Chromatin immunoprecipitation coupled with sequencing (ChIP-seq) is a technique used to identify protein-DNA interaction sites through antibody pull-down, sequencing and analysis; with enrichment 'peak' calling being the most critical analytical step. Benchmarking studies have consistently shown that peak callers have distinct selectivity and specificity characteristics that are not additive and seldom completely overlap in many scenarios, even after parameter optimization. We therefore developed ChIP-AP, an integrated ChIP-seq analysis pipeline utilizing four independent peak callers, which seamlessly processes raw sequencing files to final result. This approach enables (1) better gauging of peak confidence through detection by multiple algorithms, and (2) more thoroughly surveys the binding landscape by capturing peaks not detected by individual callers. Final analysis results are then integrated into a single output table, enabling users to explore their data by applying selectivity and sensitivity thresholds that best address their biological questions, without needing any additional reprocessing. ChIP-AP therefore presents investigators with a more comprehensive coverage of the binding landscape without requiring additional wet-lab observations.

The mechanism underlying cell type-specific gene induction conferred by ubiquitous transcription factors as well as disruptions caused by their chimeric derivatives in leukemia is not well understood. Here, we investigate whether RNAs coordinate with transcription factors to drive myeloid gene transcription. In an integrated genome-wide approach surveying for gene loci exhibiting concurrent RNA and DNA interactions with the broadly expressed Runt-related transcription factor 1 (RUNX1), we identified the long noncoding RNA (lncRNA) originating from the upstream regulatory element of PU.1 (LOUP). This myeloid-specific and polyadenylated lncRNA induces myeloid differentiation and inhibits cell growth, acting as a transcriptional inducer of the myeloid master regulator PU.1. Mechanistically, LOUP recruits RUNX1 to both the PU.1 enhancer and the promoter, leading to the formation of an active chromatin loop. In t(8;21) acute myeloid leukemia (AML), wherein RUNX1 is fused to ETO, the resulting oncogenic fusion protein, RUNX1-ETO, limits chromatin accessibility at the LOUP locus, causing inhibition of LOUP and PU.1 expression. These findings highlight the important role of the interplay between cell-type-specific RNAs and transcription factors, as well as their oncogenic derivatives in modulating lineage-gene activation and raise the possibility that RNA regulators of transcription factors represent alternative targets for therapeutic development.

The mechanisms by which epigenetic modifications are established in gene regulatory regions of active genes remain poorly understood. The data presented show that the establishment and recycling of a major epigenetic mark, the acetylated form of the replacement histone H2A.Z, is regulated by cell cycle-specific long noncoding RNAs encoded in regions adjacent to the promoters of active genes. These transcripts, termed SPEARs (S Phase EArly RNAs), are induced in early S phase: their expression precedes that of the downstream genes on which they exert their regulatory action. SPEARs drive the modification and deposition of the acetylated form of histone H2A.Z by bringing together the replacement histone and the histone acetyl transferase TIP60. This widespread bimodal pathway constitutes a novel RNA-mediated mechanism for the establishment of epigenetic marks and cell-specific epigenetic profiles, thereby providing a unifying explanation for the accuracy and persistence of epigenetic marks on chromatin.

ChIP-Seq is a technique used to analyse protein-DNA interactions. The protein-DNA complex is pulled down using a protein antibody, after which sequencing and analysis of the bound DNA fragments is performed. A key bioinformatics analysis step is “peak” calling - identifying regions of enrichment. Benchmarking studies have consistently shown that no optimal peak caller exists. Peak callers have distinct selectivity and specificity characteristics which are often not additive and seldom completely overlap in many scenarios. In the absence of a universal peak caller, we rationalized one ought to utilize multiple peak-callers to 1) gauge peak confidence as determined through detection by multiple algorithms, and 2) more thoroughly survey the protein-bound landscape by capturing peaks not detected by individual peak callers owing to algorithmic limitations and biases. We therefore developed an integrated ChIP-Seq Analysis Pipeline (ChIP-AP) which performs all analysis steps from raw fastq files to final result, and utilizes four commonly used peak callers to more thoroughly and comprehensively analyse datasets. Results are integrated and presented in a single file enabling users to apply selectivity and sensitivity thresholds to select the consensus peak set, the union peak set, or any sub-set in-between to more confidently and comprehensively explore the protein-bound landscape. ( https://github.com/JSuryatenggara/ChIP-AP ).

Leukemic stem cells (LSCs) can acquire non-mutational resistance following drug treatment leading to therapeutic failure and relapse. However, oncogene-independent mechanisms of drug persistence in LSCs are incompletely understood, which is the primary focus of this study. We integrated proteomics, transcriptomics, and metabolomics to determine the contribution of STAT3 in promoting metabolic changes in tyrosine kinase inhibitor (TKI) persistent chronic myeloid leukemia (CML) cells. Proteomic and transcriptional differences in TKI persistent CML cells revealed BCR-ABL-independent STAT3 activation in these cells. While knockout of STAT3 inhibited the CML cells from developing drug-persistence, inhibition of STAT3 using a small molecule inhibitor sensitized the persistent CML cells to TKI treatment. Interestingly, given the role of phosphorylated STAT3 as a transcription factor, it localized uniquely to genes regulating metabolic pathways in the TKI-persistent CML stem and progenitor cells. Subsequently, we observed that STAT3 dysregulated mitochondrial metabolism forcing the TKI-persistent CML cells to depend on glycolysis, unlike TKI-sensitive CML cells, which are more reliant on oxidative phosphorylation. Finally, targeting pyruvate kinase M2, a rate-limiting glycolytic enzyme, specifically eradicated the TKI-persistent CML cells. By exploring the role of STAT3 in altering metabolism, we provide critical insight into identifying potential therapeutic targets for eliminating TKI-persistent LSCs.

45 The mechanism underlying cell type-specific gene induction conferred by ubiquitous transcription 46 factors as well as disruptions caused by their chimeric derivatives in leukemia is not well understood. 47 Here we investigate whether RNAs coordinate with transcription factors to drive myeloid gene 48 transcription. In an integrated genome-wide approach surveying for gene loci exhibiting concurrent 49 RNAand DNA-interactions with the broadly expressed transcription factor RUNX1, we identified the 50 long noncoding RNA LOUP. This myeloid-specific and polyadenylated lncRNA induces myeloid 51 differentiation and inhibits cell growth, acting as a transcriptional inducer of the myeloid master 52 regulator PU.1. Mechanistically, LOUP recruits RUNX1 to both the PU.1 enhancer and the promoter, 53 leading to the formation of an active chromatin loop. In t(8;21) acute myeloid leukemia, wherein RUNX1 54 is fused to ETO, the resulting oncogenic fusion protein RUNX1-ETO limits chromatin accessibility at the 55 LOUP locus, causing inhibition of LOUP and PU.1 expression. These findings highlight the important 56 role of the interplay between cell type-specific RNAs and transcription factors as well as their oncogenic 57 derivatives in modulating lineage-gene activation and raise the possibility that RNA regulators of 58 transcription factors represent alternative targets for therapeutic development. 59 60 INTRODUCTION 61 Lineage-control genes that dictate cellular identities are often expressed in dynamic and 62 hierarchical patterns.1-3 Disturbance of these established normal patterns results in anomalies.4 In the 63 blood system, the ETS-family transcription factor PU.1 (also known as Spi-1) is essential for myeloid 64 differentiation. PU.1 is silent in most tissues and cell types but expressed at highest levels in myeloid 65 cells including granulocytes and monocytes.5 Downregulation of PU.1 impairs myeloid cell 66 differentiation leading to acute myeloid leukemia (AML).6,7 PU.1 is a major downstream transcriptional 67 was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprint (which this version posted July 25, 2020. ; https://doi.org/10.1101/2020.07.24.220020 doi: bioRxiv preprint

The bone marrow microenvironment is composed of heterogeneous cell populations of non-hematopoietic cells with complex phenotypes and undefined trajectories of maturation. Among them, mesenchymal cells maintain the production of stromal, bone, fat, and cartilage cells. Resolving these unique cellular subsets within the bone marrow remains challenging. Here, we used single-cell RNA sequencing of non-hematopoietic bone marrow cells to define specific subpopulations. Furthermore, by combining computational prediction of the cell state hierarchy with the known expression of key transcription factors, we mapped differentiation paths to the osteocyte, chondrocyte, and adipocyte lineages. Finally, we validated our findings using lineage-specific reporter strains and targeted knockdowns. Our analysis reveals differentiation hierarchies for maturing stromal cells, determines key transcription factors along these trajectories, and provides an understanding of the complexity of the bone marrow microenvironment.

Insulin resistance results from an intricate interaction between genetic make-up and environment, and thus may be orchestrated by epigenetic mechanisms like DNA methylation. Here, we demonstrate that DNA methyltransferase 3a (Dnmt3a) is both necessary and sufficient to mediate insulin resistance in cultured mouse and human adipocytes. Furthermore, adipose-specific Dnmt3a knock-out mice are protected from diet-induced insulin resistance and glucose intolerance without accompanying changes in adiposity. Unbiased gene profiling studies revealed Fgf21 as a key negatively regulated Dnmt3a target gene in adipocytes with concordant changes in DNA methylation at the Fgf21 promoter region. Consistent with this, Fgf21 can rescue Dnmt3a-mediated insulin resistance, and DNA methylation at the FGF21 locus was elevated in human subjects with diabetes and correlated negatively with expression of FGF21 in human adipose tissue. Taken together, our data demonstrate that adipose Dnmt3a is a novel epigenetic mediator of insulin resistance in vitro and in vivo.

Significance Thyroid hormone receptors (TRs) mediate the genomic actions of thyroid hormones. In the absence of T3 (3,3′,5-triiodothyronine), the TR recruits a multiprotein repressor complex that decreases histone acetylation in the vicinity of target genes. Nuclear receptor corepressor 1 (NCoR1) is hypothesized to be the main corepressor that interacts with TR. Here we report that the deletion of NCoR1 does not prevent all gene repression and histone deacetylation across a variety of mouse models, whereas only the lack of TR was able to overturn the effects of hypothyroidism. Thus, we conclude that NCoR1 is not sufficient to mediate the actions of the unliganded TR; furthermore, our data suggest that alternative mechanisms of repression may be involved in the action of TRs.

Hematopoietic stem cells are capable of perpetual self-renewal and multi-lineage differentiation, properties that are maintained throughout life by minimal cell cycle activity. Our work has focused on deciphering transcriptional driven differentiation versus self-renewal pathways in stem and progenitor cells. To this end, we have studied transcription factors that control the fate of hematopoietic stem cells by combining mouse models of activated self-renewal with models that can report transcription factor expression. We chose to study the Wnt pathway, activated in several types of leukemia, in combination with the ets family PU.1 transcription factor, vital to almost all myeloid and lymphoid lineages. PU.1 regulates a number of important myeloid specific genes that mediate differentiation to a specific cell fate.