IRF8 Is an AML-Specific Susceptibility Factor That Regulates Signaling Pathways and Proliferation of AML Cells

Liss, Franziska; Frech, Miriam; Wang, Ying; Giel, Gavin; Fischer, Sabrina; Simon, Clara; Weber, Lisa Marie; Nist, Andrea; Stiewe, Thorsten; Neubauer, Andreas; Burchert, Andreas; Liefke, Robert

doi:10.3390/cancers13040764

Cited by 17 publications

(24 citation statements)

References 88 publications

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“…Several recent reports have explored the roles of IRF8 and MEF2D in AML ( Cao et al 2021 ; Liss et al 2021 ; Zhao et al 2021 ). In Zhao et al (2021) , MEF2D is reported to inhibit a CEBPE-mediated program of leukemia differentiation.…”

Section: Discussionmentioning

confidence: 99%

A distinct core regulatory module enforces oncogene expression in KMT2A-rearranged leukemia

et al. 2022

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Acute myeloid leukemia with KMT2A (MLL) rearrangements is characterized by specific patterns of gene expression and enhancer architecture, implying unique core transcriptional regulatory circuitry. Here, we identified the transcription factors MEF2D and IRF8 as selective transcriptional dependencies of KMT2A-rearranged AML, where MEF2D displays partially redundant functions with its paralog, MEF2C. Rapid transcription factor degradation followed by measurements of genome-wide transcription rates and superresolution microscopy revealed that MEF2D and IRF8 form a distinct core regulatory module with a narrow direct transcriptional program that includes activation of the key oncogenes MYC, HOXA9, and BCL2. Our study illustrates a mechanism of context-specific transcriptional addiction whereby a specific AML subclass depends on a highly specialized core regulatory module to directly enforce expression of common leukemia oncogenes.

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Section: Discussionmentioning

confidence: 99%

A distinct core regulatory module enforces oncogene expression in KMT2A-rearranged leukemia

et al. 2022

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“…As an orthogonal validation of the cell-type specificity of the TF regulons from GRaNIE eGRNs, we compared the regulons with differential expression data upon TF knockout (K/O) in the same cell type. We obtained data for one or two of the top five important TFs in each cell type: NFKB1 in macrophages (49), IRF8 in AML (50) and IRF1 and IRF2 in T-cells (44). The genes downregulated upon TF K/O were significantly enriched in the TF regulons of the respective cell types (Fig.…”

Section: Resultsmentioning

confidence: 99%

“…We obtained cell-type specific knock-out (K/O) data for THP1 derived macrophages (NFKB1) (49), the human AML cell line MV4-11 (IRF8) (50), and processed differential expression data from primary human CD4+ T cells (IRF1 and IRF2) (44). For the NKB1 and IRF8 datasets, raw sequencing files were obtained from GSE162015 and GSE163275 respectively, and data processing and quality control was performed with an in-house Snakemake pipeline as described previously (27).…”

Section: Methodsmentioning

confidence: 99%

GRaNIE and GRaNPA: Inference and evaluation of enhancer-mediated gene regulatory networks applied to study macrophages

Kamal

Arnold

Claringbould

et al. 2021

Preprint

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Among the biggest challenges in the post-GWAS (genome-wide association studies) era is the interpretation of disease-associated genetic variants in non-coding genomic regions. Enhancers have emerged as key players in mediating the effect of genetic variants on complex traits and diseases. Their activity is regulated by a combination of transcription factors (TFs), epigenetic changes and genetic variants. Several approaches exist to link enhancers to their target genes, and others that infer TF-gene connections. However, we currently lack a framework that systematically integrates enhancers into TF-gene regulatory networks. Furthermore, we lack an unbiased way of assessing whether inferred regulatory interactions are biologically meaningful. Here we present two methods, implemented as user-friendly R-packages, for building and evaluating enhancer-mediated gene regulatory networks (eGRNs) called GRaNIE (Gene Regulatory Network Inference including Enhancers - https://git.embl.de/grp-zaugg/GRaNIE) and GRaNPA (Gene Regulatory Network Performance Analysis - https://git.embl.de/grp-zaugg/GRaNPA), respectively. GRaNIE jointly infers TF-enhancer, enhancer-gene and TF-gene interactions by integrating open chromatin data such as ATAC-Seq or H3K27ac with RNA-seq across a set of samples (e.g. individuals), and optionally also Hi-C data. GRaNPA is a general framework for evaluating the biological relevance of TF-gene GRNs by assessing their performance for predicting cell-type specific differential expression. We demonstrate the power of our tool-suite by investigating gene regulatory mechanisms in macrophages that underlie their response to infection, and their involvement in common genetic diseases including autoimmune diseases.Among the biggest challenges in the post-GWAS (genome-wide association studies) era is the interpretation of disease-associated genetic variants in non-coding genomic regions. Enhancers have emerged as key players in mediating the effect of genetic variants on complex traits and diseases. Their activity is regulated by a combination of transcription factors (TFs), epigenetic changes and genetic variants. Several approaches exist to link enhancers to their target genes, and others that infer TF-gene connections. However, we currently lack a framework that systematically integrates enhancers into TF-gene regulatory networks. Furthermore, we lack an unbiased way of assessing whether inferred regulatory interactions are biologically meaningful. Here we present two methods, implemented as user-friendly R-packages, for building and evaluating enhancer-mediated gene regulatory networks (eGRNs) called GRaNIE (Gene Regulatory Network Inference including Enhancers - https://git.embl.de/grp-zaugg/GRaNIE) and GRaNPA (Gene Regulatory Network Performance Analysis - https://git.embl.de/grp-zaugg/GRaNPA), respectively. GRaNIE jointly infers TF-enhancer, enhancer-gene and TF-gene interactions by integrating open chromatin data such as ATAC-Seq or H3K27ac with RNA-seq across a set of samples (e.g. individuals), and optionally also Hi-C data. GRaNPA is a general framework for evaluating the biological relevance of TF-gene GRNs by assessing their performance for predicting cell-type specific differential expression. We demonstrate the power of our tool-suite by investigating gene regulatory mechanisms in macrophages that underlie their response to infection, and their involvement in common genetic diseases including autoimmune diseases.Among the biggest challenges in the post-GWAS (genome-wide association studies) era is the interpretation of disease-associated genetic variants in non-coding genomic regions. Enhancers have emerged as key players in mediating the effect of genetic variants on complex traits and diseases. Their activity is regulated by a combination of transcription factors (TFs), epigenetic changes and genetic variants. Several approaches exist to link enhancers to their target genes, and others that infer TF-gene connections. However, we currently lack a framework that systematically integrates enhancers into TF-gene regulatory networks. Furthermore, we lack an unbiased way of assessing whether inferred regulatory interactions are biologically meaningful. Here we present two methods, implemented as user-friendly R-packages, for building and evaluating enhancer-mediated gene regulatory networks (eGRNs) called GRaNIE (Gene Regulatory Network Inference including Enhancers - https://git.embl.de/grp-zaugg/GRaNIE) and GRaNPA (Gene Regulatory Network Performance Analysis - https://git.embl.de/grp-zaugg/GRaNPA), respectively. GRaNIE jointly infers TF-enhancer, enhancer-gene and TF-gene interactions by integrating open chromatin data such as ATAC-Seq or H3K27ac with RNA-seq across a set of samples (e.g. individuals), and optionally also Hi-C data. GRaNPA is a general framework for evaluating the biological relevance of TF-gene GRNs by assessing their performance for predicting cell-type specific differential expression. We demonstrate the power of our tool-suite by investigating gene regulatory mechanisms in macrophages that underlie their response to infection, and their involvement in common genetic diseases including autoimmune diseases.

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“…Other authors have used publicly available data to identify essential factors for AML growth. In this sense, IRF8 was uncovered as an AML-specific susceptibility factor [ 83 ]. Using data uploaded in DepMap ( , accessed on 25 June 2022), the glutamine–cysteine ligase catalytic subunit ( GCLC ) was extracted as a fitness gene in AML cell growth, survival, clonogenicity and leukaemogenesis [ 84 ].…”

Section: Hematologic Dependency Map Through Crispr Screens: Essential...mentioning

confidence: 99%

High-Throughput CRISPR Screening in Hematological Neoplasms

Ancos-Pintado

Bragado-García

Morales

et al. 2022

Cancers

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CRISPR is becoming an indispensable tool in biological research, revolutionizing diverse fields of medical research and biotechnology. In the last few years, several CRISPR-based genome-targeting tools have been translated for the study of hematological neoplasms. However, there is a lack of reviews focused on the wide uses of this technology in hematology. Therefore, in this review, we summarize the main CRISPR-based approaches of high throughput screenings applied to this field. Here we explain several libraries and algorithms for analysis of CRISPR screens used in hematology, accompanied by the most relevant databases. Moreover, we focus on (1) the identification of novel modulator genes of drug resistance and efficacy, which could anticipate relapses in patients and (2) new therapeutic targets and synthetic lethal interactions. We also discuss the approaches to uncover novel biomarkers of malignant transformations and immune evasion mechanisms. We explain the current literature in the most common lymphoid and myeloid neoplasms using this tool. Then, we conclude with future directions, highlighting the importance of further gene candidate validation and the integration and harmonization of the data from CRISPR screening approaches.

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IRF8 Is an AML-Specific Susceptibility Factor That Regulates Signaling Pathways and Proliferation of AML Cells

Cited by 17 publications

References 88 publications

A distinct core regulatory module enforces oncogene expression in KMT2A-rearranged leukemia

A distinct core regulatory module enforces oncogene expression in KMT2A-rearranged leukemia

GRaNIE and GRaNPA: Inference and evaluation of enhancer-mediated gene regulatory networks applied to study macrophages

High-Throughput CRISPR Screening in Hematological Neoplasms

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