Petra Zeisberger

Identification of Regulatory Networks in HSCs and Their Immediate Progeny via Integrated Proteome, Transcriptome, and DNA Methylome Analysis

In this study, we present integrated quantitative proteome, transcriptome, and methylome analyses of hematopoietic stem cells (HSCs) and four multipotent progenitor (MPP) populations. From the characterization of more than 6,000 proteins, 27,000 transcripts, and 15,000 differentially methylated regions (DMRs), we identified coordinated changes associated with early differentiation steps. DMRs show continuous gain or loss of methylation during differentiation, and the overall change in DNA methylation correlates inversely with gene expression at key loci. Our data reveal the differential expression landscape of 493 transcription factors and 682 lncRNAs and highlight specific expression clusters operating in HSCs. We also found an unexpectedly dynamic pattern of transcript isoform regulation, suggesting a critical regulatory role during HSC differentiation, and a cell cycle/DNA repair signature associated with multipotency in MPP2 cells. This study provides a comprehensive genome-wide resource for the functional exploration of molecular, cellular, and epigenetic regulation at the top of the hematopoietic hierarchy.

Identification of DNA methylation changes at cis-regulatory elements during early steps of HSC differentiation using tagmentation-based whole genome bisulfite sequencing

Epigenetic alterations during cellular differentiation are a key molecular mechanism which both instructs and reinforces the process of lineage commitment. Within the haematopoietic system, progressive changes in the DNA methylome of haematopoietic stem cells (HSCs) are essential for the effective production of mature blood cells. Inhibition or loss of function of the cellular DNA methylation machinery has been shown to lead to a severe perturbation in blood production and is also an important driver of malignant transformation. HSCs constitute a very rare cell population in the bone marrow, capable of life-long self-renewal and multi-lineage differentiation. The low abundance of HSCs has been a major technological barrier to the global analysis of the CpG methylation status within both HSCs and their immediate progeny, the multipotent progenitors (MPPs). Within this Extra View article, we review the current understanding of how the DNA methylome regulates normal and malignant hematopoiesis. We also discuss the current methodologies that are available for interrogating the DNA methylation status of HSCs and MPPs and describe a new data set that was generated using tagmentation-based whole genome bisulfite sequencing (TWGBS) in order to comprehensively map methylated cytosines using the limited amount of genomic DNA that can be harvested from rare cell populations. Extended analysis of this data set clearly demonstrates the added value of genome-wide sequencing of methylated cytosines and identifies novel important cis-acting regulatory regions that are dynamically remodeled during the first steps of haematopoietic differentiation.

A Myc enhancer cluster regulates normal and leukaemic haematopoietic stem cell hierarchies

The transcription factor Myc is essential for the regulation of haematopoietic stem cells and progenitors and has a critical function in haematopoietic malignancies. Here we show that an evolutionarily conserved region located 1.7 megabases downstream of the Myc gene that has previously been labelled as a ‘super-enhancer’ is essential for the regulation of Myc expression levels in both normal haematopoietic and leukaemic stem cell hierarchies in mice and humans. Deletion of this region in mice leads to a complete loss of Myc expression in haematopoietic stem cells and progenitors. This caused an accumulation of differentiation-arrested multipotent progenitors and loss of myeloid and B cells, mimicking the phenotype caused by Mx1-Cre-mediated conditional deletion of the Myc gene in haematopoietic stem cells. This super-enhancer comprises multiple enhancer modules with selective activity that recruits a compendium of transcription factors, including GFI1b, RUNX1 and MYB. Analysis of mice carrying deletions of individual enhancer modules suggests that specific Myc expression levels throughout most of the haematopoietic hierarchy are controlled by the combinatorial and additive activity of individual enhancer modules, which collectively function as a ‘blood enhancer cluster’ (BENC). We show that BENC is also essential for the maintenance of MLL–AF9-driven leukaemia in mice. Furthermore, a BENC module, which controls Myc expression in mouse haematopoietic stem cells and progenitors, shows increased chromatin accessibility in human acute myeloid leukaemia stem cells compared to blasts. This difference correlates with MYC expression and patient outcome. We propose that clusters of enhancers, such as BENC, form highly combinatorial systems that allow precise control of gene expression across normal cellular hierarchies and which also can be hijacked in malignancies.

BCAT1 restricts αKG levels in AML stem cells leading to IDHmut-like DNA hypermethylation

The branched-chain amino acid (BCAA) pathway and high levels of BCAA transaminase 1 (BCAT1) have recently been associated with aggressiveness in several cancer entities. However, the mechanistic role of BCAT1 in this process remains largely uncertain. Here, by performing high-resolution proteomic analysis of human acute myeloid leukaemia (AML) stem-cell and non-stem-cell populations, we find the BCAA pathway enriched and BCAT1 protein and transcripts overexpressed in leukaemia stem cells. We show that BCAT1, which transfers α-amino groups from BCAAs to α-ketoglutarate (αKG), is a critical regulator of intracellular αKG homeostasis. Further to its role in the tricarboxylic acid cycle, αKG is an essential cofactor for αKG-dependent dioxygenases such as Egl-9 family hypoxia inducible factor 1 (EGLN1) and the ten-eleven translocation (TET) family of DNA demethylases. Knockdown of BCAT1 in leukaemia cells caused accumulation of αKG, leading to EGLN1-mediated HIF1α protein degradation. This resulted in a growth and survival defect and abrogated leukaemia-initiating potential. By contrast, overexpression of BCAT1 in leukaemia cells decreased intracellular αKG levels and caused DNA hypermethylation through altered TET activity. AML with high levels of BCAT1 (BCAT1high) displayed a DNA hypermethylation phenotype similar to cases carrying a mutant isocitrate dehydrogenase (IDHmut), in which TET2 is inhibited by the oncometabolite 2-hydroxyglutarate. High levels of BCAT1 strongly correlate with shorter overall survival in IDHWTTET2WT, but not IDHmut or TET2mut AML. Gene sets characteristic for IDHmut AML were enriched in samples from patients with an IDHWTTET2WTBCAT1high status. BCAT1high AML showed robust enrichment for leukaemia stem-cell signatures, and paired sample analysis showed a significant increase in BCAT1 levels upon disease relapse. In summary, by limiting intracellular αKG, BCAT1 links BCAA catabolism to HIF1α stability and regulation of the epigenomic landscape, mimicking the effects of IDH mutations. Our results suggest the BCAA–BCAT1–αKG pathway as a therapeutic target to compromise leukaemia stem-cell function in patients with IDHWTTET2WT AML.

Author Correction: A Myc enhancer cluster regulates normal and leukaemic haematopoietic stem cell hierarchies

In the originally published version of this Letter, ref. 43 was erroneously provided twice. In the ‘Estimation of relative cell-type-specific composition of AML samples’ section in the Methods, the citation to ref. 43 after the GEO dataset GSE24759 is correct. However, in the ‘Mice’ section of the Methods, the citation to ref. 43 after ‘TAMERE’ should have been associated with a new reference1. The original Letter has been corrected online (with the new reference included as ref. 49).

Author Correction: BCAT1 restricts αKG levels in AML stem cells leading to IDH mut -like DNA hypermethylation

In Extended Data Fig. 1a of this Letter, the flow cytometry plot depicting the surface phenotype of AML sample DD08 was a duplicate of the plot for AML sample DD06. Supplementary Data 4 has been added to the Supplementary Information of the original Letter to clarify the proteome data acquisition and presentation. The original Letter has been corrected online.