Polycomb repressive complex 1.1 coordinates homeostatic and emergency myelopoiesis

  1. Yaeko Nakajima-Takagi
  2. Motohiko Oshima
  3. Junichiro Takano
  4. Shuhei Koide
  5. Naoki Itokawa
  6. Shun Uemura
  7. Masayuki Yamashita
  8. Shohei Andoh
  9. Kazumasa Aoyama
  10. Yusuke Isshiki
  11. Daisuke Shinoda
  12. Atsunori Saraya
  13. Fumio Arai
  14. Kiyoshi Yamaguchi
  15. Yoichi Furukawa
  16. Haruhiko Koseki
  17. Tomokatsu Ikawa
  18. Atsushi Iwama  Is a corresponding author
  1. Division of Stem Cell and Molecular Medicine, Center for Stem Cell Biology and Regenerative Medicine, The Institute of Medical Science, University of Tokyo, Japan
  2. Laboratory for Developmental Genetics, RIKEN Center for Integrative Medical Sciences, Japan
  3. Department of Cellular and Molecular Medicine, Chiba University, Japan
  4. Department of Stem Cell Biology and Medicine, Kyushu University, Japan
  5. Division of Clinical Genome Research,Advanced Clinical Research Center, The Institute of Medical Science, University of Tokyo, Japan
  6. Division of Immunobiology, Research Institute for Biomedical Sciences, Tokyo University of Science, Japan
  7. Laboratoty of Cellular and Molecular Chemistry, Graduate School of Pharmaceutical Sciences, University of Tokyo, Japan

Abstract

Polycomb repressive complex (PRC) 1 regulates stem cell fate by mediating mono-ubiquitination of histone H2A at lysine 119. While canonical PRC1 is critical for hematopoietic stem and progenitor cell (HSPC) maintenance, the role of non-canonical PRC1 in hematopoiesis remains elusive. PRC1.1, a non-canonical PRC1, consists of PCGF1, RING1B, KDM2B, and BCOR. We recently showed that PRC1.1 insufficiency induced by the loss of PCGF1 or BCOR causes myeloid-biased hematopoiesis and promotes transformation of hematopoietic cells in mice. Here we show that PRC1.1 serves as an epigenetic switch that coordinates homeostatic and emergency hematopoiesis. PRC1.1 maintains balanced output of steady-state hematopoiesis by restricting C/EBPα-dependent precocious myeloid differentiation of HSPCs and the HOXA9- and β-catenin-driven self-renewing network in myeloid progenitors. Upon regeneration, PRC1.1 is transiently inhibited to facilitate formation of granulocyte-macrophage progenitor (GMP) clusters, thereby promoting emergency myelopoiesis. Moreover, constitutive inactivation of PRC1.1 results in unchecked expansion of GMPs and eventual transformation. Collectively, our results define PRC1.1 as a novel critical regulator of emergency myelopoiesis, dysregulation of which leads to myeloid transformation.

Editor's evaluation

The authors present a manuscript aiming to understand the mechanism(s) underlying myeloid bias in HSCs, specifically focused on the role of Pcgf1, and therefore PRC1.1, in the regulation of hematopoiesis. The current study demonstrates that conditional deletion of Pcgf1 in HSCs causes a lineage switch from lymphoid to myeloid fates and that a key mechanism for this lineage switch is regulation of the H2AK119ub1 chromatin mark, leading to de-repression of CEBPalpha, a key transcription factor that promotes myeloid cell fate. This important work is of interest to the community of researchers interested in myeloid differentiation, lineage fate decisions in hematopoietic stem cells, and the molecular mechanisms that contribute to the initiation of myeloid malignancies. The methods are rigorous and the results convincingly support the authors' conclusions.

https://doi.org/10.7554/eLife.83004.sa0

Introduction

While lifelong hematopoiesis is considered driven by hematopoietic stem cells (HSCs) (Sawai et al., 2016; Säwen et al., 2018), recent evidence pointed out a major role for multipotent progenitors (MPPs) and lineage-committed progenitors in hematopoiesis (Busch et al., 2015; Sun et al., 2014). Hematopoietic stem and progenitor cells (HSPCs) are highly responsive to various stresses such as infection, inflammation, and myeloablation (Trumpp et al., 2010; Zhao and Baltimore, 2015), which facilitate myelopoiesis by activating HSPCs to undergo precocious myeloid differentiation and transiently amplifying myeloid progenitors that rapidly differentiate into mature myeloid cells (Hérault et al., 2017; Pietras et al., 2016). This reprogramming of HSPCs, termed ‘emergency myelopoiesis,’ serves to immediately replenish mature myeloid cells to control infection and regeneration (Manz and Boettcher, 2014). Recent evidence further suggested that uncontrolled activation of the myeloid regeneration programs results in the development of chronic inflammatory diseases and hematological malignancies (Chiba et al., 2018; Zhao and Baltimore, 2015). Emergency myelopoiesis is driven via activation of key myeloid transcriptional networks at the HSPC and myeloid progenitor cell levels (Rosenbauer and Tenen, 2007). However, the epigenetic regulatory mechanisms governing emergency myelopoiesis remained largely unknown.

Polycomb group (PcG) proteins are the key epigenetic regulators of a variety of biological processes (Piunti and Shilatifard, 2021). They comprise the multiprotein complexes, polycomb repressive complex (PRC) 1 and PRC2, which establish and maintain the transcriptional repression through histone modifications. PRC1 and PRC2 add mono-ubiquitination at lysine 119 of histone H2A (H2AK119ub) and mono-, di-, and tri-methylation at lysine 27 of histone H3 (H3K27me1/me2/me3), respectively, and cooperatively repress transcription (Blackledge et al., 2015; Iwama, 2017). PRC1 complexes are divided into subgroups (PRC1.1 to PRC1.6) according to the subtype of the Polycomb group ring finger (PCGF) subunits (PCGF1-6). PCGF2/MEL18 and PCGF4/BMI1 act as components of canonical PRC1 (PRC1.2 and 1.4, respectively) that are recruited to its target sites in a manner dependent on H3K27me3, whereas the others (PCGF1, 3, 5, and 6) constitute non-canonical PRC1 (PRC1.1, PRC1.3, PRC1.5, and PRC1.6, respectively) that are recruited independently of H3K27me3 (Blackledge et al., 2014; Gao et al., 2012; Wang et al., 2004).

PCGF4/BMI1-containing canonical PRC1 (PRC1.4) has been characterized for its role in maintaining self-renewal capacity and multipotency of HSCs (Sashida and Iwama, 2012). We and others have reported that BMI1 transcriptionally represses the loci for CDKN2A and developmental regulator genes (e.g., B cell regulators) to maintain self-renewal capacity and multipotency of HSPCs (Iwama et al., 2004; Oguro et al., 2010; Park et al., 2003). We also reported that PCGF5-containing PRC1.5 regulates global levels of H2AK119ub, but is dispensable for HSPC function (Si et al., 2016). On the other hand, we and others recently showed that PRC1.1 components, PCGF1, KDM2B, and BCOR, maintain normal hematopoiesis and suppress malignant transformation of hematopoietic cells (Andricovich et al., 2016; Isshiki et al., 2019; Tara et al., 2018). PRC1.1 consists of PCGF1, RING1A/B, KDM2B, and BCOR or BCLRL1. KDM2B binds to non-methylated CpG islands through its DNA-binding domain, thereby recruiting other PRC1.1 components (Farcas et al., 2012; He et al., 2013). PCGF1 was found to restrict the proliferative capacity of myeloid progenitor cells by downregulating Hoxa family genes in in vitro knockdown experiments (Ross et al., 2012). Correspondingly, we demonstrated that Pcgf1 loss induces myeloid-biased hematopoiesis and promotes JAK2V617F-induced myelofibrosis in mice (Shinoda et al., 2022). Bcor loss also showed myeloid-biased hematopoiesis and promoted the initiation and progression of myelodysplastic syndrome in collaboration with Tet2 loss (Tara et al., 2018). However, detailed analysis of the role for PRC1.1 in hematopoiesis, especially in the context of hematopoietic regeneration and emergency myelopoiesis, has not yet been reported.

Here, we analyzed the murine hematopoiesis in the absence of PCGF1 and uncovered critical roles of PCGF1-containing PRC1.1 in homeostatic, emergency, and malignant hematopoiesis.

Results

PCGF1 restricts myeloid commitment of HSPCs

To understand the function of PCGF1 in hematopoiesis, we crossed Pcgf1fl mice, in which exons 2–7 are floxed (Figure 1—figure supplement 1A), with Rosa26::Cre-ERT2 (Rosa26CreERT) mice (Rosa26CreERT;Pcgf1fl/fl). To delete Pcgf1 specifically in hematopoietic cells, we transplanted bone marrow (BM) cells from Rosa26CreERT control and Rosa26CreERT;Pcgf1fl/fl CD45.2 mice into lethally irradiated CD45.1 recipient mice and deleted Pcgf1 by intraperitoneal injection of tamoxifen (Figure 1A and Figure 1—figure supplement 1B). We confirmed the efficient deletion of Pcgf1 in donor-derived hematopoietic cells from the peripheral blood (PB) by genomic PCR (Figure 1—figure supplement 1C). RT-qPCR confirmed the significant reduction of Pcgf1 mRNA lacking exons 2–7 in donor-derived BM Lineage markerSca-1+c-Kit+ (LSK) HSPCs (Figure 1—figure supplement 1D). We hereafter refer to the recipient mice reconstituted with Rosa26CreERT control and Rosa26CreERT;Pcgf1fl/fl cells treated with tamoxifen as control and Pcgf1Δ/Δ mice, respectively.

Figure 1 with 4 supplements see all
PCGF1 regulates myelopoiesis but not self-renewal of hematopoietic stem and progenitor cells (HSPCs).

(A) Strategy for analyzing Pcgf1Δ/Δ hematopoietic cells. Total bone marrow (BM) cells (5 × 106) from Rosa26CreERT and Rosa26CreERT;Pcgf1fl/fl were transplanted into lethally irradiated CD45.1 recipient mice. Pcgf1 was deleted by intraperitoneal injections of tamoxifen at 4 wk post-transplantation. (B) The proportions of Mac-1+ and/or Gr-1+ myeloid cells, B220+ B cells, and CD4+ or CD8+ T cells among CD45.2+ donor-derived hematopoietic cells in the peripheral blood (PB) from control (n = 9) and Pcgf1Δ/Δ (n = 14) mice. (C) Absolute numbers of total BM cells, hematopoietic stem cells (HSCs), multipotent progenitors (MPPs), myeloid progenitors, and CLPs (ALP and BLP) in a unilateral pair of femur and tibia 4 wk after the tamoxifen injection (n = 4–5). (D) Cell cycle status of CD34LSK HSCs and CD34+LSK MPPs assessed by Ki67 and 7-AAD staining 4 wk after the tamoxifen injection. (E) In vivo limiting dilution assay. Limiting numbers of BM cells (1 × 104, 4 × 104, 8 × 104, and 2 × 105) isolated from BM of primary recipients (control and Pcgf1Δ/Δ mice after transplantation) were transplanted into sublethally irradiated secondary recipient mice with 2 × 105 of competitor CD45.1 BM cells (n = 5 each). Due to the low contribution of Pcgf1Δ/Δ HSPCs to B cells, mice with chimerism of donor myeloid and T cells more than 1% in the PB at 16 wk after transplantation were considered to be engrafted successfully, and the others were defined as non-engrafted mice. The frequencies of HSPCs that contributed to both myeloid and T cells are indicated. (F) Strategy for analyzing Pcgf1Δ/Δ hematopoietic cells. Total BM cells (2 × 106) from Rosa26CreERT and Rosa26CreERT;Pcgf1fl/fl CD45.2 mice were transplanted into lethally irradiated CD45.1 recipient mice with the same number of competitor CD45.1 BM cells. Pcgf1 was deleted by intraperitoneal injections of tamoxifen at 4 wk post-transplantation. Secondary transplantation was performed using 5 × 106 total BM cells from primary recipients at 4 mo post-intraperitoneal injections of tamoxifen. (G) The chimerism of CD45.2 donor cells in PB CD45+ leukocytes, Mac-1+ and/or Gr1+ myeloid cells, B220+ B cells, and CD4+ or CD8+ T cells in control and Pcgf1Δ/Δ mice (n = 6 each) after the tamoxifen injection. (H) The chimerism of CD45.2 donor-derived cells in BM 4 wk after the tamoxifen injection (n = 5). (I) Absolute numbers of CD45.1 and CD45.2 total BM cells, HSCs, MPPs, myeloid progenitors, and Mac-1+ mature myeloid cells in a unilateral pair of femur and tibia 4 wk after the tamoxifen injection (n = 5). Statistical significance is based on the overall number of cells. (J) The chimerism of CD45.2 donor-derived cells in PB in primary (n = 3 each) and secondary (n = 4–5) transplantation. Data are shown as the mean ± SEM. *p<0.05, **p<0.01, ***p<0.001 by the Student’s t-test. Each symbol is derived from an individual mouse. A representative of more than two independent experiments is shown.

Pcgf1Δ/Δ mice exhibited mild anemia and leukopenia, which was mainly attributed to the reduction in B cell numbers (Figure 1B, Figure 1—figure supplement 2A and B). Myeloid cell numbers in PB were relatively maintained and their proportion increased over time in Pcgf1Δ/Δ mice (Figure 1B and Figure 1—figure supplement 2B). While BM cellularity was comparable between control and Pcgf1Δ/Δ mice, Mac1+ mature myeloid cells were increased at the expense of B cells in Pcgf1Δ/Δ BM and spleen (Figure 1C and Figure 1—figure supplement 2C–E). Monocytes but not neutrophils or F4/80+ macrophages significantly increased in Pcgf1Δ/Δ BM while both neutrophils and monocytes significantly increased in Pcgf1Δ/Δ spleen (Figure 1—figure supplement 2C and D). These findings indicate that Pcgf1Δ/Δ HSPCs generate more mature myeloid cells than the control without showing evident differentiation block. Among the committed progenitor cells, the numbers of granulocyte-macrophage progenitors (GMPs) were significantly increased in Pcgf1Δ/Δ BM, whereas those of megakaryocyte-erythroid progenitors (MEPs), pre- and pro-B cells, all-lymphoid progenitors (ALPs) and B-cell-biased lymphoid progenitors (BLPs) were decreased (Figure 1C, Figure 1—figure supplement 2C, and Figure 1—figure supplement 3B). Of interest, Pcgf1Δ/Δ mice showed reduction in the numbers of HSCs and all subsets of MPPs in BM (Figure 1C and Figure 1—figure supplement 3A). The reduction in the HSPC pool size was accompanied by decreased cells in cycling phases in Pcgf1Δ/Δ HSPCs (CD34 and CD34+ LSK cells) (Figure 1D). Despite the reduction in phenotypic HSPCs, limiting dilution assays with competitive BM transplantation revealed that the numbers of HSPCs that established long-term repopulation of myeloid and T cells (B cells were excluded due to the low contribution of Pcgf1Δ/Δ HSPCs to B cells, see below) were comparable between control and Pcgf1Δ/Δ BM (Figure 1E). Extramedullary hematopoiesis was evident in the Pcgf1Δ/Δ spleen, as judged by markedly increased numbers of LSK HSPCs, GMPs, and mature myeloid cells including monocytes and neutrophils (Figure 1—figure supplement 2C, D, and F and Figure 1—figure supplement 3C). Differentiation of thymocytes in the Pcgf1Δ/Δ thymus was largely normal (Figure 1—figure supplement 2C).

To further evaluate the role of PCGF1 in hematopoiesis, we transplanted BM cells with the same number of CD45.1 wild-type (WT) competitor cells (Figure 1F). In this competitive setting, only a mild decrease was detected in the overall chimerism of CD45.2+ Pcgf1Δ/Δ cells in PB (Figure 1G). In contrast, the chimerism of Pcgf1Δ/Δ cells in myeloid cells (Mac-1+ and/or Gr1+) was markedly increased while that in B cell lineage (B220+) was decreased (Figure 1G). In BM, Pcgf1Δ/Δ cells outcompeted the competitor cells in the myeloid lineage compartments from the common myeloid progenitor (CMP) stage (Figure 1H). Since Pcgf1Δ/Δ cell showed reductions in the numbers of HSCs and MPPs in a non-competitive setting (Figure 1C), we examined the absolute numbers of test and competitor cells in this competitive repopulation. Of interest, the competitive Pcgf1Δ/Δ recipients also exhibit similar changes in BM hematopoietic cell numbers. Both CD45.2+ Pcgf1Δ/Δ and CD45.1+ WT cells were depleted in HSPC fractions in Pcgf1Δ/Δ recipients, while the total numbers of myeloid progenitors and mature myeloid cells were maintained or rather increased (Figure 1I). These findings suggest that Pcgf1Δ/Δ hematopoietic cells suppress expansion of both Pcgf1Δ/Δ and co-existing WT HSPCs through non-autonomous mechanisms. It is possible that accumulating Pcgf1Δ/Δ mature myeloid cells produce inflammatory cytokines that are suppressive to HSPCs. The underlying mechanism is an intriguing issue to be analyzed. To evaluate the impact of PCGF1 loss on long-term hematopoiesis, we harvested BM cells from primary recipient mice 4 mo after tamoxifen injection and transplanted them into secondary recipients. Pcgf1Δ/Δ cells reproduced the myeloid-biased hematopoiesis in secondary recipients (Figure 1J).

We next evaluated the capacity of Pcgf1Δ/Δ HSCs to differentiate to myeloid and lymphoid cells in culture. Pcgf1Δ/Δ HSCs displayed slower population doubling under HSPC-expanding culture conditions (Figure 1—figure supplement 4A), which is in good agreement with fewer cycling Pcgf1Δ/Δ HSPCs (Figure 1D). Nevertheless, Pcgf1Δ/Δ HSCs showed better population doubling than control cells under myeloid culture conditions (Figure 1—figure supplement 4A). On the other hand, limiting dilution assays using a co-culture system with TSt-4 stromal cells (Masuda et al., 2005) revealed that the capacity of Pcgf1Δ/Δ HSCs to produce B and T cells was declined by two- and fivefold, respectively, compared to the control (Figure 1—figure supplement 4B). The discrepancy in T cell production between in vitro and in vivo may be due to the compensatory expansion of T cells in the thymus. These results indicate that PCGF1 loss enhances myelopoiesis at the expense of lymphopoiesis. These phenotypes were similar to those of mice expressing a carboxyl-terminal truncated BCOR that cannot interact with PCGF1 (Tara et al., 2018).

PCGF1 inhibits precocious myeloid commitment of HSPCs through repression of C/EBPα, which is critical for balanced output of HSPCs

To clarify the molecular mechanisms underlying myeloid-biased differentiation of Pcgf1Δ/Δ HSPCs, we performed RNA sequence (RNA-seq) analysis of HSPCs from mice 4 wk after the tamoxifen injection. Principal component analysis (PCA) showed shifts of the transcriptomic profiles of Pcgf1Δ/Δ MPP2 and MPP3 toward pre-GMs (Figure 2A). Gene set enrichment analysis (GSEA) revealed upregulation of genes involved in myeloid cell development (Brown et al., 2006), CEBP targets (Gery et al., 2005), and genes upregulated upon C/EBPα overexpression (Loke et al., 2018) in Pcgf1Δ/Δ HSPCs compared to controls (Figure 2B and Supplementary file 1). C/EBP family are master transcription factors for myeloid differentiation (Avellino and Delwel, 2017; Rosenbauer and Tenen, 2007). RT-PCR analysis demonstrated a significant upregulation of Cepba in HSPCs, but not in GMPs (Figure 2C). C/EBPα drives myelopoiesis and antagonizes lymphoid differentiation (Fukuchi et al., 2006; Rosenbauer and Tenen, 2007; Xie et al., 2004). Disruption of Cebpa blocks the transition from CMPs to GMPs (Zhang et al., 1997). So, we evaluated the contribution of de-repressed Cebpa in Pcgf1Δ/Δ HSPCs. First, we overexpressed Cebpa in WT LSK cells and cultured them on TSt-4 stromal cells. Only twofold upregulation of Cebpa was sufficient to enhance myeloid differentiation and suppress B cell differentiation of HSPCs (Figure 2D). Conversely, we next tested whether myeloid skewing of Pcgf1Δ/Δ HSPCs could be canceled by reducing Cebpa expression. We seeded HSPCs from Rosa26CreERT, Rosa26CreERT;Pcgf1fl/fl, and Rosa26CreERT;Pcgf1fl/flCebpafl/+ mice on TSt-4 stromal cells and recombined Pcgf1fl and Cebpafl alleles by supplementation of 4-hydroxytamoxifen (4-OHT) (Figure 2—figure supplement 1A). Of note, the reduction in the levels of Cebpa expression by introducing a Cebpafl/+ allele in Pcgf1Δ/Δ LSK cells was sufficient to restore balanced production of myeloid and B cells by Pcgf1Δ/Δ HSPCs (Figure 2E). These results demonstrate that de-repression of Cebpa largely accounts for at least the myeloid-biased differentiation of Pcgf1Δ/Δ HSPCs.

Figure 2 with 1 supplement see all
Pcgf1-deficient hematopoietic stem and progenitor cells (HSPCs) undergo myeloid reprogramming.

(A) Principal component analyses (PCA) based on total gene expression obtained by RNA-seq of hematopoietic stem cells (HSCs), multipotent progenitors (MPPs), pre-GM, and granulocyte-macrophage progenitors (GMPs) from control and Pcgf1Δ/Δ mice. Magnified view of the boxed part is depicted on the right. (B) Gene set enrichment analysis (GSEA) using RNA-seq data. Summary of GSEA data of representative gene sets is shown. Normalized enrichment scores (NES), nominal p-values (NOM), and false discovery rates (FDR) are indicated. The gene sets used are indicated in Supplementary file 1. (C) Quantitative RT-PCR analysis of Cebpa in LSK cells and GMPs. Hprt1 was used to normalize the amount of input RNA. Data are shown as the mean ± SEM (n = 3). ***p<0.001 by the Student’s t-test. (D) Effects of Cebpa overexpression on HSPC differentiation in vitro. LSK cells were transduced with either control (GFP) or Cebpa retrovirus, then cultured on TSt-4 stromal cells in the presence of IL-7 (upper). The proportions of myeloid (Mac1+ and /or Gr-1+), B cells (CD19+), and others (Mac1-Gr-1-CD19-) among CD45.2+GFP+ hematopoietic cells on day 17 of culture are indicated (lower left; n = 4 each). RT-qPCR analysis of Cebpa in LSK cells transduced with control or Cebpa retrovirus on day 14 of culture (n = 3). Hprt1 was used to normalize the amount of input RNA (lower right). Each symbol is derived from an individual culture. Data are shown as the mean ± SEM. **p<0.01, ***p<0.001 by the Student’s t-test. (E) Impact of Cebpa haploinsufficiency on myeloid-biased differentiation of Pcgf1Δ/Δ HSPCs. LSK cells from Rosa26CreERT, Rosa26CreERT;Pcgf1fl/fl and Rosa26CreERT;Pcgf1fl/fl;Cebpafl/+ mice were treated with 4-OHT (200 nM) for 2 d in culture to delete Pcgf1 and Cebpa. Cells were further cultured on TSt-4 stromal cells in the presence of IL-7 (upper). The proportions of myeloid (Mac1+ and /or Gr-1+), B cells (CD19+), and others (Mac1-Gr-1-CD19-) among CD45.2+GFP+ hematopoietic cells on day 17 of culture are indicated (lower; n = 4 each;* versus control). Each symbol is derived from an individual culture (lower left). RT-qPCR data of Cebpa in LSK cells on day 14 of culture (n = 3). Hprt1 was used to normalize the amount of input RNA (lower right). Data are shown as the mean ± SEM. *p<0.05, **p<0.01, ***p<0.001 by the Student’s t-test (lower left) or the one-way ANOVA (lower right).

We next attempted to rescue the myeloid-biased differentiation of Pcgf1Δ/Δ HSPCs by exogenous Pcgf1 or a canonical PRC1 gene Bmi1/Pcgf4. We transduced Rosa26CreERT;Pcgf1fl/fl HSPCs to induce Pcgf1 or Bmi1 expression, transplanted them into lethally irradiated mice, and deleted endogenous Pcgf1 (Figure 2—figure supplement 1B). The myeloid skew in Pcgf1Δ/Δ PB leukocytes was completely prevented by ectopic expression of Pcgf1 but not of Bmi1 (Figure 2—figure supplement 1B), highlighting distinct roles of non-canonical PRC1.1 and canonical PRC1 in hematopoietic differentiation.

We also noticed that the E2F targets (Ishida et al., 2001) were downregulated in Pcgf1Δ/Δ HSPCs (Figure 2B), which may underlie the disturbed cell cycle progression and delayed proliferation observed in Pcgf1Δ/Δ HSPCs (Figure 1D and Figure 1—figure supplement 4A). C/EBPα represses E2F-mediated transcription (D’Alo’ et al., 2003; Slomiany et al., 2000), inhibits HSC cell cycle entry (Ye et al., 2013; Zhang et al., 2004), and promotes precocious IL-1β-driven emergency myelopoiesis (Higa et al., 2021). Thus, upregulated Cebpa upon PCGF1 loss may inhibit cell cycle and promote myeloid commitment of HSPCs.

Deletion of Pcgf1 affects levels of H2AK119ub1

To understand how PCGF1 loss affects H2AK119ub1 status in HSPCs, we performed chromatin immunoprecipitation followed by sequencing (ChIP-seq) analysis using control and Pcgf1Δ/Δ HSPCs. Since none of the anti-PCGF1 antibodies were suitable for ChIP analysis, we used 3×Flag-PCGF1-expressing BM LK cells obtained by retrovirally transducing LSK cells and transplanting them to lethally irradiated mice. We defined ‘PRC1 targets’ and ‘PRC2 targets’ as genes with H2AK119ub1 and H3K27me3 enrichment greater than twofold over the input signals in control LSK cells at promoter regions (transcription start site [TSS] ± 2.0 kb), respectively (Supplementary file 2). GSEA revealed that both PRC1 and PRC2 targets were upregulated in Pcgf1Δ/Δ HSPCs and GMPs (Figure 2B). K-means clustering divided PRC1 targets into two clusters depending on the levels of H2AK119ub1 and H3K27me3. Cluster 1 genes (1835 RefSeq ID genes) were marked with high levels of H2AK119ub1 and H3K27me3 at promoter regions, while cluster 2 genes (2691 RefSeq ID genes) showed moderate levels of H2AK119ub1 and H3K27me3 (Figure 3A). PCGF1 showed stronger binding to the promoters of cluster 2 genes than cluster 1 genes (Figure 3A). Interestingly, only cluster 2 genes showed moderate but significant reductions in H2AK119ub1 and H3K27me3 levels in Pcgf1Δ/Δ HSPCs (Figure 3B). The loss of PCGF1 was also significantly associated with de-repression of PRC1 target genes in clusters 1 and 2 (Figure 3B). These results suggest that cluster 2 genes are the major targets of PCGF1-containing PRC1.1 and Cebpa was included in cluster 2 genes. Bivalent genes, which are enriched for developmental regulator genes marked with both active and repressive histone marks (H3K4me3 and H3K27me3, respectively, mostly with H2AK119ub1), are classical targets of canonical PRC1 and PRC2 (Bernstein et al., 2006; Ku et al., 2008) and are implicated in multipotency of HSPCs (Oguro et al., 2010). Of note, bivalent genes defined by our previous ChIP-seq data of HSPCs (Aoyama et al., 2018; Supplementary file 2) were more enriched in cluster 1 genes than cluster 2 genes (Figure 3C).

PCGF1 regulates local H2AK119ub1 levels in hematopoietic stem and progenitor cells (HSPCs).

(A) K-means clustering of 3×FLAG-PCGF1, H2AK119ub1, and H3K27me3 ChIP peaks around transcription start site (TSS) (±8.0 kb) of PRC1 target genes. The average levels of chromatin immunoprecipitation (ChIP) peaks in each cluster are plotted in upper columns. (B) Box-and-whisker plots showing H2AK119ub1, H3K27me3, and transcription levels of genes in PRC1 targets (clusters 1 and 2) and non-PRC1 targets in control and Pcgf1Δ/Δ LSK cells. Boxes represent 25–75 percentile ranges. The whiskers represent 10–90 percentile ranges. Horizontal bars represent medians. Mean values are indicated by ‘+’. **p<0.01; ***p<0.001 by the Student’s t-test. (C) Proportion of bivalent genes in PRC1 targets (clusters 1 and 2) and non-PRC1 targets in LSK cells. Bivalent genes were defined using our previous ChIP-seq data of wild-type LSK cells (Aoyama et al., 2018). (D) Scatter plots showing the correlation of the fold enrichment values against the input signals (ChIP/Input) (TSS ± 2 kb) of H2AK119ub1 between control and Pcgf1Δ/Δ LSK cells. PRC1 targets are indicated in a green box. (E) Snapshots of Flag-PCGF1 and H2AK119ub1 ChIP signals at the Cebpa and Cebpe gene loci. (F) ChIP qPCR assays for H2AK119ub1 at the Cebpa promoter in control and Pcgf1Δ/Δ LSK cells. The relative amounts of immunoprecipitated DNA are depicted as a percentage of input DNA. Data are shown as the mean ± SEM (n = 3). ***p<0.001 by the Student’s t-test. (G) Box-and-whisker plots showing ATAC signal levels at proximal promoters (TSS ± 2 kb) in Pcgf1Δ/Δ CD135-LSK cells relative to those in control cells. The data of all ATAC peaks (n = 18,417), PCGF1 target genes (TG) (n = 670), and non-PCGF1 TG (n = 17,747) are shown. Boxes represent 25–75 percentile ranges. The whiskers represent 10–90 percentile ranges. Horizontal bars represent medians. Mean values are indicated by red crosses. ***p<0.001 by the one-way ANOVA. (H) Top DNA motifs identified in ATAC peaks at proximal promoters (TSS ± 2 kb) positively or negatively enriched in Pcgf1Δ/Δ CD135-LSK cells compared to corresponding controls.

We then defined ‘PCGF1 targets’ whose H2AK119ub1 levels were decreased by Pcgf1 deletion greater than twofold at promoter regions in HSPCs (997 RefSeq ID genes; Supplementary file 2). We found that Cebpa and Cebpe were included in PCGF1 targets, showed reductions in H2AK119ub1 levels (Figure 3D and E), and were de-repressed in expression in Pcgf1Δ/Δ LSK cells (Figure 2C and Figure 2—figure supplement 1C). ChIP-qPCR confirmed a significant reduction in H2AK119ub1 levels at the promoter region of Cebpa in Pcgf1Δ/Δ HSPCs (Figure 3F). These results indicate that the deletion of Pcgf1 compromises PRC1.1 function and causes precocious activation of key myeloid regulator genes in HSPCs.

To clarify whether the Pcgf1 deletion has any impact on the chromatin accessibility in HSPCs, we performed an assay for transposase accessible chromatin with high-throughput sequencing (ATAC-seq) analysis using CD135LSK HSPCs, which include HSCs and MPP1-3 but not lymphoid-primed MPP4. ATAC-seq profiles open chromatin regions enriched for transcriptional regulatory regions, such as promoters and enhancers. ATAC peaks were significantly enriched at the promoter regions of PCGF1 target genes, but not of PCGF1 non-target genes, in Pcgf1Δ/Δ HSPCs compared to the corresponding controls (Figure 3G), further validating de-repression of PCGF1 targets upon the deletion of Pcgf1. Motif analysis of ATAC peaks in the proximal promoter regions (TSS ± 2 kb) revealed that the CTCF motif, which has been reported to be associated with differentiation of HSCs (Buenrostro et al., 2018; Takayama et al., 2021), was highly enriched in Pcgf1Δ/Δ HSPCs (Figure 3H). Interestingly, the other top-ranked motifs were related to stress response transcription factors, such as Bach family (Bach1 and 2), CNC family (Nrf2 and NF-E2), AP1 family, and Atf3 (Figure 3H and Supplementary file 3). In contrast, the binding motifs for transcription factors essential for T and B cell commitment, including HEB and E2A (de Pooter and Kee, 2010), were negatively enriched in Pcgf1Δ/Δ HSPCs (Figure 3H). Together with the significant upregulation of Cebpa and Cebpe in Pcgf1Δ/ΔHSPCs (Figure 2C and Figure 2—figure supplement 1C), these results further support the notion that PRC1.1 deficiency caused precocious activation of myeloid differentiation program at the expense of lymphoid differentiation program in HSPCs.

PCGF1 inhibition facilitates emergency myelopoiesis

Myeloid-biased hematopoiesis in Pcgf1-deficient mice reminded us of the myeloproliferative reactions caused by emergencies such as regeneration (Manz and Boettcher, 2014). Individual GMPs scatter throughout the BM in the steady state, while expanding GMPs evolve into GMP clusters during regeneration, which, in turn, differentiate into granulocytes. Inducible activation of β-catenin and Irf8 controls the formation and differentiation of GMP clusters, respectively (Hérault et al., 2017). Immunofluorescence analyses of BM sections readily detected GMP clusters in steady-state Pcgf1Δ/Δ BM (Figure 4A), which was reminiscent of those observed during regeneration after 5-fluorouracil (5-FU) treatment (Figure 4—figure supplement 1A). To address whether PCGF1 also regulates myelopoiesis at the GMP level during regeneration, we challenged control and Pcgf1Δ/Δ mice with a single dose of 5-FU (Figure 4B). Consistent with the previous report (Hérault et al., 2017), control mice showed transient expansion of BM HSPCs and GMPs around day 14 and subsequent burst of circulating PB myeloid cells around day 21. In sharp contrast, Pcgf1Δ/Δ mice displayed sustained GMP expansion until day 28 without efficient production of PB myeloid cells, leading to the accumulation of excess GMPs (Figure 4C).

Figure 4 with 1 supplement see all
PCGF1 negatively regulates granulocyte-macrophage progenitor (GMP) self-renewal.

(A) Immunofluorescence staining of bone marrow (BM) sections from control and Pcgf1Δ/Δ mice. Magnified images are depicted in the middle panels. Dotted lines denote clusters of GMP (LinSca-1CD41IL-7Rαc-Kit+FcγR+) (c-Kit, red; FcγR, blue; merged, purple). The right panel shows c-Kit+FcγR+ purple cell area in BM sections relative to that in control. Data are shown as the mean ± SEM (n = 3). (B) Strategy to analyze emergency myelopoiesis induced by one shot of 5-FU (150 mg/kg). (C) Absolute numbers of total BM cells, LSK cells, GMPs, and Mac-1+ myeloid cells in a unilateral pair of femur and tibia and Mac-1+ myeloid cells in peripheral blood (PB) at the indicated time points post-5-FU injection. Data are shown as the mean ± SEM (n = 4;* versus day 0 for each genotype). Circles represent the data from individual mice. (D) A Venn diagram showing the overlap between upregulated differentially expressed genes (DEGs) in 5-FU treated GMPs on day 8 and upregulated DEGs in Pcgf1Δ/Δ GMPs. (E) Quantitative RT-PCR analysis of Hoxa9 in LSK cells and GMPs. Hprt1 was used to normalize the amount of input RNA. Data are shown as the mean ± SEM (n = 3). (F) Chromatin immunoprecipitation (ChIP) qPCR assays for H2AK119ub1 at the Hoxa9 locus in GMPs from WT mice on days 0, 10, and 14 post-5-FU treatment. The relative amounts of immunoprecipitated DNA are depicted as a percentage of input DNA. Data are shown as the mean ± SEM (n = 3). (G) Fold changes in H2AK119ub1 levels in Pcgf1Δ/Δ GMPs relative to control GMPs at the promoters of non-DEGs and DEGs up- and downregulated in day 8 GMPs compared to day 0 GMPs post-5-FU treatment. (H) UMAP plots illustrating the identification of cell clusters based on single-cell transcriptomic profiling of control and Pcgf1Δ/Δ myeloid progenitors (LinSca-1c-Kit+). (I) Principal component analysis (PCA) plots of control and Pcgf1Δ/Δ GMPs individually and in combination. (J) UMAP and violin plots showing expression of Hoxa9, Irf8, Csf1r, and Il-6ra in control and Pcgf1Δ/Δ myeloid progenitors. (K) Gene Ontology and pathway enrichment analyses using DEGs in the indicated clusters. (L) Proportion of common myeloid progenitors (CMPs), megakaryocyte-erythroid progenitors (MEPs), steady-state GMPs (ssGMPs), and self-renewing GMPs (srGMPs) in control and Pcgf1Δ/Δ myeloid progenitors. *p<0.05; **p<0.01; ***p<0.001 by the Student’s t-test.

GMPs can be divided into steady-state GMPs (ssGMP) and self-renewing GMPs (srGMP), the latter of which transiently increase during regeneration (Hérault et al., 2017). We performed RNA-seq analysis of GMPs isolated from 5-FU-treated WT mice at various time points (Figure 4—figure supplement 1B and C), and defined differentially expressed genes (DEGs) on day 8 after 5-FU treatment since srGMPs reportedly most expand on that day (Figure 4—figure supplement 1B and C, Supplementary file 4; Hérault et al., 2017). Of note, a significant portion of upregulated DEGs in day 8 5-FU-treated GMPs were also upregulated in Pcgf1Δ/Δ GMPs (Figure 4D and Supplementary file 4). These overlapping genes included Hoxa9, a PRC1.1 target (Figure 4—figure supplement 1D; Ross et al., 2012; Shinoda et al., 2022; Tara et al., 2018), which is highly expressed in srGMPs (Hérault et al., 2017). RT-qPCR confirmed significantly higher expression of Hoxa9 in Pcgf1Δ/Δ GMPs than control GMPs (Figure 4E). Correspondingly, 5-FU treatment transiently decreased H2AK119ub1 levels at Hoxa9 locus around day 10 (Figure 4F). ChIP-seq analysis also revealed significant reductions in H2AK119ub1 levels at promoters of upregulated DEGs in day 8 5-FU-treated GMPs in Pcgf1Δ/Δ GMPs (Figure 4G). These results suggest that transient inhibition of PRC1.1 de-represses genes critical to expand srGMP during myeloid regeneration, although the expression of PRC1.1 genes remained largely unchanged during regeneration (Figure 4—figure supplement 1E).

To better understand the role of PRC1.1 in myeloid progenitors, we performed single-cell RNA-seq (scRNA-seq) of LinSca-1c-Kit+ myeloid progenitors from control and Pcgf1Δ/Δ mice at steady state. We used data from 6171 control and 6198 Pcgf1Δ/Δ single cells and identified 10 major clusters based on dimension reduction by UMAP (Figure 4H). Functional annotation of respective UMAP clusters using previously reported myeloid progenitor cell gene expression profiles (Nestorowa et al., 2016) assigned clusters 0, 2, and 4 to GMPs (Figure 4H). PCA subdivided GMPs into two major groups (Figure 4I). These two groups exhibited distinct expression profiles of Hoxa9, Irf8, Csf1r, and Il6ra, key genes differentially expressed between ssGMPs (Hoxa9lo, Irf8hi, Csf1rhi, Il6rahi) and srGMPs (Hoxa9hi, Irf8lo, Csf1rlo, Il6ralo) (Hérault et al., 2017), and we classified clusters 2 and 4 as ssGMPs and cluster 0 as srGMPs (Figure 4J). Gene Ontology and pathway enrichment analyses using DEGs (cluster 2 or 4 versus cluster 0) revealed that clusters 2 and 4 represented more mature myeloid cell populations than cluster 0 (Figure 4K). As expected, Pcgf1Δ/Δ myeloid progenitors had a greater proportion of total GMPs including srGMPs than controls (Figure 4L). Of note, the frequency of srGMPs was also increased in Pcgf1Δ/Δ myeloid progenitors (Figure 4L). These results indicate that PRC1.1 restricts expansion of self-renewing GMPs and suggest that transient PRC1.1 inhibition allows for temporal amplification of GMPs and their subsequent differentiation to mature myeloid cells.

PCGF1 restricts GMP self-renewal network

To further investigate the mechanism by which PCGF1 regulates GMPs, we took advantage of in vitro culture experiments. Remarkably, Pcgf1Δ/Δ GMPs displayed better proliferation than control GMPs under myeloid-expanding culture conditions (Figure 5A). Moreover, while comparable numbers of cells expressing immunophenotypic GMP markers (CD34+FcγR+c-Kit+Sca-1-Lineage-) (immunophenotypic GMPs) were produced by control and Pcgf1Δ/Δ HSCs on day 7 of culture, control HSCs showed a rapid decline in immunophenotypic GMP production afterward but Pcgf1Δ/Δ HSCs persistently produced immunophenotypic GMPs until day 23 (Figure 5B). Of interest, differentiation of the expanded Pcgf1Δ/Δ GMPs was largely blocked, as indicated by reduced Mac1+ differentiated myeloid cells/GMP ratios until day 19 (Figure 5—figure supplement 1A), suggesting that Pcgf1Δ/Δ GMPs underwent enhanced self-renewal rather than differentiation. However, Pcgf1Δ/Δ GMPs did not lose its differentiation potential to mature myeloid cells and did not show evident differentiation block, with more Mac1+ cells ultimately generated from HSC cultures than control GMPs (Figure 5—figure supplement 1A). This was also consistent that the number of mature myeloid cells expressing myeloid differentiation markers increased in Pcgf1Δ/Δ BM and spleen (Figure 1—figure supplement 2C and D). Furthermore, Pcgf1Δ/Δ HSPCs showed remarkably sustained colony formation activity upon serial replating with myeloid cytokines, which is in line with the elevated self-renewing activity of Pcgf1Δ/Δ GMPs (Figure 5C).

Figure 5 with 1 supplement see all
PCGF1 restricts self-renewal of granulocyte-macrophage progenitors (GMPs) by attenuating Hoxa9 expression.

(A) Growth of control and Pcgf1Δ/Δ GMPs in culture. Cells were cultured in triplicate under myeloid culture condition-2 (25 ng/mL SCF, TPO, Flt3L, and IL-11 and 10 ng/mL IL-3 and GM-CSF). Data are shown as the mean ± SD (n = 3). (B) Growth of control and Pcgf1Δ/Δ hematopoietic stem cells (HSCs) under myeloid culture condition-2 (25 ng/mL SCF, TPO, Flt3L, and IL-11 and 10 ng/mL IL-3 and GM-CSF). Cells were cultured in triplicate. The proportion of GMPs in culture is depicted on the right panel. Data are shown as the mean ± SD (n = 3). (C) Replating assay data. 700 LSK cells were plated in a methylcellulose medium containing 20 ng/mL of SCF, TPO, IL-3, and GM-CSF. After 10 d of culture, colonies were counted and pooled, and 1 × 104 cells were then replated in the same medium every 7 d. Data are shown as the mean ± SEM (n = 3). (D) Proportion of immunophenotypic GMPs with nuclear β-catenin in control and Pcgf1Δ/Δ immunophenotypic GMPs in HSC culture on day 16 in (B). Representative immunofluorescent signals of β-catenin in control immunophenotypic GMPs are shown on the right panel. Data are shown as the mean ± SEM (n = 3). (E) Quantitative RT-PCR analysis of Hoxa9, Irf8, Csf1r, and Il-6ra in sorted control and Pcgf1Δ/Δ immunophenotypic GMPs in HSC culture in (B) at the indicated time points. Hprt1 was used to normalize the amount of input RNA. Data are shown as the mean ± SEM (n = 3). (F) Gene set enrichment analysis (GSEA) using RNA-seq data. The gene sets used are indicated in Supplementary file 1. (G) Growth of mock control and Hoxa9-expressing LSK cells. LSK cells transduced with a Hoxa9 retrovirus harboring mCherry marker gene were cultured in triplicate under myeloid culture condition-2 (25 ng/mL SCF, TPO, Flt3L, and IL-11 and 10 ng/mL IL-3 and GM-CSF). The proportion of GMPs in culture is depicted on the right panel. Data are shown as the mean ± SD (n = 4). (H) Proportion of GMPs with nuclear β-catenin in mock control and Hoxa9-expressing GMPs in LSK culture on day 12 in (G). Data are shown as the mean ± SEM (n = 5–6). (I) Model of the molecular network controlling GMP self-renewal and differentiation. *p<0.05; **p<0.01; ***p<0.001 by the Student’s t-test (A–D, H, and G) or the one-way ANOVA (E). Each symbol is derived from an individual culture.

srGMPs have increased levels of nuclear β-catenin, which is known to confer aberrant self-renewal features to leukemic GMPs (Wang et al., 2010) and directly suppresses Irf8 expression (Hérault et al., 2017). The proportion of nuclear β-catenin was significantly increased in Pcgf1Δ/Δ GMPs at later time points of culture when GMPs in control culture shrunk but GMPs in Pcgf1Δ/Δ culture kept expanding (Figure 5B and D). Pcgf1Δ/Δ GMPs in culture possessed a transcriptional profile typical to srGMPs; upregulation of Hoxa9 and downregulation of Irf8, Csf1r, and Il6ra (Figure 5E). GSEA revealed activation of the Wnt/β-catenin pathway (Shooshtarizadeh et al., 2019) and downregulation of the Irf8 targets (Kubosaki et al., 2010) in Pcgf1Δ/Δ GMPs (Figure 5F). We hypothesized that Hoxa9, a direct target of PCGF1, could have a role in the GMP self-renewal network. Overexpression of Hoxa9 in HSPCs significantly enhanced their growth and induced persistent production of GMPs for a long period (Figure 5G and Figure 5—figure supplement 1B). Most Hoxa9-overexpressing GMPs had nuclear β-catenin (Figure 5H). These results indicate that HoxaA9 can reinforce activation of β-catenin, thus placing Hoxa9 as a component of the GMP self-renewal network and PCGF1-PRC1.1 as a negative regulator of this network (Figure 5I). The promoter region of the Ctnnb1 gene contains at least one predicted sequence for Hoxa9 binding (CTTATAAATCG) (data not shown). We performed qPCR analysis using Hoxa9 overexpression samples at day 12 of culture, in which nuclear localization of β-catenin was observed (Figure 5H). Ctnnb1 gene expression was slightly but significantly upregulated in Hoxa9 overexpression samples compared to mock control (Figure 5—figure supplement 1C). These results raise the possibility that Hoxa9 is a direct transcriptional activator of the Ctnnb1 gene, but this warrants further analysis.

Constitutive PCGF1 loss promotes malignant transformation

In leukemia, GMP clusters are constantly produced owing to persistent activation of the myeloid self-renewal network and a lack of termination cytokines that normally restore HSC quiescence (Hérault et al., 2017). A significant portion of Pcgf1Δ/Δ mice, which exhibit constant production of GMP clusters, developed lethal myeloproliferative neoplasms (MPN) with severe anemia and massive accumulation of mature myeloid cells in PB, BM, and spleen (Figure 6A–E). Morphological analysis of BM and spleen sections revealed accumulation of mature myeloid cells but no obvious differentiation block like acute myeloid leukemia (AML) was observed (Figure 6F). Indeed, AML was not observed in long-term observation of Pcgf1Δ/Δ mice. It is assumed that the epigenetic status that allows GMP self-renewal was enforced over time in the absence of Pcgf1, leading to enhanced production of mature myeloid cells that mimic MPN. A part of Pcgf1Δ/Δ mice also developed lethal T-cell acute lymphoblastic leukemia (T-ALL) (Figure 6A and G) like Bcor mutant mice (Tara et al., 2018). These results indicate that constitutive activation of the GMP self-renewal network in Pcgf1Δ/Δ mice serves to promote malignant transformation. Taken together, these results highlight the importance of PRC1.1-dependent suppression of the myeloid self-renewing network to prevent malignant transformation.

Development of lethal myeloproliferative neoplasm in Pcgf1Δ/Δ mice.

(A) Kaplan–Meier survival curves of control (n = 7) and Pcgf1Δ/Δ (n = 25) mice after the tamoxifen injection. (B) White blood cell (WBC) and hemoglobin (Hb) in peripheral blood (PB) from control (n = 8) and moribund Pcgf1Δ/Δ myeloproliferative neoplasms (MPN) mice (n = 6). Bars indicate median values. (C) Absolute numbers of total bone marrow (BM) cells and spleen weight in control (n = 8) and moribund Pcgf1Δ/Δ MPN mice (n = 6). (D) The proportions of Mac-1+ and/or Gr-1+ myeloid cells, B220+ B cells, and CD4+ or CD8+ T cells in PB and BM in control (n = 8) and moribund Pcgf1Δ/Δ MPN mice (n = 6). Data are shown as the mean ± SEM. (E) Representative flow cytometric profiles of PB, BM, and spleen of control and moribund Pcgf1Δ/Δ MPN mice. The percentages of gated populations over CD45.2+ live cells are indicated. (F) Representative histology of BM and spleen from control and moribund Pcgf1Δ/Δ MPN mice observed by hematoxylin-eosin staining (left). The high-power field images of BM from control and moribund Pcgf1Δ/Δ MPN mice observed by hematoxylin-eosin staining (right). (G) Representative flow cytometric profiles of thymus from control mice and moribund Pcgf1Δ/Δ T-ALL mice. (H) Model for the stage-specific roles of non-canonical PRC1 in hematopoietic differentiation. *p<0.05; **p<0.01; ***p<0.001 by the Student’s t-test. Each symbol is derived from an individual mouse.

Discussion

In this study, we demonstrated that PCGF1 contributes to balanced hematopoiesis by restricting precocious myeloid commitment of HSPCs and expansion of myeloid progenitors while its inhibition promotes emergency myelopoiesis and myeloid transformation. These findings present a sharp contrast with PCGF4/BMI1 essential for self-renewal of HSCs (Iwama et al., 2004; Oguro et al., 2006; Park et al., 2003) and underscore distinct functions between canonical PRC1 and non-canonical PRC1.1 in hematopoiesis (Figure 6H).

PcG and trithorax group proteins mark developmental regulator gene promoters with bivalent histone domains to keep them poised for activation in ES cells (Bernstein et al., 2006). We previously reported that canonical PRC1 reinforces bivalent domains at the B cell regulator genes, Ebf1 and Pax5, to maintain B cell lineage commitment poised for activation in HSPCs (Oguro et al., 2010). In contrast, PCGF1 appeared to target non-bivalent PRC1 target genes marked with moderate levels of H2AK119ub1 and H3K27me3. Among these, PCGF1 targets myeloid regulator genes, such as Cebpa, thereby negatively regulating myeloid commitment. Our findings indicate that canonical and non-canonical PRC1 restrict the lymphoid and myeloid commitment of HSPCs, respectively, by targeting different transcriptional programs of differentiation, thereby fine-tuning the balance of HSPC commitment (Figure 6H). Although there might be considerable functional redundancy between canonical and non-canonical PRC1 complexes, our results uncovered a unique function of PRC1.1 in the lineage commitment of HSPCs. The reduction in the levels of Cebpa expression by introducing a Cebpafl/+ allele in Pcgf1Δ/Δ LSK cells was sufficient to restore balanced production of myeloid and B cells by Pcgf1Δ/Δ HSPCs in culture (Figure 2E). However, we could not confirm these data in mice probably due to inefficient deletion of Cebpa using a Cre-ERT system (data not shown). The real impact of de-repressed Cebpa in myeloid-biased differentiation of Pcgf1Δ/Δ HSPCs requires further validation in vivo.

Myeloid-biased output from HSPCs is one of the hallmarks of emergency hematopoiesis (Trumpp et al., 2010; Zhao and Baltimore, 2015). In mouse models of regeneration, myeloid-biased MPP2 and MPP3 are transiently overproduced, suggesting that HSCs produce functionally distinct lineage-biased MPPs to adapt blood production to hematopoietic demands (Pietras et al., 2015). In the present study, we found that Pcgf1-deficient hematopoiesis recapitulates sustained emergency myelopoiesis, although the production of circulating myeloid cells was not enhanced. Expanding GMPs, GMP clusters during regeneration, which, in turn, differentiate into granulocytes (Hérault et al., 2017). Of note, PCGF1 loss induced constitutive GMP cluster formation at steady state and sustained GMP expansion in mice after myeloablation and in culture. Correspondingly, Pcgf1-deficient mice had a greater number of self-renewing GMPs than control mice. This unique phenotype may implicate the importance of transient but not constitutive PCGF1 repression for proper myeloid regeneration. β-catenin and Irf8 constitute an inducible self-renewal progenitor network controlling GMP cluster formation, with β-catenin directly suppressing Irf8 expression while restoration of Irf8 expression terminating the self-renewal network and inducing GMP differentiation (Hérault et al., 2017; Figure 5I). Hoxa9, which is upregulated in srGMPs, is one of the PRC1.1 targets in myeloid progenitors (Figure 4—figure supplement 1D; Ross et al., 2012; Shinoda et al., 2022; Tara et al., 2018). We demonstrated that Hoxa9 expression activates β-catenin and promotes GMP self-renewal, identifying Hoxa9 as a component of the GMP self-renewal network. Of note, PRC1.1 is transiently inhibited to de-repress such GMP self-renewal network genes. This transient nature of PRC1.1 inhibition allows for srGMP expansion and GMP cluster formation followed by proper differentiation of expanded GMPs. As expression levels of PRC1.1 components remained unchanged during hematopoietic regeneration, non-canonical PRC1.1 activity could be modulated by post-translational modifications in response to extracellular stimuli like canonical PRC1 (Banerjee Mustafi et al., 2017; Liu et al., 2012; Nacerddine et al., 2012; Voncken et al., 2005). How extrinsic signals modulate PRC1.1 functions to regulate myelopoiesis remains an important question.

The molecular machineries that drive emergency myelopoiesis are often hijacked by transformed cells (Hérault et al., 2017). A significant portion of Pcgf1-deficient mice eventually developed lethal MPN after a sustained myeloproliferative state. These findings indicate that PRC1.1 functions as a critical negative regulator of myeloid transformation. Among the components of PRC1.1, BCOR and BCLRL1, but not PCGF1 are targeted by somatic gene mutations in various hematological malignancies, including myelodysplastic syndrome (MDS), chronic myelomonocytic leukemia (CMML), and acute myeloid leukemia (AML) (Isshiki and Iwama, 2018). We reported that mice expressing a carboxyl-terminal truncated BCOR, which cannot interact with PCGF1, showed myeloid-biased hematopoiesis like Pcgf1-deficient mice. Importantly, HSPCs in these mice showed a growth advantage in the myeloid compartment, which was further enhanced by the concurrent deletion of Tet2, leading to the development of lethal MDS (Tara et al., 2018). De-repression of myeloid regulator genes, such as Cebp family and Hoxa cluster genes, were also detected in Bcor mutant progenitor cells (Tara et al., 2018). These findings also support the idea that PRC1.1 restricts myeloid transformation by transcriptionally repressing aberrant activation of myeloid regeneration programs.

Collectively, our findings highlight a critical role of PRC1.1 in coordinating steady-state and emergency hematopoiesis and preventing malignant transformation. They also suggest that transient inhibition of PRC1.1 would be a novel approach to temporarily induce emergency myelopoiesis and enhance myeloid cell supply while avoiding the potential risk for malignant transformation.

Materials and methods

Mice

Wild-type mice (C57BL/6) and Rosa::Cre-ERT2 mice were purchased from the Japan SLC and TaconicArtemis GmbH, respectively. Pcgf1fl and Cebpafl mice were kindly provided by Haruhiko Koseki and Daniel G. Tenen, respectively, and previously reported (Almeida et al., 2017; Zhang et al., 2004). All experiments using mice were performed in accordance with our institutional guidelines for the use of laboratory animals and approved by the Review Board for Animal Experiments of Chiba University (approval ID: 30-56) and the University of Tokyo (approval ID: PA18-03).

Bone marrow transplantation

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To generate hematopoietic cell-specific Pcgf1 KO mice, we transplanted total BM cells (5 × 106) from Rosa26CreERT and Rosa26CreERT;Pcgf1fl/fl mice into lethally irradiated (9.5 Gy) CD45.1 recipient mice. For competitive bone marrow transplantation assay, we transplanted total BM cells (2 × 106) from CD45.2 donor mice with CD45.1+ competitor total BM cells (2 × 106) into lethally irradiated (9.5 Gy) CD45.1 recipient mice. To induce Cre activity, transplanted mice were injected with 100 μL of tamoxifen (Sigma-Aldrich) dissolved in corn oil (Sigma-Aldrich) at a concentration of 10 mg/mL intraperitoneally once a day for five consecutive days 4 wk after transplantation.

Locus-specific genotyping of Pcgf1, Cebpa, and Rosa::Cre-ERT

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To detect Pcgf1fl, Pcgf1Δ, Cebpafl, CebpaΔ, and Rosa::Cre-ERT PCR reactions were performed using the specific oligonucleotides. The oligonucleotide sequences used are shown in Supplementary file 5.

5-FU challenge

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8–12-week-old wild-type mice or control and Pcgf1Δ/Δ were injected with 300 μL PBS or 150 mg/kg (3.75 mg per 25 g body weight mouse) 5-FU (Kyowa KIRIN) dissolved in 300 μL PBS intraperitoneally once.

Flow cytometry analyses and antibodies

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The monoclonal antibodies recognizing the following antigens were used in flow cytometry and cell sorting: CD45.1 (A20), CD45.2 (104), Gr-1 (RB6-8C5), CD11b/Mac-1 (M1/70), Ter-119 (TER-119), B220 (RA3-6B2), CD127/IL-7R (SB/119), CD4 (GK1.5), CD8a (53–6.7), CD117/c-Kit (2B8), Sca-1 (D7), CD34 (RAM34), CD150 (TC15-12F12.2), CD48 (HM48-1), CD135 (A2F10), CD16/32/FcγRII-III (93), CD41 (eBioMWReg30), CD105 (MJ7/18), Ly6D (49-H4), Ly6G (HK1.4), Ly6G (1A8), F4/80 (BM8), lineage mixture (Gr-1, Mac-1, Ter-119, CD127/IL-7R, B220, CD4, CD8α), and lineage mixture for CLP (Gr-1, Mac-1, Ter-119, B220, CD4, CD8α). Monoclonal antibodies were purchased from BioLegend, Tonbo Biosciences, Thermo Fisher Scientific, or BD Biosciences. Dead cells were eliminated by staining with 0.5 μg/mL propidium iodide (Sigma-Aldrich). All flow cytometric analyses and cell sorting were performed on FACSAria IIIu, FACSCanto II, and FACSCelesta (BD Biosciences). Cell surface protein expression used to define hematopoietic cell types were as follows:

  • HSC: CD150+CD48-CD135-CD34-c-Kit+Sca-1+Lineage-

  • MPP1: CD150+CD48-CD135-CD34+c-Kit+Sca-1+Lineage-

  • MPP2: CD150+CD48+CD135-CD34+c-Kit+Sca-1+Lineage-

  • MPP3: CD150-CD48+CD135-CD34+c-Kit+Sca-1+Lineage-

  • MPP4: CD150-CD48+CD135+CD34+c-Kit+Sca-1+Lineage-

  • CMP: CD34+FcγR-c-Kit+Sca-1-Lineage-

  • GMP: CD34+FcγR+c-Kit+Sca-1-Lineage-

  • MEP: CD34-FcγR-c-Kit+Sca-1-Lineage-

  • pre-GM: CD150-CD105-FcγR-CD41-c-Kit+Sca-1-Lineage-

  • CLP: c-KitlowSca-1lowCD135+IL7R+Lineage (for CLP)-

  • ALP: Ly6D-cKitlowSca-1lowCD135+IL7R+Lineage-

  • BLP: Ly6D+cKitlowSca-1lowCD135+IL7R+Lineage-

  • LSK: c-Kit+Sca-1+Lineage-

  • LK: c-Kit+Lineage-

  • Pro-B: B220+CD43+IgM-

  • Pre-B: B220+CD43-IgM-

Quantitative RT-PCR

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Total RNA was extracted using a RNeasy Micro Plus Kit (QIAGEN) or TRIZOL LS solution (MOR) and reverse transcribed by the SuperScript IV First-Strand Synthesis System (Invitrogen) or the ReverTra Ace α- (TOYOBO) with an oligo-dT primer. Real-time quantitative PCR was performed with a StepOnePlus Real-Time PCR System (Life Technologies) using FastStart Universal Probe Master (Roche) and the indicated combinations of the Universal Probe Library (Roche), or TB Green Premix Ex Taq II (TaKaRa Bio). All data are presented as relative expression levels normalized to Hprt expression. The primer sequences used are shown in Supplementary file 5 (Murakami et al., 2021; Sonntag et al., 2018).

Limiting dilution assay

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For in vivo limiting dilution assay, we transplanted limiting numbers of total BM cells (1 × 104, 4 × 104, 8 × 104, and 2 × 105) isolated from primary recipients (control and Pcgf1Δ/Δ mice 1 mo after tamoxifen injections) with CD45.1+ competitor total BM cells (2 × 105) into lethally irradiated CD45.1 recipient mice. PB analyses were performed at 16 wk after transplantation.

For in vitro limiting dilution assay, we sorted HSCs from control and Pcgf1Δ/Δ mice 1 mo after tamoxifen injections and cultured limiting numbers of the cells (1, 5, 25, and 125) with TSt-4 (B cells) or TSt-4/DLL1 stromal cells (T cells) in RPMI (Thermo Fisher Scientific) supplemented with 10% BSA (093001; STEMCELL Technologies), 50 μM 2-ME (Sigma-Aldrich), 100 μM MEM Non-Essential Amino Acids solution (Gibco), 100 μM sodium pyruvate (Gibco), and 2 ng/mL recombinant mouse IL-7 (577802; BioLegend) for 28 d. The generation of CD19+ B cells or Thy1.2+ T cells in each well was detected by flow cytometry.

Cell cycle assay

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BM cells were stained with antibodies against cell-surface markers. After washing, cells were fixed and permeabilized with a BD Phosflow Lyse/Fix Buffer and a BD Phosflow Perm Buffer II (BD Biosciences) according to the manufacturer’s instructions. Cells were stained with FITC-Ki67 antibody (#11-5698-82; Thermo Fisher Scientific) at room temperature for 30 min and then with 1 μg/mL 7-AAD (Sigma-Aldrich). Flow cytometric analyses were performed on FACSAria IIIu (BD Biosciences).

Cell culture

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For growth assays, sorted CD34-CD150+LSK HSCs were cultured in S-Clone SF-O3 (Sanko Junyaku) supplemented with 0.1% BSA (093001; STEMCELL Technologies), 50 μM 2-ME (Sigma-Aldrich) and 1% penicillin/streptomycin/glutamine (Gibco). 20 ng/mL of recombinant mouse SCF (579706; BioLegend) and recombinant human TPO (763706; BioLegend) for HSC culture conditions and 10 ng/mL of SCF, TPO, recombinant mouse IL-3 (575506; BioLegend), and recombinant murine GM-CSF (315-03; PeproTech) for myeloid culture condition-1 were added to cultures. In the case of myeloid culture condition-2, sorted CD150+CD48-CD135-CD34-LSK HSCs, LSK cells, and GMPs were cultured in IMDM (Gibco) supplemented with 5% FBS, 50 μM 2-ME (Sigma-Aldrich), 1% penicillin/streptomycin/glutamine (Gibco), 1 mM sodium pyruvate (Gibco), and 0.1 mM MEM Non-Essential Amino Acids solution (Gibco). 25 ng/mL of SCF, TPO, recombinant human Flt3L (300-19; PeproTech) and recombinant murine IL-11 (220-11; PeproTech) and 10 ng/mL of IL-3 and GM-CSF were added to cultures.

For replating assays, LSK cells were plated in methylcellulose medium (Methocult M3234; STEMCELL Technologies) containing 20 ng/mL of SCF, TPO, IL-3, and GM-CSF.

Retroviral vector and virus production

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Full-length Pcgf1 and Bmi1 cDNA tagged with a 3×Flag at the N-terminus was subcloned into the retroviral vector pGCDNsam-IRES-EGFP. Full-length Hoxa9 cDNA was subcloned into the retroviral vector pMYs-IRES-mCherry. A recombinant retrovirus was generated by a 293gpg packaging cell line. The virus in supernatants of 293gpg cells was concentrated by centrifugation at 6000 × g for 16 hr.

Immunofluorescence imaging of bone marrow and spleen sections

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Isolated mouse femurs were immediately placed in ice-cold 2% paraformaldehyde solution (PFA/PBS) and fixed under gentle agitation for 16 hr. The samples were then incubated in 15 and 30% sucrose for cryoprotection overnight. Samples were embedded in O.C.T. (Sakura) and frozen in cooled hexane. The 7 μm frozen sections were generated with a cryostat (Cryostar NX70, Thermo Scientific) using Kawamoto’s tape method (Kawamoto, 2003). Sections on slide glasses were blocked with staining buffer (10% normal donkey serum in TBS) and an Avidin/Biotin Blocking Kit (VECTOR), then stained with biotinylated anti-lineage antibody cocktail and anti-c-Kit antibody (#AF 1356; R&D Systems), or anti-FcγR-Alexa Fluor 647 (#101314; BioLegend) in staining buffer overnight at 4℃. For secondary staining, sections were incubated with streptavidin-Alexa Fluor 488 (#S11223; Invitrogen) and donkey anti-goat Alexa Fluor 555 (#A21432; Invitrogen) antibody for 3 hr at room temperature. Finally, sections were incubated with 1 μg/mL DAPI/TBS for 10 min and mounted with ProLong Glass Antifade Mountant (Thermo Scientific). Images of sections were captured on a confocal microscope (Dragonfly, Andor, or A1Rsi, Nikon) and processed using Fiji. ImageJ was used for image quantification.

Immunofluorescence imaging of purified GMPs

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GMPs were sorted directly onto glass slides using BD AriaIIIu. The cells were washed three times with PBS for 5 min between each staining step. Cells were fixed with 4% PFA for 15 min, permeabilized with 0.1% Triton X-100 for 10 min, and then blocked with 3% BSA for 1 hr at room temperature. The cells were then incubated with rabbit anti-mouse β-catenin (#9582S; Cell Signaling) primary antibody at 4°C overnight. The cells were then stained with anti-rabbit AF488A (#20015; Biotium) secondary antibody for 2 hr at room temperature. After staining with 1 μg/mL DAPI/PBS for 5 min, the cells were mounted with ProLong Glass Antifade Mountant (Thermo Fisher). DragonFly (Andor, ×40 objective) was used for image acquisition.

Bulk RNA-seq and data processing

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Total RNAs were extracted from 1000 to 5000 cells using an RNeasy Plus Micro Kit (QIAGEN) and cDNAs were synthesized using a SMART-Seq v4 Ultra Low Input RNA Kit for Sequencing (Clontech) according to the manufacturer’s instructions. The ds-cDNAs were fragmented using S220 or M220 Focused-ultrasonicator (Covaris), then cDNA libraries were generated using a NEBNext Ultra DNA Library Prep Kit (New England BioLabs) according to the manufacturer’s instructions. Sequencing was performed using HiSeq1500 or HiSeq2500 (Illumina) with a single-read sequencing length of 60 bp. TopHat2 (version 2.0.13; with default parameters) was used to map the reads to the reference genome (UCSC/mm10) with annotation data from iGenomes (Illumina). Levels of gene expression were quantified using Cuffdiff (Cufflinks version 2.2.1; with default parameters). Significant expression differences were detected edgeR (version 3.14; with default parameters), with raw counts generated from String Tie. The super-computing resource was provided by the Human Genome Center, the Institute of Medical Science, the University of Tokyo (http://sc.hgc.jp/shirokane.html). The enrichment analyses were performed using g:Profiler tool.

Single-cell RNA-seq and data processing

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Control (1.2 × 104) and Pcgf1Δ/Δ (1.2 × 104) LK cells were collected for single-cell RNA-seq. mRNA were isolated and libraries were prepared according to Chromium Next GEM Single Cell 3ʹ Reagent Kits v3.1 (10X Genomics). Raw data files (Base call files) were demultiplexed into fastq files using Cell Ranger with mkfastq command. Then, ‘cellranger count’ command was used for feature counts, barcode counts with reference ‘refdata-gex-mm10-2020-A.’ Filtered_feature_bc_matrix included 6565 control LK cells and 7651 Pcgf1Δ/Δ LK cells. We subsampled 6565 cells from 7651 Pcgf1Δ/Δ LK cells to adjust cell numbers between Pcgf1Δ/Δ and control LK. Subsequent analyses were performed using Seurat 4.1.0. Quality filtering for each feature and cell was conducted based on these criteria (min.cells = 3 & min.features = 200 & nFeature_RNA >200 & nFeature_RNA <10000 & percent.mt <5). After quality filtering, 6171 control LKs and 6198 KO LKs were used for further analysis. Feature counts are log-normalized with the function of ‘NormalizeData.’ 2000 highly variable features are selected for PCA. PC 1–10 components are used for UMAP and graph-based clustering with the functions of FindNeighbors(object, reduction = "pca", dims = 1:15) and FindClusters(object, resolution = 0.28). Cluster 0, 2, and 4 cells are extracted and reanalyzed with PCA. DEGs are selected with the function of ‘FindMarkers(object, min.pct=0.25)’.

Chromatin immunoprecipitation (ChIP) assays and ChIP-sequencing

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ChIP assays for histone modifications were performed as described previously (Aoyama et al., 2018) using an anti-H2AK119ub1 (#8240S; Cell Signaling Technology) and an anti-H3K27me3 (#07-449; Millipore). BM LSK cells were fixed with 1% FA at 37°C for 2 min, lysed in ChIP buffer (10 mM Tris-HCl pH 8.0, 200 mM NaCl, 1 mM CaCl2, 0.5% NP-40 substitute and cOmplete proteases inhibitor cocktail) and sonicated for 5 s ×3 times by a Bioruptor (UCD-300; Cosmo Bio). Then, cells were digested with Micrococcal Nuclease at 37°C for 40 min (New England BioLabs) and added 10 mM EDTA to stop the reaction. After the addition of an equal volume of RIPA buffer (50 mM Tris-HCl pH 8.0, 150 mM NaCl, 2 mM EDTA pH 8.0, 1% NP-40 substitute, 0.5% sodium deoxycholate, 0.1% SDS and cOmplete proteases inhibitor cocktail), cells were sonicated again for 5 s × 10 times by a Bioruptor. After centrifugation, supernatants were immunoprecipitated at 4°C overnight with Dynabeads Sheep Anti-Rabbit IgG (Invitrogen) conjugated with each antibody. Immunoprecipitates were washed with ChIP wash buffer (10 mM Tris-HCl pH 8.0, 500 mM NaCl, 1 mM CaCl2, 0.5% NP-40 substitute, and cOmplete proteases inhibitor cocktail) four times and TE buffer (10 mM Tris-HCl pH 8.0 and 1 mM EDTA pH 8.0) twice. Bound chromatins and 30 μL of input DNA were suspended in 95 μL and 65 μL elution buffer (50 mM Tris-HCl pH 8.0, 10 mM EDTA pH 8.0, 1% SDS, and 250 mM NaCl), respectively. After the addition of 5 μL of 5 M NaCl, the solutions were incubated at 65°C for 4 hr, treated with 25 μg/mL RNase A (Sigma-Aldrich) at 37°C for 30 min and 0.1 mg/mL proteinase K (Roche) at 50°C for 1 hr and were purified with a MinElute PCR Purification Kit (QIAGEN).

In 3×Flag-Pcgf1ChIP assay, BM LK cells were fixed with 1% FA at 25°C for 10 min, lysed in RIPA buffer, and sonicated for 11 s ×15 times by a homogenizer (NR-50M; Micro-tec Co.). After centrifugation, supernatants were immunoprecipitated at 4°C overnight with Dynabeads Sheep Anti-Mouse IgG (Invitrogen) conjugated with an anti-FLAG antibody (Sigma-Aldrich). Then, the samples were treated in the same way as ChIP assays for histone modifications.

In ChIP-qPCR assay, quantitative real-time PCR was performed with a StepOnePlus Thermal Cycler (Thermo Fisher Scientific) using SYBR Premix Ex Taq II or TB Green Premix Ex Taq II (Takara Bio). The primer sequences used are shown in Supplementary file 5.

ChIP-seq libraries were prepared using a ThruPLEX DNA-seq Kit (Clontech) according to the manufacturer’s instructions. Bowtie2 (version 2.2.6; with default parameters) was used to map the reads to the reference genome (UCSC/mm10). The RPM (reads per million mapped reads) values of the sequenced reads were calculated every 1000 bp bin with a shifting size of 100 bp using bedtools. In order to visualize with Integrative Genomics Viewer (IGV) (http://www.broadinstitute.org/igv), the RPM values of the immunoprecipitated samples were normalized by subtracting the RPM values of the input samples in each bin and converted to a bigwig file using wigToBigWig tool. The super-computing resource was provided by the Human Genome Center, the Institute of Medical Science, the University of Tokyo (http://sc.hgc.jp/shirokane.html).

Assay for transposase accessible chromatin with high-throughput (ATAC)-sequencing

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BM CD135-LSK cells (1.6–3.0 × 104) and GMPs (3.0 × 104) were lysed in cold lysis buffer (10 mM Tris-HCl pH 7.4, 10 mM NaCl, 3 mM MgCl2, 0.1% IGEPAL CA-630) on ice for 10 min. After centrifugation, nuclei pellets were resuspended with 50 μL of transposase reaction mix (25 μL Tagment DNA buffer (illumine), 2.5 μL Tagment DNA enzyme (illumine) and 22.5 μL water), incubated at 37°C for 35 min and were purified with a MinElute PCR Purification Kit (QIAGEN). After the optimization of PCR cycle number using SYBER Green I Nucleic Acid gel Stain (Takara Bio), transposed fragments were amplified using NEBNext High Fidelity 2×PCR Master mix and index primers, and were purified with a MinElute PCR Purification Kit (QIAGEN). Library DNA was sized selected (240–360 bps) with BluePippin (Sage Science). Sequencing was performed using HiSeq1500 or HiSeq2500 (Illumina) with a single-read sequencing length of 60 bp. Bowtie2 (version 2.2.6; with default parameters) was used to map reads to the reference genome (UCSC/mm10) with annotation data from iGenomes (Illumina). Reads mapped to mitochondria were removed. To ensure even processing, reads were randomly downsampled from each sample to adjust to the smallest read number of samples. MACS (version 2.1.1; with default parameters) was used to call peaks in downsampled reads. The catalogue of all peaks called in any samples was produced by merging all called peaks that overlapped by at least one base pair using bedtools. The MACS bdgcmp function was used to compute the fold enrichment over the background for all populations, and the bedtools map function was used to count fragments in the catalogue in each population. Fragment counts at each site in the catalogue were quantile normalized between samples using the PreprocessCore package in R (3.3.2). We used the Homer package with command annotatePeaks.pl using default parameters to annotate regions with promoter and distal labels and the nearest gene, and with command findMotifsGenome.pl using default parameters to identify enriched motifs, and the catalogue of all called peaks as a background.

Quantification and statistical analysis

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Statistical tests were performed using Prism version 9 (GraphPad). The significance of difference was measured by the Student’s t-test or one-way ANOVA. Data are shown as the mean ± SEM. Significance was taken at values of *p<0.05, **p<0.01, and ***p<0.001.

Appendix 1

Appendix 1—key resources table
Reagent type (species) or resourceDesignationSource or referenceIdentifiersAdditional information
Strain, strain background (Mus musculus)Wild-type mice: C57BL/6 (CD45.2)Japan SLCC57BL6JJmsSlc
Strain, strain background (M. musculus)Wild-type mice: C57BL/6 (CD45.1)Sankyo-Lab Service
Strain, strain background (M. musculus)Rosa26::Cre-ERT2: C57BL/6-Gt(ROSA)26Sortm9(Cre/ESR1)ArteTaconicArtemis GmbHModel 10471
Strain, strain background (M. musculus)Pcgf1flThis paperKoseki H Lab
Strain, strain background (M. musculus)Cebpafl: B6.129S6(CBA)-Cebpatm1Dgt/JThe Jackson LaboratoryJAX: 006447Daniel G. Tenen Lab
Cell line (Homo sapiens)293gpgOry et al., 1996.
Cell line (M. musculus)TSt-4Masuda et al., 2005.
Cell line (M. musculus)TSt-4/DLL1Masuda et al., 2005.
AntibodyBiotin anti-mouse Ly-6G/Ly-6C (Gr-1) (RB6-8C5) (rat monoclonal)Tonbo BiosciencesCat# 30-5931; RRID:AB_2621652FACS (3.5 × 10–3 μL/1 × 106 cell)
AntibodyBiotin anti-human/mouse CD11b (M1/70) (rat monoclonal)Tonbo BiosciencesCat# 30-0112; RRID:AB_2621639FACS (1.8 × 10–2 μL/1 × 106 cell)
AntibodyBiotin anti-mouse TER-119 (TER-119) (rat monoclonal)Tonbo BiosciencesCat# 30-5921; RRID:AB_2621651FACS (3.5 × 10–3 μL/1 × 106 cell)
AntibodyBiotin anti-human/mouse CD45R (B220) (RA3-6B2) (rat monoclonal)Tonbo BiosciencesCat# 30-0452; RRID:AB_2621644FACS (1.8 × 10–2 μL/1 × 106 cell)
AntibodyBiotin anti-mouse CD127 (IL-7Rα) antibody (rat monoclonal)BioLegendCat# 121104; RRID:AB_312989FACS (1.8 × 10–2 μL/1 × 106 cell)
AntibodyBiotin anti-mouse CD4 antibody (rat monoclonal)BioLegendCat# 100404; RRID:AB_493502FACS (9 × 10–3 μL/1 × 106 cell)
AntibodyBiotin anti-mouse CD8a (53–6.7) (rat monoclonal)Tonbo BiosciencesCat# 30-0081; RRID:AB_2621638FACS (9 × 10–3 μL/1 × 106 cell)
AntibodyBiotin anti-mouse Ly-6A/E (Sca-1) antibody (rat monoclonal)BioLegendCat# 108104; RRID:AB_3133418FACS (2.5 × 10–2 μL/1 × 106 cell)
AntibodyCD41a monoclonal antibody (eBioMWReg30 (MWReg30)), Biotin (rat monoclonal)eBioscienceCat# 13-0411-85; RRID:AB_76348FACS (0.1 μL/1 × 106 cell)
AntibodyPerCP/Cyanine5.5 StreptavidinBioLegendCat# 405214; RRID:AB_2716577FACS (5 × 10–2 μL/1 × 106 cell)
AntibodyAPC anti-human/mouse CD45R (B220) (RA3-6B2) (rat monoclonal)Tonbo BiosciencesCat# 20-0452; RRID:AB_2621574FACS (2.5 × 10–2 μL/1 × 106 cell)
AntibodyAPC anti-mouse CD117 (c-Kit) antibody (rat monoclonal)BioLegendCat# 105812; RRID:AB_313221FACS (2.5 × 10–2 μL/1 × 106 cell)
AntibodyAPC anti-human/mouse CD11b (M1/70) (rat monoclonal)Tonbo BiosciencesCat# 20-0112; RRID:AB_2621556FACS (5 × 10–3 μL/1 × 106 cell)
AntibodyAPC anti-mouse CD4 antibody (rat monoclonal)BioLegendCat# 100412; RRID:AB_312697FACS (0.1 μL/1 × 106 cell)
AntibodyAPC/Cyanine7 anti-mouse CD4 antibody (rat monoclonal)BioLegendCat# 100526; RRID:AB_312727FACS (5 × 10–2 μL/1 × 106 cell)
AntibodyAPC/Cyanine7 anti-mouse CD8a antibody (rat monoclonal)BioLegendCat# 100714; RRID:AB_312753FACS (5 × 10–2 μL/1 × 106 cell)
AntibodyAPC/Cyanine7 anti-mouse CD48 antibody (Armenian hamster monoclonal)BioLegendCat# 103431; RRID:AB_2621462FACS (2.5 × 10–2 μL/1 × 106 cell)
AntibodyAPC/Cyanine7 anti-mouse CD45.2 antibody (mouse monoclonal)BioLegendCat# 109824; RRID:AB_830789FACS (4 × 10–2 μL/1 × 106 cell)
AntibodyAPC/Cyanine7 StreptavidinBioLegendCat# 405208FACS (5 × 10–2 μL/1 × 106 cell)
AntibodyPE anti-mouse Ly-6A/E (Sca-1) antibody (rat monoclonal)BioLegendCat# 108108; RRID:AB_313345FACS (2.5 × 10–2 μL/1 × 106 cell)
AntibodyPE anti-mouse/human CD11b antibody (rat monoclonal)BioLegendCat# 101208; RRID:AB_312791FACS (5 × 10–3 μL/1 × 106 cell)
AntibodyPE anti-mouse CD150 (SLAM) antibody (rat monoclonal)BioLegendCat# 115904; RRID:AB_313683FACS (0.1 μL/1 × 106 cell)
AntibodyPE anti-mouse CD16/32 antibody (rat monoclonal)BioLegendCat# 101308; RRID:AB_312807FACS (2.5 × 10–2 μL/1 × 106 cell)
AntibodyPE anti-mouse CD127 (IL-7Rα) antibody (rat monoclonal)BioLegendCat# 135010; RRID:AB_1937251FACS (0.1 μL/1 × 106 cell)
AntibodyPE anti-mouse Ly-6G/Ly-6C (Gr-1) antibody (rat monoclonal)BioLegendCat# 108408; RRID:AB_313373FACS (2.5 × 10–3 μL/1 × 106 cell)
AntibodyPE anti-mouse CD8a antibody (rat monoclonal)BioLegendCat# 100708; RRID:AB_312747FACS (0.2 μL/1 × 106 cell)
AntibodyPE anti-mouse CD43 antibody (rat monoclonal)BioLegendCat# 143206; RRID:AB_11124719FACS (2.5 × 10–2 μL/1 × 106 cell)
AntibodyPE anti-mouse F4/80 antibody (rat monoclonal)BioLegendCat# 123110; RRID:AB_8934865FACS (0.1 μL/1 × 106 cell)
AntibodyPE/Cyanine7 anti-mouse Ly-6A/E (Sca-1) antibody (rat monoclonal)BioLegendCat# 108114; RRID:AB_493596FACS (2.5 × 10–2 μL/1 × 106 cell)
AntibodyPE/Cyanine7 anti-mouse CD45.1 antibody (mouse monoclonal)BioLegendCat# 110730; RRID:AB_1134168FACS (4 × 10–2 μL/1 × 106 cell)
AntibodyCD34 monoclonal antibody (RAM34), FITC (rat monoclonal)eBioscienceCat# 11-0341-85; RRID:AB_1465022FACS (0.25 μL/1 × 106 cell)
AntibodyFITC anti-mouse Ly-6D antibody (rat monoclonal)BioLegendCat# 138606; RRID:AB_11203888FACS (2.5 × 10–2 μL/1 × 106 cell)
AntibodyFITC anti-mouse IgM antibody (rat monoclonal)BioLegendCat# 406506; RRID:AB_315056FACS (5 × 10–2 μL/1 × 106 cell)
AntibodyFITC anti-mouse Ly6C antibody (rat monoclonal)BioLegendCat# 128005; RRID:AB_1186134FACS (0.1 μL/1 × 106 cell)
AntibodyKi-67 monoclonal antibody (SolA15), FITC (rat monoclonal)eBioscienceCat# 11-5698-82; RRID:AB_11151330FACS (0.25 μL/1 × 106 cell)
AntibodyAlexa Fluor 488 anti-mouse CD105 antibody (rat monoclonal)BioLegendCat# 120406; RRID:AB_961053FACS (0.25 μL/1 × 106 cell)
AntibodyvioletFluor 450 anti-mouse CD45.2 (104) (mouse monoclonal)Tonbo BiosciencesCat# 75-0454; RRID:AB_2621950FACS (4 × 10–2 μL/1 × 106 cell)
AntibodyBrilliant Violet 421 anti-mouse CD135 antibody (rat monoclonal)BioLegendCat# 135314; RRID:AB_2562339FACS (0.25 μL/1 × 106 cell)
AntibodyBrilliant Violet 421 anti-mouse CD150 (SLAM) antibody (rat monoclonal)BioLegendCat# 115925; RRID:AB_10896787FACS (0.1 μL/1 × 106 cell)
AntibodyBrilliant Violet 421 anti-mouse Ly6G antibody (rat monoclonal)BioLegendCat# 127627; RRID:AB_10897944FACS (0.1 μL/1 × 106 cell)
AntibodyBrilliant Violet 510 anti-mouse CD16/32 antibody (rat monoclonal)BioLegendCat# 101333; RRID:AB_2563692FACS (2.5 × 10–2 μL/1 × 106 cell)
AntibodyCD34 monoclonal antibody (RAM34), eFluor 450 (rat monoclonal)eBioscienceCat# 48-0341-82; RRID:AB_2043837FACS (0.25 μL/1 × 106 cell)
AntibodyBrilliant Violet 605 StreptavidinBioLegendCat# 405229FACS (5 × 10–2 μL/1 × 106 cell)
AntibodyV500 mouse anti-mouse CD45.2 (mouse monoclonal)BD BiosciencesCat# 562129; RRID:AB_10897142FACS (4 × 10–2 μL/1 × 106 cell)
AntibodyBV510 Mouse Anti-Mouse CD45.1 (mouse monoclonal)BD BiosciencesCat# 565278; RRID:AB_2739150FACS (4 × 10–2 μL/1 × 106 cell)
AntibodyUbiquityl-Histone H2A (Lys119) (D27C4) XP rabbit mAb (rabbit monoclonal)Cell Signaling TechnologyCat# 8240S; RRID:AB_10891618ChIP (2 μg)
AntibodyAnti-trimethyl-Histone H3 (Lys27) antibody (rabbit polyclonal)MilliporeCat# 07-449; RRID:AB_310624ChIP (2 μg)
AntibodyMonoclonal anti-FLAG M2, antibody produced in mouse (mouse monoclonal)SigmaCat# F1084ChIP (2 μg)
Antibodyβ-Catenin (6B3) rabbit mAb (rabbit monoclonal)Cell Signaling TechnologyCat# 9582S; RRID:AB_823447IF (1:100)
AntibodyHuman/mouse CD117/c-kit antibody (goat polyclonal)R&D SystemsCat# AF1356; RRID:AB_354750IF (1:100)
AntibodyCD41a monoclonal antibody (eBioMWReg30 (MWReg30)), Biotin (rat monoclonal)eBioscienceCat# 13-0411-85; RRID:AB_763489IF (1:200)
AntibodyAlexa Fluor 647 anti-mouse CD16/32 antibody (rat monoclonal)BioLegendCat# 101314; RRID:AB_2278396IF (1:50)
AntibodyBiotin anti-mouse Ly-6A/E (Sca-1) antibody (rat monoclonal)BioLegendCat# 108104; RRID:AB_313341IF (1:100)
AntibodyStreptavidin, Alexa Fluor 488 conjugateInvitrogenCat# S11223IF (1:200)
AntibodyDonkey anti-rabbit IgG (H+L), highly cross-adsorbed antibody, CF488A conjugated (donkey polyclonal)BiotiumCat# 20015; RRID:AB_10559669IF (1:400)
AntibodyDonkey anti-goat IgG (H+L) cross-adsorbed secondary antibody, Alexa Fluor 555 (donkey polyclonal)InvitrogenCat# A-21432; RRID:AB_2535853IF (1:400)
AntibodyDonkey anti-goat IgG (H+L) cross-adsorbed secondary antibody, Alexa Fluor 568 (donkey polyclonal)InvitrogenCat# A-11057; RRID:AB_2534104IF (1:400)
Sequence-based reagentPrimer pairs for genotypingThis studySupplementary file 5
Sequence-based reagentPrimer pairs for qPCRUniversal ProbeLibrary Assay Design Center (Roche)Supplementary file 5
Sequence-based reagentPrimer pairs for ChIP-qPCRThis studySupplementary file 5
Peptide, recombinant proteinMicrococcal NucleaseNew England BioLabsM0247S
Peptide, recombinant proteinRNase ASigma-AldrichR6513
Peptide, recombinant proteinProteinase KRoche3115852001
Peptide, recombinant proteinRecombinant mouse IL-7 (carrier-free)BioLegend577802
Peptide, recombinant proteinRecombinant mouse SCF (carrier-free)BioLegend579706
Peptide, recombinant proteinRecombinant human TPO (carrier-free)BioLegend763706
Peptide, recombinant proteinRecombinant mouse IL-3 (carrier-free)BioLegend575506
Peptide, recombinant proteinRecombinant murine GM-CSFPeproTech315-03
Peptide, recombinant proteinRecombinant human Flt3-ligandPeproTech300-19
Peptide, recombinant proteinRecombinant murine IL-11PeproTech220-11
Commercial assay or kitRNeasy Micro Plus KitQIAGEN74034
Commercial assay or kitTRI Reagent LSMORTS120
Commercial assay or kitSuperScript IV First-Strand Synthesis SystemInvitrogen18091050
Commercial assay or kitReverTra Ace a-TOYOBOFSQ-301
Commercial assay or kitFastStart Universal Probe MasterRoche4914058001
Commercial assay or kitMethocult M3234STEMCELL Technologies03234
Commercial assay or kitBD Phosflow Lyse/Fix BufferBD Biosciences558049
Commercial assay or kitBD Phosflow Perm Buffer IIBD Biosciences558052
Commercial assay or kitTB Green Premix Ex Taq IITakara BioRR820S
Commercial assay or kitcOmplete, Mini, Protease Inhibitor CocktailRoche11836170001
Commercial assay or kitDynabeads M-280 sheep anti-rabbit IgGInvitrogen11203D
Commercial assay or kitDynabeads M-280 sheep anti-mouse IgGInvitrogen11202D
Commercial assay or kitMinElute PCR Purification KitQIAGEN28006
Commercial assay or kitSMART-Seq v4 Ultra Low Input RNA KitClontech634890
Commercial assay or kitNEBNext Ultra DNA Library Prep KitNew England BioLabsE7370
Commercial assay or kitThruPLEX DNA-seq KitClontechR400428
Commercial assay or kitTagment DNA bufferIllumina15027866
Commercial assay or kitTagment DNA enzymeIllumina15027865
Commercial assay or kitSYBER Green I Nucleic Acid gel StainTakara Bio5761A
Commercial assay or kitNEBNext High Fidelity 2x PCR Master mixNew England BioLabsM0541S
Commercial assay or kitChromium Next GEM Single Cell 3ʹ Reagent Kits v3.110X Genomics1000269
Commercial assay or kitO.T.C CompoundSakuraD3571
Commercial assay or kitProLong Glass Antifade MountantThermo ScientificP36982
Commercial assay or kitAvidin/Biotin Blocking KitVECTORSP-2001
Chemical compound, drugTamoxifenSigma-AldrichT5648
Chemical compound, drug4-HydroxytamoxifenSigma-AldrichH7904
Chemical compound, drugCorn oilSigma-AldrichC8267
Chemical compound, drugPropidium iodideSigma-AldrichP4170
Chemical compound, drug7-AADSigma-AldrichA94001 μg/mL
Chemical compound, drug2-Mercaptoethanol (2-ME)Sigma-AldrichM3148
Chemical compound, drugFormaldehyde solution (FA)Sigma-AldrichF8775
Chemical compound, drugIGEPAL CA-630Sigma-AldrichI8896
Chemical compound, drugSodium pyrubateThermo Scientific11360070
Chemical compound, drugDAPIInvitrogenD35711 μg/mL
Chemical compound, drugPierce 16% formaldehyde (w/v), methanol-freeThermo Scientific289068%
Chemical compound, drug5-fluorouracil (5-FU)Kyowa KIRIN5-FU 250 mg3.75 mg per 25 g body weight mouse
Software, algorithmPrism 9GraphPadhttps://www.graphpad.com/
software, algorithmL-CalcSTEMCELL Technologieshttps://www.stemcell.com/l-calc-software.html
Software, algorithmGene set enrichment analysisBroad Institutehttp://software.broadinstitute.org/gsea/index.jsp
Software, algorithmg:ProfilerRaudvere et al.,
2019.
https://biit.cs.ut.ee/gprofiler/
Software, algorithmIntegrate Genomics Viewer v2.4.4Broad Institutehttps://software.broadinstitute.org/software/igv/
Software, algorithmFijiSchindelin et al., 2012.https://fiji.sc
Software, algorithmImageJNational Institutes of Healthhttps://imagej.nih.gov/ij/index.html
OtherNormal donkey serumJackson ImmunoResearch017-000-121Blocking reagent for immunostaining
OtherS-Clone SF-O3EIDIA Co., Ltd1303Medium for cell cluture
Other10% bovine serum albumin in Iscove’s MDMSTEMCELL Technologies09300Supplement for cell cluture

Data availability

RNA sequence, ChIP sequence and ATAC sequence data were deposited in the DDBJ (accession numbers DRA008518 and DRA013523).

The following data sets were generated
    1. Iwama A
    (2022) DDBJ
    ID DRA013523. Polycomb repressive complex 1.1 orchestrates homeostatic and emergency hematopoiesis and restricts transformation by acting as a rheostat of myelopoiesis.
    1. Iwama A
    (2019) DDBJ
    ID DRA008518. Non-canonical PRC1 orchestrates homeostatic and emergency hematopoiesis and restricts transformation by acting as a rheostat of myeloid differentiation.

References

Decision letter

  1. Yelena Ginzburg
    Reviewing Editor; Icahn School of Medicine at Mount Sinai, United States
  2. Mone Zaidi
    Senior Editor; Icahn School of Medicine at Mount Sinai, United States
  3. Carl R Walkley
    Reviewer; St. Vincent's Institute of Medical Research, Australia

Our editorial process produces two outputs: (i) public reviews designed to be posted alongside the preprint for the benefit of readers; (ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.

Decision letter after peer review:

Thank you for submitting your article "Polycomb repressive complex 1.1 coordinates homeostatic and emergency myelopoiesis" for consideration by eLife. Your article has been reviewed by 3 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Mone Zaidi as the Senior Editor. The following individuals involved in the review of your submission have agreed to reveal their identity: Carl R Walkley (Reviewer #2).

The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.

The authors present a manuscript aiming to understand the mechanism(s) underlying myeloid bias in HSCs, specifically focused on the role of Pcgf1, and therefore PRC1.1, in the regulation of hematopoiesis. This work is of interest to the community of researchers interested in myeloid differentiation, lineage fate decisions in HSCs, and the molecular mechanisms that contribute to the initiation of myeloid malignancies. This work also is of novel significance for translational purposes and could lead to the design of better therapeutics for myeloid malignancies, in the least the utility of the novel mouse model presented in this work. The current study demonstrates that conditional deletion of Pcgf1 in HSCs causes a lineage switch from lymphoid to myeloid fates and that a key mechanism for this lineage switch is regulation of the H2AK119ub1 chromatin mark, leading to de-repression of CEBPalpha, a key transcription factor that promotes myeloid cell fate. The authors also demonstrate that deletion of Pcgf1 in HSCs can also lead to deregulated myelopoiesis, leading to a lethal MPN in a subset of animals, implicating Pcgf1 as a critical regulator of emergency myelopoiesis with the potential to prevent malignant transformation of myeloid progenitors.

The reviewers found significant merit in the work both technically and conceptually. However, the evaluation identified weaknesses that prevent the publication of this work in the present form. The main shortcomings of the presented work include:

1) Lack of clarity on whether the KO findings result in a normal but excessive myeloid differentiation, or from a myeloid skew with a block in the maturation of the mature myeloid cells;

2) Need for further delineation of the subtypes of mature myeloid cells in vivo;

3) Provide in vivo data to support the in vitro evidence that restoring CEBPa rescues the myeloid bias in the Pcgf1 KO HSPCs.

A complete list of reviewer comments can be found below.

Reviewer #1 (Recommendations for the authors):

1. The authors show that HoxA9 overexpression is associated with the activation of b-catenin, but the mechanism for this is unclear. Is HoxA9 a direct transcriptional activator of b-catenin? If not, can the authors speculate about a mechanism for the increase in β-catenin activation?

2. Is this really "emergency myelopoiesis", or is it pathologic myelopoiesis? The authors state that "the production of circulating myeloid cells was not enhanced". Is that because there is also a differentiation block? Addressing the question in of whether there is an expansion of undifferentiated cells would help to make this distinction. In Figure 6, if there is an increase in self-renewing GMPs in Pcgf1 KO mice with increased HoxA9 expression, why do the mice develop MPN and not AML? Is there any differentiation block along with the myeloid expansion? What happens to c-kit expression? Is this disease transplantable? It would also be helpful to see some high-power cytospins of the bone marrow or spleens of mice with MPN, to better assess the differentiation status.

3. It is suggested that Figure S6H should be in the last main figure, as it is a really helpful summary of the roles of PRC 1.1 in hematopoiesis.

Reviewer #2 (Recommendations for the authors):

Specific comments:

1. The work clearly shows a myeloid bias in hematopoiesis. It is not clear from the data as presented whether this is resulting in a normal but excessive myeloid differentiation, or from a myeloid skew with a block in the maturation of the mature myeloid cells. This is an important difference and would be important to understand. The present data demonstrate that the mice accumulate GMP and myeloid progenitors, but they also develop MPN and myeloid malignant/pre-malignant states. An alternative interpretation could be that the mature myeloid cells are not differentiating normally and that this is leading to excessive myeloid production to try to generate a functionally competent myeloid population for host defence.

2. The authors should more clearly describe the mature myeloid cells in the animals (across PB, BM, Spleen) and it would be useful to provide FACS plots of Mac-1/Gr-1 profiles (+/- F4/80, Ly6C/G).

3. The authors describe myeloid cells in the figure legends as "Mac-1+ and/or Gr-1+ myeloid cells" positive cells – please be more specific and define the populations. This applies throughout the manuscript and assays.

4. In the data in Figure 2E, can the authors provide qPCR or similar levels of Cebpa in the different genotypes. Is it demonstrated that germ-line heterozygous Cebpa would cause a reduction in the expression of Cebpa?

5. Did the authors age any mice post-5-FU to determine if this was sufficient to predispose to MPN generation or induce a more pronounced block in myeloid maturation?

6. The Hoxa9 reexpression studies in Figure 5G – This doesn't appear to have a WT control. The overexpression of Hoxa9 may be expected to have the same effect in a WT cell as that displayed (I am assuming this is a Pcgf1 deficient cell but this isn't completely clear to me from the text or legend).

7. Include an n for sick mice in Figure 6 (25 in total aged but how many got what diagnosis). Related to this, but potentially beyond the scope, is the disease transplantable?

Reviewer #3 (Recommendations for the authors):

Line 127: KO mice have mild anemia and leukopenia. Figure S2A is the only data associated with this statement – please show neutrophil, monocyte, and lymphocyte counts separately.

Figure 1I: Green/Red bar plotting could be problematic for people with color blindness. The labeling of the HSPC populations underneath the genotypes is poorly aligned – which adds to the confusion. Because the bars are stacked with multiple colors, the statistical significance is indicated for the total or specific colored portion?

How does PCGF1 non-cell autonomously control hematopoiesis? What % of Pcgf1Δ/Δ cells is needed to achieve this non-cell autonomous inhibition? This seems to be a potent aspect of regulation – some discussion would be helpful.

Line 167 and 169, "… cells display slower growth under HSPC expanding condition" and "better growth". The word "growth" in these sentences is more accurate to be replaced by "population doubling".

S5A is never referenced in the text. S5B, middle plot. Significance is indicated by a star in the B cell portion of the stacked bars. This is for comparing which samples/cell types, as multiple ways of comparison can be made.

Figure 2D/E provided in vitro results for restoring CEBPa in rescuing the myeloid bias in the Pcgf1Δ/Δ HSPCs. Support this claim with in vivo results.

Line 242 refers to Figure S3D: there is no panel D in Figure S3.

Line 311 Figure 3D – this should have been 3G, if I understood correctly. If I did not understand correctly, then Figure 3G is never cited. This is so terribly confusing!

Figure 3I – so poorly aligned/labelled.

Line 320-322 "Moreover, ATAC-seq analyses also revealed trend toward enrichment of ATAC peaks containing CEBP motif in Pcgf1Δ/Δ HSPCs compared to the control, suggesting that chromatin regions containing CEBP motif tended to open in Pcgf1Δ/Δ HSPCs (Figure 3I)." The figure supporting this claim seems to have "p=0.3". Such a p-value is "ns" everywhere else. Why is it different here? As far as this reviewer can gather, the only significant difference in this figure is between control LSK and control GMP. Therefore, these results cannot and should not be cited as evidence to support "chromatin regions containing CEBP motif tended to open in HSPCs (Figure 3I)." Authors should remove any misleading claim based on this strenuous comparison.

Figure 4A. Can the difference in cluster GMPs be quantified?

Line 412-413 figure legend for Figure 4C: "Each symbol is derived from an individual culture". This is supposed to be from in vivo experiments? No statistical significance indicated?

Figure 5B,5G/H, states that HSC cultures produced GMPs. This statement is misleading and contributes to the over-interpretation of the results. To the best that this data can support, perhaps it is fair to say cells expressing immunophenotypic GMP markers are produced. The authors interpreted the difference in myeloid culture condition 1 and 2 being the different responses of HSC and GMP toward the loss of Pcgf1. However, it is equally possible that the different growth factors (type and concentrations) caused the differential response between the control and Pcgf1Δ/Δ cells. The authors need to culture HSC and GMP in both myeloid culture condition 1 and 2 to justify their claims.

Figure 5D. The images are missing the labels of genotypes.

Figure 5E legend says the samples are the GMPs from HSC cultures in 5B. Were these "GMPs" sorted? Please specify. Why are the day 16 control "GMPs" missing?

Figure 5F. What samples are being compared by RNA-seq? This reviewer is guessing the same samples shown in Figure 5E. If so, it begs the same question as to why are the day 16 control "GMPs" missing, and why are the day 0 Pcgf1Δ/Δ GMPs missing.

https://doi.org/10.7554/eLife.83004.sa1

Author response

The authors present a manuscript aiming to understand the mechanism(s) underlying myeloid bias in HSCs, specifically focused on the role of Pcgf1, and therefore PRC1.1, in the regulation of hematopoiesis. This work is of interest to the community of researchers interested in myeloid differentiation, lineage fate decisions in HSCs, and the molecular mechanisms that contribute to the initiation of myeloid malignancies. This work also is of novel significance for translational purposes and could lead to the design of better therapeutics for myeloid malignancies, in the least the utility of the novel mouse model presented in this work. The current study demonstrates that conditional deletion of Pcgf1 in HSCs causes a lineage switch from lymphoid to myeloid fates and that a key mechanism for this lineage switch is regulation of the H2AK119ub1 chromatin mark, leading to de-repression of CEBPalpha, a key transcription factor that promotes myeloid cell fate. The authors also demonstrate that deletion of Pcgf1 in HSCs can also lead to deregulated myelopoiesis, leading to a lethal MPN in a subset of animals, implicating Pcgf1 as a critical regulator of emergency myelopoiesis with the potential to prevent malignant transformation of myeloid progenitors.

The reviewers found significant merit in the work both technically and conceptually. However, the evaluation identified weaknesses that prevent the publication of this work in the present form. The main shortcomings of the presented work include:

1) Lack of clarity on whether the KO findings result in a normal but excessive myeloid differentiation, or from a myeloid skew with a block in the maturation of the mature myeloid cells;

2) Need for further delineation of the subtypes of mature myeloid cells in vivo;

3) Provide in vivo data to support the in vitro evidence that restoring CEBPa rescues the myeloid bias in the Pcgf1 KO HSPCs.

A complete list of reviewer comments can be found below.

Thank you for your suggestions. In response to the points raised above, we performed additional experiments, presented data in new figures, and revised the main text. Among the 3 main shortcomings of the presented work you pointed out, we clearly addressed the points 1 and 2. Regarding the point 3, we tried competitive BM transplantation assays using conditional Pcgf1 KO, Cebpa KO, and compound mice to rescue dysregulated differentiation of Pcgf1Δ/Δ HSPCs. However, we found that the deletion efficiency of Cebpa was poor in vivo using tamoxifen, while it is quite efficient in vitro using 4-hydroxytamoxifen. The data we obtained was hard to interpret. We would like to address this issue in our future experiments using different approaches. Details are described in the following responses to the reviewers.

Reviewer #1 (Recommendations for the authors):

1. The authors show that HoxA9 overexpression is associated with the activation of b-catenin, but the mechanism for this is unclear. Is HoxA9 a direct transcriptional activator of b-catenin? If not, can the authors speculate about a mechanism for the increase in β-catenin activation?

We examined the promoter region of the Ctnnb1 gene by JASPER database (https://jaspar.genereg.net/) and found at least one predicted sequence for HoxA9 binding (CTTATAAATCG) (data not shown). We performed qPCR analysis using HoxA9 overexpression samples at day 12 of culture, in which nuclear localization of β-catenin was observed upon HoxA9 overexpression (Figure 5H). Ctnnb1 gene expression was slightly but significantly upregulated in HoxA9 overexpression samples compared to mock control (Figure 5—figure supplement 1C). These results raise the possibility that HoxA9 is a direct transcriptional activator of the Ctnnb1 gene, but this warrants further analysis. We described these points in lines 385-392.

2. Is this really "emergency myelopoiesis", or is it pathologic myelopoiesis? The authors state that "the production of circulating myeloid cells was not enhanced". Is that because there is also a differentiation block? Addressing the question in of whether there is an expansion of undifferentiated cells would help to make this distinction. In Figure 6, if there is an increase in self-renewing GMPs in Pcgf1 KO mice with increased HoxA9 expression, why do the mice develop MPN and not AML? Is there any differentiation block along with the myeloid expansion? What happens to c-kit expression? Is this disease transplantable? It would also be helpful to see some high-power cytospins of the bone marrow or spleens of mice with MPN, to better assess the differentiation status.

We think the reviewer’s points are very important. The data in Figure S2 (Figure 1—figure supplement 2 in revised manuscript) indicate accumulation of mature myeloid cells in Pcgf1 KO BM and spleen. To further confirm this, we performed additional FACS analyses using mature myeloid surface marker (Ly6G, Ly6C, F4/80) antibodies. The results showed that the number of mature myeloid cells expressing myeloid differentiation markers increased in Pcgf1 KO BM and spleen. Monocytes but not neutrophils or F4/80+ macrophages significantly increased in Pcgf1 KO BM while both neutrophils and monocytes significantly increased in Pcgf1 KO spleen. We replaced the flow cytometric profiles of Mac1 and Gr1 staining with those of Mac1, Ly6G, and Ly6c staining and added graphs of their absolute numbers (Figure 1—figure supplement 2C and D). These findings indicate that the expanded Pcgf1 KO GMPs can supply mature myeloid cells and do not show evident differentiation block. This notion was supported by better production of Mac-1+ mature myeloid cells by Pcgf1 KO GMPs than control GMPs in culture (Figure 5—figure supplement 1A). Even in Pcgf1 KO mice at the MPN stage, GMPs actively produced mature myeloid cells and no obvious differentiation block like AML was observed. As we discussed, the molecular machineries that drive emergency myelopoiesis are often hijacked by transformed cells (Hérault et al., 2017). It is assumed that the epigenetic status that allows GMP self-renewal was enforced over time in the absence of Pcgf1, leading to enhanced production of mature myeloid cells that mimic MPN. Indeed, we did not see accumulation of c-Kit+ leukemic blasts in BM by FACS or accumulation of leukemic blasts in BM images with higher magnification (Figure 6F). Although we haven't investigated whether the disease is transplantable or not, we would like to address this in our future experiments. We described these points in lines 131-138 and 400-405.

3. It is suggested that Figure S6H should be in the last main figure, as it is a really helpful summary of the roles of PRC 1.1 in hematopoiesis.

Thank you for your suggestion. We presented Figure S6H as Figure 6H.

Reviewer #2 (Recommendations for the authors):

Specific comments:

1. The work clearly shows a myeloid bias in hematopoiesis. It is not clear from the data as presented whether this is resulting in a normal but excessive myeloid differentiation, or from a myeloid skew with a block in the maturation of the mature myeloid cells. This is an important difference and would be important to understand. The present data demonstrate that the mice accumulate GMP and myeloid progenitors, but they also develop MPN and myeloid malignant/pre-malignant states. An alternative interpretation could be that the mature myeloid cells are not differentiating normally and that this is leading to excessive myeloid production to try to generate a functionally competent myeloid population for host defence.

We think the reviewer’s points are very important. The data in Figure S2 (Figure 1—figure supplement 2 in revised manuscript) indicate accumulation of mature myeloid cells in Pcgf1 KO BM and spleen. To further confirm this, we performed additional FACS analyses using mature myeloid surface marker (Ly6G, Ly6C, F4/80) antibodies. The results showed that the number of mature myeloid cells expressing myeloid differentiation markers increased in Pcgf1 KO BM and spleen. Monocytes but not neutrophils or F4/80+ macrophages significantly increased in Pcgf1 KO BM while both neutrophils and monocytes significantly increased in Pcgf1 KO spleen. We replaced the flow cytometric profiles of Mac1 and Gr1 staining with those of Mac1, Ly6G, and Ly6c staining and added graphs of their absolute numbers (Figure 1—figure supplement 2C and D). These findings indicate that the expanded Pcgf1 KO GMPs can supply mature myeloid cells and do not show evident differentiation block. This notion was supported by better production of Mac-1+ mature myeloid cells by Pcgf1 KO GMPs than control GMPs in culture (Figure 5—figure supplement 1A). Even in Pcgf1 KO mice at the MPN stage, GMPs actively produced mature myeloid cells and no obvious differentiation block like AML was observed. As we discussed, the molecular machineries that drive emergency myelopoiesis are often hijacked by transformed cells (Hérault et al., 2017). It is assumed that the epigenetic status that allows GMP self-renewal was enforced over time in the absence of Pcgf1, leading to enhanced production of mature myeloid cells that mimic MPN. Indeed, we did not see accumulation of c-Kit+ leukemic blasts in BM by FACS or accumulation of leukemic blasts in BM images with higher magnification (Figure 6F). We described these points in lines 131-138 and 400- 405.

2. The authors should more clearly describe the mature myeloid cells in the animals (across PB, BM, Spleen) and it would be useful to provide FACS plots of Mac-1/Gr-1 profiles (+/- F4/80, Ly6C/G).

Thank you for your valuable suggestion. As described in response to your point 1, we performed an additional FACS analysis and presented the results in Figure 1—figure supplement 2C and D.

3. The authors describe myeloid cells in the figure legends as "Mac-1+ and/or Gr-1+ myeloid cells" positive cells – please be more specific and define the populations. This applies throughout the manuscript and assays.

For PB analysis, we used a mixture of Mac-1 and Gr-1 antibodies with the same conjugation. So, we defined myeloid cells as those positively reacted with a mixture of Mac-1 and Gr-1 antibodies. In this case, all types of myeloid cells were included. Instead, we performed detailed profiling of BM and spleen myeloid cells using Mac-1, Ly6C and Ly6G antibodies as described above (Figure 1—figure supplement 2C and D).

4. In the data in Figure 2E, can the authors provide qPCR or similar levels of Cebpa in the different genotypes. Is it demonstrated that germ-line heterozygous Cebpa would cause a reduction in the expression of Cebpa?

We have added qPCR data to Figure 2E. Cebpa gene expression levels in Pcgf1Δ/Δ ;CebpaΔ/+ cells were significantly reduced (Figure 2E lower right).

5. Did the authors age any mice post-5-FU to determine if this was sufficient to predispose to MPN generation or induce a more pronounced block in myeloid maturation?

Thank you for your very interesting suggestion. We would like to test in our future experiments.

6. The Hoxa9 reexpression studies in Figure 5G – This doesn't appear to have a WT control. The overexpression of Hoxa9 may be expected to have the same effect in a WT cell as that displayed (I am assuming this is a Pcgf1 deficient cell but this isn't completely clear to me from the text or legend).

We apologize for the lack of clarity in our description. The experiment in Figure 5G was performed using Hoxa9-overexpressing WT LSK cells, not in Pcgf1 KO cells. This experiment was designed to test whether Hoxa9 overexpression alone can maintain the immunophenotypic GMPs for a long period as in Pcgf1 KO cells.

7. Include an n for sick mice in Figure 6 (25 in total aged but how many got what diagnosis). Related to this, but potentially beyond the scope, is the disease transplantable?

We have indicated the number of mice per diagnosis in Figure 6A. Although we haven't investigated whether the disease is transplantable or not, we would like to address this in our future experiments.

Reviewer #3 (Recommendations for the authors):

Line 127: KO mice have mild anemia and leukopenia. Figure S2A is the only data associated with this statement – please show neutrophil, monocyte, and lymphocyte counts separately.

Thank you for your suggestion. We showed the lineage cell counts (myeloid/B/T) 1 and 6 months after tamoxifen injection in Figure 1—figure supplement 2B. We also analyzed myeloid cells (neutrophils, monocytes, and macrophages) more in detail in the BM and spleen and presented the data in Figure 1—figure supplement 2C and D. We described these points in lines 131-138.

Figure 1I: Green/Red bar plotting could be problematic for people with color blindness. The labeling of the HSPC populations underneath the genotypes is poorly aligned – which adds to the confusion. Because the bars are stacked with multiple colors, the statistical significance is indicated for the total or specific colored portion?

Thank you for your important point. We changed the bar plotting color from Green/Red to Blue/Red. We also improved the labeling of genotypes and cell populations to make it easier to understand. Statistical significance is based on the overall number of cells. We indicated this point in Figure 1I legend.

How does PCGF1 non-cell autonomously control hematopoiesis? What % of Pcgf1Δ/Δ cells is needed to achieve this non-cell autonomous inhibition? This seems to be a potent aspect of regulation – some discussion would be helpful.

Thank you very much for your very interesting suggestion. These findings suggest that Pcgf1Δ/Δ hematopoietic cells suppress expansion of both Pcgf1Δ/Δ and co-existing WT HSPCs through non- autonomous mechanisms. It is possible that accumulating Pcgf1Δ/Δ mature myeloid cells produce inflammatory cytokines that are suppressive to HSPCs. We are very interested in this issue and would like to address it in our future project. We discussed this point in lines 169-173.

Line 167 and 169, "… cells display slower growth under HSPC expanding condition" and "better growth". The word "growth" in these sentences is more accurate to be replaced by "population doubling".

As the reviewer pointed out, we replaced the word "growth" with "population doubling".

S5A is never referenced in the text. S5B, middle plot. Significance is indicated by a star in the B cell portion of the stacked bars. This is for comparing which samples/cell types, as multiple ways of comparison can be made.

We unintentionally missed to reference Figure S5A (Figure 2—figure supplement 1A) in revised manuscript in the text. We referred to Figure S5A in line 215. The significance shown in the Figure S5B (Figure 2—figure supplement 1B in revised manuscript) middle plot was calculated by comparing the proportion of each lineage cell of each genotype to that in GFP control. We described this point in the legend.

Figure 2D/E provided in vitro results for restoring CEBPa in rescuing the myeloid bias in the Pcgf1Δ/Δ HSPCs. Support this claim with in vivo results.

Thank you for your suggestion. We tried competitive BM transplantation assays using conditional Pcgf1 KO, Cebpa KO, and compound mice to rescue dysregulated differentiation of Pcgf1Δ/Δ HSPCs. However, we found that the deletion efficiency of Cebpa was poor in vivo using tamoxifen, while it is quite efficient in vitro using 4-hydroxytamoxifen. The data we obtained was hard to interpret. We would like to address this issue in our future experiments using different approaches.

Line 242 refers to Figure S3D: there is no panel D in Figure S3.

Thank you for pointing this out. We corrected Figure S3D to Figure 1D.

Line 311 Figure 3D – this should have been 3G, if I understood correctly. If I did not understand correctly, then Figure 3G is never cited. This is so terribly confusing!

We apologize for the incorrect description. We corrected Figure 3D to Figure 3G.

Figure 3I – so poorly aligned/labelled.

Line 320-322 "Moreover, ATAC-seq analyses also revealed trend toward enrichment of ATAC peaks containing CEBP motif in Pcgf1Δ/Δ HSPCs compared to the control, suggesting that chromatin regions containing CEBP motif tended to open in Pcgf1Δ/Δ HSPCs (Figure 3I)." The figure supporting this claim seems to have "p=0.3". Such a p-value is "ns" everywhere else. Why is it different here? As far as this reviewer can gather, the only significant difference in this figure is between control LSK and control GMP. Therefore, these results cannot and should not be cited as evidence to support "chromatin regions containing CEBP motif tended to open in HSPCs (Figure 3I)." Authors should remove any misleading claim based on this strenuous comparison.

We are sorry for misleading statements. We deleted Figure 3I and related statements.

Figure 4A. Can the difference in cluster GMPs be quantified?

We quantified cKit+FcγR+ cells (GMP clusters) using ImageJ software and presented the data in Figure 4A right panel.

Line 412-413 figure legend for Figure 4C: "Each symbol is derived from an individual culture". This is supposed to be from in vivo experiments? No statistical significance indicated?

Thank you for pointing this mistake out. We corrected “an individual culture” to “individual mice”. We have already shown statistical significance against day 0 for each genotype. For example, in the case of total BM, statistical significance was confirmed only on day 8 for both Control and Pcgf1Δ/Δ. We described these points in the legend.

Figure 5B,5G/H, states that HSC cultures produced GMPs. This statement is misleading and contributes to the over-interpretation of the results. To the best that this data can support, perhaps it is fair to say cells expressing immunophenotypic GMP markers are produced. The authors interpreted the difference in myeloid culture condition 1 and 2 being the different responses of HSC and GMP toward the loss of Pcgf1. However, it is equally possible that the different growth factors (type and concentrations) caused the differential response between the control and Pcgf1Δ/Δ cells. The authors need to culture HSC and GMP in both myeloid culture condition 1 and 2 to justify their claims.

Thank you for your suggestions. We changed the designation from GMP to immunophenotypic GMP. We also performed GMP culture in Figure 5A using myeloid culture condition 2, the same condition as HSCs in Figure 5B, and presented new data in Figure 5A.

Figure 5D. The images are missing the labels of genotypes.

These images are included simply as examples of nuclear β-Cat determination. All images are from control cells. We indicated this information in its legend.

Figure 5E legend says the samples are the GMPs from HSC cultures in 5B. Were these "GMPs" sorted? Please specify. Why are the day 16 control "GMPs" missing?

We sorted immunophenotypic GMPs (CD34+FcγR+c-Kit+Sca-1-Lineage-). We clarified this point in the legend. Unfortunately, control GMPs on day 16 were too few to analyze.

Figure 5F. What samples are being compared by RNA-seq? This reviewer is guessing the same samples shown in Figure 5E. If so, it begs the same question as to why are the day 16 control "GMPs" missing, and why are the day 0 Pcgf1Δ/Δ GMPs missing.

We are sorry for the lack of clarity, All samples were compared to the day 0 control GMPs. It is indicated in the upper left corner of the table. The day 16 control GMPs was too few to perform analysis. This is why we used day 0 Pcgf1Δ/Δ GMPs as a reference.

https://doi.org/10.7554/eLife.83004.sa2

Article and author information

Author details

  1. Yaeko Nakajima-Takagi

    Division of Stem Cell and Molecular Medicine, Center for Stem Cell Biology and Regenerative Medicine, The Institute of Medical Science, University of Tokyo, Tokyo, Japan
    Contribution
    Conceptualization, Data curation, Formal analysis, Validation, Investigation, Writing - original draft
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-2341-2409
  2. Motohiko Oshima

    Division of Stem Cell and Molecular Medicine, Center for Stem Cell Biology and Regenerative Medicine, The Institute of Medical Science, University of Tokyo, Tokyo, Japan
    Contribution
    Data curation, Formal analysis
    Competing interests
    No competing interests declared
  3. Junichiro Takano

    Laboratory for Developmental Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
    Contribution
    Formal analysis, Investigation
    Competing interests
    No competing interests declared
  4. Shuhei Koide

    Division of Stem Cell and Molecular Medicine, Center for Stem Cell Biology and Regenerative Medicine, The Institute of Medical Science, University of Tokyo, Tokyo, Japan
    Contribution
    Formal analysis, Investigation
    Competing interests
    No competing interests declared
  5. Naoki Itokawa

    Division of Stem Cell and Molecular Medicine, Center for Stem Cell Biology and Regenerative Medicine, The Institute of Medical Science, University of Tokyo, Tokyo, Japan
    Contribution
    Formal analysis, Investigation
    Competing interests
    No competing interests declared
  6. Shun Uemura

    Division of Stem Cell and Molecular Medicine, Center for Stem Cell Biology and Regenerative Medicine, The Institute of Medical Science, University of Tokyo, Tokyo, Japan
    Contribution
    Formal analysis, Investigation
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1520-6808
  7. Masayuki Yamashita

    Division of Stem Cell and Molecular Medicine, Center for Stem Cell Biology and Regenerative Medicine, The Institute of Medical Science, University of Tokyo, Tokyo, Japan
    Contribution
    Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-9459-4329
  8. Shohei Andoh

    Division of Stem Cell and Molecular Medicine, Center for Stem Cell Biology and Regenerative Medicine, The Institute of Medical Science, University of Tokyo, Tokyo, Japan
    Contribution
    Formal analysis, Investigation
    Competing interests
    No competing interests declared
  9. Kazumasa Aoyama

    Division of Stem Cell and Molecular Medicine, Center for Stem Cell Biology and Regenerative Medicine, The Institute of Medical Science, University of Tokyo, Tokyo, Japan
    Contribution
    Formal analysis, Investigation
    Competing interests
    No competing interests declared
  10. Yusuke Isshiki

    Department of Cellular and Molecular Medicine, Chiba University, Chiba, Japan
    Contribution
    Formal analysis, Investigation
    Competing interests
    No competing interests declared
  11. Daisuke Shinoda

    Division of Stem Cell and Molecular Medicine, Center for Stem Cell Biology and Regenerative Medicine, The Institute of Medical Science, University of Tokyo, Tokyo, Japan
    Contribution
    Formal analysis, Investigation
    Competing interests
    No competing interests declared
  12. Atsunori Saraya

    Department of Cellular and Molecular Medicine, Chiba University, Chiba, Japan
    Contribution
    Formal analysis, Investigation
    Competing interests
    No competing interests declared
  13. Fumio Arai

    Department of Stem Cell Biology and Medicine, Kyushu University, Fukuoka, Japan
    Contribution
    Formal analysis, Investigation
    Competing interests
    No competing interests declared
  14. Kiyoshi Yamaguchi

    Division of Clinical Genome Research,Advanced Clinical Research Center, The Institute of Medical Science, University of Tokyo, Tokyo, Japan
    Contribution
    Formal analysis, Investigation
    Competing interests
    No competing interests declared
  15. Yoichi Furukawa

    Division of Clinical Genome Research,Advanced Clinical Research Center, The Institute of Medical Science, University of Tokyo, Tokyo, Japan
    Contribution
    Formal analysis, Investigation
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0462-8631
  16. Haruhiko Koseki

    Laboratory for Developmental Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
    Contribution
    Resources
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8424-5854
  17. Tomokatsu Ikawa

    Division of Immunobiology, Research Institute for Biomedical Sciences, Tokyo University of Science, Chiba, Japan
    Contribution
    Formal analysis, Investigation, Writing – review and editing
    Competing interests
    No competing interests declared
  18. Atsushi Iwama

    1. Division of Stem Cell and Molecular Medicine, Center for Stem Cell Biology and Regenerative Medicine, The Institute of Medical Science, University of Tokyo, Tokyo, Japan
    2. Laboratoty of Cellular and Molecular Chemistry, Graduate School of Pharmaceutical Sciences, University of Tokyo, Tokyo, Japan
    Contribution
    Conceptualization, Supervision, Funding acquisition, Writing – review and editing
    For correspondence
    03aiwama@ims.u-tokyo.ac.jp
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-9410-8992

Funding

Japan Society for the Promotion of Science (19H05653)

  • Atsushi Iwama

Japan Society for the Promotion of Science (20K08728)

  • Yaeko Nakajima-Takagi

Japan Society for the Promotion of Science (19H05746)

  • Atsushi Iwama

Japan Agency for Medical Research and Development (21zf0127003h0001)

  • Atsushi Iwama

Japan Agency for Medical Research and Development (JP223fa627001)

  • Atsushi Iwama

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

We thank Dr. Daniel G Tenen for providing us with Cebpa mutant mice. The super-computing resource was provided by The Human Genome Center, The Institute of Medical Science, The University of Tokyo. This work was supported in part by Grants-in-Aid for Scientific Research (#19H05653, #20K08728) and Scientific Research on Innovative Area 'Replication of Non-Genomic Codes' (#19H05746) from Japanese Society for the Promotion of Science, and Moonshot (#21zf0127003h0001) and UTOPIA (JP223fa627001) projects from the Japan Agency for Medical Research and Development.

Ethics

All experiments using mice were performed in accordance with our institutional guidelines for the use of laboratory animals and approved by the Review Board for Animal Experiments of Chiba University (approval ID: 30-56) and the University of Tokyo (approval ID: PA18-03).

Senior Editor

  1. Mone Zaidi, Icahn School of Medicine at Mount Sinai, United States

Reviewing Editor

  1. Yelena Ginzburg, Icahn School of Medicine at Mount Sinai, United States

Reviewer

  1. Carl R Walkley, St. Vincent's Institute of Medical Research, Australia

Version history

  1. Received: August 26, 2022
  2. Preprint posted: September 8, 2022 (view preprint)
  3. Accepted: June 1, 2023
  4. Accepted Manuscript published: June 2, 2023 (version 1)
  5. Version of Record published: June 22, 2023 (version 2)

Copyright

© 2023, Nakajima-Takagi et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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  1. Yaeko Nakajima-Takagi
  2. Motohiko Oshima
  3. Junichiro Takano
  4. Shuhei Koide
  5. Naoki Itokawa
  6. Shun Uemura
  7. Masayuki Yamashita
  8. Shohei Andoh
  9. Kazumasa Aoyama
  10. Yusuke Isshiki
  11. Daisuke Shinoda
  12. Atsunori Saraya
  13. Fumio Arai
  14. Kiyoshi Yamaguchi
  15. Yoichi Furukawa
  16. Haruhiko Koseki
  17. Tomokatsu Ikawa
  18. Atsushi Iwama
(2023)
Polycomb repressive complex 1.1 coordinates homeostatic and emergency myelopoiesis
eLife 12:e83004.
https://doi.org/10.7554/eLife.83004

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https://doi.org/10.7554/eLife.83004

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