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.

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.

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.

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.

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 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.

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.

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.

Genome-wide mapping of genetic determinants influencing DNA methylation and gene expression in human hippocampus.

Emerging evidence emphasizes the strong impact of regulatory genomic elements in neurodevelopmental processes and the complex pathways of brain disorders. The present genome-wide quantitative trait loci analyses explore the cis-regulatory effects of single-nucleotide polymorphisms (SNPs) on DNA methylation (meQTL) and gene expression (eQTL) in 110 human hippocampal biopsies. We identify cis-meQTLs at 14,118 CpG methylation sites and cis-eQTLs for 302 3'-mRNA transcripts of 288 genes. Hippocampal cis-meQTL-CpGs are enriched in flanking regions of active promoters, CpG island shores, binding sites of the transcription factor CTCF and brain eQTLs. Cis-acting SNPs of hippocampal meQTLs and eQTLs significantly overlap schizophrenia-associated SNPs. Correlations of CpG methylation and RNA expression are found for 34 genes. Our comprehensive maps of cis-acting hippocampal meQTLs and eQTLs provide a link between disease-associated SNPs and the regulatory genome that will improve the functional interpretation of non-coding genetic variants in the molecular genetic dissection of brain disorders.

Identification of novel risk loci for restless legs syndrome in genome-wide association studies in individuals of European ancestry: a meta-analysis.

BACKGROUND: Restless legs syndrome is a prevalent chronic neurological disorder with potentially severe mental and physical health consequences. Clearer understanding of the underlying pathophysiology is needed to improve treatment options. We did a meta-analysis of genome-wide association studies (GWASs) to identify potential molecular targets. METHODS: In the discovery stage, we combined three GWAS datasets (EU-RLS GENE, INTERVAL, and 23andMe) with diagnosis data collected from 2003 to 2017, in face-to-face interviews or via questionnaires, and involving 15 126 cases and 95 725 controls of European ancestry. We identified common variants by fixed-effect inverse-variance meta-analysis. Significant genome-wide signals (p≤5 × 10-8) were tested for replication in an independent GWAS of 30 770 cases and 286 913 controls, followed by a joint analysis of the discovery and replication stages. We did gene annotation, pathway, and gene-set-enrichment analyses and studied the genetic correlations between restless legs syndrome and traits of interest. FINDINGS: We identified and replicated 13 new risk loci for restless legs syndrome and confirmed the previously identified six risk loci. MEIS1 was confirmed as the strongest genetic risk factor for restless legs syndrome (odds ratio 1·92, 95% CI 1·85-1·99). Gene prioritisation, enrichment, and genetic correlation analyses showed that identified pathways were related to neurodevelopment and highlighted genes linked to axon guidance (associated with SEMA6D), synapse formation (NTNG1), and neuronal specification (HOXB cluster family and MYT1). INTERPRETATION: Identification of new candidate genes and associated pathways will inform future functional research. Advances in understanding of the molecular mechanisms that underlie restless legs syndrome could lead to new treatment options. We focused on common variants; thus, additional studies are needed to dissect the roles of rare and structural variations. FUNDING: Deutsche Forschungsgemeinschaft, Helmholtz Zentrum München-Deutsches Forschungszentrum für Gesundheit und Umwelt, National Research Institutions, NHS Blood and Transplant, National Institute for Health Research, British Heart Foundation, European Commission, European Research Council, National Institutes of Health, National Institute of Neurological Disorders and Stroke, NIH Research Cambridge Biomedical Research Centre, and UK Medical Research Council.

Genetic regulatory effects modified by immune activation contribute to autoimmune disease associations.

The immune system plays a major role in human health and disease, and understanding genetic causes of interindividual variability of immune responses is vital. Here, we isolate monocytes from 134 genotyped individuals, stimulate these cells with three defined microbe-associated molecular patterns (LPS, MDP, and 5'-ppp-dsRNA), and profile the transcriptomes at three time points. Mapping expression quantitative trait loci (eQTL), we identify 417 response eQTLs (reQTLs) with varying effects between conditions. We characterize the dynamics of genetic regulation on early and late immune response and observe an enrichment of reQTLs in distal cis-regulatory elements. In addition, reQTLs are enriched for recent positive selection with an evolutionary trend towards enhanced immune response. Finally, we uncover reQTL effects in multiple GWAS loci and show a stronger enrichment for response than constant eQTLs in GWAS signals of several autoimmune diseases. This demonstrates the importance of infectious stimuli in modifying genetic predisposition to disease.Insight into the genetic influence on the immune response is important for the understanding of interindividual variability in human pathologies. Here, the authors generate transcriptome data from human blood monocytes stimulated with various immune stimuli and provide a time-resolved response eQTL map.

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.

Connecting Anxiety and Genomic Copy Number Variation: A Genome-Wide Analysis in CD-1 Mice.

Genomic copy number variants (CNVs) have been implicated in multiple psychiatric disorders, but not much is known about their influence on anxiety disorders specifically. Using next-generation sequencing (NGS) and two additional array-based genotyping approaches, we detected CNVs in a mouse model consisting of two inbred mouse lines showing high (HAB) and low (LAB) anxiety-related behavior, respectively. An influence of CNVs on gene expression in the central (CeA) and basolateral (BLA) amygdala, paraventricular nucleus (PVN), and cingulate cortex (Cg) was shown by a two-proportion Z-test (p = 1.6 x 10-31), with a positive correlation in the CeA (p = 0.0062), PVN (p = 0.0046) and Cg (p = 0.0114), indicating a contribution of CNVs to the genetic predisposition to trait anxiety in the specific context of HAB/LAB mice. In order to confirm anxiety-relevant CNVs and corresponding genes in a second mouse model, we further examined CD-1 outbred mice. We revealed the distribution of CNVs by genotyping 64 CD 1 individuals using a high-density genotyping array (Jackson Laboratory). 78 genes within those CNVs were identified to show nominally significant association (48 genes), or a statistical trend in their association (30 genes) with the time animals spent on the open arms of the elevated plus-maze (EPM). Fifteen of them were considered promising candidate genes of anxiety-related behavior as we could show a significant overlap (permutation test, p = 0.0051) with genes within HAB/LAB CNVs. Thus, here we provide what is to our knowledge the first extensive catalogue of CNVs in CD-1 mice and potential corresponding candidate genes linked to anxiety-related behavior in mice.

Identification and characterization of HPA-axis reactivity endophenotypes in a cohort of female PTSD patients.

Analysis of the function of the hypothalamic-pituitary-adrenal (HPA)-axis in patients suffering from posttraumatic stress disorder (PTSD) has hitherto produced inconsistent findings, inter alia in the Trier Social Stress Test (TSST). To address these inconsistencies, we compared a sample of 23 female PTSD patients with either early life trauma (ELT) or adult trauma (AT) or combined ELT and AT to 18 age-matched non-traumatized female healthy controls in the TSST which was preceded by intensive baseline assessments. During the TSST, we determined a variety of clinical, psychological, endocrine and cardiovascular parameters as well as expression levels of four HPA-axis related genes. Using a previously reported definition of HPA-axis responsive versus non-responsive phenotypes, we identified for the first time two clinically and biologically distinct HPA-axis reactivity subgroups of PTSD. One subgroup ("non-responders") showed a blunted HPA-axis response and distinct clinical and biological characteristics such as a higher prevalence of trauma-related dissociative symptoms and of combined AT and ELT as well as alterations in the expression kinetics of the genes encoding for the mineralocorticoid receptor (MR) and for FK506 binding protein 51 (FKBP51). Interestingly, this non-responder subgroup largely drove the relatively diminished HPA axis response of the total cohort of PTSD patients. These findings are limited by the facts that the majority of patients was medicated, by the lack of traumatized controls and by the relatively small sample size. The here for the first time identified and characterized HPA-axis reactivity endophenotypes offer an explanation for the inconsistent reports on HPA-axis function in PTSD and, moreover, suggest that most likely other factors than HPA-axis reactivity play a decisive role in determination of PTSD core symptom severity.

Characterizing the genetic basis of innate immune response in TLR4-activated human monocytes.

Toll-like receptors (TLRs) play a key role in innate immunity. Apart from their function in host defense, dysregulation in TLR signalling can confer risk to autoimmune diseases, septic shock or cancer. Here we report genetic variants and transcripts that are active only during TLR signalling and contribute to interindividual differences in immune response. Comparing unstimulated versus TLR4-stimulated monocytes reveals 1,471 expression quantitative trait loci (eQTLs) that are unique to TLR4 stimulation. Among these we find functional SNPs for the expression of NEU4, CCL14, CBX3 and IRF5 on TLR4 activation. Furthermore, we show that SNPs conferring risk to primary biliary cirrhosis (PBC), inflammatory bowel disease (IBD) and celiac disease are immune response eQTLs for PDGFB and IL18R1. Thus, PDGFB and IL18R1 represent plausible candidates for studying the pathophysiology of these disorders in the context of TLR4 activation. In summary, this study presents novel insights into the genetic basis of the innate immune response and exemplifies the value of eQTL studies in the context of exogenous cell stimulation.

Resistance to antidepressant treatment is associated with polymorphisms in the leptin gene, decreased leptin mRNA expression, and decreased leptin serum levels.

Leptin, a peptide hormone from adipose tissue and key player in weight regulation, has been suggested to be involved in sleep and cognition and to exert antidepressant-like effects, presumably via its action on the HPA-axis and hippocampal function. This led us to investigate whether genetic variants in the leptin gene, the level of leptin mRNA-expression and leptin serum concentrations are associated with response to antidepressant treatment. Our sample consisted of inpatients from the Munich Antidepressant Response Signature (MARS) project with weekly Hamilton Depression ratings, divided into two subsamples. In the exploratory sample (n=251) 17 single nucleotide polymorphisms (SNPs) covering the leptin gene region were genotyped. We found significant associations of several SNPs with impaired antidepressant treatment outcome and impaired cognitive performance after correction for multiple testing. The SNP (rs10487506) showing the highest association with treatment response (p=3.9×10(-5)) was analyzed in the replication sample (n=358) and the association could be verified (p=0.021) with response to tricyclic antidepressants. In an additional meta-analysis combining results from the MARS study with data from the Genome-based Therapeutic Drugs for Depression (GENDEP) and the Sequenced Treatment Alternatives to Relieve Depression (STAR(⁎)D) studies, nominal associations of several polymorphisms in the upstream vicinity of rs10487506 with treatment outcome were detected (p=0.001). In addition, we determined leptin mRNA expression in lymphocytes and leptin serum levels in subsamples of the MARS study. Unfavorable treatment outcome was accompanied with decreased leptin mRNA and leptin serum levels. Our results suggest an involvement of leptin in antidepressant action and cognitive function in depression with genetic polymorphisms in the leptin gene, decreased leptin gene expression and leptin deficiency in serum being risk factors for resistance to antidepressant therapy in depressed patients.

Proteomic and metabolomic profiling reveals time-dependent changes in hippocampal metabolism upon paroxetine treatment and biomarker candidates.

Most of the commonly used antidepressants block monoamine reuptake transporters to enhance serotonergic or noradrenergic neurotransmission. Effects besides or downstream of monoamine reuptake inhibition are poorly understood and yet presumably important for the drugs' mode of action. In the present study we aimed at identifying hippocampal cellular pathway alterations in DBA/2 mice using paroxetine as a representative Selective Serotonin Reuptake Inhibitor (SSRI). Furthermore we identified biomarker candidates for the assessment of antidepressant treatment effects in plasma. Hippocampal protein levels were compared between chronic paroxetine- and vehicle-treated animals using in vivo(15)N metabolic labeling combined with mass spectrometry. We also studied the time course of metabolite level changes in hippocampus and plasma using a targeted polar metabolomics profiling platform. In silico pathway analyses revealed profound alterations related to hippocampal energy metabolism. Glycolytic metabolite levels acutely increased while Krebs cycle metabolite levels decreased upon chronic treatment. Changes in energy metabolism were influenced by altered glycogen metabolism rather than by altered glycolytic or Krebs cycle enzyme levels. Increased energy levels were reflected by an increased ATP/ADP ratio and by increased ratios of high-to-low energy purines and pyrimidines. In the course of our analyses we also identified myo-inositol as a biomarker candidate for the assessment of antidepressant treatment effects in the periphery. This study defines the cellular response to paroxetine treatment at the proteome and metabolome levels in the hippocampus of DBA/2 mice and suggests novel SSRI modes of action that warrant consideration in antidepressant development efforts.

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.

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.

Identification of common variants associated with human hippocampal and intracranial volumes.

Identifying genetic variants influencing human brain structures may reveal new biological mechanisms underlying cognition and neuropsychiatric illness. The volume of the hippocampus is a biomarker of incipient Alzheimer's disease and is reduced in schizophrenia, major depression and mesial temporal lobe epilepsy. Whereas many brain imaging phenotypes are highly heritable, identifying and replicating genetic influences has been difficult, as small effects and the high costs of magnetic resonance imaging (MRI) have led to underpowered studies. Here we report genome-wide association meta-analyses and replication for mean bilateral hippocampal, total brain and intracranial volumes from a large multinational consortium. The intergenic variant rs7294919 was associated with hippocampal volume (12q24.22; N = 21,151; P = 6.70 × 10(-16)) and the expression levels of the positional candidate gene TESC in brain tissue. Additionally, rs10784502, located within HMGA2, was associated with intracranial volume (12q14.3; N = 15,782; P = 1.12 × 10(-12)). We also identified a suggestive association with total brain volume at rs10494373 within DDR2 (1q23.3; N = 6,500; P = 5.81 × 10(-7)).