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Published in final edited form as: J Mol Biol. 2020 Oct 22;433(6):166684. doi: 10.1016/j.jmb.2020.10.018

Human histone interaction networks: an old concept, new trends

Yunhui Peng 1, Yaroslav Markov 1,2, Alexander Goncearenco 1,3, David Landsman 1,*, Anna R Panchenko 4,*
PMCID: PMC7987645  NIHMSID: NIHMS1639816  PMID: 33098859

Abstract

To elucidate the properties of human histone interactions on the large scale, we perform a comprehensive mapping of human histone interaction networks by using data from structural, chemical cross-linking and high-throughput studies. Histone interactomes derived from different data sources show limited overlap and complement each other. It inspires us to integrate these data into the combined histone global interaction network which includes 5,308 proteins and 10,330 interactions. The analysis of topological properties of the human histone interactome reveals its scale free behavior and high modularity. Our study of histone binding interfaces uncovers a remarkably high number of residues involved in histone interactions, 80–90% of residues in histones H3 and H4 have at least one binding partner. Two types of histone binding modes are detected: interfaces conserved in most histone variants and variant specific interfaces. Finally, different types of chromatin factors recognize histones in nucleosomes via different localized binding modes, and many of these interfaces utilize acidic patches. Interaction networks are available at https://github.com/Panchenko-Lab/Human-histone-interactome.

Introduction

Histones are basic nuclear proteins that help to pack DNA into nucleosomes and chromatosomes and are involved in a wide spectrum of epigenetic pathways. Nucleosome comprises ~147 bp of DNA wrapped around a histone octamer and is a central point in coordinating of various chromatin signaling pathways [1]. The molecular recognition of nucleosomes by chromatin factors frequently occurs through the interactions with the nucleosomal and linker DNA, histone tails, histone globular domains [2–4] and by recognizing their specific covalent modifications [5]. Elucidating the physicochemical properties of histone interactions within and outside nucleosomal context is essential for our understanding the principles of chromatin organization and regulation. Recent advances in high-throughput (HTP) experimental techniques have allowed for the large-scale measurements of protein-protein interactions (PPIs) in various cellular compartments of different species. The complexity of interactions between proteins in human nuclei has been recently assessed, identifying hundreds of proteins interacting with different histone types [6–13] and histone post-translationally modified sites [14–19].

Although high-throughput approaches have been widely applied for mapping of protein interactomes, the identified PPIs still suffer from high false-positive and false-negative rates and by inability to provide data of high resolution on physiological chromatin states. These issues can be addressed in part by using experimental data produced by X-ray crystallography, NMR spectroscopy and cryo-electron microscopy (cryo-EM) techniques which provide atomic or near-atomic resolution on specific biologically relevant interactions in chromatin [20, 21]. These studies enable a systematic analysis of histone and nucleosome interactions and their binding interfaces to complement and interpret interactomes obtained from the high-throughput approaches. The number of histone and nucleosome complex structures in Protein Data Bank (PDB) has been exponentially increasing in recent years [3] but still is very low due to the complexity of structural characterization. Moreover, most of the low-throughput approaches focus on individual interactions or sub-networks of histone or tail peptide interactions.

A comprehensive human protein interactome characterization remains a daunting challenge, which requires the integration of different experimental and computational approaches [22, 23]. Herein, we perform a comprehensive mapping of human histone and nucleosome interactions by systematically analyzing the structural, chemical cross-linking and HTP data. Our global human histone interactome combined from structural, cross-linking and high-throughput data has 5,308 protein nodes and 10,330 interactions between them. Finally, we characterize binding interfaces and identify binding hotspots on individual histones, histone vatiants and on histones in the context of nucleosomes.

Methods

Construction of human histone/nucleosome interactomes

To improve our understanding of the biological processes mediated by nucleosome or histone interactions in human, we used three different sources of experimental data (Figure 1). We further explored histone interactions at different levels of granularity: protein, domain and residue-levels. The details are outlined below.

Figure 1.

Figure 1.

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A workflow to construct human histone interactomes from different resources using PDB structures of histone complexes, crosslinking mass spectrometry and high-throughput data from the APID database.

Histone structural interactome

The histone structural interactome includes histone interactions collected from the available histone and nucleosome complex structures in PDB [20]. To build this network, first we performed the text search against PDB using a list of keywords associated with histones (Table SM1). The PDB identifiers of obtained structures were further used to extract information on species, protein names, UniProt accessions and chain identifiers with the RCSB PDB RESTful Web Service interface (https://www.rcsb.org/pdb/software/rest.do). Second, three rounds of filtering were applied to extract the structures of human histones or nucleosomes in complex with other proteins: i) structures that did not contain any human histones were excluded; ii) ambiguous cases of synthetic constructs mimicking histone tails without relevant UniProtKB accessions that could not be mapped to known histone sequences in HistoneDB 2.0 database were excluded [24, 25]; iii) structures that contained histones without any protein binding partners were removed. It should be noted that a small fraction of collected PDB histone structures contained human histones in complex with proteins from other species. Such inter-species histone interactions were checked and included in the interaction network if binding partners were evolutionary conserved based on the corresponding reference. Proteins were considered as unique histone-binding partners if they had different UniProtKB accessions. In total, we found 208 histone interactions with 164 different binding proteins from 345 structures of individual histones or histones in nucleosome complexes (Table SM2).

In order to identify binding interfaces we retrieved coordinates of biological assemblies from the PDB and analyzed their inter-protein contacts. Interfaces consisted of residues located within 5 Å distance between heavy atoms of histones and their binding partners. Residue locations were mapped to the sequences of the corresponding UniprotKB entries using SIFTS [26]. Next, using protein domain family annotations from the Conserved Domain Database (a collection of manually curated and annotated multiple sequence alignments for protein domain families and full-length proteins) [27], we identified domain families of histone-binding proteins. Proteins interacting with histones via the same domain family were grouped together in the “domain-level network” totaling in 137 interactions from 113 unique domain families.

Histone cross-linking interactome

Cross-linking mass spectrometry is a powerful experimental approach for identifying protein-protein interactions and probing PPI interfaces containing certain residues [28]. Recently, PPIs in human nuclei were studied using this technique and interaction network was built from the inter-protein crosslinks between spatially closely located lysine residues (cross-linked using disuccinimidyl sulfoxide, DSSO) [10]. Using the interactions observed in both fractionated and unfractionated crosslinked nuclei from [10] (in total 1855 PPIs in human nuclei), we extracted the histone interactions where one human histone was cross-linked with another human non-histone protein. This so called “cross-linking histone network” included 274 interactions with 200 histone-binding proteins. In addition, we used domain annotations from the Conserved Domain Database [27] to construct the domain-level cross-linking network which contained 107 interactions from 70 different domain families. Finally, binding interfaces were extracted by mapping specific lysine residues forming inter-protein crosslinks from XL-MS data [10].

Histone high-throughput interactome

APID database integrates PPIs from several major databases of molecular interactions for more than 1100 organisms [29]. Herein, we extracted human histone interactions from the APID database supported by “binary” methods which provided data on direct physical interactions between proteins; inter-species interactions were excluded. We used UniProtKB accessions to identify histone proteins. Histone interaction was identified if one human histone interacted with another human non-histone protein in APID database. As a consequence, 220 interactions between histones and 163 histone-binding proteins were extracted from APID database to build the human histone high-throughput interactome. For most APID entries no data on binding interfaces was available.

Interactome visualization and analysis

All constructed human histone interactomes were visualized using Cytoscape [30]. Nodes were annotated with the UniProtKB accession identifiers and the BioPAX_SIF style was used in the network visualization. Histone-binding proteins were classified by their functions using the PANTHER server (http://www.pantherdb.org) [31] and further categorized into different groups using the PANTHER protein class. All human histone interactome Cytoscape session files are available at https://github.com/Panchenko-Lab/Human-histone-interactome.

The topological properties of networks were analyzed as follows. We applied cytoHubba program to identify hub nodes in the networks by calculating Maximal Clique Centrality (MCC) score [32] which is defined as MCC(v) = ∑C ∈ S(v)(|C| − 1)!, where S(v) is the collection of maximal cliques which contains node v and C is the size of maximum clique. A clique is defined as a subset of nodes in an undirected graph where every two distinct nodes are adjacent. Maximal clique is a clique that cannot be extended by including adjacent nodes. MCC is equal to the node degree if there is no edge between the neighbors of the node v. We used the DyNet program [33] to identify the overlapping nodes between different networks. Other topological properties of networks, including clustering coefficient, topological coefficient, betweenness centrality and node degree, were analyzed with the Network Analyzer module in Cytoscape [30]. The node degree of a node n is a number of edges linked to this node. Local clustering coefficient Cn of a node n is defined as Cn = 2en/(kn(kn − 1)), where kn is a number of immediate directly connected neighbors of a node n, and en is an actual number of connections between all neighbors of node n. Local clustering coefficient was then averaged over all nodes. The topological coefficient Tn of a node n with kn neighbors is computed as Tn = avg(J(n,m)/kn, where node m is a node that shares at least one immediate neighbor with a node n. J(n,m) is calculated as a number of neighbors shared between nodes n and m plus one if there is a direct link between nodes n and m; it is then averaged over all nodes m. The betweenness centrality Cb(n) of a node n is computed as Cb(n) = ∑S ≠ n ≠ t(σst(n)/σst), where s and t denote nodes in the network different from n, σst denotes the number of shortest paths from s to t, and σst(n) is a number of shortest paths from s to t that include node n.

Identification of binding hotspots

Using the interfacial residue locations from the structural and cross-linking interactomes, we counted the number of different binding proteins with unique UniProtKB accessions per each histone interfacial residue. For the cross-linking interactome, we also included one residue before and after the cross-linked lysine residue to define binding interfacial residues. Sequences of all histone variants present in structural and cross-linking networks were extracted from UniProt [34] and HistoneDB 2.0 [24, 25]. Then, we performed multiple sequence alignments for each histone family using Clustal Omega 1.2.3 [35] and further mapped the number of binding proteins for each residue onto the consensus sequence.

Results

Different histone interaction networks highly complement each other

To elucidate the physicochemical properties of human histone/nucleosome interactions, we construct three human histone interaction networks including structural, cross-linking and high-throughput interactomes (Figure 2 and Table SM2). First, we observe that the numbers of interactions for each histone family dramatically differ in the structural interactome at all levels of granularity (Figure 2): H3 and H4 histones have the largest number of interactions while very few H1 interactions are present. Such observations could arise from the bias of PDB database which contains many structures of histone H3 and H4 tail peptides with the reader domains, for example, JmjC and MBT domains. On the other hand, long disordered N-and C-terminal regions of H1 histone bear difficulties in their experimental structural characterization and therefore very few H1 interactions are present in the structural interactome. Next, we compare domain-level histone structural interactomes between Homo sapiens, Xenopus laevis and Saccharomyces cerevisiae. Histone structural interactomes of Xenopus laevis and Saccharomyces cerevisiae share about one third of their nodes (five domain families in Xenopus laevis and seven domain families in Saccharomyces cerevisiae) with Homo sapiens domain-level network (Figure SM1, Table SM3). Some protein domain families having conserved interactions between all three networks include: Bromodomain-containing acetyltransferase and ASF1_hist_chap family which comprises histone chaperone proteins and participates in both the replication-dependent and replication-independent pathways in human, yeast and frog.

Figure 2.

Figure 2.

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Human histone interactions at different granularity levels. The protein-level and domain-level networks are shown as the preferred layout in Cytoscape and nodes of histones and histone-binding proteins are colored as green and pink while hub nodes in histone binding proteins (nodes with the high node degree) are highlighted as orange. Degree sorted circle layout in Cytoscape is applied to show the residue-level interactions, where histone residues are colored by purple, yellow, red, blue and green per histone colour convention while binding proteins are shown as pink nodes.

In contrast to the human structural interactome and in concordance with the previous study [10], we observe that H1 and H2B histones harbor the majority of interactions in cross-linking interactome, while H3 has the least number of interactions. Such observations could be explained by different lysine content in different histone types since XL-MS experiments are based on Lys-Lys chemical crosslinks. As we can see from the lysine content of human canonical histones (Table SM4), H1 and H2B have indeed the highest lysine content, which is about four and two times higher than H3. As a consequence, the number of H3 interactions is probably underestimated by XL-MS data due to the low lysine content. Lastly, we observe that high-throughput histone interactome has smaller differences in the number of interactions for each histone family compared to structural and cross-linking interactomes. Overall, 6% of nodes in structural interactome overlap with cross-linking interactome and 32% of nodes in structural interactome overlap with HTP interactome.

In order to analyze histone associated pathways using our networks, we further identify an additional layer of partners which interact with histone-binding proteins and construct so called “global histone interactomes” (Figure 3A, Table SM5). We systemically compare three global histone interactomes (structural, cross-linking and high-throughput) by identifying the overlapping nodes (Figure SM2 and SM3) [33]. A very low fraction of overlap (~4%) has been observed between global structural and cross-linking interactomes (Figure SM2), and only 49% of nodes in structural interactome are present in the high-throughput interactome (Figure SM3).

Figure 3.

Figure 3.

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Topological properties of human histone interactomes. a) Partners which interact with histone-binding proteins are identified (orange nodes) and added as one additional layer to the initial networks (green and pink nodes) to construct histone global interactomes. b) and c) Comparison of the network topological properties between structural, cross-linking and high-throughput global interactomes.

Histone networks are scale-free and have high modularity

Next, we systematically analyze and compare topological properties of global structural, cross-linking and high-throughput networks (Figure 3 and Figures SM5–7). The average clustering coefficient quantifies how well the nodes in a graph are clustered together and the topological coefficient measures the extent to which a node in the network shares neighbors with other nodes. For all networks, the majority of nodes have low clustering and topological coefficients and more than 80% and 60% of nodes respectively have clustering and topological coefficients less than 0.1. These nodes generally have sparse connections with the neighboring nodes. Compared to cross-linking interactome, structural and high-throughput interactomes show higher average clustering coefficients pointing to more dense connections and possible complex formation in these networks (0.01 compared to 0.13 and 0.1) (Figure 3B). Among all three networks, only very few nodes have high betweenness centrality values (a measure of the centrality of a node in a graph) (Figure SM5). These nodes generally comprise histones and other regulatory proteins playing ubiquitous functions in biological processes such as ubiquitin, transporter and defense/immunity proteins. As many biological networks are scale-free [23, 36, 37], we show that the node degree distributions for all three types of histone networks follow a power law since it shows a remarkedly strong linear association between the fraction of nodes and the node degree on a log-log plot (Figure SM6). In scale-free networks, the majority of proteins represent low degree nodes and only a small number of proteins act as hubs which play critical roles in mediating chromatin signal transduction. This makes a network relatively tolerant to perturbations as it can maintain its integrity even if vast majority of low degree nodes are damaged [38, 39].

Furthermore, we observe a strong power-law decay of values of clustering coefficients with the increasing node degree (Figure SM7). Such observation indicates a high modularity of these networks having more dense connections between the nodes within the modules compared to connections between different modules. It suggests the existence of at least two levels of organization, as was previously observed in human and yeast protein interactomes [23, 36, 37, 40] The first level represents the separate dense modules formed by proteins with the low node degrees. The second level is composed of high degree nodes such as histone proteins and others which act as hubs to meditate the global connectivity between different modules [36]. We further analyze the correlation between topological coefficient and the node degree, where a power-law decay is also clearly identified (Figure 3c). It demonstrates that in all three types of networks, the high degree nodes generally do not have more common neighbors compared to the nodes with the low node degree, which also points to high modularity of networks.

Different networks demonstrate similar functional types of histone binding proteins

Proteins in both structural and cross-linking networks have been categorized into different groups using the PANTHER protein class (Figure 4). Although structural and cross-linking interactomes share only a small portion of nodes, as shown previously, we observe relatively small differences in terms of their functional types. However, the number of proteins in each functional category varies between two networks. These networks mostly contain nucleic acid binding proteins, especially transcription regulatory proteins, since the vast majority of proteins belong to nuclear proteins (Figure SM8). Moreover, we find fewer protein-modifying enzymes and chromatin-binding/regulatory proteins in the cross-linking interactome compared to the structural interactome.

Figure 4.

Figure 4.

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Functional classification of histone-binding proteins in the global histone interactomes at protein level. Proteins were classified using PANTHER classification system (ten top ranked protein types are shown) and categorized by PANTHER protein classes. Histones are represented as large green rectangular nodes while binding proteins are shown as circular nodes and colored by their functions. Proteins which cannot be clearly classified by PANTHER protein classes are colored by grey. The hubs nodes of the networks (MCC score >=4) are highlighted as middle size squares.

Furthermore, we identify and rank nodes in all three global histone networks with respect to their numbers of interactions. Hub nodes with MCC score >= 4 are shown as squares in Figure 4 and a full list of the hub nodes is provided in Table SM 6 and 7. We observe two major functional classes of hub proteins: first class encompasses proteins playing ubiquitous functions in biological processes and includes defense/immunity proteins, transporter proteins, cytoskeletal proteins and ubiquitin (Table SM 8 and 9). These proteins usually interact with a large spectrum of partners and are not specific to chromatin. Consistent with recent XL-MS studies [10], interactions between these proteins may occur across subcellular compartments such as nucleus-cytosol/plasma membrane, nucleus-mitochondria and nucleus-endoplasmic reticulum. Another set of hubs is specific to chromatin signaling pathways (Table SM8 and 9) and includes polycomb protein EED, transcriptional repressor protein YY1, transcription initiation factor TFIID and others.

Binding hotspots in human histones and nucleosomes

Using data on binding interfaces extracted from structural and cross-linking interactomes, we systematically analyze binding sites of all human histone variants present in PDB complex structures and cross-linking data. We perform multiple sequence alignments of human histone variants per each histone type and map the protein binding sites onto histone sequences using binding interfaces extracted from structural and cross-linking interactomes (Figure 5, SM9, 13–17). As can be seen on these figures, histone tails of H3 and H4 have the largest number of interactions followed by the acidic patches on H2A and H2B. Consistent with our observations, recent XL-MS studies highlighted a large fraction of the identified cross-links mapped to the flexible histone tails [10]. Importantly we identify binding sites which are aligned in most histone variants, many of them belong to sites of post-translationally modified residues in H3 histone tails (not shown). However, the vast majority of binding sites are not aligned between all histone variants pointing to possible interactions characteristic for certain histone variants. For instance, we identify binding site residues 169–180 as a variant specific interface on macro-H2A and indeed this interface has been found to be specifically targeted by the speckle-type POZ protein and these interactions are involved in the X-chromosome inactivation pathway [41](Figure SM9).

Figure 5.

Figure 5.

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Mapping of protein binding sites onto human histones using the data extracted from structural and cross-linking interactomes. a) The number of binding proteins per residue mapped onto the consensus sequence of the full alignment of human histone sequences (see Figure SM 9, 13–17). Red asterisks denote acidic patches and globular domains are indicated by purple, yellow, red, blue and green bars per histone colour convention. The full sequence alignment of H2A variants contains residue 1 to 378 (Figure SM 14) and the plot only shows the region of 1 to 200 which has the vast majority of histone interactions. b) Binding hotspots are highlighted on histone structures. Binding hotspots are defined if a residue interacts with more than five different proteins. H1 structure is taken from PDB: 4QLC and structures of H2A, H2B, H3 and H4 are from PDB: 1KX5. We illustrate H1 C-and N-terminal tails by linearly extending them since they are not resolved in the structures.

Next, to identify the histone binding hotspots, as sites contributing the most to binding energy, we map the number of unique binding proteins for each residue onto the consensus sequences of the alignment using binding interfaces extracted from the structural and cross-linking interactomes (Figure 5, Figure SM 10, 11). As shown in Figure 5, binding hotspots of H1 are mostly distributed over its globular domain and certain regions of the C-terminal tail. In structural interactome, H2A has more or less uniformly distributed binding sites, including both tails and acidic patch (Figure SM10). In contrast, H2A acidic patch is not identified as binding hotspot in cross-linking interactome (Figure SM11), which could be explained by the lack of solvent accessible lysine residues around this region. Binding hotspots of H2B in structural interactome are distributed over both tails and globular domains while XL-MS study indicates that about 25% of the identified inter-protein cross-links in histone interactions are located at the α-helical C-terminal region of histone H2B (Figure SM11) [10]. Both H3 and H4 have the highest number of interaction partners, their binding hotspots are concentrated within the first ten residues of their N-terminal tails rich in post-translational modifications and in case of H3 in certain regions of alpha-helices in globular domains.

Finally, we systemically analyze histone binding sites in the context of the full nucleosomes. We collect 26 human nucleosome complex structures from PDB, classify them by functions and map binding interfaces onto the molecular surface of nucleosomes (Figure 6). As can be seen in Figure 6, many binding proteins with the exception of transcription regulatory proteins recognize nucleosomes via the localized acidic patches. At the same time, in some cases histones participate in multivalent binding (in this study we only focus on histones while nucleosomal DNA could be also involved in the recognition). For example, binding of centromere proteins involves both H4 tails and acidic patch, whereas chromatin remodelers recognize the acidic patch, H2A-C tails and H3 core regions. Proteins involved in DNA repair mostly bind nucleosomes via acidic patches and H3 core regions. Our observations are consistent with recent screening of nucleosome interactions in mouse embryonic stem cell [42], indicating that acidic patch and its surrounding residues are the primary binding hotspots in nucleosome.

Figure 6.

Figure 6.

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Mapping of protein binding sites on human histones within nucleosomes. The nucleosome-binding proteins are classified by their functions using the PATHNER classification system (all PDB IDs are provided in Table SM10). The nucleosome representation is generated from PDB 1KX5 and histones are colored by yellow, pink, light blue and light green per histone colour convention. The binding interfaces are colored according to the categories in the pie chart. Acidic patch is marked with a red circle.

Discussion

Even though proteome-wide experiments prove to be useful for mapping protein networks, it remains very challenging to map chromatin related interaction networks with high resolution due to their dynamic transient nature and the involvement of both DNA and proteins via multivalent interactions. Here we make an attempt to characterize the histone related interactions and show that networks derived from the three different experimental sources largely complement each other. Therefore, we integrate the data from these sources. Our combined global network from structural and cross-linking interactome comprises 987 edges and 754 nodes and network combined from all three interactomes includes 5,308 nodes and 10,330 edges (Table SM5 and Figure SM4). Even though the three networks do not show much overlap, their topological properties are relatively similar. Structural, cross-linking, high-throughput networks and combined networks (Figure SM12) exhibit scale-free behavior pointing to their robustness to perturbation and have high modularity where identified hubs are not preferentially clustered together. High modularity and scale-free networks were previously identified in H3 tail interactomes [17] and in human and yeast proteome-wide interactomes [23, 36, 37, 40].

As a result of our analysis of binding interfaces of different human variants, we observe a remarkably high number of residues involved in histone interactions, 80–90% of all residues in histones H3 and H4 are involved in at least one interaction. We argue that it can explain their high degree of evolutionary conservation. Our systematic study of structures of histone binding interfaces and their alignments uncovers two types of histone binding modes. First type includes interactions conserved in most histone variants, such as binding sites in H3 tails regions. The second type comprises variant specific interactions characteristic for certain histone variants, extensively studied previously [7, 43], such as interactions between speckle-type POZ protein and macro-H2A and interactions of CENP-A with CENP-B and -C [44, 45]. In addition, we map histone interactions in the context of the full nucleosomes. In contrast to widely distributed binding interfaces on histones, histones within nucleosomes utilize localized specific regions with the abundance of acidic patch mediated interactions, consistent with the previous studies [2, 42, 46–48]. Moreover, we show that distinctive binding modes are used by chromatin factors with different functions. Although the scope of our analysis is influenced by the limited data on histone and nucleosome interactions, all evidence points to the abundance and high specificity of histone interactions in a large variety of cellular processes. The modulation of these interactions by mutations and post-translational modifications merits further study.

Supplementary Material

1

Acknowledgements

YP, YM, AG and DL were supported by the Intramural Research Program of the National Library of Medicine at the U.S. National Institutes of Health. ARP was in part supported by the Intramural Research Program of the National Library of Medicine at the U.S. National Institutes of Health. ARP were supported by the Department of Pathology and Molecular Medicine, Queen’s University, Canada. ARP is the recipient of a Senior Canada Research Chair in Computational Biology and Biophysics and a Senior Investigator award from the Ontario Institute of Cancer Research, Canada.

Footnotes

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