Cell-specific expression of ENACα gene by FOXA1 in the glucocorticoid receptor pathway

Introduction: The glucocorticoid receptor (GR) is one of the most widely studied ligand-dependent nuclear receptors. The combination of transcriptional regulatory factors required for the expression of individual genes targeted by GR varies across cell types; however, the mechanisms underlying this cell type–specific regulation of gene expression are not yet clear. Methods: Here, we investigated genes regulated by GR in two different cell lines, A549 and ARPE-19, and examined how gene expression varied according to the effect of pioneer factors using RNA-seq and RT-qPCR. Results: Our RNA-seq results identified 19 and 63 genes regulated by GR that are ARPE-19-specific and A549-specific, respectively, suggesting that GR induces the expression of different sets of genes in a cell type–specific manner. RT-qPCR confirmed that the epithelial sodium channel ( ENACα) gene is an ARPE-19 cell-specific GR target gene, whereas the FK506 binding protein 5 ( FKBP5) gene was A549 cell-specific. There was a significant decrease in ENACα expression in FOXA1-deficient ARPE-19 cells, suggesting that FOXA1 might function as a pioneer factor enabling the selective expression of ENACα in ARPE-19 cells but not in A549 cells. Conclusion: These findings indicate that ENACα expression in ARPE-19 cells is regulated by FOXA1 and provide insights into the molecular mechanisms of cell type–specific expression of GR-regulated genes.

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Cell-type Specific Expression Quantitative Trait Loci Associated with Alzheimer Disease in Blood and Brain Tissue

10.1101/2020.11.23.20237008 ◽

2020 ◽

Author(s):

Devanshi Patel ◽

Xiaoling Zhang ◽

John J. Farrell ◽

Jaeyoon Chung ◽

Thor D. Stein ◽

...

Keyword(s):

Gene Expression ◽

Quantitative Trait ◽

Expression Patterns ◽

Regulation Of Gene Expression ◽

Cell Types ◽

Eqtl Analysis ◽

Cell Type ◽

Specific Expression ◽

Cell Type Specific Expression ◽

Cell Type Specific

ABSTRACTBecause regulation of gene expression is heritable and context-dependent, we investigated AD-related gene expression patterns in cell-types in blood and brain. Cis-expression quantitative trait locus (eQTL) mapping was performed genome-wide in blood from 5,257 Framingham Heart Study (FHS) participants and in brain donated by 475 Religious Orders Study/Memory & Aging Project (ROSMAP) participants. The association of gene expression with genotypes for all cis SNPs within 1Mb of genes was evaluated using linear regression models for unrelated subjects and linear mixed models for related subjects. Cell type-specific eQTL (ct-eQTL) models included an interaction term for expression of “proxy” genes that discriminate particular cell type. Ct-eQTL analysis identified 11,649 and 2,533 additional significant gene-SNP eQTL pairs in brain and blood, respectively, that were not detected in generic eQTL analysis. Of note, 386 unique target eGenes of significant eQTLs shared between blood and brain were enriched in apoptosis and Wnt signaling pathways. Five of these shared genes are established AD loci. The potential importance and relevance to AD of significant results in myeloid cell-types is supported by the observation that a large portion of GWS ct-eQTLs map within 1Mb of established AD loci and 58% (23/40) of the most significant eGenes in these eQTLs have previously been implicated in AD. This study identified cell-type specific expression patterns for established and potentially novel AD genes, found additional evidence for the role of myeloid cells in AD risk, and discovered potential novel blood and brain AD biomarkers that highlight the importance of cell-type specific analysis.

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Chromatin-enriched RNAs mark active and repressive cis-regulation: an analysis of nuclear RNA-seq

10.1101/646950 ◽

2019 ◽

Author(s):

Xiangying Sun ◽

Zhezhen Wang ◽

Carlos Perez-Cervantes ◽

Alex Ruthenburg ◽

Ivan Moskowitz ◽

...

Keyword(s):

Gene Expression ◽

Noncoding Rna ◽

Molecular Mechanisms ◽

Specific Gene ◽

Rna Seq ◽

Cell Type ◽

Neighboring Gene ◽

Cis Regulation ◽

Nuclear Rna ◽

Cell Type Specific

AbstractLong noncoding RNAs (lncRNAs) localize in the cell nucleus and influence gene expression through a variety of molecular mechanisms. RNA sequencing of two biochemical fractions of nuclei reveals a unique class of lncRNAs, termed chromatin-enriched nuclear RNAs (cheRNAs) that are tightly bound to chromatin and putatively function to cis-activate gene expression. Until now, a rigorous analytic pipeline for nuclear RNA-seq has been lacking. In this study, we survey four computational strategies for nuclear RNA-seq data analysis and show that a new pipeline, Tuxedo, outperforms other approaches. Tuxedo not only assembles a more complete transcriptome, but also identifies cheRNA with higher accuracy. We have used Tuxedo to analyze gold-standard K562 cell datasets and further characterize the genomic features of intergenic cheRNA (icheRNA) and their similarity to those of enhancer RNA (eRNA). Moreover, we quantify the transcriptional correlation of icheRNA and adjacent genes, and suggest that icheRNA may be the cis-acting transcriptional regulator that is more positively associated with neighboring gene expression than eRNA predicted by state-of-art method or CAGE signal. We also explore two novel genomic associations, suggesting cheRNA may have diverse functions. A possible new role of H3K9me3 modification coincident with icheRNA may be associated with active enhancer derived from ancient mobile elements, while a potential cis-repressive function of antisense cheRNA (as-cheRNA) is likely to be involved in transiently modulating cell type-specific cis-regulation.Author SummaryChromatin-enriched nuclear RNA (cheRNA) is a class of gene regulatory non-coding RNAs. CheRNA provides a powerful way to profile the nuclear transcriptional landscape, especially to profile the noncoding transcriptome. The computational framework presented here provides a reliable approach to identifying cheRNA, and for studying cell-type specific gene regulation. We found that intergenic cheRNA, including intergenic cheRNA with high levels of H3K9me3 (a mark associated with closed/repressed chromatin), may act as a transcriptional activator. In contrast, antisense cheRNA, which originates from the complementary strand of the protein-coding gene, may interact with diverse chromatin modulators to repress local transcription. With our new pipeline, one future challenge will be refining the functional mechanisms of these noncoding RNA classes through exploring their regulatory roles, which are involved in diverse molecular and cellular processes in human and other organisms.

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A computational method for direct imputation of cell type-specific expression profiles and cellular compositions from bulk-tissue RNA-Seq in brain disorders

10.1101/2020.05.28.121483 ◽

2020 ◽

Author(s):

Abolfazl Doostparast Torshizi ◽

Jubao Duan ◽

Kai Wang

Keyword(s):

Gene Expression ◽

Expression Profiles ◽

Complex Diseases ◽

Specific Gene ◽

Cellular Composition ◽

Rna Seq ◽

Cell Type ◽

Specific Expression ◽

Cell Type Specific Expression ◽

Cell Type Specific

AbstractThe importance of cell type-specific gene expression in disease-relevant tissues is increasingly recognized in genetic studies of complex diseases. However, the vast majority of gene expression studies are conducted on bulk tissues, necessitating computational approaches to infer biological insights on cell type-specific contribution to diseases. Several computational methods are available for cell type deconvolution (that is, inference of cellular composition) from bulk RNA-Seq data, but cannot impute cell type-specific expression profiles. We hypothesize that with external prior information such as single cell RNA-seq (scRNA-seq) and population-wide expression profiles, it can be a computationally tractable and identifiable to estimate both cellular composition and cell type-specific expression from bulk RNA-Seq data. Here we introduce CellR, which addresses cross-individual gene expression variations by employing genome-wide tissue-wise expression signatures from GTEx to adjust the weights of cell-specific gene markers. It then transforms the deconvolution problem into a linear programming model while taking into account inter/intra cellular correlations, and uses a multi-variate stochastic search algorithm to estimate the expression level of each gene in each cell type. Extensive analyses on several complex diseases such as schizophrenia, Alzheimer’s disease, Huntington’s disease, and type 2 diabetes validated efficiency of CellR, while revealing how specific cell types contribute to different diseases. We conducted numerical simulations on human cerebellum to generate pseudo-bulk RNA-seq data and demonstrated its efficiency in inferring cell-specific expression profiles. Moreover, we inferred cell-specific expression levels from bulk RNA-seq data on schizophrenia and computed differentially expressed genes within certain cell types. Using predicted gene expression profile on excitatory neurons, we were able to reproduce our recently published findings on TCF4 being a master regulator in schizophrenia and showed how this gene and its targets are enriched in excitatory neurons. In summary, CellR compares favorably (both accuracy and stability of inference) against competing approaches on inferring cellular composition from bulk RNA-seq data, but also allows direct imputation of cell type-specific gene expression, opening new doors to re-analyze gene expression data on bulk tissues in complex diseases.

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Cell-type specific regulation of gene expression by simian virus 40 T antigens

Virology ◽

10.1016/j.virol.2008.12.038 ◽

2009 ◽

Vol 386 (1) ◽

pp. 183-191 ◽

Cited By ~ 27

Author(s):

Paul G. Cantalupo ◽

Maria Teresa Sáenz-Robles ◽

Abhilasha V. Rathi ◽

Rebecca W. Beerman ◽

William H. Patterson ◽

...

Keyword(s):

Gene Expression ◽

Regulation Of Gene Expression ◽

Simian Virus 40 ◽

Simian Virus ◽

Cell Type ◽

T Antigens ◽

Cell Type Specific ◽

Specific Regulation

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A computational method for direct imputation of cell type-specific expression profiles and cellular compositions from bulk-tissue RNA-Seq in brain disorders

NAR Genomics and Bioinformatics ◽

10.1093/nargab/lqab056 ◽

2021 ◽

Vol 3 (2) ◽

Author(s):

Abolfazl Doostparast Torshizi ◽

Jubao Duan ◽

Kai Wang

Keyword(s):

Gene Expression ◽

Expression Profiles ◽

Complex Diseases ◽

Specific Gene ◽

Cellular Composition ◽

Rna Seq ◽

Cell Type ◽

Specific Expression ◽

Cell Type Specific Expression ◽

Cell Type Specific

Abstract The importance of cell type-specific gene expression in disease-relevant tissues is increasingly recognized in genetic studies of complex diseases. However, most gene expression studies are conducted on bulk tissues, without examining cell type-specific expression profiles. Several computational methods are available for cell type deconvolution (i.e. inference of cellular composition) from bulk RNA-Seq data, but few of them impute cell type-specific expression profiles. We hypothesize that with external prior information such as single cell RNA-seq and population-wide expression profiles, it can be computationally tractable to estimate both cellular composition and cell type-specific expression from bulk RNA-Seq data. Here we introduce CellR, which addresses cross-individual gene expression variations to adjust the weights of cell-specific gene markers. It then transforms the deconvolution problem into a linear programming model while taking into account inter/intra cellular correlations and uses a multi-variate stochastic search algorithm to estimate the cell type-specific expression profiles. Analyses on several complex diseases such as schizophrenia, Alzheimer’s disease, Huntington’s disease and type 2 diabetes validated the efficiency of CellR, while revealing how specific cell types contribute to different diseases. In summary, CellR compares favorably against competing approaches, enabling cell type-specific re-analysis of gene expression data on bulk tissues in complex diseases.

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Cell-type-specific expression quantitative trait loci associated with Alzheimer disease in blood and brain tissue

Translational Psychiatry ◽

10.1038/s41398-021-01373-z ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Devanshi Patel ◽

Xiaoling Zhang ◽

John J. Farrell ◽

Jaeyoon Chung ◽

Thor D. Stein ◽

...

Keyword(s):

Gene Expression ◽

Quantitative Trait ◽

Expression Patterns ◽

Regulation Of Gene Expression ◽

Cell Types ◽

Eqtl Analysis ◽

Cell Type ◽

Specific Expression ◽

Cell Type Specific Expression ◽

Cell Type Specific

AbstractBecause regulation of gene expression is heritable and context-dependent, we investigated AD-related gene expression patterns in cell types in blood and brain. Cis-expression quantitative trait locus (eQTL) mapping was performed genome-wide in blood from 5257 Framingham Heart Study (FHS) participants and in brain donated by 475 Religious Orders Study/Memory & Aging Project (ROSMAP) participants. The association of gene expression with genotypes for all cis SNPs within 1 Mb of genes was evaluated using linear regression models for unrelated subjects and linear-mixed models for related subjects. Cell-type-specific eQTL (ct-eQTL) models included an interaction term for the expression of “proxy” genes that discriminate particular cell type. Ct-eQTL analysis identified 11,649 and 2533 additional significant gene-SNP eQTL pairs in brain and blood, respectively, that were not detected in generic eQTL analysis. Of note, 386 unique target eGenes of significant eQTLs shared between blood and brain were enriched in apoptosis and Wnt signaling pathways. Five of these shared genes are established AD loci. The potential importance and relevance to AD of significant results in myeloid cell types is supported by the observation that a large portion of GWS ct-eQTLs map within 1 Mb of established AD loci and 58% (23/40) of the most significant eGenes in these eQTLs have previously been implicated in AD. This study identified cell-type-specific expression patterns for established and potentially novel AD genes, found additional evidence for the role of myeloid cells in AD risk, and discovered potential novel blood and brain AD biomarkers that highlight the importance of cell-type-specific analysis.

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Shisa6 mediates cell-type specific regulation of depression in the nucleus accumbens

Molecular Psychiatry ◽

10.1038/s41380-021-01217-8 ◽

2021 ◽

Author(s):

Hee-Dae Kim ◽

Jing Wei ◽

Tanessa Call ◽

Nicole Teru Quintus ◽

Alexander J. Summers ◽

...

Keyword(s):

Nucleus Accumbens ◽

Molecular Mechanisms ◽

Cell Types ◽

Current Treatment ◽

Specific Cell ◽

Excitatory Synapses ◽

Cell Type ◽

Cell Type Specific ◽

Specific Regulation ◽

Circuit Function

AbstractDepression is the leading cause of disability and produces enormous health and economic burdens. Current treatment approaches for depression are largely ineffective and leave more than 50% of patients symptomatic, mainly because of non-selective and broad action of antidepressants. Thus, there is an urgent need to design and develop novel therapeutics to treat depression. Given the heterogeneity and complexity of the brain, identification of molecular mechanisms within specific cell-types responsible for producing depression-like behaviors will advance development of therapies. In the reward circuitry, the nucleus accumbens (NAc) is a key brain region of depression pathophysiology, possibly based on differential activity of D1- or D2- medium spiny neurons (MSNs). Here we report a circuit- and cell-type specific molecular target for depression, Shisa6, recently defined as an AMPAR component, which is increased only in D1-MSNs in the NAc of susceptible mice. Using the Ribotag approach, we dissected the transcriptional profile of D1- and D2-MSNs by RNA sequencing following a mouse model of depression, chronic social defeat stress (CSDS). Bioinformatic analyses identified cell-type specific genes that may contribute to the pathogenesis of depression, including Shisa6. We found selective optogenetic activation of the ventral tegmental area (VTA) to NAc circuit increases Shisa6 expression in D1-MSNs. Shisa6 is specifically located in excitatory synapses of D1-MSNs and increases excitability of neurons, which promotes anxiety- and depression-like behaviors in mice. Cell-type and circuit-specific action of Shisa6, which directly modulates excitatory synapses that convey aversive information, identifies the protein as a potential rapid-antidepressant target for aberrant circuit function in depression.

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Genetic influences on cell type specific gene expression and splicing during neurogenesis elucidate regulatory mechanisms of brain traits

10.1101/2020.10.21.349019 ◽

2020 ◽

Author(s):

Nil Aygün ◽

Angela L. Elwell ◽

Dan Liang ◽

Michael J. Lafferty ◽

Kerry E. Cheek ◽

...

Keyword(s):

Gene Expression ◽

Regulatory Elements ◽

Regulatory Mechanisms ◽

Specific Gene ◽

Cell Type ◽

Specific Expression ◽

Risk Variants ◽

Allele Specific ◽

Cell Type Specific ◽

Regulatory Effects

SummaryInterpretation of the function of non-coding risk loci for neuropsychiatric disorders and brain-relevant traits via gene expression and alternative splicing is mainly performed in bulk post-mortem adult tissue. However, genetic risk loci are enriched in regulatory elements of cells present during neocortical differentiation, and regulatory effects of risk variants may be masked by heterogeneity in bulk tissue. Here, we map e/sQTLs and allele specific expression in primary human neural progenitors (n=85) and their sorted neuronal progeny (n=74). Using colocalization and TWAS, we uncover cell-type specific regulatory mechanisms underlying risk for these traits.

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Inducible cell-specific mouse models for paired epigenetic and transcriptomic studies of microglia and astroglia

Communications Biology ◽

10.1038/s42003-020-01418-x ◽

2020 ◽

Vol 3 (1) ◽

Cited By ~ 1

Author(s):

Ana J. Chucair-Elliott ◽

Sarah R. Ocañas ◽

David R. Stanford ◽

Victor A. Ansere ◽

Kyla B. Buettner ◽

...

Keyword(s):

Gene Expression ◽

Affinity Purification ◽

Regulation Of Gene Expression ◽

Cell Types ◽

Tissue Level ◽

Cell Phenotype ◽

Specific Gene ◽

Cell Type ◽

A Cell ◽

Cell Type Specific

AbstractEpigenetic regulation of gene expression occurs in a cell type-specific manner. Current cell-type specific neuroepigenetic studies rely on cell sorting methods that can alter cell phenotype and introduce potential confounds. Here we demonstrate and validate a Nuclear Tagging and Translating Ribosome Affinity Purification (NuTRAP) approach for temporally controlled labeling and isolation of ribosomes and nuclei, and thus RNA and DNA, from specific central nervous system cell types. Analysis of gene expression and DNA modifications in astrocytes or microglia from the same animal demonstrates differential usage of DNA methylation and hydroxymethylation in CpG and non-CpG contexts that corresponds to cell type-specific gene expression. Application of this approach in LPS treated mice uncovers microglia-specific transcriptome and epigenome changes in inflammatory pathways that cannot be detected with tissue-level analysis. The NuTRAP model and the validation approaches presented can be applied to any brain cell type for which a cell type-specific cre is available.

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