Identification of Gene Regulatory Networks in B-Cell Progenitor Differentiation and Leukemia.
Stefan NagelCorinna MeyerPublished in: Genes (2024)
Pro-B- and pre-B-cells are consecutive entities in early B-cell development, representing cells of origin for B-cell precursor acute lymphoid leukemia (BCP-ALL). Normal B-cell differentiation is critically regulated by specific transcription factors (TFs). Accordingly, TF-encoding genes are frequently deregulated or mutated in BCP-ALL. Recently, we described TF-codes which delineate physiological activities of selected groups of TF-encoding genes in hematopoiesis including B-cell development. Here, we exploited these codes to uncover regulatory connections between particular TFs in pro-B- and pre-B-cells via an analysis of developmental TFs encoded by NKL and TALE homeobox genes and by ETS and T-box genes. Comprehensive expression analyses in BCP-ALL cell lines helped identify validated models to study their mutual regulation in vitro. Knockdown and overexpression experiments and subsequent RNA quantification of TF-encoding genes in selected model cell lines revealed activating, inhibitory or absent connections between nine TFs operating in early B-cell development, including HLX, MSX1, IRX1, MEIS1, ETS2, ERG, SPIB, EOMES, and TBX21. In addition, genomic profiling revealed BCP-ALL subtype-specific copy number alterations of ERG at 21q22, while a deletion of the TGFbeta-receptor gene TGFBR2 at 3p24 resulted in an upregulation of EOMES . Finally, we combined the data to uncover gene regulatory networks which control normal differentiation of early B-cells, collectively endorsing more detailed evaluation of BCP-ALL subtypes.
Keyphrases
- transcription factor
- genome wide
- genome wide identification
- copy number
- bioinformatics analysis
- mitochondrial dna
- dna methylation
- single cell
- acute myeloid leukemia
- genome wide analysis
- bone marrow
- cell proliferation
- signaling pathway
- dna binding
- oxidative stress
- induced apoptosis
- drug induced
- electronic health record
- deep learning
- respiratory failure
- cell cycle arrest
- cell death
- mechanical ventilation