scholarly journals Functional classification of noncoding RNAs associated with distinct histone modifications by PIRCh-seq

2019 ◽  
Author(s):  
Jingwen Fang ◽  
Qing Ma ◽  
Ci Chu ◽  
Beibei Huang ◽  
Lingjie Li ◽  
...  
Keyword(s):  
Histone Modification ◽  
Histone Modifications ◽  
Noncoding Rnas ◽  
Cell Types ◽  
Single Nucleotide Variants ◽  
Chromatin Interactions ◽  
Allele Specific ◽  
Rna Interaction ◽  
Nascent Transcripts ◽  
Different Cell Types

ABSTRACTMany long noncoding RNAs (lncRNAs) regulate gene transcription through binding to histone modification complexes. Therefore, a comprehensive study of nuclear RNAs in a histone modification-specific manner is critical to understand their regulatory mechanisms. Here we develop a method named Profiling Interacting RNAs on Chromatin by deep sequencing (PIRCh-seq), in which we profile chromatin-associated transcriptome in 5 different cell types using antibodies recognizing histone H3 and 6 distinct histone modifications associated with active or repressive chromatin states. PIRCh-seq identified chromatin-associated RNAs with substantially less contamination by nascent transcripts, as compared to existing methods. We classified chromatin-enriched lncRNAs into 6 functional groups based on the patterns of their association with specific histone modifications. LncRNAs were enriched with different chromatin modifications in different cell types, suggesting lncRNAs’ regulation may also be cell type-specific. By integrating profiles of RNA secondary structure and RNA m6A modification, we found that RNA bases which bind to chromatin tend to be more single stranded. We discovered hundreds of allele-specific RNA-chromatin interactions, nominating specific single nucleotide variants that alter RNA association with chromatin. These results provide a unique resource to globally study the functions of chromatin-associated lncRNAs and elucidate the basic mechanisms of chromatin-RNA interaction.

scholarly journals PIRCh-seq: functional classification of non-coding RNAs associated with distinct histone modifications

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Jingwen Fang ◽  
Qing Ma ◽  
Ci Chu ◽  
Beibei Huang ◽  
Lingjie Li ◽  
...  
Keyword(s):  
Histone Modifications ◽  
Cell Types ◽  
Functional Classification ◽  
Chromatin Interactions ◽  
Specific Manner ◽  
Comprehensive Survey ◽  
Allele Specific ◽  
Non Coding Rnas ◽  
Nascent Transcripts

AbstractWe develop PIRCh-seq, a method which enables a comprehensive survey of chromatin-associated RNAs in a histone modification-specific manner. We identify hundreds of chromatin-associated RNAs in several cell types with substantially less contamination by nascent transcripts. Non-coding RNAs are found enriched on chromatin and are classified into functional groups based on the patterns of their association with specific histone modifications. We find single-stranded RNA bases are more chromatin-associated, and we discover hundreds of allele-specific RNA-chromatin interactions. These results provide a unique resource to globally study the functions of chromatin-associated lncRNAs and elucidate the basic mechanisms of chromatin-RNA interactions.

scholarly journals Large organized chromatin lysine domains help distinguish primitive from differentiated cell populations

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Seyed Ali Madani Tonekaboni ◽  
Benjamin Haibe-Kains ◽  
Mathieu Lupien
Keyword(s):  
Transposable Elements ◽  
Histone Modifications ◽  
Cell Types ◽  
Regulatory Elements ◽  
Cell Populations ◽  
Genomic Features ◽  
Differentiated Cells ◽  
Chromatin Interactions ◽  
Topologically Associating Domains ◽  
Highly Expressed Genes

AbstractThe human genome is partitioned into a collection of genomic features, inclusive of genes, transposable elements, lamina interacting regions, early replicating control elements and cis-regulatory elements, such as promoters, enhancers, and anchors of chromatin interactions. Uneven distribution of these features within chromosomes gives rise to clusters, such as topologically associating domains (TADs), lamina-associated domains, clusters of cis-regulatory elements or large organized chromatin lysine (K) domains (LOCKs). Here we show that LOCKs from diverse histone modifications discriminate primitive from differentiated cell types. Active LOCKs (H3K4me1, H3K4me3 and H3K27ac) cover a higher fraction of the genome in primitive compared to differentiated cell types while repressive LOCKs (H3K9me3, H3K27me3 and H3K36me3) do not. Active LOCKs in differentiated cells lie proximal to highly expressed genes while active LOCKs in primitive cells tend to be bivalent. Genes proximal to bivalent LOCKs are minimally expressed in primitive cells. Furthermore, bivalent LOCKs populate TAD boundaries and are preferentially bound by regulators of chromatin interactions, including CTCF, RAD21 and ZNF143. Together, our results argue that LOCKs discriminate primitive from differentiated cell populations.

scholarly journals The Histone Modification Code in the Pathogenesis of Autoimmune Diseases

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Yasuto Araki ◽  
Toshihide Mimura
Keyword(s):  
Autoimmune Diseases ◽  
Histone Modifications ◽  
Lupus Erythematosus ◽  
Cell Types ◽  
Biliary Cirrhosis ◽  
Loss Of Self ◽  
Self Tolerance ◽  
Different Cell Types

Autoimmune diseases are chronic inflammatory disorders caused by a loss of self-tolerance, which is characterized by the appearance of autoantibodies and/or autoreactive lymphocytes and the impaired suppressive function of regulatory T cells. The pathogenesis of autoimmune diseases is extremely complex and remains largely unknown. Recent advances indicate that environmental factors trigger autoimmune diseases in genetically predisposed individuals. In addition, accumulating results have indicated a potential role of epigenetic mechanisms, such as histone modifications, in the development of autoimmune diseases. Histone modifications regulate the chromatin states and gene transcription without any change in the DNA sequence, possibly resulting in phenotype alteration in several different cell types. In this paper, we discuss the significant roles of histone modifications involved in the pathogenesis of autoimmune diseases, including rheumatoid arthritis, systemic lupus erythematosus, systemic sclerosis, primary biliary cirrhosis, and type 1 diabetes.

scholarly journals Graph–based integration of histone modifications profiles: haematopoietic cell differentiation as a case study

2020 ◽  
Author(s):  
Federica Baccini ◽  
Monica Bianchini ◽  
Filippo Geraci
Keyword(s):  
Histone Modification ◽  
Histone Modifications ◽  
Cell Types ◽  
Lymphoid Cells ◽  
Differential Analysis ◽  
Cell Type ◽  
Similarity Networks ◽  
Unique Cell ◽  
Per Gene

AbstractIn this paper, we show that quantifying histone modifications by counting the number of high– resolution peaks in each gene allows to build profiles of these epigenetic marks, associating them to a phenotype. The significance of this approach is verified by applying graph–cut techniques for assessing the differentiation between myeloid and lymphoid cells in haematopoiesis, i.e. the process through which all the different types of blood cells originate starting from a unique cell type. The experiments are conducted on a population of samples from 24 cell types involved in haematopoiesis. Six profiles are constructed for each cell type, based on a different histone modification signal. Following the experimentally verified idea that the peak number distribution per gene behaves similarly to gene expression, the profile computation employs standard differential analysis tools to find genes whose epigenetic modifications are related to a given phenotype. Next, six similarity networks of cell types are constructed, based on each histone modification, and then combined into a unique one through similarity network fusion. Finally, the similarity networks are transformed into dissimilarity graphs, to which two different cuts are applied and compared to evaluate the classic differentiation between myeloid and lymphoid cells. The results show that all histone modifications contribute almost equally to the myeloid/lymphoid differentiation, and this is also confirmed by the analysis of the fused network. However, they also suggest that histone modifications may not be the only mechanism for regulating the differentiation of hematopoietic cells.

scholarly journals Deconvolving multiplexed histone modifications in single cells

2021 ◽  
Author(s):  
Jake Yeung ◽  
Maria Florescu ◽  
Peter Zeller ◽  
Buys Anton de Barbanson ◽  
Alexander van Oudenaarden
Keyword(s):  
Bone Marrow ◽  
B Cells ◽  
Histone Modification ◽  
Histone Modifications ◽  
Blood Cells ◽  
Single Cells ◽  
Cell Types ◽  
Mouse Bone Marrow ◽  
Immunoglobulin Genes ◽  
Computational Framework

Recent advances have enabled mapping of histone modifications in single cells, but current methods are constrained to profile only one histone modification per cell. Here we present an integrated experimental and computational framework, scChIX (single-cell chromatin immunocleavage and unmixing), to map multiple histone modifications in single cells. We first validate this method using purified blood cells and show that although the two repressive marks, H3K27me3 and H3K9me3, are generally mutually exclusive, the transitions between the two regions can vary between cell types. Next we apply scChIX to a heterogenous cell population from mouse bone marrow to generate linked maps of active (H3K4me1) and repressive (H3K27me3) chromatin landscapes in single cells, where coordinates in the active modification map correspond to coordinates in the repressive map. Linked analysis reveals that immunoglobulin genes in the region are in a repressed chromatin state in pro-B cells, but become activated in B cells. Overall, scChIX unlocks systematic interrogation of the interplay between histone modifications in single cells.

scholarly journals Principles of 3D Compartmentalization of the Human Genome

2020 ◽  
Author(s):  
Michael H. Nichols ◽  
Victor G. Corces
Keyword(s):  
Histone Modifications ◽  
Transcriptional Activity ◽  
Cell Types ◽  
Computer Simulation Model ◽  
Domain Formation ◽  
Biochemical Characteristics ◽  
Different Types ◽  
Nuanced Understanding ◽  
Fundamental Forces ◽  
Different Cell Types

AbstractChromatin is organized in the nucleus by CTCF loops and compartmental domains, the latter of which contain sequences bound by proteins capable of mediating interactions among themselves. While compartmental domains are one of the most prominent features of genome 3D organization at the chromosome scale, we lack a nuanced understanding of the different types of compartmental domains present in chromosomes and a mechanistic knowledge of the forces responsible for their formation. In this study, we compared different cell types to identify distinct paradigms of compartmental domain formation in human tissues. We identified and quantified compartmental forces correlated with histone modifications characteristic of transcriptional activity as well as previously underappreciated roles for compartmental domains correlated with the presence of H3K9me3, H3K27me3, or none of these histone modifications. We present a simple computer simulation model capable of predicting compartmental organization based on the biochemical characteristics of independent chromatin features. Using this computational model, we show that the underlying forces responsible for compartmental domain formation in human cells are conserved and that the diverse compartmentalization patterns seen across cells are due to differences in chromatin features. We extend these findings to Drosophila to suggest that the same fundamental forces are at work beyond humans. These results offer mechanistic insights into the fundamental forces driving the 3D organization of the genome.

scholarly journals Significance of Long Noncoding RNAs in Regenerative Medicine

2017 ◽  
Vol 3 ◽  
pp. 0
Author(s):  
Raheleh Amirkhah
Keyword(s):  
Next Generation Sequencing ◽  
Noncoding Rnas ◽  
Cell Types ◽  
Long Noncoding Rnas ◽  
Rna Seq ◽  
Huge Data ◽  
Paracrine Action ◽  
Different Cell Types ◽  
Generation Sequencing ◽  
Heterogeneous Class

Long noncoding RNAs (lncRNAs) are a heterogeneous class of RNAs with generally longer than 200 nucleotides. It has been proposed that LncRNAs as a piece of paracrine action would control cellular pluripotency, differentiation, maintenance and regulate tissue development, organogenesis and regeneration. Next generation sequencing (RNA-seq) has produced huge data about lncRNAs expression profile in different cell types and condition, but understanding the roles and functions of these novel lncRNAs is poorly understood.

scholarly journals Noncoding RNAs in Cardiac Autophagy following Myocardial Infarction

2019 ◽  
Vol 2019 ◽  
pp. 1-6 ◽  
Author(s):  
Annie Turkieh ◽  
Henri Charrier ◽  
Emilie Dubois-Deruy ◽  
Sina Porouchani ◽  
Marion Bouvet ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Left Ventricle ◽  
Noncoding Rnas ◽  
Cell Types ◽  
Cardiac Cells ◽  
Left Ventricle Remodeling ◽  
Pathological Conditions ◽  
Evolutionarily Conserved ◽  
Different Cell Types

Macroautophagy is an evolutionarily conserved process of the lysosome-dependent degradation of damaged proteins and organelles and plays an important role in cellular homeostasis. Macroautophagy is upregulated after myocardial infarction (MI) and seems to be detrimental during reperfusion and protective during left ventricle remodeling. Identifying new regulators of cardiac autophagy may help to maintain the activity of this process and protect the heart from MI effects. Recently, it was shown that noncoding RNAs (microRNAs and long noncoding RNAs) are involved in autophagy regulation in different cell types including cardiac cells. In this review, we summarized the role of macroautophagy in the heart following MI and we focused on the noncoding RNAs and their targeted genes reported to regulate autophagy in the heart under these pathological conditions.

scholarly journals Revealing Hi-C subcompartments by imputing high-resolution inter-chromosomal chromatin interactions

2018 ◽  
Author(s):  
Kyle Xiong ◽  
Jian Ma
Keyword(s):  
High Resolution ◽  
Genome Organization ◽  
Cell Types ◽  
High Coverage ◽  
Chromatin Interactions ◽  
Gm12878 Cell ◽  
Additional Cell ◽  
Multiple Cell ◽  
Spatial Genome Organization ◽  
Different Cell Types

AbstractThe higher-order genome organization and its variation in different cellular conditions remains poorly understood. Recent high-resolution genome-wide mapping of chromatin interactions using Hi-C has revealed that chromosomes in the human genome are spatially segregated into distinct subcompartments. However, due to the requirement on sequencing coverage of the Hi-C data to define subcompartments, to date subcompartment annotation is only available in the GM12878 cell line, making it impractical to compare Hi-C subcompartment patterns across multiple cell types. Here we develop a new computational approach, named Sniper, based on an autoencoder and multilayer perceptron classifier to infer subcompartments using typical Hi-C datasets with moderate coverage. We demonstrated that Sniper can accurately reveal subcompartments based on Hi-C datasets with moderate coverage and can significantly outperform an existing method that uses numerous epigenomic datasets as input features in GM12878. We applied Sniper to eight additional cell lines to identify the variation of Hi-C subcompartments across different cell types. Sniper revealed that chromosomal regions with conserved and more dynamic subcompartment annotations across cell types have different patterns of functional genomic features. This work demonstrates that Sniper is effective in identifying subcompartments without the need of high-coverage Hi-C data and has the potential to provide new insights into the spatial genome organization variation across different cell types.

scholarly journals Deciphering epigenomic code for cell differentiation using deep learning

BMC Genomics ◽  
2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Pengyu Ni ◽  
Zhengchang Su
Keyword(s):  
Cell Differentiation ◽  
T Cell ◽  
Histone Modification ◽  
Cell Types ◽  
Histone Mark ◽  
Cell Models ◽  
Cell Type ◽  
Histone Marks ◽  
Different Cell Types ◽  
Histone Modification Patterns

Abstract Background Although DNA sequence plays a crucial role in establishing the unique epigenome of a cell type, little is known about the sequence determinants that lead to the unique epigenomes of different cell types produced during cell differentiation. To fill this gap, we employed two types of deep convolutional neural networks (CNNs) constructed for each of differentially related cell types and for each of histone marks measured in the cells, to learn the sequence determinants of various histone modification patterns in each cell type. Results We applied our models to four differentially related human CD4+ T cell types and six histone marks measured in each cell type. The cell models can accurately predict the histone marks in each cell type, while the mark models can also accurately predict the cell types based on a single mark. Sequence motifs learned by both the cell or mark models are highly similar to known binding motifs of transcription factors known to play important roles in CD4+ T cell differentiation. Both the unique histone mark patterns in each cell type and the different patterns of the same histone mark in different cell types are determined by a set of motifs with unique combinations. Interestingly, the level of sharing motifs learned in the different cell models reflects the lineage relationships of the cells, while the level of sharing motifs learned in the different histone mark models reflects their functional relationships. These models can also enable the prediction of the importance of learned motifs and their interactions in determining specific histone mark patterns in the cell types. Conclusion Sequence determinants of various histone modification patterns in different cell types can be revealed by comparative analysis of motifs learned in the CNN models for multiple cell types and histone marks. The learned motifs are interpretable and may provide insights into the underlying molecular mechanisms of establishing the unique epigenomes in different cell types. Thus, our results support the hypothesis that DNA sequences ultimately determine the unique epigenomes of different cell types through their interactions with transcriptional factors, epigenome remodeling system and extracellular cues during cell differentiation.

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