Role of TET enzymes in DNA methylation, development, and cancer.

The pattern of DNA methylation at cytosine bases in the genome is tightly linked to gene expression, and DNA methylation abnormalities are often observed in diseases. The ten eleven translocation (TET) enzymes oxidize 5-methylcytosines (5mCs) and promote locus-specific reversal of DNA methylation. TET genes, and especially TET2, are frequently mutated in various cancers, but how the TET proteins contribute to prevent the onset and maintenance of these malignancies is largely unknown. Here, we highlight recent advances in understanding the physiological function of the TET proteins and their role in regulating DNA methylation and transcription. In addition, we discuss some of the key outstanding questions in the field.

The KDM4/JMJD2 histone demethylases are required for hematopoietic stem cell maintenance.

KDM4/JMJD2 are H3K9- and H3K36-specific demethylases, which are considered promising therapeutic targets for the treatment of acute myeloid leukemia (AML) harboring MLL translocations. Here, we investigate the long-term effects of depleting KDM4 activity on normal hematopoiesis to probe potential side effects of continuous inhibition of these enzymes. Utilizing conditional Kdm4a/Kdm4b/Kdm4c triple-knockout mice, we show that KDM4 activity is required for hematopoietic stem cell (HSC) maintenance in vivo. The knockout of the KDM4 demethylases leads to accumulation of H3K9me3 on transcription start sites and the corresponding downregulation of expression of several genes in HSCs. We show that 2 of these genes, Taf1b and Nom1, are essential for the maintenance of hematopoietic cells. Taken together, our results show that the KDM4 demethylases are required for the expression of genes essential for the long-term maintenance of normal hematopoiesis.

Oligoasthenoteratozoospermia and infertility in mice deficient for miR-34b/c and miR-449 loci.

Male fertility requires the continuous production of high quality motile spermatozoa in abundance. Alterations in all three metrics cause oligoasthenoteratozoospermia, the leading cause of human sub/infertility. Post-mitotic spermatogenesis inclusive of several meiotic stages and spermiogenesis (terminal spermatozoa differentiation) are transcriptionally inert, indicating the potential importance for the post-transcriptional microRNA (miRNA) gene-silencing pathway therein. We found the expression of miRNA generating enzyme Dicer within spermatogenesis peaks in meiosis with critical functions in spermatogenesis. In an expression screen we identified two miRNA loci of the miR-34 family (miR-34b/c and miR-449) that are specifically and highly expressed in post-mitotic male germ cells. A reduction in several miRNAs inclusive of miR-34b/c in spermatozoa has been causally associated with reduced fertility in humans. We found that deletion of both miR34b/c and miR-449 loci resulted in oligoasthenoteratozoospermia in mice. MiR-34bc/449-deficiency impairs both meiosis and the final stages of spermatozoa maturation. Analysis of miR-34bc-/-;449-/- pachytene spermatocytes revealed a small cohort of genes deregulated that were highly enriched for miR-34 family target genes. Our results identify the miR-34 family as the first functionally important miRNAs for spermatogenesis whose deregulation is causal to oligoasthenoteratozoospermia and infertility.

Comprehensive and unbiased multiparameter high-throughput screening by compaRe finds effective and subtle drug responses in AML models.

Large-scale multiparameter screening has become increasingly feasible and straightforward to perform thanks to developments in technologies such as high-content microscopy and high-throughput flow cytometry. The automated toolkits for analyzing similarities and differences between large numbers of tested conditions have not kept pace with these technological developments. Thus, effective analysis of multiparameter screening datasets becomes a bottleneck and a limiting factor in unbiased interpretation of results. Here we introduce compaRe, a toolkit for large-scale multiparameter data analysis, which integrates quality control, data bias correction, and data visualization methods with a mass-aware gridding algorithm-based similarity analysis providing a much faster and more robust analyses than existing methods. Using mass and flow cytometry data from acute myeloid leukemia and myelodysplastic syndrome patients, we show that compaRe can reveal interpatient heterogeneity and recognizable phenotypic profiles. By applying compaRe to high-throughput flow cytometry drug response data in AML models, we robustly identified multiple types of both deep and subtle phenotypic response patterns, highlighting how this analysis could be used for therapeutic discoveries. In conclusion, compaRe is a toolkit that uniquely allows for automated, rapid, and precise comparisons of large-scale multiparameter datasets, including high-throughput screens.

Biology has seen huge advances in technology in recent years. This has led to state-of-the-art techniques which can test hundreds of conditions simultaneously, such as how cancer cells respond to different drugs. In addition to this, each of the tens of thousands of cells studied can be screened for multiple variables, such as certain proteins or genes. This generates massive datasets with large numbers of parameters, which researchers can use to find similarities and differences between the tested conditions. Analyzing these 'high-throughput' experiments, however, is no easy task, as the data is often contaminated with meaningless information, or 'background noise', as well as sources of bias, such as non-biological variations between experiments. As a result, most analysis methods can only probe one parameter at a time, or are unautomated and require manual interpretation of the data. Here, Chalabi Hajkarim et al. have developed a new toolkit that can analyze multiparameter datasets faster and more robustly than current methods. The kit, which was named 'compaRe', combines a range of computational tools that automatically 'clean' the data of background noise or bias: the different conditions are then compared and any similarities are visually displayed using a graphical interface that is easy to explore. Chalabi Hajkarim et al. used their new method to study data from patients with acute myeloid leukemia (AML) and myelodysplastic syndrome, two forms of cancer that disrupt the production of functional immune cells. The toolkit was able to identify subtle differences between the patients and categorize them into groups based on the proteins present on immune cells. Chalabi Hajkarim et al. also applied compaRe to high-throughput data on cells from patients and mouse models with AML that had been treated with large numbers of specific drugs. This revealed that different cell types in the samples responded to the treatments in distinct ways. These findings suggest that the toolkit created by Chalabi Hajkarim et al. can automatically, rapidly and precisely compare large multiparameter datasets collected using high-throughput screens. In the future, compaRe could be used to identify drugs that illicit a specific response, or to predict how newly developed treatments impact different cell types in the body.

Quantification of Differential Transcription Factor Activity and Multiomics-Based Classification into Activators and Repressors: diffTF.

Transcription factors (TFs) regulate many cellular processes and can therefore serve as readouts of the signaling and regulatory state. Yet for many TFs, the mode of action-repressing or activating transcription of target genes-is unclear. Here, we present diffTF (https://git.embl.de/grp-zaugg/diffTF) to calculate differential TF activity (basic mode) and classify TFs into putative transcriptional activators or repressors (classification mode). In basic mode, it combines genome-wide chromatin accessibility/activity with putative TF binding sites that, in classification mode, are integrated with RNA-seq. We apply diffTF to compare (1) mutated and unmutated chronic lymphocytic leukemia patients and (2) two hematopoietic progenitor cell types. In both datasets, diffTF recovers most known biology and finds many previously unreported TFs. It classifies almost 40% of TFs based on their mode of action, which we validate experimentally. Overall, we demonstrate that diffTF recovers known biology, identifies less well-characterized TFs, and classifies TFs into transcriptional activators or repressors.

Tumor suppressor ASXL1 is essential for the activation of INK4B expression in response to oncogene activity and anti-proliferative signals.

ASXL1 mutations are frequently found in hematological tumors, and loss of Asxl1 promotes myeloid transformation in mice. Here we present data supporting a role for an ASXL1-BAP1 complex in the deubiquitylation of mono-ubiquitylated lysine 119 on Histone H2A (H2AK119ub1) in vivo. The Polycomb group proteins control the expression of the INK4B-ARF-INK4A locus during normal development, in part through catalyzing mono-ubiquitylation of H2AK119. Since the activation of the locus INK4B-ARF-INK4A plays a fail-safe mechanism protecting against tumorigenesis, we investigated whether ASXL1-dependent H2A deubiquitylation plays a role in its activation. Interestingly, we found that ASXL1 is specifically required for the increased expression of p15(INK4B) in response to both oncogenic signaling and extrinsic anti-proliferative signals. Since we found that ASXL1 and BAP1 both are enriched at the INK4B locus, our results suggest that activation of the INK4B locus requires ASXL1/BAP1-mediated deubiquitylation of H2AK119ub1. Consistently, our results show that ASXL1 mutations are associated with lower expression levels of p15(INK4B) and a proliferative advantage of hematopoietic progenitors in primary bone marrow cells, and that depletion of ASXL1 in multiple cell lines results in resistance to growth inhibitory signals. Taken together, this study links ASXL1-mediated H2A deubiquitylation and transcriptional activation of INK4B expression to its tumor suppressor functions.