Browse
Submit Data
Databases
API
Help

Dataset Information

23 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Fruitless decommissions regulatory elements to implement cell-type-specific neuronal masculinization.

ABSTRACT: In the fruit fly Drosophila melanogaster, male-specific splicing and translation of the Fruitless transcription factor (FruM) alters the presence, anatomy, and/or connectivity of >60 types of central brain neurons that interconnect to generate male-typical behaviors. While the indispensable function of FruM in sex-specific behavior has been understood for decades, the molecular mechanisms underlying its activity remain unknown. Here, we take a genome-wide, brain-wide approach to identifying regulatory elements whose activity depends on the presence of FruM. We identify 436 high-confidence genomic regions differentially accessible in male fruitless neurons, validate candidate regions as bona fide, differentially regulated enhancers, and describe the particular cell types in which these enhancers are active. We find that individual enhancers are not activated universally but are dedicated to specific fru+ cell types. Aside from fru itself, genes are not dedicated to or common across the fru circuit; rather, FruM appears to masculinize each cell type differently, by tweaking expression of the same effector genes used in other circuits. Finally, we find FruM motifs enriched among regulatory elements that are open in the female but closed in the male. Together, these results suggest that FruM acts cell-type-specifically to decommission regulatory elements in male fruitless neurons.

SUBMITTER: Brovkina MV

PROVIDER: S-EPMC7924761 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Fruitless decommissions regulatory elements to implement cell-type-specific neuronal masculinization

Project description:This SuperSeries is composed of the SubSeries listed below.

2021-01-22 | GSE160931 | GEO

Fruitless decommissions regulatory elements to implement cell-type-specific neuronal masculinization [RNA-Seq]

Project description:Gene expression profile of neurons comprising most of the central brain courtship circuit in adult male Drosophila brains, with sex and cell-type control populations.

2021-01-22 | GSE160930 | GEO

Fruitless decommissions regulatory elements to implement cell-type-specific neuronal masculinization [ATAC-seq]

Project description:Chromatin accessibility of neurons comprising most of the central brain courtship circuit in adult male Drosophila brains, with sex and cell-type control populations.

2021-01-22 | GSE159600 | GEO

Fruitless decommissions regulatory elements to implement cell-type-specific neuronal masculinization

Project description:Fruitless decommissions regulatory elements to implement cell-type-specific neuronal masculinization

| PRJNA674852 | ENA

Fruitless decommissions regulatory elements to implement cell-type-specific neuronal masculinization [ATAC-seq]

Project description:Fruitless decommissions regulatory elements to implement cell-type-specific neuronal masculinization [ATAC-seq]

| PRJNA669853 | ENA

Fruitless decommissions regulatory elements to implement cell-type-specific neuronal masculinization [RNA-Seq]

Project description:Fruitless decommissions regulatory elements to implement cell-type-specific neuronal masculinization [RNA-Seq]

| PRJNA674857 | ENA

Inference of putative cell-type-specific imprinted regulatory elements and genes during human neuronal differentiation.

Project description:Genomic imprinting results in gene expression bias caused by parental chromosome of origin and occurs in genes with important roles during human brain development. However, the cell-type and temporal specificity of imprinting during human neurogenesis is generally unknown. By detecting within-donor allelic biases in chromatin accessibility and gene expression that are unrelated to cross-donor genotype, we inferred imprinting in both primary human neural progenitor cells and their differentiated neuronal progeny from up to 85 donors. We identified 43/20 putatively imprinted regulatory elements (IREs) in neurons/progenitors, and 133/79 putatively imprinted genes in neurons/progenitors. Although 10 IREs and 42 genes were shared between neurons and progenitors, most putative imprinting was only detected within specific cell types. In addition to well-known imprinted genes and their promoters, we inferred novel putative IREs and imprinted genes. Consistent with both DNA methylation-based and H3K27me3-based regulation of imprinted expression, some putative IREs also overlapped with differentially methylated or histone-marked regions. Finally, we identified a progenitor-specific putatively imprinted gene overlapping with copy number variation that is associated with uniparental disomy-like phenotypes. Our results can therefore be useful in interpreting the function of variants identified in future parent-of-origin association studies.

| S-EPMC9851749 | biostudies-literature

Machine-guided design of synthetic cell type-specific <i>cis</i>-regulatory elements.

Project description:Cis-regulatory elements (CREs) control gene expression, orchestrating tissue identity, developmental timing, and stimulus responses, which collectively define the thousands of unique cell types in the body. While there is great potential for strategically incorporating CREs in therapeutic or biotechnology applications that require tissue specificity, there is no guarantee that an optimal CRE for an intended purpose has arisen naturally through evolution. Here, we present a platform to engineer and validate synthetic CREs capable of driving gene expression with programmed cell type specificity. We leverage innovations in deep neural network modeling of CRE activity across three cell types, efficient in silico optimization, and massively parallel reporter assays (MPRAs) to design and empirically test thousands of CREs. Through in vitro and in vivo validation, we show that synthetic sequences outperform natural sequences from the human genome in driving cell type-specific expression. Synthetic sequences leverage unique sequence syntax to promote activity in the on-target cell type and simultaneously reduce activity in off-target cells. Together, we provide a generalizable framework to prospectively engineer CREs and demonstrate the required literacy to write regulatory code that is fit-for-purpose in vivo across vertebrates.

| S-EPMC10441439 | biostudies-literature

Cell-type-specific enrichment of risk-associated regulatory elements at ovarian cancer susceptibility loci.

Project description:Understanding the regulatory landscape of the human genome is a central question in complex trait genetics. Most single-nucleotide polymorphisms (SNPs) associated with cancer risk lie in non-protein-coding regions, implicating regulatory DNA elements as functional targets of susceptibility variants. Here, we describe genome-wide annotation of regions of open chromatin and histone modification in fallopian tube and ovarian surface epithelial cells (FTSECs, OSECs), the debated cellular origins of high-grade serous ovarian cancers (HGSOCs) and in endometriosis epithelial cells (EECs), the likely precursor of clear cell ovarian carcinomas (CCOCs). The regulatory architecture of these cell types was compared with normal human mammary epithelial cells and LNCaP prostate cancer cells. We observed similar positional patterns of global enhancer signatures across the three different ovarian cancer precursor cell types, and evidence of tissue-specific regulatory signatures compared to non-gynecological cell types. We found significant enrichment for risk-associated SNPs intersecting regulatory biofeatures at 17 known HGSOC susceptibility loci in FTSECs (P = 3.8 × 10(-30)), OSECs (P = 2.4 × 10(-23)) and HMECs (P = 6.7 × 10(-15)) but not for EECs (P = 0.45) or LNCaP cells (P = 0.88). Hierarchical clustering of risk SNPs conditioned on the six different cell types indicates FTSECs and OSECs are highly related (96% of samples using multi-scale bootstrapping) suggesting both cell types may be precursors of HGSOC. These data represent the first description of regulatory catalogues of normal precursor cells for different ovarian cancer subtypes, and provide unique insights into the tissue specific regulatory variation with respect to the likely functional targets of germline genetic susceptibility variants for ovarian cancer.

| S-EPMC4459387 | biostudies-literature

Discovery of cell-type specific DNA motif grammar in cis-regulatory elements using random Forest.

Project description:It has been observed that many transcription factors (TFs) can bind to different genomic loci depending on the cell type in which a TF is expressed in, even though the individual TF usually binds to the same core motif in different cell types. How a TF can bind to the genome in such a highly cell-type specific manner, is a critical research question. One hypothesis is that a TF requires co-binding of different TFs in different cell types. If this is the case, it may be possible to observe different combinations of TF motifs - a motif grammar - located at the TF binding sites in different cell types. In this study, we develop a bioinformatics method to systematically identify DNA motifs in TF binding sites across multiple cell types based on published ChIP-seq data, and address two questions: (1) can we build a machine learning classifier to predict cell-type specificity based on motif combinations alone, and (2) can we extract meaningful cell-type specific motif grammars from this classifier model.We present a Random Forest (RF) based approach to build a multi-class classifier to predict the cell-type specificity of a TF binding site given its motif content. We applied this RF classifier to two published ChIP-seq datasets of TF (TCF7L2 and MAX) across multiple cell types. Using cross-validation, we show that motif combinations alone are indeed predictive of cell types. Furthermore, we present a rule mining approach to extract the most discriminatory rules in the RF classifier, thus allowing us to discover the underlying cell-type specific motif grammar.Our bioinformatics analysis supports the hypothesis that combinatorial TF motif patterns are cell-type specific.

| S-EPMC5780765 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data