Stem cell plasticity, acetylation of H3K14, and de novo gene activation rely on KAT7.

In the conventional model of transcriptional activation, transcription factors bind to response elements and recruit co-factors, including histone acetyltransferases. Contrary to this model, we show that the histone acetyltransferase KAT7 (HBO1/MYST2) is required genome wide for histone H3 lysine 14 acetylation (H3K14ac). Examining neural stem cells, we find that KAT7 and H3K14ac are present not only at transcribed genes but also at inactive genes, intergenic regions, and in heterochromatin. KAT7 and H3K14ac were not required for the continued transcription of genes that were actively transcribed at the time of loss of KAT7 but indispensable for the activation of repressed genes. The absence of KAT7 abrogates neural stem cell plasticity, diverse differentiation pathways, and cerebral cortex development. Re-expression of KAT7 restored stem cell developmental potential. Overexpression of KAT7 enhanced neuron and oligodendrocyte differentiation. Our data suggest that KAT7 prepares chromatin for transcriptional activation and is a prerequisite for gene activation.

The Use of Subgroup Disproportionality Analyses to Explore the Sensitivity of a Global Database of Individual Case Safety Reports to Known Pharmacogenomic Risk Variants Common in Japan.

INTRODUCTION: Genetic variations of enzymes that affect the pharmacokinetics and hence effects of medications differ between ethnicities, resulting in variation in the risk of adverse drug reactions (ADR) between different populations. Previous work has demonstrated that risk-group considerations can be incorporated into approaches of statistical signal detection. It is unknown whether databases of individual case safety reports (ICSRs) are sensitive to pharmacogenomic differences between populations. OBJECTIVE: The aim of this study was to explore the sensitivity of a global database of ICSRs to known pharmacogenomic risk variants common in Japan. METHODS: The data source was VigiBase, the global database of ICSRs, including all reports entered in the version frozen on 5 January 2020. Subgroup disproportionality analysis was used to compare ICSRs of two subgroups, Japan and rest of world (RoW). Reports for UGT1A1-metabolized irinotecan and the CYP2C19-metabolized drugs voriconazole, escitalopram and clopidogrel were selected for comparison between the subgroups based upon known genetic polymorphisms with high prevalence in Japan. Contrast between the subgroups was quantified by IC delta [Formula: see text]), a robust shrinkage observed-to-expected (OE) ratio on a log scale. Harmonic mean p values (HMP) were calculated for each drug to evaluate whether a list of pre-specified ADRs were collectively significantly over- (or under-)reported as hypothesized. Daily drug dosages were calculated for ICSRs with sufficient information, and dose distributions were compared between Japan and RoW and related to differences in regionally approved doses. RESULTS: The predictions of over-reporting patterns for specific ADRs were observed and confirmed in bootstrap HMP analyses (p = 0.004 for irinotecan and p < 0.001 for each of voriconazole, escitalopram and clopidogrel) and compared with similar drugs with different metabolic pathways. The impact of proactive regulatory action, such as recommended dosing and therapeutic drug monitoring (TDM), was also observable within the global database. For irinotecan and escitalopram, there was evidence of use of lower dosages as recommended in the Japanese labels; for voriconazole, there was evidence of use of TDM with an over-reporting of terms related to drug level measurements and an under-reporting of liver toxicity. CONCLUSIONS: Pharmaco-ethnic vulnerabilities caused by pharmacogenomic differences between populations may contribute to differences in ADR reporting between countries in a global database of ICSRs. Regional analyses within a global database can inform on the effectiveness of local risk minimization measures and should be leveraged to catalyse the conversion of real-world usage into safer use of drugs in ethnically tailored ways.

Cellular Microenvironment Stiffness Regulates Eicosanoid Production and Signaling Pathways.

Pathological changes in the biomechanical environment are implicated in the progression of idiopathic pulmonary fibrosis (IPF). Stiffened matrix augments fibroblast proliferation and differentiation and activates TGF-ß1 (transforming growth factor-ß1). Stiffened matrix impairs the synthesis of the antifibrogenic lipid mediator prostaglandin E2 (PGE2) and reduces the expression of the rate-limiting prostanoid biosynthetic enzyme cyclooxygenase-2 (COX-2). We now show that prostaglandin E synthase (PTGES), the final enzyme in the PGE2 biosynthetic pathway, is expressed at lower levels in the lungs of patients with IPF. We also show substantial induction of COX-2, PTGES, prostaglandin E receptor 4 (EP4), and cytosolic phospholipase A2 (cPLA2) expression in human lung fibroblasts cultured in soft collagen hydrogels or in spheroids compared with conventional culture on stiff plastic culture plates. Induction of COX-2, cPLA2, and PTGES expression in spheroid cultures was moderately inhibited by the p38 mitogen-activated protein kinase inhibitor SB203580. The induction of prostanoid biosynthetic enzyme expression was accompanied by an increase in PGE2 levels only in non-IPF-derived fibroblast spheroids. Our study reveals an extensive dysregulation of prostanoid biosynthesis and signaling pathways in IPF-derived fibroblasts, which are only partially abrogated by culture in soft microenvironments.

Comparison of clustering tools in R for medium-sized 10x Genomics single-cell RNA-sequencing data.

Background: The commercially available 10x Genomics protocol to generate droplet-based single cell RNA-seq (scRNA-seq) data is enjoying growing popularity among researchers. Fundamental to the analysis of such scRNA-seq data is the ability to cluster similar or same cells into non-overlapping groups. Many competing methods have been proposed for this task, but there is currently little guidance with regards to which method to use. Methods: Here we use one gold standard 10x Genomics dataset, generated from the mixture of three cell lines, as well as multiple silver standard 10x Genomics datasets generated from peripheral blood mononuclear cells to examine not only the accuracy but also running time and robustness of a dozen methods. Results: We found that Seurat outperformed other methods, although performance seems to be dependent on many factors, including the complexity of the studied system. Furthermore, we found that solutions produced by different methods have little in common with each other. Conclusions: In light of this we conclude that the choice of clustering tool crucially determines interpretation of scRNA-seq data generated by 10x Genomics. Hence practitioners and consumers should remain vigilant about the outcome of 10x Genomics scRNA-seq analysis.

FC1000: normalized gene expression changes of systematically perturbed human cells.

The systematic study of transcriptional responses to genetic and chemical perturbations in human cells is still in its early stages. The largest available dataset to date is the newly released L1000 compendium. With its 1.3 million gene expression profiles of treated human cells it offers many opportunities for biomedical data mining, but also data normalization challenges of new dimensions. We developed a novel and practical approach to obtain accurate estimates of fold change response profiles from L1000, based on the RUV (Remove Unwanted Variation) statistical framework. Extending RUV to a big data setting, we propose an estimation procedure, in which an underlying RUV model is tuned by feedback through dataset specific statistical measures, reflecting p-value distributions and internal gene knockdown controls. Applying these metrics - termed evaluation endpoints - to disjoint data splits and integrating the results to select an optimal normalization, the procedure reduces bias and noise in the L1000 data, which in turn broadens the potential of this resource for pharmacological and functional genomic analyses. Our pipeline and normalization results are distributed as an R package (nelanderlab.org/FC1000.html).

Case-specific potentiation of glioblastoma drugs by pterostilbene.

Glioblastoma multiforme (GBM, astrocytoma grade IV) is the most common malignant primary brain tumor in adults. Addressing the shortage of effective treatment options for this cancer, we explored repurposing of existing drugs into combinations with potent activity against GBM cells. We report that the phytoalexin pterostilbene is a potentiator of two drugs with previously reported anti-GBM activity, the EGFR inhibitor gefitinib and the antidepressant sertraline. Combinations of either of these two compounds with pterostilbene suppress cell growth, viability, sphere formation and inhibit migration in tumor GBM cell (GC) cultures. The potentiating effect of pterostilbene was observed to a varying degree across a panel of 41 patient-derived GCs, and correlated in a case specific manner with the presence of missense mutation of EGFR and PIK3CA and a focal deletion of the chromosomal region 1p32. We identify pterostilbene-induced cell cycle arrest, synergistic inhibition of MAPK activity and induction of Thioredoxin interacting protein (TXNIP) as possible mechanisms behind pterostilbene's effect. Our results highlight a nontoxic stilbenoid compound as a modulator of anticancer drug response, and indicate that pterostilbene might be used to modulate two anticancer compounds in well-defined sets of GBM patients.

Deciphering clonality in aneuploid breast tumors using SNP array and sequencing data.

Intra-tumor heterogeneity concerns the existence of genetically different subclones within the same tumor. Single sample quantification of heterogeneity relies on precise determination of chromosomal copy numbers throughout the genome, and an assessment of whether identified mutation variant allele fractions match clonal or subclonal copy numbers. We discuss these issues using data from SNP arrays, whole exome sequencing and pathologist purity estimates on several breast cancers characterized by ERBB2 amplification. We show that chromosomal copy numbers can only be estimated from SNP array signals or sequencing depths for subclonal tumor samples with simple subclonal architectures under certain assumptions.

Physical fitness, but not muscle strength, is a risk factor for death in amyotrophic lateral sclerosis at an early age.

BACKGROUND: Amyotrophic lateral sclerosis (ALS) is a rare neurodegenerative disorder mainly characterised by motor symptoms. Extensive physical activity has been implicated in the aetiology of ALS. Differences in anthropometrics, physical fitness and isometric strength measured at 18-19 years were assessed to determine if they are associated with subsequent death in ALS. METHOD: Data on body weight and height, physical fitness, resting heart rate and isometric strength measured at conscription were linked with data on death certificates in men born in 1951-1965 in Sweden (n=809 789). Physical fitness was assessed as a maximal test on an electrically braked bicycle ergometer. Muscle strength was measured as the maximal isometric strength in handgrip, elbow flexion and knee extension in standardised positions, using a dynamometer. Analyses were based on 684 459 (84.5%) men because of missing data. A matched case control study within this sample was performed. The population was followed until 31 December 2006, and 85 men died from ALS during this period. RESULTS: Weight adjusted physical fitness (W/kg), but not physical fitness per se, was a risk factor for ALS (OR 1.98, 95% CI 1.32 to 2.97), whereas resting pulse rate, muscle strength and other variables were not. CONCLUSIONS: Physical fitness, but not muscle strength, is a risk factor for death at early age in ALS. This may indicate that a common factor underlies both fitness (W/kg) and risk of ALS.

Normalization and expression changes in predefined sets of proteins using 2D gel electrophoresis: a proteomic study of L-DOPA induced dyskinesia in an animal model of Parkinson's disease using DIGE.

BACKGROUND: Two-Dimensional Difference In Gel Electrophoresis (2D-DIGE) is a powerful tool for measuring differences in protein expression between samples or conditions. However, to remove systematic variability within and between gels the data has to be normalized. In this study we examined the ability of four existing and four novel normalization methods to remove systematic bias in data produced with 2D-DIGE. We also propose a modification of an existing method where the statistical framework determines whether a set of proteins shows an association with the predefined phenotypes of interest. This method was applied to our data generated from a monkey model (Macaca fascicularis) of Parkinson's disease. RESULTS: Using 2D-DIGE we analysed the protein content of the striatum from 6 control and 21 MPTP-treated monkeys, with or without de novo or long-term L-DOPA administration. There was an intensity and spatial bias in the data of all the gels examined in this study. Only two of the eight normalization methods evaluated ('2D loess+scale' and 'SC-2D+quantile') successfully removed both the intensity and spatial bias. In 'SC-2D+quantile' we extended the commonly used loess normalization method against dye bias in two-channel microarray systems to suit systems with three or more channels.Further, by using the proposed method, Differential Expression in Predefined Proteins Sets (DEPPS), several sets of proteins associated with the priming effects of L-DOPA in the striatum in parkinsonian animals were identified. Three of these sets are proteins involved in energy metabolism and one set involved proteins which are part of the microtubule cytoskeleton. CONCLUSION: Comparison of the different methods leads to a series of methodological recommendations for the normalization and the analysis of data, depending on the experimental design. Due to the nature of 2D-DIGE data we recommend that the p-values obtained in significance tests should be used as rankings only. Individual proteins may be interesting as such, but by studying sets of proteins the interpretation of the results are probably more accurate and biologically informative.

Hierarchical Bayes models for cDNA microarray gene expression.

cDNA microarrays are used in many contexts to compare mRNA levels between samples of cells. Microarray experiments typically give us expression measurements on 1000-20 000 genes, but with few replicates for each gene. Traditional methods using means and standard deviations to detect differential expression are not satisfactory in this context. A handful of alternative statistics have been developed, including several empirical Bayes methods. In the present paper we present two full hierarchical Bayes models for detecting gene expression, of which one (D) describes our microarray data very well. We also compare the full Bayes and empirical Bayes approaches with respect to model assumptions, false discovery rates and computer running time. The proposed models are compared to existing empirical Bayes models in a simulation study and for a set of data (Yuen et al., 2002), where 27 genes have been categorized by quantitative real-time PCR. It turns out that the existing empirical Bayes methods have at least as good performance as the full Bayes ones.

Empirical bayes microarray ANOVA and grouping cell lines by equal expression levels.

In the exploding field of gene expression techniques such as DNA microarrays, there are still few general probabilistic methods for analysis of variance. Linear models and ANOVA are heavily used tools in many other disciplines of scientific research. The usual F-statistic is unsatisfactory for microarray data, which explore many thousand genes in parallel, with few replicates. We present three potential one-way ANOVA statistics in a parametric statistical framework. The aim is to separate genes that are differently regulated across several treatment conditions from those with equal regulation. The statistics have different features and are evaluated using both real and simulated data. Our statistic B1 generally shows the best performance, and is extended for use in an algorithm that groups cell lines by equal expression levels for each gene. An extension is also outlined for more general ANOVA tests including several factors. The methods presented are implemented in the freely available statistical language R. They are available at http://www.math.uu.se/staff/pages/?uname=ingrid.

Growth hormone-mediated alteration of fuel metabolism in the aged rat as determined from transcript profiles.

Age-related changes in body composition and serum lipids resemble symptoms of adult-onset growth hormone (GH) deficiency. GH treatment has been shown to normalize these changes in both GH-deficient adult patients and elderly subjects. The aim of this study was to identify GH-responsive genes that might mediate positive effects of GH treatment on fuel metabolism and body composition. cDNA microarrays were used to analyze age- and GH-induced changes in gene expression patterns in male rats. Tissues analyzed were liver, adipose tissue, and skeletal muscle from animals on or off GH treatment. A value of 1.5 was chosen to denote differences (increased or decreased expression) in the level of mRNA expression. In the liver, 7.3% of the expressed genes were affected by age and 6.5% by GH. Similar values for the other tissues were 8.3% and 5.3% (fat), and 7.9% and 9.6% (muscle), respectively. Among the differentially expressed genes, we identified several that encode proteins involved in fuel metabolism. Old rats were shown to have induced expression of genes involved in hepatic glucose oxidation and lipid synthesis, whereas these pathways were reduced in adipose tissue. GH treatment induced the expression of genes for lipid oxidation in liver and for glucose oxidation in skeletal muscle. In adipose tissue, GH reduced the expression of genes involved in lipogenesis even further. Changes in transcript levels were reflected in serum in terms of altered lipid profiles. Serum levels of triglycerides, high-density lipoprotein (HDL) cholesterol, and total cholesterol were higher in the old animals than in the young and normalized by GH treatment.