Autosomal genetic variation is associated with DNA methylation in regions variably escaping X-chromosome inactivation

This is the final version of the article. Available from Springer Nature via the DOI in this record.Raw data were submitted to the European Genome-phenome Archive (EGA) under accession EGAS00001001077.X-chromosome inactivation (XCI), i.e., the inactivation of one of the female X chromosomes, restores equal expression of X-chromosomal genes between females and males. However, ~10% of genes show variable degrees of escape from XCI between females, although little is known about the causes of variable XCI. Using a discovery data-set of 1867 females and 1398 males and a replication sample of 3351 females, we show that genetic variation at three autosomal loci is associated with female-specific changes in X-chromosome methylation. Through cis-eQTL expression analysis, we map these loci to the genes SMCHD1/METTL4, TRIM6/HBG2, and ZSCAN9. Low-expression alleles of the loci are predominantly associated with mild hypomethylation of CpG islands near genes known to variably escape XCI, implicating the autosomal genes in variable XCI. Together, these results suggest a genetic basis for variable escape from XCI and highlight the potential of a population genomics approach to identify genes involved in XCI.This research was financially supported by several institutions: BBMRI-NL, a Research Infrastructure financed by the Dutch government (NWO, numbers 184.021.007 and 184.033.111); the UK Medical Research Council; Wellcome (www.wellcome.ac.uk; [grant number 102215/2/13/2 to ALSPAC]); the University of Bristol to ALSPAC; the UK Economic and Social Research Council (www.esrc.ac.uk; [ES/N000498/1] to CR); the UK Medical Research Council (www.mrc.ac.uk; grant numbers [MC_UU_12013/1, MC_UU_12013/2 to JLM, CR]); the Helmholtz Zentrum München – German Research Center for Environmental Health, which is funded by the German Federal Ministry of Education and Research (BMBF) and by the State of Bavaria; the Munich Center of Health Sciences (MC-Health), Ludwig-Maximilians-Universität, as part of LMUinnovativ; the Wellcome Trust, Medical Research Council, European Union (EU), and the National Institute for Health Research (NIHR)- funded BioResource, Clinical Research Facility, and Biomedical Research Centre based at Guy’s and St Thomas’ NHS Foundation Trust in partnership with King’s College London

At-risk individuals display altered brain activity following stress

Stress is a major risk factor for almost all psychiatric disorders, however, the underlying neurobiological mechanisms remain largely elusive. In healthy individuals, a successful stress response involves an adequate neuronal adaptation to a changing environment. This adaptive response may be dysfunctional in vulnerable individuals, potentially contributing to the development of psychopathology. In the current study, we investigated brain responses to emotional stimuli following stress in healthy controls and at-risk individuals. An fMRI study was conducted in healthy male controls (N = 39) and unaffected healthy male siblings of schizophrenia patients (N = 39) who are at increased risk for the development of a broad range of psychiatric disorders. Brain responses to pictures from the International Affective Picture System (IAPS) were measured 33 min after exposure to stress induced by the validated trier social stress test (TSST) or a control condition. Stress-induced levels of cortisol, alpha-amylase, and subjective stress were comparable in both groups. Yet, stress differentially affected brain responses of schizophrenia siblings versus controls. Specifically, control subjects, but not schizophrenia siblings, showed reduced brain activity in key nodes of the default mode network (PCC/precuneus and mPFC) and salience network (anterior insula) as well as the STG, MTG, MCC, vlPFC, precentral gyrus, and cerebellar vermis in response to all pictures following stress. These results indicate that even in the absence of a psychiatric disorder, at-risk individuals display abnormal functional activation following stress, which in turn may increase their vulnerability and risk for adverse outcomes

Improved RMR rock mass classification using artificial intelligence algorithms

Rock mass classification systems such as rock mass rating (RMR) are very reliable means to provide information about the quality of rocks surrounding a structure as well as to propose suitable support systems for unstable regions. Many correlations have been proposed to relate measured quantities such as wave velocity to rock mass classification systems to limit the associated time and cost of conducting the sampling and mechanical tests conventionally used to calculate RMR values. However, these empirical correlations have been found to be unreliable, as they usually overestimate or underestimate the RMR value. The aim of this paper is to compare the results of RMR classification obtained from the use of empirical correlations versus machine-learning methodologies based on artificial intelligence algorithms. The proposed methods were verified based on two case studies located in northern Iran. Relevance vector regression (RVR) and support vector regression (SVR), as two robust machine-learning methodologies, were used to predict the RMR for tunnel host rocks. RMR values already obtained by sampling and site investigation at one tunnel were taken into account as the output of the artificial networks during training and testing phases. The results reveal that use of empirical correlations overestimates the predicted RMR values. RVR and SVR, however, showed more reliable results, and are therefore suggested for use in RMR classification for design purposes of rock structures

Inhibition of Bcl-2 family members sensitises soft tissue leiomyosarcomas to chemotherapy

BACKGROUND: Leiomyosarcoma is an aggressive soft tissue sarcoma with a 5-year survival rate of 15 to 60%. Treatment options for inoperable or metastatic patients are limited owing to frequent resistance of tumours to chemotherapy and radiation. In this study, we hypothesised that antiapoptotic Bcl-2 family proteins might contribute to leiomyosarcoma chemoresistance and therefore inhibition of Bcl-2 family proteins might sensitise leiomyosarcomas to conventional chemotherapy. METHODS: Expression of the Bcl-2 family proteins Bcl-xL, Bcl-w and Bcl-2 was investigated using immunohistochemistry on a tissue microarray containing 43 leiomyosarcomas. Furthermore, we investigated whether ABT-737, a potent BH3 mimetic, sensitises leiomyosarcoma cells to doxorubicin treatment in vitro. RESULTS: Seventy-seven per cent, 84% and 42% of leiomyosarcomas demonstrated high expression of Bcl-2, Bcl-xL and Bcl-w, respectively. Single-agent treatment with ABT-737 resulted in a minor reduction of cell viability. However, combination treatment of ABT-737 and doxorubicin revealed synergism in all four cell lines, by inducing apoptosis. CONCLUSIONS: In conclusion, Bcl-2 family proteins contribute to soft tissue leiomyosarcoma chemoresistance. Antiapoptotic proteins are highly expressed in leiomyosarcoma of soft tissue, and inhibition of these proteins using a BH3 mimetic increases leiomyosarcoma sensitivity to doxorubicin