Whereas results for all explored diseases are inconsistent, we believe this reflects the need for concerted, multisite data collection efforts with a strong focus on measuring the generalizability of results. Finally, whereas functional MRI is the best way of measuring brain function available to date, its low signal-to-noise ratio and elevated monetary cost make it a poor clinical alternative. Even with technology progressing and costs decreasing, this might incentivize the search for more accessible, clinically ready functional proxies in the future.

Gene expression profiling in the stress control brain region hypothalamic paraventricular nucleus reveals a novel gene network including amyloid beta precursor protein.

BACKGROUND: The pivotal role of stress in the precipitation of psychiatric diseases such as depression is generally accepted. This study aims at the identification of genes that are directly or indirectly responding to stress. Inbred mouse strains that had been evidenced to differ in their stress response as well as in their response to antidepressant treatment were chosen for RNA profiling after stress exposure. Gene expression and regulation was determined by microarray analyses and further evaluated by bioinformatics tools including pathway and cluster analyses. RESULTS: Forced swimming as acute stressor was applied to C57BL/6J and DBA/2J mice and resulted in sets of regulated genes in the paraventricular nucleus of the hypothalamus (PVN), 4 h or 8 h after stress. Although the expression changes between the mouse strains were quite different, they unfolded in phases over time in both strains. Our search for connections between the regulated genes resulted in potential novel signalling pathways in stress. In particular, Guanine nucleotide binding protein, alpha inhibiting 2 (GNAi2) and amyloid ß (A4) precursor protein (APP) were detected as stress-regulated genes, and together with other genes, seem to be integrated into stress-responsive pathways and gene networks in the PVN. CONCLUSIONS: This search for stress-regulated genes in the PVN revealed its impact on interesting genes (GNAi2 and APP) and a novel gene network. In particular the expression of APP in the PVN that is governing stress hormone balance, is of great interest. The reported neuroprotective role of this molecule in the CNS supports the idea that a short acute stress can elicit positive adaptational effects in the brain.

Gene Expression in Spontaneous Experimental Autoimmune Encephalomyelitis Is Linked to Human Multiple Sclerosis Risk Genes.

Recent genome-wide association studies have identified over 230 genetic risk loci for multiple sclerosis. Current experimental autoimmune encephalomyelitis (EAE) models requiring active induction of disease may not be optimally suited for the characterization of the function of these genes. We have thus used gene expression profiling to study whether spontaneous opticospinal EAE (OSE) or MOG-induced EAE mirrors the genetic contribution to the pathogenesis of multiple sclerosis more faithfully. To this end, we compared gene expression in OSE and MOG EAE models and analyzed the relationship of both models to human multiple sclerosis risk genes and T helper cell biology. We observed stronger gene expression changes and an involvement of more pathways of the adaptive immune system in OSE than MOG EAE. Furthermore, we demonstrated a more extensive enrichment of human MS risk genes among transcripts differentially expressed in OSE than was the case for MOG EAE. Transcripts differentially expressed only in diseased OSE mice but not in MOG EAE were significantly enriched for T helper cell-specific transcripts. These transcripts are part of immune-regulatory pathways. The activation of the adaptive immune system and the enrichment of both human multiple sclerosis risk genes and T helper cell-specific transcripts were also observed in OSE mice showing only mild disease signs. These expression changes may, therefore, be indicative of processes at disease onset. In summary, more human multiple sclerosis risk genes were differentially expressed in OSE than was observed for MOG EAE, especially in TH1 cells. When studying the functional role of multiple sclerosis risk genes and pathways during disease onset and their interactions with the environment, spontaneous OSE may thus show advantages over MOG-induced EAE.

Treatment response classes in major depressive disorder identified by model-based clustering and validated by clinical prediction models.

The identification of generalizable treatment response classes (TRC[s]) in major depressive disorder (MDD) would facilitate comparisons across studies and the development of treatment prediction algorithms. Here, we investigated whether such stable TRCs can be identified and predicted by clinical baseline items. We analyzed data from an observational MDD cohort (Munich Antidepressant Response Signature [MARS] study, N = 1017), treated individually by psychopharmacological and psychotherapeutic means, and a multicenter, partially randomized clinical/pharmacogenomic study (Genome-based Therapeutic Drugs for Depression [GENDEP], N = 809). Symptoms were evaluated up to week 16 (or discharge) in MARS and week 12 in GENDEP. Clustering was performed on 809 MARS patients (discovery sample) using a mixed model with the integrated completed likelihood criterion for the assessment of cluster stability, and validated through a distinct MARS validation sample and GENDEP. A random forest algorithm was used to identify prediction patterns based on 50 clinical baseline items. From the clustering of the MARS discovery sample, seven TRCs emerged ranging from fast and complete response (average 4.9 weeks until discharge, 94% remitted patients) to slow and incomplete response (10% remitted patients at week 16). These proved stable representations of treatment response dynamics in both the MARS and the GENDEP validation sample. TRCs were strongly associated with established response markers, particularly the rate of remitted patients at discharge. TRCs were predictable from clinical items, particularly personality items, life events, episode duration, and specific psychopathological features. Prediction accuracy improved significantly when cluster-derived slopes were modelled instead of individual slopes. In conclusion, model-based clustering identified distinct and clinically meaningful treatment response classes in MDD that proved robust with regard to capturing response profiles of differently designed studies. Response classes were predictable from clinical baseline characteristics. Conceptually, model-based clustering is translatable to any outcome measure and could advance the large-scale integration of studies on treatment efficacy or the neurobiology of treatment response.

Variants in the neuronal nitric oxide synthase (nNOS, NOS1) gene are associated with restless legs syndrome.

Sixty percent of the patients with restless legs syndrome (RLS) report a positive family history. To date five loci have been mapped on chromosome 12q, 14q, 9p, 2q, and 20p (RLS1-5) but no gene has been identified so far. To identify genes related to RLS, we performed a three-stage association study (explorative study, replication study, high-density mapping) in two Caucasian RLS case-control samples of altogether 918 independent cases and controls. In the explorative study (367 cases and controls, respectively), we screened 1536 SNPs in 366 genes in a 21 Mb region encompassing the RLS1 critical region on chromosome 12. Armitage trend test revealed three genomic regions that were significant (P < 0.05). In the replication study (551 cases and controls, respectively) we genotyped the most significant SNPs of Stage 1. After correction for multiple testing, association was observed with SNP rs7977109 (P(nominal) = 0.00175, P(Westfall-Young) = 0.04895, OR = 0.76228, 95% CI = 0.64310-0.90355), which is in the neuronal nitric oxide synthase (NOS1) gene. High-density mapping using altogether 34 tagging and coding SNPs of the NOS1 gene in both case-control samples further confirmed the significant association results to NOS1. Ten more SNPs revealed significance with nominal P-values from 0.0001 to 0.0482 (genotypic test and Armitage test). Altogether, this study provides evidence for an association of variants in the NOS1 gene and RLS, and suggests the involvement of the NO/arginine pathway in the pathogenesis of RLS. Potential usage of NO modulating agents as new treatment options for RLS have become a challenging aspect for future research of this disorder.

Profiling of behavioral changes and hippocampal gene expression in mice chronically treated with the SSRI paroxetine.

INTRODUCTION: Monoamine-based antidepressants inhibit neurotransmitter reuptake within short time. However, it commonly takes several weeks until clinical symptoms start to resolve--indicating the involvement of effects distant from reuptake inhibition. OBJECTIVE: To unravel other mechanisms involved in drug action, a "reverse" pharmacological approach was applied to determine antidepressant-induced alterations of hippocampal gene expression. MATERIALS AND METHODS: The behavioral response to long-term paroxetine administration of male DBA/2Ola mice was assessed by the forced swim test (FST), the modified hole board (mHB), and the dark/light box. Hippocampi of test-naive mice were dissected, and changes in gene expression by paroxetine treatment were investigated by means of microarray technology. RESULTS AND DISCUSSION: Robust effects of paroxetine on passive stress-coping behavior in the FST were observed. Furthermore, anxiolytic properties of long-term antidepressant treatment could be identified in DBA mice in both, the mHB and dark/light box. Analysis of microarray results revealed a list of 60 genes differentially regulated by chronic paroxetine treatment. Preproenkephalin 1 and inhibin beta-A showed the highest level of transcriptional change. Furthermore, a number of candidates involved in neuroplasticity/neurogenesis emerged (e.g., Bdnf, Gfap, Vim, Sox11, Egr1, Stat3). Seven selected candidates were confirmed by in situ hybridization. Additional immunofluorescence colocalization studies of GFAP and vimentin showed more positive cells to be detected in long-term paroxetine-treated DBA mice. CONCLUSION: Candidate genes identified in the current study using a mouse strain validated for its responsiveness to long-term paroxetine treatment add, in our opinion, to unraveling the mechanism of action of paroxetine as a representative for SSRIs.