The packaging of chromosomal DNA by nucleosomes condenses and organizes the genome, but occludes many regulatory DNA elements. However, this constraint also allows nucleosomes and other chromatin components to actively participate in the regulation of transcription, chromosome segregation, DNA replication, and DNA repair. To enable dynamic access to packaged DNA and to tailor nucleosome composition in chromosomal regions, cells have evolved a set of specialized chromatin remodeling complexes (remodelers). Remodelers use the energy of ATP hydrolysis to move, destabilize, eject, or restructure nucleosomes. Here, we address many aspects of remodeler biology: their targeting, mechanism, regulation, shared and unique properties, and specialization for particular biological processes. We also address roles for remodelers in development, cancer, and human syndromes.

The Biology of Chromatin Remodeling Complexes

Transcription factors are adaptor molecules that detect regulatory sequences in the DNA and target the assembly of protein complexes that control gene expression. Yet much of the DNA in the eukaryotic cell is in nucleosomes and thereby occluded by histones, and can be further occluded by higher-order chromatin structures and repressor complexes. Indeed, genome-wide location analyses have revealed that, for all transcription factors tested, the vast majority of potential DNA-binding sites are unoccupied, demonstrating the inaccessibility of most of the nuclear DNA. This raises the question of how target sites at silent genes become bound de novo by transcription factors, thereby initiating regulatory events in chromatin. Binding cooperativity can be sufficient for many kinds of factors to simultaneously engage a target site in chromatin and activate gene expression. However, in cases in which the binding of a series of factors is sequential in time and thus not initially cooperative, special "pioneer transcription factors" can be the first to engage target sites in chromatin. Such initial binding can passively enhance transcription by reducing the number of additional factors that are needed to bind the DNA, culminating in activation. In addition, pioneer factor binding can actively open up the local chromatin and directly make it competent for other factors to bind. Passive and active roles for the pioneer factor FoxA occur in embryonic development, steroid hormone induction, and human cancers. Herein we review the field and describe how pioneer factors may enable cellular reprogramming.

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Pioneer transcription factors: establishing competence for gene expression

Dynamic Regulation of Nucleosome Positioning in the Human Genome

Polycomb proteins form chromatin-modifying complexes that implement transcriptional silencing in higher eukaryotes. Hundreds of genes are silenced by Polycomb proteins, including dozens of genes that encode crucial developmental regulators in organisms ranging from plants to humans. Two main families of complexes, called Polycomb repressive complex 1 (PRC1) and PRC2, are targeted to repressed regions. Recent studies have advanced our understanding of these complexes, including their potential mechanisms of gene silencing, the roles of chromatin modifications, their means of delivery to target genes and the functional distinctions among variant complexes. Emerging concepts include the existence of a Polycomb barrier to transcription elongation and the involvement of non-coding RNAs in the targeting of Polycomb complexes. These findings have an impact on the epigenetic programming of gene expression in many biological systems.

Mechanisms of Polycomb gene silencing: knowns and unknowns

The field of machine learning, which aims to develop computer algorithms that improve with experience, holds promise to enable computers to assist humans in the analysis of large, complex data sets. Here, we provide an overview of machine learning applications for the analysis of genome sequencing data sets, including the annotation of sequence elements and epigenetic, proteomic or metabolomic data. We present considerations and recurrent challenges in the application of supervised, semi-supervised and unsupervised machine learning methods, as well as of generative and discriminative modelling approaches. We provide general guidelines to assist in the selection of these machine learning methods and their practical application for the analysis of genetic and genomic data sets.

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Machine learning applications in genetics and genomics

Eukaryotic genomes are packaged into nucleosome particles that occlude the DNA from interacting with most DNA binding proteins. Nucleosomes have higher affinity for particular DNA sequences, reflecting the ability of the sequence to bend sharply, as required by the nucleosome structure. However, it is not known whether these sequence preferences have a significant influence on nucleosome position in vivo, and thus regulate the access of other proteins to DNA. Here we isolated nucleosome-bound sequences at high resolution from yeast and used these sequences in a new computational approach to construct and validate experimentally a nucleosome–DNA interaction model, and to predict the genome-wide organization of nucleosomes. Our results demonstrate that genomes encode an intrinsic nucleosome organization and that this intrinsic organization can explain ∼50% of the in vivo nucleosome positions. This nucleosome positioning code may facilitate specific chromosome functions including transcription factor binding, transcription initiation, and even remodelling of the nucleosomes themselves. Eukaryotic genomes do not exist in vivo as naked DNA, but in complexes known as chromatin. Chromatin contains nucleosomes, short stretches of DNA tightly wrapped around a histone protein core, which exclude most DNA binding proteins and so act as repressors. A combined computational and experimental approach has been used to determine DNA sequence preferences of nucleosomes and to predict genome-wide nucleosome organization. The yeast genome encodes an intrinsic nucleosome organization that explains about half of the in vivo nucleosome positions. Highly conserved across eukaryotes, the code directs transcription factors to their binding sites and facilitates many other specific chromosome functions. An accompanying News and Views piece discusses the role of DNA sequence and other regulators in nucleosome positioning. The cover graphic represents a stretch of chromatin including several nucleosomes. A combined computational and experimental approach is used to determine the DNA sequence preferences of nucleosomes and predict genome-wide nucleosome organization. The yeast genome encodes an intrinsic nucleosome organization, which can explain about 50% of in vivo nucleosome positions.

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Annchristine Thåström

Papers

A genomic code for nucleosome positioning