The DMT1 isoform lacking the iron-response element regulates normal and malignant hematopoiesis via NOTCH pathway activation

Natural resistance-associated macrophage protein 2 (NRAMP 2; also known as DMT1 and encoded by SLC11A2) is mainly known for its iron transport activity. Recently, the DMT1 isoform lacking the iron-response element (nonIRE) was associated with aberrant NOTCH pathway activity. In this report, we investigated the function of DMT1 nonIRE in normal and malignant hematopoiesis. Knockdown of Dmt1 nonIRE in mice showed that it has non-canonical functions in hematopoietic stem cell differentiation: its knockdown suppressed development along the myeloid and lymphoid lineages, while promoting the production of platelets. These phenotypic effects on the hematopoietic system induced by Dmt1 nonIRE knockdown were linked to suppression of Notch/Myc pathway activity. Conversely, our data indicate a non-canonical function for DMT1 nonIRE overexpression in boosting NOTCH pathway activity in T-cell leukemia homeobox protein 1 (TLX1)-defective leukemia. This work sets the stage for future investigation using a multiple-hit T-cell acute lymphoblastic leukemia (T-ALL) model to further investigate the function of DMT1 nonIRE in T-ALL disease development and progression.

Cross-species analysis of enhancer logic using deep learning

Deciphering the genomic regulatory code of enhancers is a key challenge in biology because this code underlies cellular identity. A better understanding of how enhancers work will improve the interpretation of noncoding genome variation and empower the generation of cell type-specific drivers for gene therapy. Here, we explore the combination of deep learning and cross-species chromatin accessibility profiling to build explainable enhancer models. We apply this strategy to decipher the enhancer code in melanoma, a relevant case study owing to the presence of distinct melanoma cell states. We trained and validated a deep learning model, called DeepMEL, using chromatin accessibility data of 26 melanoma samples across six different species. We show the accuracy of DeepMEL predictions on the CAGI5 challenge, where it significantly outperforms existing models on the melanoma enhancer of IRF4 Next, we exploit DeepMEL to analyze enhancer architectures and identify accurate transcription factor binding sites for the core regulatory complexes in the two different melanoma states, with distinct roles for each transcription factor, in terms of nucleosome displacement or enhancer activation. Finally, DeepMEL identifies orthologous enhancers across distantly related species, where sequence alignment fails, and the model highlights specific nucleotide substitutions that underlie enhancer turnover. DeepMEL can be used from the Kipoi database to predict and optimize candidate enhancers and to prioritize enhancer mutations. In addition, our computational strategy can be applied to other cancer or normal cell types.