Down-Regulation of Glucose-Regulated Protein (GRP) 78 Potentiates Cytotoxic Effect of Celecoxib in Human Urothelial Carcinoma Cells

Celecoxib is a selective cyclooxygenase-2 (COX-2) inhibitor that has been reported to elicit anti-proliferative response in various tumors. In this study, we aim to investigate the antitumor effect of celecoxib on urothelial carcinoma (UC) cells and the role endoplasmic reticulum (ER) stress plays in celecoxib-induced cytotoxicity. The cytotoxic effects were measured by MTT assay and flow cytometry. The cell cycle progression and ER stress-associated molecules were examined by Western blot and flow cytometry. Moreover, the cytotoxic effects of celecoxib combined with glucose-regulated protein (GRP) 78 knockdown (siRNA), (−)-epigallocatechin gallate (EGCG) or MG132 were assessed. We demonstrated that celecoxib markedly reduces the cell viability and causes apoptosis in human UC cells through cell cycle G1 arrest. Celecoxib possessed the ability to activate ER stress-related chaperones (IRE-1α and GRP78), caspase-4, and CCAAT/enhancer binding protein homologous protein (CHOP), which were involved in UC cell apoptosis. Down-regulation of GRP78 by siRNA, co-treatment with EGCG (a GRP78 inhibitor) or with MG132 (a proteasome inhibitor) could enhance celecoxib-induced apoptosis. We concluded that celecoxib induces cell cycle G1 arrest, ER stress, and eventually apoptosis in human UC cells. The down-regulation of ER chaperone GRP78 by siRNA, EGCG, or proteosome inhibitor potentiated the cytotoxicity of celecoxib in UC cells. These findings provide a new treatment strategy against UC

Impact of Diabetes on Postinfarction Heart Failure and Left Ventricular Remodeling

Diabetes mellitus, the metabolic syndrome, and the underlying insulin resistance are increasingly associated with diastolic dysfunction and reduced stress tolerance. The poor prognosis associated with heart failure in patients with diabetes after myocardial infarction is likely attributable to many factors, important among which is the metabolic impact from insulin resistance and hyperglycemia on the regulation of microvascular perfusion and energy generation in the cardiac myocyte. This review summarizes epidemiologic, pathophysiologic, diagnostic, and therapeutic data related to diabetes and heart failure in acute myocardial infarction and discusses novel perceptions and strategies that hold promise for the future and deserve further investigation

Contribution of copy number variants to schizophrenia from a genome-wide study of 41,321 subjects

Copy number variants (CNVs) have been strongly implicated in the genetic etiology of schizophrenia (SCZ). However, genome-wide investigation of the contribution of CNV to risk has been hampered by limited sample sizes. We sought to address this obstacle by applying a centralized analysis pipeline to a SCZ cohort of 21,094 cases and 20,227 controls. A global enrichment of CNV burden was observed in cases (OR=1.11, P=5.7×10−15), which persisted after excluding loci implicated in previous studies (OR=1.07, P=1.7 ×10−6). CNV burden was enriched for genes associated with synaptic function (OR = 1.68, P = 2.8 ×10−11) and neurobehavioral phenotypes in mouse (OR = 1.18, P= 7.3 ×10−5). Genome-wide significant evidence was obtained for eight loci, including 1q21.1, 2p16.3 (NRXN1), 3q29, 7q11.2, 15q13.3, distal 16p11.2, proximal 16p11.2 and 22q11.2. Suggestive support was found for eight additional candidate susceptibility and protective loci, which consisted predominantly of CNVs mediated by non-allelic homologous recombination

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

Analysis of shared heritability in common disorders of the brain

ience, this issue p. eaap8757 Structured Abstract INTRODUCTION Brain disorders may exhibit shared symptoms and substantial epidemiological comorbidity, inciting debate about their etiologic overlap. However, detailed study of phenotypes with different ages of onset, severity, and presentation poses a considerable challenge. Recently developed heritability methods allow us to accurately measure correlation of genome-wide common variant risk between two phenotypes from pools of different individuals and assess how connected they, or at least their genetic risks, are on the genomic level. We used genome-wide association data for 265,218 patients and 784,643 control participants, as well as 17 phenotypes from a total of 1,191,588 individuals, to quantify the degree of overlap for genetic risk factors of 25 common brain disorders. RATIONALE Over the past century, the classification of brain disorders has evolved to reflect the medical and scientific communities' assessments of the presumed root causes of clinical phenomena such as behavioral change, loss of motor function, or alterations of consciousness. Directly observable phenomena (such as the presence of emboli, protein tangles, or unusual electrical activity patterns) generally define and separate neurological disorders from psychiatric disorders. Understanding the genetic underpinnings and categorical distinctions for brain disorders and related phenotypes may inform the search for their biological mechanisms. RESULTS Common variant risk for psychiatric disorders was shown to correlate significantly, especially among attention deficit hyperactivity disorder (ADHD), bipolar disorder, major depressive disorder (MDD), and schizophrenia. By contrast, neurological disorders appear more distinct from one another and from the psychiatric disorders, except for migraine, which was significantly correlated to ADHD, MDD, and Tourette syndrome. We demonstrate that, in the general population, the personality trait neuroticism is significantly correlated with almost every psychiatric disorder and migraine. We also identify significant genetic sharing between disorders and early life cognitive measures (e.g., years of education and college attainment) in the general population, demonstrating positive correlation with several psychiatric disorders (e.g., anorexia nervosa and bipolar disorder) and negative correlation with several neurological phenotypes (e.g., Alzheimer's disease and ischemic stroke), even though the latter are considered to result from specific processes that occur later in life. Extensive simulations were also performed to inform how statistical power, diagnostic misclassification, and phenotypic heterogeneity influence genetic correlations. CONCLUSION The high degree of genetic correlation among many of the psychiatric disorders adds further evidence that their current clinical boundaries do not reflect distinct underlying pathogenic processes, at least on the genetic level. This suggests a deeply interconnected nature for psychiatric disorders, in contrast to neurological disorders, and underscores the need to refine psychiatric diagnostics. Genetically informed analyses may provide important "scaffolding" to support such restructuring of psychiatric nosology, which likely requires incorporating many levels of information. By contrast, we find limited evidence for widespread common genetic risk sharing among neurological disorders or across neurological and psychiatric disorders. We show that both psychiatric and neurological disorders have robust correlations with cognitive and personality measures. Further study is needed to evaluate whether overlapping genetic contributions to psychiatric pathology may influence treatment choices. Ultimately, such developments may pave the way toward reduced heterogeneity and improved diagnosis and treatment of psychiatric disorders

Impact of opioid-free analgesia on pain severity and patient satisfaction after discharge from surgery: multispecialty, prospective cohort study in 25 countries

Background: Balancing opioid stewardship and the need for adequate analgesia following discharge after surgery is challenging. This study aimed to compare the outcomes for patients discharged with opioid versus opioid-free analgesia after common surgical procedures.Methods: This international, multicentre, prospective cohort study collected data from patients undergoing common acute and elective general surgical, urological, gynaecological, and orthopaedic procedures. The primary outcomes were patient-reported time in severe pain measured on a numerical analogue scale from 0 to 100% and patient-reported satisfaction with pain relief during the first week following discharge. Data were collected by in-hospital chart review and patient telephone interview 1 week after discharge.Results: The study recruited 4273 patients from 144 centres in 25 countries; 1311 patients (30.7%) were prescribed opioid analgesia at discharge. Patients reported being in severe pain for 10 (i.q.r. 1-30)% of the first week after discharge and rated satisfaction with analgesia as 90 (i.q.r. 80-100) of 100. After adjustment for confounders, opioid analgesia on discharge was independently associated with increased pain severity (risk ratio 1.52, 95% c.i. 1.31 to 1.76; P < 0.001) and re-presentation to healthcare providers owing to side-effects of medication (OR 2.38, 95% c.i. 1.36 to 4.17; P = 0.004), but not with satisfaction with analgesia (beta coefficient 0.92, 95% c.i. -1.52 to 3.36; P = 0.468) compared with opioid-free analgesia. Although opioid prescribing varied greatly between high-income and low- and middle-income countries, patient-reported outcomes did not.Conclusion: Opioid analgesia prescription on surgical discharge is associated with a higher risk of re-presentation owing to side-effects of medication and increased patient-reported pain, but not with changes in patient-reported satisfaction. Opioid-free discharge analgesia should be adopted routinely

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

Which method is best for the induction of labour?: A systematic review, network meta-analysis and cost-effectiveness analysis

Background: More than 150,000 pregnant women in England and Wales have their labour induced each year. Multiple pharmacological, mechanical and complementary methods are available to induce labour. Objective: To assess the relative effectiveness, safety and cost-effectiveness of labour induction methods and, data permitting, effects in different clinical subgroups. Methods: We carried out a systematic review using Cochrane methods. The Cochrane Pregnancy and Childbirth Group’s Trials Register was searched (March 2014). This contains over 22,000 reports of controlled trials (published from 1923 onwards) retrieved from weekly searches of OVID MEDLINE (1966 to current); Cochrane Central Register of Controlled Trials (The Cochrane Library); EMBASE (1982 to current); Cumulative Index to Nursing and Allied Health Literature (1984 to current); ClinicalTrials.gov; the World Health Organization International Clinical Trials Registry Portal; and hand-searching of relevant conference proceedings and journals. We included randomised controlled trials examining interventions to induce labour compared with placebo, no treatment or other interventions in women eligible for third-trimester induction. We included outcomes relating to efficacy, safety and acceptability to women. In addition, for the economic analysis we searched the Database of Abstracts of Reviews of Effects, and Economic Evaluations Databases, NHS Economic Evaluation Database and the Health Technology Assessment database. We carried out a network meta-analysis (NMA) using all of the available evidence, both direct and indirect, to produce estimates of the relative effects of each treatment compared with others in a network. We developed a de novo decision tree model to estimate the cost-effectiveness of various methods. The costs included were the intervention and other hospital costs incurred (price year 2012–13). We reviewed the literature to identify preference-based utilities for the health-related outcomes in the model. We calculated incremental cost-effectiveness ratios, expected costs, utilities and net benefit. We represent uncertainty in the optimal intervention using cost-effectiveness acceptability curves. Results: We identified 1190 studies; 611 were eligible for inclusion. The interventions most likely to achieve vaginal delivery (VD) within 24 hours were intravenous oxytocin with amniotomy [posterior rank 2; 95% credible intervals (CrIs) 1 to 9] and higher-dose (≥ 50 μg) vaginal misoprostol (rank 3; 95% CrI 1 to 6). Compared with placebo, several treatments reduced the odds of caesarean section, but we observed considerable uncertainty in treatment rankings. For uterine hyperstimulation, double-balloon catheter had the highest probability of being among the best three treatments, whereas vaginal misoprostol (≥ 50 μg) was most likely to increase the odds of excessive uterine activity. For other safety outcomes there were insufficient data or there was too much uncertainty to identify which treatments performed ‘best’. Few studies collected information on women’s views. Owing to incomplete reporting of the VD within 24 hours outcome, the cost-effectiveness analysis could compare only 20 interventions. The analysis suggested that most interventions have similar utility and differ mainly in cost. With a caveat of considerable uncertainty, titrated (low-dose) misoprostol solution and buccal/sublingual misoprostol had the highest likelihood of being cost-effective. Limitations: There was considerable uncertainty in findings and there were insufficient data for some planned subgroup analyses. Conclusions: Overall, misoprostol and oxytocin with amniotomy (for women with favourable cervix) is more successful than other agents in achieving VD within 24 hours. The ranking according to safety of different methods was less clear. The cost-effectiveness analysis suggested that titrated (low-dose) oral misoprostol solution resulted in the highest utility, whereas buccal/sublingual misoprostol had the lowest cost. There was a high degree of uncertainty as to the most cost-effective intervention

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