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Record W4310106485 · doi:10.1182/blood-2022-170366

Metformin Treatment Abrogates the Competitive Advantage of <i>Dnmt3a</i> R878H HSPCs By Enhancing DNA Methylation Activity

2022· article· en· W4310106485 on OpenAlexaff
Mohsen Hosseini, Séverine Cathelin, Alex C.H. Liu, Yitong Yang, Amit Subedi, Dhanoop Manikoth Ayyathan, Shraddha Pai, Steven M. Chan

Bibliographic record

VenueBlood · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEpigenetics and DNA Methylation
Canadian institutionsUniversity of TorontoOccupational Cancer Research CentrePrincess Margaret Cancer CentreUniversity Health Network
Fundersnot available
KeywordsBiologyHaematopoiesisProgenitor cellDNA methylationGene knockdownMetforminMethylationCancer researchStem cellMolecular biologyCell biologyDNAGeneticsGeneGene expression

Abstract

fetched live from OpenAlex

Clonal hematopoiesis of indeterminate potential (CHIP) refers to the clonal expansion of hematopoietic stem and progenitor cells (HSPCs) carrying leukemogenic mutations in individuals without evidence of a blood disorder. CHIP carriers are at an increased risk of blood cancers and cardiovascular diseases. Targeting the clonal expansion of mutant HSPCs has the potential to mitigate CHIP-related illnesses. The most commonly mutated gene in CHIP is DNMT3A, which encodes an enzyme that catalyzes the methyl transfer from S-adenosylmethionine (SAM) to cytosines in DNA. The heterozygous DNMT3A hotspot mutation at R882 exerts a dominant negative effect on the wild-type (WT) allele, resulting in decreased DNA methylation activity. Prior studies have shown that murine HSPCs carrying mutant Dnmt3a R878H (equivalent to R882 in humans) have an advantage over WT cells in competition assays. Here, we aimed to first identify differences in the functional properties between Dnmt3a-mutated and WT HSPCs. Given the crosstalk between mitochondrial metabolism and DNA methylation, we investigated potential differences in mitochondrial activity and found that the level of mitochondrial respiration was higher in Dnmt3aR878H/+ HSPCs than in WT cells. To determine if this translated to differential dependencies, we silenced the expression of critical subunits in the each of five electron transport chain (ETC) complexes. Knockdown of NDUFV1 in Complex I was the most effective in reducing the competitive advantage of Dnmt3aR878H/+ cells in vitro. To explore translational relevance of this finding, we tested the impact of metformin, a pharmacologic inhibitor of Complex I, on Dnmt3a-mutated HSPCs. Treatment with metformin at clinically relevant concentrations suppressed the competitive advantage of mutant cells in vitro. Importantly, this effect was rescued by expression of NDI1, a metformin-resistant yeast analog of Complex I. To extend these findings, we conducted a competitive repopulation assay between CD45.2+ Dnmt3aR878H/+ and CD45.1+ Dnmt3a+/+ HSPCs. Metformin treatment abrogated the in vivo competitive advantage of mutant cells over an 8-month period based on peripheral blood chimerism. To decipher the mechanism of action, we performed transcriptome and metabolome profiling of mutant HSPCs treated with or without metformin for 1 month. GSEA analysis of the transcriptome data revealed a downregulation of genes associated with stemness in metformin-treated cells. Intriguingly, we found that metformin increased the expression of genes involved in one-carbon metabolism, which generates SAM. Consistent with these findings, metabolomics analysis showed that the levels of several key metabolites in one-carbon metabolism, including SAM, were higher in metformin-treated cells compared with controls. Genetic and pharmacologic inhibition of serine hydroxymethyltransferase 2 (SHMT2), a key enzyme in one-carbon metabolism, as well as the addition of exogeneous S-adenosylhomocysteine (SAH), an inhibitor of SAM-dependent methyltransferases, effectively rescued the effect of metformin on mutant cells in vitro. Based on the above findings, we hypothesized that metformin treatment might enhance the residual DNA methylation activity found in Dnmt3aR878H/+ mutated cells by increasing SAM concentration. In support of this hypothesis, we found that exogeneous SAM reproduced the effect of metformin, and HSPCs with homozygous Dnmt3aR878H mutations were insensitive to metformin. Furthermore, we performed DNA methylome profiling of untreated and metformin-treated mutant and WT HSPCs using RRBS. This analysis revealed that the hypomethylated regions (HMRs) in untreated Dnmt3aR878H/+ HSPCs relative to WT controls were enriched for regions of repressed chromatin with H3K27 methylation. Importantly, metformin treatment increased the level of DNA methylation at these regions in mutated HSPCs. In summary, our findings demonstrate that Dnmt3aR878H/+ HSPCs depend on ETC activity and particularly complex I activity to maintain their competitive advantage over WT cells. We propose a model in which inhibition of complex I activity with metformin increases one-carbon metabolism and SAM level, thereby restoring DNA methylation at HMRs and the differentiation of mutant HSPCs. Our findings have important implications for the development of preventive interventions against DNMT3A-mutated CHIP.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.007
GPT teacher head0.244
Teacher spread0.237 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2022
Admission routes1
Has abstractyes

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