tagHi-C Reveals 3D Chromatin Architecture Dynamics during Mouse Hematopoiesis

Zhang, Chao; Xu, Zihan; Yang, Shangda; Sun, Guohuan; Jia, Lumeng; Zheng, Zhaofeng; Gu, Quan; Tao, Wei; Cheng, Tao; Cheng, Li; Cheng, Hui

doi:10.1016/j.celrep.2020.108206

Cited by 53 publications

(60 citation statements)

References 65 publications

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“…Previous analysis by Lara-Astiaso et al [54] reported a set of critical genes for hematopoietic lineage differentiation; we identified 12 of the 13 at an FDR value < 0.01 ( Supplemental Figure S4 ). We also found supporting evidence for some examples of lineage-specific genes highlighted by Zhang et al, 2020 [28]. The authors showed that increased gene-body associating domains (GAD) scores are linked to active transcription and indicate cell-type specific features.…”

Section: Resultssupporting

confidence: 86%

“…We observed very similar proportions for A and B compartments except for two cell types (granulocyte-macrophage progenitor (GMP) and megakaryocyte-erythrocyte progenitor (MEP)), where the primary cells showed a lower frequency of A compartments ( Figure 5A ). We detected a total of 2,569 differential compartments across the ten cell types encompassing many of the genomic regions previously shown to undergo dynamic changes [28]. Another observation was an overall higher proportion of A compartments in the differential regions other than MEPs.…”

Section: Resultsmentioning

confidence: 97%

“…Hematopoiesis is a complex biological process that involves multiple cell-types and requires a systematic approach to study the dynamic genome organization. A recent study by Zhang et al 2020 [28] provided a detailed overview of the spatiotemporal dynamics of chromatin architecture during hematopoiesis. The study involved the classic hematopoietic model with ten primary stem, progenitor, and differentiated cell populations from mouse bone marrow ( Figure 5A-B ).…”

Section: Resultsmentioning

confidence: 99%

“…Technical challenges such as selecting the correct PC representing compartments, differences in the orientation of the sign of PC values, and their scaling across different datasets become larger problems while comparing many Hi-C contact maps. Thus far, comparative compartment analysis has been mainly limited to examining compartment flips between two Hi-C maps at a time [27, 28].…”

Section: Introductionmentioning

confidence: 99%

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dcHiC: differential compartment analysis of Hi-C datasets

Wang

Chakraborty

2021

Preprint

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Compartmental organization plays a role in important biological processes. However, its comparative analysis has been mainly limited to pairwise comparisons of contact maps with focus on finding compartment flips (e.g., A-to-B). Here, we introduce dcHiC, which utilizes Multiple Factor Analysis and a multivariate distance measure to systematically identify all compartmentalization differences among multiple contact maps. Evaluating dcHiC on three different collections of Hi-C data, we show its effectiveness and sensitivity in detecting biologically meaningful differences associated with cellular identity, gene expression, and lamin association. By providing a multivariate formulation, dcHiC immediately expands compartment analysis to new modalities in comparative genomics.

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Section: Resultssupporting

confidence: 86%

Section: Resultsmentioning

confidence: 97%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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dcHiC: differential compartment analysis of Hi-C datasets

Wang

Chakraborty

2021

Preprint

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“…A CoolBox figure representing differential interactions of Hi-C contact matrices. Shown Hi-C and RNA-Seq data are produced from the process of hematopoietic differentiation[25]. Its clearly that there is a topological association domain(TAD) formation at the Abca13 gene region and its RNA expression is up-regulated at the same time after the differentiation.…”

Section: Resultsmentioning

confidence: 99%

CoolBox: A flexible toolkit for visual analysis of genomics data

Zhong

Lin

et al. 2021

Preprint

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We developed CoolBox, an open source toolkit for visual analysis of genomics data, which is highly compatible with the Python ecosystem, easy to use and highly customizable with a well-designed user interface. It can be used in various visualization situations like a Swiss army knife, for example, to produce high-quality genome track plots or fetch common used genomic data files with a Python script or command line, interactively explore genomic data within Jupyter environment or web browser. Moreover, owing to the highly extensible API design, users can customize their own tracks without difficulty, which can greatly facilitate analytical, comparative genomic data visualization tasks.

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Activating mutations remodel the chromatin accessibility landscape to drive distinct regulatory networks in KMT2A‐rearranged acute leukemia

Zhang,

Falqués‐Costa,

Pilheden

et al. 2024

HemaSphere

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Activating FLT3 and RAS mutations commonly occur in leukemia with KMT2A‐gene rearrangements (KMT2A‐r). However, how these mutations cooperate with the KMT2A‐r to remodel the epigenetic landscape is unknown. Using a retroviral acute myeloid leukemia (AML) mouse model driven by KMT2A::MLLT3, we show that FLT3ITD, FLT3N676K, and NRASG12D remodeled the chromatin accessibility landscape and associated transcriptional networks. Although the activating mutations shared a common core of chromatin changes, each mutation exhibits unique profiles with most opened peaks associating with enhancers in intronic or intergenic regions. Specifically, FLT3N676K and NRASG12D rewired similar chromatin and transcriptional networks, distinct from those mediated by FLT3ITD. Motif analysis uncovered a role for the AP‐1 family of transcription factors in KMT2A::MLLT3 leukemia with FLT3N676K and NRASG12D, whereas Runx1 and Stat5a/Stat5b were active in the presence of FLT3ITD. Furthermore, transcriptional programs linked to immune cell regulation were activated in KMT2A‐r AML expressing NRASG12D or FLT3N676K, and the expression of NKG2D‐ligands on KMT2A‐r cells rendered them sensitive to CAR T cell‐mediated killing. Human KMT2A‐r AML cells could be pharmacologically sensitized to NKG2D‐CAR T cells by treatment with the histone deacetylase inhibitor LBH589 (panobinostat) which caused upregulation of NKG2D‐ligand levels. Co‐treatment with LBH589 and NKG2D‐CAR T cells enabled robust AML cell killing, and the strongest effect was observed for cells expressing NRASG12D. Finally, the results were validated and extended to acute leukemia in infancy. Combined, activating mutations induced mutation‐specific changes in the epigenetic landscape, leading to changes in transcriptional programs orchestrated by specific transcription factor networks.

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tagHi-C Reveals 3D Chromatin Architecture Dynamics during Mouse Hematopoiesis

Cited by 53 publications

References 65 publications

dcHiC: differential compartment analysis of Hi-C datasets

dcHiC: differential compartment analysis of Hi-C datasets

CoolBox: A flexible toolkit for visual analysis of genomics data

Activating mutations remodel the chromatin accessibility landscape to drive distinct regulatory networks in KMT2A‐rearranged acute leukemia

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