Article

Dynamic Control of Enhancer Repertoires Drives Lineage and Stage-Specific Transcription during Hematopoiesis

Graphical Abstract Authors Jialiang Huang, Xin Liu, Dan Li, ..., Guo-Cheng Yuan, Stuart H. Orkin, Jian Xu

Correspondence [email protected] (S.H.O.), [email protected] (J.X.)

In Brief Enhancers are crucial determinants of cell identity. Huang et al. compare enhancer landscapes in human hematopoietic stem/progenitor cells and erythroid progenitors. They uncover driver transcription factors and their combinatorial patterns in enhancer turnover. Genomic editing of constituent enhancers reveals functional hierarchy and diversity of enhancer clusters during hematopoiesis.

Highlights Accession Numbers d Pervasive changes in enhancer landscapes occur during GSE70660 blood stem cell specification GSE36985 GSE52924 d Analysis of developmentally regulated enhancers uncover GSE59087 driver TFs and combinations d Genomic editing reveals functional hierarchy of super- enhancer constituents d Functionally divergent GATA switch enhancers cooperate within enhancer clusters

Huang et al., 2016, Developmental Cell 36, 9–23 January 11, 2016 ª2016 Elsevier Inc. http://dx.doi.org/10.1016/j.devcel.2015.12.014 Developmental Cell Article

Dynamic Control of Enhancer Repertoires Drives Lineage and Stage-Specific Transcription during Hematopoiesis

Jialiang Huang,2,3,7 Xin Liu,1,7 Dan Li,4,7 Zhen Shao,5,7 Hui Cao,1 Yuannyu Zhang,1,5 Eirini Trompouki,2,8 Teresa V. Bowman,2,9 Leonard I. Zon,2,6 Guo-Cheng Yuan,3 Stuart H. Orkin,2,6,* and Jian Xu1,* 1Children’s Medical Center Research Institute, Department of Pediatrics, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA 2Division of Hematology/Oncology, Boston Children’s Hospital and Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Stem Cell Institute, Harvard Medical School, Boston, MA 02115, USA 3Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Harvard School of Public Health, Boston, MA 02115, USA 4Harvard College, Cambridge, MA 02138, USA 5Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China 6Howard Hughes Medical Institute, Boston, MA 02115, USA 7Co-first author 8Present address: Cellular and Molecular Immunology, Max Planck Institute of Immunology and Epigenetics, D-79108 Freiburg, Germany 9Present address: Department of Developmental and Molecular Biology, Albert Einstein College of Medicine, Bronx, NY 10461, USA *Correspondence: [email protected] (S.H.O.), [email protected] (J.X.) http://dx.doi.org/10.1016/j.devcel.2015.12.014

SUMMARY expression. Enhancers are cis-acting DNA sequences that can increase the transcription of through cooperative and syn- Enhancers are the primary determinants of cell ergistic binding of transcription factors (TFs), DNA binding effec- identity, but the regulatory components controlling tors of signaling pathways, and chromatin-modifying complexes enhancer turnover during lineage commitment remain (Banerji et al., 1981). Enhancers function from distal regions in an largely unknown. Here we compare the enhancer orientation-independent manner, and harbor distinct chromatin landscape, transcriptional factor occupancy, and features including increased chromatin accessibility, character- transcriptomic changes in human fetal and adult istic histone modifications and DNA hypomethylation, and bidi- rectional transcription (Bulger and Groudine, 2011). Although hematopoietic stem/progenitor cells and committed major progress has been made toward genome-wide annotation erythroid progenitors. We find that enhancers are of candidate enhancers (Andersson et al., 2014; Heintzman modulated pervasively and direct lineage- and et al., 2007, 2009; Visel et al., 2009a), the molecular processes stage-specific transcription. GATA2-to-GATA1 switch controlling enhancer activation and deactivation during lineage is prevalent at dynamic enhancers and drives commitment remain poorly understood. erythroid enhancer commissioning. Examination of A defining feature of enhancers is their ability to function lineage-specific enhancers identifies transcription as integrated platforms for TF binding, where cell-intrinsic and factors and their combinatorial patterns in enhancer -extrinsic signaling cues are interpreted in a highly lineage- and turnover. Importantly, by CRISPR/Cas9-mediated context-dependent manner (Buecker and Wysocka, 2012). genomic editing, we uncover functional hierarchy Despite recent advances in profiling enhancer-associated of constituent enhancers within the SLC25A37 su- biochemical features, the biological importance of individual enhancers in lineage differentiation is often limited by lack of per-enhancer. Despite indistinguishable chromatin insights in enhancer regulation in vivo and in molecular details features, we reveal through genomic editing the func- of enhancer structure-function in situ. The fundamental ques- tional diversity of several GATA switch enhancers tions related to enhancer function and mechanisms remain un- in which enhancers with opposing functions coop- answered: how do enhancers regulate precise spatiotemporal erate to coordinate transcription. Thus, genome- expression patterns? How are enhancers organized and wide enhancer profiling coupled with in situ enhancer regulated in a high-dimensional chromatin environment? How editing provide critical insights into the functional do enhancers communicate with their target genes in vivo during complexity of enhancers during development. development? Furthermore, how do mutations and genetic var- iations in enhancers influence human disease? Recently, intensely marked clusters of enhancers or super-en- INTRODUCTION hancers containing an exceptionally high degree of enrichment of master TFs, Mediator, and chromatin marks have been iden- Stem cell self-renewal and differentiation require precisely regu- tified in a broad range of mammalian cell types (Hnisz et al., lated tissue-specific and developmental stage-specific gene 2013; Parker et al., 2013; Whyte et al., 2013). Super-enhancers

Developmental Cell 36, 9–23, January 11, 2016 ª2016 Elsevier Inc. 9 A CDF0 F5 A0 A5 Lineage-Specific Enhancers CD34 CD36, CD71, CD235 A0-A5-Lost A0-A5-Gained Fetal (FL) H3K4me1 A0 or H3K4me1 H3K27ac H3K4me1 H3K27ac H3K4me1 H3K27ac H3K4me1 H3K27ac H3K27ac Adult (BM) Lost Lost H3K4me1 0 357 A5 CD34+ Proerythroblasts H3K27ac Gained (HSPC) (ProE) Gained CSF1R HEMGN Shared Shared

B F0 A0 F5 A5 E Developmental Stage-Specific Enhancers 6081 3323 10283 F5-A5-Lost F5-A5-Gained HSPC ProE H3K4me1 H3K4me1 H3K27ac H3K4me1 H3K27ac H3K4me1 H3K27ac H3K4me1 H3K27ac F5 F0 vs F5 H3K27ac Lost Lost Fetal H3K4me1 A5 H3K27ac Gained F0 F5 Gained 2389 28562856 LIN28B

Shared THRB Shared 10674 F0 vs A0 F5 vs A5 82398239 FGLineage Specification Developmental Stage Specificity 1916 2535 A0 A5 F0 F5 A0 A5 F0 A0 F5 A5 Lost Gained Lost Gained Adult Lost Gained Lost Gained A0 vs A5 ETS1 ETS1 PU.1 GFI ERG PU.1 Fetal GFI GATA1::TAL1 Fetal HSPC HSPC GFI1B PRRX2 FLI1 FLI1 NFE2 MYB 8632 3996 8496 PU.1 ERG GATA1 STAT1::STAT2 Adult Adult GATA1 GATA1 NFIC IRF2 ProE ProE GATA1::TAL1 GATA1::TAL1 GATA1::TAL1 IRF1 -3 -2 -1 0 1 2 3 -2 -1 0 1 2 -2 -1 0 1 2 -2 -1 0 1 2 A0-A5-Lost A0-A5-Shared A0-A5-Gained Fold Changes Fold Changes Fold Changes Fold Changes (A0-Specific) (A5-Specific) TF elements enriched in Lost enhancers (p < 0.01) TF elements enriched in Lost enhancers (p < 0.01) TF elements enriched in Gained enhancers (p < 0.01) TF elements enriched in Gained enhancers (p < 0.01)

Figure 1. Comparative Analysis of Enhancer Repertoires during Human Erythropoiesis (A) Ex vivo erythroid differentiation of fetal liver (FL) or adult bone marrow (BM) CD34+ HSPCs. Cells at matched stages of differentiation (HSPC: F0 and A0; ProE: F5 and A5) were collected for transcriptomic profiling and ChIP-seq analyses. (B) Identification of lineage or developmental stage-specific enhancers. Venn diagram shows the overlap between HSPC and ProE, or fetal and adult enhancers. The numbers of lost, shared, or gained enhancers in each comparison are shown. (C) ChIP-seq density heatmaps for H3K4me1 and H3K27ac within lost, gained, and shared enhancers in each comparison. (D) Representative lineage-specific enhancers are shown. The putative active enhancers are depicted by shaded lines. (E) Representative developmental stage-specific enhancers are shown. The putative active enhancers are depicted by shaded lines. (F) Enrichment of lineage-defining TFs in lineage-specific enhancers. The top enriched TF motifs in lost or gained enhancers between HSPCs and ProEs at fetal or adult stage are shown. p values were calculated using the hypergeometric test. (G) Enrichment of distinct coregulators in stage-specific enhancers. The top enriched TF motifs in lost or gained enhancers between fetal and adult HSPCs (F0 versus A0) or ProEs (F5 versus A5) are shown. p values were calculated using the hypergeometric test. See also Figure S1. differ from regular enhancers in both the size and intensity of and their combinatorial rules as putative drivers of enhancer the associated chromatin features, and are frequently found in turnover during differentiation. GATA2-to-GATA1 switch is a crit- genomic proximity of cell identity genes and disease-associated ical molecular driver of developmentally dynamic enhancers dur- variants. Super-enhancers are also found at key oncogenic ing erythroid specification. Most importantly, through CRISPR/ drivers, thus selective inhibition of oncogenes may be achieved Cas9-mediated loss-of-function analysis, we uncover functional by disruption of super-enhancers (Loven et al., 2013). These hierarchy and complexity of constituent enhancers within the studies suggested a model that a relatively small set of line- same enhancer clusters. Thus, genome-wide enhancer profiling age-defining super-enhancers might determine cell identity in coupled with in-depth enhancer editing provides important development and disease. Despite the proposed prominent insights into the regulatory components controlling enhancer roles, the regulatory components and functional features associ- functions during erythropoiesis. ated with super-enhancers are currently unknown. The critical questions are whether the super-enhancer represents a single RESULTS functional unit in vivo, and how the individual constituent en- hancers contribute to maximal enhancer activity through coop- Pervasive Changes in Enhancer Landscape during erativity or interaction within their native chromatin environment Human Erythropoiesis (Pott and Lieb, 2015). Primary fetal- and adult-stage human CD34+ hematopoietic We reasoned that modeling human fetal and adult-stage stem/progenitor cells (HSPCs) were expanded and differentiated erythropoiesis combined with epigenomic enhancer annotation ex vivo into highly enriched stage-matched populations of and analysis of the underlying DNA sequences can be used as erythroid progenitor cells (proerythroblasts or ProEs) (Figure 1A). an unbiased approach to identify causative TFs driving enhancer We selected fetal or adult-stage HSPCs (F0 or A0; Figure 1B), and temporal activities in lineage specification. Comparing and con- lineage-committed ProEs (F5 or A5) for genome-wide enhancer trasting TF binding associated with developmentally regulated annotation and expression profiling. Specifically, we analyzed enhancers facilitates identification of lineage-regulating factors active enhancer-associated histone modifications H3K4me1

10 Developmental Cell 36, 9–23, January 11, 2016 ª2016 Elsevier Inc. and H3K27ac by chromatin immunoprecipitation sequencing analyses demonstrate that an increasing number of enhancers (ChIP-seq) and transcriptomic profiles in four distinct popula- strongly correlate with more rapid kinetics and pronounced tions of human HSPCs or ProEs at the fetal or adult stage (Fig- transcriptional changes. It has been suggested that clustered ures 1B–1E and S1A). enhancers, including super-enhancers, associate with critical Comparative analysis of the enhancer landscape illustrates developmental or cancer-associated transcription units (Hnisz pervasive temporal changes in enhancer usage underlying et al., 2013; Loven et al., 2013; Whyte et al., 2013). Consistent lineage and developmental stage specificity. For example, with this notion, we observed that the lineage-specific super- 8,632 enhancers are lost (A0-A5-lost) and 8,496 enhancers are enhancers correlate with more robust transcriptional changes acquired (A0-A5-gained) upon differentiation of adult HSPCs than regular enhancers (Figure 2D). (A0) to ProEs (A5), whereas only 3,996 enhancers are preserved Enhancers can act from distance to regulate transcription. We (A0-A5-shared, Figures 1B and 1C). The number of developmen- then compared the expression of genes targeted by enhancers tally dynamic enhancers is much higher than that of differentially located at varying distance (Figure 2E). Interestingly, the prox- expressed genes (Figure S1B), suggesting that the enhancer imal enhancers correlate with more rapid kinetics and larger landscape undergoes more extensive turnover than transcrip- quantitative changes in compared with distal tomic changes during lineage specification. Furthermore, we enhancers. Taken together, these analyses illustrate critical roles identified 1,916 to 2,856 enhancers uniquely active at fetal or of enhancers in modulating lineage- and stage-specific gene adult stage in HSPCs or ProEs, indicating a substantial change expression patterns, and suggest that the quantity and physical in genome-wide regulatory architecture across developmental proximity of enhancers can influence the transcriptional robust- stages within the erythroid lineage (Figure 1B). During the transi- ness of their gene targets during development. tion from HSPCs to ProEs, lost enhancers at both fetal and adult stages are significantly enriched in recognition sites (or motifs) In Situ Genomic Editing of the SLC25A37 for HSPC-regulating TFs including ETS1, ERG, FLI1, and PU.1 Super-enhancer (Orkin and Zon, 2008; Wilson et al., 2010), whereas gained en- Intensely marked enhancer clusters or super-enhancers, con- hancers are enriched in motifs for erythroid master regulators sisting of multiple discrete enhancers spanning larger chromatin GATA1 and TAL1 (Cantor and Orkin, 2002)(Figure 1F). By domains, have been proposed to control genes essential for contrast, the comparison between fetal and adult stage-specific cell identity. The enhancer cluster upstream of the SLC25A37 enhancers reveals enrichment of distinct TF motifs (Figure 1G). gene, consisting of three distinct constituent enhancers as Of note, the most enriched motifs in F5-A5-gained enhancers measured by H3K4me1 and H3K27ac ChIP-seq, is defined as are IRF1, IRF2, and STAT1/2, consistent with recent studies an erythroid-specific super-enhancer in both human (A5 ProE) demonstrating a role of inflammatory signaling pathways in es- and mouse (G1ER) erythroid cells (Figure 3A and Table S4). tablishing HSPC programs (Espin-Palazon et al., 2014; He The orthologous mouse super-enhancer displays high primary et al., 2015; Li et al., 2014; Xu et al., 2012). Taken together, these , syntenic position, and similar chromatin results indicate that the lineage-defining TFs are functionally signature and TF occupancy (Figure 3A). The SLC25A37 gene conserved within enhancers during lineage specification; how- encodes Mitoferrin 1 that functions as an essential mitochondrial ever, they cooperate with distinct stage-specific cofactors to importer for iron metabolism and heme biogenesis. A genetic modulate enhancer landscape for fetal and adult erythropoiesis. deficiency of SLC25A37 results in profound hypochromic ane- mia in vertebrate species (Amigo et al., 2011; Shaw et al., Enhancers Control Lineage and Stage-Specific 2006). The expression of SLC25A37, but not the neighboring Transcription ENTPD4 gene, is progressively and significantly induced during To directly examine the correlation between enhancer activities human and mouse erythropoiesis (Figures S2A and 3C), sug- and lineage or developmental stage-specific gene expression, gesting that it is trans-activated through the upstream super- we mapped enhancers to target genes using the ‘‘nearest enhancer. neighbor gene’’ approach (Heintzman et al., 2009; Visel et al., To define the regulatory components of the SLC25A37 super- 2009a; Xu et al., 2012) and compared these with lineage or enhancer, we asked whether the function of each constituent stage-specific gene expression (Figure 2A and Table S3). Impor- enhancer depends on the activity of neighboring enhancers tantly, the erythroid lineage-specific enhancers (F0-F5-gained in situ. We employed site-directed loss-of-function analysis of and A0-A5-gained) strongly associate with genes induced during the SLC25A37 super-enhancer constituents using CRISPR/ erythropoiesis (F0-F5-up and A0-A5-up), whereas the HSPC- Cas9-mediated genomic engineering. We focused on the or- associated enhancers (F0-F5-lost and A0-A5-lost) strongly thologous mouse super-enhancer in the murine G1E/G1ER associate with downregulated genes (F0-F5-down and A0- erythroid cell model (Welch et al., 2004). Analogous to the A5-down). Similarly, the presence of stage-specific enhancers ex vivo erythroid maturation of human HSPCs, the Gata1-null highly correlates with gene expression changes in the respective G1E cells are maintained in an undifferentiated state and ex- fetal or adult stage (Figure 2A). press a high level of Gata2. Upon activation of the Gata1-ER Of note, by focusing on representative A0-A5-up genes, we transgene by b-estradiol treatment in G1ER cells, Gata1 mRNA observed that genes with an increasing number of enhancers was progressively elevated whereas Gata2 expression was display faster expression kinetics and higher mRNA levels during sharply downregulated, resulting in a ‘‘GATA switch’’ (Figures differentiation (Figure 2B). We then compared on a global scale S3A and S3B). the expression kinetics of genes associated with single and mul- We designed sequence-specific single-guide RNAs (sgRNAs) tiple lost or gained lineage-specific enhancers (Figure 2C). These flanking each constituent enhancer (E1, E2, and E3) or the

Developmental Cell 36, 9–23, January 11, 2016 ª2016 Elsevier Inc. 11 ABEnhancer Activity H3K4me1 6 A0 ADD2 F0-F5-Gained H3K27ac GFI1B H3K4me1 F0 F5 A5 5 SLC25A37

A0-A5-Gained H3K27ac ADD2 TMEM56 (1 Enhancer) F0-F5-Lost 4 A0 A5 H3K4me1 A0-A5-Lost A0 H3K27ac 3 H3K4me1 F0-A0-Gained A5 F0 A0 H3K27ac GFI1B F5-A5-Gained 2 (2 Enhancers)

F0-A0-Lost H3K4me1 F5 A5 A0 Expression mRNA H3K27ac 1 F5-A5-Lost H3K4me1 A0) log2 (fold change relative to A5 0 100 H3K27ac 0 SLC25A37 -log10 (p-value) (3 Enhancers) H3K4me1 Developmental Stage A0 H3K27ac F0-F5-Up F0-A0-Up F5-A5-Up A0-A5-Up Specificity H3K4me1 0 357 F0-F5-Down F0-A0-Down F5-A5-Down A0-A5-Down A5 vs. H3K27ac CD34+ Proerythroblasts TMEM56

(4 Enhancers) (HSPC) (ProE) Gene Expression Lineage Specification

CDSingle Enhancer vs. Multiple Enhancers Regular Enhancers vs. Super-Enhancers EDistal Enhancers vs. Proximal Enhancers

Enhancer Gene Enhancer Gene Enhancer Gene Enhancer Gene Enhancer Gene Enhancer Gene -0.8 -0.8 -0.5 Super-Enhancer <10kb (1082) ≥4 (437) (229) -0.3 -0.4 10~20kb (745) -0.4 3 (369)

Lost Regular Enhancer Lost 20~50kb (1071) Lost 2 (799) A0 (3821) A0 A0 -0.1 ≥50kb (904) 1 (2216) 0 0 1 (2207) Regular Enhancer 0.1 ≥50kb (664) A5 (3835) A5 A5 2 (776) 20~50kb (915) 0.4 0.4 3 (344) Super-Enhancer log2 (fold change) 10~20kb (765) log2 (fold change) 0.3 Gained Gained log2 (fold change) Gained ≥4 (508) (314) <10kb (1483)

0.8 0.8 Differentiation 0.5 Differentiation Differentiation HSPC ProE HSPC ProE HSPC ProE

Figure 2. Enhancers Control Lineage- and Stage-Specific Transcription (A) Enhancers positively associate with lineage or stage-specific gene expression changes. The enrichment significance of differentially expressed genes (columns) harboring different types of enhancers (rows) is calculated using Fisher’s exact test. (B) Representative genes targeted by one, two, three, and four ‘‘A0-A5-gained’’ enhancers are shown, respectively. The putative enhancers are depicted by shaded lines. The mRNA expression of each gene is shown on the right. (C) mRNA expression of genes harboring single versus multiple enhancers. (D) The correlation between mRNA expression and regular enhancers versus super-enhancers. (E) mRNA expression of genes harboring enhancers with varying distance. Values are shown as mean ± SEM between replicates. promoter (P) (Figure 3B). Upon transfection into undifferentiated enhancers (Figures 3B, S3C, and S3D). Consistent with a G1E cells together with an SpCas9-expressing construct, we prominent role of E3 in super-enhancer activation, combined screened and obtained multiple independent single-cell-derived deletion of E2 and E3 (E2-3) or all three enhancers (E1-3) clones containing biallelic deletion of each enhancer (Figures completely abolishes Slc25a37 expression upon differentia- S2B and S2C). Surprisingly, knockout of individual enhancers tion (Figure 3C). Importantly, deletion of individual or multiple confers markedly varying effects on Slc25a37 expression. constituent enhancers has only a subtle effect on Slc25a37 Specifically, while deletion of E1 or E2 individually only modestly baseline expression in G1E cells (Figures 3C, S3C, and or slightly impairs Slc25a37 activation during differentiation, S3D), indicating that enhancer usage is highly context spe- respectively, E3 deletion abolishes its activation, resulting in a cific. Loss of E3 leads to near absence of H3K27ac, GATA1 15-fold decrease in expression (48 hr after b-estradiol treatment; and TAL1 occupancy at the neighboring E1 and E2 enhancers, Figure 3C). The expression of the neighboring Entpd4 gene whereas loss of E1 or E2 has minimal impact on H3K27ac remains low and unchanged in control and enhancer-deletion or GATA1/TAL1 binding at neighboring enhancers (Figures cells (Figure S2D). ChIP-seq analysis of CTCF reveals two 3D–3G). These results strongly suggest that, despite the CTCF binding sites between Slc25a37 and Entpd4 genes indistinguishable chromatin features and TF occupancy at (Figure S2E), suggesting that the Slc25a37 super-enhancer the Slc25a37 constituent enhancers, the E3 enhancer is does not control Entpd4 transcription in erythroid cells. The dif- functionally more potent than its neighboring enhancers in ferentiation kinetics of G1E cells, as measured by Gata1 and directing transcriptional activation. Thus, our in-depth in situ Gata2 expression, appear ostensibly normal in the absence of enhancer-deletion analyses demonstrate that the Slc25a37 Slc25a37 enhancers (Figures S3A and S3B). super-enhancer is composed of a functional hierarchy con- Of note, by using combinations of sgRNAs, we also ob- taining both critical and dispensable constituent components tained G1E cells containing deletion of multiple constituent (Figure 3H).

12 Developmental Cell 36, 9–23, January 11, 2016 ª2016 Elsevier Inc. ABSuper-Enhancer E3 E2 E1 P 5kb DHS + H3K4me1 A0 H3K27ac Cas9 Guide RNA GATA1 TFs

DHS C mRNA H3K4me1 A5 H3K27ac P GATA1 E1 PhastCons E2 E3 E2 E1 P SLC25A37 5kb DHS E3 H3K4me1 G1E H3K27ac E1-2 Gata1 E2-3 DHS H3K4me1 E1-3 G1ER H3K27ac E3 E2 E1 P Gata1 Super-Enhancer Super-Enhancer Slc25a37 C 0.018 Control (C) Prom-del (P) H Super-Enhancer 0.014 E1-del (E1) E3 E2 E1 E2-del (E2) 0.010 E3-del (E3) E1,2-del (E1-2) 0.006 E2,3-del (E2-3) Differentiation * * E1,2,3-del (E1-3) HSPC ProE * mRNA Expression mRNA 0.002 ** 0.002 *

0.001 ** ** * * ** ** ** *** *** ** ** 0 P P P P P P C C C C C C E1 E2 E3 E1 E2 E3 E1 E2 E3 E1 E2 E3 E1 E2 E3 E1 E2 E3 E1-2 E2-3 E1-3 E1-2 E2-3 E1-3 E1-2 E2-3 E1-3 E1-2 E2-3 E1-3 E1-2 E2-3 E1-3 E1-2 E2-3 E1-3 0 hr 4 hr 8 hr 12 hr 24 hr 48 hr

D H3K4me1 E H3K27ac

60 5 Slc25a37 Slc25a37 50 4

40 3 Control (C) 30 2 Prom-del (P) 20 * * * * 1 * 10 * E1-del (E1) * * * * ChIP Enrichment (% of Input) ChIP * * * * *** * * Enrichment (% of Input) ChIP * ** * *** * ** * * 0 0 E2-del (E2) Oct4 (ctrl) E3 E2 E1 P Oct4 (ctrl) E3 E2 E1 P E3-del (E3) F GATA1 G TAL1 E1,2-del (E1-2) 0.8 2.5 Slc25a37 Slc25a37 E2,3-del (E2-3) 2.0 0.6 E1,2,3-del (E1-3) 1.5 0.4

1.0 * * * * 0.2 * * 0.5 * * ChIP Enrichment (% of Input) ChIP * ** * * ** * * * * Enrichment (% of Input) ChIP * ** * * ** * 0 0 * * * * Oct4 (ctrl) E3 E2 E1 P Oct4 (ctrl) E3 E2 E1 P

(legend on next page)

Developmental Cell 36, 9–23, January 11, 2016 ª2016 Elsevier Inc. 13 Control of Enhancer Activities by Distinct Combinations et al., 2015; Su et al., 2013; Xu et al., 2012, 2015)(Table S1). of Transcription Factors Consistent with the motif analysis, ChIP-seq of a panel of identi- A common approach to identifying enhancer-associated TFs is fied TFs clearly demonstrates enrichment of distinct TF clusters to survey for the presence of TF binding consensus sequences within each enhancer type (Figures 4C and S4B). (or motifs), without knowing whether the putative motifs are To explore the TF combinatorial rules, we determined the occupied by the cognate TFs in the cell type of interest. We co-association between TFs within the enhancer repertoires reasoned that uncovering the functionally relevant TFs that asso- (Figure 4D). By hierarchical clustering analysis, we identified ciate with developmentally dynamic enhancers should help to five distinct TF combinatorial modules, including expected asso- infer lineage-specific regulators and their combinations in con- ciations such as GATA1 and TAL1, and some less expected as- trolling cellular identity. To this end, we compared the motif sociations such as NFE2 and MYB. Importantly, we found that enrichment and ChIP-seq occupancy of a panel of lineage-regu- the ubiquitous ‘‘housekeeping’’ -SP1 and AP1 modules, lating TFs (GATA1, TAL1, FLI1, and PU.1) and the ubiquitously and the HSPC-specific RUNX1-FLI1-PU.1-ETS module, are in- expressed CTCF (Figure 4A). Strikingly, while only 3% (22,570 terconnected but dissociable, suggesting that distinct TF combi- of 707,718) of the GATA1 motif-matched loci are covered by nations cooperate to modulate the spatiotemporal activities of GATA1 ChIP-seq at a genome scale in ProEs, 34% of identified enhancers in a highly context-specific manner. Taken together, GATA1 motifs are covered by GATA1 within the enhancer these analyses uncover TF combinatorial regulatory patterns context. Furthermore, 55% of GATA1 motifs at A0-A5-gained with known and unknown roles as putative drivers of enhancer enhancers are occupied by GATA1, in contrast to only 9% in turnover during erythropoiesis. A0-A5-lost enhancers, consistent with a prominent role of GATA1 in erythroid specification. A similar pattern is observed Enhancer-Centered TF Combinatorial Regulatory for another principal erythroid regulator, TAL1 (Figure 4A). In Networks stark contrast, the opposite patterns are observed for HSPC- To illustrate the temporal control of enhancer activities, we devel- regulating FLI1 and PU.1. Of note, comparable frequencies of oped a computational methodology to delineate enhancer-medi- CTCF motif-matched loci are covered by CTCF ChIP-seq in ated transcriptional networks by connecting motif enrichment, TF both cell types. These analyses demonstrate that functionally occupancy, and enhancer activity (Figure 5A). In brief, to quantify relevant TF motifs are highly enriched in lineage-selective en- the relative importance of individual TF on enhancer specificity, hancers of lineage-relevant cell types. we calculated the enrichment score as the significance of the To comprehensively identify TFs involved in developmentally enrichment of TF ChIP-seq peaks (or motif-matched loci) within dynamic enhancers, we performed motif enrichment analysis each type of lineage or stage-specific enhancers using the whole and identified 86 TF motifs that are significantly enriched in at genome as background. To quantify the likelihood of TF cooper- least one enhancer type across 12 types of lineage or stage- ation, we calculated the combinational score as the frequency of specific enhancers (Figures 4B and S4A). Specifically, GATA1 co-occurrence of two TFs at the same enhancer relative to and GATA1:TAL1 motifs are highly enriched in the erythroid-spe- genome background. We then selected TF/motif pairs with signif- cific F0-F5-gained and A0-A5-gained enhancers. In contrast, icant combinational scores to assemble the networks for each PU.1, RUNX1, ETS1, and FLI1 motifs are highly enriched in enhancer type in Cytoscape (Shannon et al., 2003). the HSPC-specific F0-F5-lost and A0-A5-lost enhancers. To Notably, the A0-A5-lost network is predominantly modulated validate the motif analysis, we performed 30 additional ChIP- by the PU.1-RUNX1-FLI1-ETS-GATA2 combinatorial interac- seq analyses of identified TFs in both HSPCs and ProEs, tions. In contrast, the A0-A5-gained network is dominated by including GATA1, GATA2, TAL1, PU.1, and RUNX1 in HSPCs GATA1-TAL1 together with an IRF2-STAT1-STAT2 interaction (F0 and A0) and/or ProEs (F5 and A5) (Figures 4C, 5F, and Table module, suggesting that the temporal changes in TF occupancy S1). We also re-analyzed 45 previously published ChIP-seq control differential enhancer activity (Figure 5B). Distinct sets datasets of TFs and chromatin regulators obtained in the same of TF combinatorial modules were found at lineage or stage- cell populations (Abraham et al., 2013; Beck et al., 2013; Dogan specific enhancers (Figure S5 and Table S5). Importantly, the

Figure 3. CRIPSR/Cas9-Mediated Deletion Analysis of the SLC25A37 Super-enhancer (A) Chromatin signatures and TF occupancy within the human or mouse SLC25A37 locus in HSPC (A0) versus ProE (A5) or undifferentiated G1E versus differentiated G1ER cells are shown, respectively. The SLC25A37 constituent enhancers (E1, E2, and E3) and the proximal promoter (P) are depicted by shaded lines. Super-enhancers were called using Rose (Whyte et al., 2013) base on the H3K27ac ChIP-seq signal. The sequence conservation by PhastCons analysis is shown. (B) Schematic of CRISPR/Cas9-mediated genomic editing to dissect the SLC25A37 super-enhancer. The scheme is adapted from Pott and Lieb (2015). The scissors indicate DNA double-strand breaks induced by CRISPR/Cas9. The red dashed lines indicate the deleted genomic DNA. (C) Expression of Slc25a37 mRNA in unmodified (control) and enhancer-deletion G1E/G1ER cells at various time points (0–48 hr) after b-estradiol treatment. The mRNA expression levels relative to GAPDH are shown. Each colored circle represents an independent single-cell-derived biallelic enhancer or promoter-deletion clone. Results are means ± SD of multiple independent clones. The p value measures the statistical significance between control (C) and each experimental group. *p < 0.01; **p < 0.001. (D–G) ChIP-qPCR analysis of H3K4me1, H3K27ac, GATA1, and TAL1 in control and enhancer-deletion cells. Primers against Slc25a37 promoter (P) and each constituent enhancer (E1, E2, and E3) are used. Oct4 promoter is analyzed as a negative control. Results are means ± SD of multiple independent clones. *p < 0.01. (H) Schematic of the hierarchical structure of the SLC25A37 super-enhancer. See also Figures S2 and S3.

14 Developmental Cell 36, 9–23, January 11, 2016 ª2016 Elsevier Inc. A All A0-A5-Lost A0-A5-Gained B Genome Enhancers Enhancers Enhancers 0 100

-log (p-value) Enhancers 3% 9% 10 GATA1 34% 55% 707,718 24,111 4,440 6,344 F0-F5-Gained A0-A5-Gained F0 F5 F0-F5-Lost 3% 5% TAL1 33% A0-A5-Lost A0 A5 57% 322,767 13,224 2,240 3,857 F0-F5-Shared A0-A5-Shared F0-A0-Gained 3% 14% 18% 3% FLI1 F5-A5-Gained F0 A0

613,371 32,717 10,158 4,183 F0-A0-Lost F5-A5-Lost F5 A5 F0-A0-Shared 5% 6% PU.1 26% 38% F5-A5-Shared

1,043,882 46,699 13,879 6,521 Developmental Stage Myb AP1 JUN FLI1 FOS IRF1 ERG SPIB Prrx2 E2F6 ELF1 Specificity ELK4 ELK1 ETS1 MZF1 JUNB JUND EGR2 Nfe2l2 GATA1 GABPA RUNX1 RUNX2 ARID3A TFAP2A TFAP2C vs. 6% 13%

8% 12% BATF::JUN NFE2::MAF CTCF SPI1 (PU.1) Lineage Specification TAL1::GATA1 STAT2::STAT1 140,936 7,769 2,039 1,254 TF Motifs

D GATA1-TAL1 AP1 RUNX1-FLI1-PU.1-ETS C Enhancers PRRX2 ARID3A GATA1 1 CTCF GATA2 F0-F5-Gained EGR2 EVI1 E2F6 A0-A5-Gained TAL1::GATA1 MZF1 F0-F5-Lost SP1 2 SP2 A0-A5-Lost BATF::JUN AP1 EGR1 F0-F5-Shared JUN Arnt::Ahr FOS 3 A0-A5-Shared JUNB JUND NRC2C F0-A0-Gained ZFP423 MZF1 NHLH1 F5-A5-Gained USF1 F0-A0-Lost USF2 TFAP2A ERG TFAP2C F5-A5-Lost Mafb ETS1 F0-A0-Shared GABPA FLI1 ELK4 4 RUNX1 F5-A5-Shared RUNX2 SPI1 (PU.1) TCF3 0 100 -1 SPIB NFE2L2 FLI1-A0 PU.1-F0 IRF2-A0 IRF2-A5 ERG-A0 PU.1-A0 TAL1-F5 TAL1-F0 LYL1-A0 TAL1-A5 TAL1-A0 NFE2-F5 NFE2-F0 NFE2-A5 NFE2-A0 CTCF-F5 CTCF-A5 LMO2-A0 -log10(p-value) NFE2::MAF GATA1-F5 GATA1-F0 GATA2-A5 GATA1-A5 GATA1-A0 GATA2-A0 RUNX1-F0 RUNX1-A0 5 +1 MYB PCC Value PRDM1 TF ChIP-seq E2F-SP1 NFE2-MYB

Figure 4. Combinatorial Control of Enhancers by Transcription Factors (A) Enrichment of functionally relevant TF motifs. The numbers of identified motif-matched loci and the percentage of motifs overlapped with ChIP-seq peaks are shown. (B) Hierarchical clustering of 86 TF motifs significantly enriched in the lineage- and/or stage-specific enhancers. Heatmap shows the enrichment score of TF motifs. (C) Hierarchical clustering of ChIP-seq occupancy of the indicated TFs within enhancers. Heatmap shows the enrichment score based on the overlap significance calculated by Fisher’s exact test. (D) Combinatorial TF modules within enhancers during erythropoiesis. TF modules were defined by hierarchical clustering of enriched TF motifs based on their enrichment score across all 12 enhancer types. Five distinct TF motif modules are shown for (1) GATA1-TAL1, (2) E2F-SP1, (3) AP1, (4) RUNX1-FLI1-PU.1-ETS, and (5) NFE2-MYB. The color code indicates the Pearson correlation coefficient (PCC) of the enrichment scores. See also Figure S4.

GATA2 and GATA1 combinatorial interactions are present in respectively, compared with random interactions (p values both A0 and A5 networks, with GATA2 dominating the A0 8.0 3 1033 and 1.9 3 1066 by Fisher’s exact test; Figure 5C). network while GATA1 dominates the A5 network (Figure 5B). We then investigated the extent to which disease-associated Thus, our data suggest that the combinatorial assembly of SNPs occur in enhancers containing network-predicted TF pairs lineage-defining TFs and transcriptional coregulators at lineage- (Figure 5D). We observed that enhancers containing network- and stage-specific enhancers directs temporal regulation of predicted TF interactions (TF pairs in network; Figure 5D) transcriptional networks during erythroid specification. are highly enriched in GWAS SNPs, particularly the hematologic To validate the network predictions, we first compared the and erythroid trait-associated SNPs. Furthermore, A0-A5-gained network-identified interactions with known -protein inter- erythroid-specific enhancers are more significantly enriched in actions (PPIs) from Lit-BM-13 (Rolland et al., 2014) and STRING erythroid SNPs than A0-A5-lost HSPC-specific enhancers (Fig- (Szklarczyk et al., 2015) databases. We observed that the ure 5E). These analyses confirm that the enhancer-centered TF network-predicted TF interactions are 7.3- and 3.8-fold more combinatorial networks can be used to identify functionally rele- enriched of known PPIs in Lit-BM-13 and STRING databases, vant and disease-associated regulatory interactions.

Developmental Cell 36, 9–23, January 11, 2016 ª2016 Elsevier Inc. 15 A Motif-Matched Loci Enhancers TF ChIP-seq Peaks Input Enhancer TF Combinatorial Networks

Node (Enrichment Score) (Figure 4B) (Figure 4C) Scoring Output Edge (Combinational Score) E=mtf2mtf -log10(p)E tf2mtf = -log10(p)E tf2tf = -log10(p)

JUND JUN JUNB ZNF263 B TF-to-TF (tf2tf) EWSR1-FLI1 TF FOSL2BATF::JUN MZF1_5-13 NR1H2::RXRA TF-to-Motif (tf2mtf) AP1 NR2C2 Motif NFE2::MAF FOS CTCF EGR2 EGR1 Motif-to-Motif (mtf2mtf) FOSL1 SP1 MAFK IRF2_A5 E2F3 SP2 INSM1 E2F1 E2F6 E2F4E2F3 CTCF_A5 IRF2 IRF2_A0 PLAG1 Nobox Mafb Nfe2l2 CTCF_A5 TF/Motif TF/Motif MAFF E2F1 TFAP2C EGR1 ESR2 Zfp423 Enrichment Score Combinatorial Score IRF1 TFAP2A NFE2_A0 PRDM1 SP2 ESR1 CTCF HOXA5 PPARG NRF1 NFE2_A5 STAT2::STAT1 ZBTB33 Lhx3EBF1 RUNX1_A0 RREB1 Tcfcp2l1 Arnt::Ahr SP1 EGR2 GATA2 MZF1_1-4 TAL1::GATA1 HIF1A::ARNT Mycn GATA2_A0 FLI1_A0 Evi1 Gata4 Bhlhe40MYC::MAX ARID3A MAX RUNX1 Gata1 Gata4 GATA3 Prrx2 NR1H2::RXRA Klf1 Pdx1 LYL1_A0 RUNX2 NR2C2 GATA2_A0 Arnt Gata1 TAL1_A5 ERG_A0 GATA1_A5 USF1 USF2 LMO2_A0 HNF4A

TAL1_A5 GATA1_A5 GFI1B_A5 PPARG::RXRA GFI1B_A5 NFE2L1::MafG GATA2 Hltf NR2F1 HNF4G Pax2 NFE2_A5 LYL1_A0 GATA3 RUNX1_A0 MAFK NFE2_A0 Nfe2l2 TAL1::GATA1 FLI1_A0 NFE2::MAF LMO2_A0 MAFF PU1_A0 JUN FOSL1 TFAP2C Sox17 Arnt::Ahr BATF::JUN ARID3A SOX10 ERG_A0 AP1JUNB Mafb MZF1_1-4 EBF1 JUND HOXA5 Spi1 Bhlhe40HIF1A::ARNT RUNX1 FOSFOSL2 TFAP2A IRF2_A0 Sox6 Sox3 RUNX2 Pdx1 ELF1 Erg IRF1 Mycn Arnt PRDM1 FLI1 Ets1 PU1_A0 STAT2::STAT1 Prrx2 USF1 MYC::MAX GABPA USF2 IRF2_A5 A0 A5 Tcfcp2l1 SOX9 FEV IRF2 ELK4 Myc SPIB ESR1 Spi1 MAX ELF1Erg A0 vs A5 ELK1 Ets1FLI1 Sox5 Foxa2 Bcl6 GABPA ESR2 FOXD1 Foxq1 FOXA1 Stat4 STAT1 FOXF2 ELK4FEV 8632 3996 8496 Stat5a::Stat5b SRY STAT3 PPARG

A0-A5-Lost A0-A5-Shared A0-A5-Gained

C Enrichment of Known PPIs D Enrichment of GWAS SNPs E Enrichment of Erythroid SNPs

8.0 x7.3 2.0 All SNPs 0.4 Lit-BM-13 ** A0-A5-Lost Hematologic SNPs * A0-A5-Gained 6.0 STRING 1.5 Erythroid SNPs 0.3 * * ** x3.8 ** ** 4.0 1.0 0.2

2.0 0.5 0.1 % of Enhancers Fold Enrichment Fold Enrichment Containing SNPs 0.0 0.0 0.0 Random Network All TF TF Pairs TF Pairs All TF TF Pairs TF Pairs TF Interactions Enhancersin Network Enhancers in Network

F A0 A5 A0 A5

GATA Switch

H3K4me1 H3K27ac H3K4me1 H3K27ac GATA2 FLI1 PU.1 p300 BRG1 GATA1 TAL1 KLF1 p300 BRG1 Non-Switch

5%

A0-A5-Lost 95%

A0 Lost 30%

70%

A5 A0-A5-Shared

17% Gained

83% 0-A5-Gained A

(legend on next page) 16 Developmental Cell 36, 9–23, January 11, 2016 ª2016 Elsevier Inc. In addition, we generated ChIP-seq density heatmaps within bution of enhancers occupied by GATA2-only, GATA1-only, or the lost, shared, and gained enhancers in HSPCs (A0) and ProEs GATA2-to-GATA1 switch (GATA switch) (Figure 6A and Table (A5). Strikingly, while the HSPC-regulating TFs (FLI1 and PU.1), S6). By overlap analysis, we identified 3,101 GATA switch en- p300, and BRG1 exclusively associate with HSPC-specific hancers and the remainder as GATA2-only (2,697) or GATA1- (A0-A5-lost) and shared enhancers, GATA2 can occupy a subset only (4,348) enhancers, respectively. Importantly, the GATA of erythroid-specific (A0-A5-gained) enhancers prior to their acti- switch enhancers consist of 39% of A0-A5-shared and 47% of vation (Figure 5F). A significant portion of A0-A5-gained (17%) A0-A5-gained enhancers, and only 14% of A0-A5-lost en- and -shared (30%) enhancers display GATA2-to-GATA1 switch. hancers, consistent with the role of GATA switch as a major These results, together with the network analysis, strongly sug- driver for enhancer activation. gest that GATA switch plays a major role in modulating enhancer We then examined the differentially enriched TF motifs within turnover during erythroid specification. GATA switch and GATA1/2-only (or non-switch) enhancers. We found that GATA motifs are highly enriched in GATA switch GATA2-to-GATA1 Switch Functions as a Molecular and GATA1-only enhancers, but not in GATA2-only enhancers. Driver of Enhancer Turnover By contrast, the GATA2-only enhancers are highly enriched To further dissect the molecular processes controlling enhancer with TF motifs known to be important for HSPC identity (Fig- turnover, we focused on enhancers that are lost or gained be- ure 6B). These analyses indicate that the recruitment of GATA tween adult HSPCs (A0) and ProE (A5). Specifically, we subdi- factors within the GATA switch enhancers are mediated primarily vided A0-A5-lost enhancers into two groups depending on through GATA motifs (motif-driven). However, GATA2 binding to whether they have lost H3K27ac (‘‘active / primed’’), or both the non-switch GATA2-only enhancers are mediated largely H3K27ac and H3K4me1 (‘‘active / silent’’) (Figure S6A). Simi- through transcriptional cofactors (cofactor-driven). We then larly, we subdivided A0-A5-gained enhancers into two groups compared the mRNA expression of genes targeted by GATA2- depending on the prior chromatin states in HSPCs (‘‘silent / only, GATA switch, or GATA1-only enhancers. The GATA2-only active’’ and ‘‘primed / active’’). Notably, the active / silent en- targets are progressively downregulated, whereas the GATA1- hancers are highly enriched for TF motifs required for HSPC only targets are progressively upregulated during erythropoiesis. identity such as RUNX1, PU.1, FLI1, and ETS1. In contrast, the Interestingly, the expression levels of GATA switch enhancer silent / active enhancers are highly enriched for GATA1 and targets are much higher than that of genomic average, and TAL1 motifs. We next examined the occupancy of various line- remain largely unchanged during differentiation (Figure 6C). age-specific TFs and chromatin regulators at the enhancer These results suggest that, on the global scale, GATA switch en- groups. Strikingly, GATA2-to-GATA1 switch (or GATA switch) hancers function to maintain the high level of gene expression is highly prevalent within transcriptionally primed enhancers, critical for cellular differentiation and housekeeping functions, consisting of 30% of active / primed lost enhancers and 41% in contrast to GATA1-only or GATA2-only enhancers (Figure 6D). of the primed / active gained enhancers (Figure S6B). By network (Figure S6C) and motif analysis (Figure S6D), we also In Situ Genomic Editing of GATA Switch Enhancers in identified GATA switch as the major TF combinatorial module Lineage Differentiation in primed enhancers. These data strongly suggest that GATA To gain functional insights into the role of GATA switch in regu- switch functions as a major molecular driver of enhancer turn- lating enhancer activities, we employed CRISPR/Cas9-medi- over during erythropoiesis. ated genomic editing to remove GATA switch or non-switch enhancers at multiple independent loci in G1E cells (Figures 7 Genomic Features of GATA Switch Enhancers and S7). Strikingly, loss of GATA switch and non-switch en- To further dissect the role of GATA switch in enhancer turnover, hancers results in pleiotropic effects on target gene expression we compared GATA2 and GATA1 occupancy within A0 and A5 during erythroid differentiation. At the Pinx1 gene, biallelic dele- enhancers by ChIP-seq. Specifically, we enumerated the distri- tion of the GATA2-only enhancer (E1) markedly impairs Pinx1

Figure 5. TF Combinatorial Regulatory Networks within Enhancers (A) Schematic of the construction of enhancer-mediated TF combinational regulatory networks. (B) Representative TF combinational regulatory networks in adult HSPCs (A0, left) and ProEs (A5, right). In the network, the nodes represent TF with ChIP-seq data (red) or motif information (gray). The size of node represents the enrichment score. The color of edges represents different types of combination (red: TF-to-TF; green: TF-to-Motif; gray: Motif-to-Motif). The width of edges represents the combinatorial score. The blue arrows and the areas outlined with dashed lines indicate the most significantly enriched TF interactions. (C) Enrichment of known protein-protein interactions (PPIs) in network-predicted TF interactions. ‘‘Network’’ on the x axis represents TF interactions predicted in A0 or A5 network, whereas ‘‘Random’’ represents all possible interactions among TFs shown in the network. (D) Enrichment of hematologic and erythroid trait-associated SNPs in enhancers containing network-predicted TF interactions. All, all A0-A5-gained and A0-A5- lost enhancers; TF, enhancers occupied by at least one TF based on ChIP-seq analysis; TF Pairs, enhancers occupied by TF pairs; TF Pairs in Network, enhancers occupied by TF pairs identified in the A0 or A5 network. The p value measures statistical significance between all enhancers and each enhancer group. *p < 0.05; **p < 0.001. (E) A0-A5-gained enhancers are highly enriched for erythroid SNPs. The y axis shows the percentage of enhancers containing erythroid trait-associated SNPs. The x axis is the same as in (D). (F) ChIP-seq density heatmaps are shown for H3K4me1, H3K27ac, GATA1, GATA2, FLI1, PU.1, TAL1, KLF1, p300, and BRG1 within the indicated lost or gained enhancers. The percentage of GATA2-to-GATA1 switch enhancers is shown on the right. See also Figures S5 and S6.

Developmental Cell 36, 9–23, January 11, 2016 ª2016 Elsevier Inc. 17 A A0 A5 B Enhancers D

A0 vs A5 Regulation of immune system process Immune response Regulation of immune response 8632 3996 8496 Abnormal adaptive immunity Abnormal immune cell physiology A0-A5-Lost A0-A5-Shared A0-A5-Gained GATA2-Only Switch GATA GATA1-Only Abnormal hematopoiesis ELF1 Enhancers SPI1 (PU.1) GATA2-Only B cell activation Interleukin signaling pathway 426 1204 1471 FEV 2035 254 512 673 150 3421 PDGF signaling pathway FLI1 1 10 100 GABPA -driven Cofactor GATA2-Only ERG Regulation of myeloid cell differentiation 3101 GATA Switch ETS1 Peptidyl-tyrosine phosphorylation 2697 4348 Actin cytoskeleton reorganization GATA1-Only GATA2 GATA4 Abnormal bone marrow cell morphology Abnormal hematopoietic cell number 14% 39% 47% GATA1

Motif Abnormal lymphocyte morphology

Lost Shared Gained -driven GATA3 Enhancers TAL1::GATA1 Switch GATA Apoptosis signaling pathway Distribution of GATA Switch Enhancers B cell activation 1 10 100 C 3.0 Negative regulation of cell-cell adhesion GATA2-Only Sphingosine metabolic process 2.5 (1721) Regulation of myeloid cell differentiation GATA Switch Abnormal hemoglobin 2.0 (2108) Abnormal mean corpuscular volume GATA1-Only Increased red blood cell distribution width (z-score)

1.5 (2905) Enhancers GATA1-Only EPO signaling pathway Genome Average Signaling mediated by HDAC class I mRNA Expression mRNA 1.0 (19851) 0357 0357 0357 0357 1 10 100 -log10(p-value)

HSPC ProE HSPC ProE HSPC ProE HSPC ProE GO Biological Process Mouse Phenotypes Pathways

Figure 6. GATA Switch Controls Enhancer Activities during Erythroid Specification (A) Distribution of GATA switch and non-switch enhancers within the lost, gained, or shared enhancers in adult HSPCs (A0) and ProEs (A5). The numbers of GATA1, GATA2, or both (GATA switch) occupied enhancers are shown. (B) Differential enriched TF motifs in GATA2-only, GATA switch, or GATA1-only enhancers. (C) mRNA expression of genes associated with GATA2-only, GATA switch, or GATA1-only enhancers during erythroid differentiation of adult HSPCs (A0) to ProEs (A5). The normalized expression level in particular cell types (Z score) were calculated using barcode (McCall et al., 2014). (D) GREAT analysis (McLean et al., 2010) of GATA2-only, GATA switch, or GATA1-only enhancers. Top enriched (GO) biological processes, mouse phenotypes, and pathways are shown, respectively. See also Figure S6. expression in G1E cells and during early erythroid differentiation of histone marks H3K4me1 and H3K27ac, and occupancy by (4 hr and 8 hr after b-estradiol treatment; Figures 7A–7C), GATA2 in HSPCs (or G1E) and GATA1 in ProEs (or G1ER), whereas Pinx1 is activated at a comparable level as in the un- respectively (Figure 7F). Surprisingly, deletion of E1 has no effect modified control cells during late differentiation (24 hr and on Gata2 expression in G1E and during early differentiation, but 48 hr). In contrast, deletion of the GATA switch enhancer (E2) Gata2 expression is markedly induced during late differentiation significantly and selectively impairs Pinx1 expression during (Figures 7G and 7H). In contrast, deletion of E2 or E3 significantly late differentiation. The differentiation kinetics of G1E cells impairs Gata2 expression in G1E, with minimal effects on its remain ostensibly normal in the absence of Pinx1 enhancers (Fig- expression upon differentiation. Of note, these findings are ures S7A–S7D). Similarly, biallelic deletion of the Mrto4 GATA2- consistent with previous studies using engineered mouse only enhancer (E1) impairs baseline Mrto4 expression, whereas models lacking critical GATA motifs at the Gata2 upstream en- deletion of the GATA switch enhancer (E2) selectively diminishes hancers. In mice lacking a single palindromic GATA motif 1.8 Mrto4 expression upon differentiation (Figures 7B, 7D, 7E, and kb upstream of the Gata2 TSS, which overlaps with the E1 S7E–S7H). The distinct effects upon loss of GATA2-only versus enhancer, Gata2 expression is reactivated in late-stage erythro- GATA switch enhancers strongly suggest that multiple spatially blasts, resulting in defective erythropoiesis (Snow et al., 2010). or temporally distinct enhancers cooperate to control the optimal By contrast, in mice lacking GATA motifs 2.8 kb upstream of expression of target genes. Gata2 TSS, which overlaps with the E2 enhancer, Gata2 expres- We then employed genomic editing to dissect the requirement sion is compromised in HSPCs (Snow et al., 2011). of GATA switch enhancers within the paradigmatic Gata2 locus Together with prior studies, our analyses demonstrate that (Bresnick et al., 2010; Dore et al., 2012; Kaneko et al., 2010). qualitatively distinct and functionally divergent GATA switch en- Gata2 is a potent regulator of hematopoietic stem/progenitor hancers cooperate within the same enhancer cluster at the cells and is highly expressed in HSPCs, then rapidly silenced in Gata2 locus. While the E2 and E3 enhancers are indispensable erythroid cells (Bresnick et al., 2010; Tsai et al., 1994)(Figure 7H). for maximal Gata2 activation in stem/progenitor cells, the E1 The transcription of Gata2 gene is controlled by multiple distal enhancer is required to maintain Gata2 repression in committed regulatory elements including an enhancer cluster immediately erythroid cells. Thus, despite the indistinguishable chromatin upstream of the Gata2 transcriptional start site (TSS) (E1, E2, features among the GATA switch enhancers at the Gata2 locus, and E3; Figures 7F and 7G). The upstream enhancers display we reveal through in situ genomic editing the functional diversity indistinguishable chromatin features, such as the enrichment of GATA switch enhancers whereby enhancers with opposing

18 Developmental Cell 36, 9–23, January 11, 2016 ª2016 Elsevier Inc. A B E1 E2 5kb Pinx1 or Mrto4 GATA1 H3K4me1 0-10 GATA2 H3K27ac 0-10 HSPC G1E A0 or GATA1 0-40 0-40 ProE G1ER GATA2 E1 E2 0-10 H3K4me1 C * H3K27ac 0-10 n.s. A5 0.15 GATA1 0-40 Control (C) GATA2 0-40 E1-del (E1) E2-del (E2) 0.10 PINX1 * E1 E2 5kb n.s. H3K4me1 0-100 0-20 H3K27ac n.s. n.s. n.s. G1E 0.05 Gata1 0-30 * * * 0-40

Gata2 mRNA Expression 0-200 H3K4me1 0-30 0 H3K27ac C E1 E2 CE1E2 CE1E2 CE1E2 CE1E2 G1ER 0-30 Gata1 0 hr 4 hr 8 hr 24 hr 48 hr Gata2 0-40 Control (C) Pinx1 E 0.12 E1-del (E1) n.s. n.s. * * 1kb E2-del (E2) n.s. n.s. D E1 E2 0.09 0-60 n.s. H3K4me1 * n.s. H3K27ac 0-25 * G1E Gata1 0-100 0.06 Gata2 0-10 0-100 H3K4me1 0.03 0-75

H3K27ac mRNA Expression G1ER Gata1 0-100 0-10 0 Gata2 CE1E2 CE1E2 CE1E2 CE1E2 CE1E2 0 hr 4 hr 8 hr 24 hr 48 hr C230096C10Rik Mrto4 G1E +β-estradiol G1ER

E3 E2 E1 1kb F 0-10 G H3K4me1 0-20 H3K27ac Gata2 GATA1 A0 GATA1 0-100 GATA2 GATA2 0-100 HSPC G1E 0-10 or H3K4me1 0-20 ProE G1ER H3K27ac E3 E2 E1 A5 GATA1 0-100 * 0-100 H n.s.* GATA2 0.06 Control (C) E1-del (E1) E3 E2 E1 GATA2 1kb E2-del (E2) 0-100 0.04 H3K4me1 n.s. E3-del (E3) 0-20 n.s. H3K27ac n.s. G1E n.s. n.s. Gata1 0-100 n.s. n.s. * Gata2 0-100 n.s. 0.02 n.s. 0-300 n.s. H3K4me1 n.s. H3K27ac 0-75 mRNA Expression G1ER 0-100 Gata1 0 Gata2 0-100 CE1E2E3 CE1E2E3 C E1 E2 E3 C E1 E2 E3 CE1E2E3 0 hr 4 hr 8 hr 24 hr 48 hr G1E +β-estradiol G1ER Gata2

Figure 7. Genome Editing of GATA Switch-Mediated Enhancer Turnover (A) Chromatin signatures and TF occupancy within the human or mouse PINX1 locus. The putative GATA2-only (E1) and GATA switch (E2) enhancers are shown. The putative active enhancers are depicted by shaded lines. (B) Schematic of CRISPR/Cas9-mediated deletion of Pinx1 or Mrto4 enhancers. The scissors indicate DNA double-strand breaks induced by CRISPR/Cas9. (C) Expression of Pinx1 mRNA in unmodified (control) and enhancer-deletion cells at various time points (0–48 hr) after b-estradiol treatment. Each colored circle represents an independent biallelic enhancer-deletion clone. Results are means ± SD of multiple independent clones. *p < 0.01; n.s., not significant. (D) Chromatin signatures and TF occupancy within the mouse Mrto4 locus. The putative GATA2-Only (E1) and GATA switch (E2) enhancers are shown. The putative active enhancers are depicted by shaded lines. (E) Expression of Mrto4 mRNA in control and enhancer-deletion cells. Results are means ± SD of multiple independent clones. *p < 0.01; n.s., not significant. (F) Chromatin signatures and TF occupancy within the human or mouse GATA2 locus. The putative GATA switch (E1, E2, and E3) enhancers are shown. The putative active enhancers are depicted by shaded lines. (G) Schematic of CRISPR/Cas9-mediated deletion of Gata2 enhancers. The scissors indicate DNA double-strand breaks induced by CRISPR/Cas9. (H) Expression of Gata2 mRNA in control and enhancer-deletion cells. Results are means ± SD of multiple independent clones. *p < 0.01; n.s., not significant. See also Figure S7.

Developmental Cell 36, 9–23, January 11, 2016 ª2016 Elsevier Inc. 19 functions cooperate to orchestrate gene expression during composed of a functional hierarchy of constituent components, cellular differentiation. with some significantly stronger than others for target gene expression during cellular differentiation. Strikingly, although all DISCUSSION constituent enhancers possess similar levels of enhancer-asso- ciated histone marks and TF occupancy, deletion of each In Situ Perturbation of Enhancers enhancer has a markedly distinct effect on Slc25a37 transcrip- The term ‘‘transcriptional enhancer’’ was first introduced to tion. Our findings on the distinct requirement of enhancer describe the effects of SV40 DNA on the transcription of the rab- constituents are consistent with a recent analysis of several bit b-globin gene (Banerji et al., 1981). Since then, enhancers super-enhancers in mouse embryonic stem cells (Hnisz et al., have been routinely identified and characterized in ectopic heter- 2015). One critical difference, however, is that our studies were ologous reporter assays or by sequence analysis (Bulger and performed in cells undergoing lineage differentiation. Hence, Groudine, 2011; Pennacchio et al., 2013; Visel et al., 2007). we were able to analyze and compare the effects of each With the advent of genome-scale chromatin profiling, many pu- enhancer deletion during the spectrum of erythroid differentia- tative enhancers have been identified in the tion. Importantly, while deletion of individual Slc25a37 constitu- (Heintzman et al., 2007, 2009; Visel et al., 2009a, 2009b). While ent enhancers has minimal effects at the undifferentiated stage, the biological importance of enhancers could be inferred from deletion of E3 has a profound impact on Slc25a37 expression at these genome-scale studies, it remains challenging to ascertain later differentiation (Figure 3C). The developmental stage- the precise in vivo function of individual enhancers using conven- specific requirement of enhancer functions would have been tional loss-of-function methods. Thus, advances in genome en- overlooked if one had only analyzed the steady-state undifferen- gineering technologies hold enormous promise for systematic tiated cells. evaluation of the functional significance of enhancers in physio- Moreover, our results suggest a cooperative behavior in the logically relevant contexts (Bauer et al., 2013; Canver et al., recruitment of master TFs at the Slc25a37 super-enhancer. 2015; Groschel et al., 2014; Hnisz et al., 2015; Mansour et al., While deletion of E1 or E2 has a minimal effect on histone marks 2014; Xu et al., 2015). and TF occupancy at neighboring enhancers, loss of E3 substan- In this study, we employed the CRISPR/Cas9 system to tially reduces H3K27ac and completely abolishes GATA1/TAL1 examine the functional requirements of a panel of discrete en- binding at the neighboring enhancers. Combined deletion of hancers in an experimental model recapitulating normal E3 and neighboring enhancers further diminishes the tran- erythroid development. Our results reveal functional hierarchy scriptional activity, the level of H3K27ac, and TF occupancy at of enhancer regulation within their native chromatin. We first Slc25a37 enhancer and promoter regions, suggesting that E3 demonstrate the distinct roles of GATA switch and GATA2-only cooperates with other constituent enhancers to achieve maximal (or non-switch) enhancers in the temporal control of gene tran- activity. Interestingly, despite the profound impact on H3K27ac scription. Moreover, by systematic dissection of three distinct level, loss of individual enhancers or their combinations does GATA switch enhancers at the Gata2 locus, our studies uncover not affect H3K4me1, suggesting that the level of H3K27ac is functional divergence of enhancers cooperating within the same more sensitive and predictive to transcriptional activities. Taken cluster. Despite the indistinguishable chromatin features based together, our results indicate that at least a subset of super- on ChIP-seq analyses, the neighboring GATA switch enhancers enhancers is organized in a hierarchical structure composed of display distinct requirements for the precise control of Gata2 enhancer constituents with non-redundant functions. Some con- transcription. Hence, our studies highlight the power and neces- stituent enhancers may possess significantly stronger effects on sity of combining genome-scale enhancer annotation with transcription while others cooperate with the dominant en- genomic editing for analyzing enhancer cooperation during line- hancers for maximal activity. These findings also raise the possi- age differentiation. We speculate that further in-depth studies of bility that further in-depth dissection of super-enhancers by enhancer regulation through high-throughput, high-resolution high-resolution genomic editing may unravel the regulatory com- in situ enhancer editing will likely uncover novel regulatory prin- ponents and distinct vulnerabilities underlying enhancer clus- ciples underlying the context-specific actions of enhancers in tering in gene regulation. mammalian genomes. TF Combinatorial Rules Underlying Enhancer Dynamics Hierarchical Composition of Erythroid Super-enhancers during Erythropoiesis Highly marked clusters of enhancers or super-enhancers have Enhancers are known to function as multifactorial platforms been identified in various cell types (Hnisz et al., 2013; Parker for binding of lineage-regulating TFs, chromatin regulators, and et al., 2013; Whyte et al., 2013). Despite the proposed roles of signaling effectors (Buecker and Wysocka, 2012; Xu and Smale, super-enhancers in gene regulation and disease, the functional 2012). A long-standing question is how enhancers acquire the significance of enhancer clustering within their native chromatin ability to translate intra- and extracellular signals to cell-type- environment remains largely unexplored. More specifically, it is specific transcriptional responses in development and disease. unclear whether super-enhancer represents a simple assembly On the other hand, it is estimated that there are 2,000–3,000 of regular enhancers or whether it behaves as a single functional sequence-specific DNA binding TFs encoded by the human unit through cooperative activities of its constituent enhancers genome (Babu et al., 2004), with 200–300 TFs being expressed (Pott and Lieb, 2015). in each cell type (Vaquerizas et al., 2009). However, the underly- By focusing on the Slc25a37 super-enhancer, our studies ing principles by which TFs collaborate to regulate enhancer dy- demonstrate that the Slc25a37 clustered enhancers are namics are poorly understood.

20 Developmental Cell 36, 9–23, January 11, 2016 ª2016 Elsevier Inc. j j In this study, we developed a new network approach to con- only keep enhancers with MH3K27ac > 1; for shared enhancers we keep en- j j % nect TF occupancy, motif enrichment, and enhancer activity hancers with MH3K27ac 1. for illustration of the combinational regulatory mechanisms in Motif Enrichment Analysis complex differentiation processes. The rationale rests on accu- The position weight matrices of 196 core vertebrate motifs were downloaded mulating evidence that enhancers are critical modulators of line- from the JASPAR database (Mathelier et al., 2014). The motif enrichment score age- and stage-specific gene expression, and enrich for binding was defined as log10(p value), where p value is the significance of observed sequences (or motifs) for lineage-regulating TFs (Figure 4A) over-representation of each motif in enhancer regions compared with (ENCODE Project Consortium, 2012; Neph et al., 2012; Xu randomly selected control regions. Motif modules were detected based on et al., 2012). Using an ex vivo model for human erythropoiesis, the hierarchical clustering of motif enrichment scores across lineage or stage-specific enhancers (Table S2). we identified distinct sets of TF combinatorial modules as puta- tive drivers of enhancer turnover during erythroid differentiation. Construction of Enhancer-Mediated TF Regulatory Networks Of note, the HSPC-specific enhancer network is predomi- The enrichment score was calculated to measure the significance of the nantly modulated by the PU.1-RUNX1-FLI1-ETS-GATA2 combi- enrichment of TF ChIP-seq peaks (or motif-matched loci) within each enhancer natorial interactions. In contrast, the erythroid-specific enhancer type relative to the genome background. The combinational score was calcu- network is dominated by GATA1-TAL1 together with IRF2- lated to measure the frequency of co-occurrence of two TFs at the same STAT1-STAT2 interactions (Figures 5, S5, and Table S5). More enhancer compared with the genome background. TF or motif pairs with significant combinational scores were selected to assemble the networks in importantly, we identified previously unrecognized TF interaction Cytoscape (Shannon et al., 2003). modules including NFE2-MYB and PRRX2-ARID3A-GATA2 within the enhancer context, thus providing a platform for sub- Enhancer Editing by CRISPR/Cas9 stantive future investigations. Finally, in-depth experimental The clustered regularly interspersed palindromic repeats (CRISPR)/CRISPR- validation by ChIP-seq and CRISPR/Cas9-mediated loss-of- associated (Cas) 9 nuclease system was used for enhancer-deletion analyses function studies not only validated the overall approach but following published protocols (Cong et al., 2013; Mali et al., 2013). also provided novel insights into the structure-function - ACCESSION NUMBERS tionship of enhancer dynamics during erythroid specification. Taken together, our studies demonstrate that the integrative All ChIP-seq and microarray datasets have been deposited in GEO under acces- analysis of genome-wide enhancer annotation coupled with sion numbers NCBI-GEO: GSE70660, GSE36985, GSE52924, and GSE59087. in situ enhancer editing has the potential to identify the underly- ing regulatory components of enhancer-directed cellular pheno- SUPPLEMENTAL INFORMATION types in complex developmental processes. Supplemental Information includes Supplemental Experimental Procedures, seven figures, and six tables and can be found with this article online at EXPERIMENTAL PROCEDURES http://dx.doi.org/10.1016/j.devcel.2015.12.014.

Cells and Cell Culture AUTHOR CONTRIBUTIONS Primary human adult and fetal CD34 + HSPCs were isolated as previously described (Van Handel et al., 2010; Xu et al., 2012). Primary fetal or adult X.L., D.L., H.C., and J.X. performed experiments and analyzed the data. J.H., committed proerythroblasts (ProEs) were generated ex vivo as previously X.L., Z.S., Y.Z., G.C.Y., and J.X. performed bioinformatics analyses. E.T., described (Sankaran et al., 2009; Xu et al., 2012). G1E/G1ER cells were T.V.B., and L.I.Z. contributed the GATA2 ChIP-seq datasets and analyzed cultured as described by Welch et al. (2004). the data. J.H., X.L., S.H.O., and J.X. wrote the manuscript. S.H.O. and J.X. su- pervised the project. ChIP-Seq and Data Analysis ChIP-seq was performed as described by Xu et al. (2012). Other ChIP-seq ACKNOWLEDGMENTS datasets were obtained from previous publications (Abraham et al., 2013; Beck et al., 2013; ENCODE Project Consortium, 2012; Dogan et al., 2015; We thank Matthew Canver, Daniel Bauer, Luca Pinello, and members of the Pinello et al., 2014; Su et al., 2013; Trompouki et al., 2011; Xu et al., 2012, Orkin laboratory for assistance and discussion, John Stamatoyannopoulos 2015). for assistance with DNase-seq, and Ben van Handel and Hanna Mikkola for providing the fetal CD34+ cells. This work was supported by NIH/NHLBI grant Enhancer Annotation R01HL119099 (to G.C.Y and S.H.O.). L.I.Z and S.H.O. are Investigators of the Putative active enhancers were annotated using the peaks of H3K4me1, Howard Hughes Medical Institute (HHMI). This work was also supported by H3K27ac, and H3K27me3. In brief, H3K27ac peaks were used to define NIH/NIDDK grants K01DK093543 and R03DK101665, by a Cancer Prevention the enhancer boundary, followed by filtering based on the criteria: (1) excluded and Research Institute of Texas (CPRIT) New Investigator Award (RR140025), H3K27ac peaks not overlapped with H3K4me1 peaks; (2) excluded H3K27ac by the American Cancer Society (IRG-02-196) award and the Harold C. peaks located within ±2-kb region of RefSeq-annotated promoters; (3) Simmons Comprehensive Cancer Center at UT Southwestern, and by an excluded H3K27ac peaks overlapped with H3K27me3 peaks. American Society of Hematology Scholar Award (to J.X.).

Identification of Lineage or Developmental Stage-Specific Received: July 10, 2015 Enhancers Revised: October 15, 2015 Lineage-specific enhancers were identified by comparing A0 and A5, or F0 Accepted: December 10, 2015 and F5 enhancers. Similarly, stage-specific enhancers were identified by Published: January 11, 2016 comparing F0 and A0, or F5 and A5 enhancers. The overlapped (R1 bp) enhancers were considered as ‘‘shared’’ enhancers and the remainder as REFERENCES ‘‘lost’’ or ‘‘gained’’ enhancers. To obtain the high-confidence enhancers, we filtered the enhancers using MAnorm (Shao et al., 2012; Xu et al., 2012) for Abraham, B.J., Cui, K., Tang, Q., and Zhao, K. (2013). Dynamic regulation of

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