Maternal pluripotency factors initiate extensive chromatin remodelling to predefine first response to inductive signals

Abstract Embryonic development yields many different cell types in response to just a few families of inductive signals. The property of signal-receiving cells that determines how they respond to inductive signals is known as competence, and it differs in different cell types. Here, we explore the ways in which maternal factors modify chromatin to specify initial competence in the frog Xenopus tropicalis. We identify early-engaged regulatory DNA sequences, and infer from them critical activators of the zygotic genome. Of these, we show that the pioneering activity of the maternal pluripotency factors Pou5f3 and Sox3 determines competence for germ layer formation by extensively remodelling compacted chromatin before the onset of inductive signalling. This remodelling includes the opening and marking of thousands of regulatory elements, extensive chromatin looping, and the co-recruitment of signal-mediating transcription factors. Our work identifies significant developmental principles that inform our understanding of how pluripotent stem cells interpret inductive signals.

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The role of maternal pioneer factors in predefining first zygotic responses to inductive signals

10.1101/306803 ◽

2018 ◽

Cited By ~ 5

Author(s):

George E. Gentsch ◽

Thomas Spruce ◽

Nick D. L. Owens ◽

James C. Smith

Keyword(s):

Dna Sequences ◽

Cell Types ◽

Regulatory Elements ◽

Regulatory Dna Sequences ◽

Cell Type Specific ◽

Pioneer Factors ◽

Developmental Principles ◽

Regulatory Dna ◽

Different Cell Types ◽

Inductive Signals

ABSTRACTEmbryonic development yields many different cell types in response to just a few families of inductive signals. The property of a signal-receiving cell that determines how it responds to such signals, including the activation of cell type-specific genes, is known as its competence. Here, we show how maternal factors modify chromatin to specify initial competence in the frog Xenopus tropicalis. We identified the earliest engaged regulatory DNA sequences, and inferred from them critical activators of the zygotic genome. Of these, we showed that the pioneering activity of the maternal pluripotency factors Pou5f3 and Sox3 predefines competence for germ layer formation by extensively remodeling compacted chromatin before the onset of signaling. The remodeling includes the opening and marking of thousands of regulatory elements, extensive chromatin looping, and the co-recruitment of signal-mediating transcription factors. Our work identifies significant developmental principles that inform our understanding of how pluripotent stem cells interpret inductive signals.

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Sequence determinants of human gene regulatory elements

10.1101/2021.03.18.435942 ◽

2021 ◽

Author(s):

Biswajyoti Sahu ◽

Tuomo Hartonen ◽

Paivi Pihlajamaa ◽

Bei Wei ◽

Kashyap Dave ◽

...

Keyword(s):

Gene Expression ◽

Dna Sequences ◽

Transcriptional Activity ◽

Cell Types ◽

Regulatory Elements ◽

Binding Motif ◽

Atomic Unit ◽

Gene Regulatory Elements ◽

A Cell ◽

Different Cell Types

DNA determines where and when genes are expressed, but the full set of sequence determinants that control gene expression is not known. To obtain a global and unbiased view of the relative importance of different sequence determinants in gene expression, we measured transcriptional activity of DNA sequences that are in aggregate ~100 times longer than the human genome in three different cell types. We show that enhancers can be classified to three main types: classical enhancers1, closed chromatin enhancers and chromatin-dependent enhancers, which act via different mechanisms and differ in motif content. Transcription factors (TFs) act generally in an additive manner with weak grammar, with classical enhancers increasing expression from promoters by a mechanism that does not involve specific TF-TF interactions. Few TFs are strongly active in a cell, with most activities similar between cell types. Chromatin-dependent enhancers are enriched in forkhead motifs, whereas classical enhancers contain motifs for TFs with strong transactivator domains such as ETS and bZIP; these motifs are also found at transcription start site (TSS)-proximal positions. However, some TFs, such as NRF1 only activate transcription when placed close to the TSS, and others such as YY1 display positional preference with respect to the TSS. TFs can thus be classified into four non-exclusive subtypes based on their transcriptional activity: chromatin opening, enhancing, promoting and TSS determining factors — consistent with the view that the binding motif is the only atomic unit of gene expression.

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Targeted optimization of regulatory DNA sequences with neural editing architectures

10.1101/714402 ◽

2019 ◽

Cited By ~ 2

Author(s):

Anvita Gupta ◽

Anshul Kundaje

Keyword(s):

Gene Expression ◽

Dna Sequences ◽

Binding Sites ◽

Network Architecture ◽

Regulatory Elements ◽

Biological Knowledge ◽

Specific Gene ◽

Learning Approaches ◽

Regulatory Dna Sequences ◽

Regulatory Dna

AbstractTargeted optimizing of existing DNA sequences for useful properties, has the potential to enable several synthetic biology applications from modifying DNA to treat genetic disorders to designing regulatory elements to fine tune context-specific gene expression. Current approaches for targeted genome editing are largely based on prior biological knowledge or ad-hoc rules. Few if any machine learning approaches exist for targeted optimization of regulatory DNA sequences.Here, we propose a novel generative neural network architecture for targeted DNA sequence editing – the EDA architecture – consisting of an encoder, decoder, and analyzer. We showcase the use of EDA to optimize regulatory DNA sequences to bind to the transcription factor SPI1. Compared to other state-of-the-art approaches such as a textual variational autoencoder and rule-based editing, EDA significantly improves predicted binding of SPI1 of genomic sequences with the minimal set of edits. We also use EDA to design regulatory elements with optimized grammars of CREB1 binding sites that can tune reporter expression levels as measured by massively parallel reporter assays (MPRA). We analyze the properties of the binding sites in the edited sequences and find patterns that are consistent with previously reported grammatical rules which tie gene expression to CRE binding site density, spacing and affinity.

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Haystack: systematic analysis of the variation of epigenetic states and cell-type specific regulatory elements

10.1101/199067 ◽

2017 ◽

Author(s):

Luca Pinello ◽

Rick Farouni ◽

Guo-Cheng Yuan

Keyword(s):

Dna Sequences ◽

Cell Types ◽

Regulatory Elements ◽

Epigenetic Mark ◽

Cell Type ◽

Epigenetic Variability ◽

Bioinformatics Pipeline ◽

Systematic Analysis ◽

Regulatory Dna Sequences ◽

Cell Type Specific

AbstractMotivationWith the increasing amount of genomic and epigenomic data in the public domain, a pressing challenge is how to integrate these data to investigate the role of epigenetic mechanisms in regulating gene expression and maintenance of cell-identity. To this end, we have implemented a computational pipeline to systematically study epigenetic variability and uncover regulatory DNA sequences that play a role in gene regulation.ResultsHaystack is a bioinformatics pipeline to characterize hotspots of epigenetic variability across different cell-types as well as cell-type specific cis-regulatory elements along with their corresponding transcription factors. Our approach is generally applicable to any epigenetic mark and provides an important tool to investigate cell-type identity and the mechanisms underlying epigenetic switches during development. Additionally, we make available a set of precomputed tracks for a number of epigenetic marks across several cell types. These precomputed results may be used as an independent resource for functional annotation of the human genome.AvailabilityThe Haystack pipeline is implemented as an open-source, multiplatform, Python package called haystack_bio available at https://github.com/pinellolab/[email protected], [email protected]

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Role of Transcriptional Read-Through in PRE Activity in Drosophila melanogaster

Acta Naturae ◽

10.32607/20758251-2016-8-2-79-86 ◽

2016 ◽

Vol 8 (2) ◽

pp. 79-86 ◽

Cited By ~ 3

Author(s):

P. V. Elizar’ev ◽

D. V. Lomaev ◽

D. A. Chetverina ◽

P. G. Georgiev ◽

M. M. Erokhin

Keyword(s):

Dna Sequences ◽

Cell Types ◽

Transcription Terminator ◽

Response Elements ◽

Polycomb Response Elements ◽

The Individual ◽

Multicellular Organisms ◽

Different Cell Types ◽

Main Factor

Maintenance of the individual patterns of gene expression in different cell types is required for the differentiation and development of multicellular organisms. Expression of many genes is controlled by Polycomb (PcG) and Trithorax (TrxG) group proteins that act through association with chromatin. PcG/TrxG are assembled on the DNA sequences termed PREs (Polycomb Response Elements), the activity of which can be modulated and switched from repression to activation. In this study, we analyzed the influence of transcriptional read-through on PRE activity switch mediated by the yeast activator GAL4. We show that a transcription terminator inserted between the promoter and PRE doesnt prevent switching of PRE activity from repression to activation. We demonstrate that, independently of PRE orientation, high levels of transcription fail to dislodge PcG/TrxG proteins from PRE in the absence of a terminator. Thus, transcription is not the main factor required for PRE activity switch.

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SeqEnhDL: sequence-based classification of cell type-specific enhancers using deep learning models

10.1101/2020.05.13.093997 ◽

2020 ◽

Author(s):

Yupeng Wang ◽

Rosario B. Jaime-Lara ◽

Abhrarup Roy ◽

Ying Sun ◽

Xinyue Liu ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Dna Sequences ◽

Cell Types ◽

Learning Models ◽

Cell Type ◽

Coding Sequences ◽

Sequence Features ◽

Cell Type Specific ◽

Different Cell Types

AbstractWe propose SeqEnhDL, a deep learning framework for classifying cell type-specific enhancers based on sequence features. DNA sequences of “strong enhancer” chromatin states in nine cell types from the ENCODE project were retrieved to build and test enhancer classifiers. For any DNA sequence, sequential k-mer (k=5, 7, 9 and 11) fold changes relative to randomly selected non-coding sequences were used as features for deep learning models. Three deep learning models were implemented, including multi-layer perceptron (MLP), Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN). All models in SeqEnhDL outperform state-of-the-art enhancer classifiers including gkm-SVM and DanQ, with regard to distinguishing cell type-specific enhancers from randomly selected non-coding sequences. Moreover, SeqEnhDL is able to directly discriminate enhancers from different cell types, which has not been achieved by other enhancer classifiers. Our analysis suggests that both enhancers and their tissue-specificity can be accurately identified according to their sequence features. SeqEnhDL is publicly available at https://github.com/wyp1125/SeqEnhDL.

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Expression of the K-fgf proto-oncogene is controlled by 3' regulatory elements which are specific for embryonal carcinoma cells

Molecular and Cellular Biology ◽

10.1128/mcb.10.6.2475-2484.1990 ◽

1990 ◽

Vol 10 (6) ◽

pp. 2475-2484

Author(s):

A M Curatola ◽

C Basilico

Keyword(s):

Dna Sequences ◽

Embryonal Carcinoma ◽

Protein S ◽

Cell Types ◽

Regulatory Elements ◽

Developmentally Regulated ◽

Cis Acting ◽

Dna Elements ◽

Ec Cells ◽

Cat Expression

Expression of the K-fgf/hst proto-oncogene appears to be restricted to cells in the early stages of development, such as embryonal carcinoma (EC) cells. When EC cells are induced to differentiate, K-fgf expression is drastically repressed. To identify cis-acting DNA elements responsible for this type of regulation, we constructed a plasmid in which cat gene expression was driven by about 1 kilobase of upstream K-fgf human DNA sequences, including the putative promoter, and transfected it into undifferentiated F9 EC cells or HeLa cells as prototypes of cells which express or do not express, respectively, the K-fgf proto-oncogene. This plasmid was essentially inactive in both cell types, and the addition of more than 8 kilobases of DNA sequences upstream of the K-fgf promoter did not lead to any increase in chloramphenicol acetyltransferase (CAT) expression. On the other hand, when we inserted in this plasmid DNA sequences which are 3' of the human K-fgf coding sequences, we could detect a significant stimulation of CAT activity. Analysis of these sequences led to the identification of enhancerlike DNA elements which are part of the 3' noncoding region of K-fgf exon 3 and promote CAT expression only in undifferentiated mouse F9 or human NT2/D1 EC cells, but not in HeLa, 3T3, or differentiated F9 cells, therefore mimicking the physiological expression of the K-fgf proto-oncogene. Similar elements are also present in the 3' region of the murine K-fgf proto-oncogene, in a region showing high homology to the human K-fgf sequences. These regulatory elements can promote CAT expression from heterologous promoters in an EC-specific manner, suggesting that they interact with a specific cellular transacting protein(s) whose expression is developmentally regulated.

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