Abstract
Complex organisms are able to rapidly induce select genes among thousands in response to diverse environmental cues. This occurs in the context of large genomes condensed with histone proteins into chromatin. The macrophage response to pathogen sensing, for example, rapidly engages highly conserved signaling pathways and transcription factors (TFs) for coordination of inflammatory gene induction1–3. Enriched integration of histone H3.3, the ancestral histone H3 variant, is a feature of inflammatory genes and, in general, dynamically regulated chromatin and transcription4–7. However, little is known of how chromatin is regulated at rapidly induced genes and what features of H3.3, conserved from yeast to human, might enable rapid and high-level transcription. The amino-terminus of H3.3 contains a unique serine residue as compared with alanine residues found in “canonical” H3.1/2. We find that this H3.3-specific serine residue, H3.3S31, is phosphorylated (H3.3S31ph) in a stimulation-dependent manner along the gene bodies of rapidly induced response genes in mouse macrophages responding to pathogen sensing. Further, this selective mark of stimulation-responsive genes directly engages histone methyltransferase (HMT) SETD2, a component of the active transcription machinery. Our structure-function studies reveal that a conserved positively charged cleft in SETD2 contacts H3.3S31ph and specifies preferential methylation of H3.3S31ph nucleosomes. We propose that features of H3.3 at stimulation induced genes, including H3.3S31ph, afford preferential access to the transcription apparatus. Our results provide insight into the function of ancestral histone variant H3.3 and the dedicated epigenetic mechanisms that enable rapid gene induction, with implications for understanding and treating inflammation.
A poorly understood feature of stimulation-induced genes is their ability to effectively engage the general transcription machinery for rapid expression. Selective, induced gene transcription, for example during heat shock8 or the inflammatory response, occurs rapidly and robustly, despite these genes’ de novo expression among thousands of constitutively expressed genes. We considered that stimulation-induced transcription may be controlled by dedicated epigenetic mechanisms in cooperation with signal-activated transcription factors (TFs). Among stimulation-responsive features of chromatin, histone phosphorylation can be an efficient and potent means of transmitting signals via kinase cascades to chromatin regions associated with stimulation-responsive genes with the potential to augment their transcription9–14.
H3.3 is the conserved, ancestral H3 variant and the only H3 present in some simple eukaryotes, including S. cerevisiae. In complex organisms, H3.3 is uniquely expressed outside of the cell cycle and plays a variety of roles in transcription, genomic stability and mitosis, while so-called “canonical” H3.1/2 histones are expressed in a “replication-dependent” manner and provide a principal packaging role to accommodate the doubling genome15,16. The amino-terminal H3.3 ‘tail’ differs from that of H3.1/2 by a single amino acid, a serine at position 31 in H3.3 in place of an alanine in H3.1/2 (Fig. 1A and fig. S1A). Despite the well-characterized enrichment of H3.3 in dynamic chromatin, the potential regulatory roles of H3.3S31 and H3.3-specific phosphorylation are unknown4–7,17. Here, we report that H3.3 phosphorylation at the conserved and H3.3-specific serine 31 (H3.3S31ph) amplifies the rapid, high-level transcription of stimulation-induced gene expression. We present a specific biophysical mechanism that provides these select genes with augmented transcriptional capacity.
To identify candidate chromatin regulatory mechanisms with a delegated role during cellular stimulation we biochemically purified histones from resting and bacterial lipopolysaccharide (LPS) stimulated macrophages and quantified residue-specific histone post translational modifications (PTMs) by mass spectrometry (MS). Given our interest in the H3.3-specific S31 we targeted peptides containing the H3.3S31 residue in our MS analysis. H3.3S31ph is undetectable in resting macrophages and increases upon stimulation, while the total level of H3.3 protein remains unchanged (Fig. 1B). In support, we developed a specific antibody (fig. S1B-F) and confirmed, by western blot, the stimulation-induced nature and rapid kinetics of H3.3S31ph, paralleling ERK phosphorylation (Fig 1C). Importantly, given the extensive phosphorylation of histones in mitosis, including H3.3S31 (fig.S1B-F) 18, the post-mitotic nature of primary mouse bone marrow derived macrophages (BMDM) enabled us to distinguish stimulation-associated histone phosphorylation from mitotic events in bulk populations of cells (fig. S1G).
To establish the genomic location of stimulation-induced H3.3S31ph, we performed chromatin immunoprecipitation followed by whole genome sequencing (ChIP seq) in resting and stimulated (60’ LPS) macrophages. We compared H3.3S31ph localization to H3S28ph. H3S28ph is enriched at promoters, enhancers, and generally across large domains that contain LPS-induced genes, consistent with its role in early events of chromatin activation and transcription14. While the H3.3S31ph ChIP signal is enriched in stimulated versus resting macrophages, in striking contrast to H3S28ph, it strictly delineates the “gene bodies” (transcription start site, TSS, to transcription end site, TES) of many LPS-induced genes (Fig. 1D).
A preliminary survey of H3.3S31ph ChIP distribution revealed that its deposition appeared to be specific for stimulation-induced genes (including Tnf, Nfkbia, Il1a, Il1b, Ccl4, Cxcl2, Tnfaip3) and is not simply a feature of highly transcribed or constitutively expressed genes (fig. S2). To better evaluate the identity of H3.3S31ph-enriched genes in an unbiased manner and explore the relationship between genic ChIP signal densities of H3.3S31ph and LPS-induced genes, we ranked all annotated genes by H3.3S31ph ChIP signal density (TSS-TES) in resting and stimulated macrophages. This analysis shows that many more genes acquire high-density H3.3S31ph upon stimulation compared with resting cells (Fig. 1E), which is consistent with our MS and other global analysis of H3.3S31ph levels. Additionally, several of the top ranked genes (note, by density, not fold change) are prominent LPS-induced genes, including Tnfaip3 (A20), Tnf, Il1a, and Plk2 (Fig. 1E). We then defined a threshold for the top 1% of genes by H3.3S31ph density in stimulated macrophages (167 genes) for gene ontology analysis and found that the most enriched category is “response to stimulus” (p-value=2.88 × 10−21) reflecting the stimulation-induced nature of genes featuring H3.3S31ph (Fig. 1F). We compared the H3.3S31ph chromatin state to other “active” chromatin states including H3K27ac, H3K36me3, and H3S28ph as they relate to stimulation-induced gene expression. Our analysis showed that the top 1% of H3.3S31ph genes (by ChIP density in stimulated macrophages) was highly enriched for stimulation-induced genes (Fig. 1G, fig. S3). Thus, selective deposition of H3.3S31ph at genes with de novo, signal-induced transcription indicates a dedicated role in stimulation-responsive transcription rather than constitutive transcription.
In considering possible mechanisms by which H3.3S31ph may regulate transcription, we focused on the gene body localization of this stimulation-dependent histone phosphorylation event. We considered the possibility that H3.3S31ph may be linked to another well-studied histone PTM, the co-transcriptional H3K36me3. H3K36me3 is mediated by a single histone methyltransferase (HMT), SETD2, while members of the NSD family of H3K36-specific methyltransferases can mono- and di-methylate H3K3619. SETD2, and specifically the tri-methylation of H3K36, are considered to play an important role in transcription fidelity at highly expressed genes, transcription-associated genic DNA methylation, and mRNA splicing20–22. Therefore, we assessed the colocalization and correlation between these two histone PTMs at stimulation-induced genes. We found strikingly similar gene body localization of H3.3S31ph and H3K36me3 in stimulated macrophages, distinct from enhancer and promoter regions delineated by H3K27ac and intergenic regions marked by H3K36me2 (Fig. 2A). Intriguingly, while H3.3S31ph was stimulation-dependent, H3K36me3 was present at modest levels in resting macrophages and increased upon stimulation and induction of associated genes, likely representing a transcriptionally poised state of these genes (Fig. 2A, fig. S3B). While overall, we find enrichment of H3.3 and “active” histone PTMs at LPS-induced genes, H3.3S31ph and H3K36me3 are especially prominent in their enrichment at these genes (Fig. 2A-B, fig. S3A-C). Further, average ChIP density profiling for H3.3S31ph and H3K36me3 across all LPS-induced genes revealed their matching gene-body distribution and stimulation-induced enrichment in this class of genes (Fig. 2A, C). While co-localized at LPS-induced genes, an important distinction between these two histone PTMs is that H3K36me3 is a ubiquitous feature of transcribed genes, while H3.3S31ph appears to have a dedicated function at stimulation-induced genes (fig. S3C).
Thus, H3.3S31ph is a feature of stimulation-responsive chromatin, is rapidly and specifically deposited along the gene bodies of stimulation-induced genes, and at these genes shares a common genomic distribution and stimulated deposition with H3K36me3. These findings suggested cross-talk between these two histone PTMs, and we hypothesized that H3.3S31ph may endow stimulation-induced genes with the capacity for augmented transcription, in part through the stimulation of H3K36me3. To test if H3.3S31ph may determine H3K36me3 densities at LPS-induced genes, we compared H3.3S31ph ChIP density changes between resting and stimulated BMDM with changes in H3K36me3 (catalyzed by SETD2) and H3K36me2 (NSD1, NSD2, NSD3, ASH1L, SMYD2, SETMAR). This analysis demonstrated a high correlation between the density change in H3.3S31ph and H3K36me3 (Spearman’s correlation, 0.8) but not H3K36me2 (Spearman’s correlation, −0.2) (Fig. 2D).
Given this link between H3.3S31ph and H3K36me3 as well as their physical proximity on the H3.3 tail (Fig. 1A), we considered the possibility that H3.3S31ph may directly augment the activity of HMT SETD2, the enzyme catalyzing H3K36me3. To test this hypothesis, we assessed recombinant SETD2-SET domain enzymatic activity in vitro on nucleosome substrates assembled from recombinant core histones, either with normal H3.3 tail sequence, or bearing the phospho-mimicking glutamic acid mutation at residue 31 (S31E). Processive SETD2 HMT activity on H3.3K36 was measured by western blot read out during a reaction time course using antibodies specific for K36me2 and K36me3. For comparison, we also performed these assays with the K36me2-specific enzyme NSD2. Under standard assay conditions, both enzymes accumulated their products throughout the 25 minute time course, however, SETD2 activity was potently stimulated by the phospho-mimicking H3.3S31E mutant, while NSD2 activity was substantially reduced (Fig. 3A).
Structural studies of the SETD2 SET domain have revealed a basic patch along the path of the H3 amino-terminal tail as it extends from the catalytic site23,24. We speculated that such a feature could provide the basis of a specific enhanced interaction between SETD2 and H3.3S31ph nucleosome substrates and that these interactions might link the augmented enzymatic activity we observed to structural properties.
Therefore, we solved the crystal structure of the human SETD2 catalytic domain bound to the H3.3 peptide H3.3S31phK36M (S31 phosphorylated, and K36 mutated to M to stabilize the H3.3 peptide in the catalytic site) at 1.78Å (Fig. 3B-C, Table S1). In the resulting structure, the electrostatic surface view of SETD2 shows that the H3.3 peptide is embedded in the substrate-binding channel of SETD2 (Fig. 3B). Notably, the N-terminal fragment of H3.3 extends from the active site to the exit of SETD2 substrate channel, which is exclusively enriched with basic residues. The electron density of the H3.3S31 phosphate group is clearly visualized. Specifically, the hydroxyl oxygen from H3.3S31ph forms a salt bridge with K1673 of SETD2, and water-mediated hydrogen bonding with adjacent K1600 of SETD2 (Fig. 3B-C). Thus, SETD2 K1600 and K1673 provide a channel with positive charge that accommodates and provides charge-complementarity for H3.3S31ph substrate while H3.3K36 is positioned at the active site. Given their potential significance in the observed interactions between H3.3S31ph and SETD2, we evaluated the sequence conservation at and around these lysine residues across phylogeny (Fig. 3D top) and within H3K36 methyltransferases (Fig. 3D bottom). Remarkably, we find that the basic residues K1600 and K1673 are highly conserved in metazoan SETD2 (conserved across vertebrates and replaced by highly similar Arg in C. elegans and D. melanogaster and His in S. cerevisiae). In contrast to this high degree of cross-species conservation within SETD2 orthologs, other H3K36 HMTs (NSD family, etc.) frequently replace these basic residues with acidic or polar amino acids (Fig. 3D, bottom).
To directly assess the function of these conserved SETD2 lysine residues that engage in specific interactions with H3.3S31ph, we generated recombinant SETD2 SET-domain proteins with mutated lysines, individually and combined (K1600E, K1673E, and K1600E/K1673E). Wild type and mutant SETD2 enzymes were then assessed for their activity on unmodified as well as H3.3S31E-containing nucleosomes. As before, we observed potent stimulatory activity of H3.3S31E nucleosomes over unmodified nucleosomes (Fig. 3A, E), however, H3.3S31E-augmented SETD2 activity was decreased in single mutants (K1600E and K1673E) and reversed in the double K1600E/K1673E SETD2 mutant.
Together, our cellular, epigenomic, and structure-function studies suggest H3.3S31ph-augmented SETD2 activity as a feature of enhanced stimulation-induced transcription. This indicates a mechanism by which stimulation-induced genes may be endowed with preferential access to (and dependency on) SETD2 for rapid, high-level expression. To test this hypothesis, we performed SETD2 siRNA knockdown in BMDM before LPS stimulation. We find that expression of LPS-induced genes with H3.3S31ph, Tnf, Plk2, Cxcl2, is highly dependent on SETD2, compared with constitutively expressed Tbp (Fig. 3F, fig. S4A).
Functional perturbations of histone genes are made difficult by their essential role in diverse cellular function and their genetic complexity (there are 15 copies of the H3 gene in mouse and human). However, because there are only two genes for the histone H3.3 variant containing the S31 residue (H3f3a and H3f3b) we were able to target these genes by CRISPR. H3.3 is required for embryogenesis25–28 and spermatogenesis29. Further, as we find here (fig. S3), H3.3 is enriched at inflammatory genes, though its function in this context is unknown6,30. To study the function of H3.3 in inflammatory gene induction we generated H3f3a/H3f3b double knockout (DKO) RAW264.7 (macrophage-like) mouse cell lines through CRISPR targeting of both H3f3a and H3f3b. Given its critical role in development, we also selected a hypomorphic (HYPO) RAW264.7 clone, with a null H3f3a allele and hypomorphic H3f3b allele (fig. S4B).
These wild type, DKO, and HYPO macrophage cell lines were then assessed for their ability to induce inflammatory genes following stimulation with LPS in the absence of the H3 protein containing the H3.3-specific S31 residue. While these cell lines grow comparably (not shown), assessment of their ability to rapidly respond to stimulation by RNAseq revealed substantial decreases in induced expression of LPS-induced genes in both DKO and HYPO macrophage cell lines (Fig 4A-B, fig. S4C-D). At 60 minutes and 120 minutes following stimulation with LPS, we observed a global reduction in LPS-induced gene expression in DKO and HYPO cell lines (Fig. 4B-C, fig. S4D). We found that LPS-induced genes characterized by the highest levels of H3.3S31ph were expressed, on average, at 3-times the level of all LPS-induced genes and also had consistently decreased expression in H3.3 HYPO and DKO cells (Fig. 4C-D, fig. S4D).
The inflammatory gene induction defect in H3.3 HYPO and DKO cells responding to LPS occurs despite the presence of 13 other copies of H3.1/2, abundantly expressed in these rapidly cycling cells. Given that the only H3.3 “tail” sequence difference is S31 and our results that demonstrate a dedicated role for H3.3S31ph at stimulation responsive genes, we suggest that H3.3S31ph contributes to the function of H3.3 that we observe in these experiments (Fig. 4). Our structural and enzymatic studies of Setd2 activity (Fig. 3) highlight specific biophysical mechanisms that may link H3.3 to augmented transcription. However, key unknowns remain on the function of this ancestral H3.3 variant, including the relative function of the H3.3S31 residue and its phosphorylation, the signaling pathways that link stimulation to H3.3S31ph in chromatin, and the breadth of the mechanisms that we describe here, both across species and cell types.
Dedicated mechanisms enabling rapid stimulation-induced transcription are relevant to diverse cell responses and disease states, and may represent more selective therapeutic targets than the general transcription machinery31–33. In the context of inflammatory gene induction, numerous studies have revealed signals, TFs, and chromatin features that drive stimulation responsive genes (reviewed in 2,34,35). However, explanation of inducible genes’ preferential access to the transcription apparatus and suitability for speed and scope of transcription in the form of dedicated chromatin mechanisms have remained obscure. Our epigenomic and biochemical studies link selectively deposited H3.3S31ph at stimulation-induced genes to augmented SETD2 activity and co-transcriptional H3K36me3, enabling rapid and high-level transcription of these genes. Together with our previous characterization of H3S28 phosphorylation in early stimulation-induced chromatin activation14, these studies reveal mechanisms for the dedicated role of histone phosphorylation in de novo transcription. We propose that selectively employed deposition of histone PTMs at these genes, including H3.3-specific H3.3S31ph, provides a signature that specifies preferential access to the transcription apparatus, endowing cells with the essential capacity for rapid and selective environmental responsiveness.
Author Contributions
A.A. and S.Z.J. designed the study, performed biochemical, cellular, and epigenomic experiments and analyzed the data. S.Y. performed structural studies supervised by H.L.. L.E.R., C.D., A.W.D., J.Q.J., A.L.M., A.R., performed experiments and analyzed data supervised by S.Z.J.. T.P. assisted A.A. with nucleosome assembly and enzymatic assays. T.A. and S.B.H. developed and tested the H3.3 antibody. S.L. performed mass spectrometry studies supervised by B.A.G.. S.Z.J. wrote the manuscript with input from all authors. C.D.A., H.L., and S.Z.J. supervised the study.
Competing Interests
None
Materials and Correspondence
S.Z.J., szj2001{at}med.cornell.edu
Materials and Methods
ChIP-seq data processing and analysis
H3S31ph, H3K36me3, H3K36me2, H3K27ac, H3S28ph, and H3.3 ChIP-seq analyses were performed in bone marrow derived macrophages (BMDM) with an average range of 20-25 × 106 reads per independent ChIP-seq experiment. ChIP-seq reads were mapped to the mm10 genome using Bowtie2 v.2.3.4.11 with the following parameters: -p 8 ‒k 1 ‒N 1. The aligned reads underwent three stages of filtering using SAMtools v.1.52. First, the unmapped, non-primary, qc failed, and multi-mapped reads were discarded. PCR duplicates were then marked by Picard Tools v.2.14.0 (http://broadinstitute.github.io/picard/) using ‘VALIDATION_STRINGENCY=SILENT and REMOVE_DUPLICATES=false” options and removed by SAMtools (−F 1796). Then, chromosome M and scaffolds were removed to create the final filtered bam file. The final bam files were used to generate average profiles for RNA-seq define LPS-stimulated genes at time 60 for H3S31ph signal using ngs.plot v.2.613 at genebody using the following parameters: −FL 200 −MW 2. For visualization in IGV v.2.3.944, the final bam files were converted to a tiled data file (.tdf) using igvtools v.2.3.985 including duplicates. Final bam files were converted to bigWig files of read coverages normalized to 1x depth of coverage as reads per genomic content (RPGC) using deeptools v2.5.46 bamCoverage. To obtain a tab-delimited file of average scores comprised of all bigWig files for each experiment, deeptools multiBigwigSummary performed the analysis for regions defined by a General Transfer Format (GTF) vM3 Annotation BED file. The BED file was constructed using the BEDOPS v.2.4.297 gtf2bed conversion utility and, depending on strand direction, extending the feature at both the start and end position by 2kb (H3S31ph, H3K36me3, H3K36me2, H3.3) or 4kb (H3S28ph, H3K27ac) to account for promoters (+/−2kb) or histone marks found outside of gene body (+/− 4kb). The resulting tab-delimited file of read densities was used for downstream analysis in R v.3.4.08. Top H3S31ph genes were defined by a 2-fold or greater increase in H3S31ph enrichment at time 60 after LPS stimulation with FDR < 0.05. Top genes for all other epigenetic marks, such as H3K27ac, H3K36me3, H3S28ph, were defined in the same manner. The top H3S31ph genes enriched at time 60 were used as a target list for gene ontology analysis by the tools Gorilla9 and REViGO10.
RNA-seq data processing and DESeq2 analysis
Paired-end RNA-seq reads were obtained from biological triplicates at times 0, 60, and 120 after LPS stimulation in BMDMs. Single-end RNA-seq reads were also obtained from technical duplicates at times 0, 60, and 120 after LPS stimulation for KO comparisons for WT BMDM, cell line hypomorph 3.205, and cell line knockout 264. Both paired-end and single-end RNA-seq were processed the same. The fastq files underwent adapter trimming and quality control analysis using wrapper Trim Galore v.0.5.0. The resulting trimmed fastq files were aligned to the GENCODE vM3 transcriptome in mm10 using STAR aligner v.2.4.211 with default settings. The utility featureCounts12 from Subread v.1.4.6 was used to calculate raw counts reads per gene to be used as input for differential expression analysis by DESeq213.
Antibodies
a-H3.3S31ph (developed by Pineda Antikörper-Service), a-H3S28ph (clone E191, ab32388 Abcam), H3.3 (09-838, EMD), a-p44/42 MAPK, Erk1/2 (4695 Cell Signaling), a-phospho-p44/42 MAPK (Erk1/2) (4370, Cell Signaling), a-H3 (ab1791 Abcam), a-H3K27ac (39133, Active Motif), a-H3K36me3 (61021, Active Motif), a-H3K36me2 (2901, Cell Signaling).
a-H3.3S31ph Antibody Development
For the generation of an H3.3S31ph-specific polyclonal antibody, a peptide spanning amino acids 26 to 37 from H3.3 containing phosphorylated serine 31 (RKSAPS(ph)TGGYKK, note the exchange of V35Y due to enhanced immunicity) was used for immunization of three rabbits by the Pineda - Antikörper-Service company (Berlin, Germany). Last bleed from animal 1 was affinity purified and used in this study. Antibody specificity was tested in immunoblots and 2D-Triton Acid Urea (2D-TAU) gels with acid-extracted histones as described previously14. Peptide competition experiments were done as described previously15 using peptides that were N-terminally biotinylated and synthesized with higher than 80% purity by GenScript USA Inc. All peptides contained the general H3.3 sequence (aa 20-39; BIO-LATKAARKSAPSTGGVKKPH) with respective phosphorylations on serines 10, 28 and/or 31. For Immunofluorescence microscopy HeLa Kyoto cells were grown on coverslips, washed, fixed, permeabilized and stained as descibed previously16. Chromosome spreads were generated as described17. Wide-field fluorescence imaging was performed on a PersonalDV microscope system (Applied Precision) equipped with a 60x/1.42 PlanApo oil objective (Olympus), CoolSNAP ES2 interline CCD camera (Photometrics); Xenon illumination and appropriate filtersets. Iterative 3D deconvolution of image z-stacks was performed with the SoftWoRx 3.7 imaging software package (Applied Precision).
Chromatin Immunoprecipitation
As previously described in Josefowicz et al., 2016.
Primary Cell Culture
As previously described in Josefowicz et al., 2016. HeLa Kyoto cells were grown as described15.
Cell Culture, siRNA transfection
For siRNA transfection RAW cells were reverse transfected with Lipofectamine RNAiMAX (Life Technologies) and ON-TARGETplus SMARTpool siRNAs against mouse SETD2, CHK1 and CHK2. After 72h, cells were either harvested for gene expression or western blot analysis.
RNA extraction, quantitative real-time PCR and RNA sequencing
RNA was isolated using RNAeasy Kit (Quiagen). For RT-PCR extracted RNA was treated with DNAse and cDNA was synthesized using High-Capacity cDNA Reverse Transcription Kit (Applied Biosystems). qPCR was performed using SYBR green dye (Applied Biosystems) and normalized to GAPDH. For RNA sequencing libraries were prepared using according to the Illumina TruSeq protocol and were sequenced on Illumina HiSeq 2500 / NextSeq 500.
Antibody-based methods
(flow cytometry and western blotting) As previously described in Josefowicz et al., 2016.
Mass Spectrometry Analysis of Histone Post-Translational Modifications
As previously described in Josefowicz et al., 2016.
Nucleosome reconstitution
All histones were expressed and purified as previously described (Ruthenburg et al., 2011). Nucleosome Assembly Octamers were reconstituted as described (Ruthenburg et al., 2011). The 601 nucleosome positioning sequence was used for nucleosome reconstitution (Lowary and Widom, 1998). The DNA was amplified by PCR using HPLC purified primers containing a biotin tag on the 5’ end to produce 189 bp linear DNA and purified using QIAEXII kit (Qiagen). Nucleosomes were assembled using the standard step-wise dialysis method (Dyer et al., 2004).
Bacterial recombinant protein
Human SETD21347-1711 (original plasmid was a generous gift of Danny Reinberg) and point mutants were cloned into pETduet–smt3 (Mossessova E, Lima CD, 2000). The SETD2 wt and mutant fragments were expressed with an N-terminal His-tag in Rosetta (DE3, pLysS) cells with LB Media for 18 h at 17°C by induction with 0.5 mM Isopropyl β-D-1-thiogalactopyranoside (IPTG). E. coli cells were resuspended in50 mM Tris pH 8.0, 500 mM NaCl, 1 mM PMSF, 2 mM BME, 10% glycerol, 10 mM imidazole supplemented with ROCHE COMPLETE protease inhibitors. After lysis with tip sonicator and centrifugation the cleared lysate was incubated for 1h with Ni-NTA resin slurry (Clonetech). After washing beads with the same buffer, the protein was eluted. The samples were incubated with Ubiquitin-like protease (Ulp) overnight at 4°C and subsequently incubated again with Ni-NTA resin to remove protease and cleaved tag. Supernatant was further purified by size-exclusion chromatography (Superdex 75, GE Healthcare).
HMT assay
Standard HMT assays were performed in a total volume of 20 μL containing HMT buffer (50 mM Tris-HCl, pH 8.5, 50mM NaCl, 5 mM MgCl2, and 1 mM DTT) with 100 uM S-Adenosylmethionine (NEB) and 1.2ug of nucleosomes. The enzymes used were 30nM NSD2 full-length (Reaction Biology Corp), 800 nM SETD2-SET wt, and 3200 nM of SETD2K1600E, SETD2K1673E, SETD2K1600EK1673E. The reaction mixtures were incubated for 0,5,10,15,20 and 25 min at 30°C and stopped by adding 20ul of Laemmli Buffer. The results were analyzed by Western Blot.
Crystallography study of SETD2-H3.3S31phK36M complex
Human SETD2 catalytic domain (residues 1434–1711) was expressed in E. coli and purified as previously described (Yang et al. 2016). Crystallization was performed via vapor diffusion method under 277K by mixing equal volumes (0.5ul) of SETD2-H3.329-42S31phK36M-SAM (1:5:10 molar ratio, 8mg/ml) and reservoir solution containing 0.2M potassium thiocyanate, 0.1M Bis-Tris propane, pH 8.5, and 20% PEG 3350. The crystals were briefly soaked in a cryo- protectant drop composed of the reservoir solution supplemented with 20% glycerol and then flash frozen in liquid nitrogen for data collection. Diffraction data were collected at Shanghai Synchrotron Radiation Facility beamline BL17U under cryo conditions and processed with the HKL2000 software packages. The structures were solved by molecular replacement using the MolRep program (Vagin and Teplyakov 2010), with the SETD2-H3.3K36M complex structure (PDB code: 5JJY) as the search model. All structures were refined using PHENIX (Adams et al. 2010) with iterative manual model building with COOT (Emsley and Cowtan 2004). Detailed structural refinement statistics are in Supplemental Table S1. Structural figures were created using the PYMOL (http://www.pymol.org/) or Chimera (http://www.cgl.ucsf.edu/chimera) programs.
In vitro kinase assay and dot blot
Recombinant CHK1 kinase (Sigma) was incubated with kinase buffer (40mM HEPES pH7.4, 20mM MgCl2), Magnesium/ATP cocktail (EMD) and histone tail peptides for overnight at 37°C (Total reaction 15ul, 2ug Peptide, Mg(4.5mM)/ATP(30uM) cocktail and 4ng Enzyme). The samples were then added with 5ul of 0.5%SDS followed by boiling for 5min at 95°C. The samples were dropped on a dry nitrocellulose membrane and probed with a-H3S31ph antibody.
CRISPR targeting of H3.3
CRISPR targeting H3f3b and H3f3a was performed in RAW264.7 cells using methods described in Ran et al. 201318. Targeting was done consecutively first targeting H3f3b, then using H3f3b mutants to target H3f3a.
The gRNAs (Primers caccTAGAAATACCTGTAACGATG forward aaacCATCGTTACAGGTATTTCTA reverse for H3f3a and caccGAAAGCCCCCCGCAAACAGC forward aaacGCTGTTTGCGGGGGGCTTTC reverse for H3f3b) were cloned into PX458 (from Addgene) and sorted for GFP 24h after transfection, cells were first sorted as bulk and after recovery sorted into single cell clones. Positive clones were tested by PCR, sequencing and Western Blot.
Acknowledgements
This work was supported by the following funding sources: R01GM040922 (C.D.A), R00GM113019 (S.Z.J), CIPSM (S.B.H.). We thank John Zinder for contributing the SETD2-pETduet-smt3 construct; Congcong Lu, Simone Sidoli (lab of B.A.G.) for H3.3 peptide analysis; members of Weill Cornell Applied Bioinformatics Core, Doron Betel, Paul Zumbo, Friederike Dundar, and Luce Skrabanek for suggestions and assistance with bioinformatics; Alexey Soshnev for help with figures.