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Bipolar disorder (BD) is a genetically complex mental illness characterized by severe oscillations of mood and behavior. Genome-wide association studies (GWAS) have identified several risk loci that together account for a small portion of the heritability. To identify additional risk loci, we performed a two-stage meta-analysis of >9 million genetic variants in 9,784 bipolar disorder patients and 30,471 controls, the largest GWAS of BD to date. In this study, to increase power we used ~2,000 lithium-treated cases with a long-term diagnosis of BD from the Consortium on Lithium Genetics, excess controls, and analytic methods optimized for markers on the Xchromosome. In addition to four known loci, results revealed genome-wide significant associations at two novel loci: an intergenic region on 9p21.3 (rs12553324, p = 5.87×10-9; odds ratio = 1.12) and markers within ERBB2 (rs2517959, p = 4.53×10-9; odds ratio = 1.13). No significant X-chromosome associations were detected and X-linked markers explained very little BD heritability. The results add to a growing list of common autosomal variants involved in BD and illustrate the power of comparing well-characterized cases to an excess of controls in GWAS.
Chromosomal rearrangements of the human MLL (mixed lineage leukemia) gene are associated with high-risk infant, pediatric, adult and therapy-induced acute leukemias. We used long-distance inverse-polymerase chain reaction to characterize the chromosomal rearrangement of individual acute leukemia patients. We present data of the molecular characterization of 1590 MLL-rearranged biopsy samples obtained from acute leukemia patients. The precise localization of genomic breakpoints within the MLL gene and the involved translocation partner genes (TPGs) were determined and novel TPGs identified. All patients were classified according to their gender (852 females and 745 males), age at diagnosis (558 infant, 416 pediatric and 616 adult leukemia patients) and other clinical criteria. Combined data of our study and recently published data revealed a total of 121 different MLL rearrangements, of which 79 TPGs are now characterized at the molecular level. However, only seven rearrangements seem to be predominantly associated with illegitimate recombinations of the MLL gene (~ 90%): AFF1/AF4, MLLT3/AF9, MLLT1/ENL, MLLT10/AF10, ELL, partial tandem duplications (MLL PTDs) and MLLT4/AF6, respectively. The MLL breakpoint distributions for all clinical relevant subtypes (gender, disease type, age at diagnosis, reciprocal, complex and therapy-induced translocations) are presented. Finally, we present the extending network of reciprocal MLL fusions deriving from complex rearrangements.
Reduced social functioning in depression has been explained by different factors. Reduced social connectedness and prosocial motivation may contribute to interpersonal difficulties, particularly in chronic depression. In the present study, we tested whether social connectedness and prosocial motivation are reduced in chronic depression. Forty-seven patients with persistent depression and 49 healthy controls matched for age and gender completed the Inclusion of the Other in the Self Scale (IOS), the Compassionate Love Scale (CLS), the Beck Depression Inventory-II, and the Childhood Trauma Questionnaire. A Multivariate analysis of variance (MANOVA) with IOS and CLS as dependent variables revealed a highly significant difference between both groups. The IOS and the CLS-subscale Close Others were lower in persistent depression, whereas there was no difference in the CLS-subscale Strangers/Humanity. IOS and CLS-Close Others showed significant negative correlations with depressive symptoms. Connectedness to family members as measured by the IOS was negatively correlated with childhood trauma in patients with chronic depression. The results indicate that compassion and perceived social connection are reduced in depressed patients toward close others, but not to others in general. Implications for the treatment of depression are discussed.
Objectives: Current treatments for chronic depression have focused on reducing interpersonal problems and negative affect, but paid little attention to promoting prosocial motivation and positive affect. Following this treatment focus, the objective of the present study was to examine whether the combination of metta (Loving Kindness) group meditation and subsequent tailored individual therapy focusing on kindness towards oneself and others (metta-based therapy, MBT) shows greater improvements in depressive symptoms than a wait list control group in patients with chronic depression. Methods: Forty-eight patients with DSM-5 persistent depressive disorder were randomly assigned to MBT or a wait list control condition. Outcome was assessed after group meditation, after subsequent individual therapy, and at 6-month follow-up. The primary outcome measure was an independent blind rating of depressive symptoms at post-test. Secondary outcome included changes in self-reported depression, behavioral activation, rumination, social functioning, mindfulness, compassion, and clinician-rated emotion regulation. Results: Mixed-design analyses showed significant differences between MBT and WLC in changes from pre- to post-test in clinician-rated and self-rated depression, behavioral activation, rumination, social functioning, mindfulness, and emotion regulation. Most of the changes occurred during group meditation and were associated with large effect sizes. Improvements were maintained at 6-month follow-up. Conclusions: The results provide preliminary support for the effectiveness of MBT in treating chronic depression. Trial Registration: ISRCTN, ISRCTN97264476.
Conventional treatments for mood disorders primarily focus on reducing negative affect, but little on enhancing positive affect. Loving-kindness meditation (LKM) is a traditional meditation practice directly oriented toward enhancing unconditional and positive emotional states of kindness towards oneself and others. We report here two independent and uncontrolled studies carried out at different centers, one in Boston, USA (n = 10), and one in Frankfurt, Germany (n = 8), to examine the potential therapeutic utility of a brief LKM group intervention for symptoms of dysthymia and depression. Results at both centers suggest that LKM was associated with large-sized effects on self-reported symptoms of depression (d = 3.33 and 1.90), negative affect (d = 1.98 and 0.92), and positive affect (d = 1.63 and 0.94). Large effects were also found for clinician-reported changes in depression, rumination and specific positive emotions, and moderate effects for changes in adaptive emotion regulation strategies. The qualitative data analyses provide additional support for the potential clinical utility of the intervention. This proof-of-concept evaluation of LKM as a clinical strategy warrants further investigation.
Long non-coding RNAs (lncRNAs) contribute to cardiac (patho)physiology. Aging is the major risk factor for cardiovascular disease with cardiomyocyte apoptosis as one underlying cause. Here, we report the identification of the aging-regulated lncRNA Sarrah (ENSMUST00000140003) that is anti-apoptotic in cardiomyocytes. Importantly, loss of SARRAH (OXCT1-AS1) in human engineered heart tissue results in impaired contractile force development. SARRAH directly binds to the promoters of genes downregulated after SARRAH silencing via RNA-DNA triple helix formation and cardiomyocytes lacking the triple helix forming domain of Sarrah show an increase in apoptosis. One of the direct SARRAH targets is NRF2, and restoration of NRF2 levels after SARRAH silencing partially rescues the reduction in cell viability. Overexpression of Sarrah in mice shows better recovery of cardiac contractile function after AMI compared to control mice. In summary, we identified the anti-apoptotic evolutionary conserved lncRNA Sarrah, which is downregulated by aging, as a regulator of cardiomyocyte survival.
Fragment mass distributions for fission after full momentum transfer were measured in the reactions of 30Si,34,36 S,31P,40Ar + 238U at bombarding energies around the Coulomb barrier. Mass distributions change significantly as a function of incident beam energy. The asymmetric fission probability increases at sub-barrier energy. The phenomenon is interpreted as an enhanced quasifission probability owing to orientation effects on fusion and/or quasifission. The evaporation residue (ER) cross sections were measured in the reactions of 30Si + 238U and 34S + 238U to obtain information on fusion. In the latter reaction, significant suppression of fusion was implied. This suggests that fission events different from compound nucleus are included in the masssymmetric fragments. The results are supported by a model calculation based on a dynamical calculation using Langevin equation, in which the mass distribution for fusion-fission and quasifission fragments are separately determined.
Background: Biological psychiatry aims to understand mental disorders in terms of altered neurobiological pathways. However, for one of the most prevalent and disabling mental disorders, Major Depressive Disorder (MDD), patients only marginally differ from healthy individuals on the group-level. Whether Precision Psychiatry can solve this discrepancy and provide specific, reliable biomarkers remains unclear as current Machine Learning (ML) studies suffer from shortcomings pertaining to methods and data, which lead to substantial over-as well as underestimation of true model accuracy.
Methods: Addressing these issues, we quantify classification accuracy on a single-subject level in N=1,801 patients with MDD and healthy controls employing an extensive multivariate approach across a comprehensive range of neuroimaging modalities in a well-curated cohort, including structural and functional Magnetic Resonance Imaging, Diffusion Tensor Imaging as well as a polygenic risk score for depression.
Findings Training and testing a total of 2.4 million ML models, we find accuracies for diagnostic classification between 48.1% and 62.0%. Multimodal data integration of all neuroimaging modalities does not improve model performance. Similarly, training ML models on individuals stratified based on age, sex, or remission status does not lead to better classification. Even under simulated conditions of perfect reliability, performance does not substantially improve. Importantly, model error analysis identifies symptom severity as one potential target for MDD subgroup identification.
Interpretation: Although multivariate neuroimaging markers increase predictive power compared to univariate analyses, single-subject classification – even under conditions of extensive, best-practice Machine Learning optimization in a large, harmonized sample of patients diagnosed using state-of-the-art clinical assessments – does not reach clinically relevant performance. Based on this evidence, we sketch a course of action for Precision Psychiatry and future MDD biomarker research.
Fission fragment mass distributions were measured in heavy-ion induced fissions using 238U target nucleus. The measured mass distributions changed drastically with incident energy. The results are explained by a change of the ratio between fusion and qasifission with nuclear orientation. A calculation based on a fluctuation dissipation model reproduced the mass distributions and their incident energy dependence. Fusion probability was determined in the analysis, and the values were consistent with those determined from the evaporation residue cross sections.