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DNA methylation disruption reshapes the hematopoietic differentiation landscape
Open accessAbstract
Mutations in genes involved in DNA methylation (DNAme; for example, TET2 and DNMT3A) are frequently observed in hematological malignancies 1-3 and clonal hematopoiesis 4,5. Applying single-cell sequencing to murine hematopoietic stem and progenitor cells, we observed that these mutations disrupt hematopoietic differentiation, causing opposite shifts in the frequencies of erythroid versus myelomonocytic progenitors following Tet2 or Dnmt3a loss. Notably, these shifts trace back to transcriptional priming skews in uncommitted hematopoietic stem cells. To reconcile genome-wide DNAme changes with specific erythroid versus myelomonocytic skews, we provide evidence in support of differential sensitivity of transcription factors due to biases in CpG enrichment in their binding motif. Single-cell transcriptomes with targeted genotyping showed similar skews in transcriptional priming of DNMT3A-mutated human clonal hematopoiesis bone marrow progenitors. These data show that DNAme shapes the topography of hematopoietic differentiation, and support a model in which genome-wide methylation changes are transduced to differentiation skews through biases in CpG enrichment of the transcription factor binding motif.
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“However, analysis of DNA methylation and gene expression patterns in mouse (15,17) and human (17,18) systems have yet to identify a unifying molecular mechanism to explain the increased fitness of these mutant HSCs.”
From Txnip Enhances Fitness of Dnmt3a -Mutant Hematopoietic Stem Cells via p21 (2022) · cites this paper as [17]
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“yet focal methylation alterations appear physiologically relevant (17,18,37,41).”
From Txnip Enhances Fitness of Dnmt3a -Mutant Hematopoietic Stem Cells via p21 (2022) · cites this paper as [17]
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Cites
Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2
Love, M. I., Huber, W., & Anders, S. (2014) · Genome Biology · doi:10.1186/s13059-014-0550-8 · Cited by 95,106
Fast gapped-read alignment with Bowtie 2
Langmead, B., & Salzberg, S. L. (2012) · Nature Methods · doi:10.1038/nmeth.1923 · Cited by 56,400
STAR: ultrafast universal RNA-seq aligner
Dobin, A., Davis, C. A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., Batut, P., Chaisson, M., & Gingeras, T. R. (2013) · Bioinformatics · doi:10.1093/bioinformatics/bts635 · Cited by 51,709
Heinz, S., Benner, C., Spann, N., Bertolino, E., Lin, Y. C., Laslo, P., Cheng, J. X., Murre, C., Singh, H., & Glass, C. K. (2010) · Molecular Cell · doi:10.1016/j.molcel.2010.05.004 · Cited by 13,382
Enrichr: a comprehensive gene set enrichment analysis web server 2016 update
Kuleshov, M. V., Jones, M. R., Rouillard, A. D., Fernandez, N. F., Duan, Q., Wang, Z., Koplev, S., Jenkins, S. L., Jagodnik, K. M., Lachmann, A., et al. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw377 · Cited by 10,799
Generalized linear mixed models: a practical guide for ecology and evolution
Bolker, B. M., Brooks, M. E., Clark, C. J., Geange, S. W., Poulsen, J. R., Stevens, M. H. H., & White, J.-S. S. (2009) · Trends in Ecology & Evolution · doi:10.1016/j.tree.2008.10.008 · Cited by 8,910
DNA methylation patterns and epigenetic memory
Bird, A. (2002) · Genes & Development · doi:10.1101/gad.947102 · Cited by 7,124
Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets.
Macosko, E. Z., Basu, A., Satija, R., Nemesh, J., Shekhar, K., Goldman, M., Tirosh, I., Bialas, A. R., Kamitaki, N., Martersteck, E. M., et al. (2015) · Cell · doi:10.1016/j.cell.2015.05.002 · Cited by 7,110
Scenic: single-cell regulatory network inference and clustering.
Aibar, S., González-Blas, C. B., Moerman, T., Huynh-Thu, V. A., Imrichova, H., Hulselmans, G., Rambow, F., Marine, J.-C., Geurts, P., Aerts, J., et al. (2017) · Nature Methods · doi:10.1038/nmeth.4463 · Cited by 6,520
Mutational heterogeneity in cancer and the search for new cancer-associated genes
Lawrence, M. S., Stojanov, P., Polak, P., Kryukov, G. V., Cibulskis, K., Sivachenko, A., Carter, S. L., Stewart, C., Mermel, C. H., Roberts, S. A., et al. (2013) · Nature · doi:10.1038/nature12213 · Cited by 5,461
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Metadata
- Authors
- Izzo, F., Lee, S. C., Poran, A., Chaligne, R., Gaiti, F., Gross, B., Murali, R. R., Deochand, S. D., Ang, C., Jones, P. W., Nam, A. S., Kim, K.-T., Kothen-Hill, S., Schulman, R. C., Ki, M., Lhoumaud, P., Skok, J. A., Viny, A. D., Levine, R. L., Kenigsberg, E., Abdel-Wahab, O., & Landau, D. A.
- Affiliations
- New York Genome Center (US)
- Memorial Sloan Kettering Cancer Center (US)
- Weill Cornell Medicine (US)
- NYU Langone Health (US)
- Icahn School of Medicine at Mount Sinai (US)
- Corresponding author
- Landau, Dan A.
- ORCID
- Venue
- Nature Genetics
- Published
- 2020-03-23
- Volume
- 52
- Issue
- 4
- Pages
- 378-387
- ISSN
- 1061-4036 (ISSN-L)
- 1546-1718
- PMC
- PMC7216752
- OpenAlex
- W3012899267
- Cited by
- 236
- References
- 86
- Citations by year
- 2026: 16 · 2025: 25 · 2024: 46 · 2023: 42 · 2022: 45 · 2021: 38 · 2020: 24
- FWCI
- 23.53
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Nature Genetics (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Burroughs Wellcome Fund
- Pershing Square Foundation
- Foundation for the National Institutes of Health (DP2-CA239065)
- EIF | Stand Up To Cancer (SU2C-AACR-IRG-0616)
- American Society of Hematology
- Marie Skłodowska-Curie
- National Cancer Center
- U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI R00 CA218896, K08 CA215317)
- U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (1R35GM122515)
- Damon Runyon Cancer Research Foundation (DRG 117-15)
- Edward P. Evans Foundation
- Leukemia and Lymphoma Society
- U.S. Department of Health & Human Services | National Institutes of Health (P30 CA008748)
- Topics
- Health Sciences › Medicine › Hematology › Acute Myeloid Leukemia Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Epigenetics and DNA Methylation
- Health Sciences › Medicine › Hematology › Hematopoietic Stem Cell Transplantation
- Keywords
- DNA methylation, hematopoietic differentiation, TET2, DNMT3A, clonal hematopoiesis, hematologic malignancies, hematopoietic stem cells, erythroid progenitor cells, transcriptional priming, transcription factor binding motifs, single-cell transcriptomics, targeted genotyping
- MeSH terms
- Animals, Cell Differentiation, DNA (Cytosine-5-)-Methyltransferases, DNA-Binding Proteins, Hematopoiesis, Hematopoietic Stem Cells, Humans, Male, Mice, Transgenic, Mutation, Transcription, Genetic, DNA Methylation, Mice, Transcriptome
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