Abstract

   The competitive advantage of mutant hematopoietic stem and progenitor
   cells (HSPCs) underlies clonal hematopoiesis (CH). Drivers of CH
   include aging and inflammation; however, how CH-mutant cells gain a
   selective advantage in these contexts is an unresolved question. Using
   a murine model of CH (Dnmt3a^R878H/+), we discover that mutant HSPCs
   sustain elevated mitochondrial respiration which is associated with
   their resistance to aging-related changes in the bone marrow
   microenvironment. Mutant HSPCs have DNA hypomethylation and increased
   expression of oxidative phosphorylation gene signatures, increased
   functional oxidative phosphorylation capacity, high mitochondrial
   membrane potential (Δψm), and greater dependence on mitochondrial
   respiration compared to wild-type HSPCs. Exploiting the elevated Δψm of
   mutant HSPCs, long-chain alkyl-TPP molecules (MitoQ, d-TPP) selectively
   accumulate in the mitochondria and cause reduced mitochondrial
   respiration, mitochondrial-driven apoptosis and ablate the competitive
   advantage of HSPCs ex vivo and in vivo in aged recipient mice. Further,
   MitoQ targets elevated mitochondrial respiration and the selective
   advantage of human DNMT3A-knockdown HSPCs, supporting species
   conservation. These data suggest that mitochondrial activity is a
   targetable mechanism by which CH-mutant HSPCs gain a selective
   advantage over wild-type HSPCs.

   Subject terms: Ageing, Haematopoietic stem cells, Mitochondria
     __________________________________________________________________

   Clonal hematopoiesis is driven by the selective advantage of mutant
   hematopoietic stem cells. Here, authors discover that elevated
   mitochondrial activity is a targetable mechanism by which mutant
   hematopoietic stem cells gain a selective advantage.

Introduction

   Throughout life, hematopoietic stem and progenitor cells (HSPCs) are
   vital to sustaining hematopoiesis and maintaining balanced production
   of mature hematopoietic cells. Aging is associated with the development
   of clonal hematopoiesis (CH), a condition that occurs when a subset of
   HSPCs gain a selective fitness advantage based on accrual of somatic
   mutation(s). The gene most frequently observed to accrue somatic
   mutations that drive human CH is the DNA methyltransferase
   DNMT3A^[64]1–[65]3. While CH itself is not a pathological condition, a
   greater burden of larger clones is associated with increased risk of
   numerous aging-associated pathologies, including cardiovascular
   disease^[66]4, hematologic malignancies^[67]2, and inflammatory bone
   loss^[68]5. Understanding how and why HSPCs with DNMT3A and other
   somatic mutations gain a selective advantage in the context of aging
   provides an opportunity for intervention to prevent the expansion of
   clones that increase disease risk.

   Given the positive association between aging and inflammation, much
   work has focused on acute and chronic inflammation as selective
   pressures promoting clonal selection and CH^[69]6. Inflammation is
   proposed to underly the positive associations between CH and smoking,
   chronic liver disease, autoimmune conditions, increased body mass
   index, and bone loss^[70]6–[71]9. While acute and chronic inflammatory
   states irreversibly deplete wild-type HSCs^[72]10,[73]11, Dnmt3a-mutant
   and other CH-mutant HSCs (ex. Tet2, Asxl1) either resist the response
   to inflammatory factors or mount a distinct response to inflammatory
   factors resulting in maintenance of their self-renewal
   capacity^[74]12–[75]16. We recently reported that Dnmt3a-mutant HSCs
   have an increased competitive advantage over wild-type HSCs in
   middle-aged recipient mice^[76]14 in which we have not observed strong
   signatures of elevated inflammation^[77]17. This suggests that other
   mechanisms contribute to the competitive advantage of Dnmt3a-mutant
   HSCs. Here, we investigated the mechanisms underlying HSPC competition
   that adapt Dnmt3a-mutant HSPCs to aging environments.

Results

Dnmt3a-mutant HSPCs have a competitive advantage over wild-type HSPCs that is
associated with higher mitochondrial respiration

   To narrow down potential mechanisms by which Dnmt3a-mutant HSCs have an
   increased competitive advantage in middle-aged recipients^[78]14, we
   employed a defined genetic model of IGF1 deficiency that mimics aspects
   of the middle-aged BM microenvironment^[79]17,[80]18. We first
   evaluated whether Dnmt3a-mutant HSCs also have a competitive advantage
   in an Igf1-deficient environmental context using a competitive bone
   marrow transplant. Donor CD45.2^+ BM cells from a mouse model of human
   DNMT3A-mutant hematopoiesis (Dnmt3a^R878H/+ Mx-Cre)^[81]19 or Mx-Cre
   control BM cells were mixed with wild-type CD45.1^+ BM cells at a 1:1
   ratio. This mixture was transplanted into Igf1 conditional knockout
   recipient mice (Igf1^fl/fl Cre-ER^T2) or control recipient mice
   (Igf1^+/+ Cre-ER^T2) (Fig. [82]1A). Dnmt3a^R878H/+ hematopoietic cells
   had an increased competitive advantage over wild-type cells in
   Igf1-deficient recipient mice in the peripheral blood at 28 weeks
   post-transplant (Fig. [83]1B). Igf1-deficient recipients also had
   increased frequency and total number of Dnmt3a^R878H/+ HSCs in the bone
   marrow (Fig. [84]1C, Supplementary Fig. [85]1a). Unlike Mx-Cre control
   cells, Dnmt3a^R878H/+ hematopoietic cells in Igf1-deficient recipients
   maintained a balanced mature cell lineage composition (Supplementary
   Fig. [86]1B–D), suggesting the competitive advantage of Dnmt3a^R878H/+
   hematopoiesis in Igf1-deficient recipients is established at the level
   of multipotent HSCs rather than expansion of lineage-biased progenitor
   cells.

Fig. 1. Dnmt3a^R878H/+ enhances oxidative phosphorylation in HSPCs.

   [87]Fig. 1
   [88]Open in a new tab

   A Experimental design. B, C Frequency of donor-derived (CD45.2^+) cells
   in peripheral blood (B), frequency of hematopoietic stem cells (HSCs)
   in CD45.2^+ bone marrow at 28 weeks post-transplant (c, left) and total
   number of CD45.2^+ HSCs in BM (C, right) of recipient mice. Symbols
   represent mean ± SEM, bars represent mean ± SEM with points from
   biological replicate mice (n = 5 control into Igf1 + /+, n = 4 control
   into Igf1−/−, n = 8 R878H/+ into Igf1 + /+, n = 7 R878H/+ into
   Igf1−/−). Statistical analyses used two-way ANOVA with Tukey’s multiple
   comparisons test (B) or Brown-Forsythe and Welch one-way ANOVA (C). D
   Experimental design. e Oxygen consumption rate plot (left) used to
   quantify basal and maximal respiration (right) and (F), spare
   respiratory capacity in control and Dnmt3a^R878H/+ HSPCs with and
   without IGF1. E, F Symbols represent mean ± SEM, bars represent mean ±
   SEM with points from biological replicate mice (n = 4). Statistical
   analyses used two-way ANOVA with Sidak’s multiple comparisons test (E)
   or one-way ANOVA with Tukey’s multiple comparisons test (F). G OCR plot
   (left) used to quantify basal and maximal respiration (right) and (H),
   spare respiratory capacity in control and Dnmt3a^R878H/+ HSPCs after
   16 h in vitro culture. G, H Symbols represent mean ± SEM, bars
   represent mean ± SEM with points from biological replicate mice
   (n = 4). Statistical analyses used two-way ANOVA with Sidak’s multiple
   comparisons test (G) or one-way ANOVA with Tukey’s multiple comparisons
   test (H). I Extracellular acidification rate plot of control and
   Dnmt3a^R878H/+ HSPCs after 16 h in vitro culture. Symbols represent
   mean ± SEM from biological replicate mice (n = 3). J Experimental
   design. K OCR profile plot (left) used to quantify basal and maximal
   respiration (right) and (l), spare respiratory capacity in de novo
   control and Dnmt3a^R878H/+ HSPCs. K, L Symbols represent mean ± SEM,
   bars represent mean ± SEM with points from biological replicate mice
   (n = 4). Statistical analyses used two-way ANOVA with Sidak’s multiple
   comparisons test (K) and unpaired two-tailed t test with Welch’s
   correction (l). Source data are provided as a Source Data file.

   Based on our previous work^[89]18, we hypothesized that Dnmt3a^R878H/+
   HSCs may have a competitive advantage in aging and Igf1-deficient
   environments due to differential regulation of metabolism. Using
   Seahorse extracellular metabolic flux assays (Fig. [90]1D), we observed
   that unstimulated Dnmt3a^R878H/+ HSPCs had higher maximal respiration
   and spare respiratory capacity compared to unstimulated control HSPCs
   (Fig. [91]1E, F). A similar signal was observed in control HSPCs in
   response to IGF1 whereas the elevated respiration in Dnmt3a^R878H/+
   HSPCs was not further increased in response to IGF1. The phenotype of
   increased maximal respiration and spare respiratory capacity in
   unstimulated Dnmt3a^R878H/+ HSPCs compared to control HSPCs was
   confirmed in independent experiments (Fig. [92]1G, H), and no change in
   glycolytic capacity was observed between Dnmt3a^R878H/+ and control
   HSPCs (Fig. [93]1I). To examine whether this phenotype was exclusive to
   Dnmt3a-mutant hematopoiesis or conserved among CH-associated mutations,
   we evaluated Tet2^−/− and control HSPCs. We observed that Tet2^−/−
   HSPCs also had increased basal respiration and maximal respiration
   (Supplementary Fig. [94]1e, f), consistent with the Dnmt3a^R878H/+
   phenotype. As these phenotypes were evaluated after ex vivo culture, we
   also tested Dnmt3a^R878H/+ HSPCs isolated directly from mice
   (Fig. [95]1J). De novo isolated Dnmt3a^R878H/+ HSPCs had increased
   maximal cellular respiration (Fig. [96]1K) and increased spare
   respiratory capacity (Fig. [97]1L) compared to de novo isolated control
   HSPCs.

Dnmt3a-mutant HSPCs exhibit elevated mitochondrial membrane potential
associated with DNA hypomethylation and increased expression of electron
transport chain complex and supercomplex machinery

   To evaluate if enhanced mitochondrial cellular respiration in
   Dnmt3a^R878H/+ HSPCs was associated with loss of DNA methyltransferase
   function of the Dnmt3a protein and DNA hypomethylation, we performed
   whole genome bisulfite sequencing of Dnmt3a^R878H/+ and control HSCs.
   Genes with DNA hypomethylation in Dnmt3a^R878H/+ HSCs were enriched for
   an oxidative phosphorylation gene signature (Fig. [98]2A). We also
   examined published transcriptome and DNA methylation datasets from
   mouse Dnmt3a^R878H/+ HSCs and Dnmt3a knockout HSCs^[99]20,[100]21. In
   all cases, genes with DNA hypomethylation and increased transcript
   expression were enriched for the same oxidative phosphorylation gene
   signature (Fig. [101]2B–D). Analysis of single cell DNA methylation and
   transcriptome data from human DNMT3A^R882-mutant CH^[102]22 revealed
   DNA hypomethylation and increased expression of oxidative
   phosphorylation genes in DNMT3A^R882 CD34^+ cells compared to
   DNMT3A^+/+ CD34^+ cells in the same individuals (Fig. [103]2E, F).
   These data support that the enhanced molecular signatures of oxidative
   phosphorylation in Dnmt3a-mutant and Dnmt3a-knockout HSPCs are
   conserved between mouse and human and are associated with DNA
   hypomethylation.

Fig. 2. DNA hypomethylation and increased expression of electron transport
chain machinery and elevated mitochondrial membrane potential in
Dnmt3a-mutant HSPCs.

   [104]Fig. 2
   [105]Open in a new tab

   A–F Enrichment analysis of an oxidative phosphorylation gene signature
   in DNA methylation data (A, C, E) and RNA-seq data (B, D, F) comparing
   Dnmt3a^R878H/+ vs. control HSC (A, B), Dnmt3a KO versus control HSC (C,
   D), DNMT3A^R882 CD34^+ cells versus DNMT3A^+/+ CD34^+ cells (E, F).
   Includes data from Jeong et al^[106]20 and Nam et al.^[107]22. G DNA
   methylation and expression heatmap of oxidative phosphorylation genes
   in Dnmt3a-KO and Dnmt3a^R878H/+ BM and HSCs. Includes data from Li et
   al^[108]21 and Jeong et al.^[109]20. H TMRE mean fluorescence intensity
   in control and Dnmt3a^R878H/+ HSCs. Bars represent mean ± SEM with
   points from biological replicate mice (n = 6). Statistical analysis
   used unpaired, two-tailed t test. I Calcium ion uptake in control and
   Dnmt3a^R878H/+ HSCs. Bars represent mean ± SEM with points from
   biological replicate mice (n = 4). Statistical analysis used unpaired,
   two-tailed t test. J Representative images (left) of control and
   Dnmt3a^R878H/+ HSCs stained with TOM20 (red) and DAPI (blue). Scale bar
   denotes 2 μm. Mitochondrial volume (center) and mitochondrial number
   (right) in control and Dnmt3a^R878H/+ HSCs. Bars represent mean ± SEM
   with data points from biological replicate HSCs (n = 30) from three
   mice per genotype. Statistical analysis used unpaired, two-tailed t
   test. K Representative transmission electron microscopy images (left)
   of control and Dnmt3a^R878H/+ HSPCs. Scale bar denotes 0.5 μm. Red
   squares denote zoomed region. Mitochondrial area (center) and
   mitochondrial cristae area (right) in control and Dnmt3a^R878H/+ HSPCs.
   Bars represent mean ± SEM with points are from biological replicate
   HSPCs (n = 23 control HSPC, n = 32 R878H/ + HSPC) from three mice per
   genotype. Statistical analysis used unpaired, two-tailed t test. Source
   data are provided as a Source Data file.

   To explore the mechanisms by which DNA hypomethylation relates to
   increased oxidative phosphorylation, we examined four independent DNA
   methylation datasets and two transcriptome datasets comparing
   Dnmt3a^R878H/+ or Dnmt3a-knockout BM cells and HSCs to their control
   counterparts^[110]20,[111]21. Genes with both DNA hypomethylation and
   increased expression included mediators of electron transport chain
   supercomplexes (Cox7a2l, Ndufa6, Pink1, Ndufa11), ETC complex V
   activity (Atp6v0a1, Atp6v1b2) and mitochondrial DNA and RNA synthesis
   (Slc25a33) (Fig. [112]2G). Cox7a2l was the only gene hypomethylated and
   increased in expression across all datasets. By mediating the assembly
   of respiratory chain supercomplexes, Cox7a2l is essential for
   mitochondrial efficiency and mitochondrial membrane potential
   (Δψm)^[113]23–[114]26. We observed that Dnmt3a^R878H/+ HSCs have
   increased Δψm compared to control HSCs by functional analysis using
   tetramethyl rhodamine ethyl ester (TMRE) (Fig. [115]2H). Consistent
   with increased Δψm, Dnmt3a^R878H/+ HSCs also exhibited enhanced uptake
   of positively charged calcium ions (Fig. [116]2I). Dnmt3a^R878H/+ HSCs
   did not have alterations in volume or number of mitochondria assessed
   by immunofluorescence staining (Fig. [117]2J), nor gross changes
   mitochondrial morphology, area, or cristae area assessed by electron
   microscopy (Fig. [118]2K). Dnmt3a^R878H/+ and control HSPCs also had
   similar mitochondrial DNA content (Supplementary Fig. [119]2).
   Together, these data suggest that enhanced mitochondrial cellular
   respiration observed in Dnmt3a^R878H/+ HSPCs is a consequence of
   elevated Δψm rather than altered mitochondrial morphology or number.

Elevated mitochondrial membrane potential sensitizes Dnmt3a-mutant HSPCs to
inhibition of the electron transport chain

   To assess the dependency of Dnmt3a^R878H/+ HSCs on cellular
   respiration, we utilized the SCENITH assay (single cell energetic
   metabolism by profiling translation inhibition)^[120]27 which
   quantifies changes in protein translation as a measure of metabolic
   activity in response to metabolic inhibitors. We found that
   Dnmt3a^R878H/+ HSCs have greater dependence on oligomycin-targetable
   oxidative phosphorylation compared to control HSCs (Fig. [121]3A) and
   have reduced glycolytic capacity when mitochondrial oxidative
   phosphorylation is inhibited (Supplementary Fig. [122]3a–d). Enhanced
   electron transport chain function and supercomplex formation are
   targetable by lipophilic triphenylphosphonium (TPP^+) molecules,
   including the small molecule mitoquinol (MitoQ)^[123]28. Similar to the
   effects of oligomycin, we found that MitoQ decreased metabolic activity
   in Dnmt3a^R878H/+ HSCs but not in control HSCs (Fig. [124]3B). As MitoQ
   has been shown to have beneficial effects on enhancing wild-type HSC
   function in the context of aging^[125]29, we pursued this molecule for
   further study. Using Seahorse extracellular metabolic flux assays
   (Fig. [126]3C), MitoQ treatment decreased the maximal respiration
   (Fig. [127]3D) and spare respiratory capacity (Fig. [128]3E) of
   Dnmt3a^R878H/+ HSPCs and did not impact control HSPCs. Evaluating this
   phenotype in human cells, primary cord blood (CB) CD34^+ HSPCs with
   shRNA knockdown of DNMT3A (Fig. [129]3F) had increased basal
   respiration (Fig. [130]3G), maximal respiration, and spare respiratory
   capacity (Fig. [131]3H). MitoQ treatment decreased these parameters in
   DNMT3A knockdown CD34^+ HSPCs to levels consistent with control HSPCs.
   Thus, elevated mitochondrial cellular respiration in Dnmt3a-mutant
   HSPCs is conserved between mouse and human and represents a selective
   molecular dependency that can be targeted using electron transport
   chain inhibitors including MitoQ.

Fig. 3. Mouse and human Dnmt3a-mutant HSPCs are sensitive to electron
transport chain inhibitors.

   [132]Fig. 3
   [133]Open in a new tab

   A Quantification of puromycin incorporation in control and
   Dnmt3a^R878H/+ HSCs treated with vehicle control (DMSO), 2-deoxygluocse
   (2-DG), oligomycin (Oligo) or 2-DG + Oligo using SCENITH assay. Bars
   represent mean ± SEM with points from biological replicate mice (n = 5
   DMSO, n = 5 2-DG, n = 5 Oligo, n = 6 2-DG+Oligo). Statistical analyses
   used two-way ANOVA with Tukey’s multiple comparisons test. B
   Quantification of metabolic function in control and Dnmt3a^R878H/+ HSCs
   after treatment with MitoQ using SCENITH assay. Bars represent mean ±
   SEM with points from biological replicate mice (n = 4 DMSO, n = 7
   MitoQ). Statistical analyses used two-way ANOVA with Dunnett’s multiple
   comparisons test. C Experimental design. D OCR profile plot (left) used
   to quantify basal and maximal respiration (right) and (E), spare
   respiratory capacity. D, E Symbols represent mean ± SEM, bars represent
   mean ± SEM with points from biological replicate mice (n = 4).
   Statistical analyses used two-way ANOVA with Tukey’s multiple
   comparisons test (D) and one-way ANOVA with Tukey’s multiple
   comparisons test (E). F Experimental design. G OCR profile plot (left)
   used to quantify basal and maximal respiration (right) and (H), spare
   respiratory capacity. F–H Symbols represent mean ± SEM, bars represent
   mean ± SEM with points from biological replicates (n = 4). Statistical
   analyses used two-way ANOVA with Tukey’s multiple comparisons test (G)
   and one-way ANOVA with Fisher’s LSD (H). Source data are provided as a
   Source Data file.

Elevated Δψm in Dnmt3a-mutant HSPCs is linked to greater uptake and
accumulation of long-chain alkyl-TPP molecules

   MitoQ consists of a lipophilic cation (triphenylphosphonium [TPP])
   coupled to an antioxidant component (quinone) by a 10-carbon alkyl
   chain (Fig. [134]4A). Based on this structure, a potential mechanism by
   which MitoQ selectively targets Dnmt3a^R878H/+ HSPCs is by accumulation
   in the mitochondrial matrix due to increased Δψm in these cells. As
   mitochondrial accumulation decreases with decreased alkyl chain
   length^[135]30, we tested how TPP conjugates with varying carbon chain
   lengths impact protein translation as a proxy for metabolic activity.
   We evaluated carbon alkyl chain lengths of 12 (dodecyl-TPP [d-TPP]), 7
   (heptyl-TPP [h-TPP]) and 1 (methyl-TPP [m-TPP]) (Fig. [136]4A) compared
   to the positive control MitoQ. We observed that MitoQ and d-TPP reduced
   protein translation/metabolic activity in Dnmt3a^R878H/+ HSCs to a
   similar level while the shorter TPP conjugates h-TPP and m-TPP did not
   have a significant effect (Fig. [137]4B). Using Seahorse extracellular
   metabolic flux assays (Fig. [138]4C), d-TPP treatment of Dnmt3a^R878H/+
   HSPCs decreased their basal and maximal cellular respiration and spare
   respiratory capacity (Fig. [139]4D) to levels comparable to untreated
   control HSPCs. As longer-chain alkyl-TPP molecules have been reported
   to have additional effects of enhancing glycolysis and increasing
   proton leak^[140]30, we assessed these phenotypes following d-TPP
   treatment. Consistent with the reduced glycolytic capacity of
   Dnmt3a^R878H/+ HSPCs when oxidative phosphorylation is inhibited
   (Supplementary Fig. [141]3b), Dnmt3a^R878H/+ HSPCs treated with d-TPP
   have a reduced ability to undergo glycolysis (Fig. [142]4E). In
   addition, d-TPP treatment caused proton leak-driven cellular
   respiration to a similar degree in control and Dnmt3a^R878H/+ HSPCs
   (Fig. [143]4F), supporting that this does not underlie differential
   sensitivity of Dnmt3a^R878H/+ HSPCs to long-chain alkyl-TPP molecules.
   To formally evaluate accumulation in the mitochondria, we performed
   mass spectrometry-based quantitation of MitoQ in mitochondrial lysates
   and cytoplasmic lysates (Fig. [144]4G). This analysis revealed greater
   accumulation of MitoQ in the mitochondria of Dnmt3a^R878H/+ HSPCs
   compared to control HSPCs (Fig. [145]4H). Thus, elevated Δψm of
   Dnmt3a-mutant HSPCs can be harnessed using long-chain alkyl-TPP
   molecules to specifically target enhanced mitochondrial cellular
   respiration of Dnmt3a-mutant HSPCs.

Fig. 4. Dnmt3a^R878H/+ HSPCs have greater uptake and accumulation of
long-chain alkyl-TPP molecules.

   [146]Fig. 4
   [147]Open in a new tab

   A Chemical structures of TPP^+ derivatives MitoQ, d-TPP, h-TPP and
   m-TPP. Created in BioRender: [148]https://BioRender.com/j69r060. B
   Quantification of puromycin incorporation in control and Dnmt3a^R878H/+
   HSCs treated with vehicle control (DMSO), MitoQ, d-TPP, h-TPP, or
   m-TPP. Bars represent mean ± SEM, points from biological replicate mice
   (n = 7 Control DMSO, n = 6 R878H/ + DMSO, n = 5 Control MitoQ, n = 6
   R878H/+ MitoQ, n = 5 Control d-TPP, n = 6 R878H/+ d-TPP, n = 3 Control
   h-TPP, n = 3 R878H/+ h-TPP, n = 3 Control m-TPP, n = 3 R878H/+ m-TPP).
   Statistical analysis used two-way ANOVA with Dunnett’s multiple
   comparisons test. C Experimental design (left) and OCR plot (right)
   used to quantify (D), basal and maximal respiration (left) and spare
   respiratory capacity (right) in d-TPP treated control and
   Dnmt3a^R878H/+ HSPCs. E Quantification of d-TPP induced ECAR in d-TPP
   treated control and Dnmt3a^R878H/+ HSPCs. F Proton-leak driven
   respiration in d-TPP treated control and Dnmt3a^R878H/+ HSPCs. D–F Bars
   represent mean ± SEM, points are from biological replicate mice
   (n = 4). Statistical analyses used two-way ANOVA with Sidak’s multiple
   comparisons test (D, left), one-way ANOVA with Tukey’s multiple
   comparisons test (D, right, F), and unpaired, two-tailed t test (E). G
   Experimental design. H Ratio of MitoQ in the mitochondria vs. cytoplasm
   of control and Dnmt3a^R878H/+ HSPCs. Bars represent mean ± SEM, points
   from biological replicate mice (n = 4). Statistical analysis used ratio
   paired, two-tailed t test. Source data are provided as a Source Data
   file.

MitoQ induces the mitochondrial apoptotic pathway selectively in
Dnmt3a-mutant HSPCs

   We next sought to understand the molecular and cellular consequences of
   mitochondrial accumulation of long-chain alkyl-TPP molecules in
   Dnmt3a^R878H/+ HSPCs. We profiled the transcriptional effects of MitoQ
   on Dnmt3a^R878H/+ HSPCs using single cell RNA-seq (Fig. [149]5A).
   Clustering and cell type annotation identified 9 HSPC subsets
   (Fig. [150]5B) and differential expression analysis was performed on
   each cluster (Supplementary Fig. [151]4a). We focused first on the
   transcriptional changes induced by MitoQ in Dnmt3a^R878H/+ HSCs that
   were not induced in control HSCs. MitoQ treatment decreased expression
   of molecular signatures of Δψm, mitochondrial transport and import into
   the inner mitochondrial membrane in Dnmt3a^R878H/+ HSCs (Fig. [152]5C),
   consistent with the reduced cellular respiration phenotypes we
   observed. In addition, MitoQ-treated Dnmt3a^R878H/+ HSCs had increased
   expression of molecular signatures of the lysosome, including increased
   expression of Cathepsin B (Ctsb) which is involved in apoptotic cell
   death (Supplementary Fig. [153]4b), mitochondrial membrane
   permeability, cytochrome C release and apoptosis (Supplementary
   Fig. [154]4c). These signatures were also significantly increased in
   other Dnmt3a^R878H/+ HSPC populations such as MPP, GMP, MonoPro and
   LyPro (Supplementary Fig. [155]4d), supporting that MitoQ impacts
   multiple populations within the HSPC compartment. In contrast,
   transcriptional changes induced by MitoQ in control HSCs reflected
   reduced reactive oxygen species and increased mitochondrial function
   (Supplementary Fig. [156]4e, f), consistent with MitoQ being an
   antioxidant that has a beneficial effect on metabolism and function of
   aged wild-type HSCs^[157]29,[158]31. These data support fundamentally
   distinct molecular actions of MitoQ in Dnmt3a^R878H/+ HSPCs compared to
   control HSPCs.

Fig. 5. MitoQ induces activation of the mitochondrial apoptotic pathway
selectively in Dnmt3a^R878H/+ HSPCs.

   [159]Fig. 5
   [160]Open in a new tab

   A Experimental design for single-cell RNA-seq from biological replicate
   mice (n = 4). B UMAP plots of scRNA-seq showing 9 cell clusters from a
   total of 134,844 cells. C Negative and positive enrichment of gene
   ontology terms in control and Dnmt3a^R878H/+ HSCs treated with MitoQ
   vs. vehicle. D Experimental design. E Quantification of loss of calcein
   retention in control and Dnmt3a^R878H/+ HSCs treated with MitoQ vs.
   vehicle. F Quantification of JC-1 exclusion in control and
   Dnmt3a^R878H/+ HSC treated with MitoQ vs. vehicle. E, F Bars represent
   mean ± SEM, points from biological replicate mice (n = 4). Statistical
   analyses used one-way ANOVA with Sidak’s multiple comparisons test. G
   Quantification of mitochondrial membrane potential in control and
   Dnmt3a^R878H/+ HSC treated with MitoQ vs. vehicle. Bars represent mean
   ± SEM, points from biological replicate mice (n = 3). Statistical
   analysis used one-way ANOVA with Sidak’s multiple comparisons test. H
   Representative images (left) of Dnmt3a^R878H/+ HSPCs treated with MitoQ
   or vehicle using TEM. Scale bar denotes 0.5 μm. Red squares denote
   zoomed area. Quantification of mitochondrial cristae width (right) in
   Dnmt3a^R878H/+ HSPCs treated with MitoQ or vehicle. Bars represent mean
   ± SEM, points from biological replicate HSPCs (n = 32 R878H/+ veh,
   n = 35 R878H/+ +MitoQ) from three mice per genotype. Statistical
   analysis used unpaired, two-tailed t test. I Frequency of AnnexinV+
   Dnmt3a^R878H/+ HSPCs after treatment with MitoQ or vehicle. Bars
   represent mean ± SEM, points from biological replicate mice (n = 3).
   Statistical analysis used unpaired, two-tailed t test. J Model of MitoQ
   uptake in Dnmt3a^R878H/+ HSPCs leading to increased permeability
   transition pore, increased swelling, depolarization, decreased MMP,
   cytochrome C release and apoptosis. Created in Biorender:
   [161]https://BioRender.com/m381057. Source data are provided as a
   Source Data file.

   We then tested candidate mechanisms nominated by our molecular analysis
   at the cellular level (Fig. [162]5D). MitoQ treatment of Dnmt3a^R878H/+
   HSPCs resulted in opening of the mitochondrial transition pore, an
   early step in the mitochondrial apoptotic pathway, indicated by reduced
   retention of the mitochondrially-localized molecule calcein
   (Fig. [163]5E). Further, MitoQ treatment of Dnmt3a^R878H/+ HSPCs caused
   mitochondrial depolarization (Fig. [164]5F), reduced Δψm
   (Fig. [165]5G), mitochondrial swelling (Fig. [166]5H), and increased
   frequency of apoptotic cells assessed by Annexin V (Fig. [167]5I).
   MitoQ reduced the proportion of control HSPCs in cell cycle but did not
   impact cycling of Dnmt3a^R878H/+ HSPCs (Supplementary Fig. [168]4g).
   Thus, molecular and cellular phenotypes support that MitoQ reduces
   cellular respiration and can induce the mitochondrial apoptotic pathway
   selectively in Dnmt3a-mutant HSPCs (Fig. [169]5J).

Long-chain alkyl-TPP molecules ablate the competitive advantage of
Dnmt3a-mutant HSPCs ex vivo and in vivo

   We evaluated the functional consequences of long-chain alkyl-TPP
   molecules on the competitive advantage of Dnmt3a^R878H/+ HSPCs and
   their progeny. First, we used ex vivo serial colony-forming unit (CFU)
   assays to assess the growth advantage of HSPCs (Fig. [170]6A). MitoQ
   treatment decreased the secondary CFU replating of Dnmt3a^R878H/+ HSPCs
   but had no effect on control HSPCs (Fig. [171]6B). Treatment with d-TPP
   (Fig. [172]6C) had a similar effect of decreased secondary CFU
   replating of Dnmt3a^R878H/+ HSPCs while the short form m-TPP had no
   effect (Fig. [173]6D).

Fig. 6. Long-chain alkyl-TPP molecules ablate the competitive advantage of
mouse and human Dnmt3a-mutant HSPCs and their progeny.

   [174]Fig. 6
   [175]Open in a new tab

   A Experimental design. B Number of myeloid CFU in secondary plating of
   control and Dnmt3a^R878H/+ BM cells. Bars represent mean ± SEM, points
   from biological replicate mice (n = 5 control veh, n = 5 control
   +MitoQ, n = 6 R878H/+ veh, n = 6 R878H/+ +MitoQ). Statistical analysis
   used one-way ANOVA with Sidak’s multiple comparisons test. C
   Experimental design. D Number of myeloid CFU in secondary plating of
   control and Dnmt3a^R878H/+ BM cells. Bars represent mean ± SEM, points
   from biological replicate mice (n = 3). Statistical analysis used
   one-way ANOVA with Sidak’s multiple comparisons test. E Experimental
   design. F Frequency of donor-derived tdTomato^+ CD45.2^+ cells in PB,
   BM and HSPCs of control and Dnmt3a^R878H/+ recipient mice after
   treatment with MitoQ. Bars represent mean ± SEM, points from biological
   replicate mice (n = 11 control veh, n = 5 control +MitoQ, n = 10
   R878H/+ veh, n = 5 R878H/+ +MitoQ). The transplant was performed 3
   independent times. Statistical analysis used two-way ANOVA with
   Fisher’s LSD test. G Experimental design. H Frequency of
   DNMT3A-knockdown human CD34+ cells after 14 days of MitoQ treatment.
   Bars represent mean ± SEM, points are from one human sample with
   technical replicates (n = 6). Statistical analysis used two-way ANOVA
   with Tukey’s multiple comparisons test. Source data are provided as a
   Source Data file.

   We then assessed the in vivo effects on MitoQ based on our reported
   physiologically relevant observation that Dnmt3a-mutant HSCs have an
   enhanced competitive advantage in middle-aged recipient mice^[176]14.
   We transplanted control or Dnmt3a^R878H/+ BM cells into 9-month-old
   recipient mice, allowed these recipients to age to 12 months old, and
   administered MitoQ in vivo for five consecutive days^[177]29,[178]32
   (Fig. [179]6E). As expected, Dnmt3a^R878H/+ cells had greater
   contribution to hematopoiesis compared to control cells in middle-aged
   recipients assessed at the peripheral blood, bone marrow and HSPC
   levels (Fig. [180]6F). Treatment of middle-aged mice with MitoQ
   decreased the contribution of Dnmt3a^R878H/+ cells to hematopoiesis at
   all cellular levels, mirroring the proportions of control cells. To
   evaluate this phenotype in primary human cells, ex vivo cultures of
   cord blood (CB) CD34^+ HSPCs with DNMT3A knockdown were competed
   against control CD34^+ HSPCs at a 2:3 ratio (Fig. [181]6G). In
   vehicle-treated control cultures, we observed a clear competitive
   advantage of DNMT3A knockdown human HSPCs (Fig. [182]6H). The addition
   of MitoQ significantly reduced the competitive growth advantage of
   DNMT3A knockdown HSPCs. Thus, long-chain alkyl-TPP molecules such as
   MitoQ can be used to impair the competitive growth advantage of mouse
   and human DNMT3A-mutant HSPCs, reducing the burden of clonal
   hematopoiesis.

Discussion

   Here we have shown that Dnmt3a-mutant HSPCs achieve a selective
   advantage over wild-type HSPCs through elevated mitochondrial membrane
   potential and cellular respiration. Associated with DNA hypomethylation
   and increased expression of components of the electron transport chain
   and respiratory supercomplex formation, Dnmt3a-mutant HSPCs have an
   elevated Δψm and greater dependency on mitochondrial respiration which
   sensitizes them to inhibition of the electron transport chain. Greater
   uptake and accumulation of long-chain alkyl-TPP molecules selectively
   reduce mitochondrial respiration in Dnmt3a-mutant HSPCs, induce the
   mitochondrial apoptotic pathway, and ablate the competitive advantage
   of Dnmt3a-mutant HSPCs and their progeny. Importantly, key observations
   were reproduced in human DNMT3A-knockdown HSPCs, supporting species
   conservation of this mechanism and translational relevance of our
   findings.

   In wild-type HSCs, Δψm is positively associated with self-renewal and
   regenerative capacity, and Δψm is reduced with HSC dysfunction as a
   consequence of aging and post-chemotherapy^[183]18,[184]33. Our data
   suggests that Dnmt3a-mutant HSPCs have increased Δψm as a consequence
   of DNA hypomethylation and increased gene expression of components of
   the electron transport chain and respiratory supercomplex formation.
   Interestingly, we and others have observed enhanced oxidative
   phosphorylation and cellular respiration as a consequence of other
   recurrent CH mutations including Tet2, Asxl1^[185]34, and Idh1^[186]35
   although the mechanisms causing these phenotypes remain to be
   discerned. Taken together, Δψm and enhanced mitochondrial respiration
   may be a common mechanism by which stem cells have a competitive
   survival advantage in the context of aging, inflammation or stress
   environments.

   Targeting strategies based on mitochondrial accumulation of lipophilic,
   positively charged long-chain alkyl-TPP molecules (MitoQ, d-TPP) were
   able to successfully reduce Δψm and mitochondrial respiration in
   Dnmt3a-mutant HSPCs. In addition to disrupting respiratory supercomplex
   formation^[187]28, these molecules destabilize the mitochondrial
   membrane and inhibit ETC complexes I, II, III and IV in a
   dose-dependent manner^[188]36. To what extent mitochondrially targeted
   compounds reduce mitochondrial respiration in Dnmt3a-mutant HSPCs by
   mitochondrial matrix destabilization versus direct inhibition of ETC
   activity or supercomplex activity will require additional investigation
   currently limited by the lack of techniques adapted for low input cell
   number. In addition to reduced mitochondrial respiration, our
   observation that MitoQ induces the mitochondrial apoptotic pathway in a
   subset of Dnmt3a-mutant HSPCs also contributes to the efficacy of this
   molecule in reducing Dnmt3a-mutant hematopoiesis.

   From a therapeutic standpoint, given that CH is not an overt disease
   state, the only reasonable interventions should be efficacious at a
   dose and duration with no toxicity and low to no side effects.
   TPP-based compounds can be given to rodents at high doses without
   significant damage to organs including the heart, liver and
   kidneys^[189]37. MitoQ and d-TPP have been administered continuously to
   mice in drinking water for 28 weeks with no adverse events including no
   effect on whole-body metabolism, motor function, and food or liquid
   consumption^[190]38. In humans, MitoQ has been administered orally to
   patients in Phase II studies from 28 days to 6 weeks duration with no
   significant adverse side effects^[191]39–[192]42. Further studies are
   needed to determine the dose at which systemic administration of MitoQ
   or d-TPP would be effective in reducing the selective advantage of
   Dnmt3a-mutant HSPCs, the durability of the treatment effect, and side
   effect(s) observed with that treatment regimen. Apart from TPP-based
   compounds, FDA-approved molecules that reduce electron transport chain
   function and mitochondrial respiration such as metformin, tamoxifen,
   and diclofenac^[193]43 may also have utility in selectively targeting
   CH-mutant HSPCs. An intriguing aspect of targeting CH-mutant cells with
   molecules like MitoQ is that this same molecule has been shown through
   its antioxidant function to enhance wild-type aged HSC function and
   boost adaptive immune cell production^[194]44. Thus, it is conceivable
   that a single mitochondrially-targeted molecule could be employed to
   increase the contribution of wild-type HSPCs to hematopoiesis and
   lymphopoiesis in aged individuals while decreasing clonal
   hematopoiesis, therein theoretically reducing risk of aging- and
   CH-associated disease.

Methods

Experimental animals

   B6.SJL-Ptprc^aPepc^b/BoyJ mice^[195]45 (referred to as CD45.1) were
   obtained from, and aged within, The Jackson Laboratory.
   B6.129(FVB)-Igf1^tm1Dlr/J mice^[196]46 (referred to as Igf1^fl/fl) were
   crossed to B6.129-Gt(ROSA)26Sor^tm1(cre/ERT2)Tyj/J mice^[197]47
   (referred to as Cre-ER^T2). Dnmt3a^fl-R878H/+ mice^[198]19 were crossed
   to B6.Cg-Tg (Mx1-cre)1Cgn/J mice^[199]48 (referred to as Mx-Cre) or
   C57BL/6N-Fgd5t^m3(cre/ERT2)Djr/J mice^[200]49 (referred to as
   Fgd5-CreER^T2) crossed to B6.Cg-Gt-ROSA)26Sor^tm14(CAG-tdTomato)Hze/J
   mice^[201]50 (referred to as LSL-tdTomato)^[202]50. Tet2^fl/fl
   mice^[203]51 were crossed to Vav-Cre mice^[204]52. All experiments used
   Cre-only mice as a control group, adult (2–6 months of age) female and
   male mice were used throughout these studies. Mice are housed in cages
   with same sex, 12 hr/12 hr light and dark cycle with controlled
   temperature and humidity. All strains were obtained from The Jackson
   Laboratory repository. To induce Mx1-Cre, mice were intraperitoneally
   (IP) injected once every other day for five total injections with
   15 mg/kg high molecular weight polyinosinic-polycytidylic acid
   (InvivoGen). To induce Cre-ER^T2 or Fgd5-CreER^T2, mice received
   125 mg/kg tamoxifen for three consecutive days by oral gavage. All
   mouse work was approved by The Animal Care and Use Committee at The
   Jackson Laboratory.

In vivo transplantation

   For transplantation experiments into Igf1^fl/fl Cre-ER^T2 recipient
   mice, 1 x 10^6 mononuclear cells (MNCs) from donor CD45.1 mice and
   1 x 10^6 MNCs from donor Dnmt3a^+/+ Mx-Cre or Dnmt3a^R878H/+ Mx-Cre
   mice were transplanted into lethally irradiated Igf1^+/+ Cre-ER^T2 mice
   or Igf1^fl/fl Cre-ER^T2 recipient mice. Injection of tamoxifen (TAM) to
   induce Igf1 knockout was performed 4 weeks after transplant to avoid
   confounding responses to irradiation^[205]53. For transplantation
   experiments into middle-aged CD45.1 recipient mice, 3 × 10^6 MNCs from
   Dnmt3a^+/+ Fgd5-CreER^T2 LSL-tdTomato or Dnmt3a^R878H/+ Fgd5-CreER^T2
   LSL-tdTomato donor mice were transplanted into 9-month-old
   busulfan-conditioned (20 mg/kg/per day for consecutive 3 days,
   Sigma-Aldrich B2635) CD45.1 recipient mice.

Peripheral blood analysis

   Blood was collected from mice via the retro-orbital sinus and red blood
   cells were lysed using 1X RBC lysis buffer. Remaining MNCs were stained
   with fluorochrome-conjugated antibodies recognizing CD45.1 (clone A20,
   Biolegend, 1:200), CD45.2 (clone 104, Biolegend, 1:50), B220 (clone
   RA3-6B2, BD Biosciences, 1:200), CD3e (clone 145-2C11, BD Biosciences,
   1:200), CD11b (clone M1/70, Biolegend, 1:200), Ter-119 (clone TER-119,
   Biolegend, 1:50), Gr-1 (clone RB6-8C5, Biolgend, 1:200), Ly6g (clone
   1A8, Biolegend, 1:200), Ly6c (clone HK1.4, Biolegend, 1:200) and F4/80
   (clone BM8, Invitrogen, 1:200). DAPI was used as a viability stain.
   Data was captured using a LSRII (BD) and analyzed using FlowJo V10.

Isolation of hematopoietic stem and progenitor cells

   BM cells were isolated from pooled and crushed femurs, tibiae, and
   iliac crests of individual mice. Following red blood cell lysis using
   1X RBC lysis buffer (eBioscience), BM MNCs were stained with a
   combination of fluorochrome-conjugated antibodies recognizing c-Kit
   (clone 2B8, Biolegend, 1:50), Sca-1 (clone D7, Biolegend, 1:200), CD150
   (clone TC15-12F12.2, Biolegend, 1:200), CD48 (clone HM48-1, Biolegend,
   1:200), CD34 (clone RAM34, BD Biosciences, 1:100), Flt3 (clone A2F10,
   Biolegend, 1:50), and a mature lineage (Lin) marker mix including B220
   (clone RA3-6B2, Biolegend, 1:200), CD11b (clone M1/70, Biolegend,
   1:200), CD4 (clone RM4-5, Biolegend, 1:200), CD5 (clone 53-7.3,
   Biolegend, 1:200), CD8a (clone 53-6.7, Biolegend, 1:200), Ter-119
   (clone TER-119, Biolegend, 1:200) and Gr-1 (clone RB6-8C5, Biolgend,
   1:200). DAPI was used as a viability stain. HSC isolation and/or
   phenotyping was based on the surface markers Lin- Sca-1+ c-Kit+ Flt3-
   CD150 + CD48- using a FACSAria II, FACSymphony S6 or FACSymphony A5
   (BD). HSPCs were isolated by incubating BM cells with c-Kit microbeads
   (Miltenyi Biotec, 130-091-224), washing with MACS Buffer and enriched
   using an AutoMACS Pro cell separator (Miltenyi Biotec). Additionally,
   HSPCs were isolated by sortwelcomefor Lin- Sca-1+ c-Kit+ population.
   For experiments using Ki67 BM cells were fixed and permeabilized using
   the eBioscience Foxp3/Transcription Factor Staining Buffer Set
   (ThermoFisher) per the manufacturer’s instructions. Stained cells were
   sorted using a FACSAria II or a FACSymphony S6 Sorter or analyzed on a
   FACSymphony A5. Flow cytometry data was analyzed using FlowJo V10
   software.

Mitochondria analysis

   Staining for mitochondrial membrane potential was performed as
   previously described in ref. ^[206]18. Briefly, whole BM was freshly
   isolated and stained with fluorochrome-conjugated antibodies for HSPC
   markers as described above, washed, and incubated at 37 °C and 5% CO[2]
   for 30 min with TMRE (0.1 μM, Sigma). For MitoTracker staining, freshly
   isolated BM cells were stained with fluorochrome-conjugated antibodies
   for HSPC markers, incubated with 25 nM MitoTracker (ThermoFisher) in
   PBS supplemented with 50 μM Verapamil at 37 °C for 30 min, then washed.
   For MitoTracker staining, freshly isolated BM cells were stained with
   fluorochrome-conjugated antibodies for HSPC markers, incubated with
   25 nM MitoTracker (ThermoFisher) in PBS supplemented with 50 μM
   Verapamil at 37 °C for 30 min, then washed. For MitoProbe^TM JC-1 Assay
   Kit (ThermoFisher), MitoProbe^TM Transition Pore Assay Kit
   (ThermoFisher), MitoProbe™ TMRM Assay Kit (ThermoFisher),
   Invitrogen™Rhod-2, AM, Calcium Labeling Assay Kit (ThermoFisher), and
   Annexin V Conjugates for Apoptosis Detection Assay Kit (ThermoFisher)
   were used per the manufacturer’s instructions. Stained cells were
   sorted using a FACSAria II or a FACSymphony S6 Sorter or analyzed on a
   FACSymphony A5 within 60 min of staining. Flow cytometry data was
   analyzed using FlowJo V10 software.

Immunofluorescence

   Sorted HSCs were resuspended in 20 μl of Stemspan SFEM II (StemCell
   Technologies) supplemented with 100 ng/ml rmSCF (Peprotech) and
   200 ng/ml rmTPO (Peprotech) and 1% Pen-Strep (Gibco) then seeded on
   retronectin-coated coverslips and allowed to adhere for 2 h at 37 °C
   and 5% CO[2]. Cells were fixed in 4% PFA (Electron Microscopy Services)
   for 15 min at 4 °C and stored at 4 °C overnight. Then cells were washed
   once with cold PBS (Gibco) and permeablized with 0.1% Trition X-100
   (Fisher Scientific) in PBS for 20 min at room temperature (RT) and
   blocked with 10% goat serum (Sigma Aldrich) for 20 min at RT. Rabbit
   polyclonal anti-TOM20 (FL-145, Santa Cruz, dilution 1:100) and DAPI
   containing mounting media (Vectashield, Vector Laboratories) were used
   for the staining. Z-stack were acquired on a Leica SP5 equipped with
   63X oil immersion lens (NA 1.4), pinhole set at 1 airy unit with voxel
   size 80/200 nm on XY/Z. Stacks were deconvolved using the
   Richardson-Lucy algorithm^[207]54 using measured PSF. Representative
   image renderings were obtained by Imaris 7 (Bitplane).

Transmission electron microscopy

   For mitochondrial analysis, HSPCs (Lin-, Sca-1 + , c-Kit + ) were
   sorted, spun down, and red blood cells were added to assist in
   identifying cell pellet. Cells were fixed in 2.5% glutaraldehyde in
   0.1 M sodium cacodylate buffer 0.1 M, pH 7.4 for 1 hr at RT. Samples
   were post-fixed in 1.0% aqueous osmium tetroxide (pH 7.4) followed by
   1% uranyl acetate, dehydrated in a graded series of ethanol, and
   embedded in Lx112 resin (LADD Research Industries). Ultrathin (80 nm)
   sections were cut on a Leica UC7, contrasted with uranyl acetate
   followed by lead citrate, and viewed on a JEOL 1400Plus transmission
   electron microscope (Jeol Ltd.) at 120 kV. Images were manually
   segmented using ImageJ.

Extracellular flux assays

   For experiments using mouse Dnmt3a-mutant HSPCs, 150,000 naïve or
   stimulated c-Kit+ HSPCs were seeded on poly-D lysine (Sigma)-coated
   XF96 cell culture microplates. Oxygen consumption rate (OCR) and
   extracellular acidification rate (ECAR) were measured using Seahorse XF
   Mito Stress kit and Glycolysis Stress kits (Agilent Technologies,
   102416-100) using the Seahorse XFe96 analyzer (Agilent). For
   experiments using Tet2-knockout HSPCs, c-Kit+ HSPCs were plated and
   cultured in Stempro plus SCF for 12 h, then transferred to microplates
   at 100,000 cells per well for assessment using the Seahorse XF Mito
   Stress kit. All tests performed on human cord blood samples were
   carried out using the XF96 Extracellular Flux Analyzer from Seahorse
   Bioscience (North Billerica, MA). The sensor cartridge was hydrated
   overnight in a non-CO[2] incubator using the calibration buffer medium
   supplied by Seahorse Biosciences (200 μl of buffer per well) on the day
   prior to the assay. The wells of Seahorse XFe96 microplates were coated
   the following day at 4 °C with a 40 μl solution of Cell-Tak (Corning;
   Cat#354240) containing 22.4 μg/ml. The Cell Tak-coated Seahorse
   microplate wells were subsequently rinsed with distilled water. For
   plating, all cells were seeded at a density of 80,000 cells per well on
   Seahorse XFe96 microplates, using XF base minimal DMEM media containing
   11 mM glucose, 2 mM glutamine, and 1 mM sodium pyruvate. Following cell
   seeding, 180 μl of XF base minimal DMEM medium was added to each well,
   and the plate was centrifuged at 100 g for 5 min. Following a one-hour
   incubation of the seeded cells at 37 °C in a non-CO[2] incubator, the
   oxygen consumption rate (OCR) and extracellular acidification rate
   (ECAR) were evaluated under the baseline and in response to 1 μM
   Oligomycin, 1 μM carbonylcyanide-4-(trifluoromethoxy)-phenylhydrazone
   (FCCP), and 1 μM Antimycin and Rotenone (all from Sigma-Aldrich) using
   the XFe96 analyzer.

SCENITH assay

   c-Kit+ HSPCs were cultured in StemPro^TM-34 SFM with complete nutrient
   supplement (Gibco) media for 1 hour at 37 °C and 5% CO[2] in a 6-well
   plate. Then c-Kit+ HSPCs were transferred to a 96-well plate (150,000
   cells/well) with or without inhibitors (1 μM oligomycin, Millipore
   Sigma; 100 mM 2-DG, Millipore Sigma; or 100 nM MitoQ, Cayman Chemical)
   for 1 hr at 37 °C and 5% CO[2]. Puromycin (10 μg/ml, Thermo Fisher
   Scientific) was then added to each well and incubated for 30 min at
   37 °C and 5% CO[2]. Cells were then harvested, washed, and stained with
   Live/Dead viability Ghost Red 780 Dye (13-0865-T100, Tonobo) and
   fluorochrome-conjugated antibodies for HSPC markers as described above.
   Cells were then fixed and permeabilized (eBioscience Staining Buffer
   Set, 00-5523-00, Invitrogen) followed by intracellular staining for
   puromycin (clone 12D10, Alexa Fluor 647, Millipore Sigma). Cells were
   collected and analyzed by flow cytometry on a FACSymphony A5 (BD).
   Dependency and capacity were quantified using algorithms that are based
   on inhibiting a single metabolic pathway compared to complete
   inhibition of ATP synthesis^[208]27.

In vitro cultures

   c-Kit+ HSPCs were cultured in StemPro^TM-34 SFM with complete nutrient
   supplement (Gibco) media for 16 h at 37 °C and 5% CO[2]. Addition of
   MitoQ (100 nM) (Cayman Chemical, 89950), IGF1 (100 ng/ml) (StemCell
   Technologies), m-TPP (1, 10, 100 nM) (Thermo Scientific Chemicals,
   AAA1587818), h-TPP (1, 10, 100 nM) (Sigma, 377538), d-TPP (1, 10,
   100 nM) (Thermo Scientific Chemicals, AAA1429509), or vehicle control
   (DMSO) were added to the media as indicated.

Quantitative Real-Time PCR (qRT-PCR)

   HSPCs were collected after 16hrs of treatment with MitoQ or vehicle
   control, then washed with 1 × PBS. Whole cell DNA was extracted using
   the DNeasy blood and tissue kit (Qiagen, # 69504) according to the
   manufacturer’s protocol. The DNA was eluted in UltraPure™
   DNase/RNase-free distilled water (Invitrogen, #10977015), and processed
   with a spectrophotometer (DeNovix®, DS-11 FX+) for concentration
   determination. The qRT-PCR was performed using the Power SYBR™ green
   PCR master mix (Applied Biosystems™, 4367659), and technical
   triplicates of each sample were prepared. The qRT-PCR was performed on
   the QuantStudio™ 7 Flex platform (Applied Biosystems™) using a standard
   comparative CT (ΔΔC[T]) method. The mtDNA abundance was calculated
   using the ΔΔC[T] method relative to nuclear DNA.

Single-cell multi-omics analysis of DNA Methylation and RNA expression in
DNMT3A R882 mutant CD34+ cells

   Single-cell DNA methylation (RRBS) data, combined with single-cell RNA
   sequencing (Smart-Seq2) data from CD34+ enriched cells in
   GCSF-mobilized bone marrow samples (n = 2), were retrieved from
   [209]GSE158067 [GSE158067_gene_exp_mtx.txt and Supplementary
   Table [210]5 from Nam et al. ^[211]22]. Seurat V5.1 was used to process
   the scRNA data. The counts were log-normalized, scaled, and centered
   using the number of RNA features, mitochondrial percentage, and plates
   as covariates. Differential expression between the DNMT3A R882 mutant
   and wild-type (WT) phenotype, determined through targeted genotyping,
   was assessed using the Wilcoxon Rank Sum test via the limma method.
   Genes were ranked based on the –log10 p-value and the sign of the log
   fold change. To perform GSEA on the DNA methylation data, 12,131 genes
   were ranked by the differential methylation region (DMR) value within
   the ±1 kb TSS region using the –log10 p-value and fold change sign. The
   enrichment of GO:0006119 (Oxidative Phosphorylation) was evaluated
   using the GSEA method in ClusterProfiler 4.8 for both RNA and DNA
   methylation data.

Whole Genome Bisulfite Sequencing (WGBS)

   DNA was extracted from hematopoietic cells using the PureLink Genomic
   DNA Mini Kit (Invitrogen) and quantified using Qubit. 200 ng of genomic
   DNA, including 0.2% Lambda DNA (N6-methyladenine-free; NEB) was
   fragmented in a final volume of 50 mL using the Covaris LE220 targeting
   ~350 bp inserts. A 1.5x AMPure clean-up was performed post
   fragmentation resulting in a final volume of 20 mL. Fragmented DNA was
   bisulfite converted with the EZ-96 DNA Methylation-Gold Mag Prep Kit
   (Zymo Research) according to manufacturer recommendations. Whole genome
   bisulfite (WGBS) libraries were constructed with ~100 ng of bsDNA using
   the xGen Methyl-Seq Library Prep Kit (IDT) and unique dual indexes
   (IDT). Nine PCR cycles were performed during the indexing PCR step,
   followed by a final 0.85x AMPure cleanup. Final libraries were assessed
   on the bioanalyzer instrument for average library size and
   concentration measured by qPCR using KAPA library quantification kits
   (Roche). Libraries were sequenced using 2×150 paired end reads on an
   Illumina NovaSeq X Plus.

Whole Genome Bisulfite Sequencing Analysis

   FastqQC v0.11.8 was used to assess the quality of the raw reads. Paired
   end reads were trimmed to remove adapter sequences and low quality
   reads with Cutadapt v1.18 using the parameters (“-q 15,10 -u 10 -U 15
   --minimum-length 36”) and reassessed using FastqQC. The mouse reference
   genome mm10 was first converted into a bisulfite converted version
   using Bismark v0.20.0 (bismark_genome_preparation). The paired end
   reads were aligned to the mm10 bisulfite converted genome using the
   options: “-N 1 -L 28 --score_min L,0,-0.6” and deduplicated using the
   deduplicate_bismark command from Bismark. Bismark command
   bismark_methylation_extractor was used to calculate DNA methylation
   levels. Bisulfite conversion was estimated using the conversion rate of
   cytosine to thymine in the lambda reference genome. DMRs were
   identified with DSS v2.43.2 using the CpG methylation with coverage and
   methylation counts and calling DMRs between groups using the two-group
   comparison for biological replicates. The DMRs were called using the
   following parameters: DMLtest: “smoothing = TRUE, smoothing.span = 500”
   and callDMR: “delta = 0.2, p.threshold = 0.001, minlen = 200, minCG =
   3, dis.merge = 50, pct.sig = 0.5”. Biological replicates were combined
   by merging replicate -CX_report.txt files from Bismark and summing the
   methylated and unmethylated values per CpG locus. DMRs with missing CpG
   values in any of the samples were removed. Intersections for control
   DMR comparisons were performed and visualized by Intervene Upset
   function using default parameters.

CFU assay

   BM mononuclear cells were plated in MethoCult GF M3434 (StemCell
   Technologies) supplemented with MitoQ (100 nM), m-TPP (1 nM) (Thermo
   Scientific Chemicals, AAA1587818), d-TPP (1 nM) (Thermo Scientific
   Chemicals, AAA1429509), or vehicle and cultured at 37 °C and 5% CO[2].
   Colonies were scored between 7 and 10 days post-plating. For secondary
   plating CFUs were harvested, counted and plated at equal cell input
   with supplementation. Colonies were scored using a Nikon Eclipse TS100
   inverted microscope.

MitoQ treatment

   For in vivo experiments, Mitoquinol (MitoQ) or vehicle control was
   administered to mice by IP injection (2 mg/kg, Cayman Chemical, #89950)
   for 5 consecutive days.

Targeted MitoQ Selected Reaction Monitoring (SRM) assay

   Cell and mitochondrial pellets were transferred to Mass Spectrometry
   and Protein Chemistry Service (MSPC) at the Jackson Laboratory. Each
   2 mL sample tube had a 5 mm stainless steel bead added, along with
   150 µL of 2:2:1(acetonitrile: methanol: water) extraction buffer.
   Sample tubes were then placed in the Tissue Lyser II (QIAGEN) for
   2 minutes at a frequency of 20 oscillations per second. Beads were then
   removed from the samples and the samples were allowed to extract for
   one hour at −20 °C. Sample extract was then clarified by centrifugation
   at 12,000 x g at 4 °C for 10 minutes. The supernatant was transferred
   to a new tube and the samples were dried using a vacuum centrifuge.
   Dried samples were then reconstituted with 20 µL of 60%
   acetonitrile/40% water and transferred to a sample vial for liquid
   chromatography tandem mass spectrometry (LC-MS/MS) SRM quantification.
   In addition to the individual samples, a cell lysate pool was also
   extracted for use as a standard eight-point quantification curve
   ranging from 0 pg on column to 1500 pg on column. During the LC-MS/MS
   analysis 2 uL equivalents of the reconstituted samples were injected
   onto the column per replicate (three technical replicates per sample).
   LC-MS/MS SRM analysis was performed on a Thermo TSQ Altis Plus mass
   spectrometer coupled to a Vanquish ultra high-performance liquid
   chromatography (UHPLC) system using a 10-minute gradient on an Agilent
   InfinityLab Poroshell 120 EC-C18 Column (2.1 × 50 mm 2.7 µm, part #
   699775-902). Chromatography separation utilized water with 10 mM
   ammonium acetate and 0.1% formic acid (Buffer A) and acetonitrile with
   0.1% formic acid (Buffer B). The gradient used a flow rate of 250 µL
   per minute and consisted of the following steps: 98% Buffer A/2% Buffer
   B for 1 min, ramped to 2% Buffer A/98% Buffer B by 7 minutes, held at
   2% Buffer A/98% Buffer B until 8.5 minutes, and adjusted to 98% Buffer
   A/2% Buffer B by 9.5 minutes where it held until 10 minutes. Mito-Q
   (Cayman Chemical) analyte transitions (default of +1 charge) used were
   585.4 m/z for the precursor ion with fragmentation ions of 288.967 m/z
   (collision energy = 61.98 V), 387.133 m/z (collision energy = 40.47 V),
   and 555.133 m/z (collision energy = 39.05 V). Other general method
   conditions included an autosampler temperature of 6 °C, column chamber
   of 40 °C, the H-ESI ion source, voltage of 3700 V, positive polarity,
   sheath gas of 50 (arb), auxiliary gas of 10 (arb), sweep gas of 1
   (arb), an ion transfer tube temperature of 380 °C, and a vaporizer
   temperature of 350 °C. Specific SRM settings included a 6 second peak
   width, a 0.6 s cycle time, 10 points per peak, a Q1 resolution of 0.7
   (FWHM), a Q3 resolution of 1.2 (FWHM), and a CID gas of 1.5 mTorr. Peak
   area for Mito-Q in the sample runs was then extracted using the Peak
   Detection in the Thermo FreeStyle 1.8 SP2 software (Version 1.8.63.0)
   and averages were calculated for the groups. These group peak areas
   were then compared to the Mito-Q standard curve of known concentrations
   and concentrations were calculated. Mito-Q concentrations were
   normalized to cell input.

Single cell RNA-seq

   c-Kit+ HSPCs were cultured with MitoQ (100 nM) or vehicle in
   StemPro^TM-34 SFM with complete nutrient supplement (Gibco) for 16 h at
   37 °C and 5% CO[2]. Cells were harvested and stained with
   fluorochrome-conjugated antibodies for HSPC markers. Lin- Sca-1+ c-Kit+
   cells were sorted directly into tubes containing DMEM/10%FBS at 4 ^oC.
   Cells were counted on a Countess II automated cell counter (Thermo
   Fisher), and 12,000 cells were loaded onto one lane of a 10X Chromium
   microfluidic chip (10X Genomics). Single-cell capture, barcoding, and
   library preparation were performed using the 10X Chromium version 3.1
   chemistry according to the manufacturer’s protocol (#[212]CG000315).
   cDNA and libraries were checked for quality on Agilent 4200 Tapestation
   and quantified by KAPA qPCR before sequencing. Libraries were sequenced
   using an Illumina NovaSeq 6000 S4 flow cell lane with an average
   sequencing depth of 75,000 reads per cell. Illumina base call files for
   all libraries were demultiplexed and converted to FASTQs using
   bcl2fastq v2.20.0.422. The Cell Ranger pipeline (10X Genomics, version
   6.0.0) was used to align reads to the mouse reference GRCm38.p93 (mm10
   10X Genomics reference 2020-A), deduplicate reads, call cells, and
   generate cell by gene digital count matrices for each library. The
   resultant count matrices were uploaded into PartekFlow (version
   10.0.22.0428) for downstream analysis and visualization. This included
   log transformation of count data, principal component analysis,
   graph-based clustering from the top 20 principal components using the
   Louvain algorithm, Uniform Manifold Approximation and Projection (UMAP)
   visualization, and pathway enrichment analysis. Differentially
   expressed genes were investigated for overlap with published datasets
   using Gene Set Enrichment Analysis (RRID:SCR_003199).

Human cord blood samples

   Human mPB samples were obtained with informed consent from Princess
   Margaret Cancer Centre following the procedures approved by the
   University Health Network (UHN) research ethic board. Mononuclear cells
   were isolated by density gradient centrifugation using Ficoll-Paque ™
   (Fisher Scientific, #45001749), and hematopoietic progenitors were
   enriched through lineage marker depletion (STEMCELL, #14056). The CD34+
   cells were then isolated using the CD34 Microbead kit and purified
   using LS columns, following the directions provided by the manufacturer
   (Miltenyi Cat# 130-042-401).

Lentivirus production and transduction

   HEK293T cells were grown in RPMI 1640 medium (Wisent, #350-035-CL)
   supplemented with 10% FBS (Wisent, #080–150) and 1% Gluta-Plus (Wisent,
   #609-066-EL). Cells were seeded in 15 cm tissue culture plates
   (Sarstedt, #83.3903) at a density of 7 × 10^6 cells per plate one day
   before transfection. On the day of transfection, cells were
   co-transfected with lentiviral plasmid vectors, psPAX2 (Addgene,
   #12259), and pCMV-VSVG (Cell biolabs, #320023) using the jetPRIME
   transfection reagent (Polyplus, #CA89129-924) according to
   manufacturer’s protocol. Supernatant containing viral particles was
   collected at 48- and 72- hours post transfection and filtered through a
   0.45 µm PVDF membrane (Sigma, #SE1M003M00). Viral particles were
   precipitated in 40%(w/v) polyethylene glycol (Sigma, #89510-1KG-F)
   overnight. On the next day, the viral particles were collected by
   centrifugation at 2500 x g for 30 min at 4 °C. The pellet was
   resuspended in HBSS (Gibco, #14170112) + 25 mM HEPES (Thermo Fisher,
   #15630-080) and stored at −80 °C for long term storage. For lentiviral
   transductions, non-TC-treated 24 well plates were coated with 20 µg/mL
   of Retronectin (Takara, Cat #T100B) for 2 h at room temperature
   followed by aspiration and blocking with PBS containing 2% (w/v) BSA
   (Wisent Bioproducts, Cat #800-096-EG) for 30 min at room temperature.
   After aspiration of the blocking buffer, the concentrated virus
   suspension was added to wells. The plates were then centrifuged at
   2500 x g for two hours at 4 °C to allow virus binding. Following
   centrifugation, unbound virus was aspirated, and cells were added. The
   plates were then transferred to a 37 °C incubator to initiate
   lentiviral infection.

Human in vitro competition assays

   Competition experiments were conducted using 96-well flat-bottom tissue
   culture plates (Corning, Ref #351172). Following transduction of CD34+
   cells with lentiviruses encoding either shDNMT3A or shNT, cells were
   sorted by flow cytometry based on their fluorescent markers (BFP or
   GFP). DNMT3A knockdown (KD) BFP+ cells or non-targeting BFP+ control
   cells were subsequently mixed with non-targeting GFP+ control cells
   with 2000 cells per well in a proportion of 40% and 60%, respectively.
   The cell mixture was added to human MethoCultTM GF H4330 medium
   (StemCell Technologies, Cat #04330) supplemented with StemSpan™ CC100
   (StemCell Technologies, Cat #02690) and BIT 9500 Serum Substitute
   (StemCell Technologies, Cat #09500) with a density of 2000 cells per
   well, treatment was administered as indicated (MitoQ at 100 nM). Cells
   were incubated at 37 °C with 5% CO[2] for 14 days. Following the
   incubation period, cells were washed with 1X PBS, and flow cytometry
   was performed to determine the ratio of GFP- to BFP-positive cells.
   Annexin V staining was employed to exclude apoptotic cells, ensuring
   that the analysis was restricted to viable cell populations.

Statistical analyses

   Statistical analyses and graphing were performed using Prism 9
   (GraphPad). Individual mice and/or biological replicates are presented
   as individual dots and/or mean ± SEM. No blinding was performed for
   these studies. All experiments were performed a minimum of 2
   independent times. When comparing more than 2 groups, ANOVA was
   performed with multiple comparisons as reported in the figure legends.
   When only 2 groups were compared an unpaired, two-tailed Student’s
   t-test was used unless otherwise stated. Exact p values are reported in
   figures. P-values ≤ 0.05 were considered significant.

Reporting summary

   Further information on research design is available in the [213]Nature
   Portfolio Reporting Summary linked to this article.

Supplementary information

   [214]Supplementary Information^ (5.7MB, pdf)
   [215]Reporting Summary^ (3.5MB, pdf)
   [216]Transparent Peer Review file^ (15.5KB, pdf)

Source data

   [217]Source Data^ (87KB, xlsx)

Acknowledgements