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Although originally described as transcriptional activator, SPI1/PU.1, a major player in haematopoiesis whose alterations are associated with haematological malignancies, has the ability to repress transcription. Here, we investigated the mechanisms underlying gene repression in the erythroid lineage, in which SPI1 exerts an oncogenic function by blocking differentiation. We show that SPI1 represses genes by binding active enhancers that are located in intergenic or gene body regions. HDAC1 acts as a cooperative mediator of SPI1-induced transcriptional repression by deacetylating SPI1-bound enhancers in a subset of genes, including those involved in erythroid differentiation. Enhancer deacetylation impacts on promoter acetylation, chromatin accessibility and RNA pol II occupancy. In addition to the activities of HDAC1, polycomb repressive complex 2 (PRC2) reinforces gene repression by depositing H3K27me3 at promoter sequences when SPI1 is located at enhancer sequences. Moreover, our study identified a synergistic relationship between PRC2 and HDAC1 complexes in mediating the transcriptional repression activity of SPI1, ultimately inducing synergistic adverse effects on leukaemic cell survival. Our results highlight the importance of the mechanism underlying transcriptional repression in leukemic cells, involving complex functional connections between SPI1 and the epigenetic regulators PRC2 and HDAC1., (© The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Comparing histone modification profiles between cancer and normal states, or across different tumor samples, can provide insights into understanding cancer initiation, progression and response to therapy. ChIP-seq histone modification data of cancer samples are distorted by copy number variation innate to any cancer cell. We present HMCan-diff, the first method designed to analyze ChIP-seq data to detect changes in histone modifications between two cancer samples of different genetic backgrounds, or between a cancer sample and a normal control. HMCan-diff explicitly corrects for copy number bias, and for other biases in the ChIP-seq data, which significantly improves prediction accuracy compared to methods that do not consider such corrections. On in silico simulated ChIP-seq data generated using genomes with differences in copy number profiles, HMCan-diff shows a much better performance compared to other methods that have no correction for copy number bias. Additionally, we benchmarked HMCan-diff on four experimental datasets, characterizing two histone marks in two different scenarios. We correlated changes in histone modifications between a cancer and a normal control sample with changes in gene expression. On all experimental datasets, HMCan-diff demonstrated better performance compared to the other methods., (© The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Acute leukemias are characterized by deregulation of transcriptional networks that control the lineage specificity of gene expression. The aberrant overexpression of the Spi-1/PU.1 transcription factor leads to erythroleukemia. To determine how Spi-1 mechanistically influences the transcriptional program, we combined a ChIP-seq analysis with transcriptional profiling in cells from an erythroleukemic mouse model. We show that Spi-1 displays a selective DNA-binding that does not often cause transcriptional modulation. We report that Spi-1 controls transcriptional activation and repression partially through distinct Spi-1 recruitment to chromatin. We revealed several parameters impacting on Spi-1-mediated transcriptional activation. Gene activation is facilitated by Spi-1 occupancy close to transcriptional starting site of genes devoid of CGIs. Moreover, in those regions Spi-1 acts by binding to multiple motifs tightly clustered and with similar orientation. Finally, in contrast to the myeloid and lymphoid B cells in which Spi-1 exerts a physiological activity, in the erythroleukemic cells, lineage-specific cooperating factors do not play a prevalent role in Spi-1-mediated transcriptional activation. Thus, our work describes a new mechanism of gene activation through clustered site occupancy of Spi-1 particularly relevant in regard to the strong expression of Spi-1 in the erythroleukemic cells.

Dramatic progress in the development of next-generation sequencing technologies has enabled accurate genome-wide characterization of the binding sites of DNA-associated proteins. This technique, baptized as ChIP-Seq, uses a combination of chromatin immunoprecipitation and massively parallel DNA sequencing. Other published tools that predict binding sites from ChIP-Seq data use only positional information of mapped reads. In contrast, our algorithm MICSA (Motif Identification for ChIP-Seq Analysis) combines this source of positional information with information on motif occurrences to better predict binding sites of transcription factors (TFs). We proved the greater accuracy of MICSA with respect to several other tools by running them on datasets for the TFs NRSF, GABP, STAT1 and CTCF. We also applied MICSA on a dataset for the oncogenic TF EWS-FLI1. We discovered >2000 binding sites and two functionally different binding motifs. We observed that EWS-FLI1 can activate gene transcription when (i) its binding site is located in close proximity to the gene transcription start site (up to approximately 150 kb), and (ii) it contains a microsatellite sequence. Furthermore, we observed that sites without microsatellites can also induce regulation of gene expression--positively as often as negatively--and at much larger distances (up to approximately 1 Mb).