scholarly journals Polycomb Requires Chaperonin Containing TCP-1 Subunit 7 for Maintaining Gene Silencing in Drosophila

2021 ◽  
Vol 9 ◽  
Author(s):  
Najma Shaheen ◽  
Jawad Akhtar ◽  
Zain Umer ◽  
Muhammad Haider Farooq Khan ◽  
Mahnoor Hussain Bakhtiari ◽  
...  
Keyword(s):  
Genetic Interaction ◽  
Expression Patterns ◽  
Cell Types ◽  
Polycomb Group ◽  
Specific Gene ◽  
Cellular Memory ◽  
Trithorax Group ◽  
T Complex ◽  
Ring Complex ◽  
Active Expression

In metazoans, heritable states of cell type-specific gene expression patterns linked with specialization of various cell types constitute transcriptional cellular memory. Evolutionarily conserved Polycomb group (PcG) and trithorax group (trxG) proteins contribute to the transcriptional cellular memory by maintaining heritable patterns of repressed and active expression states, respectively. Although chromatin structure and modifications appear to play a fundamental role in maintenance of repression by PcG, the precise targeting mechanism and the specificity factors that bind PcG complexes to defined regions in chromosomes remain elusive. Here, we report a serendipitous discovery that uncovers an interplay between Polycomb (Pc) and chaperonin containing T-complex protein 1 (TCP-1) subunit 7 (CCT7) of TCP-1 ring complex (TRiC) chaperonin in Drosophila. CCT7 interacts with Pc at chromatin to maintain repressed states of homeotic and non-homeotic targets of PcG, which supports a strong genetic interaction observed between Pc and CCT7 mutants. Depletion of CCT7 results in dissociation of Pc from chromatin and redistribution of an abundant amount of Pc in cytoplasm. We propose that CCT7 is an important modulator of Pc, which helps Pc recruitment at chromatin, and compromising CCT7 can directly influence an evolutionary conserved epigenetic network that supervises the appropriate cellular identities during development and homeostasis of an organism.

scholarly journals Polycomb requires Tcp-1η chaperonin for maintaining gene silencing inDrosophila

2021 ◽  
Author(s):  
Najma Shaheen ◽  
Jawad Akhtar ◽  
Zain Umer ◽  
Muhammad Haider Farooq Khan ◽  
Murtaza Saleem ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Fate ◽  
Molecular Interaction ◽  
Hox Genes ◽  
Genetic Interaction ◽  
Expression Patterns ◽  
Polycomb Group ◽  
Gene Expression Patterns ◽  
Specific Gene ◽  
Cellular Memory

AbstractIn metazoans, heritable states of cell type specific gene expression patterns linked with specialization of various cell types constitute transcriptional cellular memory. Evolutionarily conserved Polycomb group (PcG) and trithorax group (trxG) proteins contribute to the transcriptional cellular memory by maintaining heritable patterns of repressed and active expression states, respectively. Although chromatin structure and modifications appear to play a fundamental role in maintenance of repression by PcG, the precise targeting mechanism and the specificity factors that bind PcG complexes to a defined region in chromosomes remain elusive. Here we report a serendipitous discovery that uncovers a direct molecular interaction between Polycomb (PC) and TCP-1 Ring Complex (TRiC) chaperonin subunit, Tcp-1η inDrosophila. Tcp-1η interacts with PC at chromatin to maintain repressed states of homeotic and non-homeotic targets of PcG, which supports a strong genetic interaction observed betweenPcandTcp-1ηmutants. Depletion of Tcp-1η results in dissociation of PC from chromatin and redistribution of an abundant amount of PC in cytoplasm. We propose that Tcp-1η is an important modulator of PC, which helps PC recruitment at chromatin and compromising Tcp-1η can directly influence an evolutionary highly conserved epigenetic network that supervises the appropriate cellular identities during development and homoeostasis of an organism.Significance StatementSilencing of key developmental genes, e.g, Hox genes, by PcG is a hallmark of differential gene expression patterns associated with cell fate determination. Here we describe a previously unknown molecular and genetic interaction of Polycomb (PC) with Tcp-1η subunit of TRiC chaperonin complex inDrosophila. Compromising Tcp-1η function results in de-repression of PcG targets and a concomitant loss of PC from chromatin. Moreover, depletion of Tcp-1η leads to redistribution of PC in cytoplasm. Molecular interaction of PC with Tcp-1η highlights a novel factor which helps PC recruitment at chromatin. We propose that Tcp-1η chaperonin is one of the specificity factors and part of the targeting mechanism that binds PC to specific regions on chromosomes.

scholarly journals Analysis of Gal4 Expression Patterns in Adult Drosophila Females

2020 ◽  
Vol 10 (11) ◽  
pp. 4147-4158
Author(s):  
Lesley N. Weaver ◽  
Tianlu Ma ◽  
Daniela Drummond-Barbosa
Keyword(s):  
Genetic Manipulation ◽  
Expression Patterns ◽  
Cell Types ◽  
Tissue Expression ◽  
Whole Body ◽  
Specific Gene ◽  
Specific Cell ◽  
Gene Pathway ◽  
Gal4 Expression ◽  
Adult Drosophila

Precise genetic manipulation of specific cell types or tissues to pinpoint gene function requirement is a critical step in studies aimed at unraveling the intricacies of organismal physiology. Drosophila researchers heavily rely on the UAS/Gal4/Gal80 system for tissue-specific manipulations; however, it is often unclear whether the reported Gal4 expression patterns are indeed specific to the tissue of interest such that experimental results are not confounded by secondary sites of Gal4 expression. Here, we surveyed the expression patterns of commonly used Gal4 drivers in adult Drosophila female tissues under optimal conditions and found that multiple drivers have unreported secondary sites of expression beyond their published cell type/tissue expression pattern. These results underscore the importance of thoroughly characterizing Gal4 tools as part of a rigorous experimental design that avoids potential misinterpretation of results as we strive for understanding how the function of a specific gene/pathway in one tissue contributes to whole-body physiology.

scholarly journals Global Analysis of Cell Type-Specific Gene Expression

2003 ◽  
Vol 4 (2) ◽  
pp. 208-215 ◽  
Author(s):  
David W. Galbraith
Keyword(s):  
Gene Expression ◽  
Fluorescent Protein ◽  
Global Analysis ◽  
Expression Patterns ◽  
Three Dimensional ◽  
Global Gene Expression ◽  
Cell Types ◽  
Reasonable Assumption ◽  
Specific Gene ◽  
Different Cell Types

The tissues and organs of multicellular eukaryotes are frequently observed to comprise complex three-dimensional interspersions of different cell types. It is a reasonable assumption that different global patterns of gene expression are found within these different cell types. This review outlines general experimental strategies designed to characterize these global gene expression patterns, based on a combination of methods of transgenic fluorescent protein (FP) expression and targeting, of flow cytometry and sorting and of high-throughput gene expression analysis.

scholarly journals CT-FOCS: a novel method for inferring cell type-specific enhancer-promoter maps

2019 ◽  
Author(s):  
Tom Aharon Hait ◽  
Ran Elkon ◽  
Ron Shamir
Keyword(s):  
Gene Expression ◽  
Target Genes ◽  
Expression Patterns ◽  
Cell Types ◽  
Specific Gene ◽  
Cell Type ◽  
Chromatin Interactions ◽  
Cell Type Specific ◽  
Novel Method ◽  
Validation Scheme

AbstractSpatiotemporal gene expression patterns are governed to a large extent by enhancer elements, typically located distally from their target genes. Identification of enhancer-promoter (EP) links that are specific and functional in individual cell types is a key challenge in understanding gene regulation. We introduce CT-FOCS, a new statistical inference method that utilizes multiple replicates per cell type to infer cell type-specific EP links. Computationally predicted EP links are usually benchmarked against experimentally determined chromatin interactions measured by ChIA-PET and promoter-capture HiC techniques. We expand this validation scheme by using also loops that overlap in their anchor sites. In analyzing 1,366 samples from ENCODE, Roadmap epigenomics and FANTOM5, CT-FOCS inferred highly cell type-specific EP links more accurately than state-of-the-art methods. We illustrate how our inferred EP links drive cell type-specific gene expression and regulation.

scholarly journals Expression pattern determines regulatory logic

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0244864
Author(s):  
Carlos Mora-Martinez
Keyword(s):  
Gene Expression ◽  
Gene Regulatory Networks ◽  
Dna Sequences ◽  
In Silico ◽  
Regulatory Networks ◽  
Expression Patterns ◽  
Cell Types ◽  
Evolutionary Strategy ◽  
Specific Gene ◽  
Gene Regulatory

Large amounts of effort have been invested in trying to understand how a single genome is able to specify the identity of hundreds of cell types. Inspired by some aspects of Caenorhabditis elegans biology, we implemented an in silico evolutionary strategy to produce gene regulatory networks (GRNs) that drive cell-specific gene expression patterns, mimicking the process of terminal cell differentiation. Dynamics of the gene regulatory networks are governed by a thermodynamic model of gene expression, which uses DNA sequences and transcription factor degenerate position weight matrixes as input. In a version of the model, we included chromatin accessibility. Experimentally, it has been determined that cell-specific and broadly expressed genes are regulated differently. In our in silico evolved GRNs, broadly expressed genes are regulated very redundantly and the architecture of their cis-regulatory modules is different, in accordance to what has been found in C. elegans and also in other systems. Finally, we found differences in topological positions in GRNs between these two classes of genes, which help to explain why broadly expressed genes are so resilient to mutations. Overall, our results offer an explanatory hypothesis on why broadly expressed genes are regulated so redundantly compared to cell-specific genes, which can be extrapolated to phenomena such as ChIP-seq HOT regions.

scholarly journals Deep feature extraction of single-cell transcriptomes by generative adversarial network

2020 ◽  
Author(s):  
Mojtaba Bahrami ◽  
Malosree Maitra ◽  
Corina Nagy ◽  
Gustavo Turecki ◽  
Hamid R Rabiee ◽  
...  
Keyword(s):  
Single Cell ◽  
Expression Patterns ◽  
Cell Types ◽  
Individual Variability ◽  
Superior Performance ◽  
Supplementary Information ◽  
Specific Gene ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Deep Feature Extraction

Abstract Motivation Single-cell RNA-sequencing (scRNA-seq) offers the opportunity to dissect heterogeneous cellular compositions and interrogate the cell-type-specific gene expression patterns across diverse conditions. However, batch effects such as laboratory conditions and individual-variability hinder their usage in cross-condition designs. Results Here, we present a single-cell Generative Adversarial Network (scGAN) to simultaneously acquire patterns from raw data while minimizing the confounding effect driven by technical artifacts or other factors inherent to the data. Specifically, scGAN models the data likelihood of the raw scRNA-seq counts by projecting each cell onto a latent embedding. Meanwhile, scGAN attempts to minimize the correlation between the latent embeddings and the batch labels across all cells. We demonstrate scGAN on three public scRNA-seq datasets and show that our method confers superior performance over the state-of-the-art methods in forming clusters of known cell types and identifying known psychiatric genes that are associated with major depressive disorder. Availabilityand implementation The scGAN code and the information for the public scRNA-seq datasets are available at https://github.com/li-lab-mcgill/singlecell-deepfeature. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals The insulin-like growth factor (IGF)-I E-peptides are required for isoform-specific gene expression and muscle hypertrophy after local IGF-I production

2010 ◽  
Vol 108 (5) ◽  
pp. 1069-1076 ◽  
Author(s):  
Elisabeth R. Barton ◽  
J DeMeo ◽  
Hanqin Lei
Keyword(s):  
Growth Factor ◽  
Muscle Hypertrophy ◽  
Biological Effects ◽  
Expression Patterns ◽  
Cell Types ◽  
Specific Gene ◽  
Insulin Like Growth Factor ◽  
Igf I ◽  
Carboxy Terminal ◽  
Mmp13 Expression

Insulin-like growth factor I (IGF-I) coordinates proliferation and differentiation in a wide variety of cell types. The igf1 gene not only produces IGF-I, but also generates multiple carboxy-terminal extensions, the E-peptides, through alternative splicing leading to different isoforms. It is not known if the IGF-I isoforms share a common pathway for their actions, or if there are specific actions of each protein. Viral administration of murine IGF-IA, IGF-IB, and mature IGF, which lacked an E-peptide extension, was utilized to identify IGF-I isoform-specific responsive genes in muscles of young growing mice. Microarray analysis revealed responses that were driven by increased IGF-I regardless of the presence of E-peptide, such as Bcl-XL. In contrast, distinct expression patterns were observed after viral delivery of IGF-IA or IGF-IB, which included matrix metalloproteinase 13 (MMP13). Expression of Bcl-XL was prevented when viral administration of the IGF-I isoforms was performed into muscles of MKR mice, which lack functional IGF-I receptors on the muscle fibers. However, MMP13 expression persisted under the same conditions after viral injection of IGF-IB. At 4 mo after viral delivery, expression of IGF-IA or IGF-IB promoted muscle hypertrophy, but viral delivery of mature IGF-I failed to increase muscle mass. These studies provide evidence that local production of IGF-I requires the E-peptides to drive hypertrophy in growing muscle and that both common and unique pathways exist for the IGF-I isoforms to promote biological effects.

Detection of genes with tissue-specific expression patterns using Akaike’s information criterion procedure

2003 ◽  
Vol 12 (3) ◽  
pp. 251-259 ◽  
Author(s):  
Koji Kadota ◽  
Shin-Ichiro Nishimura ◽  
Hidemasa Bono ◽  
Shugo Nakamura ◽  
Yoshihide Hayashizaki ◽  
...  
Keyword(s):  
Expression Patterns ◽  
Information Criterion ◽  
Cell Types ◽  
Specific Gene ◽  
Akaike's Information Criterion ◽  
Expression Array ◽  
Specific Expression ◽  
Tissue Specific ◽  
Significance Level ◽  
Akaike’S Information Criterion

We applied a method based on Akaike’s information criterion (AIC) to detect genes whose expression profile is considerably different in some tissue(s) than in others. Such observations are detected as outliers, and the method we used was originally developed to detect outliers. The main advantage of the method is that objective decisions are possible because the procedure is independent of a significance level. We applied the method to 48 expression ratios corresponding to various tissues in each of 14,610 clones obtained from the RIKEN Expression Array Database (READ; http://read.gsc.riken.go.jp ). As a result, for several tissues (e.g., muscle, heart, and tongue tissues that contain similar cell types) we objectively obtained specific clones without any “thresholding.” Our study demonstrates the feasibility of the method for detecting tissue-specific gene expression patterns.

scholarly journals Deep feature extraction of single-cell transcriptomes by generative adversarial network

2020 ◽  
Author(s):  
Mojtaba Bahrami ◽  
Malosree Maitra ◽  
Corina Nagy ◽  
Gustavo Turecki ◽  
Hamid R. Rabiee ◽  
...  
Keyword(s):  
Single Cell ◽  
Negative Binomial ◽  
Expression Patterns ◽  
Cell Types ◽  
Individual Variability ◽  
Superior Performance ◽  
Specific Gene ◽  
Batch Effects ◽  
Generative Adversarial Network ◽  
Adversarial Network

AbstractMotivationSingle-cell RNA-sequencing (scRNA-seq) has opened the opportunities to dissect the heterogeneous cellular composition and interrogate the cell-type-specific gene expression patterns across diverse conditions. However, batch effects such as laboratory conditions and individual-variability hinder their usage in cross-condition design.ResultsWe present single-cell Generative Adversarial Network (scGAN). Our main contribution is to introduce an adversarial network to predict batch effects using the embeddings from the variational autoencoder network, which does not only need to maximize the Negative Binomial data likelihood of the raw scRNA-seq counts but also minimize the correlation between the latent embeddings and the batch effects. We demonstrate scGAN on three public scRNA-seq datasets and show that our method confers superior performance over the state-of-the-art methods in forming clusters of known cell types and identifying known psychiatric genes that are associated with major depressive disorder.AvailabilityThe code is available at https://github.com/li-lab-mcgill/[email protected]

Mesonephric cell migration induces testis cord formation and Sertoli cell differentiation in the mammalian gonad

Development ◽  
1999 ◽  
Vol 126 (13) ◽  
pp. 2883-2890 ◽  
Author(s):  
C. Tilmann ◽  
B. Capel
Keyword(s):  
Gene Expression ◽  
Cell Migration ◽  
Single Gene ◽  
Expression Patterns ◽  
Critical Role ◽  
Cell Types ◽  
Specific Gene ◽  
Specific Gene Expression ◽  
Male Specific ◽  
Testis Cord

In mammals a single gene on the Y chromosome, Sry, controls testis formation. One of the earliest effects of Sry expression is the induction of somatic cell migration from the mesonephros into the XY gonad. Here we show that mesonephric cells are required for cord formation and male-specific gene expression in XY gonads in a stage-specific manner. Culturing XX gonads with an XY gonad at their surface, as a ‘sandwich’, resulted in cell migration into the XX tissue. Analysis of sandwich gonads revealed that in the presence of migrating cells, XX gonads organized cord structures and acquired male-specific gene expression patterns. From these results, we conclude that mesonephric cell migration plays a critical role in the formation of testis cords and the differentiation of XY versus XX cell types.

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