Nuclear morphogenesis: forming a heterogeneous nucleus during embryogenesis

An embryo experiences progressively complex spatial and temporal patterns of gene expression that guide the morphogenesis of its body plan as it matures. Using super-resolution fluorescence microscopy in Drosophila melanogaster embryos, we observed a similar increase in complexity in the nucleus: the spatial distributions of transcription factors became increasingly heterogeneous as the embryo matured. We also observed a similar trend in chromatin conformation with the establishment of specific histone modification patterns. However, transcription sites of specific genes had distinct local preferences for histone marks separate from the average nuclear trend, depending on the time and location of their expression. These results suggest that reconfiguring the nuclear environment is an integral part of embryogenesis and that the physical organization of the nucleus a key element in developmental gene regulation.

Current Epigenetic Insights in Kidney Development

The kidney is among the best characterized developing tissues, with the genes and signaling pathways that regulate embryonic and adult kidney patterning and development having been extensively identified. It is now widely understood that DNA methylation and histone modification patterns are imprinted during embryonic development and must be maintained in adult cells for appropriate gene transcription and phenotypic stability. A compelling question then is how these epigenetic mechanisms play a role in kidney development. In this review, we describe the major genes and pathways that have been linked to epigenetic mechanisms in kidney development. We also discuss recent applications of single-cell RNA sequencing (scRNA-seq) techniques in the study of kidney development. Additionally, we summarize the techniques of single-cell epigenomics, which can potentially be used to characterize epigenomes at single-cell resolution in embryonic and adult kidneys. The combination of scRNA-seq and single-cell epigenomics will help facilitate the further understanding of early cell lineage specification at the level of epigenetic modifications in embryonic and adult kidney development, which may also be used to investigate epigenetic mechanisms in kidney diseases.

Deciphering epigenomic code for cell differentiation using deep learning

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.

MicroRNA Regulation of Epigenetic Modifiers in Breast Cancer

Epigenetics refers to the heritable changes in gene expression without a change in the DNA sequence itself. Two of these major changes include aberrant DNA methylation as well as changes to histone modification patterns. Alterations to the epigenome can drive expression of oncogenes and suppression of tumor suppressors, resulting in tumorigenesis and cancer progression. In addition to modifications of the epigenome, microRNA (miRNA) dysregulation is also a hallmark for cancer initiation and metastasis. Advances in our understanding of cancer biology demonstrate that alterations in the epigenome are not only a major cause of miRNA dysregulation in cancer, but that miRNAs themselves also indirectly drive these DNA and histone modifications. More explicitly, recent work has shown that miRNAs can regulate chromatin structure and gene expression by directly targeting key enzymes involved in these processes. This review aims to summarize these research findings specifically in the context of breast cancer. This review also discusses miRNAs as epigenetic biomarkers and as therapeutics, and presents a comprehensive summary of currently validated epigenetic targets in breast cancer.

Unique Epigenetic Programming Distinguishes Regenerative Spermatogonial Stem Cells in the Developing Mouse Testis

Spermatogonial stem cells (SSCs) both self-renew and give rise to progenitor spermatogonia that enter steady-state spermatogenesis in the mammalian testis. However, questions remain regarding the extent to which SSCs and progenitors represent stably distinct spermatogonial subtypes. Here we provide the first multiparametric integrative analysis of mammalian germ cell epigenomes comparable to that done by the ENCODE Project for >100 somatic cell types. Differentially expressed genes distinguishing SSCs and progenitors showed distinct histone modification patterns as well as differences in distal intergenic low-methylated regions. Motif-enrichment analysis predicted transcription factors that regulate this spermatogonial subtype-specific epigenetic programming, and gene-specific chromatin immunoprecipitation analyses confirmed subtype-specific differences in binding of a subset of these factors to target genes. Collectively, these results suggest that SSCs and progenitors are stably distinct spermatogonial subtypes differentially programmed to either self-renew and maintain regenerative capacity as SSCs, or lose regenerative capacity and initiate lineage commitment as progenitors.

Deciphering epigenomic code for cell differentiation using deep learning

ABSTRACTEpigenomic markers, such as histone modifications, play important roles in cell fate determination and type maintenance during cell differentiation. Although genomic sequence plays a crucial role in establishing the unique epigenome in each cell type produced during cell differentiation, little is known about the sequence determinants that lead to the unique epigenomes of the cells. Here, using a dataset of six histone markers measured in four human CD4+ T cell types produced at different stages of T cell development, we showed that two types of highly accurate deep convolutional neural networks (CNNs) constructed for each cell type and for each histone marker are a powerful strategy to uncover the sequence determinants of the various histone modification patterns in difference cell types. We found that sequence motifs learned by the CNN models are highly similar to known binding motifs of transcription factors known to play important roles in CD4+ T cell differentiation. Our results suggest that both the unique histone modification patterns in each cell type and the different patterns of the same histone marker in different cell types are determined by a set of motifs with unique combinations. Interestingly, the level of shared few motifs learned in the different cell models reflect the lineage relationships of the cells, while the level of few shared motifs learned in different histone marker models reflect their functional relationships. Furthermore, using these models, we can predict the importance of the learned motifs and their interactions in determining specific histone marker patterns in the cell types.