ETV6 Represses TNF during Stress Hematopoiesis and Regulates HSC Self Renewal

Bloom, Mackenzie; Baskin-Doerfler, Rebekah; Oak, Ninad; Feng, Ruopeng; Zhao, Xujie; Li, Chunliang; Ozark, Patrick; Verbist, Katherine; Stroh, Alexa; Albeituni, Sabrin; Maise, JaQuel; Weiss, Mitchell J.; McKinney‐Freeman, Shannon; Yang, Jun; Nichols, Kim E.

doi:10.1182/blood-2022-168076

Cited by 3 publications

(5 citation statements)

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“…Additionally, we uncover a role for ETV6 P216L in impairing HSC self-renewal, and we propose that mutation-dependent HSC defects observed in mice explain why human carriers are strongly predisposed to MDS and leukemia. Of note, ETV6 has been demonstrated to repress inflammatory response genes 43 , and elevated innate inflammation is a hallmark of MDS stem cells 44 . Future studies are needed to determine whether ETV6 P216L is sufficient for transformation to MDS with aging, or whether cooperating mutations, as have been described in patients 18 , are required.…”

Section: Discussionmentioning

confidence: 99%

Enigmatic missense mutations can cause disease via creation ofde novonuclear export signals

McConville,

Thomas,

Beckner

et al. 2024

Preprint

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Disease-causing missense mutations that occur within structurally and functionally unannotated protein regions can guide researchers to new mechanisms of protein regulation and dysfunction. Here, we report that the thrombocytopenia-, myelodysplastic syndromes-, and leukemia-associated P214L mutation in the transcriptional regulator ETV6 creates an XPO1-dependent nuclear export signal to cause protein mislocalization. Strategies to disrupt XPO1 activity fully restore ETV6 P214L protein nuclear localization and transcription regulation activity. Mechanistic insight inspired the design of humanized ETV6 mice, which we employ to demonstrate that the germline P214L mutation is sufficient to elicit severe defects in thrombopoiesis and hematopoietic stem cell maintenance. Beyond ETV6, we employed computational methods to uncover rare disease-associated missense mutations in unrelated proteins that create a nuclear export signal to disrupt protein function. Thus, missense mutations that operate through this mechanism should be predictable and may suggest rational therapeutic strategies for associated diseases.

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Section: Discussionmentioning

confidence: 99%

Enigmatic missense mutations can cause disease via creation ofde novonuclear export signals

McConville,

Thomas,

Beckner

et al. 2024

Preprint

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“…Concerning to these databanks, the dataset of HSCs was obtained by Bloom et al (52) (accession number: GSE213596); The MEPs dataset was obtained by Lu et al (53) (accession number: GSE112692), and the GMPs' dataset was obtained by Yáñez et al (54) (accession number: GSE88982). Next, we normalize these data using the Transcripts Per Million (TPM) convention (55).…”

Section: Processing Of Rna-seq Datasetsmentioning

confidence: 99%

“…Figure 4Comparison between predictions vs real gene expression. gene expression pattern corresponding to HSCs vs discretized RNA-seq data(52). (b) Boolean prediction equivalent to MEPs compared to discretized RNA-seq data(53).…”

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confidence: 99%

A System-Level Model Reveals that Transcriptional Stochasticity is Required for Hematopoietic Stem Cell Differentiation

Álvarez-Buylla,

Herrera,

Bensussen

et al. 2024

Preprint

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Background The differentiation of hematopoietic stem cells towards their different cell fates, has been difficult to study experimentally due to the diverse number of physiological conditions involved in it. These experimental obstacles have generated antagonistic positions to explain this differentiation process.Methods Here, we build a gene regulation network that integrates several aspects documented in the literature on the regulation of the differentiation of hematopoietic stem cells towards their different cell fates. Based on this network, we used Boolean, stochastic and continuous models to simulate the conditions of the hematopoietic stem cell niche under physiological conditions.Results We found that the differentiation of hematopoietic stem cells is a process in which cell fates can transdifferentiate among themselves through plasticity phenomena, which are supported by the stochasticity of transcription. Finally, we found that oxygen activates the production of reactive oxygen species that inhibits the quiescence of hematopoietic stem cells and promote the growth and differentiation pathways of the hematopoietic stem cells.Conclusion Hematopoiesis is a plastic process strongly conditioned at a genetic level that can be altered by the microenvironment in which it takes place, as well as by metabolism.

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“…We found three matching motifs: GATA1, SP4, and ETV6. In K562 cells, GATA1 is a well-known master regulator necessary for erythroid differentiation, playing a crucial role in the activation and repression of various genes involved in hematopoiesis 41,42 ; ETV6 is known for its involvement in hematopoiesis and oncogenesis, contributing to the regulation of genes essential for blood cell development and differentiation 43,44 ; The SP4 transcription factor (TF) is a member of the SP/KLF family of zinc finger transcription factors, and is known to bind to GC-rich promoter elements and influence the transcription of target genes 41,45 . While these insights are derived from predictive analysis, they lay the groundwork for empirical validation.…”

Section: Epinformer Recapitulates Tf Motifs Required For Enhancer Act...mentioning

confidence: 99%

“…We utilized TF-MoDISco-lite v2.1.0 (available at https://github.com/jmschrei/tfmodisco-lite) to identify motifs in nucleotide contribution scores across enhancer sequences from the testing set, derived from a 12-fold cross-chromosome validation process. This tool, an efficient version of TF-MoDISco 28 , was used with its default settings to find seqlet patterns which were then compared against the JASPAR2024 CORE vertebrates non-redundant database 39 using Tomtom 44 . To analyze nucleotide contributions to enhancer activity predictions accurately, we used In-silico Saturation Mutagenesis (ISM) from TangerMEME 29 v0.2.1, generating attribution scores for each base within targeted regions.…”

Section: Nucleotide Contribution and Motif Discoverymentioning

confidence: 99%

EPInformer: A Scalable Deep Learning Framework for Gene Expression Prediction by Integrating Promoter-enhancer Sequences with Multimodal Epigenomic Data

Lin,

Luo,

Pinello

2024

Preprint

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Transcriptional regulation, critical for cellular differentiation and adaptation to environmental changes, involves coordinated interactions among DNA sequences, regulatory proteins, and chromatin architecture. Despite extensive data from consortia like ENCODE, understanding the dynamics of cis-regulatory elements (CREs) in gene expression remains challenging. Deep learning is a powerful tool for learning gene expression and epigenomic signals from DNA sequences, exhibiting superior performance compared to conventional machine learning approaches. However, even the most advanced deep learning-based methods may fall short in capturing the regulatory effects of distal elements such as enhancers, limiting their predictive accuracy. In addition, these methods may require significant resources to train or to adapt to newly generated data. To address these challenges, we present EPInformer, a scalable deep-learning framework for predicting gene expression by integrating promoter-enhancer interactions with their sequences, epigenomic signals, and chromatin contacts. Our model outperforms existing gene expression prediction models in rigorous cross-chromosome validation, accurately recapitulates enhancer-gene interactions validated by CRISPR perturbation experiments, and identifies crucial transcription factor motifs within regulatory sequences. EPInformer is available as open-source software at https://github.com/pinellolab/EPInformer.

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ETV6 Represses TNF during Stress Hematopoiesis and Regulates HSC Self Renewal

Cited by 3 publications

References 0 publications

Enigmatic missense mutations can cause disease via creation ofde novonuclear export signals

Enigmatic missense mutations can cause disease via creation ofde novonuclear export signals

A System-Level Model Reveals that Transcriptional Stochasticity is Required for Hematopoietic Stem Cell Differentiation

EPInformer: A Scalable Deep Learning Framework for Gene Expression Prediction by Integrating Promoter-enhancer Sequences with Multimodal Epigenomic Data

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