Dynamic nucleosome landscape elicits a noncanonical gata2 pioneer model

ABSTRACT Knowledge gaps remain on how nucleosome organization and dynamic reorganization are governed by specific pioneer factors in a genome-wide manner. In this study, we generate over

three billons of multi-omics sequencing data to exploit dynamic nucleosome landscape governed by pioneer factors (PFs), FOXA1 and GATA2. We quantitatively define nine functional nucleosome

states each with specific characteristic nucleosome footprints in LNCaP prostate cancer cells. Interestingly, we observe dynamic switches among nucleosome states upon androgen stimulation,

accompanied by distinct differential (gained or lost) binding of FOXA1, GATA2, H1 as well as many other coregulators. Intriguingly, we reveal a noncanonical pioneer model of GATA2 that it

initially functions as a PF binding at the edge of a nucleosome in an inaccessible crowding array. Upon androgen stimulation, GATA2 re-configures an inaccessible to accessible nucleosome

state and subsequently acts as a master transcription factor either directly or recruits signaling specific transcription factors to enhance WNT signaling in an androgen receptor

(AR)-independent manner. Our data elicit a pioneer and master dual role of GATA2 in mediating nucleosome dynamics and enhancing downstream signaling pathways. Our work offers structural and

mechanistic insight into the dynamics of pioneer factors governing nucleosome reorganization. SIMILAR CONTENT BEING VIEWED BY OTHERS IDENTIFYING SYNERGISTIC HIGH-ORDER 3D CHROMATIN

CONFORMATIONS FROM GENOME-SCALE NANOPORE CONCATEMER SEQUENCING Article 30 May 2022 DECODING THE EPIGENETICS AND CHROMATIN LOOP DYNAMICS OF ANDROGEN RECEPTOR-MEDIATED TRANSCRIPTION Article

Open access 03 November 2024 A PIONEER FACTOR LOCALLY OPENS COMPACTED CHROMATIN TO ENABLE TARGETED ATP-DEPENDENT NUCLEOSOME REMODELING Article 19 December 2022 INTRODUCTION Nucleosome

organization (positioning, spacing, and regularity) plays a central role in gene regulation1,2. The dynamic nucleosome reorganization is the interplay among nucleosome, wrapped DNA, and

nucleosome-binding factors such that nucleosomes sterically occlude their wrapped DNA from DNA-binding factors and ATP-dependent chromatin remodelers unwrap nucleosomal DNA or slide

nucleosomes to reposition along DNA3,4. Recent genome-wide nucleosome mapping highlighted functionally important regular nucleosomal arrays as their array regularity is often aligned at

biological features5,6. In contrast, impaired genic arrays are correlated with increased cryptic transcription with suppressive activities7. High-resolution mapping studies suggested that

DNA contains information specifying the position of nucleosomes called positioning code which facilitates the shunting of nucleosomes from one array to another by chromatin remodeling

machines8,9,10. Some studies also showed nucleosomes are subject to extensive reversible post-translational modifications that can alter the local chromatin structure poised for activation

and transcription11,12. Other studies further elucidated coordinated and antagonistic functional relationships between nucleosome remodeling and modifying machineries13,14,15. A group of

special transcription factors (TFs) called pioneer factors (PFs) such as FOXA families16,17, GATA families18,19,20, PAX715, HOXB1321, and P5322 can access target DNA sequences on

nucleosomes. A paradigm of competence for transcription is thus established that nucleosome-binding properties of PFs can engage in the assembly of regulatory factors on the DNA by either

opening the chromatin locally, positioning nucleosomes, or enabling intrinsic cooperative binding effects among other TFs23,24, or directly recruiting other chromatin modifiers and

coregulators25. Despite many advances in the understanding of nucleosome dynamics, there still remain knowledge gaps on how nucleosome organization and dynamic reorganization are governed by

specific pioneer factors in a genome-wide manner. Many studies26,27,28, including ours19,29, have found both FOXA1 and GATA2 act as PFs to trigger the androgen-induced androgen receptor

(AR) signaling pathway. It is believed that both PFs can recruit chromatin modifiers, chromatin remodelers, and chaperone molecules to establish an “open” chromatin environment or a

nucleosome-free region (NFR) to facilitate the accessibility for other factors, then initiate subsequent regulatory events. However, the pioneer capacity of GATA2 is inconclusive. Several

studies illustrated that GATA2 is simply a non-pioneer TF30 but others demonstrated it is indeed a pioneer TF19. To fully understand the detailed pioneer capacities of GATA2 in regulating

nucleosome organization in androgen stimulated prostate cancer cells, it is critical to use integrative approaches combining high-resolution genomic techniques (ChIP-exo and MNase-ChIP-seq)

and computational analyses to examine the relationship among GATA2 and nucleosome organization. In this study, we conducted high-resolution ChIP-exo, MNase-seq, and MNase-ChIP-seq in LNCaP

cell model under Vehicle (Veh) and 5α-dihydroxytestosterone (DHT)-treated conditions and generated over three billions multi-omics sequencing data. Here, we exploit the landscape of dynamic

nucleosome footprints and quantitatively define functional nucleosome states based on histone marks, genomic regions, nucleosome positioning, spacing, and regularity. We further identify

GATA2-associated dynamic nucleosome state switching upon DHT treatment. Using various in vitro assays, we demonstrate that GATA2 initially functions as a PF binding at the edge of a

nucleosome in an inaccessible crowding array. Under the DHT-treated condition, GATA2 reconfigures inaccessible to accessible nucleosome state and subsequently, it acts as a master

transcription factor either directly or to recruit signaling-specific TFs to enhance oncogenic Wnt/β-catenin signaling in an AR-independent manner. Our work thus elicits a noncanonical GATA2

pioneer model, providing a structural and mechanistic insight into the dynamics of pioneer factors governed by nucleosome reorganization. RESULTS GENOME-WIDE IDENTIFICATION OF NUCLEOSOME

POSITIONING AND SPACING We conducted multi-omics sequencing profiling in LNCaP cells in Veh and DHT-treated conditions, including MNase-seq for identifying genome-wide nucleosome

positioning, spacing, and regularity, MNase-ChIP-seq of H3K4me1, H3K4me2, H3K4me3, H3K27ac, H3K27me3, H3K36me3, and H3K79me2 for detecting enriched histone marks at a nucleosome level, as

well as ChIP-ePENS of FOXA1 and GATA2 for detecting 1 bp binding resolution, each with biological replicates (Fig. 1a). In total, we generated over three billons of multi-omics sequencing

data to investigate the landscape of nucleosome organization and dynamic reorganization (Supplementary Table 1). A Pearson correlation coefficient between two MNase-seq replicates in LNCaP

cells was very high with an _r_ value of 0.95 (Fig. 1b), demonstrating the high reproducibility and good quality of the data. We applied a nucleosome positioning tool iNPS31 on MNase-seq

data, and identified ~12.6 million nucleosomes, in equivalent to 48.6% of the whole genome DNA wrapping on the nucleosomes, which were proportionally distributed in each of 23 chromosomes

(Supplementary Table 2). We then plotted a down-sampling saturation curve and showed the data sequencing depth was sufficient to capture whole genome-wide nucleosomes (Supplementary Fig. 1).

We further used a nucleosome density map to illustrate the robust correlations of detected nucleosomes in each of 23 chromosomes between two replicates (Fig. 1c), with all _r_ values larger

than 0.93. The accumulation of nucleosome dyads with the enrichment of Mono-nucleosome, Di-nucleosomes, Tri-nucleosomes, and Penta-nucleosomes showed relatively high robustness of

nucleosome detection with adjacent three nucleosomes in a genome-wide scale. (Fig. 1d). We also observed a range of 150-350 bp with a peak of 187 bp nucleosome spacing, i.e., the distance

between two neighboring nucleosome dyads (Fig. 1e). QUANTITATIVELY DEFINING FUNCTIONAL NUCLEOSOME STATES To understand the relationship between the nucleosome positioning, spacing, and

regularity with eight histone marks which characterize genomic regulatory elements as a promoter, enhancer, repressor, and others, we examined the enrichment of histone marks on positioned

nucleosomes in three genomic regions, the Promoter (−1 Kb to 1 Kb away from TSS), the Proximal (−5 Kb to −1 Kb away from TSS), and the Distal (−50 Kb to −5 Kb away from TSS). We observed a

distinct distribution of nucleosomes enriched with different histone marks in three genomic regions (Fig. 2a). For example, H3K4me1-nucleosomes were mostly located in far the Proximal and

Distal regions and H3K27me3-nucleosomes were in the far Distal region. When looking into MNase-seq read signal distribution of histone marks in different genomic regions, we found the

characteristic spacing between nucleosomes. For example, we found a clear spacing between nucleosomes of H3K4me1 and H3K27ac in all three regions, of H3K4me3 in the Promoter/Proximal region,

of H3K27me3 in the Proximal/Distal, of H3K4me2 in the Promoter, and of H3K36me3 in the Distal region respectively (Fig. 2b). Further, we observed histone marks and genomic region-specific

nucleosome spacing patterns (Fig. 2c). For instance, H3K4me3, H3K27ac, and H3K36me3 have the shortest nucleosome spacing ranging from 170–180 bp in the Promoter region, H3K27me3 has two

peaks at 188 bp and 215 bp in the Proximal region, and H3K9me3 and H3K27me3 have a wide peak in the Distal region. Furthermore, we compared the density of histone mark-enriched nucleosomes

and gene expression, i.e., No. of nucleosomes per 1000 bp, in three regions, and found active marks, H3K4me1/2/3 and H3K27ac had a higher density than repressive marks, H3K9me3 and H3K27me3

in all three regions (Fig. 2d and Supplementary Fig. 2). Finally, we developed an empirical formula to quantitatively define the nucleosome states (Methods), which takes into account

nucleosome positioning, spacing, and histone marks, including the similarity score γ between nucleosome αn +1 and α1 to αn, where γ is calculated by nucleosome position factor λ, the peak

area of nucleosome S, the width of nucleosome position W and spacing between nucleosomes d and histone mark factor β. After iteratively running the formula to optimize the parameters, we

were able to determine the trajectory of optimized parameters used to define the number of nucleosome states. Indeed, when further incorporating various genomic features with the trajectory,

including histone marks, the number of the grouped nucleosomes, genomic location, degree of positioning, regularity score, and average spacing, we were able to define nine functional

states, S1–S9 (Fig. 2e, f and Supplementary Fig. 3). S1 was defined as transcriptional initial due to its location around the up-promoter or 5′TSS region with active H3Kme3/K27ac marks. S2

was defined as an accessible edge due to its mainly located in proximal/distal regions and having 1–4 nucleosomes, a shorter spacing (180.43 bp shortest except S1), and a relatively low

regularity score of 9.85. S3 was defined as Alternative primed due to its locating in promoter/proximal regions and having primed or poised marks H3K27me3/H3K4me1/H3K4me2. S4 was defined as

Crowding array because of its higher number of 5–20 positioned nucleosomes in an array with the highest average spacing of 210.64 bp and 75.4% of states enriched with H3K9me3 and/or H3K27me3

marks. S5 was identified as well-organized due to its location in the down-promoter region with the highest regularity score of 21.00. S6 was defined as restricted accessible since a

majority (54.1%) of S6 were in the lower average spacing and in proximal/distal regions with H3K4me2/H3K27me3 marks. S7 was defined as a Steady structure due to 69.8% of states were in a

distal region with various marks. S8 was defined as Fuzzy due to its lower degree of positioning and the corresponding low regularity score and S9 was defined as Unknown due to its unclear

features. S1 and S5 have been extensively studied in previous work32,33,34, therefore, we focused on thoroughly examining the functionality of S2, S3, S4, S6, and S7 and their relationship

with FOXA1, GATA2, and other TFs and coregulators in the downstream analyses. DYNAMIC NUCLEOSOME STATES SWITCHING We extended our quantitative modeling of nucleosome states on MNase-seq and

MNase-ChIP-seq data in LNCaP cells under DHT-treatment conditions. Intriguingly, we obtained the same functional nucleosome states, demonstrating the validity and broadness of our

quantitative definition. When comparing the changes in nucleosome states before and after DHT treatment, we found the numbers of S2, S3, and S6 increase while the numbers of S4 and S7

decrease (Fig. 3a). Interestingly, we found dynamic switches among different nucleosome states upon DHT treatment. Sankey's diagram showed over 72.1% of S4 have been switched to other

states including 45.4% of them turning into S3, while 42.3% of S3 have been changed to S2, and the number of S2 increases to 202.4% in the DHT-treatment condition (Fig. 3b). Next, we wanted

to investigate what transcription factors (TFs) and coregulators could potentially instruct these dynamic nucleosome state switches particularly from two condensed states, S4 (Inaccessible)

or S3 (Alternative primed) to others. We downloaded many publicly available ChIP-seq data of PFs, TFs, and coregulators and examined their differential binding patterns35,36,37,38 (Fig. 3c

and Supplementary Fig. 4). As expected, we found FOXA1 showed a significantly lost binding from S4 to S3 or S2 accompanied by a lost linker H1. This finding is consistent with many other

studies39,40, where FOXA1 competes with its canonical binding motif with H1 to enhance nucleosome accessibility (Supplementary Fig. 5). We also found the bindings of GATA2, HOXB13, RUNX1,

and TLE3 were lost, implicating their potential pioneer capacities of opening condensed nucleosomes. We then particularly examined the expression level of genes associated with nucleosome

states switching from S4 to relatively accessible states (RAS) including S2, S3, S7, and nucleosome-free region (NFR) named as RAS1; and from S3 to relatively accessible states including S2,

S6, and NFR named as RAS2. Interestingly, we found a majority (>80%) of the genes associated with differential binding of FOXA1, GATA2, and H1 were upregulated, indicating both FOXA1 and

GATA2 play pioneer roles capable of dynamically reprogramming nucleosome accessibility, resulting in gene activation (Fig. 3d and Supplementary Figs. 6, 7). Furthermore, we found there were

257 and 293 unique GATA2 genes with only GATA2 binding but no other analyzed TF bindings from S4 to RAS1 and S3 to RAS2 respectively, suggesting that this subset of GATA2 genes can

independently exert a pioneer function upon androgen stimulation (Fig. 3e). GATA2-ASSOCIATED DYNAMIC NUCLEOSOME STATES SWITCHING To elucidate the pioneer capacity of GATA2, we conducted

ChIP-ePENS of GATA2 in both Vehicle (Veh) and DHT-treated LNCaP cells and used ePEST to identify GATA2 binding borders at one-base resolution21 (Supplementary Fig. 8 and Supplementary Table 

3). We identified a total of 32,342 and 27,613 border composed sites (BCSs) as binding footprint boundaries in Veh and DHT-treatment respectively. Interestingly, we found paired border sites

(PBSs) of GATA2 borders followed a bimodal distribution with a 13–14 bp gap, similar to the distribution of FOXA1 borders with an 11 bp in our previous study41 (Fig. 4a, Supplementary Fig. 

9, and Supplementary Data 1). About 42.1% of GATA2 borders were associated with the nucleosome states in Veh and dropped to 36.2% in DHT-treated cells (Fig. 4b, Supplementary Figs. 10, 11,

and Supplementary Data 2, 3). GATA2 borders on S2 showed a dramatic decrease at 45.5% upon the DHT treatment (Fig. 4c). By plotting the accumulating distribution of borders around the

nucleosome dyad, we found more than 80% of GATA2 borders were located ~50–60 bp on the edge of the nucleosome for S4 and S3, and the peaks tended to spread wider in the DHT treatment, while

a majority of GATA2 borders were located in the middle of nucleosomes for S6 and S7 (Fig. 4d and Supplementary Fig. 12). Two examples demonstrated GATA2-associated nucleosomes switching from

inaccessible to accessible states while maintaining its binding (Fig. 4e and Supplementary Fig. 13). We further examined the open chromatin changes for both GATA2-associated nucleosome

states switching by ATAC-seq data and observed a great increase from 11.2 to 37.1% for S4-RAS1 and 38.6 to 72.3% for S3-RAS2, respectively (Fig. 5a and Supplementary Fig. 14). Further, only

21 (7.6%) of 278 genes associated with unique GATA2-governed S4-RAS1 switching and only 9 (6.7%) of 135 genes associated with unique GATA2-governed S3-RAS2 switching were overlapped with

1036 AR-dependent DHT-treated differentially expressed genes, respectively, suggesting that the vast majority of GATA2-governed dynamic nucleosome states switching are independent of AR

signaling (Fig. 5b and Supplementary Figs. 15, 16). KEGG pathway analysis further identified WNT signaling and nuclear receptor meta pathways were the top pathways for S4-RAS1 and for

S3-RAS2 respectively (Fig. 5c). GATA2 strongly preferred binding at the edge of nucleosomes and were almost equally distributed on both sides (Fig. 5d). Remarkably, ZNF700, IRF protein

family, ZNF569, and SOX protein family were found as the top enriched motifs in GATA2-governed S4-RAS1 switching (Fig. 5e) and SOX9 was also identified as a potential co-binding TF on WNT

signaling genes by a publicly available database collected all ChIP-seq of TFs from ENCODE and ChEA42 (Fig. 5f and Supplementary Fig. 17). Taken together, our data suggested a noncanonical

pioneer model of GATA2 that it initially functions as a PF binding at the edge of a nucleosome in an inaccessible crowding array; under the DHT-treated condition, it reconfigures

inaccessible to accessible nucleosome state; subsequently, it acts as a master transcription factor either directly or to recruit other signaling-specific TFs to enhance WNT signaling in an

AR-independent manner. GATA2 IN MEDIATING NUCLEOSOME DYNAMICS AND ENHANCING WNT SIGNALING PATHWAY To substantiate this model, we conducted various in vitro assays on 20 Wnt/β-catenin

signaling genes selected from GATA2-governed S4-RAS1 switching. Competitive nucleosome-binding assays including in vitro nucleosome-binding and electrophoretic mobility shift assays were

designed to detect a binding range of nucleosome position with GATA2 binding border at 0, 41, 65, and 85 bp. The 65 bp DNA showed the highest supershift of the others, confirming that GATA2

prefers binding at the edge of nucleosomes (Fig. 6a and Supplementary Fig. 18). Open chromatin assays further demonstrated that 17 of 20 Wnt/β-catenin signaling genes showed an increase in

chromatin opening upon DHT treatment (Supplementary Fig. 19). Our genome-wide ATAC-seq data showed that a majority of 413 GATA2-governed S4/3-RAS1/2 switching genes significantly reduced the

chromatin accessibility and overall 29.6% of open chromatin regions on a genome-wide scale were lost after knockdown GATA2 gene (Fig. 6b and Supplementary Figs. 20,21). Together, we

provided several lines of evidence to support the notion that GATA2 was involved in regulating chromatin accessibility and nucleosome reorganization. Furthermore, we used siRNA to knock down

GATA2 in LNCaP cells to create a siGATA2 subline and measured the gene expression changes by RT-qPCR. We found that 14 of 20 genes in siGATA2 vs siCtrl LNCaP cells were downregulated under

the DHT-treated condition (Fig. 6c), suggesting that GATA2 regulates Wnt/β-catenin signaling gene expression levels. Collectively, our results revealed a dual pioneer and master role of

GATA2 in mediating nucleosome dynamics and enhancing downstream Wnt/β-catenin signaling in an AR-independent manner (Fig. 6d). DISCUSSION In this study, we systematically defined genome-wide

functional nucleosome states with a quantitative method (Fig. 2e). One major advantage of our method is to fully utilize the high-resolution nucleosome level genomic data including

MNase-seq and MNase-ChIP-seq. Intriguingly, we were able to obtain the same functional nucleosome states in DHT-treated LNCaP cells as in untreated cells when applying the method to the

data, demonstrating the validity and broadness of our quantitative definition. Although numerous studies have revealed the basic principles of nucleosome organization and its dynamics3,4,

our work clearly filled a knowledge gap in the field since most of the previous work were focused on qualitatively defining the nucleosome states without systematically providing the

trajectory of clear quantitative cutoff thresholds38, or on examining the nucleosome landscape in specific genomic regions4. More importantly, these functional nucleosome states could be

further used to elicit the pioneer capacity of any TFs including known PFs by integrating with one-base resolution ChIP-ePENS or ChIP-exo data. In theory, our integrative approach can

accurately define the pioneer capacity of any known PFs or distinguish the pioneer factors from non-pioneer factors by comparing two or more different biological conditions. This statement

is attested by the following four foundations of our approach: (1) we identify the PF/TF-associated condensed nucleosome states; (2) we identify the PF/TF binding borders within the

nucleosomes; (3) we determine whether the PF/TF is accompanying the nucleosome switches under at least two biological conditions; and (4) we perform competitive nucleosome-binding assays to

validate the pioneering capacity. Although our approach is able to define the pioneer functionality of any TFs, we are cautious that the PF functionality and capacity should be interpreted

tightly with the specific biological context. Nevertheless, our approach highlights the importance of utilizing the high resolution of high throughput genomic data in elucidating the pioneer

function of TFs. Remarkably, we found almost half (42.1%) of GATA2 was bound on nucleosomes (combined all states) in untreated LNCaP cells and 51.8% of these GATA2-associated nucleosomes

were switched to more accessible nucleosomes or free nucleosome regions in DHT-treated cells (Fig. 4b), suggesting that this subset of GATA2 might function as a pioneer factor in the

hormone-induced context. We unexpectedly found this GATA2 pioneer action exerts in an AR-independent manner and regulates specific downstream signaling pathways (Fig. 5b, c). It seems that

GATA2 further acts as a master transcription factor either directly or to recruit other signaling-specific TFs to the chromatin to regulate the Wnt/β-catenin pathway upon androgen

stimulation (Fig. 6). This data is in stark contrast with the archetypical pioneer function of FOXA1 such that FOXA1 opens the condensed chromatin to mainly serve for an AR binding activity

under DHT-treated conditions (Supplementary Figs. 22, 23). Collectively, our data support a noncanonical pioneer GATA2 model and elicit the pioneer capacity of GATA2 action in

hormone-induced prostate cancer cells. Despite that previous studies19,25,26,27 have demonstrated the pioneer functionality of GATA2 in hormone-induced prostate cancer cells, all of these

studies emphasized on the pioneering role of GATA2 in activating or enhancing AR-dependent gene transcription. By contrast, our results illustrated a pioneer function for GATA2 regulation in

which it regulates oncogenic Wnt/β-catenin signaling by circumventing AR signaling. Our finding that GATA2 exerts an AR-independent functionality in promoting aggressive prostate cancer is

consistent with a previous study that GATA2 regulates a core subset of clinically relevant genes in an AR-independent manner43. In summary, we provided a quantitative model of defining

functional nucleosome states to the community. We also conducted a detailed examination of the pioneer capacity of GATA2 in regulating dynamical nucleosome reorganization in hormone-induced

prostate cancer cells, and further implicated GATA2-mediated Wnt/β-catenin signaling in conferring aggressiveness in prostate cancer. Our work may provide a rationale for targeting GATA2

downstream signaling as a therapeutic strategy to treat advanced prostate cancer. Our work also offers a structural and mechanistic insight into the dynamics of pioneer factors governed by

nucleosome reorganization. METHODS MNASE-CHIP-SEQ AND MNASE-SEQ MNase-ChIP-seq and MNase-seq protocols were performed according to previous studies44. In brief, LNCaP cells were exposed to

10 nM DHT or DMSO (Veh) for 4 h. Mono-nucleosomes with solubilized chromatin was achieved by MNase digestion of 2 min at 37 °C, then immunoprecipitated with antibody-conjugated magnetic

beads. DNA is phenol extracted and ethanol precipitated. Libraries were prepared from isolated DNA and sent for sequencing on the Illumina HiSeq3000 at the UTHSA sequencing core. All samples

were performed in biological replicates. Antibodies include: H3K4me1 (ab8895) 1:500 dilution, H3K4me2 (ab7766) 1:250 dilution, H3K27ac (ab4729) 1:500 dilution, H3K27me3 (ab6002) 1:250

dilution, H3K36me3 (ab9050) 1:250 dilution, H3K79me2 (ab8898) 1:250 dilution from Abcam (Cambridge, MA). H3K4me3 (07-473) 1:500 dilution, H3K9me3 (17-10242) 1:250 dilution from Millipore

(Upstate). CHIP-EPENS A modified ChIP-exo protocol for TFs was performed as following steps21: Cells were fixed with 1% formaldehyde for 10 min at room temperature and chromatin was

sonicated and incubated overnight with 2–4 μg antibodies against GATA2(sc-9008, Santa Cruz) 1:250 dilution with biological replicates. T4 DNA polymerase, T4 PNK, and Klenow DNA Polymerase

were used together for end polishing. The ligation step was performed with 1 mM dithiothreitol. Protein A Dynal magnetic beads were washed using modified RIPA buffer (50 mM Tris-HCl pH 8.0,

1 mM EDTA, 0.25% sodium deoxycholate, 1% NP-40, 0.5 M LiCl) followed by Tris pH 8.0 twice during each step. The library was amplified with only 10–12 cycles and prepared without gel-based

size selection. Paired-end sequencing (50 bp) was performed by Illumina HiSeq2500. ATAC-SEQ About 50,000 cells were resuspended in cold ATAC-seq resuspension buffer (10 mM Tris-HCl pH 7.4,

10 mM NaCl, and 3 mM MgCl2). Cell nuclei were then prepared by incubation in 50 μl of ATAC-seq resuspension buffer containing 0.1% NP-40, 0.1% Tween-20, and 0.01% digitonin on ice for 3 min.

After centrifugation, nuclei were resuspended in 50 μl of transposition mix (25 μl 2× TD buffer, 2.5 μl Nextera Tn5 transposase (Illuminar), 16.5 μl PBS, 0.5 μl 1% digitonin, 0.5 μl 10%

Tween-20, and 5 μl water), and incubated at 37 oc for 30 min in a thermomixer with shaking at 1000 rpm. Transposed fragments were then purified with a Zymo DNA Clean and Concentrator-5 Kit.

All libraries showed sufficient amplification after the five pre-amplification cycles and were quantified using the KAPA Library Quantification Kit. Libraries were then sequenced using

Illumina Novaseq 6000 at the Duke sequencing core. All samples were performed in biological replicates. DATA MAPPING MNase-seq, MNase-ChIP-seq, and ChIP-ePENS sequencing datasets were

generated in LNCaP cells with both Veh and DHT-treated conditions. Raw sequence reads were aligned against the human genomic sequence (hg19) using bowtie2 (version 2.2.8) with -v 3 -k 2 -m 1

-I 20 -X 400 for ChIP-seq data. Only uniquely mapped reads were used for further downstream analysis. DETECTION OF NUCLEOSOME POSITIONING We applied iNPS31, which uses a Laplacian of

Gaussian convolution model to obtain smooth estimates of the stringency of nucleosome positioning, on MNase-seq data to detect nucleosome centers (dyads) and robustly estimate the degree of

positioning. The kernel bandwidth w is a key parameter to control the smoothness of the stringency profile. We initially chose w = 30 as suggested in ref. 39 to conduct the calculation, then

adjust it to make sure that it provides sufficient smoothing for the particular data without sacrificing the sharpness of the positioning estimate. We assigned nucleosomes to 21,319genes

with their longest isoforms in which each gene was divided into three genomic regions: Promoter region of −1 Kb to 1 Kb around the transcriptional start site (TSS), Proximal region of −5 Kb

to −1 Kb upstream of TSS and Distal region of −50 Kb to −5 Kb upstream of TSS. We used MACS244 to identify enriched peaks of various histone marks using nonmodel and shift options in order

to remove a technical bias and peak shifting for ChIP-seq data. NUCLEOSOMESGROUPING AND STATES CLASSIFICATION The degree of positioning describes how well the nucleosome is positioned in the

cells’ population and the regularity score indicates the periodical feature of a nucleosome array, which is measured by calculating power spectral density with an interpolating method and

Welch’s method for the nucleosome states’ array. For quantitatively defining the nucleosome states, we utilized the following features related to nucleosomes, nucleosome positioning and

spacing, histone marks, and the similarity ratio between continuous nucleosomes. $${{{{{{\rm{\lambda

}}}}}}}_{{{{{{\rm{i}}}}}}}={{{{{{\rm{S}}}}}}}_{{{{{{\rm{i}}}}}}}/{{{{{{\rm{W}}}}}}}_{{{{{{\rm{i}}}}}}}+\omega \,(\mu \,-\,{{{{{{\rm{d}}}}}}}_{{{{{{\rm{i}}}}}}})$$ (1) $${{{{{{\rm{\beta

}}}}}}}_{{{{{{\rm{i}}}}}}}=\mathop{\sum}\limits_{k=1}^{n}\frac{{dk}}{{dx}}{a}^{k}{b}_{{{{{{\rm{i}}}}}}}$$ (2) where λi represents the nucleosome positioning and spacing, as Si is the peak

area of nucleosome Ni, Wi is the width of nucleosome position, ω is the weight for the spacing factor, μ is the average spacing in a specific area and di is the actual spacing between

nucleosomes. βi represents the histone mark state factor, as \(\frac{{dk}}{{dx}}\) is the weight of a specific histone mark k, while \({a}^{k}\) is the relative number of reads of histone

mark k and \(b\)i is the relative number of reads of nucleosome Ni $${{{{{\rm{\gamma }}}}}}=({{{{{{\rm{\lambda }}}}}}}_{{{{{{\rm{i}}}}}}}{{{{{{\rm{\beta

}}}}}}}_{{{{{{\rm{i}}}}}}}-{{{{{{\rm{\lambda }}}}}}}_{{{{{{\rm{i}}}}}}+1}{{{{{{\rm{\beta }}}}}}}_{{{{{{\rm{i}}}}}}+1})/\mathop{\sum}\limits_{n=1}^{i}{{{{{{\rm{\lambda

}}}}}}}_{{{{{{\rm{i}}}}}}}{{{{{{\rm{\beta }}}}}}}_{{{{{{\rm{i}}}}}}}$$ (3) where γ calculates the similarity ratio between nucleosome Ni+1 and N1 to Ni. If γ among calculated nucleosomes is

lower than 10%, the nucleosomes were merged into the same group. We defined nucleosome dyad position, degree of positioning, regularity score, and histone peak signals as grouped nucleosome

profiles. We then performed K-means clustering on grouped nucleosome profiles to obtain distinct classes of nucleosome states. IDENTIFICATION OF GATA2 BORDERS Border-calling of ChIP-ePENS

data was conducted by ePEST (version 1.0) with the parameter of -D True -p 1e-8 -R 25 -c 0.05 -k 2.0 -o. The ePEST algorithm was specifically designed for ChIP-ePENS40 and depending on a

statistical evaluation of Chernoff inequity on exo-5′-end reads and r-scan statistic method for peak-calling on son-3′-end reads, Border-calling was conducted specifically within these

binding regions and borders were finally assigned into each individual binding site by a graph-based strategy. A GATA2-associated gene was defined as the closest gene of GATA2 border

bindings and each GATA2 border pair was assigned to only one gene according to the order of the following criterion: gene body region (TSS~ TES of a gene), promoter region (TSS~ −1 Kb

upstream of a gene), proximal region (−5 Kb ~ −1 Kb upstream of a gene), then distal region (−50 Kb ~ −5 Kb upstream of a gene) and no associated gene. DIFFERENTIALLY EXPRESSED GENE

ANDATAC-SEQ ANALYSIS RNA-seq data were aligned by STAR (version 2.5.3) with default parameters. The differentially expressed genes were performed by HTseq-count (version 0.9.1) and DESeq2

(version 1.10.1) with thresholds of log2(|folder change | ) >1 and _p_ values < 0.05. ATAC-seq data of LNCaP cells were downloaded from the Gene Expression Omnibus (GSE105116).

ATAC-seq peaks were called using HOMER (version 4.10) find peaks localSize 50000 -size 150 -minDist 50 –fragLength 0 -style dnase. Differential accessibility was called using DESeq2 and

hyper- and hypo-accessible peaks were defined with a | log2 FC | > 1 and an adjusted _p_ value < 0.01. SIRNA ASSAY Silencer® Select siRNAs of GATA2 were obtained from Thermo Fisher

Scientific (Catalog #4392420, Santa Clara, CA). Each sample was performed in triplicates with siRNA of different targets. For transfection of siRNA oligos, cells were seeded in six-cell

plates with Lipofectamine® RNAiMAX Transfection Reagent for 48 h. The knockdown efficiency was detected by qPCR. IN VITRO NUCLEOSOME-BINDING AND ELECTROPHORETIC MOBILITY SHIFT ASSAYS The

nucleosome-binding assay was performed as following22: In vitro nucleosomes were generated from H2A/H2B dimer and H3.1/H4 tetramer (NEB). Synthesized double-stranded DNA sequences were mixed

at equal molar amounts and then added to histones at octamer/DNA molar ratios of 1.5:1 in 2 M NaCl. Nucleosomes were reconstituted through salt gradient dialysis and further purified by

7–20% sucrose gradient centrifuge and concentrated by 50,000 centrifugal filter units (Millipore, Amicon ultra). The protein-nucleosome-binding assays were carried out with the purified

nucleosomes mentioned above and human full-length recombinant GATA2 protein (Abcam catalog no. ab134866) in a 7 μL DNA binding buffer and incubated for 30 min. Protein binding was analyzed

by non-denaturing polyacrylamide gel electrophoresis with ethidium bromide staining. OPEN CHROMATIN ASSAY Open chromatin assay was performed using a Chromatin Accessibility Assay Kit

(ab185901) from Abcam (Cambridge, MA)45. Briefly, LNCaP cells were lysed and chromatin was extracted by adding lysis buffer to cell pellets and incubating for 10 min. Chromatin pellets were

centrifuged and resuspended in a nuclease reaction mix and then added to a stop solution and incubated with Proteinase K. Accessible fragments were then purified by DNA binding columns and

gene targets were analyzed with PCR by comparing the nuclease-treated condition and the no-nuclease control. DATA AVAILABILITY The data that support this study are available from the

corresponding authors upon reasonable request. The MNase-seq, MNase-ChIP-seq, ChP-ePENS of GATA2, and ChIP-seq of H1 data generated in this study have been deposited in the GEO database

under accession code GSE148935 and GSE182529. Source data are provided with this paper. CODE AVAILABILITY All codes used in this study are available on Github

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  PubMed  PubMed Central  Google Scholar  Download references ACKNOWLEDGEMENTS We thank UTHSA Next Generation Sequencing Facilities (1S10OD021805) for services rendered for the production of

ChIP-ePENS, MNase-seq, MNase-ChIP-seq, and thank Duke sequencing core for services rendered for the production of ATAC-seq and ChIP-seq data. We are grateful to Dr. B Frank Pugh of the

Department of Molecular Biology and Genetics at Cornell University for reading the manuscript and providing suggestive comments. This project was partially supported by grants from NIH

R01GM114142 (V.X.J.), U54CA217297 (V.X.J. and Q.W.), and R01GM120221 (Q.W. and V.X.J.). AUTHOR INFORMATION Author notes * These authors contributed equally: Tianbao Li, Qi Liu, Zhong Chen. *

These authors jointly supervised this work: Qianben Wang, Victor X. Jin. AUTHORS AND AFFILIATIONS * Department of Molecular Medicine, University of Texas Health Science Center at San

Antonio, San Antonio, TX, 78229, USA Tianbao Li, Qi Liu, Kun Fang & Victor X. Jin * Department of Pathology and Duke Cancer Institute, Duke University School of Medicine, Durham, NC,

27710, USA Zhong Chen, Furong Huang & Qianben Wang * Edmond H. Fischer Signal Transduction Laboratory, College of Life Sciences, Jilin University, Changchun, 130012, China Xueqi Fu

Authors * Tianbao Li View author publications You can also search for this author inPubMed Google Scholar * Qi Liu View author publications You can also search for this author inPubMed 

Google Scholar * Zhong Chen View author publications You can also search for this author inPubMed Google Scholar * Kun Fang View author publications You can also search for this author

inPubMed Google Scholar * Furong Huang View author publications You can also search for this author inPubMed Google Scholar * Xueqi Fu View author publications You can also search for this

author inPubMed Google Scholar * Qianben Wang View author publications You can also search for this author inPubMed Google Scholar * Victor X. Jin View author publications You can also

search for this author inPubMed Google Scholar CONTRIBUTIONS V.X.J. conceived the project. V.X.J. and Q.W. conceived the functional validations. Q.W. provided the critical inputs to the

project. T.L, Q.L., Z.C., and F.H. conducted the experiments. T.L. and Q.L. performed the data analyses. V.X.J., T.L., Q.L., Z.C., and Q.W. wrote the manuscript, with all authors including

K.F., X.F., and F.H. contributing to the writing and providing the feedback. CORRESPONDING AUTHORS Correspondence to Qianben Wang or Victor X. Jin. ETHICS DECLARATIONS COMPETING INTERESTS

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noncanonical GATA2 pioneer model. _Nat Commun_ 13, 3145 (2022). https://doi.org/10.1038/s41467-022-30960-x Download citation * Received: 20 May 2020 * Accepted: 24 May 2022 * Published: 07

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