Document Type

Article

Publication Date

4-9-2024

Keywords

JMG, JGM, Humans, Nucleophosmin, Gene Regulatory Networks, Isocitrate Dehydrogenase, Leukemia, Myeloid, Acute, Carcinogenesis

JAX Source

NPJ Syst Biol Appl. 2024;10(1):38.

ISSN

2056-7189

PMID

38594351

DOI

https://doi.org/10.1038/s41540-024-00366-0

Grant

M. Lu and S. Li were supported by startup funds from The Jackson Laboratory and by the National Cancer Institute of the National Institutes of Health under Award Number P30CA034196. S. Li is also supported by the National Institute of General Medical Sciences of the National Institutes of Health under Award Number R35GM133562, by the National Human Genomic Research Institute of the National Institutes of Health under Award Number U01HG013175, by the National Cancer Institute of the National Institutes of Health under Award Number U01CA271830 and U01CA271830-03S1, and by the National Institute of Aging of the National Institutes of Health under Award Number R56AG071766-01A1.

Abstract

Acute myeloid leukemia (AML) is characterized by uncontrolled proliferation of poorly differentiated myeloid cells, with a heterogenous mutational landscape. Mutations in IDH1 and IDH2 are found in 20% of the AML cases. Although much effort has been made to identify genes associated with leukemogenesis, the regulatory mechanism of AML state transition is still not fully understood. To alleviate this issue, here we develop a new computational approach that integrates genomic data from diverse sources, including gene expression and ATAC-seq datasets, curated gene regulatory interaction databases, and mathematical modeling to establish models of context-specific core gene regulatory networks (GRNs) for a mechanistic understanding of tumorigenesis of AML with IDH mutations. The approach adopts a new optimization procedure to identify the top network according to its accuracy in capturing gene expression states and its flexibility to allow sufficient control of state transitions. From GRN modeling, we identify key regulators associated with the function of IDH mutations, such as DNA methyltransferase DNMT1, and network destabilizers, such as E2F1. The constructed core regulatory network and outcomes of in-silico network perturbations are supported by survival data from AML patients. We expect that the combined bioinformatics and systems-biology modeling approach will be generally applicable to elucidate the gene regulation of disease progression.

Comments

This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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