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

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.

New insights into promoter–enhancer communication mechanisms revealed by dynamic single-molecule imaging

2021 ◽  
Author(s):  
Jieru Li ◽  
Alexandros Pertsinidis
Keyword(s):  
Gene Expression ◽  
Single Molecule ◽  
Target Genes ◽  
Regulatory Elements ◽  
Specific Gene ◽  
Single Molecule Imaging ◽  
Cell Type ◽  
Regulatory Information ◽  
Cell Type Specific ◽  
Target Promoters

Establishing cell-type-specific gene expression programs relies on the action of distal enhancers, cis-regulatory elements that can activate target genes over large genomic distances — up to Mega-bases away. How distal enhancers physically relay regulatory information to target promoters has remained a mystery. Here, we review the latest developments and insights into promoter–enhancer communication mechanisms revealed by live-cell, real-time single-molecule imaging approaches.

scholarly journals Dynamic changes in cis-regulatory occupancy by Six1 and its cooperative interactions with distinct cofactors drive lineage-specific gene expression programs during progressive differentiation of the auditory sensory epithelium

2020 ◽  
Vol 48 (6) ◽  
pp. 2880-2896 ◽  
Author(s):  
Jun Li ◽  
Ting Zhang ◽  
Aarthi Ramakrishnan ◽  
Bernd Fritzsch ◽  
Jinshu Xu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Regulatory Elements ◽  
Sensory Epithelium ◽  
Hair Bundle ◽  
Specific Gene ◽  
Cell Type ◽  
Specific Gene Expression ◽  
Wide Range ◽  
Cell Type Specific ◽  
Lineage Specific Gene

Abstract The transcription factor Six1 is essential for induction of sensory cell fate and formation of auditory sensory epithelium, but how it activates gene expression programs to generate distinct cell-types remains unknown. Here, we perform genome-wide characterization of Six1 binding at different stages of auditory sensory epithelium development and find that Six1-binding to cis-regulatory elements changes dramatically at cell-state transitions. Intriguingly, Six1 pre-occupies enhancers of cell-type-specific regulators and effectors before their expression. We demonstrate in-vivo cell-type-specific activity of Six1-bound novel enhancers of Pbx1, Fgf8, Dusp6, Vangl2, the hair-cell master regulator Atoh1 and a cascade of Atoh1’s downstream factors, including Pou4f3 and Gfi1. A subset of Six1-bound sites carry consensus-sequences for its downstream factors, including Atoh1, Gfi1, Pou4f3, Gata3 and Pbx1, all of which physically interact with Six1. Motif analysis identifies RFX/X-box as one of the most significantly enriched motifs in Six1-bound sites, and we demonstrate that Six1-RFX proteins cooperatively regulate gene expression through binding to SIX:RFX-motifs. Six1 targets a wide range of hair-bundle regulators and late Six1 deletion disrupts hair-bundle polarity. This study provides a mechanistic understanding of how Six1 cooperates with distinct cofactors in feedforward loops to control lineage-specific gene expression programs during progressive differentiation of the auditory sensory epithelium.

scholarly journals Positive and negative elements regulate a melanocyte-specific promoter

1992 ◽  
Vol 12 (8) ◽  
pp. 3653-3662
Author(s):  
P Lowings ◽  
U Yavuzer ◽  
C R Goding
Keyword(s):  
Protein Interactions ◽  
Regulatory Element ◽  
Basal Layer ◽  
Regulatory Elements ◽  
Hair Follicles ◽  
Specific Gene ◽  
Cell Type ◽  
Specific Expression ◽  
Cell Type Specific Expression ◽  
Cell Type Specific

Melanocytes are specialized cells residing in the hair follicles, the eye, and the basal layer of the human epidermis whose primary function is the production of the pigment melanin, giving rise to skin, hair, and eye color. Melanogenesis, a process unique to melanocytes that involves the processing of tyrosine by a number of melanocyte-specific enzymes, including tyrosinase and tyrosinase-related protein 1 (TRP-1), occurs only after differentiation from the melanocyte precursor, the melanoblast. In humans, melanogenesis is inducible by UV irradiation, with melanin being transferred from the melanocyte in the epidermis to the surrounding keratinocytes as protection from UV-induced damage. Excessive exposure to UV, however, is the primary cause of malignant melanoma, an increasingly common and highly aggressive disease. As an initial approach to understanding the regulation of melanocyte differentiation and melanocyte-specific transcription, we have isolated the gene encoding TRP-1 and examined the cis- and trans-acting factors required for cell-type-specific expression. We find that the TRP-1 promoter comprises both positive and negative regulatory elements which confer efficient expression in a TRP-1-expressing, pigmented melanoma cell line but not in NIH 3T3 or JEG3 cells and that a minimal promoter extending between -44 and +107 is sufficient for cell-type-specific expression. Assays for DNA-protein interactions coupled with extensive mutagenesis identified three factors, whose binding correlated with the function of two positive and one negative regulatory element. One of these factors, termed M-box-binding factor 1, binds to an 11-bp motif, the M box, which acts as a positive regulatory element both in TRP-1-expressing and -nonexpressing cell lines, despite being entirely conserved between the melanocyte-specific tyrosinase and TRP-1 promoters. The possible mechanisms underlying melanocyte-specific gene expression are discussed.

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

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.

scholarly journals Positive and negative elements regulate a melanocyte-specific promoter.

1992 ◽  
Vol 12 (8) ◽  
pp. 3653-3662 ◽  
Author(s):  
P Lowings ◽  
U Yavuzer ◽  
C R Goding
Keyword(s):  
Protein Interactions ◽  
Regulatory Element ◽  
Basal Layer ◽  
Regulatory Elements ◽  
Hair Follicles ◽  
Specific Gene ◽  
Cell Type ◽  
Specific Expression ◽  
Cell Type Specific Expression ◽  
Cell Type Specific

Melanocytes are specialized cells residing in the hair follicles, the eye, and the basal layer of the human epidermis whose primary function is the production of the pigment melanin, giving rise to skin, hair, and eye color. Melanogenesis, a process unique to melanocytes that involves the processing of tyrosine by a number of melanocyte-specific enzymes, including tyrosinase and tyrosinase-related protein 1 (TRP-1), occurs only after differentiation from the melanocyte precursor, the melanoblast. In humans, melanogenesis is inducible by UV irradiation, with melanin being transferred from the melanocyte in the epidermis to the surrounding keratinocytes as protection from UV-induced damage. Excessive exposure to UV, however, is the primary cause of malignant melanoma, an increasingly common and highly aggressive disease. As an initial approach to understanding the regulation of melanocyte differentiation and melanocyte-specific transcription, we have isolated the gene encoding TRP-1 and examined the cis- and trans-acting factors required for cell-type-specific expression. We find that the TRP-1 promoter comprises both positive and negative regulatory elements which confer efficient expression in a TRP-1-expressing, pigmented melanoma cell line but not in NIH 3T3 or JEG3 cells and that a minimal promoter extending between -44 and +107 is sufficient for cell-type-specific expression. Assays for DNA-protein interactions coupled with extensive mutagenesis identified three factors, whose binding correlated with the function of two positive and one negative regulatory element. One of these factors, termed M-box-binding factor 1, binds to an 11-bp motif, the M box, which acts as a positive regulatory element both in TRP-1-expressing and -nonexpressing cell lines, despite being entirely conserved between the melanocyte-specific tyrosinase and TRP-1 promoters. The possible mechanisms underlying melanocyte-specific gene expression are discussed.

scholarly journals Super-Enhancers and CTCF in Early Embryonic Cell Fate Decisions

2021 ◽  
Vol 9 ◽  
Author(s):  
Puja Agrawal ◽  
Sridhar Rao
Keyword(s):  
Gene Expression ◽  
Cell Fate ◽  
Critical Role ◽  
Regulatory Elements ◽  
Specific Gene ◽  
Cell Type ◽  
Mammalian Development ◽  
Cell Fate Decisions ◽  
Cell Type Specific ◽  
Early Mammalian Development

Cell fate decisions are the backbone of many developmental and disease processes. In early mammalian development, precise gene expression changes underly the rapid division of a single cell that leads to the embryo and are critically dependent on autonomous cell changes in gene expression. To understand how these lineage specifications events are mediated, scientists have had to look past protein coding genes to the cis regulatory elements (CREs), including enhancers and insulators, that modulate gene expression. One class of enhancers, termed super-enhancers, is highly active and cell-type specific, implying their critical role in modulating cell-type specific gene expression. Deletion or mutations within these CREs adversely affect gene expression and development and can cause disease. In this mini-review we discuss recent studies describing the potential roles of two CREs, enhancers and binding sites for CTCF, in early mammalian development.

WED 139 Gene expression regulation in CD4+ t-cell dysfunction in ms

2018 ◽  
Vol 89 (10) ◽  
pp. A16.1-A16
Author(s):  
Hrastelj James ◽  
Bray Nicholas ◽  
Williams Nigel ◽  
Robertson Neil
Keyword(s):  
Gene Expression ◽  
T Lymphocytes ◽  
Gene Expression Regulation ◽  
Cell Types ◽  
Expression Data ◽  
Specific Expression ◽  
Complex Disorders ◽  
Risk Variants ◽  
Allele Specific ◽  
Regulatory Effects

BackgroundGenetic risk variants for complex disorders such as multiple sclerosis (MS) rarely encode protein. Growing evidence suggests risk variants regulate gene expression in specific disease-relevant cells by altering regulatory genetic sequences. This can be explored by correlating genotype at risk loci with expression of nearby genes (cis-regulation), with greatest sensitivity demonstrated in the most relevant cell types. Identifying genes under regulation by the MS risk variants could be a crucial step in identifying novel therapeutic targets.Aims1) Interrogate MS risk loci for cis-regulatory effects using allele-specific expression (ASE) analysis. 2) Establish a publicly available resource of genome-wide RNA-seq expression data in CSF-derived CD4 +T lymphocytes. 3) Correlate gene expression data with longitudinal clinical data to seek biomarkers of prognosis.Method and progressWe have extracted RNA from FACS-sorted CSF-derived CD4 +T lymphocytes from over 100 individuals. cDNA library preparation and sequencing for each sample is underway.Data analysisASE analysis makes use of a pre-selected transcribed SNP to differentiate maternal and paternal transcripts of each gene of interest. When an individual is heterozygous at an additional functional regulatory locus the ratio of maternal: paternal expression will deviate from 1:1. Each MS risk locus will be interrogated for this effect.

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

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.

scholarly journals Enhancers and Transcriptional Regulation

Blood ◽  
2016 ◽  
Vol 128 (22) ◽  
pp. SCI-14-SCI-14
Author(s):  
Joanna Wysocka
Keyword(s):  
Gene Expression ◽  
Transcriptional Regulation ◽  
Conflicts Of Interest ◽  
Expression Patterns ◽  
Regulatory Elements ◽  
Specific Gene ◽  
Regulatory Sequence ◽  
Cell Type ◽  
Control Of Gene Expression ◽  
Cell Type Specific

Abstract Interactions between the genome and its cellular and signaling environments, which ultimately occur at the level of chromatin, are the key to comprehending how cell-type-specific gene expression patterns arise and are maintained during development or are misregulated in disease. Central to the cell type-specific transcriptional regulation are distal cis-regulatory elements called enhancers, which function in a modular way to provide exquisite spatiotemporal control of gene expression during development. We are using a combination of genomic, genetic, biochemical, and single-cell approaches to investigate how enhancers are activated in response to developmental stimuli, how they communicate with target promoters over large genomic distances to regulate transcriptional outputs, what is the role of chromatin modification and remodeling in facilitating or restricting enhancer activity and how regulatory sequence change leads to the phenotypic divergence in humans. I will discuss our latest results on the mechanisms underlying enhancer function and gene regulation in development and disease. Disclosures No relevant conflicts of interest to declare.

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