Integrative genomic strategies applied to a lymphoblast cell line model reveal specific transcriptomic signatures associated with clozapine response

AbstractClozapine is an important antipsychotic drug. However, its use is often accompanied by metabolic adverse effects and, in rare instances, agranulocytosis. The molecular mechanisms underlying these adverse events are unclear. To gain more insights into the response to clozapine at the molecular level, we exposed lymphoblastoid cell lines (LCLs) to increasing concentrations of clozapine and measured genome-wide gene expression and DNA methylation profiles. We observed robust and significant changes in gene expression levels due to clozapine (n = 463 genes at FDR < 0.05) affecting cholesterol and cell cycle pathways. At the level of DNA methylation, we find significant changes upstream of the LDL receptor, in addition to global enrichments of regulatory, immune and developmental pathways. By integrating these data with human tissue gene expression levels obtained from the Genotype-Tissue Expression project (GTEx), we identified specific tissues, including liver and several tissues involved in immune, endocrine and metabolic functions, that clozapine treatment may disproportionately affect. Notably, differentially expressed genes were not enriched for genome-wide disease risk of schizophrenia or for known psychotropic drug targets. However, we did observe a nominally significant association of genetic signals related to total cholesterol and low-density lipoprotein levels. Together, these results shed light on the biological mechanisms through which clozapine functions. The observed associations with cholesterol pathways, its genetic architecture and specific tissue effects may be indicative of the metabolic adverse effects observed in clozapine users. LCLs may thus serve as a useful tool to study these molecular mechanisms further.

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A Genome-Wide Study of DNA Methylation Patterns and Gene Expression Levels in Multiple Human and Chimpanzee Tissues

PLoS Genetics ◽

10.1371/journal.pgen.1001316 ◽

2011 ◽

Vol 7 (2) ◽

pp. e1001316 ◽

Cited By ~ 142

Author(s):

Athma A. Pai ◽

Jordana T. Bell ◽

John C. Marioni ◽

Jonathan K. Pritchard ◽

Yoav Gilad

Keyword(s):

Gene Expression ◽

Dna Methylation ◽

Expression Levels ◽

Genome Wide ◽

A Genome ◽

Methylation Patterns ◽

Genome Wide Study ◽

Gene Expression Levels

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Gene expression levels of DNA methyltransferase enzymes in Shank3-deficient mouse model of autism during early development

Endocrine Regulations ◽

10.2478/enr-2021-0025 ◽

2021 ◽

Vol 55 (4) ◽

pp. 234-237

Author(s):

Annamaria Srancikova ◽

Alexandra Reichova ◽

Zuzana Bacova ◽

Jan Bakos

Keyword(s):

Gene Expression ◽

Dna Methylation ◽

Brain Development ◽

Early Development ◽

Molecular Mechanisms ◽

Altered Expression ◽

Expression Levels ◽

The Brain ◽

Deficient Mice ◽

Gene Expression Levels

Abstract Objectives. The balance between DNA methylation and demethylation is crucial for the brain development. Therefore, alterations in the expression of enzymes controlling DNA methylation patterns may contribute to the etiology of neurodevelopmental disorders, including autism. SH3 and multiple ankyrin repeat domains 3 (Shank3)-deficient mice are commonly used as a well-characterized transgenic model to investigate the molecular mechanisms of autistic symptoms. DNA methyltransferases (DNMTs), which modulate several cellular processes in neurodevelopment, are implicated in the pathophysiology of autism. In this study, we aimed to describe the gene expression changes of major Dnmts in the brain of Shank3-deficient mice during early development. Methods and Results. The Dnmts gene expression was analyzed by qPCR in 5-day-old homo-zygous Shank3-deficient mice. We found significantly lower Dnmt1 and Dnmt3b gene expression levels in the frontal cortex. However, no such changes were observed in the hippocampus. However, significant increase was observed in the expression of Dnmt3a and Dnmt3b genes in the hypothalamus of Shank3-deficient mice. Conclusions. The present data indicate that abnormalities in the Shank3 gene are accompanied by an altered expression of DNA methylation enzymes in the early brain development stages, therefore, specific epigenetic control mechanisms in autism-relevant models should be more extensively investigated.

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A Sparse and Low-Rank Regression Model for Identifying the Relationships Between DNA Methylation and Gene Expression Levels in Gastric Cancer and the Prediction of Prognosis

Genes ◽

10.3390/genes12060854 ◽

2021 ◽

Vol 12 (6) ◽

pp. 854

Author(s):

Yishu Wang ◽

Lingyun Xu ◽

Dongmei Ai

Keyword(s):

Gene Expression ◽

Gastric Cancer ◽

Dna Methylation ◽

Regression Model ◽

The Cancer Genome Atlas ◽

Low Rank ◽

Expression Levels ◽

Rank Regression ◽

Prediction Of Prognosis ◽

Gene Expression Levels

DNA methylation is an important regulator of gene expression that can influence tumor heterogeneity and shows weak and varying expression levels among different genes. Gastric cancer (GC) is a highly heterogeneous cancer of the digestive system with a high mortality rate worldwide. The heterogeneous subtypes of GC lead to different prognoses. In this study, we explored the relationships between DNA methylation and gene expression levels by introducing a sparse low-rank regression model based on a GC dataset with 375 tumor samples and 32 normal samples from The Cancer Genome Atlas database. Differences in the DNA methylation levels and sites were found to be associated with differences in the expressed genes related to GC development. Overall, 29 methylation-driven genes were found to be related to the GC subtypes, and in the prognostic model, we explored five prognoses related to the methylation sites. Finally, based on a low-rank matrix, seven subgroups were identified with different methylation statuses. These specific classifications based on DNA methylation levels may help to account for heterogeneity and aid in personalized treatments.

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Genome-Wide Expression and Alternative Splicing in Domesticated Sunflowers (Helianthus annuus L.) under Flooding Stress

Agronomy ◽

10.3390/agronomy11010092 ◽

2021 ◽

Vol 11 (1) ◽

pp. 92

Author(s):

Joon Seon Lee ◽

Lexuan Gao ◽

Laura Melissa Guzman ◽

Loren H. Rieseberg

Keyword(s):

Gene Expression ◽

Alternative Splicing ◽

Mixed Model ◽

Agricultural Land ◽

Alcohol Fermentation ◽

Expression Levels ◽

Flooding Stress ◽

Genome Wide ◽

And Control ◽

Gene Expression Levels

Approximately 10% of agricultural land is subject to periodic flooding, which reduces the growth, survivorship, and yield of most crops, reinforcing the need to understand and enhance flooding resistance in our crops. Here, we generated RNA-Seq data from leaf and root tissue of domesticated sunflower to explore differences in gene expression and alternative splicing (AS) between a resistant and susceptible cultivar under both flooding and control conditions and at three time points. Using a combination of mixed model and gene co-expression analyses, we were able to separate general responses of sunflower to flooding stress from those that contribute to the greater tolerance of the resistant line. Both cultivars responded to flooding stress by upregulating expression levels of known submergence responsive genes, such as alcohol dehydrogenases, and slowing metabolism-related activities. Differential AS reinforced expression differences, with reduced AS frequencies typically observed for genes with upregulated expression. Significant differences were found between the genotypes, including earlier and stronger upregulation of the alcohol fermentation pathway and a more rapid return to pre-flooding gene expression levels in the resistant genotype. Our results show how changes in the timing of gene expression following both the induction of flooding and release from flooding stress contribute to increased flooding tolerance.

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Intra- and inter-specific variations of gene expression levels in yeast are largely neutral

10.1101/089995 ◽

2016 ◽

Cited By ~ 1

Author(s):

Jian-Rong Yang ◽

Calum Maclean ◽

Chungoo Park ◽

Huabin Zhao ◽

Jianzhi Zhang

Keyword(s):

Gene Expression ◽

Genome Sequence ◽

Large Fraction ◽

General Role ◽

Expression Levels ◽

Genome Wide ◽

Sequence Variations ◽

Expression Evolution ◽

Molecular Phenotypes ◽

Gene Expression Levels

ABSTRACTIt is commonly, although not universally, accepted that most intra- and inter-specific genome sequence variations are more or less neutral, whereas a large fraction of organism-level phenotypic variations are adaptive. Gene expression levels are molecular phenotypes that bridge the gap between genotypes and corresponding organism-level phenotypes. Yet, it is unknown whether natural variations in gene expression levels are mostly neutral or adaptive. Here we address this fundamental question by genome-wide profiling and comparison of gene expression levels in nine yeast strains belonging to three closely related Saccharomyces species and originating from five different ecological environments. We find that the transcriptome-based clustering of the nine strains approximates the genome sequence-based phylogeny irrespective of their ecological environments. Remarkably, only ∼0.5% of genes exhibit similar expression levels among strains from a common ecological environment, no greater than that among strains with comparable phylogenetic relationships but different environments. These and other observations strongly suggest that most intra- and inter-specific variations in yeast gene expression levels result from the accumulation of random mutations rather than environmental adaptations. This finding has profound implications for understanding the driving force of gene expression evolution, genetic basis of phenotypic adaptation, and general role of stochasticity in evolution.

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Occupational exposure to gases/fumes and mineral dust affect DNA methylation levels of genes regulating expression

Human Molecular Genetics ◽

10.1093/hmg/ddz067 ◽

2019 ◽

Vol 28 (15) ◽

pp. 2477-2485 ◽

Cited By ~ 1

Author(s):

Diana A van der Plaat ◽

Judith M Vonk ◽

Natalie Terzikhan ◽

Kim de Jong ◽

Maaike de Vries ◽

...

Keyword(s):

Gene Expression ◽

Dna Methylation ◽

Occupational Exposure ◽

Health Effects ◽

Mineral Dust ◽

Occupational Exposures ◽

Expression Levels ◽

Adverse Health Effects ◽

Never Smokers ◽

Gene Expression Levels

Abstract Many workers are daily exposed to occupational agents like gases/fumes, mineral dust or biological dust, which could induce adverse health effects. Epigenetic mechanisms, such as DNA methylation, have been suggested to play a role. We therefore aimed to identify differentially methylated regions (DMRs) upon occupational exposures in never-smokers and investigated if these DMRs associated with gene expression levels. To determine the effects of occupational exposures independent of smoking, 903 never-smokers of the LifeLines cohort study were included. We performed three genome-wide methylation analyses (Illumina 450 K), one per occupational exposure being gases/fumes, mineral dust and biological dust, using robust linear regression adjusted for appropriate confounders. DMRs were identified using comb-p in Python. Results were validated in the Rotterdam Study (233 never-smokers) and methylation-expression associations were assessed using Biobank-based Integrative Omics Study data (n = 2802). Of the total 21 significant DMRs, 14 DMRs were associated with gases/fumes and 7 with mineral dust. Three of these DMRs were associated with both exposures (RPLP1 and LINC02169 (2×)) and 11 DMRs were located within transcript start sites of gene expression regulating genes. We replicated two DMRs with gases/fumes (VTRNA2-1 and GNAS) and one with mineral dust (CCDC144NL). In addition, nine gases/fumes DMRs and six mineral dust DMRs significantly associated with gene expression levels. Our data suggest that occupational exposures may induce differential methylation of gene expression regulating genes and thereby may induce adverse health effects. Given the millions of workers that are exposed daily to occupational exposures, further studies on this epigenetic mechanism and health outcomes are warranted.

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