Myelodysplastic syndromes (MDS) are heterogeneous hematopoietic disorders that are incurable with conventional therapy. Their incidence is increasing with global population aging. Although many genetic, epigenetic, splicing, and metabolic aberrations have been identified in patients with MDS, their clinical features are quite similar. Here, we show that hypoxia-independent activation of hypoxia-inducible factor 1α (HIF1A) signaling is both necessary and sufficient to induce dysplastic and cytopenic MDS phenotypes. The HIF1A transcriptional signature is generally activated in MDS patient bone marrow stem/progenitors. Major MDS-associated mutations (Dnmt3a, Tet2, Asxl1, Runx1, and Mll1) activate the HIF1A signature. Although inducible activation of HIF1A signaling in hematopoietic cells is sufficient to induce MDS phenotypes, both genetic and chemical inhibition of HIF1A signaling rescues MDS phenotypes in a mouse model of MDS. These findings reveal HIF1A as a central pathobiologic mediator of MDS and as an effective therapeutic target for a broad spectrum of patients with MDS.
Significance: We showed that dysregulation of HIF1A signaling could generate the clinically relevant diversity of MDS phenotypes by functioning as a signaling funnel for MDS driver mutations. This could resolve the disconnection between genotypes and phenotypes and provide a new clue as to how a variety of driver mutations cause common MDS phenotypes. Cancer Discov; 8(11); 1438–57. ©2018 AACR.
See related commentary by Chen and Steidl, p. 1355.
This article is highlighted in the In This Issue feature, p. 1333
Myelodysplastic syndromes (MDS) are a group of heterogeneous clonal disorders that are characterized by ineffective hematopoiesis and unilineage or multilineage dysplasia (1, 2). Because of its diversity and complexity, the pathogenesis of MDS remains to be elucidated. Limited preclinical models are available for dissecting the pathogenesis and testing new drugs, and each model has its limitations (3, 4). Stem-cell transplantation is a curative strategy for MDS; however, few patients are eligible for transplantation. Further elucidation of the pathogenesis of MDS and development of novel therapeutic strategies are needed. MDS are associated with mutations in chromatin-modifying enzymes, splicing factors, transcription factors, cohesin complex, and metabolic enzymes that regulate hematopoietic stem cell (HSC) self-renewal, survival, and differentiation. Cooperating genetic lesions occur, involving signaling molecules that regulate cell growth and proliferation (1, 2, 5). Although a number of heterogeneous genomic aberrations have been identified in patients with MDS (6, 7), their key clinical phenotypes are similar. Therefore, we hypothesized that driver mutations activate common underlying mechanisms involved in MDS phenotypes. Identification of these key mediators would reveal fundamental insights into MDS pathogenesis and present novel opportunities for therapeutic intervention beyond specific mutations for MDS.
Hypoxia-inducible factor-1α (HIF1A) is a critical transcription factor for the hypoxic response, angiogenesis, normal HSC regulation, and cancer development (8, 9). Importantly, HIF1A is also essential for the activation of innate and adaptive immunity (10). HIF1A is regulated by both oxygen-dependent and oxygen-independent mechanisms (11). HSCs and progenitor cells (HSPC) isolated from patients with MDS display abnormal self-renewal and differentiation, and accumulating clinical and research evidence suggests an important role for systemic inflammation and immune activation in MDS pathogenesis (12). Thus, we tested the impact of HIF1A signaling in MDS.
Activated HIF1A Pathway in a Broad Spectrum of Patients with MDS
HIF1A is mainly regulated at translational and protein levels. Thus, analyzing downstream HIF1A signature gene expression is a reliable approach to measure HIF1A activation. To determine whether patients with MDS have an activated HIF1A gene signature, we analyzed a published cohort of CD34+ bone marrow (BM) cells isolated from healthy donors (n = 17) or from patients with MDS (n = 183; ref. 13). This MDS cohort contains patients with refractory anemia (RA; n = 55), refractory anemia with ring sideroblasts (RARS; n = 48), or refractory anemia with excess blast type 1 (RAEB1; n = 37) or type 2 (RAEB2; n = 43). HIF1A regulates many genes, which could be either HIF1A direct targets or indirect targets (such as those regulated by HIF1A-regulated miRNAs). The number of HIF1A-regulated genes exceeds 1,000 and continues to increase (11). Notably, there are unique sets of target genes activated by HIF1A signaling in individual cell types (different lineage, at different maturation stage; ref. 11). Recently, HIF1A-induced genes (which include both direct and indirect targets) in human cord blood (CB) CD34+ cells have been defined (14). To limit the number of HIF1A-induced genes and generate the gene set for analysis, we selected 3-fold upregulated genes in the constitutively active HIF1A-transduced CD34+ CB cells compared with control cells. Gene set enrichment analysis (GSEA) revealed that expression of these genes is significantly enriched in the MDS cohort (Fig. 1A; Supplementary Fig. S1A). Some well-known classic HIF1A target genes were also significantly upregulated in the MDS patient data set (Supplementary Fig. S1B). Using IHC staining, we then determined HIF1A activation (protein accumulation) in the BM biopsies from patients with MDS (n = 38), versus deidentified adult BM samples (diagnosed as apparently normal; n = 6). Consistent with transcriptional analysis of CD34+ cells (Fig. 1A), we found a high frequency of HIF1A-expressing cells in both low-risk and high-risk revised international prognostic scoring system (IPSS-R; ref. 15) groups (Fig. 1B and C; Supplementary Fig. S2). The expression levels of HIF1A mRNA in these patients with MDS were not increased (Fig. 1D). These results suggest that the HIF1A pathway (but not HIF1A mRNA) is widely activated in a broad spectrum of patients with MDS in both low-risk and high-risk groups.
The frequency of HIF1A-expressing cells did not associate with the mutational profile (Fig. 1C). Mutations in DNMT3A, TET2, ASXL1, RUNX1, and MLL-partial tandem duplication (MLL-PTD) have been reported in patients with MDS (5). To determine whether MDS-associated mutations induce HIF1A signaling, we performed immunoblot analysis of c-KIT+ BM cells from Dnmt3aΔ/Δ mice, Tet2Δ/Δ mice, Runx1Δ/Δ mice, Asxl1Δ/Δ mice, and Mll-PTD knock-in heterozygous mice (MllPTD/WT mice). The expression of HIF1A protein in c-KIT+ cells isolated from these mice was significantly increased compared with control cells (Fig. 1E; Supplementary Fig. S3A–S3J). These results suggest that MDS-associated mutations induce HIF1A signaling in mouse models without obvious anemia or other MDS phenotypes.
HIF1A Activation Is Sufficient for the Development of MDS
To determine whether HIF1A activation is sufficient to induce MDS phenotypes, we generated transgenic mice with blood cell–specific and inducible HIF1A expression. Using a Vav1-Cre/Rosa26-loxP-Stop-loxP (LSL) reverse-tetracycline-controlled transactivator (rtTA) driver (Rosa26-LSL-rtTA), we achieved doxycycline-inducible expression of both a stable and constitutively active human HIF1A triple-point-mutant (TPM; ref. 16) and wild-type (WT) ARNT (also known as HIF1B, a subunit for dimerization with HIF1A; tet-on-TPM/ARNT; B6J/129 × 1 SvJ; Fig. 2A). After doxycycline administration, we found increased HIF1A protein expression in the c-KIT+ BM cells from Vav1-Cre/TPM mice (Fig. 2B). The resulting HIF1A protein expression level was 1.5-fold higher than in MllPTD/WT cells (Fig. 2C). To identify gene-expression changes, we performed RNA-sequencing (RNA-seq) analysis of Vav1-Cre/TPM and wild-type c-KIT+ BM cells. Pathway enrichment analysis revealed that a number of HIF1A-related gene sets were significantly upregulated in the c-KIT+ BM cells from Vav1-Cre/TPM mice (Fig. 2D). Notably, ARNT dimerizes not only with HIF1A but also with aryl hydrocarbon receptor (AHR). Genes downregulated by the small molecule SR1, a known AHR antagonist, were not upregulated in the c-KIT+ BM cells from Vav1-Cre/TPM mice. This clearly suggests that coexpression of TPM protein with WT ARNT protein selectively activates HIF1A signaling (but not AHR signaling) in c-KIT+ BM cells (Supplementary Fig. S4A).
Although doxycycline administration had no observable effect on control mice, Vav1-Cre/TPM mice quickly developed thrombocytopenia, then within 2 to 8 months leukocytopenia, macrocytic anemia, and splenomegaly gradually developed (Fig. 2E; Supplementary Fig. S4B). Induced Vav1-Cre/TPM BM consistently showed multilineage dysplasia, with micromegakaryocytes and megakaryocytes with multiple separate nuclei, which are major diagnostic criteria for MDS (Fig. 2F). Most of the mice also developed BM reticulin (but not collagen) fibrosis (Supplementary Fig. S4C), which is associated with a worse outcome in patients with MDS (17). The frequency of HSPCs in BM from Vav1-Cre/TPM mice was increased (Supplementary Fig. S4D and S4E). Among the myeloid progenitors, increase in the megakaryocyte-erythrocyte progenitors was prominent (Supplementary Fig. S4E). To evaluate the clonal advantage of the Vav1-Cre/TPM cells, we performed in vitro serial colony-forming unit (CFU) replating assay and in vivo competitive bone marrow transplantation (CBMT) assay (1:1 ratio; Supplementary Fig. S4F). The number of colonies derived from Vav1-Cre/TPM BM cells was significantly higher even after serial replating than that of control cells (Supplementary Fig. S4G and S4H). We also found long-term repopulating advantage of Vav1-Cre/TPM cells in the CBMT assay (Supplementary Fig. S4I). We note that the phenotypes of the primary Vav1-Cre/TPM mice were transplantable when we transferred whole BM cells into lethally irradiated WT mice (Supplementary Fig. S4J).
Concordant with anemia, the late-stage erythroblasts were decreased, accompanied by a significant reduction of basophilic and polychromatic erythroblasts (Fig. 2G–I). The impairment of late-stage erythropoiesis causes ineffective erythropoiesis, which is one of the key features of MDS. It has been proposed for several decades that macrophages play a critical role in the late stage of erythropoiesis (18). We therefore examined the potential impact of activated HIF1A signaling on macrophage subsets, which could indirectly influence erythropoiesis. We found significant decreases in both the frequency and absolute number of F4/80+ Ter119+ BM erythroblastic islands (Fig. 2J and K), along with phenotypic inflammatory macrophages in the Vav1-Cre/TPM BM (Fig. 2L and M).
To dissect the cell-intrinsic/extrinsic effect of HIF signaling activation, we utilized tissue-specific Cre transgenes to restrict HIF1A signaling within the hematopoietic system. Specifically, we combined Rosa26-LSL-rtTA and tet-on-TPM/ARNT alleles with an erythroid lineage–specific EpoR-Cre allele (EpoR-Cre/TPM), a megakaryocyte lineage–specific PF4-Cre allele (PF4-Cre/TPM), and a myelomonocytic lineage LysM-Cre allele (LysM-Cre/TPM). Mice with the corresponding tissue-specific Cre expression served as controls. HIF1A induction in the erythrocyte lineage did not cause anemia (Supplementary Fig. S5A), and the differentiation pattern of erythroblasts (Supplementary Fig. S5B–S5D) and the number of BM erythroblastic islands (Supplementary Fig. S5E) were not altered in the EpoR-Cre/TPM mice. When we induced HIF1A expression in the megakaryocyte lineage, the PF4-Cre/TPM mice quickly developed thrombocytopenia (Supplementary Fig. S5F) and micromegakaryocyte formation (Supplementary Fig. S5G), but not anemia. On the other hand, the LysM-Cre/TPM mice showed a significant reduction of BM erythroblastic islands and eventually developed anemia (Supplementary Fig. S5H and S5I). Notably, LysM-Cre/TPM mice developed anemia more slowly compared with Vav1-Cre/TPM mice, suggesting that other additional cell types/factors participate in the development of anemia. Taken together, these data suggest that HIF1A is sufficient to trigger a variety of key MDS features via both cell-intrinsic and cell-extrinsic mechanisms. Considering that HIF1A is broadly activated in MDS from low risk to high risk, HIF1A may be the underlying mechanism that dictates MDS phenotypes, unifying genetically heterogeneous MDS under one umbrella.
Activation of HIF1A Signaling Is Critical for MLL-PTD–Mediated Clonal Advantage
The MLL-PTD is found in patients with MDS and acute myeloid leukemia (AML), but not in other hematologic malignancies (19, 20). Previously, we showed that MllPTD/WT mice present with MDS-associated features, such as increased self-renewal and apoptosis in HSPCs, expansion of the myeloid progenitor population, and ineffective hematopoiesis; however, these HSPC phenotypes are insufficient to progress to bona fide MDS or AML (19, 21). Given that MDS-associated mutations induce HIF1A signaling (Fig. 1E; Supplementary Fig. S3), we focused on MllPTD/WT mice (as an example) to dissect the significance of HIF1A in the context of MDS pathogenesis.
MLL is an epigenetic regulator: Its C-terminal SET domain has methyltransferase activity on lysine 4 on histone 3 (H3K4; ref. 22), which is retained in the MLL-PTD mutant. Trimethylation of H3K4 (H3K4me3) is associated with transcription activation. To clarify the effects of MLL-PTD on target gene regulation, we performed H3K4me3 chromatin immnoprecipitation sequencing (ChIP-seq) analysis of lineage marker− SCA1+ c-KIT+ (LSK) cells isolated from WT and MllPTD/WT mice. Interestingly, the DNA sequences underlying H3K4me3 peaks uniquely enriched in MllPTD/WT mice were significantly enriched for the Hif1a/Arnt heterodimer DNA binding motif (Fig. 3A and B). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis revealed enrichment in several inflammatory/immune response–related pathways, such as systemic lupus erythematosus (P = 1.61e−48), chemokine signaling (P = 9.06e−37), cytokine–cytokine receptor (P = 1.09e−36), and Toll-like receptor (P = 5.78e−29) signaling pathways (Fig. 3C; Supplementary Table S1), as well as glycolysis/gluconeogenesis pathways (P = 2.84e−14). We note that HIF1A is a well-known regulator of glycolysis/gluconeogenesis (8, 23). We also compared the H3K4me3 ChIP-seq data of LSKs from MllPTD/WT mice with the ENCODE binding data of human CD34+ myeloid progenitors. We then performed KEGG pathway analysis using genes on the overlapped H3K4me3 peaks. Similar to what was enriched in the initial analysis (Fig. 3C), we found upregulation of several inflammation- and HIF signaling–related pathways (Supplementary Fig. S6A).
MLL-PTD confers a clonal advantage in stress hematopoiesis (19). To evaluate the biological impact of HIF1A upregulation on MLL-PTD–induced phenotypes, we generated MllPTD/WT/Hif1aflox/flox/Mx1-Cre mice. It has been reported that assays of BM CFU-C and CFU-S did not show a significant difference between Hif1aflox/flox cells and Hif1aΔ/Δ cells (9). We have shown that MllPTD/WT HSPCs can be replated more than three times, and MllPTD/WT HSCs show significantly enhanced fitness in a CBMT assay (1:1 ratio), even after the secondary BMT (19). However, MllPTD/WT/Hif1aΔ/Δ cells stopped growing after the second replating (Supplementary Fig. S6B and S6C), similar to the WT cells. In a CBMT assay (1:1 ratio), Hif1a abrogation attenuated the repopulating advantage in MllPTD/WT cells to the level of WT cells (Hif1af/f cells; Supplementary Fig. S6B, S6D, and S6E). Taken together, these results suggest that, similar to Vav1-Cre/TPM mice, MLL-PTD–mediated HIF1A signaling controls HSC clonal expansion and fitness.
MLL-PTD Stabilizes HIF1A through Pseudohypoxia
Because ubiquitination is critical for HIF1A protein degradation in normoxia (or even in hypoxia), we tested whether downregulation of E3 ligases contributes to MLL-PTD–mediated HIF1A protein stabilization in normoxia. Von Hippel–Lindau tumor suppressor (VHL), which recognizes prolyl hydroxylase (PHD)–mediated hydroxylation of proline residues, is a well-known E3 ubiquitin ligase for oxygen-dependent HIF1A protein degradation (24). MDM2 E3 ubiquitin ligase is also involved in HIF1A protein degradation independent of oxygen condition (ref. 25; Fig. 3D). MDM2 expression is positively regulated by p53, whereas HIF1A expression is negatively regulated by p53. However, mRNA expression levels of Vhl and Mdm2 were increased in c-KIT+ cells isolated from MllPTD/WT mice compared with control cells. Trp53 mRNA expression was not altered in the c-KIT+ BM cells from MllPTD/WT mice (Supplementary Fig. S7A and S7B). This suggests that MLL-PTD stabilizes HIF1A through other mechanisms.
PHDs are α-ketoglutarate (α-KG)–dependent dioxygenase, catalyzing the hydroxylation of the proline residue using O2, Fe2+, and α-KG as substrates. Notably, α-KG is an intermediate metabolite of the tricarboxylic acid (TCA) cycle in mitochondria. Thus, accumulation of subsequent metabolites of α-KG, such as succinate, fumarate, and malate, inhibits PHD activity even in normoxia, leading to pseudohypoxia. Indeed, mitochondrial dysfunction or the Warburg effect is known to result in accumulation of TCA intermediate metabolites, leading to activation of HIF signaling (Fig. 3D). Thus, in order to determine whether MLL-PTD affects mitochondrial function, we first measured cellular respiration using c-KIT+ BM cells from MllPTD/WT mice (Fig. 3E and F). We found significantly decreased basal respiration, ATP production, and maximal respiration of MllPTD/WT HSPCs compared with WT cells (Fig. 3F). The activity of mitochondrial complexes I, II, and III was significantly decreased in the c-KIT+ BM cells from MllPTD/WT mice (Fig. 3G). To assess the mechanism of mitochondrial dysfunction, we first measured the mitochondrial DNA copy number. However, the copy number of mitochondrial DNA in the c-KIT+ BM cells was comparable in the WT and MllPTD/WT mice (Fig. 3H), suggesting similar mitochondrial mass in MllPTD/WT HSPCs. Among the mitochondrial electron transport chain complexes, complex II [succinate dehydrogenase (SDH)] also functions as a critical enzyme in the mitochondrial TCA cycle. Inactivation of SDHs has been reported in several cancers (26–28), resulting in a blockade of the TCA cycle, impairing cellular respiration (29). It has been well documented that the SDH inactivation causes HIF stabilization (29, 30). Thus, we first determined the gene-expression level of SDH subunits as well as genes that code for other key TCA cycle enzymes, which may result in a similar pseudohypoxia as SDH inactivation. We found that the expression level of Sdha, Sdhb, and Sdhd mRNA was significantly decreased in MllPTD/WT HSPCs (Fig. 3I). Consistent with the reduction of the transcripts, the expression levels of their proteins were also decreased (Fig. 3J). These results indicate that a defective complex II may lead to mitochondrial unfitness and pseudohypoxia in MLL-PTD cells.
Indeed, concordant with decreased complex II activity, nuclear magnetic resonance (NMR) assay showed a metabolic shift in the MllPTD/WT mice (Fig. 4A). Several TCA cycle metabolites, such as fumarate and malate, were significantly accumulated in the plasma of MllPTD/WT mice, whereas the concentration of pyruvate was increased in MllPTD/WT mice as a result of activation of glycolysis (Fig. 4B and C). Notably, the ratio of fumarate/α-KG and malate/α-KG was significantly increased (Fig. 4D). This concentration gradient could inhibit the α-KG–dependent dioxygenases including PHDs (Fig. 4D and E). Using the Vhlf/f Vav1-Cre allele, we confirmed that HIF1A protein is constantly degraded through the PHD–VHL axis in c-KIT+ HSPCs (Supplementary Fig. S8). However, adding succinate, fumarate, and malate into the culture medium stabilized HIF1A protein even in normoxia (Fig. 4F). Besides PHDs, TETs and histone demethylases are also α-KG–dependent dioxygenases (Fig. 4E; refs. 31–34). Both TETs and histone demethylases are critical epigenetic modifiers that are involved in DNA demethylation through oxidizing 5-methylcytosine (5mC) on DNA or regulation of methylation status on histones. Inactivation of TETs and histone demethylase function results in hypermethylation of DNA or histones. Notably, aberrant methylation status of DNA and histones are well-known features of MDS. To assess the levels of 5mC and 5-hydroxymethylcytosine (5hmC), we performed dot-blot analysis using genomic DNA in c-KIT+ cells from MllPTD/WT mice. We found increased 5mC levels and decreased 5hmC levels in DNA from MllPTD/WT cells compared with WT cells, indicating DNA hypermethylation in MllPTD/WT mice (Fig. 4G). We also found increased levels of histone methylation in MllPTD/WT HSPCs (Fig. 4H). Taken together, these results suggest that the Warburg effect and metabolic rewiring lead to accumulation of TCA cycle intermediate metabolites, which results in pseudohypoxia-mediated HIF1A signaling activation, in addition to the hypermethylation features in HSPCs of MllPTD/WT mice.
MLL-PTD and Mutant RUNX1 Cooperate to Induce MDS Phenotypes
Although MllPTD/WT mice present with several MDS-related phenotypes, MllPTD/WT mice do not generate MDS or AML in primary or transplant recipient mice (19, 35). This suggests that additional genetic and/or epigenetic defects are necessary for transformation to MDS or AML. Importantly, almost all patients with MDS have more than one mutation (36), and several mouse models of MDS have been established by combining multiple MDS-associated mutations (37–39). In secondary AML and de novo AML, MLL-PTD mutation is significantly associated with mutations in the RUNX1 gene. Thus, we combined an Mll-PTD allele with an MDS patient–derived RUNX1 mutant, RUNX1-S291fsX300 (RX1-S291fs; ref. 40). We retrovirally introduced the RUNX1-S291fsX300 mutant into MllPTD/WT BM cells and examined their functional properties in vitro and in vivo. In CFU assays, introducing RUNX1 mutation into the MllPTD/WT BM cells allowed the cells to undergo indefinite serial replating, whereas MllPTD/WT/Empty cells replated 3 to 4 times (Fig. 5A). The MllPTD/WT/RX1-S291fs BMT mice developed leukocytopenia, macrocytic anemia, thrombocytopenia (Fig. 5B and C), and multilineage dysplasia in the BM (Fig. 5D). Total BM erythroblasts were significantly decreased in MllPTD/WT/RX1-S291fs mice (Fig. 5E). We found a significant reduction of basophilic and polychromatic erythroblasts (Fig. 5F and G) and BM erythroblastic islands in MllPTD/WT/RX1-S291fs mice, compared with the control mice (Fig. 5H and I). MllPTD/WT/RX1-S291fs mice also developed BM fibrosis (Supplementary Fig. S9A). Eventually, all MllPTD/WT/RX1-S291fs mice died of MDS complications, mainly severe anemia or infection (Supplementary Fig. S9B).
Notably, RUNX family proteins have also been shown to affect HIF1A transcriptional activity, cooperative target gene regulation, and protein degradation (41–43). WT RUNX1 has been reported to inhibit HIF1A transcriptional activity (41). Indeed, Runx1-deleted cells display a dramatic increase of HIF1A protein compared with WT cells (Fig. 1E; Supplementary Fig. S3A). Interestingly, as contrasted with the case of MllPTD/WT cells, we found that the expression level of Mdm2 mRNA was significantly decreased in c-KIT+ BM cells from Runx1Δ/Δ mice compared with WT cells (Fig. 5J). Trp53 mRNA expression was not altered in the c-KIT+ BM cells from Runx1-deficient mice (Fig. 5K), which is consistent with the results reported in a recently published paper showing decreased p53 protein (but not mRNA) expression levels and ribosome biogenesis (RiBi) in Runx1-deficient HSPCs (44). On the other hand, the expression levels of SDH subunits, which were decreased in the MllPTD/WT cells, were not decreased in c-KIT+ HSPCs from Runx1Δ/Δ mice compared with WT cells (Supplementary Fig. S9C). In CFU assays, the expression levels of HIF1A protein in RUNX1 mutant–transduced MllPTD/WT BM cells (MllPTD/WT/RX1-S291fs cells) were further increased compared with those in control vector–transduced MllPTD/WT BM cells (MllPTD/WT/Empty cells; Supplementary Fig. S9D and S9E). The expression of HIF1A protein in c-KIT+ cells isolated from MllPTD/WT/RX1-S291fs mice was increased compared with MllPTD/WT/Empty cells (Fig. 5L). Importantly, this HIF1A protein expression level was similar to that in Vav1-Cre/TPM mice, which presented a variety of clinically relevant MDS phenotypes. The expression level of known HIF1A target genes was also increased, suggesting the additive activation of transcriptional activity (Supplementary Fig. S9F). In contrast, MDM2 and p53 proteins were decreased in MllPTD/WT/RX1-S291fs cells compared with MllPTD/WT/Emptycells (Fig. 5L). The downregulation of MDM2 and p53 is consistent with results obtained from patients with MDS with RUNX1 mutations (Supplementary Fig. S9G and S9H) and (given the ability of MDM2 to control HIF1A protein; ref. 25) may explain the additive accumulation of HIF1A protein in MDS with RUNX1 mutations (Fig. 5M) as the expression levels of SDH subunits remained low in MllPTD/WT/RX1-S291fs mice (Supplementary Fig. S9I). Taken together, these results suggest that RUNX1 and MLL mutations cooperate to additively activate HIF1A expression in a hypoxia-independent manner, which is sufficient to induce phenotypic MDS in a mouse model.
Essential Role of HIF1A in MDS Development
Neither MllPTD/WT mice alone nor Runx1Δ/Δ mice alone develop MDS or AML phenotypes, whereas each of them exhibits increased HIF1A protein expression and clonal advantage of HSPCs. On the other hand, additively increased expression of HIF1A protein was found in the MllPTD/WT/RUNX1-mutant MDS model, which induces a variety of clinically relevant MDS phenotypes. This suggests a threshold effect, requiring the additive effect of hypoxia-independent HIF1A signaling activation for MDS development. To test the essential role of HIF1A in MDS, we genetically eliminated HIF1A expression in our MDS model. We generated MllPTD/WT/Runx1flox/flox/Mx1-Cre mice (to model loss-of-function RUNX1 mutations; refs. 44, 45) and MllPTD/WT/Runx1flox/flox/Hif1aflox/flox/Mx1-Cre mice. Similar to mutant-RUNX1 overexpression, the expression of HIF1A protein in c-KIT+ cells isolated from MllPTD/WT/Runx1Δ/Δ mice was approximately 1.5-fold higher than in cells isolated from MllPTD/WT mice (Fig. 6A; Supplementary Fig. S10A). In CFU assays, Runx1-deleted MllPTD/WT BM cells (MllPTD/WT/Runx1Δ/Δ cells) produced a higher number of colonies than WT or MllPTD/WT BM cells and could be serially replated more than six times (Supplementary Fig. S10B). MllPTD/WT/Runx1Δ/Δ mice also presented with multilineage dysplasia (Supplementary Fig. S10C–A10G), and recipient mice transplanted with MllPTD/WT/Runx1Δ/Δ cells developed MDS (Fig. 6B). In contrast, Hif1a deletion rescued dysplasia formation (Supplementary Fig. S10C–S10G). Hif1a deletion also abrogated disease development (Fig. 6B). This was not due to the elimination of the donor-derived cells (Fig. 6C), as most of the donor-derived cells in MllPTD/WT/Runx1Δ/Δ BMT mice were CD11b+ myeloid cells (Fig. 6D), and after deletion of Hif1a, these cells were still CD11b+ myeloid cells (Fig. 6D). Interestingly, the recipient mice transplanted with MllPTD/WT/Runx1Δ/Δ/Hif1aΔ/Δ cells did not develop macrocytic anemia (Fig. 6E). Hif1a deletion also partially rescued the thrombocytopenia and megakaryocyte phenotype of the MllPTD/WT/Runx1Δ/Δ BMT mice (Fig. 6E; Supplementary Fig. S10F), likely because RUNX1 is a critical regulator of megakaryocytopoiesis. These results suggest that aberrantly stabilized HIF1A protein and activated HIF1A signaling play critical roles in the development of key MDS phenotypes (ineffective erythropoiesis, thrombocytopenia, and BM dysplasia).
To better characterize the effect of HIF1A in this model, we first performed RNA-seq analysis of c-KIT+ BM cells comparing MllPTD/WT/Runx1Δ/Δ with or without Hif1a (Fig. 6F). To define HIF1A target genes within the data set, we intersected differentially expressed genes with both published HIF1A ChIP-seq data sets from murine hematopoietic cell populations (GSE40918, GSE36104; refs. 46, 47), as well as HIF1A binding sites based on the literature and DNA sequence conservation (GO-Elite transcription factor (TF)–target database “PAZAR,” ref. 48; Broad Molecular Signatures Database “MSigDB” Pathway Commons, ref. 49; and Whole-Genome R-Vista, Supplementary Table S2). We found that several previously identified HIF1A targets, such as Gata2, Ndrg1, Pgam1, Ak4, Zeb2, Egln3, and Ddit4, were downregulated in response to Hif1a deletion (Fig. 6F; Supplementary Fig. S10H). In contrast, several key genes related to erythroid–megakaryocyte differentiation, such as Klf1, Gfi1b, Gata1, Pbx1, and Tal1, were upregulated in response to Hif1a deletion (Fig. 6F; Supplementary Fig. S10H). Moreover, the loss of HIF1A also upregulated Irf4 (Fig. 6F), which is a key regulator for alternative macrophage activation (but not for inflammatory macrophages). Among these upregulated genes, Klf1, Tal1, and Irf4 were also predicted as HIF1A targets (Fig. 6F). We note that Hif1a deletion did not affect the pseudohypoxia-related glycolysis/oxidative phosphorylation gene signature in this model, suggesting that HIF1A signaling activation is downstream of pseudohypoxia conditions (Supplementary Fig. S11).
Echinomycin is an inhibitor of HIF1A-mediated target gene activation (50). In CFU assays, the MllPTD/WT BM cells were more sensitive to echinomycin than the WT BM cells (Supplementary Fig. S12A), suggesting that there is a therapeutic window. MllPTD/WT/Runx1Δ/Δ cells were also sensitive to echinomycin (Fig. 7A). In CFU replating assays using sorted cells from primary MllPTD/WT/Runx1Δ/Δ CFU cells, only the CD11b and Gr-1 double-negative population formed colonies, suggesting that this fraction contains CFU-initiating cells (Fig. 7B). Notably, echinomycin reduced this CFU-initiating population and induced differentiation to mature granulocytic cells (Fig. 7C). To determine the therapeutic efficacy of echinomycin in vivo, we first treated WT mice to assess its effect on normal hematopoiesis (Supplementary Fig. S12B). There was no significant change over time in peripheral blood (PB) cell counts between echinomycin- and vehicle-treated groups (Supplementary Fig. S12C). CBMT assay showed that the frequency of functional HSCs was also comparable between echinomycin- and vehicle-treated groups (Supplementary Fig. S12D). We then treated mice transplanted with BM cells from moribund MllPTD/WT/RX1-S291fs MDS mice with echinomycin and saw a significant prolongation of their survival (Fig. 7D and E). To determine the effect of echinomycin on human MDS cells, we xenografted MDS patient-derived MDSL cells (51) into NOD/SCID-IL2Rγ3GS (NSGS) mice (Fig. 7F). After the echinomycin treatment, we first evaluated the chimerism of MDSL cells in BM and spleen (SP; Fig. 7F–L). MDSL cells were significantly decreased in both BM and SP from the echinomycin-treated group compared with the vehicle treatment group (Fig. 7G–L). Echinomycin treatment also significantly prolonged the survival of MDSL xenografted NSGS mice (Fig. 7M and N). Together, our genetic deletion and chemical inhibition data strongly suggest that the HIF1A signaling pathway is a novel therapeutic target in MDS.
Complex human diseases may bear disparate mutation profiles in individual patients but still share a common underlying mechanism. During development, different tissues utilize key regulators, such as HIF1A. Using genetic mouse models, human data sets, and unbiased approaches, we have identified an important role for hypoxia-independently activated HIF1A signaling in MDS development (Supplementary Fig. S13). HIF1A is ubiquitously expressed, and most HIF1A regulation is posttranscriptional, with HIF1A protein stability being regulated by both oxygen-dependent and oxygen-independent degradation (8, 52). Furthermore, the HIF1A transactivation domain is also inactivated via HIF1A subunit inhibitor (HIF1AN; also known as FIH1)–mediated hydroxylation of an asparagine residue. Both protein stability and transcriptional activity are required to activate HIF1A signaling. Although mouse modeling of individual MDS-associated mutations showed the significant accumulation of HIF1A protein, single mutations are not enough to develop an MDS phenotype in mice. This might indicate a threshold effect preventing MDS development, requiring the additive effect of both HIF1A protein accumulation and activated transcriptional potential for MDS development. Individual mutations may have direct or indirect effects, leading to stabilization of HIF1A protein and activation of HIF1A signaling in both HSPCs and mature cell populations. Combination of two MDS-associated mutations induced additive accumulation of HIF1A protein in a hypoxia-independent manner and resulted in MDS phenotypes. Underscoring this point, a stable and constitutively active form of HIF1A induced in hematopoietic cells (Vav1-Cre/TPM mice) resulted in a variety of MDS-related phenotypes (recapitulating the HIF1A accumulation, transcriptional activation, and phenotypes induced by combining multiple MDS-associated genetic events).
Mechanistically, we found that MLL-PTD was associated with Warburg effect–type metabolic rewiring and hypoxia-independent activation of HIF1A signaling along with DNA hypermethylation. In contrast, HIF1A expression by mutant RUNX1 proteins was associated with downregulation of both MDM2 E3 ubiquitin ligase and p53 protein (Fig. 5M). We note that Vhl and Mdm2 expression was upregulated in the HSPCs from MllPTD/WT mice. Thus, it is possible that RUNX1 mutation or Runx1 deficiency further upregulate HIF1A protein levels and signaling in the MllPTD/WT/RUNX1-mutation mouse model by disrupting the RUNX1–p53–MDM2 axis in an MllPTD/WT background. Although deficiency of Dnmt3a, Tet2, and Asxl1 genes also significantly upregulates HIF1A protein expression, further elucidation of the detailed mechanisms for these and other major MDS-associated mutations is required for a better understanding of the MDS pathobiology and target identification. Nonetheless, given the common upregulation of HIF1A protein, HIF1A could be a promising target for a broad spectrum of MDS.
When we induced HIF1A expression in individual blood cell lineages, the megakaryocyte lineage–specific HIF1A induction caused thrombocytopenia and megakaryocyte dysplasia, indicating a sufficient, HIF1A-mediated, cell-intrinsic mechanism for deregulated megakaryocytopoiesis. Erythrocyte lineage–specific HIF1A induction did not cause aberrant erythropoiesis or anemia, suggesting an extrinsic mechanism for the ineffective erythropoiesis. On the other hand, HIF1A induction in the myelomonocytic lineage affected BM erythroblastic island formation by inducing inflammatory macrophages, triggering anemia that had a slower tempo than the pan-hematopoietic cell lineage induction of HIF1A. In Vav1-Cre/TPM mice, not only innate immune cells, such as monocytes and macrophages, but also lymphoid cells were significantly activated by HIF1A. We noted that HIF1A induction in blood causes phenotypic inflammatory macrophages. Although we did not characterize the role of T cells and dendritic cells in our MDS models, activated inflammatory macrophages, T cells, and dendritic cells may cooperate to impair the late-stage erythropoiesis and cause anemia. Thus, one possible explanation could be the lack of direct T-cell activation in the setting of myelomonocytic lineage induction of HIF1A. Indeed, activated innate and adaptive immunity has been reported in patients with MDS (12, 53). Aberrant immune activation could also interact with the BM microenvironment and affect disease development through a blood cell–extrinsic manner. The interaction between innate and adaptive immunity might be involved in the ineffective erythropoiesis, resulting in the complex refractory anemia found in patients with MDS.
Genetic deletion of Hif1a does not dramatically alter steady-state hematopoiesis (9). On the other hand, genetic deletion of Hif1a abrogated clonal advantage of MDS-related mutant cells and also reversed MDS phenotypes, including anemia and thrombocytopenia in our mutant and MDS mouse models. These results indicate that the gain-of-function effect of HIF1A signaling driven by mutations induces MDS-related phenotypes in different lineages and at various differentiation stages. Our analysis of RNA-seq data indicates that the major impact of HIF1A abrogation in the disease context is to relieve the block in erythroid and megakaryocyte differentiation. HIF1A abrogation resulted in Gata2 downregulation and Gata1 upregulation (GATA factor switching), and upregulation of several critical genes for erythroid and megakaryocyte differentiation, such as Klf1, Gfi1b, Tal1, and Pbx1. Erythropoiesis is regulated by both intrinsic and extrinsic mechanisms. Macrophages at erythroblastic islands play a critical extrinsic role for erythropoiesis. Macrophages are regulated by other immune cells, especially T cells. HIF1A abrogation resulted in the reduction of Zeb2 expression and upregulation of Irf4 expression. ChIP-seq data analysis indicated ZEB2 and IRF4 as HIF1A targets. ZEB2 is a key regulator of macrophages, dendritic cells, and also memory and cytotoxic T cells (54, 55). IRF4 is a key regulator of alternative macrophage activation (but not for inflammatory macrophages). We noted that HIF1A induction in blood causes phenotypic inflammatory macrophages, impairment of late-stage erythropoiesis, and decrease of BM erythroblastic islands. Thus, downregulation of Zeb2 and upregulation of Irf4 expression may explain the rescue of anemia phenotype through an erythroid cell–extrinsic mechanism.
A number of genetic, epigenetic, splicing, cohesin, and metabolic aberrations have been identified in MDS. Interestingly, for instance, DNMT3A and TET2 have an opposite function on DNA methylation, despite the high frequency of loss-of-function mutations in both genes. Moreover, in many cases, these aberrations are not specific for MDS, as the same mutations have also been identified in other hematologic disorders (5, 56). However, there is a clear distinction in the phenotypes, clinical course, and treatment responsiveness between MDS and other hematologic malignancies, implying the complex interplay between the combination of various mutations, the genetic background, and likely the microenvironment. MDS HSPCs retain differentiation potential and keep producing aberrant mature cell populations that have the same driver mutations as their HSPCs. Once MDS HSPCs lose their differentiation ability and acquire additional mutations, the disease can transform to acute leukemia. Current studies and recent evidence indicate that not only the HSPCs, but also cells at more mature stages, such as immune cell populations, play an important role in triggering complex MDS phenotypes (12).
Although future studies are required to elucidate the detailed mechanisms and critical downstream events in individual lineages that contribute to MDS phenotypes, our studies clearly provide new clues for dissecting and targeting MDS. Given the current focus on genomics in dissecting human disease pathobiology, we believe that our work illustrates the utility of comprehensive, unbiased genomics approaches to identify/target the critical signaling funnels for human diseases. The identification of such signaling funnels may help integrate big-data genome sequencing efforts and identify new research directions with therapeutic potential.
The mice with the genotypes of MllPTD/WT, Runx1flox/flox, Dnmt3aflox/flox, Vav1-Cre, EpoR-Cre, HIF1A-TPM, and NSGS were previously described (16, 19, 45, 57–60). Mx1-Cre, Hif1aflox/flox, Vhlflox/flox, LysM-Cre, PF4-Cre, Rosa26-LSL-rtTA (129 × 1/SvJ), Asxl1flox/flox, and Tet2flox/flox mice were purchased from The Jackson Laboratory. B6.SJL (CD45.1) and C57BL/6 (CD45.2) mice were obtained from the Cincinnati Children's Hospital Medical Center (CCHMC)/Cancer and Blood Diseases Institute (CBDI) mouse core. Polyinosine–polycytosine (poly (I:C); 12–18 μg/g; GE Healthcare) was injected into mice with Mx1-Cre transgene and their controls (without Mx1-Cre) intraperitoneally every other day for 3 to 5 times. All animals were housed in the animal barrier facility at CCHMC. All animal studies were conducted according to an approved Institutional Animal Care and Use Committee protocol and federal regulations.
Transient Transfection and Retrovirus Infection
Retroviruses were generated by calcium phosphate transient cotransfection of the retroviral vectors (pMY-RUNX1-S291fs-IRES-eGFP and pMY-IRES-eGFP) with the packaging plasmids Gag and Eco-env into 293T cells. 293T cells were maintained in DMEM with 10% FBS in a humidified incubator at 37°C and 5% CO2. BM cells were collected 3 days after a single dose of 5-fluorouracil (150 mg/kg, intraperitoneally) treatment. BM mononuclear cells were isolated using Histopaque (Sigma-Aldrich) and cultured in Iscove's Modified Dulbecco's Medium containing 10% FBS, 100 ng/mL mouse stem cell factor (PeproTech), 100 ng/mL mouse thrombopoietin (PeproTech), and 100 ng/mL human G-CSF overnight at 37°C in 5% CO2. First-round retroviral infection was performed using RetroNectin (Takara Bio). Polybrene was used for the second-round infection.
Western Blot Analysis
To isolate c-KIT+ BM HSPCs, freshly harvested BM cells were incubated with CD117 MicroBeads and separated on an AutoMACS Pro separator (Miltenyi) according to the manufacturer's specifications. Whole-cell extract was prepared by sonicating cells in a sodium dodecyl sulfate (SDS) sample buffer containing 10 mmol/L NaF, 0.2 mmol/L Na3VO4, 10 mmol/L β-glycerophosphate, 1 mmol/L phenylmethylsulfonyl fluoride (93482, Sigma-Aldrich), 1 mmol/L 4-amidinophenylmethanesulfonyl fluoride hydrochloride (A6664, Sigma-Aldrich), 2.5 mmol/L dithiothreitol, 5% 2-mercaptoethanol, and proteinase inhibitors (Sigma-Aldrich, P8849; Roche Diagnosis, 11873580001). After boiling at 95°C for 10 minutes, samples were separated by SDS-PAGE and electrotransferred to polyvinylidene fluoride (PVDF) membranes (Millipore). The following primary antibodies were used in this study: anti-HIF1A (NB100-105, Novus), anti-MDM2 (sc-965, Santa Cruz), anti-mouse p53 (1C12; 2524, Cell Signaling), anti-SDHA (ab14715, Abcam), anti-SDHB (ab14714, Abcam), anti-SDHD (ABT110, Millipore), anti-H3K4me3 (ab12209, Abcam), anti-H3K9me3 (millipore 07-442, Millipore), anti-H3K36me3 (ab9050, Abcam), anti-H3K79me3 (ab2621-100, Abcam), anti-Histone H3 (ab1791, Abcam), and anti-β-Actin (sc-1616R, Santa Cruz). Horseradish peroxidase–conjugated antibodies to rabbit (NA934V, GE Healthcare) or mouse (NA931V, GE Healthcare) were used as the secondary antibody, and Super Signal West Dura Chemiluminescent Substrate (Pierce) was used for ECL detection. ImageJ software was used for quantification (61).
Bone Marrow Transplantation
Lethally irradiated (9.5 Gy) 2- to 4-month-old B6.SJL mice (CD45.1) were used as recipients. Total BM cells (1.0–2.0 × 106 cells) from primary mice (CD45.2) or retrovirus vector-transduced BM cells (CD45.2) were injected into the tail vein. For radioprotection, 1 × 105 cells of freshly harvested whole BM cells from B6.SJL mice were cotransplanted. For MllPTD/WT/Rx1-S291fs mice in vivo experiments, GFP+ BM cells (1.0–2.0 × 106 cells) from primary BMT mice were used. In the case of 1:1 ratio competitive BMT assays, recipient mice received an equal number of competitive BM cells from B6.SJL mice or C57BL/6 mice. For secondary transplantation of 1:1 ratio competitive BMT assays, BM cells from primary recipients at 16 weeks after transplantation purified by CD45.1 depletion were transplanted into lethally irradiated secondary mice along with B6.SJL BM cells.
Flow-cytometric analysis and cell sorting were performed with FACSCanto I, FACSCanto II, or FACSAria instruments (BD Biosciences). Colony formed cells, PB, and BM cells were immunostained using the following anti-mouse antibodies: CD45.1 (A20), CD45.2 (104), B220 (RA3-6B2), CD3 (17A2), CD45 (30-F11), CD71 (C2), c-KIT (2B8), CD34 (RAM34), Sca-1 (D7; BD Pharmingen), CD11b (M1/70), CD16/32 (93; eBioscience), CD115 (AFS98), Gr-1 (RB6-8C5), F4/80 (BM8), Ter119 (Ter-119), CD80 (16-10A1), and CD206 (C068C2; BioLegend). For mature cell lineage exclusion, a lineage marker cocktail containing anti-mouse CD3e (145-2C11), B220 (RA3-6B2), Ter119 (Ter-119), CD11b (M1/70), Gr-1 (RB6-8C5), CD11c (HL3; BD Bioscience), and NK1.1 (PK136; BioLegend) antibodies was used. Anti-human CD45 (HI-30; BD Bioscience) or anti-human CD45 (2D1; eBioscience) antibodies were used for detecting human cells. Data were analyzed using the FlowJo software (Tree Star).
BM samples from unselected subjects with MDS were obtained from Blood Diseases Hospital at diagnosis or referral and gave informed consent compliant with the Declaration of Helsinki. Written informed consent was obtained in all cases according to the Institutional Review Board. BM mononuclear cells (BM MNC) were isolated from BM aspirates of patients with MDS and healthy donors using a Ficoll-Histopaque 1077-1 (Sigma-Aldrich) density gradient centrifugation. Genomic DNA was extracted using the AxyPrep blood genomic DNA miniprep kit (Axygen Biosciences). Mutation sequencing PCR primers were designed to amplify the coding sequences and splice sites of DNMT3A, SF3B1, U2AF1, and SRSF2. All products were confirmed by 2% agarose gel, purified using QIAquick Spin Kit (Qiagen), and sequenced using ABI PRISM 3730xl DNA Analyzer (Applied Biosystems). The sequence data files were analyzed using the Mutation surveyor3.25 software.
The 2-μm-thick formalin-fixed, paraffin-embedded BM biopsy sections were used. The BM biopsy sections were submitted to xylene and alcohol for deparaffinization, and then blockade of endogenous peroxidase as performed. Samples were incubated in HIF1A antibody (mgc3, Abcam) for 4 hours at room temperature. A Biotin Link was used as a secondary antibody for 40 minutes followed by the streptavidin–peroxidase method, visualization with the DAB chromogen, and counterstaining using hematoxylin. Deidentified and discarded adult BM samples with a diagnosis of “no abnormalities” (n = 6) were obtained from the Pathology Research Core at CCHMC. An exemption from the Institutional Review Board was obtained for these samples. Expression was reported as the percentage of positive cells per sample.
For human BM MNC samples, the TRIzol reagent (Invitrogen) was used to isolate total RNA. Reverse transcription was carried out with 1 μg total RNA from each specimen using the RevertAid First-Strand cDNA Synthesis Kit (Thermo Fisher). The cDNA was amplified with an Applied Biosystems 7500 Real-Time PCR System Instrument (Applied Biosystems). For murine c-KIT+ BM cell and kidney samples, total RNA was isolated using the RNeasy Micro Kit (Qiagen) and converted to cDNA using SuperScript III First-Strand Synthesis System for the RT-PCR Kit (Invitrogen). The cDNA was amplified using an Applied Biosystems Step One Plus thermal cycler (Applied Biosystems). SYBR Green Master Mix (Life Technologies) and specific primers for each gene (Supplementary Table S3) were used.
Total BM cells were incubated with CD117 MicroBeads, and c-KIT+ BM cells were separated on an AutoMACS Pro separator (Miltenyi) according to the manufacturer's specifications. Total RNA was isolated using RNeasy Mini Kit (Qiagen). The NEBNext Ultra Directional RNA Library Prep Kit (New England BioLabs) was used for library preparation, which used dUTP in cDNA synthesis to maintain strand specificity. In short, the isolated RNA was Mg2+/heat fragmented (∼200 bp) and reverse transcribed to first-strand cDNA, followed by second-strand cDNA synthesis labeled with dUTP. The purified cDNA was end repaired and dA tailed and then ligated to an adapter with a stem-loop structure. The dUTP-labeled second-strand cDNA was removed by USER enzyme to maintain strand specificity. After indexing via PCR (12 cycles) enrichment, the amplified library was cleaned up by AMPure XP beads for QC analysis. To check the quality and yield of the purified library, 1 μL library was analyzed by Bioanalyzer (Agilent) using DNA high-sensitivity chip. To accurately quantify the library concentration for the clustering, the library was 1:104 diluted in dilution buffer (10 mmol/L Tris–HCl, pH 8.0 with 0.05% Tween 20), and qPCR measured by Kapa Library Quantification kit (Kapabiosystem) using Applied Biosystems 9700HT real-time PCR system (Applied Biosystems). To study differential gene expression, individually indexed and compatible libraries were proportionally pooled (20–50 million reads per sample in general) for clustering in cBot system (Illumina). Libraries at the final concentration of 15 pmol/L were clustered onto a single read (SR) flow cell using Illumina TruSeq SR Cluster kit v3 and sequenced to 50 bp using a TruSeq SBS kit on the Illumina HiSeq system (Illumina).
RNA-seq Data Analysis
For the analyses of RNA-seq data of Vav1-Cre/Control mouse and Vav1-Cre/TPM mouse samples, sequence reads were aligned to the reference genome using BowTie2/TopHat2 (62), and reads aligning to each known transcript were quantified using Bioconductor (63). The differential expression analysis between Vav1-Cre/Control mice and Vav1-Cre/TPM mice samples was performed using the negative binomial statistical model of read counts as implemented in the edgeR Bioconductor package (64). The statistical significance of differential expression is established based on the FDR-adjusted P values (65). The pathway enrichment analysis was performed separately for upregulated and downregulated genes using the LRpath methodology (66) as implemented in the CLEAN package (67) and the gene lists from the MSigDB database (68).
For the analyses of RNA-seq data of MllPTD/WT/Runx1Δ/Δ and MllPTD/WT/Runx1Δ/Δ/Hif1aΔ/Δ samples, the FASTQ files were processed in the software AltAnalyze (version 2.1.0). Transcript per million estimates were calculated in AltAnalyze through a built-in call to Kallisto for read pseudoalignment and transcript quantification (Ensembl 72, mm10; ref. 69). Differentially expressed genes between MllPTD/WT/Runx1Δ/Δ and MllPTD/WT/Runx1Δ/Δ/Hif1aΔ/Δ samples were selected based on a fold change >2 and an empirical Bayes moderated t test P < 0.05 in AltAnalyze. TF-target enrichment analysis was carried out using the GO-Elite algorithm (PAZAR and Amadeus databases) in AltAnalyze (48). HIF1A targets were obtained from multiple sources and data analyses including two reanalyzed published ChIP-seq data sets (GSE40918 and GSE36104; refs. 46, 47), HIF1A target annotations from the GO-Elite TF-target database (PAZAR; ref. 48), and prior predicted targets from the Broad Molecular Signatures Database (MSigDB), Pathway Commons (49), and Whole-Genome R-Vista binding site predictions (70). The FASTQ files from the ChIP-seq studies were mapped using bowtie2 to the Mus Musculus genome. Duplicate reads were removed using Picard (https://broadinstitute.github.io/picard/) and Multimapped reads were filtered with SAMtools (version 1.1.19; ref. 71). MACS2 (72) was used for peak calling and then followed by closest gene annotation to identify peaks with the software GREAT (http://bejerano.stanford.edu/great) using the default options. The ATAC-seq data from LSK cells were obtained from the Gene Expression Omnibus (GEO) record (GSM1463179). For visualization, data were processed with the software biowardrobe (73), and tracks were displayed using the UCSC Genome Browser (74). RNA-seq data sets are deposited at GEO under accession numbers GSE83988 (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?token=srwtkoyctnynjot&acc=GSE83988) and GSE86953 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?token=qduhsmckvvwfzcn&acc=GSE86953).
H3K4me3 ChIP-seq and Data Analysis
BM LSK cells from WT and MllPTD/WT mice were isolated as described (75). These cells were subjected to ChIP-seq as previously described (76). Anti-histone H3K4me3 antibody (ab8580, Abcam) was used. The ChIP-enriched DNA was purified using the MinElute Reaction Cleanup Kit (Qiagen).
Differential bindings were analyzed using HOMER (v4.7) package (77) using find Peaks function (FDR < 0.001, F (fold enrichment over control tag count) > 4-fold, P (Poisson P value threshold relative to control tag count) < 0.001). To show the tag intensities around transcription start sites (TSS; + 10/−10 kbp regions), heat maps were generated using HOMER's annotate Peaks function and R's heat map package. No clustering method was used to visualize data; instead, the rows in the heat map are arranged by the coverage from strong to weak. For each differentially bound peak, RefSeq genes whose TSS are located within ±20 kbp from the peak center were chosen for gene set enrichment test using webGestalt (78) to get statistically significant (FDR < 0.05) gene sets or pathways. The TSS coordinates of each Refseq gene were extracted from UCSC table browser (mm9). Enriched motifs were detected using find Motifs Genome function with regional size (-size) 200 for TFs and 1,000 for H3K4me3. The data sets are deposited at GEO under accession number GSE75608 (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?token=wbwpqagurtmdbyl&acc=GSE75608).
Measurements of Mitochondrial Respiration
The rates of oxygen consumption in fibroblast cell lines were measured with a Seahorse Bioscience XF96 extracellular flux analyzer (Seahorse Biosciences), as detailed elsewhere (79, 80). XF96 creates a transient, 7-μL chamber in specialized microplates that allows for the determination of oxygen and proton concentrations in real time. The cultured cells were transferred to an XF96 assay plate at 100,000 cells/well and allowed to grow overnight. The parameters included basal respiration, ATP production, and maximal respiratory capacity. To allow comparison between different experiments, data are expressed as the rate of oxygen consumption in pmol/minute or the rate of extracellular acidification in mpH/minute, normalized to cell protein in individual wells determined by the Bradford protein assay (Bio-Rad). The rates of O2 were determined under basal conditions and the addition of oligomycin (1.5 μmol/L), carbonyl cyanide p-(trifluoromethoxy) phenylhydrazone (FCCP; 0.5 μmol/L), rotenone (1 μmol/L), and antimycin A (1 μmol/L), as detailed elsewhere (79, 80). Additional details relating to theSeahorse XF96 analyzer can also be found at http://www.seahorsebio.com/ (81).
Mitochondrial Respiratory Chain Complex I to IV Activity Assay
The enzymatic activities of complexes I, II, III, and IV were assayed as detailed elsewhere (82, 83). Complex I (NADH dehydrogenase) activity was determined as the rotenone-sensitive NADH oxidation at 340 nm, using the coenzyme Q analogue 2,3-dimethyl-5-methyl 6-n-decyl-1,4-benzomethyluinone as an electron acceptor. The activity of complex II (succinate dehydrogenase) was analyzed by tracking the secondary reduction of 2,6 dichlorophenolindophenol by ubiquinone 2 at 600 nm. Complex III (cytochrome bc1 complex) activity was determined by measuring the reduction of cytochrome c at 550 nm with reduced decylubiquinone. Complex IV (cytochrome c oxidase) activity was measured by monitoring the oxidation of reduced cytochrome c as a decrease of absorbance at 550 nm. Citrate synthase activity was analyzed by tracking the reduction of 5,5′-dithiobis-2-nitrobenzoic acid at 412 nm in the presence of acetyl-CoA and oxaloacetate. Complex I to IV activities were normalized by citrate synthase activity and then used in the analysis.
NMR Plasma Sample Preparation
All the plasma test samples were thawed on ice on the day of the data collection. Once thawed, samples were aliquoted onto prewashed 3 kDa spin filters (∼100 μL plasma/filter; NANOSEP 3K, Pall Life Sciences) and centrifuged 10,000 × gn for 90 minutes at 4°C. The 85 μL of plasma filtrate was mixed with 115 μL of NMR buffer containing 100 mmol/L phosphate buffer in D2O, pH 7.3, which included 1.0 mmol/L TMSP (3-trimethylsilyl 2,2,3,3-d4 propionate). The final TMSP concentration was 0.575 mmol/L in the NMR sample.
NMR Spectroscopy Data Acquisition and Processing
The experiments were conducted using 180 μL samples in 103.5 mm × 3 mm NMR tubes (Bruker). One-dimensional 1H NMR spectra were acquired on a Bruker Avance II 600 MHz spectrometer. All data were collected at a calibrated temperature of 298 K using a three-pulse sequence based on the noesypr1d (or noesygppr1d) pulse sequence in the Bruker pulse sequence library. This pulse sequence provided water suppression with good baseline characteristics. Experiments were run with 4 dummy scans (DS) and 256 acquisition scans (NS) with an acquisition time (AQ) of 2.7 seconds and a relaxation delay (D1) of 3 seconds for a total repetition cycle (AQ + D1) of 5.7 seconds. The NOESY mixing time was 10 ms. The spectral width was 20 ppm, and 64 K real data points were collected. Under automation control, each sample analysis took about 10 minutes for setup and 30 minutes for acquisition. During the 10-minute setup time, the temperature was monitored for equilibration. All free induction decays were subjected to an exponential line-broadening of 0.3 Hz. Upon Fourier transformation, each spectrum was manually phased, baseline corrected, and referenced to the internal standard TMSP at 0.0 ppm for polar samples using Topspin 3.5 software (Bruker Analytik). For two-dimensional 1H–1H total correlation spectroscopy (TOCSY) data, a relaxation delay equal to 2 seconds, isotropic mixing time of 80 ms with a B1 field strength of 10 kHz were used for 2,048 data points with 128 scans per increment acquired with spectral widths of 14 ppm. In addition, for 2-D 1H-13C heteronuclear single quantum coherence (HSQC) data, a relaxation delay equal to 1.5 seconds was used between acquisitions, and a refocusing delay of 3.45 ms was implemented. The 2,048 data points with 128 scans per increment were acquired with spectral widths of 11 ppm in F2 and 180 ppm in F1 (13C).
Metabolomics Data Analysis
Principal components analysis (PCA) was performed to determine the reproducibility of the extraction and sample stability and to look for metabolic differences. 1H NMR spectra were processed and analyzed with AMIX for PCA analysis. The spectra from 0.5–10.0 ppm, excluding the region of the residual water resonance (4.6–5.0 ppm) and methanol (3.36–3.38 ppm), were reduced by uniform binning to 995 buckets 0.01 ppm wide. Signal intensities were summed for integration, and the spectra were normalized to constant total spectral area. Prior to PCA, the binned spectra were mean centered with no scaling.
The spectral bin intensity tables were further analyzed using a univariate approach, based on bin-by-bin differences between WT and PTD spectra. Metabolites were assigned to those bins based on the chemical shifts as described below. Pairwise differences within each bin were compared using a two-tailed Welch t test and ANOVA (JMP 12). In addition to the comparison of the bin intensities, the concentration of TCA cycle metabolites was calculated based on the internal standard (Chenomx Inc., version 8.1). The concentrations of these metabolites were used for correlation analysis (MetaboAnalyst 3.0). Metabolites found in plasma were assigned based on 1D 1H, 2D TOCSY, and HSQC NMR experiments. Peaks were assigned by comparing the chemical shifts and spin–spin couplings with reference spectra found in databases, such as the Human Metabolome Database (HMDB), the biological magnetic resonance data bank (BMRB), and Chenomx NMR Suite profiling software (Chenomx Inc., version 8.1).
Reagents and Cell lines
Echinomycin was purchased from Enzo Life Sciences. Stock solutions were made in DMSO. Echinomycin was injected into WT B6.SJL (CD45.1) mice intraperitoneally as follows: 100 μg/kg/day, 5 times (every other day). Echinomycin was injected into the recipient mice transplanted with MllPTD/WT/Rx1-S291fs cells intraperitoneally as follows: from day 14 after BMT; 100 μg/kg/day, every other day, total 7 times. Echinomycin was injected into the xenografted NSGS mice intraperitoneally as follows: 50 μg/kg/day, 5 times (every other day). MDSL cells were acquired in 2015. MDSL cells were a gift from Dr. Kaoru Tohyama, Kawasaki Medical School, Okayama, Japan.
Survival of mice was analyzed using the log-rank test. Statistical analyses for others were generally performed using two-tailed Student t test. One-way ANOVA with multiple comparisons correction was used for comparisons among four groups in Fig. 6E. P values of < 0.05 were considered statistically significant.
Disclosure of Potential Conflicts of Interest
No potential conflicts of interest were disclosed.
Conception and design: Y. Hayashi, Y. Zhang, X. Sun, H.L. Grimes, S.D. Nimer, Z. Xiao, G. Huang
Development of methodology: Y. Hayashi, Y. Zhang, K. Choi, Y. Peng, X. Sun, G. Huang
Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): Y. Hayashi, Y. Zhang, A. Yokota, X. Yan, J. Liu, B. Li, G. Sashida, Y. Peng, R. Huang, L. Zhang, G.M. Freudiger, J. Wang, J. Wang, A. Chen, X. Zhao, X. Sun, A. Olsson, M. Watanabe, L.E. Romick-Rosendale, L.-Y. Shih, W. Tse, J.P. Bridges, M.A. Caligiuri, T. Huang, Z. Xiao, G. Huang
Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): Y. Hayashi, Y. Zhang, A. Yokota, X. Yan, K. Choi, Y. Peng, L. Zhang, G.M. Freudiger, Y. Dong, J. Bu, X. Sun, K. Chetal, M. Watanabe, L.E. Romick-Rosendale, H. Harada, W. Tse, T. Huang, C.-K. Qu, N. Salomonis, H.L. Grimes, Z. Xiao, G. Huang
Writing, review, and/or revision of the manuscript: Y. Hayashi, Y. Zhang, K. Choi, Y. Peng, A. Chen, X. Sun, M. Watanabe, W. Tse, Y. Zheng, C.-K. Qu, H.L. Grimes, S.D. Nimer, Z. Xiao, G. Huang
Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): Y. Hayashi, Y. Zhang, Z. Xu, Y. Zhou, L. Wu, X. Sun, Y. Zheng, D.P. Witte, Q.-f. Wang, G. Huang
Study supervision: Y. Hayashi, X. Sun, T. Huang, D.P. Witte, Z. Xiao, G. Huang
We thank M. Rife and A. Woeste (Cincinnati Children's Hospital Medical Center) for experiment assistance. This work was supported by the Kyoto University Foundation (to Y. Hayashi), the MDS Foundation (Young Investigator Award to Y. Hayashi), the Cincinnati Children's Hospital Research Foundation (to G. Huang), the Leukemia Research Foundation (to G. Huang), the OCRA (to G. Huang), NIH (R01CA166835 to S.D. Nimer, R01DK105014 to G. Huang, P50CA140158 and R01CA89341 to M.A. Caligiuri), National Natural Science Funds of China (No. 81470338 to Y. Zhang, No.81370611 to Z. Xu, No. 81300392 to J. Wang, No. 81470297 and No. 81770129 to G. Huang, and No. 81530008 and No. 81470295 to Z. Xiao), CAMS Initiative Fund for Medical Sciences (2016-I2M-1-001 to Z. Xiao), Tianjin science and technology projects (13CYBJC42400 to Y. Zhang), CCHMC Research and Development Project through the Cystic Fibrosis Foundation (to J.P. Bridges), and the Ministry of Science and Technology, Taiwan (MOST 103-2321-B-182-015 to L.-Y. Shih). The mouse BMT services were conducted by the Comprehensive Mouse and Cancer Core in Cancer and Blood Diseases Institute at Children's Hospital Research Foundation in Cincinnati, which is supported through the NIDDK Centers of Excellence in Molecular Hematology (P30DK090971). RNA-seq was conducted by Genomics, Epigenomics and Sequencing Core, Department of Environmental Health, University of Cincinnati, which is supported in part through a CEG grant (NIEHS P30-ES006096). NMR Spectroscopy–based analysis was conducted by the NMR-Based Metabolomics Core (NBMC) at Children's Hospital Research Foundation. Part of the RNA-seq analyses was conducted by J. Chen and M. Medvedovic at the Laboratory for Statistical Genomics and Systems Biology, Department of Environmental Health, University of Cincinnati. MDSL cells were kindly provided by Dr. Kaoru Tohyama.
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