Reprogramming by De-bookmarking the Somatic Transcriptional ...

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Reprogramming by De-bookmarking the Somatic Transcriptional Program through Targeting of BET Bromodomains Graphical Abstract

Authors Zhicheng Shao, Chunping Yao, Alireza Khodadadi-Jamayran, Weihua Xu, Tim M. Townes, Michael R. Crowley, Kejin Hu

Correspondence [email protected]

In Brief Shao et al. show that mild chemical targeting of the acetyllysine binding pockets of the BET bromodomains enhances human cellular reprogramming. Such chemical transcriptional debookmarking efficiently dampens a fibroblast-specific transcriptional program and results in the loss of fibroblast morphology.

Highlights d

Various BET-specific chemical inhibitors enhance human iPSC reprogramming

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Chemical BET inhibition dampens the fibroblast transcriptional program

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Chemical BET inhibition results in the loss of fibroblast morphology

Shao et al., 2016, Cell Reports 16, 3138–3145 September 20, 2016 ª 2016 The Author(s). http://dx.doi.org/10.1016/j.celrep.2016.08.060

Accession Numbers GSE75364

Cell Reports

Report Reprogramming by De-bookmarking the Somatic Transcriptional Program through Targeting of BET Bromodomains Zhicheng Shao,1,5 Chunping Yao,1,3,5 Alireza Khodadadi-Jamayran,1,5 Weihua Xu,4 Tim M. Townes,1 Michael R. Crowley,2 and Kejin Hu1,6,* 1Stem Cell Institute, Department of Biochemistry and Molecular Genetics, University of Alabama at Birmingham, Birmingham, AL 35294-0024, USA 2Howell and Elizabeth Heflin Center for Genomic Science, Department of Genetics, University of Alabama at Birmingham, Birmingham, AL 35294-0024, USA 3Department of Radiation Oncology, Shandong Cancer Hospital, Shandong University, Jinan, Shandong 250117, China 4Longyan University, Fujian 364012, China 5Co-first author 6Lead Contact *Correspondence: [email protected] http://dx.doi.org/10.1016/j.celrep.2016.08.060

SUMMARY

One critical event in reprogramming to pluripotency is erasure of the somatic transcriptional program of starting cells. Here, we present the proof of principle of a strategy for reprogramming to pluripotency facilitated by small molecules that interfere with the somatic transcriptional memory. We show that mild chemical targeting of the acetyllysine-binding pockets of the BET bromodomains, the transcriptional bookmarking domains, robustly enhances reprogramming. Furthermore, we show that chemical targeting of the transcriptional bookmarking BET bromodomains downregulates or turns off the expression of somatic genes in both naive and reprogramming fibroblasts. Chemical blocking of the BET bromodomains also results in loss of fibroblast morphology early in reprogramming. We therefore experimentally demonstrate that cell fate conversion can be achieved by chemically targeting the transcriptional bookmarking BET bromodomains responsible for transcriptional memory. INTRODUCTION Conversion of somatic cells into the pluripotent state is in principle a process of epigenetic modulation. Specific and directed modulation of epigenetic status on a genome-wide scale, as for pluripotent reprogramming, is complicated and difficult (Hu, 2014a, 2014b). We hypothesized that targeted chemical intervention of somatic epigenetic reading and/or decoding may be easier to achieve and could facilitate induction of pluripotent stem cells (iPSCs). Each cell type has its unique transcriptome. This uniquely expressed gene set defines the cell identity. How-

ever, during mitosis, general and gene-specific transcription factors largely come off chromatin, and global transcription stops (Gottesfeld and Forbes, 1997; Martı´nez-Balba´s et al., 1995; Parsons and Spencer, 1997). Upon exit from mitosis, each cell type remembers its unique transcriptional details and faithfully resumes its defined transcription. The released transcriptional machinery homes in to the defined places after mitosis. This transcriptional memory during cell divisions is realized by a process called transcriptional bookmarking on mitotic chromatin (Sarge and Park-Sarge, 2009; Zaidi et al., 2010). Because reprogramming has to erase the somatic transcription, it is tempting to facilitate pluripotent reprogramming by perturbing somatic transcriptional memory, which is done by specifically disrupting the somatic transcriptional bookmarking during mitosis. One family of mitotic proteins responsible for transcriptional memory is the bromodomain and extra-terminal domain (BET) proteins (Devaiah and Singer, 2013; Dey et al., 2009; Kanno et al., 2004; Zhao et al., 2011). They are characterized by two tandem bromodomains, which bind to the acetylated histones. BET proteins are established epigenetic readers due to their ability to bind to the active acetylated histones via their bromodomains (Belkina and Denis, 2012), and they positively regulate the actively expressed genes in general (LeRoy et al., 2008). Unlike other transcriptional regulators, BET proteins remain on mitotic chromatin during all stages of mitosis when the general transcriptional machinery temporally disengages mitotic chromatin. It is suggested that this continuous association of BET proteins with mitotic chromatin contributes to the transcriptional memory by bookmarking the formerly active genes (Devaiah and Singer, 2013; Dey et al., 2009; Kanno et al., 2004; Zhao et al., 2011). Among these BET regulated genes are those defining cell identities. For example, BRD4 is critical in the control of transcriptional elongation of pluripotent genes to maintain embryonic stem cell identity (Di Micco et al., 2014; Wu et al., 2015). Therefore, the transcriptional bookmarking or reading bromodomains

3138 Cell Reports 16, 3138–3145, September 20, 2016 ª 2016 The Author(s). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

of BET proteins is an ideal target for chemical manipulation in cell fate conversions. Here, we report that mild inhibition of BET bromodomains with small molecules significantly enhances iPSC reprogramming via reduction or erasure of expressions for a large set of fibroblast genes. Our findings also raise a concern about the highly anticipated use of BET inhibitors in cancer patients. RESULTS Identification of a Set of Human Fibroblast-Specific Genes and Insufficient Erasure of Fibroblast Program by the Canonical Reprogramming Factors We first examined the number of fibroblast-specific genes that have to be downregulated during reprogramming. These fibroblast genes include not only those exclusively expressed in fibroblasts but also those with higher expression in fibroblasts than in human pluripotent stem cells (hPSCs). We have sequenced the RNA from two human embryonic stem cell (hESC) lines (H1 and H9). To avoid mouse RNA contamination from feeder cells, those hESC lines are cultured in a xeno-free culture system (Chen et al., 2011). We have also sequenced the RNA from three human BJ fibroblast samples (GSE66798), the starting cells in our reprogramming experiments. We used the DESeq algorithm for RNA sequencing (RNA-seq) differential analyses because of its sensitivity (Dillies et al., 2013). Differential expression analysis showed that as many as 3,148 genes have higher expression in fibroblasts than in hESCs (1.5 times or more, p < 0.05; normalized averaged DESeq read counts [ADRCs] for BJ are greater than 50) (Table S1). Of these 3,148 fibroblast-specific genes, 1,017 are considered not expressed in hESCs (ADRC for hESCs < 50). These indicate that we have to shut down the expression of these 1,017 genes, and downregulate the remaining 2,131 genes for successful reprogramming. Downregulation of somatic genes is generally an early event during reprogramming. Next, we sequenced the RNA from the same human fibroblasts transduced with reprogramming factors OCT4, SOX2, and KLF4 (OSK), and then we examined the degree of downregulation of these fibroblast genes early in the reprogramming (48 hr after transduction). As many as 1,940 genes were downregulated by OSK overexpression, but only 510 of those genes have fibroblast-specific transcription (Figure S3A; Table S1). These results indicate insufficient erasure of fibroblast-specific transcription and significant aberrant downregulation of transcription by OSK early in reprogramming. Mild Targeting of the Bookmarking Pockets of the BET Bromodomains Enhances Human iPSC Reprogramming Small molecules with specific binding to the acetyllysine-binding pockets of the BET bromodomains have been developed (Dawson et al., 2011; Devaiah et al., 2012; Filippakopoulos et al., 2010). Given the roles of BET proteins in transcriptional bookmarking, we explored the possibility that these chemicals may be used to promote iPSC reprogramming via de-bookmarking the somatic transcription. However, we and others reported that chemical BET inhibition severely impaired reprogramming (Liu et al., 2014; Shao et al., 2016). We observed

extensive cell death during reprogramming when 500 nM (+)-JQ1 (or JQ1), the prototype BET chemical inhibitor, was included in the reprogramming media for 3 to 5 consecutive days (data not shown). This concentration is widely used to kill cancer cells (Delmore et al., 2011; Mertz et al., 2011). Because this concentration impairs the basic BET functions for cell survival, we examined the effect of transient BET inhibition on reprogramming. Increased reprogramming activities were observed when the reprogramming cells were treated with JQ1 intermittently on days 3 and 5 at 100, 250, 500, and up to 1,000 nM (Figure 1A). A scrutiny of the reprogramming data from M.A. Esteban’s group revealed a consistent increase of reprogramming activity upon JQ1 treatment at early stages of reprogramming, although the enhancement was low (Liu et al., 2014). Encouraged by this finding, we conducted a dosage study with reduced concentrations of JQ1. We observed a dramatic increase in reprogramming activities for all low doses of JQ1 from 10 to 100 nM, even when used for an extended time (Figures 1B and S1A). The highest reprogramming activity was displayed by 50 nM of JQ1 (Figures 1B–1D and S1A). Under the optimized condition (Figure 1H), JQ1 enhanced reprogramming by 22-fold on average (Figure 1C). We then used 50 nM of JQ1 in our remaining studies. A time course study showed that JQ1 had an early impact on reprogramming (Figures 1E and S1B). Later treatments still benefitted reprogramming, but the reprogramming activity decreased over time (Figures 1E and S1B). Continuous BET inhibition for 11 days from day 3 to 14 displayed the highest reprogramming activity (Figures 1E and S1B). However, further treatment with JQ1 beyond day 14 impaired reprogramming (data not shown). This is because human iPSCs have appeared by then and this low concentration of JQ1 still impaired growth of hPSCs (Figures S1E and S1F). This result is in agreement with the report by M.A. Esteban’s group (Liu et al., 2014). Their data showed that JQ1 treatment at a late stage of reprogramming impairs reprogramming. Mouse reprogramming is faster, and we suggest that their data reflect the negative effect of JQ1 on iPSC growth rather than the reprogramming process. JQ1 is specific for BET bromodomains (Filippakopoulos et al., 2010). The reprogramming activity of JQ1 was observed at low concentrations, and reprogramming media were changed every day. Therefore, it is unlikely that the observed reprogramming activity is an off-target effect. To further rule out the possibility of an off-target effect, we tested additional chemical inhibitors specific for BET bromodomains. CPI203, a derivative of JQ1 (Devaiah et al., 2012), demonstrated robust reprogramming activity at a lower concentration (Figures 1F and S1C). I-BET151 is a structurally distinct BET-specific inhibitor (Dawson et al., 2011; Smith et al., 2014). Our data showed that I-BET151 is also a robust reprogramming promoter when used at a lower concentration (Figures 1F and S1C). In addition, vehicle (DMSO) and the inactive ( )-JQ1, the enantiomer of (+)-JQ1, exhibited no reprogramming activity (Figures 1G and S1D). In agreement with our observation that 50 nM is the optimal concentration for reprogramming, we found that a combination of the three BET inhibitors did not further improve reprogramming and even slightly impaired reprogramming compared to 50 nM JQ1 alone

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Figure 1. Mild Chemical Targeting of Bromodomains with the BET Inhibitors Enhances Reprogramming (A) Transient BET inhibition promoted human iPSC reprogramming. BJ cells transduced with OSK were treated at days 3 and 5 for 24 hr each day at the concentrations indicated. n = 3. (B) Effect of JQ1 dosage on reprogramming. JQ1 treatments were performed from day 3 to 14 at the concentrations indicated. n = 3. (C) JQ1 reprogramming efficiency as calculated by the percentage of TRA-1-60+ colonies based on the number of starting cells, at the optimized conditions (50 nM, from day 3 to 14). n = 12. (D) Representative images of reprogramming dishes stained for TRA-1-60, showing high reprogramming activity of JQ1. (E) Time course of JQ1 reprogramming activity (50 nM). n = 3. (F) Other BET inhibitors demonstrated reprogramming activity when used at low concentrations from day 3 to 14. n = 3. (G) Inactive ( )-JQ1 and DMSO do not exhibit reprogramming activity. Reprogramming cells were treated with chemicals as indicated from day 3 to 14. n = 3. (H) Schematic for the optimized JQ1 reprogramming protocol in a chemically defined medium. D, day. **p < 0.01; ***p < 0.001. Data are presented as mean ± SD in (A)–(C) and (E)–(G). See also Figures S1 and S2.

(Figure S1G). This also suggests that these three distinct inhibitors hit the same target, because a higher concentration of JQ1 alone also demonstrated decreased reprogramming activity (Figures 1B and S1A). Collectively, these data indicate that the enhancement of reprogramming is specific for BET bromodomain targeting. Vitamin C (Vc) has been reported to promote reprogramming (Esteban et al., 2010). Because the xeno-free reprogramming media we used contains Vc (Chen et al., 2011), we conducted Vc dropout experiments to compare the reprogramming activities of JQ1 with those of Vc. We used the optimized concentration of Vc (64 mg/ml) based on previous reports (Chen et al., 2011, 2013; Esteban et al., 2010; Wang et al., 2011). Vc displayed little reprogramming activity in our system (Figures 1G and S1D). This discrepancy might be a result of different media and the reprogramming system used in the current study. Our results are in agreement with another report that used the same reprogramming system as we did (Chen et al., 2011).

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We conducted our reprogramming in three-factor reprogramming, considering that c-MYC is slightly detrimental to reprogramming in defined media (Shao et al., 2016; Xu et al., 2013). Nevertheless, we found that JQ1 also enhanced reprogramming in the presence of c-MYC (Figure S2). The observed enhanced reprogramming activity for BET inhibitors was not due to shortened kinetics of reprogramming by JQ1, because enumeration of the TRA-1-60+ colonies on day 30 had similar results (Figure S2). BET iPSCs Are Pluripotent Using various criteria, we next tested the pluripotency of the iPSCs generated using BET inhibitors (BETiPSCs). Transgene silencing is a hallmark of advanced reprogramming. All three canonical reprogramming factors are co-expressed with GFP via 2A peptide in our reprogramming constructs (Shao et al., 2016); therefore, GFP expression provides a sensitive measurement of transgene silencing. BETiPSCs did not express GFP (Figure 2B), indicating silencing of the transgenes. BETiPSCs activated the pluripotent transcription factors OCT4, NANOG, and SOX2 and expressed the pluripotent surface markers TRA-1-60 and TRA-1-81 (Figure 2A) (Kang et al., 2016). The expression of OCT4 was not from the transgenic OCT4, because the multiple GFPs co-expressed with

Figure 2. BETiPSCs Are Pluripotent (A) Immunocytochemistry of BETiPSCs showing expression of pluripotent master transcription factors and pluripotent surface markers as indicated. (B) BETiPSCs silenced transgenes as indicated by the absence of GFP co-expressed with reprogramming factors as mediated by 2A peptide. (C) PCAs of BETiPSCs and selected cell types based on RNA-seq data (GSE75364 and GSE66798). (D) Euclidean distance of BETiPSCs from somatic cells, hESCs, and previously established iPSC lines based on RNA-seq data. (E) H&E staining of BETiPSC-derived teratoma, showing the ability to generate tissues of all three embryonic germ layers. (F) Normal karyotypes of BETiPSCs. Scale bars in (B), 100 mm; bars in (A) and (E), 50 mm. K, human keratinocytes.

OSK were efficiently silenced (Figure 2B). BETiPSCs had a transcriptome similar to those of hESCs and the established human iPSCs (Figures 2C and 2D). BETiPSCs had the ability to generate teratomas containing cells that represent all three embryonic germ layers (Figure 2E). BETiPSCs had normal karyotypes (Figure 2F). Mild Chemical Targeting of BET Bromodomains Erases Fibroblast Transcription Next, we test whether chemical targeting of the BET bromodomains erases somatic transcription. We first conducted deep sequencing of RNA of the starting human fibroblasts. Exposure of fibroblasts to 50 nM JQ1 for 48 hr downregulated 551 genes

(Figure 3A2; Table S2). Of these 551 downregulated genes, 390 (70.8%) demonstrated higher expression in fibroblasts than in hPSCs (Figures 3A3 and 3A4; Table S2). Furthermore, of these 390 JQ1-downregulated fibroblast genes, 162 (41.5%) are deemed no expression in hPSCs (Figures 3A3 and 3A4; Table S2). In contrast, only 95 genes were upregulated by JQ1 (Figure 3A2). BET proteins are widely regarded as positive regulators of gene expression. This 5.83 bias in numbers toward downregulated genes upon BET inhibition indicates a predominant loss of transcriptional memory. These results indicate that as expected, mild BET bromodomain perturbation has dampened the transcriptional memory of the fibroblast gene expression.


Chemical Targeting of the BET Bromodomains Erases Fibroblast Transcription during Reprogramming We further sequenced the RNA from reprogramming cells treated with JQ1 after transduction of reprogramming factors OSK. We found that twice as many genes (1,182 versus 551 genes) were downregulated by JQ1 in reprogramming cells compared to naive fibroblasts (Figure 3B2; Table S3). This indicates a synergistic effect between JQ1 and the reprogramming factors. As in non-transduced fibroblasts, a large portion of the JQ1-downregulated genes in the OSK-overexpressing reprogramming cells had higher expression in fibroblasts than in hPSCs (651 of 1,182) (Figures 3B1, 3B3, and 3B4; Table S4). Furthermore, 270 of the 651 fibroblast-enriched genes (41.5%) were deemed no expression in hPSCs (Figures 3B3 and 3B4; Table S4). Therefore, as seen in the non-transduced fibroblasts, JQ1 targeting of the BET bromodomains has reduced and/or erased the fibroblast-specific transcription in reprogramming cells. As described earlier, the OSK factors downregulate 510 fibroblast-specific genes. It appears that OSK and JQ1 treatment largely targeted different sets of fibroblast-specific genes under our conditions, and there were only 104 genes targeted by both OSK and JQ1 (Figures S3A and S3B). For these 104 fibroblast genes, addition of JQ1 showed a synergistic effect (Figures S3C and S3D). However, there appears to be some interplay between BET inhibition and OSK, because we observed that more genes were downregulated by JQ1 in reprogramming cells, and some genes downregulated by JQ1 in naive cells were not on the list of genes downregulated by JQ1 in reprogramming cells (Figures S3E and S3F). In our current data, BET inhibition did not downregulate all fibroblast-specific genes. There may be three reasons. First, BET proteins may not regulate all fibroblast-specific genes. Second, our RNA-seq was performed at early stages of reprogramming. Our RNA-seq may not reveal all changes caused by BET inhibition. Unfortunately, analyses at later stages become more complicated because the cell population becomes more heterogeneous. Third, disruption of other fibroblast transcription upon short treatment may require a more severe BET inhibition, but those conditions are not relevant to the reprogramming mechanistic study. In agreement with the erasure of fibroblast transcription by BET inhibition, we observed a loss of fibroblast morphology upon JQ1 treatment early in reprogramming (Figures 3D and S4). The same reprogramming BJ cells treated with the inactive enantiomer ( )-JQ1 mostly retained the spindle shape characteristic of fibroblasts, while most cells treated with JQ1 became polygonal or even round, similar to those of hPSCs (compare Figures 3C and 3D with S4). Other BET inhibitors caused similar changes in cell morphology early in reprogramming (Figures 3E and S4). Compared to the non-transduced fibroblasts, 9.2 times more genes (878 versus 95) were upregulated by JQ1 in the reprogramming cells overexpressing OSK (Figures 3A2 and 3B2), again indicating a synergistic effect between JQ1 and OSK. Among these JQ1-upregulated genes, 33.6% had higher expression in hPSCs, 22% had lower expression in hPSCs, and the remaining 44.4% did not have significant changes in expression levels between fibroblasts and hPSCs (Figure 3B3). Based on these


results, the JQ1 impact on the upregulated genes is more promiscuous in terms of reprogramming, but a pro-reprogramming upregulation prevails when the reprogramming factors are present (Figure 3). DISCUSSION We have demonstrated that mild chemical targeting of the BET bookmarking bromodomains significantly enhanced human iPSC reprogramming. In fibroblasts, this targeted blockage of the acetyllysine-binding pockets in the BET bromodomains also resulted in reduction and/or loss of expression of a large set of fibroblast genes. In addition, we showed that mild chemical blocking of this acetyllysine-binding activity acted in concert with the reprogramming factors OSK to further downregulate the somatic transcription. Figure 4 is a schematic model for facilitating pluripotent reprogramming by de-bookmarking somatic transcription via targeting the BET bromodomains. In brief, BET proteins bookmark the active fibroblast genes during mitosis. This bookmarking ensures the resumption of the celltype-specific transcription upon exit from mitosis. When BET inhibitors are included in the growth media, BET proteins fail to bookmark the fibroblast transcription due to their displacement by BET chemical inhibitors. This de-bookmarking results in reduction and/or loss of the fibroblast transcription and eventually enhances reprogramming. In nature, bookmarking is a concept for mitosis. In an experimental setting, this bookmarking may be required in interphase as well for the maintenance of somatic transcription. Therefore, the concept of de-bookmarking should not be restricted to mitosis. A lack of bookmarking via experimental interference in interphase also results in a loss of somatic transcription. In the future, it will be worth further testing the de-bookmarking theory in other cell fate conversions, for example, direct reprogramming from fibroblasts to myocardiocytes or other functional somatic cells. BET perturbation of transcriptional memory may be a general mechanism in cell fate conversions. One publication indicated this possibility already. BET inhibitors promoted direct conversion of murine fibroblasts to neural cells and disrupted the fibroblast expression programs (Li et al., 2015). It is possible that fibroblast-specific transcriptional programs are regulated by multiple members of BET proteins, considering that current BET chemical inhibitors target all three ubiquitous BET proteins: BRD2, BRD3, and BRD4. Further elegant research is required to understand the exact molecular mechanisms underlying the reprogramming activity of chemical BET inhibition. BET inhibitors are highly anticipated as a pharmacological intervention of tumor development. Our data and a previous report that BET perturbation can shift cell fates is a cautionary sign for their use in patients (Li et al., 2015). S.W. Lowe’s group demonstrated that Brd4 knockdown in a mouse model resulted in a loss of tissue homeostasis, including skin hyperplasia and depletion of intestinal stem cells (Bolden et al., 2014). Further research on possible induction of tumorigenesis and disruptions of tissue homeostasis by BET inhibition is warranted, because iPSCs are by nature tumorigenic and many cell types express SOX2 and/or KLF4, the two canonical reprogramming factors used with BET inhibitors in the current study.

Figure 3. Chemical Targeting of the BET Bromodomains Results in Reduction and Loss of Fibroblast Transcription both in Early Reprogramming Fibroblasts and in Naive Fibroblasts (A and B) Heatmap comparisons showing reduction and/or loss of expression of the fibroblast genes by JQ1 treatment at 50 nM for 48 hr of the non-transduced naive BJ fibroblasts (A1, the same 390 genes as in A4) and of the reprogramming fibroblasts (B1, the same 651 genes as in B4). Summary for the impact of mild BET inhibition on transcription in naive BJ fibroblasts (A2) and in reprogramming BJ cells (B2). Summary for the impact of the JQ1affected genes on reprogramming based on their expression status in hPSCs relative to that in fibroblasts. A3 is for those in non-transduced BJ cells, while B3 is for the affected genes in reprogramming cells. Heatmap presentation for the expression levels in hPSCs and BJ, of the fibroblast genes, that are large subsets of the JQ1-downregulated genes in the non-transduced fibroblasts (A4, the same 390 genes as in A1) and in the reprogramming BJ cells (B4, the same 651 genes as in B1). Heatmap expression levels in (A1), (B1), (A4), and (B4) are log2 (DESeq read counts of RNA-seq). Key in (A2), (A3), (B2), and (B3): upward arrows, upregulation; downward arrows, downregulation; red arrows, elevated or decreased expression in hPSCs compared to expression in fibroblasts; forward arrows, positive changes for pluripotency; hammer signs, negative changes for pluripotency. Thickness indicates the approximate impact on reprogramming based on the number of genes affected. Counts of genes affected or compared are listed, with the corresponding percentages included in parentheses. The corresponding denominators for percentage calculations are indicated with the same highlights for the percentages (blue or underline). See also Tables S2, S3, and S4. (C–E) Cell morphologies of BJ cells on day 5 after treatment with ( )-JQ1 (50 nM), (+)-JQ1 (50 nM), and I-BET151 (100 nM). Bars, 50 mM. See also Figures S3 and S4 and Tables S1, S2, S3, and S4. EXPERIMENTAL PROCEDURES Reprogramming UAB Institutional Animal Care and Use Committee approved the use of mice, and the animal protocols comply with ethical regulations. BJ fibroblasts were seeded into 6-well plates at 1 3 105 cells/well. Twenty-four hours after plating, premixed OSK viruses (OCT4, 10 MOI; SOX2, 5 MOI; and KLF4, 5 MOI) were added in the presence of polybrene at 4 mg/ml. The next morning, viruses were removed by replacing the spent media with fresh fibroblast media. Then, 24 hr after transduction, the transduced cells were reseeded at 3 3 104 to 4 3 104 cells/well into matrigel-coated 6-well plates. The next day, fibroblast medium was replaced with reprogramming media (E6 medium: DMEM/ F12, 13.6 mg/l sodium selenium, 1.7 g/l NaHCO3, 1 g/l sodium chloride, 10 mg/l FGF2, 20 mg/l insulin, and 10.7 mg/l transferrin; or E7 medium: E6 medium + 64 mg/l L-ascorbic acid 2-phosphate sesquimagnesium) with or without small molecules JQ1 (Catalog No. 4499, Tocris Bioscience), iBET-151(Catalog No. 4650, Tocris), or CPI203 (C6132, LKT Laboratories). Starting from day 12 to 14, E8 media were used. On day 21 of reprogramming, reprogramming cells were stained for TRA-1-60. RNA-Seq RNA-seq used 500 ng of total RNA. Polyadenylated RNAs were isolated using NEBNext Magnetic Oligo d(T)25 Beads. Next, first-strand synthesis was

performed using the NEBNext RNA first-strand synthesis module (NE Biolabs). Immediately, directional second-strand synthesis was performed using the NEBNext Ultra Directional Second-Strand Synthesis kit. The NEBNext DNA Library Prep Master Mix Set for Illumina was then used to prepare individually bar-coded next-generation sequencing expression libraries per the manufacturer’s recommended protocol. Library quality was assessed using the Qubit 2.0 Fluorometer, and library concentration was estimated by using a DNA 1000 Chip on an Agilent 2100 Bioanalyzer. Accurate quantification for sequencing applications was determined using the qPCR-based Kapa Biosystems Library Quantification Kit. Each library was diluted to a final concentration of 12.5 nM and pooled in an equimolar ratio before clustering. Paired-end sequencing was performed on an Illumina HiSeq2500 sequencer with a high-output mode. Image analysis and base calling were performed using the standard Illumina pipeline implemented in real-time analysis (RTA). Raw reads were de-multiplexed using bcl2fastq Conversion Software (Illumina) with default settings. Bioinformatics We obtained around 25 million paired 50 bp reads for each sample. RNA-seq reads were mapped to the human reference genome (GRCh37/hg19) using the STAR aligner (v.2.3.0e r291) (Dobin et al., 2013). The alignment was guided by a Gene Transfer File (GTF version GRCh37.70). The read per million (RPM) normalized BigWig files were generated using Bedtools (v.2.17.0) (Quinlan


Figure 4. A Model for Reprogramming by Transcriptional De-bookmarking via Chemical Targeting of the BET Bromodomains (A) In the absence of BET inhibitors, somatic genes continue to express and reprogramming is compromised. (B) JQ1 displaces BET proteins and de-bookmarks somatic fibroblast genes in mitosis and in interphase to promote loss or reduction of expression for a large set of fibroblast genes during reprogramming.

and Hall, 2010) and the bedGraphToBigWig tool (v.4). Read count tables were generated using HTSeq (v.0.6.0) based on the Ensembl gene annotation file (Ensembl GTF version GRCh37.70) (Anders et al., 2015). All read count tables were then corrected for their library size differences based on their geometric library size factors calculated using the DESeq R package (v.3.0) (Anders and Huber, 2010), and differential expression (DE) analysis was performed. To compare the level of similarity among the samples in our dataset based on their normalized gene expression, we used two methods: classical multidimensional scaling or principal-component analysis (PCA) and Euclidean distance-based sample clustering. All downstream statistical analyses and data visualizations were performed in R (v.3.1.1) (http://www.r-project.org/).

ACCESSION NUMBERS The accession number for the RNA-seq data reported in this paper is GEO: GSE75364. SUPPLEMENTAL INFORMATION Supplemental Information includes Supplemental Experimental Procedures, four figures, and four tables and can be found with this article online at http://dx.doi.org/10.1016/j.celrep.2016.08.060. AUTHOR CONTRIBUTIONS

Statistics For reprogramming analyses, we conducted each experiment at least in triplicate and counted the TRA-1-60+ colonies. The data are shown as mean ± SD. Statistics was conducted using a two-tailed, unpaired t test for statistical analysis. Significance designations are as follows: significance, *p < 0.05; very significant, **p < 0.01; extremely significant, ***p < 0.001. For deferential expression analyses, we used the nbinomTest function in the DESeq package after correcting for library sizes. For PCA and Euclidean distance analyses, we performed dispersion estimates of the count data using the estimateDispersions function in blind mode, and we used the varianceStabilizingTransformation function to transform the count data for fixing for large or infinite log2 (expression values) in genes with low or zero expression. We employed DESeq’s plotPCA function to calculate the first two principle components, and we created the two-dimensional plot using the ggplot2 package. For distance analysis, we used the R dist function to calculate the sample distances in the transformed count data (as explained earlier) by setting the method as Euclidean (default).


Conceptualization, K.H.; Methodology, K.H. and Z.S.; Investigation, Z.S., C.Y., W.X., K.H., and M.R.C.; Formal Analysis, A.K.-J. and K.H.; Validation, Z.S. and C.Y.; Writing – Original Draft, K.H.; Writing – Review & Editing, K.H. and Z.S.; Visualization, A.K.-J., K.H., and Z.S.; Supervision, K.H.; Funding Acquisition, K.H.; Project Administration, K.H.; Resources, T.M.T.; Data Curation, A.K. ACKNOWLEDGMENTS We thank Dr. Louise Chow for her critical reading of this manuscript. This research was supported by a UAB faculty development fund (to K.H.). Received: January 8, 2016 Revised: April 19, 2016 Accepted: August 18, 2016 Published: September 20, 2016

REFERENCES

Hu, K. (2014b). Vectorology and factor delivery in induced pluripotent stem cell reprogramming. Stem Cells Dev. 23, 1301–1315.

Anders, S., and Huber, W. (2010). Differential expression analysis for sequence count data. Genome Biol. 11, R106.

Kang, L., Yao, C., Khodadadi-Jamayran, A., Xu, W., Zhang, R., Banerjee, N.S., Chang, C.W., Chow, L.T., Townes, T., and Hu, K. (2016). The universal 3D3 antibody of human PODXL is pluripotent cytotoxic, and identifies a residual population after extended differentiation of pluripotent stem cells. Stem Cells Dev. 25, 556–568.

Anders, S., Pyl, P.T., and Huber, W. (2015). HTSeq—a Python framework to work with high-throughput sequencing data. Bioinformatics 31, 166–169. Belkina, A.C., and Denis, G.V. (2012). BET domain co-regulators in obesity, inflammation and cancer. Nat. Rev. Cancer 12, 465–477. Bolden, J.E., Tasdemir, N., Dow, L.E., van Es, J.H., Wilkinson, J.E., Zhao, Z., Clevers, H., and Lowe, S.W. (2014). Inducible in vivo silencing of Brd4 identifies potential toxicities of sustained BET protein inhibition. Cell Rep. 8, 1919–1929. Chen, G., Gulbranson, D.R., Hou, Z., Bolin, J.M., Ruotti, V., Probasco, M.D., Smuga-Otto, K., Howden, S.E., Diol, N.R., Propson, N.E., et al. (2011). Chemically defined conditions for human iPSC derivation and culture. Nat. Methods 8, 424–429. Chen, J., Guo, L., Zhang, L., Wu, H., Yang, J., Liu, H., Wang, X., Hu, X., Gu, T., Zhou, Z., et al. (2013). Vitamin C modulates TET1 function during somatic cell reprogramming. Nat. Genet. 45, 1504–1509. Dawson, M.A., Prinjha, R.K., Dittmann, A., Giotopoulos, G., Bantscheff, M., Chan, W.I., Robson, S.C., Chung, C.W., Hopf, C., Savitski, M.M., et al. (2011). Inhibition of BET recruitment to chromatin as an effective treatment for MLL-fusion leukaemia. Nature 478, 529–533. Delmore, J.E., Issa, G.C., Lemieux, M.E., Rahl, P.B., Shi, J., Jacobs, H.M., Kastritis, E., Gilpatrick, T., Paranal, R.M., Qi, J., et al. (2011). BET bromodomain inhibition as a therapeutic strategy to target c-Myc. Cell 146, 904–917. Devaiah, B.N., and Singer, D.S. (2013). Two faces of brd4: mitotic bookmark and transcriptional lynchpin. Transcription 4, 13–17. Devaiah, B.N., Lewis, B.A., Cherman, N., Hewitt, M.C., Albrecht, B.K., Robey, P.G., Ozato, K., Sims, R.J., 3rd, and Singer, D.S. (2012). BRD4 is an atypical kinase that phosphorylates serine2 of the RNA polymerase II carboxy-terminal domain. Proc. Natl. Acad. Sci. USA 109, 6927–6932.

Kanno, T., Kanno, Y., Siegel, R.M., Jang, M.K., Lenardo, M.J., and Ozato, K. (2004). Selective recognition of acetylated histones by bromodomain proteins visualized in living cells. Mol. Cell 13, 33–43. LeRoy, G., Rickards, B., and Flint, S.J. (2008). The double bromodomain proteins Brd2 and Brd3 couple histone acetylation to transcription. Mol. Cell 30, 51–60. Li, X., Zuo, X., Jing, J., Ma, Y., Wang, J., Liu, D., Zhu, J., Du, X., Xiong, L., Du, Y., et al. (2015). Small-molecule-driven direct reprogramming of mouse fibroblasts into functional neurons. Cell Stem Cell 17, 195–203. Liu, L., Xu, Y., He, M., Zhang, M., Cui, F., Lu, L., Yao, M., Tian, W., Benda, C., Zhuang, Q., et al. (2014). Transcriptional pause release is a rate-limiting step for somatic cell reprogramming. Cell Stem Cell 15, 574–588. Martı´nez-Balba´s, M.A., Dey, A., Rabindran, S.K., Ozato, K., and Wu, C. (1995). Displacement of sequence-specific transcription factors from mitotic chromatin. Cell 83, 29–38. Mertz, J.A., Conery, A.R., Bryant, B.M., Sandy, P., Balasubramanian, S., Mele, D.A., Bergeron, L., and Sims, R.J., 3rd. (2011). Targeting MYC dependence in cancer by inhibiting BET bromodomains. Proc. Natl. Acad. Sci. USA 108, 16669–16674. Parsons, G.G., and Spencer, C.A. (1997). Mitotic repression of RNA polymerase II transcription is accompanied by release of transcription elongation complexes. Mol. Cell. Biol. 17, 5791–5802. Quinlan, A.R., and Hall, I.M. (2010). BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842.

Dey, A., Nishiyama, A., Karpova, T., McNally, J., and Ozato, K. (2009). Brd4 marks select genes on mitotic chromatin and directs postmitotic transcription. Mol. Biol. Cell 20, 4899–4909.

Sarge, K.D., and Park-Sarge, O.K. (2009). Mitotic bookmarking of formerly active genes: keeping epigenetic memories from fading. Cell Cycle 8, 818–823.

Di Micco, R., Fontanals-Cirera, B., Low, V., Ntziachristos, P., Yuen, S.K., Lovell, C.D., Dolgalev, I., Yonekubo, Y., Zhang, G., Rusinova, E., et al. (2014). Control of embryonic stem cell identity by BRD4-dependent transcriptional elongation of super-enhancer-associated pluripotency genes. Cell Rep. 9, 234–247.

Shao, Z., Zhang, R., Khodadadi-Jamayran, A., Chen, B., Crowley, M.R., Festok, M.A., Crossman, D.K., Townes, T.M., and Hu, K. (2016). The acetyllysine reader BRD3R promotes human nuclear reprogramming and regulates mitosis. Nat. Commun. 7, 10869.

Dillies, M.A., Rau, A., Aubert, J., Hennequet-Antier, C., Jeanmougin, M., Servant, N., Keime, C., Marot, G., Castel, D., Estelle, J., et al.; French StatOmique Consortium (2013). A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis. Brief. Bioinform. 14, 671–683. Dobin, A., Davis, C.A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., Batut, P., Chaisson, M., and Gingeras, T.R. (2013). STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21. Esteban, M.A., Wang, T., Qin, B., Yang, J., Qin, D., Cai, J., Li, W., Weng, Z., Chen, J., Ni, S., et al. (2010). Vitamin C enhances the generation of mouse and human induced pluripotent stem cells. Cell Stem Cell 6, 71–79. Filippakopoulos, P., Qi, J., Picaud, S., Shen, Y., Smith, W.B., Fedorov, O., Morse, E.M., Keates, T., Hickman, T.T., Felletar, I., et al. (2010). Selective inhibition of BET bromodomains. Nature 468, 1067–1073. Gottesfeld, J.M., and Forbes, D.J. (1997). Mitotic repression of the transcriptional machinery. Trends Biochem. Sci. 22, 197–202. Hu, K. (2014a). All roads lead to induced pluripotent stem cells: the technologies of iPSC generation. Stem Cells Dev. 23, 1285–1300.

Smith, S.G., Sanchez, R., and Zhou, M.M. (2014). Privileged diazepine compounds and their emergence as bromodomain inhibitors. Chem. Biol. 21, 573–583. Wang, T., Chen, K., Zeng, X., Yang, J., Wu, Y., Shi, X., Qin, B., Zeng, L., Esteban, M.A., Pan, G., and Pei, D. (2011). The histone demethylases Jhdm1a/1b enhance somatic cell reprogramming in a vitamin-C-dependent manner. Cell Stem Cell 9, 575–587. Wu, T., Pinto, H.B., Kamikawa, Y.F., and Donohoe, M.E. (2015). The BET family member BRD4 interacts with OCT4 and regulates pluripotency gene expression. Stem Cell Reports 4, 390–403. Xu, Y., Wei, X., Wang, M., Zhang, R., Fu, Y., Xing, M., Hua, Q., and Xie, X. (2013). Proliferation rate of somatic cells affects reprogramming efficiency. J. Biol. Chem. 288, 9767–9778. Zaidi, S.K., Young, D.W., Montecino, M.A., Lian, J.B., van Wijnen, A.J., Stein, J.L., and Stein, G.S. (2010). Mitotic bookmarking of genes: a novel dimension to epigenetic control. Nat. Rev. Genet. 11, 583–589. Zhao, R., Nakamura, T., Fu, Y., Lazar, Z., and Spector, D.L. (2011). Gene bookmarking accelerates the kinetics of post-mitotic transcriptional re-activation. Nat. Cell Biol. 13, 1295–1304.
