All-Trans-Retinoic Acid Combined With Valproic Acid Can Promote Differentiation in Myeloid Leukemia Cells by an Autophagy Dependent Mechanism

Acute myeloid leukemia (AML) is an aggressive blood cancer with an overall survival of 30%. One form of AML, acute promyelocytic leukemia (APL) has become more than 90% curable with differentiation therapy, consisting of all-trans-retinoic acid (ATRA) and arsenic trioxide (ATO). Application of differentiation therapy to other AML subtypes would be a major treatment advance. Recent studies have indicated that autophagy plays a key role in the differentiation of ATRA-responsive APL cells. In this study, we have investigated whether differentiation could be enhanced in ATRA resistant cells by promoting autophagy induction with valproic acid (VPA). ATRA sensitive (NB4) and resistant leukemia cells (NB4R and THP-1) were co-treated with ATRA and valproic acid, followed by assessment of autophagy and differentiation. The combination of VPA and ATRA induced autophagic flux and promoted differentiation in ATRA-sensitive and -resistant cell lines. shRNA knockdown of ATG7 and TFEB autophagy regulators impaired both autophagy and differentiation, demonstrating the importance of autophagy in the combination treatment. These data suggest that ATRA combined with valproic acid can promote differentiation in myeloid leukemia cells by mechanism involving autophagy

Genetic correlation between amyotrophic lateral sclerosis and schizophrenia

A. Palotie on työryhmän Schizophrenia Working Grp Psychiat jäsen.We have previously shown higher-than-expected rates of schizophrenia in relatives of patients with amyotrophic lateral sclerosis (ALS), suggesting an aetiological relationship between the diseases. Here, we investigate the genetic relationship between ALS and schizophrenia using genome-wide association study data from over 100,000 unique individuals. Using linkage disequilibrium score regression, we estimate the genetic correlation between ALS and schizophrenia to be 14.3% (7.05-21.6; P = 1 x 10(-4)) with schizophrenia polygenic risk scores explaining up to 0.12% of the variance in ALS (P = 8.4 x 10(-7)). A modest increase in comorbidity of ALS and schizophrenia is expected given these findings (odds ratio 1.08-1.26) but this would require very large studies to observe epidemiologically. We identify five potential novel ALS-associated loci using conditional false discovery rate analysis. It is likely that shared neurobiological mechanisms between these two disorders will engender novel hypotheses in future preclinical and clinical studies.Peer reviewe

Dissecting the Shared Genetic Architecture of Suicide Attempt, Psychiatric Disorders, and Known Risk Factors

Background Suicide is a leading cause of death worldwide, and nonfatal suicide attempts, which occur far more frequently, are a major source of disability and social and economic burden. Both have substantial genetic etiology, which is partially shared and partially distinct from that of related psychiatric disorders. Methods We conducted a genome-wide association study (GWAS) of 29,782 suicide attempt (SA) cases and 519,961 controls in the International Suicide Genetics Consortium (ISGC). The GWAS of SA was conditioned on psychiatric disorders using GWAS summary statistics via multitrait-based conditional and joint analysis, to remove genetic effects on SA mediated by psychiatric disorders. We investigated the shared and divergent genetic architectures of SA, psychiatric disorders, and other known risk factors. Results Two loci reached genome-wide significance for SA: the major histocompatibility complex and an intergenic locus on chromosome 7, the latter of which remained associated with SA after conditioning on psychiatric disorders and replicated in an independent cohort from the Million Veteran Program. This locus has been implicated in risk-taking behavior, smoking, and insomnia. SA showed strong genetic correlation with psychiatric disorders, particularly major depression, and also with smoking, pain, risk-taking behavior, sleep disturbances, lower educational attainment, reproductive traits, lower socioeconomic status, and poorer general health. After conditioning on psychiatric disorders, the genetic correlations between SA and psychiatric disorders decreased, whereas those with nonpsychiatric traits remained largely unchanged. Conclusions Our results identify a risk locus that contributes more strongly to SA than other phenotypes and suggest a shared underlying biology between SA and known risk factors that is not mediated by psychiatric disorders.Peer reviewe

Spectrum of mutational signatures in T-cell lymphoma reveals a key role for UV radiation in cutaneous T-cell lymphoma

Funder: Galderma; doi: http://dx.doi.org/10.13039/501100009754Funder: NIHR-BRC Cambridge core grantFunder: National Institute for Health Research; doi: http://dx.doi.org/10.13039/501100000272Funder: NHS EnglandAbstract: T-cell non-Hodgkin’s lymphomas develop following transformation of tissue resident T-cells. We performed a meta-analysis of whole exome sequencing data from 403 patients with eight subtypes of T-cell non-Hodgkin’s lymphoma to identify mutational signatures and associated recurrent gene mutations. Signature 1, indicative of age-related deamination, was prevalent across all T-cell lymphomas, reflecting the derivation of these malignancies from memory T-cells. Adult T-cell leukemia-lymphoma was specifically associated with signature 17, which was found to correlate with the IRF4 K59R mutation that is exclusive to Adult T-cell leukemia-lymphoma. Signature 7, implicating UV exposure was uniquely identified in cutaneous T-cell lymphoma (CTCL), contributing 52% of the mutational burden in mycosis fungoides and 23% in Sezary syndrome. Importantly this UV signature was observed in CD4 + T-cells isolated from the blood of Sezary syndrome patients suggesting extensive re-circulation of these T-cells through skin and blood. Analysis of non-Hodgkin’s T-cell lymphoma cases submitted to the national 100,000 WGS project confirmed that signature 7 was only identified in CTCL strongly implicating UV radiation in the pathogenesis of cutaneous T-cell lymphoma

Interaction Testing and Polygenic Risk Scoring to Estimate the Association of Common Genetic Variants with Treatment Resistance in Schizophrenia

Importance: About 20% to 30% of people with schizophrenia have psychotic symptoms that do not respond adequately to first-line antipsychotic treatment. This clinical presentation, chronic and highly disabling, is known as treatment-resistant schizophrenia (TRS). The causes of treatment resistance and their relationships with causes underlying schizophrenia are largely unknown. Adequately powered genetic studies of TRS are scarce because of the difficulty in collecting data from well-characterized TRS cohorts. Objective: To examine the genetic architecture of TRS through the reassessment of genetic data from schizophrenia studies and its validation in carefully ascertained clinical samples. Design, Setting, and Participants: Two case-control genome-wide association studies (GWASs) of schizophrenia were performed in which the case samples were defined as individuals with TRS (n = 10501) and individuals with non-TRS (n = 20325). The differences in effect sizes for allelic associations were then determined between both studies, the reasoning being such differences reflect treatment resistance instead of schizophrenia. Genotype data were retrieved from the CLOZUK and Psychiatric Genomics Consortium (PGC) schizophrenia studies. The output was validated using polygenic risk score (PRS) profiling of 2 independent schizophrenia cohorts with TRS and non-TRS: a prevalence sample with 817 individuals (Cardiff Cognition in Schizophrenia [CardiffCOGS]) and an incidence sample with 563 individuals (Genetics Workstream of the Schizophrenia Treatment Resistance and Therapeutic Advances [STRATA-G]). Main Outcomes and Measures: GWAS of treatment resistance in schizophrenia. The results of the GWAS were compared with complex polygenic traits through a genetic correlation approach and were used for PRS analysis on the independent validation cohorts using the same TRS definition. Results: The study included a total of 85490 participants (48635 [56.9%] male) in its GWAS stage and 1380 participants (859 [62.2%] male) in its PRS validation stage. Treatment resistance in schizophrenia emerged as a polygenic trait with detectable heritability (1% to 4%), and several traits related to intelligence and cognition were found to be genetically correlated with it (genetic correlation, 0.41-0.69). PRS analysis in the CardiffCOGS prevalence sample showed a positive association between TRS and a history of taking clozapine (r2 = 2.03%; P =.001), which was replicated in the STRATA-G incidence sample (r2 = 1.09%; P =.04). Conclusions and Relevance: In this GWAS, common genetic variants were differentially associated with TRS, and these associations may have been obscured through the amalgamation of large GWAS samples in previous studies of broadly defined schizophrenia. Findings of this study suggest the validity of meta-analytic approaches for studies on patient outcomes, including treatment resistance

Estimation of Genetic Correlation via Linkage Disequilibrium Score Regression and Genomic Restricted Maximum Likelihood

J. Lönnqvist on työryhmän Psychiat Genomics Consortium jäsen.Genetic correlation is a key population parameter that describes the shared genetic architecture of complex traits and diseases. It can be estimated by current state-of-art methods, i.e., linkage disequilibrium score regression (LDSC) and genomic restricted maximum likelihood (GREML). The massively reduced computing burden of LDSC compared to GREML makes it an attractive tool, although the accuracy (i.e., magnitude of standard errors) of LDSC estimates has not been thoroughly studied. In simulation, we show that the accuracy of GREML is generally higher than that of LDSC. When there is genetic heterogeneity between the actual sample and reference data from which LD scores are estimated, the accuracy of LDSC decreases further. In real data analyses estimating the genetic correlation between schizophrenia (SCZ) and body mass index, we show that GREML estimates based on similar to 150,000 individuals give a higher accuracy than LDSC estimates based on similar to 400,000 individuals (from combinedmeta-data). A GREML genomic partitioning analysis reveals that the genetic correlation between SCZ and height is significantly negative for regulatory regions, which whole genome or LDSC approach has less power to detect. We conclude that LDSC estimates should be carefully interpreted as there can be uncertainty about homogeneity among combined meta-datasets. We suggest that any interesting findings from massive LDSC analysis for a large number of complex traits should be followed up, where possible, with more detailed analyses with GREML methods, even if sample sizes are lesser.Peer reviewe

Assessment of the effect of combining autophagy inducers on chemosensitivity in CT26 murine colorectal cells.

<p>(A) The induction of autophagy in CT26 cells, following lithium treatment (30 mM) for 24 hours, was assessed with the Cyto-ID autophagy detection kit. (B) Viable cells following treatment with lithium alone or in combination with 5-fluorouracil (5-FU) (20 μM) for 24 hours were counted and equal numbers reseeded in triplicate, in the absence of drug. (i) Cells were allowed to grow for 14 days, then were fixed and stained and colonies quantified using the Odyssey Infrared Imaging System (Li-COR). (ii) Data is presented graphically as the mean +/- SEM of three independent experiments. <i>Asterisks</i> indicate a significant difference in the number of colonies formed in combination treated cells compared to single agent treated cells (* p < 0.05) (unpaired <i>t</i>-test). (C) Morphological features of CT26 cells were examined following treatment for 24 hours. Panels to the right show the morphology induced in response to lithium alone (upper) and in combination with 5-FU (lower). Lower left panel shows morphology in cells treated with 5-FU alone. Arrows indicate the accumulation of vesicles in lithium, 5-FU and combination treated cells (Magnification 40x). (D) Cells were treated with either lithium (30 mM), 5-FU (20 μM) or a combination of both for 24 hours and autophagy levels assessed with the Cyto-ID autophagy detection kit. Representative image of FACS analysis with insert showing corresponding mean fluorescence intensity (n = 3).</p

Evaluation of the effects of Lithium and Rapamycin on autophagy induction in esophageal cancer cells.

<p>(A) KYSE450 cells were treated with lithium chloride (lithium) (10–30 mM) or rapamycin (100–300 nM) for 24 and 48 hours. Cells were assessed for autophagy induction 24 hours after treatment with lithium (i) or rapamycin (ii) with the Cyto-ID autophagy detection kit. Panels on the left show representative images of FACS analysis, with panels to the right showing corresponding mean fluorescence intensity. Data shown here is representative of three independent experiments. (B) Western blot analysis of LC3 expression in cells treated with lithium (i) or rapamycin (ii) for 24 hours. LC3I and LC3II bands were quantified using the Odyssey Infrared Imaging System (Li-COR), normalized to β-actin and presented as integrated intensities. (C) Morphological features of KYSE450 cells following lithium (30 mM, center panel) or rapamycin (300 nM, right hand panel) treatment for 24 hours. Black arrows indicate accumulation of vesicles in both lithium and rapamycin treated cells, red arrows denote peripheral vesicle accumulation (Magnification 40x). (D) Cells were pretreated with chloroquine (10 μM) for two hours, prior to treatment with lithium (10 mM) (i) or rapamycin (100 nM) (ii) for 48 hours and autophagy levels assessed with the Cyto-ID autophagy detection kit. Representative images of FACS analysis are shown (n = 3), with inset bar graphs showing corresponding mean fluorescence intensity (* p < 0.05). E Viable cells following treatment with either lithium or rapamycin for 48 hours were counted and equal numbers (1,500 cells per well) reseeded in triplicate, in the absence of drug. Cells were allowed to grow for 14 days, then were fixed and stained and colony regrowth assessed.</p