Xist was repressed, indicating no X chromosome inactivation. haESCs and ESCs shared a similar global chromatin structure. However, a nucleosome depletion region was identified in 2056 promoters in ESCs, which was absent in haESCs. Furthermore, three characteristic spatial relationships were formed between transcription factor motifs and nucleosomes in both haESCs and ESCs, specifically in the linker region, on the nucleosome central surface, and nucleosome borders. Furthermore, the chromatin state of 4259 enhancers was off in haESCs but active in ESCs. Functional annotation of these enhancers revealed enrichment in regulation of the cell cycle, a predominantly reported mechanism of haESC self‐diploidization. Notably, the transcriptome profiles and chromatin structure of haESCs were highly preserved during passaging but different from those of differentiated cell types.Haploid embryonic stem cells (haESCs) are derived from the inner cell mass of the haploid blastocyst, containing only one set of chromosomes. Extensive and accurate chromatin remodelling occurs during haESC derivation, but the intrinsic transcriptome profiles and chromatin structure of haESCs have not been fully explored. We profiled the transcriptomes, nucleosome positioning, and key histone modifications of four mouse haESC lines, and compared these profiles with those of other closely‐related stem cell lines, MII oocytes, round spermatids, sperm, and mouse embryonic fibroblasts. haESCs had transcriptome profiles closer to those of naïve pluripotent stem cells. Consistent with the one X chromosome in haESCs,  Schematic illustration of the extensive and accurate reprogramming making haESCs a unique cell type. Through accurate reprogramming, haESCs have a naïve pluripotency state. The characteristic spatial relationships between the motifs of transcription factors and nucleosomes are critical for acquisition and maintenance of pluripotency. The distinct nucleosome occupancy in the nucleosome depletion regions in the promoters of haESCs is associated with the role of haESCs as spermatoid stem cells. The chromatin state of the enhancers may play a role in self‐diploidization of haESCs. Similar to ESCs, haESCs are also generated from the inner cell mass (ICM), but the ICM is from the haploid blastocyst. If one set of chromosomes is inherited from the sperm, haESCs are androgenetic (AG‐haESCs). Accordingly, parthenogenetic haESCs inherit the genome only from the oocyte.cis‐regulatory elements (promoters and enhancers) coordinate their regulatory effect on neural stem cell differentiation.Both nucleosomes and histone modifications are fundamental epigenetic factors, and together, they determine chromatin structure, which plays a pivotal role in regulating many biological processes. Nucleosome positioning controls DNA accessibility, especially the conserved motifs of transcription factors (TFs), and therefore plays critical roles in transcriptional regulation.It is critical to compare haESCs with oocytes, round spermatids, and sperm because haESCs are used as gametes in genetic screens and mammalian‐assisted reproduction. Therefore, in addition to different types of stem cells, oocytes, round spermatids, sperm, and mouse embryonic fibroblasts (MEFs) as controls were also included in this study. We generated the transcriptomes, genome‐wide maps of nucleosome positioning, and core histone modifications of four mouse haESC lines and round spermatids. Then, we investigated the similarities and differences in gene expression and chromatin structure between haESCs and ESCs, MEFs, round spermatids, MII oocytes, and sperm. The results revealed that the gene expression profile and the global chromatin structure of haESCs are highly similar to those of ESCs but significantly different from those of the other cell types. The transcriptome profile and the chromatin structure were found to be preserved during haESC passaging. Moreover, haESCs have a naïve pluripotency state. Specifically, the 4259 enhancers are turned off in haESCs but have an active chromatin state in ESCs and likely function in the self‐diploidization of haESCs.22.12 treatment for 4–6 h and then cultured in G1 plus medium and developed to the blastocyst stage in vitro. The blastocysts were planted on feeders in 15% KSR Knockout DMEM supplemented with 2i (1 M PD0325901 and 3 M CHIR99021). AG‐haESCs were derived from the outgrowths using FACS and the haploid cells were enriched every 3–4 passages using FACS. Mouse R1 ESCs were purchased from the American Type Culture Collection and cultured in our lab. All AG‐haESCs and ESCs were cultured in an ESC medium containing 2i.Mouse androgenetic haESC lines A129‐2 and AOS‐14, derived from mouse 129Sv and C57BL/6 strains, respectively, were established previously.2.2Bilateral testes were obtained from humanely euthanized 8–10 week old 129Sv male mice. Seminiferous tubules were collected into a 1.5 mL Eppendorf tube after release from the testicular capsule. Then, 200 μL of 0.05% trypsin–EDTA was added, and the specimen was cut with surgical scissors for 3 min into small pieces; finally, 600 μL of 10% FBS DMEM (Gibco) was added to quench the dissociation reaction. The resultant germ cell mass suspension was gently pipetted 10 times with a 1 mL pipette into single cells and filtered first with a 70 μm filter. After centrifuging at 1000 rpm for 5 min, germ cells were stained with DMEM containing 15 μg/mL Hoechest 33,342 and 2.5 μM verapamil at 37 °C for 30 min. Then the stained germ cells were resuspended in 2 mL of BD FACS buffer after centrifuging at 1000 rpm for 5 min and filtered with a 40 μm filter. Haploid round spermatids were sorted out based on cell size and DNA content using a BD FACS Aria II flow cytometer.2.3, and the integrity of RNA was tested via AGE (agarose gel electrophoresis). cDNA was synthesized using random hexamer primers and purified using 1.0× Agencourt AMPure XP beads. Sequencing primers were added to the cDNA fragments, and libraries were generated through PCR amplification. The sequencing was performed with an Illumina HiSeq X‐Ten or NovaSeq platform using the 150 bp paired‐end protocol.Haploid cells at the G0/G1 phase from individual AG‐haESC lines were collected using FACS and washed with DPBS. Total RNA was extracted from each cell line using the TIANGEN RNA simple total RNA kit (DP419), and two biological replicates were performed for each sample. The RNA concentration was measured using a NanoDrop 2000 (Thermo Scientific)Raw reads of RNA‐seq data were first processed to trim adapter sequences and low‐quality bases using Fastp (version 0.20.1).2.47 haploid cells were cross‐linked with 1% formaldehyde at room temperature (RT) for 8 min, and the reaction was then terminated by adding 0.125 M glycine at RT for 5 min. After cross‐linking, the nuclei of cells were isolated and suspended in 1 mL of MNase digestion buffer ). The digestion reaction was performed by adding 3 μL of MNase  at 37 °C for 25 min and then terminated by adding EDTA to a final concentration of 10 mM. Proteinase K was added at 65 °C for 2–4 h to reverse cross‐linking. Mononucleosomal DNA fragments were purified using phenol‐chloroform and examined by running AGE and then subjected to massively parallel DNA sequencing on an Illumina HiSeq X‐Ten platform using the 150 bp paired‐end protocol.Haploid cells of mouse AG‐haESC lines A129‐2 and AOS‐14 were collected at early passage (passages 20–30) and late stage (passages 50–70) during in vitro cell culture. For each sample, ~1 × 10Fastp (version 0.20.1)The nucleosome peaks were predicted using GeneTrack (version 1.0.3).2.5Based on the nucleosome occupancy around transcription start sites (TSSs), nucleosomes are absent 180 bp upstream of the TSS for many genes. If less than 30% of a nucleosome (length) overlaps with this region, such region is defined as a promoter nucleosome depletion region (NDR). The motifs enriched in a set of the promoter NDR regions were identified using the HOMER2.6https://genome.ucsc.edu/cgi-bin/hgLiftOver) to convert the coordinates of peaks to the mm10 genome build. Then, we mapped the nucleosome signals around the peak centres of each TF and open chromatin region to analyse the spatial relationships between nucleosomes and the binding sites of the TFs in mouse haESCs and ESCs.High‐confidence peaks of TF ChIP‐seq and ATAC‐seq data in mouse ESCs were retrieved from the GEO database. The accession numbers are listed in the Data Availability Statement. As the peaks were generated using mouse mm8 or mm9 genome build, we employed the UCSC tool LiftOver ULI‐NChIP‐seq technology was employed to generate histone modification profiles in AG‐haESCs, ESCs, and round spermatids as previously described.The high‐confidence peaks of each histone modification were detected using a pipeline, as previously described with some modifications.2.8The ±1 kb regions of TSSs of all RefSeq genes were defined as promoters. The promoters overlapping with high‐confidence peaks of H3K4me3 and H3K27me3 were defined as bivalent promoters in each sample. The high‐confidence H3K4me1 peaks were defined as enhancers in each sample. To analyse the changes in the enhancer chromatin state among early‐ and late‐passage AG‐haESCs, ESCs, and round spermatids, we first merged all H3K4me1 peaks in all AG‐haESCs and ESCs, in all AG‐haESCs and round spermatids, and in early‐ and late‐passage A129‐2 and AOS‐14 cells. Then, we calculated the read densities (RPKM) of H3K27ac, H3K27me3, H3K4me1, and the input in those merged peaks. The peaks with an RPKM value of histone modification at least 1.5‐fold greater than that of the input were considered enriched, in terms of that histone modification, in each sample. The combinations of the enrichment of the three histone modifications were used to classify those peaks into five types in each sample as follows: active (H3K4me1+/H3K27ac+/H3K27me3−), poised (H3K4me1+/H3K27ac−/H3K27me3+), intermediate (H3K4me1+/H3K27ac+/H3K27me3+), primed (H3K4me1+/H3K27ac−/H3K27me3−) and off (H3K4me1−).2.9The target gene of a given enhancer is the gene whose TSS is closest to the enhancer within 50‐kb region.2.10p‐values <0.05 were considered to be enriched.ClusterProfiler (version 3.14.3)2.11p‐values <0.05 were considered significant.Gene expression level differences between groups were analysed by Wilcoxon test. The correlation analysis was done by Pearson test. 33.1We generated global gene expression profiles of four mouse AG‐haESC lines  and round spermatids via RNA‐seq. The transcriptome profiles between biological replicates were highly reproducible Figure . HierarcPou5f1, Nanog, and Sox2) were highly expressed in all pluripotent stem cells. Intriguingly, the pluripotency marker genes for naïve stem cells were expressed in AG‐haESCs and naïve ESCs (R1 and V6.5). In contrast, the pluripotency marker genes for primed stem cells are only expressed in the primed stem cells (EpiSCs) and haploid epiblast stem cells (haEpiSCs)  are in a primed pluripotency state.) Figure . This suMaternal H3K27me3 imprinting is a newly discovered type of imprinting independent of DNA methylation. The chromosomes of AG‐haESCs originate from sperm, which lack canonical histones. We then tested the expression levels of the maternal H3K27me3‐imprinted genes in AG‐haESCs. We found that approximately 40% of the 76 maternal H3K27me3‐imprinted genesXist, critical for X chromosome inactivation, was repressed in haESCs , poised (H3K4me1+/H3K27ac−/H3K27me3+), intermediate (H3K4me1+/H3K27ac+/ H3K27me3+), primed (H3K4me1+/H3K27ac−/H3K27me3−) and off (H3K4me1−). Intriguingly, approximately 60% of the enhancers in ESC R1 had the same chromatin states as those in AG‐haESCs. In contrast, only 5%–30% of the enhancers in round spermatids had the same chromatin states as those in AG‐haESCs Figure . There wFinally, we compared the chromatin state of enhancers between early‐ and late‐passage AG‐haESCs. The results showed that 40%–60% of each category of enhancers remained in the same state during passaging Figure . A close4haESCs have been shown to have pluripotency with broad developmental potential and have been used as sperm replacements to ‘fertilize' oocytes and generate animal models.Nucleosome positioning and histone modifications are two important factors that determine the chromatin structure, which distinguishes pluripotent ESCs from MEFs.P53 or P73 could sustain the haploid state of haESCs.Dnmt3b can reduce the self‐diploidization of AG‐haESCs.Etl4 deficiency facilitates the stability of haESC haploidy by regulating energy metabolism.Spontaneous diploidization during passaging in vitro results in the reversion of haESCs to diploidy, which makes it difficult to maintain haESCs over time and greatly restricts their applications. Previous studies found that cell death triggered by apoptosis‐related genes plays a critical role in maintaining haESC haploidy. The knockout of 5cis‐regulatory elements (promoters and enhancers) might confer the role of spermatoid stem cells to haESCs and make them more prone to self‐diploidization during culture in vitro.We unveiled the intrinsic transcriptome profile and chromatin structure of haESCs as a unique type of stem cell. haESCs have a gene expression profile more similar to that of naïve pluripotent stem cells. Because they have one X chromosome, no X chromosome inactivation occurs in haESCs. Although the chromatin structure is globally similar between haESCs and ESCs, the distinct chromatin state in thousands of Cizhong Jiang conceived the study. Weisheng Zheng and Liping Wang did most data analyses with the help from Qianshu Zhu. Wenteng He and Xinjie Hu cultured the cells and prepared the samples. Cizhong Jiang and Liang Gu supervised the study. Weisheng Zheng and Cizhong Jiang wrote the manuscript. All authors read and approved the final manuscript.This work was supported by the National Key R&D Program of China (2019YFA0110000 and 2021YFA1100300), the National Natural Science Foundation of China , the major project in the basic research field of Shanghai Science and Technology Innovation Action Plan (22JC1402300) and China Postdoctoral Science Foundation (2018 M640420).The authors have declared no conflict of interest.Data S1: Supporting InformationClick here for additional data file.Table S1:Click here for additional data file.


Broad-host-range synthetic biology is an emerging frontier that aims to expand our current engineerable domain of microbial hosts for biodesign applications. As more novel species are brought to “model status,” synthetic biologists are discovering that identically engineered genetic circuits can exhibit different performances depending on the organism it operates within, an observation referred to as the “chassis effect.” It remains a major challenge to uncover which genome-encoded and biological determinants will underpin chassis effects that govern the performance of engineered genetic devices. In this study, we compared model and novel bacterial hosts to ask whether phylogenomic relatedness or similarity in host physiology is a better predictor of genetic circuit performance. This was accomplished using a comparative framework based on multivariate statistical approaches to systematically demonstrate the chassis effect and characterize the performance dynamics of a genetic inverter circuit operating within 6 Gammaproteobacteria. Our results solidify the notion that genetic devices are strongly impacted by the host context. Furthermore, we formally determined that hosts exhibiting more similar metrics of growth and molecular physiology also exhibit more similar performance of the genetic inverter, indicating that specific bacterial physiology underpins measurable chassis effects. The result of this study contributes to the field of broad-host-range synthetic biology by lending increased predictive power to the implementation of genetic devices in less-established microbial hosts. Escherichia coli, Saccharomyces cerevisiae, Bacillus subtilis, and Pseudomonas putida). While these model species are easy to work with, they do not always serve as the most optimal chassis for the intended objectives and could instead limit the potential of synthetic biology applications  against the genomes of our six hosts reveals that all three genes are also present in the P. putida and P. fluorescens genome, the hosts with the highest KaTc. Active export of the inducer leading to lowered intracellular concentration could explain the wider induction range of P. putida and P. fluorescens. This is a clear example of how the presence of a single element can be a major contributor to variation. Furthermore, unpredictable elements such as the presence of DNA binding sites on the host genome recognized by the TetR and AraC repressors could lead to sequestration of the transcription factors, leading to differing steady-state concentration of free repressor available to bind to its intended cognate operator [The diverse range of observed constants across the different chassis was interesting; since both the protein sequences are identical across the data set, changes in kinetic parameters such as the activation constants cannot arise due to differences in binding affinity of the small activating ligands for the transcription factors. Similarly, variations in the Hill coefficient cannot be due to local DNA binding interactions since the promoter sequences are identical. Instead, these chassis-specific effects are more likely due to the expression level of the transcription factor and the bioavailability of the activating ligand, which may also be dependent on transport regulators and efflux. For instance, the markedly higher 1 strain ,65. Indeoperator .PBAD and PTet promoters could be due to the different modes of regulation. The PBAD promoter requires Ara-bound AraC to activate transcription; hence, the retention of past induction state is susceptible to negative growth feedback diluting the activator during exponential growth and thereby leading to memory loss. On the other hand, the constitutively active PTet promoter enjoys increased transcription upon dilution of its cognate TetR repressor. This dependence on circuit topology is in agreement with the findings of Zhang et al. [PBAD promoter contains a catabolite activator protein binding site, leading to a potential topology change as cells exit exponential growth and enter slow growth conditions. Catabolite repression effect could therefore also contribute to the inability of host cells to retain a stable sfGFP ON phenotype. As the purpose of the study was to characterize the chassis effect and not the inverter circuit, we did not conduct an extensive investigation of the inverter’s complex dynamics.Hysteresis experiments revealed that inverter-carrying hosts initially induced with Ara were unable to stably maintain past fluorescence states in late phase. The reason for the different capacities of hysteresis between the g et al. . The topg et al. ,68 not oPseudomonas taeanensis MS-3 and Halopseudomonas pachastrellae CCUG 46540 . Whether this was due to transformation methodology, genetic compatibility, or the activity of inverter circuit being toxic to the cells is unknown, indicating that the “inoperable chassis effect” is challenging to assess.The chassis effect can manifest in different ways between hosts, from complete circuit inoperability in some hosts, to variations in performance between others. The diversity of performance metric profiles exhibited by the six hosts characterized here encourages the notion of viewing chassis as pragmatic parts capable of tuning device parameters. In cases where the tuning of promoter or ribosome binding site (RBS) strengths does not yield the desired functional outcome, exploring chassis design space could be a promising direction. However, we recognize that successfully introducing a device into a novel chassis is often not possible; as exemplified during our design–build–test processes, the pS4 plasmid was unsuccessfully transformed into two initially chosen hosts: Armed with automated circuit design software , standar


Recent research integrates novel technologies and methods from the interface of RNA biology and neuroscience. This advancing integration of both fields creates new opportunities in neuroscience to deepen the understanding of gene expression programs and their regulation that underlies the cellular heterogeneity and physiology of the central nervous system. Currently, transcriptional heterogeneity can be studied in individual neural cell types in health and disease. Furthermore, there is an increasing interest in RNA technologies and their application in neurology. These aspects were discussed at an online conference that was shortly named NeuroRNA. Modern molecular and cellular biology are more and more fostered by high-throughput approaches and jointly provide novel research tools to accelerate our understanding of how biological systems are built and regulated. Neuroscience is propelled by quickly advancing genomics, transcriptomics, and other -omics technologies; CRISPR-based gene-editing technology; the development of new models such as induced pluripotent stem cells (iPSCs)-derived brain organoids; high-resolution structural and functional deep brain imaging methods; among others. Interdisciplinary approaches advance the knowledge of basic regulatory processes in neural cells, re-visit their heterogeneity and connectivity, and eventually, give promise for developing better treatment strategies for patients suffering from neurological disorders.RNA biology bestows a rich resource for neuroscience. It provides methods for studying gene expression in space and time, across different genotypes/phenotypes, and among different neural cell types. RNA biology also provides research tools for perturbing gene expression, which is used to perform basic research and is increasingly tested for biomedical translational purposes. Neural cells possess highly complex and multi-level gene expression regulation mechanisms and appear evident both for the transcriptional and post-transcriptional regulatory processes. Cell type-specific subsets of genes are being recognized to be regulated and coordinated at the RNA level, e.g., in response to activity inputs, injuries, or neuroinflammation. Transcriptional heterogeneity of neural cells within individual populations  is studied in depth with single-cell RNA sequencing (scRNA-seq) and reveals unprecedented intra-populational differences  took place from 28 to 30 September 2022. There were over 200 participants from 17 countries, 17 invited talks, 13 selected talks, and 33 poster presentations.A virtual conference sessions ; each seCellular architecture of the human brain” was given by Sten Linnarsson from the Karolinska Institute, Stockholm, Sweden. ScRNA-seq technologies are gaining momentum in neuroscience, and Sten Linnarsson has contributed to both the inception and development of these technologies. Importantly, in the Linnarsson lab, scRNA-seq has been continuously used to uncover the complexity of the mammalian brain. During the conference, the participants had a chance to hear about two new studies, which, shortly after the meeting, became publicly available as preprints , or obtained from unsorted single-cell or single-nuclei suspensions. In turn, spatial transcriptomic methods bypass tissue dissociation and retain spatial information. This allows for the assessment of gene expression across thousands of cells within the context of the entire structural organization of tissue in normal physiology or under perturbation. In the first scientific session of the conference “Bozena Kaminska  and her lab apply scRNA-seq and most recently spatial transcriptomics in order to study gliomas, with the main focus on glioma-associated brain macrophages and microglia and their heterogeneity in mouse glioma models. They observed a subpopulation of so-called glioma-activated microglia and were able to detect marker genes that clearly distinguish these cells from homeostatic microglia, resident macrophages, and macrophages that infiltrate the tumor. However, both macrophages and microglia shared the signatures of signaling-associated genes, and at this level, these different myeloid cells were indistinguishable. Further application of CITE-seq [Cellular Indexing of Transcriptomes and Epitopes by sequencing  has become revolutionary in expanding our understanding of complex biological systems such as brain tumors and their microenvironment. Ozgun's Gokce  illustrated how spatial transcriptomics may be combined with the measurement of morphological features afforded by electron microscopy. Gokce lab studies dementia and focuses on the identification of the cellular responses associated with the loss of white matter (WM) volume, which is one of the hallmarks of a dementing brain  switched gears to “Multimodal profiling of the epigenome at single-cell resolution”, particularly focusing on the application of CUT&Tag in single nuclei obtained from juvenile  mouse brain. That setup enabled the study of multiple histone marks and transcription factors Olig2 and Rad21 at single-cell resolution and cluster cells according to their chromatin profile  presented an article on herpes simplex virus (HSV)-driven encephalitis. HSV-1 infection was evoked in human brain organoids and studied at the molecular and physiological levels. The study recapitulated known features of HSV-1 infection (such as diminished synaptic gene expression and decreased synaptic firing) and delivered novel insights into perturbed gene expression due to infection, e.g., activation of antisense transcription and global increase in the length in poly(A) tails in mRNA  who showed the validity of the new optogenetic method for programmable silencing or activation of target genes. Light-inducible CRISPR/Cas9 construct and gRNAs were used to target and activate transcription from Sonic Hedgehog (SHH) promoter in a proof-of-concept experiment  in 3D brain organoids, one can provide a stimulus to develop an organoid with a certain spatial patterning mimicking in vivo situation, i.e., developing neural tube-like in case of SHH activation. The analysis of optogenetically patterned brain organoids was performed both with single-cell and spatial transcriptomic methods.Franz Ake  discussed the insights into the challenges and importance of detecting alternative polyadenylation (APA) in individual cells. Is differential APA attributed to neurological conditions and can that be investigated from the single-cell transcriptomic datasets generated with currently available platforms? To tackle that question, Mireya Plass lab applies single-cell isoform quantification in iPSC-derived neurons from patients with Alzheimer's disease (AD) and isogenic controls using an in-house developed workflow. Preliminary results show a subset of genes that switch mRNA isoforms in AD pathology as compared with controls and that it occurs in a cell type-specific manner in heterogeneous neuronal cells differentiated from iPSCs. In turn, Rotem Ben Tov Perry from the Weizmann Institute of Science  presented a study about long-non-coding RNA (lncRNA) called Silc1. Silc1 RNA has been previously shown to be important for cis-activation of the transcription factor Sox11 during neuroregeneration in the peripheral nervous system  provided a comprehensive overview of the current understanding of alternative splicing and mRNA isoform usage in the brain that is now being dissected with single-cell resolution. The application of single-cell isoform RNA sequencing , and poly(A) site have fundamental roles in mRNA maturation, stability, and localization, and all of these ultimately influence the gene expression programs, oftentimes in a cell type-specific manner. Joseph Dougherty  talked about alternative translation and local translation in glia, and it was a very valuable contribution to discussions regarding the regulation of alternative mRNA isoforms in brain cells. Local translation in neurons is nowadays an intensively studied and well-recognized phenomenon . The recognition of local translation in other neural cells, such as astroglia, has been emerging recently. From the Dougherty lab's earlier work, we have learned that translation exists in distal astrocytic processes  and their association with temporal lobe epilepsy (TLE). miRNAs and miRNA-binding proteins Argonautes are mainly localizing to the cytoplasm in cells, yet there are exceptions from that commonly accepted rule  presented her group's ongoing work on the abundant let-7 family of miRNAs and its molecular targets and function in the brain. Fmr1 KO mice have been used in the study, and they showed an activity-dependent reduction of let-7 levels in behavioral studies. Orna Issler  introduced the topic of sex-specific lncRNAs in the context of depression, and more generally — mood. A previous study highlighted that a substantial part of the differentially expressed genes in the brains of depressed humans (~1/3) belong to lncRNA-encoding genes and that these transcripts display region- and sex-specific patterns of the regulation . Cdr1as is one of the best studied to date brain-enriched circRNAs. It has the unique property of possessing multiple binding sites for miRNA, in mice >120 binding sites for miR-7 and one binding site for miR-671. Cdr1as was previously shown to be involved in the regulation of miR-7 and its mRNA targets in excitatory neurons in multiple regions of the mouse brain. Cdr1as KO mice were identified with behavioral abnormalities, namely, sensorimotor gating phenotype that is associated with neuropsychiatric disorders and dampened neural activity in in vitro autaptic neuronal cultures  in the mammalian brain and multiple pathological conditions, e.g., in the past 10 years, numerous studies have informed about changes in microRNA (miRNA) levels in the different brain regions of patients suffering from neurological disorders and in neural tissues from animal models of respective conditions. Functions and mechanism of actions of newly annotated ncRNAs in the majority of cases remain unclear, albeit there is still a growing interest in research focusing on disentangling ncRNA impact on gene expression, as some of them have been evidenced to possess important regulatory functions. Functional studies in animal models provide evidence that certain miRNAs influence pathogenesis, e.g., genetic deletion of miR-128, a crucial regulator of neuronal excitability, leads to fatal epilepsy in mice  and Alzheimer's disease (AD), and repeat expansion diseases such as Huntington's disease (HD).Gracjan Michlewski  presented insights into specific RNA–protein interaction networks. RNA-binding protein HuR — pri-miR-7 — α-synuclein (SNCA) mRNA has been investigated in the context of PD. This regulatory network provides an option for therapeutic intervention in PD, as increased levels and aggregation of α-synuclein are a hallmark of this disease, and miR-7 biogenesis via HuR might be targeted to impact SNCA expression  referred to the cellular and molecular complexity of AD pathology, including complex miRNA regulation and challenges in the use of miRNAs as therapeutics  is studying neuronal vulnerability to degeneration in the spinocerebellar ataxia type 7 (SCA7), one of the CAG repeat expansion diseases. Purkinje cells (PCs) represent the main cell type that undergoes degeneration in SCA7. They constitute approximately 2% of cells in the cerebellum and are one of the biggest and most highly structured neurons in the adult brain, thus it is impossible to isolate them without losing the integrity of neuronal processes. In the presented study, the author described the challenges and eventually the method on how to specifically enrich single nuclei of PCs from the mouse cerebellum. The nuclei have been subjected to snRNA-seq. Based on the established PC isolation method, further studies are being conducted to unravel PC-specific molecular mechanisms leading to neuronal death  presented details of the mechanism of repeat-associated non-AUG (RAN) translation occurring at repeated expanded tracts in fragile X-associated tremor/ataxia syndrome (FXTAS)  referred to the allele-specific analysis of transcript levels for selected polyglutamine (polyQ) diseases. Patients suffering from polyQ diseases usually possess both normal and mutant alleles of the affected gene  shared the results on circRNAs deregulation in AD. Altogether, 48 circRNAs have been identified to be associated with AD conditions based on RNA-seq data generated from the hippocampus and the cortex of patients' brains. Moreover, circRNA expression was shown to differ by dementia subtype. The importance of impaired circRNA expression patterns and their association with pathogenesis are still emerging topics in the AD field.In the two following short talks, insights into RNA biology in the context of the other repeat expansion diseases have been presented. First, Brain tumors are complex diseases resulting from the disruption of key cellular pathways, including those regulating cell survival and division. Genetic mutations, perturbed RNA profiles, and epigenetic alterations contribute to tumorigenesis. It is already known that all these changes are heterogeneous on a cell type basis within an individual tumor and between patients, which is increasingly appreciated as a determinant of treatment failure and disease recurrence in the case of brain tumors. During this conference session, we discussed the intratumor heterogeneity, new mechanisms of regulatory interactions between ncRNAs, and the possibilities for the identification of new RNA and epigenetics-oriented therapeutics and diagnostic approaches.Itay Tirosh  presented a single-cell transcriptomic analysis of glioblastoma samples that recapitulated the high intratumoral heterogeneity of the most aggressive brain tumor. The presented scRNA-seq data showed that glioblastoma cells exist in four main cellular states. The authors found that these states might be affected by the tumor microenvironment and exhibit plasticity, i.e., multiple possible transitions between states can be observed per transcriptome level. The relative frequency of cells in each state also varies between glioblastoma samples, and it is influenced by copy number amplifications of the CDK4, EGFR, and PDGFRA loci and by mutations in the NF1 locus  presented an interesting therapeutic approach designed to target GSCs with the oncolytic herpes virus (oHSV)-based immunotherapy. It was illustrated in the talk that oHSV infection alone may activate the host antitumor immune system by triggering “immunosecretion”, i.e., the release of antigens and cytokines that stimulate the immune response. The presented data showed that upon infection with oHSV and the transcriptome- and secretome-associated proteins are regulated in GSC. The observed changes have been linked to T-cell-mediated cytotoxicity and B-cell-dependent immune response memory. On the other hand, detailed downstream analyses identified the presence of stress-resistant and oHSV-resistant GSCs located in the hypoxic niche. Transcriptome-wide data revealed that the tumor microenvironment shapes two distinguishing characteristics of GSCs: increased cell-to-cell communication with immune cells and metabolic shift toward hypoxic adaptation, both with signatures predictive of glioblastoma patent survival. One of the antisense lncRNAs has been identified as a sensor of response and adaptation of GSCs to hypoxia. Further analyses pointed out that it might be a promising therapeutic target for glioblastoma therapy, especially in combination with oncolytic virus immunotherapy.The presence of glioblastoma stem-like cells (GSCs) within the tumor mass, hypoxic microenvironment, and poor infiltration of immune cells to the core of the tumor is perceived to be the other reasons why anti-tumor therapies fail to be efficient. Anna's Krichevsky  talk . In particular, the role of the network is composed of a few distinct ncRNAs including a miRNA, enhancer-associated RNA, promoter-associated RNA, and snRNA. The central part of that network is miR-10b, a miRNA that remains silent in the brain cortex and becomes upregulated in gliomas where it is known to serve a tumor-promoting role. The two mentioned lncRNAs are involved in the chromatin folding and concordant regulation of miR-10b and other co-transcribed genes. The network remains inactive in homeostatic astrocytes and becomes activated during neoplastic transformation. In addition, Krichevsky's group evidenced that direct binding between miR-10b and U6 has a strong impact on downstream U6 interaction with splicing factors SART3 and PRPF  presented results on circRNAs and their role in gliomagenesis and GBM progression. RNA-seq results obtained from GBM samples revealed the differential expression patterns within the tumor samples compared with the normal brain. Specific circRNAs have been found to correlate with the GBM molecular subtypes, which is another attempt for providing better stratification that may underlie the future personalized treatment of patients with GBM.Katarzyna Leszczynska  shifted the attention to the pediatric high-grade gliomas (pHGG) and one of the recognized mutations in histone H3 that is specific for these tumors: H3F3A variant. Chromatin-modifying agents, including histone deacetylase (HDAC) inhibitors, have been identified as promising candidate therapeutics against pHGG. The treatments of HDAC inhibitors provided also new directions in targeting H3K27M-expressing cells. However, the precise correspondence between the efficiency and chromatin response of that treatment is still not completely understood. The presented data focused on the chromatin alterations induced by HDAC inhibitors and the role of histone variants in response to these therapies. With the multiple cellular models expressing the H3K27M histone variant, several new drugs with sub-micromolar efficacy in killing H3K27M-expressing cells were identified. The results pointed out that direct analysis of the chromatin landscape, and particularly the expression of histone variants in pHGG, and provide new insights into chromatin response to specific epigenetic treatments.Many nucleic acid-based therapeutics, mainly antisense oligonucleotides (ASOs), have recently entered the clinical trial phase. This progress is particularly evident in the field of neurodegenerative diseases, such as HD, where the strategies for lowering the level of mutant protein with ASO or RNAi appear to be very promising. Unfortunately, despite many years of research and the positive results of preclinical studies, some clinical trials had to be terminated prematurely due to ASO ineffectiveness or unexpected side effects. The main problem is still the effective and minimally invasive delivery of the therapeutic to the brain and its specificity. One line of research is the development of new gene therapy constructs and viral vectors for the targeted delivery of therapeutic molecules such as RNAi triggers or CRISPR-Cas system components. Our speakers raised all these important issues in their speeches.Leontien van der Bent  demonstrated recent advances in the development of miRNA-based gene therapies for the treatment of various CNS disorders, including HD, temporal lobe epilepsy, amyotrophic lateral sclerosis, synucleinopathies, and AD. MiQure technology relies on vector-based RNAi triggers, also known as artificial miRNAs . This stage of research was preceded by numerous preclinical studies in rodents, mini pigs, and non-human primate models, which demonstrated the effectiveness and safety of the approach , an expert in the field of viral gene transfer, presented her research on the development of the second-generation AAV-KamiCas9 self-activating system for the efficient and safe editing of CNS disease genes. Transient expression of the Cas9 protein decreases the risk of off-target effects, while optimized AAV vectors ensure efficient transduction of specific brain circuits affected in neurodegenerative disorders . CGG repeat expansion reaching 55–200 triplets leads to the development of FXTAS syndrome. The toxicity of the RNA containing expanded CGG repeats results in the sequestration of nuclear proteins involved in RNA metabolism, the initiation of non-canonical translation of the polyglycine-containing protein which forms nuclear insoluble inclusion, and the formation of the R-loop structure. ASO LNA designed by the Sobczak group is specific to bind and disrupt the RNA structure formed by CGG repeats during transcription, thereby abolishing all these toxic effects. These ASOs have been shown to improve motor behavior and rescue gene expression abnormalities associated with FXTAS in a mouse model of the disease  . He showed that ASO targeting the splicing site of a protein-coding gene embedded in ncRNA may influence the expression and function of the ncRNA. In this study, ASO-inducing intron retention events and increased gene expression resulted in defects in axonal morphogenesis in zebrafish larvae. Without careful evaluation of this phenomenon and the application of appropriate controls, it could have been misinterpreted as an effect of the protein-coding gene.A careful evaluation of the effects induced by ASO was the topic presented by Lorea Blazquez  studies aberrant regulation of RNA processing in neurological disorders and her talk focused on frontotemporal dementia. A non-coding mutation in the GRN gene leads to an abnormal splicing pattern that causes GRN mRNA degradation and progranulin haploinsufficiency. She showed that the CRISPR-dCas13 system can not only be used to identify a target sequence in a pre-mRNA for RNA-based therapeutic strategies but also can be used as a therapeutic approach to restore the GRN open reading frame. RNA-targeting CRISPR-dCas13 has some advantages over ASO or the traditional CRISPR-Cas9 system. It can be expressed from viral vectors without the need for repeated administration and appears to be at least theoretically safer than DNA targeting Cas9. However, more research is needed to confirm its therapeutic potential.The increasing synergy between RNA biology and neuroscience brings the research in the interface of both disciplines to a new level that enables us to look into physiology with a molecular resolution and in a high-throughput manner. The first edition of the NeuroRNA conference illustrated how rapidly developing new technologies focused on RNA can enrich our understanding of the basic molecular processes in neural cells and contribute to the formulation of new research hypotheses that aim to bring us closer to unraveling the complexity of the human brain and to find cures for complex neurological conditions. In his biography, Santiago Ramón y Cajal penned “It is commonplace fact that scientific discoveries are a function of the methods used.” That notion definitely holds true when contemplating NeuroRNA 2022.A few summary points on the presented and discussed insights that are worth highlighting as follows:1. The repertoire of neural cell types and subtypes in the adult human brain, both neurons and glia, is much more heterogenous when considering transcriptome-based classification, i.e., >3,000 cell types discovered in the adult human brain as based on RNA single-nuclei sequencing.2. Plasticity of certain neural cells, i.e., the possibility to acquire new states and transit between states. It is more often evidenced with scRNA-seq and trajectory inference methods .3. Alternative transcript isoforms and 3'UTR usage (including APA) are commonplace in neural cells, cell type-specific, and likely context-dependent . This phenomenon will be surely investigated further delivering new knowledge on how RNA isoforms influence the diversification of proteome on a cell type basis.4. Local translation is not restricted to neurons. Other polarized and highly structured cell types feature regulated the transport of a specific subset of transcripts to perform local synthesis of specific proteins in cell peripheries, that is, evidenced by microglia and the production of phagocytosis-related factors in microglial processes, and astrocytes and local synthesis of proteins with roles in regulating synapses in astrocyte peripheral processes.5. Subcellular localization of regulatory RNAs is one of the determinants of their versatility, e.g., miRNAs may function in a non-canonical way in the nucleus, and re-localization of regulatory RNAs might be associated with the pathology (epilepsy and tumorigenesis).Vittorio Padovano  on lncRNA in motor neurons supported by lncRNA knockout in the mouse model was awarded the best poster award at the conference.6. The landscape of non-coding RNAs which is functional in the brain is quickly progressing with many newly identified molecular roles that influence the physiology of the CNS on a cell type basis and oftentimes in a sex-based manner. Of note, a poster presentation by Many alterations in the transcriptome, specific mutated transcripts, disease-specific transcript isoforms, mislocalized regulatory RNAs, etc., are carefully examined with the ultimate aim to find causative and treatable pathological targets in the CNS. A new generation of therapeutics that are more precise and specific in reaching the targets is on the horizon, and we hope that in the next incarnation of NeuroRNA, we will learn about the progress in that matter. Last but not the least, we would like to express our gratitude to the conference speakers for the inspiring talks and for sharing fresh, oftentimes unpublished results, the audience for the stimulating discussions, and the sponsors for their support.MP conceptualized the manuscript and edited the final version of the manuscript. MP, KR, AF, and MO wrote and edited the first draft of the manuscript. All authors approved the submitted version.


GATA1 mutations and has a favorable prognosis. In contrast, AMKL in children without DS is often associated with recurrent and mutually exclusive chimeric fusion genes and has an unfavorable prognosis. This review mainly summarizes the unique features of pediatric non-DS AMKL and highlights the development of novel therapies for high-risk patients. Due to the rarity of pediatric AMKL, large-scale multi-center studies are needed to progress molecular characterization of this disease. Better disease models are also required to test leukemogenic mechanisms and emerging therapies.Acute megakaryoblastic leukemia (AMKL) is a rare subtype of acute myeloid leukemia (AML) in which leukemic blasts have megakaryocytic features. AMKL makes up 4%–15% of newly diagnosed pediatric AML, typically affecting young children (less than 2 years old). AMKL associated with Down syndrome (DS) shows  KMT2A (lysine methyltransferase 2A) and NUP98 (nucleoporin 98) rearrangements now form independent subgroups of AML with defining genetic abnormalities , and cases with CBFA2T3 ::GLIS2 (GLIS family zinc finger 2) and RBM15 (RNA binding motif protein 15)::MKL1 (megakaryoblastic leukemia 1) fusions belong to a subgroup of AML with other defined genetic alterations. Other cases of AMKL lacking defined genetic alterations remain defined by differentiation, which requires blasts to express at least one of the platelet glycoproteins: CD41 (glycoprotein IIb), CD61 (glycoprotein IIIa), or CD42b (glycoprotein Ib). Myeloid proliferations associated with DS form a subtype of myeloid neoplasms associated with germline predisposition (Acute megakaryoblastic leukemia (AMKL) is a rare but life-threatening hematological malignancy characterized by clonal megakaryoblastic proliferation and impaired differentiation of these cells. AMKL was first described in 1931 and included in the French-American-British (FAB) classification as acute myeloid leukemia (AML) the M7 subtype . In the position .GATA1 (GATA Binding Protein 1) mutations, and a range of other somatic mutations, in particular those affecting cohesin molecules, signaling pathways, epigenetic regulators, and hematopoietic transcription factors , RBM15::MKL1, and HOX (homeobox) gene rearrangements , KMT2A rearrangements (KMT2Ar) (7%–17%), NUP98::KDM5A (11%–12%), RBM15::MKL1 (10%–11%), and HOX rearrangements (HOXr) (14%–15%) ::FLI1 (friend leukemia integration 1 transcription factor), BCR (breakpoint cluster region protein):ABL1 , and MAP2K2 (mitogen-activated protein kinase 2)::MLLT10 (myeloid/lymphoid or mixed-lineage leukemia translocated to 10)  is a highly molecularly heterogeneous disease characterized by recurrent and mutually exclusive genetic aberrations, including 14%–15%)  (de Rooi14%–15%) . A furthd to 10) . Moleculcurrence . Thus, icurrence . In contotherapy . This reotherapy , 2. OngoCBFA2T3::GLIS2 fusion) is the most frequently, but not exclusively detected in 18%–27% of pediatric non-DS AMKL (CBFA2T3::GLIS2 is not specific to AMKL and occurs in diverse morphologic subtypes of de novo AML (except for acute promyelocytic leukemia (APL) and leukemia with erythroid differentiation), overall found in approximately 8.4% of cytogenetically normal AML cases  :RHEBL1 (Ras homologue enriched in brain like 1) fusion  tend to be younger (median age of onset ∼1.5 years) and display distinct clinical and laboratory features, including stronger expression of CD56  and a more frequent extramedullary involvement compared to other AML , CD38, and CD45 referred to as the RAM immunophenotype (p13.3q24.3) (generating the -DS AMKL  (de Rooi-DS AMKL . A newer-DS AMKL . CBFA2T3ML cases . The inv cohorts . CBFA2T314%–40%) . Among C) fusion . The co-S2 alone . Childrether AML . Standarhenotype . The somubgroups . Howeverubgroups . CBFA2T3diploidy .CBFA2T3::GLIS2 fuses the 5′ portion of CBFA2T3  in frame with the 3’ region of GLIS2. The majority of chimeric CBFA2T3::GLIS2 fusions (80%) are between exon 11 of CBFA2T3 and exon 3 of GLIS2. Other rare fusion transcripts have also been reported, including between exons 9, 10 or 12 of CBFA2T3 and exons 2 or 3 of GLIS2  complex. CBFA2T3 belongs to the eight-twenty-one (ETO) family of chromatin-associated proteins, functioning as a master transcriptional coregulator and a Wnt (wingless/integrated) and Notch (neurogenic locus notch homolog protein) signaling suppressor in normal and malignant hematopoiesis . CBFA2T3turation ; whilst ntiation .GLIS2 in murine LSK (Lin-SCA1+c-KIT+) cells reduces HSC repopulation, suggesting GLIS2 could regulate HSC engraftment and hematopoietic reconstitution (GLIS2 overexpression in MOZ (monocytic leukemia zinc finger protein):TIF2  leukemic cells promotes their differentiation into mature myeloid cells and delays AML development in mice, indicating that GLIS2 inhibits AML initiation by inducing LSC differentiation  upregulation, and activation of super enhancer elements may impair megakaryocytic differentiation and increase abnormal self-renewal of leukemic cells in AMKL associated with CBFA2T3::GLIS2  involved in leukemic progression , GABRE (gamma-aminobutyric acid type A receptor subunit epsilon), miR-224 and miR-452  may be responsible for the reduction in expression of apoptotic and tumor suppressor genes, and simultaneous downregulation of tumor suppressive miRNAs  (CBFA2T3::GLIS2 is not well defined. Overexpression of miR-181b-5p downregulates apoptotic genes CASP1 (caspase 1) and TRAIL-R2 (TNF-related apoptosis-inducing ligand receptor 2), and the circadian rhythm gene PER2 (period circadian regulator 2), while loss of miR-196b is associated with ITGA2  and DNMT3B (DNA methyltransferase 3 beta) overexpression  and signaling pathways , bone morphogenetic proteins (BMP), JAK/STAT and Hedgehog) (CBFA2T3::GLIS2 leukemia.Although the molecular bases for AMKL transformation by ustrated . CBFA2T3 pattern . GATA1 d3::GLIS2 . Super e3::GLIS2 . Ectopicgression . CBFA2T3 miR-452 . Both mi-199a/b) . The rolpression . When his in AML . CBFA2T3edgehog) . OverallCBFA2T3::GLIS2, this fusion probably is the primary genomic alteration. However, the evidence is mixed on whether CBFA2T3::GLIS2 alone is sufficient for leukemic transformation. On the one hand, the introduction of CBFA2T3::GLIS2 into murine bone marrow is insufficient to induce overt leukemia in mice  and leads to highly aggressive leukemia in mice . Despite the use of intensive chemotherapy and HSCT, the outcomes of CBFA2T3::GLIS2 leukemia are poor. The mechanism of leukemogenesis associated with CBFA2T3::GLIS2 remains incompletely understood. Therefore, further mechanistic studies are required to inform the development of novel therapies.In summary, cytogenetically cryptic KMT2A gene  located at 11q23.3 are found in both acute lymphoblastic leukemia (ALL) and AML, making up ∼10% of all leukemia cases in all age groups (KMT2A rearrangements (KMT2Ar) are one of the most common recurrent genetic aberrations found in 15%–25% of all newly diagnosed cases of pediatric AML  and are much less common in other subtypes  , AFF1 , MLLT3 , MLLT6, MLLT9, MLLT10 , and SEPT9 (septin 9)  (generating KMT2A::MLLT3 fusion) and t (generating KMT2A::MLLT10 fusion)  and is more common in males, although these differences were not statistically significant in the study by de Rooij et al. , KRAS , PTPN11 (tyrosine-protein phosphatase non-receptor type 11), NF1 (neurofibromatosis type 1), EPOR (erythropoietin receptor), MPL (thrombopoietin receptor), JAK1/2/3, PIK3C2A , KIT, cohesin and epigenetic regulators, e.g., STAG3 , SETD2 , IDH1 (isocitrate dehydrogenase 1), CREBBP (cyclic adenosine monophosphate response element binding protein binding protein), transcription factors, e.g., GATA1, RNA splicing and regulatory proteins, e.g., U2AF1  and DDX3X (DEAD-Box helicase 3 X-linked)  . Numeroueptin 9) . The two fusion) . Translo fusion) . In the  fusion) . The prosurvival . KMT2Ar j et al. . Pediatrtype AML . However-linked) . KMT2Ar rognosis . Severalaryotype . The cliKMT2Ar predominantly consists of the N-terminal DNA-interacting domains of KMT2A (residues 1–∼1400) fused in frame with one of over 100 fusion partners , SIX1 (SIX homeobox 1) and SIX4 (SIX homeobox 4), highlighting that the transforming capacity of KMT2A fusion may not be limited to HOXA/MEIS1 genes  methyltransferase that serves as a master controller for the transcription of critical genes implicated in normal embryonic development and hematopoiesis . KMT2A ipartners   was initially identified in pediatric AMKL in 2006 (NUP98::KDM5A fuses NUP98 located on chromosome 11p15 with KDM5A located on the telomeric end of chromosome 12p13.3 (NUP98::KDM5A AML patients have a median age at diagnosis of 3.2 years (ranging from 3 weeks to 18.5 years)   (resulting in  in 2006 . NUP98:: 12p13.3 . The fus 12p13.3 . NUP98::5 years) . NUP98::se cases . The med2 years) . NUP98:: relapse . Additioaryotype .NUP98 has many different partner genes (over 30) to produce a series of abnormal fusion proteins in several hematopoietic malignancies  (KDM5A is the most common fusion partner of NUP98 in AML and AMKL (NSD1 (nuclear receptor) is the second most common  locus frequently associates with NUP98::KDM5A , and GATA1 mutations were present in 2 of 9 NUP98::KDM5A patients with the RB1 loss , and HOXB  genes compared with RBM::MKL1 and CBFA2T3::GLIS2 fusions ::NUP214 (nucleoporin 214), and NPM1 (nucleophosmin 1) mutated cases, suggesting HOXA and HOXB overexpression is a common alteration in leukemia development (NUP98::KDM5A and NUP98::NSD1 AML show upregulation of targets of E2F (E2F transcription factor 1) and FLT3, and downregulation of targets of TP53 and HDAC  . KDM5A iand AMKL , and NSDt common . NUP98::t common . NUP98::ower WCC . Additio8::KDM5A . In contRB1 loss . Leukemi fusions . Intriguelopment . Additioand HDAC . The uprD1 cases .NUP98::KDM5A is undetectable with conventional karyotyping, whereas newly developed next-generation sequencing (NGS) technologies can detect it  into and out of the nucleus . NUP98 aKDM5A overexpression in leukemia associates with a poor prognosis , and epigenetic activation of the HOXA gene cluster , MPIG6B (megakaryocyte and platelet inhibitory receptor G6b), and NEO1 (neogenin) as novel disease biomarkers  gene, also named as OTT (one twenty-two) located on chromosome 1p13 to the MKL1 (megakaryoblastic leukemia-1) gene, also named as MAL (megakaryocytic acute leukemia) or MRTFA (myocardin-related transcription factor A) located on chromosome 22q13  results in a fusion of the me 22q13 . Althougme 22q13 , it is ame 22q13 . The t, has two isoforms consisting of two or three N-terminal RPEL-repeats, basic regions, a glutamine-rich domain, a SAP domain , a leucine zipper-like domain, and a TAD domain  Scharen. MKL1 reryocytes . The abiy of SRF .RBM15::MKL1 causes abnormal megakaryopoiesis during embryonic and adult development but rarely generates AMKL  and ITGB1, and several components of Notch signaling  with AMKL. RBM15::MKL1 possesses the function of both RBM15 and MKL1 proteins, including constitutive activation of RBPJ, MKL1-and SRF- dependent target genes. By itself, RBM15::MKL1 appears insufficient to drive AMKL. However, with a MPL mutation, RBM15::MKL1 causes rapid transformation to AMKL. It remains to be determined how RBM15::MKL1 alters megakaryopoiesis and the mechanism through which cooperating mutations drive leukemogenesis.In summary, HOX rearrangements (HOXr) are seen in 14.9% of pediatric non-DS AMKL and associate with better outcomes  than the aforementioned molecular subgroups  ::HOXA9, NIPBL::HOXB9, GATA2::HOXA10, EWSR1 (Ewing Sarcoma breakpoint region 1)::HOXB8, PLEK (pleckstrin)::HOXA11AS (HOXA11 antisense RNA), BMP2K (BMP-2-inducible protein kinase)::HOXD10, EP300::HOXA7, C8orf76 ::HOXA11AS, HOXA11::BZW2 (basic leucine zipper and W2 domains 2), HOXA9::ANGPT1 (angiopoietin 1) and HOXA10AS::CD164  , STAG2, CTCF (CCCTC-binding factor), and other genes , ATM (ataxia-telangiesctasia mutated), SMARCA2 , NSD1, and TP53)   . The med5::MKL1) . There atein 24)  (de Rooitein 24) . HOXr caOX genes . As high-DS AMKL . Multiplnd TP53) . HOXr AM n = 12) . Murine nd STAT5 . Additioaryotype . Given tHOX genes are found, situated in clusters on four chromosomes   drives AML by deregulating expression of MEIS1, HOXA9, and PBX3, arresting differentiation and inducing long-term proliferation of human HSCs  . HOX genand HOXD . Except and HOXD . For exaand HOXD . HOXA9 ioutcomes . A recurman HSCs . Neverth in mice . Both fu in mice . HOXr ar-DS AMKL . HoweverMN1::FLI1, GRB10 (growth factor receptor bound protein 10)::SDK1 (sidekick cell adhesion molecule 1), BCR::ABL1 and MAP2K2::AF10  and STAG2::LINCO1285) are detected in 3.5% of non-DS AMKL, and all are predicted to induce a truncated STAG2 protein  act as critical players in megakaryopoiesis  have inferior outcomes, mostly because of the primary refractoriness to chemotherapy and/or early relapse  and primary leukemic cells from patients are more sensitive to GANT61 than fusion-negative cells   . NeverthHOX gene cluster, including the leukemogenic HOXA9 and its co-factor MEIS1 in myeloid stem and progenitor cells     . Leukemicluster) . VTP5046ed cells . These ftigation . For exa  . The dru trials) . It was  trials) .CBFA2T3::GLIS2 is associated with higher expression of CD56 and FOLR1, and activation of super enhancers, which provides highly plausible therapeutic targets  and the myeloid cell lymphoma-1 (MCL1) protein (using S63845) inhibits the proliferation of CBFA2T3::GLIS2 cells in vitro and abrogates leukemia progression in mouse xenografts  and JAK inhibitors (ruxolitinib and tofacitinib) (KMT2Ar (In egulated . Studiescitinib) . A combi  inhibitors (selumetinib and MEK162) have been proposed as potential options for utations . RAS mut2Ar AMKL ; thus in2Ar AMKL . Foretinrrations .KMT2Ar AML  are considered an intermediate risk, except for DS-like AMKL that has an excellent prognosis. Different subgroups of AMKL share similarities and differences in gene and transcript changes, which offer new therapeutic targets. Novel drugs that interfere with driver fusions, downstream molecules, and cooperating alterations are being developed for these patients.Pediatric non-DS AMKL is a rare, heterogeneous entity characterized by mostly poor outcomes, in contrast to excellent outcomes seen in ML-DS. Assisted by advanced genomic characterization, pediatric non-DS AMKL has been divided into distinct molecular subtypes. In the latest 2022 WHO and ICC classifications, cases with Progress in understanding AMKL pathogenesis has been immense in the last decade, but many challenges persist. We still need to improve our knowledge of this disease’s genetic and molecular landscape. Leukemogenic drivers are unknown in approximately 15% of non-DS AMKL, and the role of many known genetic alterations and cooperating events is unclear. Further progress in these areas will be critical to enable better insights into AMKL pathogenesis and improve patient outcomes.


A phenotypic hallmark of cancer is aberrant transcriptional regulation. Transcriptional regulation is controlled by a complicated array of molecular factors, including the presence of transcription factors, the deposition of histone post-translational modifications, and long-range DNA interactions. Determining the molecular identity and function of these various factors is necessary to understand specific aspects of cancer biology and reveal potential therapeutic targets. Regulation of the genome by specific factors is typically studied using chromatin immunoprecipitation followed by sequencing (ChIP-Seq) that identifies genome-wide binding interactions through the use of factor-specific antibodies. A long-standing goal in many laboratories has been the development of a ‘reverse-ChIP’ approach to identify unknown binding partners at loci of interest. A variety of strategies have been employed to enable the selective biochemical purification of sequence-defined chromatin regions, including single-copy loci, and the subsequent analytical detection of associated proteins. This review covers mass spectrometry techniques that enable quantitative proteomics before providing a survey of approaches toward the development of strategies for the purification of sequence-specific chromatin as a ‘reverse-ChIP’ technique. A fully realized reverse-ChIP technique holds great potential for identifying cancer-specific targets and the development of personalized therapeutic regimens. Among the many remarkable phenotypes differentiating cancer from healthy states, transcriptional dysregulation is one of the most marked . Indeed,cis- and trans-acting factors. Promoters proximal to gene bodies and enhancers distal from gene bodies are cis elements that contain recognition elements for transcription factors to bind to sequence-specific elements. Transcription factors in turn recruit other trans-acting factors involved in transcription and chromatin remodeling. Chromatin remodelers can install epigenetic marks, such as histone posttranslational modifications and DNA methylation, altering the standard ‘histone code’ ..29].A reliable reverse-ChIP approach would prove as powerful for reverse genomics as ChIP-Seq has been for forward genomics. Understanding how genomic differences give rise to phenotypic diversity requires identifying the molecular mediators that are present in experimentally identified regions. An ideal reverse-ChIP technique would involve the enrichment of an experimenter-defined genomic region to homogeneity from background chromatin followed by identification of the molecular composition of the region. Identifying the molecular composition of an isolated locus would involve determining the presence of protein and nucleic acid factors, their relative stoichiometry, and any associated posttranslational or posttranscriptional modifications. Achieving this ambitious goal is limited in practice by experimental realities. For example, histone proteins are ubiquitously present in the background and the experimenter-defined locus. Though histones with a specific posttranslational modification may be detected in a reverse-ChIP experiment, identifying statistically significantly enriched histone modifications is much more difficult as a result of the high background signal due to the structural importance of histones in chromatin architecture. False negatives can result from proteins truly present at a locus of interest that are also expressed at high levels elsewhere. A more tractable experimental goal is the identification of uniquely bound molecular factors in the region, such as the presence of transcription factors that specifically regulate the region of interest and nowhere else. Contemporary reverse-ChIP experiments tend to focus on identifying molecular regulators uniquely present at the chosen locus and demonstrating the importance of those factors in regulating genomic expression.Several demanding experimental difficulties not present in ChIP-Seq must be surmounted to develop a successful reverse-ChIP approach . The issAnother issue in reverse-ChIP experiments is the high background. Unlike ChIP-Seq, which can include a PCR amplification step to increase signal, a reverse-ChIP technique must directly detect proteins present at the chosen locus. For some regulatory regions of interest , there are only two copies of the locus per diploid cell , while the remaining billions of base pairs in the genome and their associated protein complexes are sources of nonspecific background signal. A typical promoter is ~1 kilobase in length, meaning it comprises approximately 0.0001% of the 3 billion base pair human genome. However, a 1 kb promoter represents approximately 0.0083% of the 12 Mb yeast genome, meaning that there is more signal relative to the background for an individual, single-copy regulatory region in yeast. Accordingly, yeast genetics has been a testing ground for locus-specific chromatin isolation. Complicating matters further is the significant biochemical similarity that exists across all genomic regions: negatively charged DNA tightly winds around positively charged nucleosomes composed of histone proteins. Distinguishing proteins present at a specific locus from ubiquitously expressed chromatin structural proteins is a nontrivial challenge.One strategy to overcome the difficulties associated with the high background is to target repetitive elements such as telomeres, which comprise ~0.01% of the human genome. Telomeres are repetitive regions that protect the ends of chromosomes during cell division and are critical to cancer biology. In healthy states, telomeres shorten as cells divide until they reach replicative senescence. Telomeres play a key role in cancer biology since cancer cells must activate mechanisms to maintain telomere length to allow for perpetual cell division ,38,39,40Nonrepetitive genomic elements require high input volumes to generate enough signal for reliable detection above the background. Detecting the proteins associated with single-copy loci is the ultimate goal of reverse-ChIP experiments. The widespread utilization of alternative promoters is a common feature across many types of cancer ,42,43,44−15 moles) level. Therefore, at least several hundred million cells are required to detect a singly-bound protein present at a unique genetic locus (~108 molecules). Fortunately, transcription factor binding sites are distributed periodically near gene promoters to increase the local concentration of regulatory factors, reducing the technical detection requirements beyond that demanding limit . . 114]. Tbacteria . Protein2 plates per condition to address this problem. By contrast, protein-based purification can have higher sensitivity. Rapid immunoprecipitation of endogenous proteins (RIME), an antibody-based ChIP strategy, can detect novel proteins involved in chromatin complexes with as few as 106 cells . . 118]. Sin yeast . Purificin yeast . The aut6 cells by using a sequential immunoprecipitation strategy. After purifying the tagged region with an anti-FLAG antibody, the chromatin was purified with an antibody against Stat1, a known protein binding to the locus. Stat1 was detected by Western blotting, demonstrating the feasibility of using iChIP as a reverse-ChIP strategy. A key step in any iChIP experiment is performing control experiments to demonstrate that the introduction of the DNA recognition sequence does not disrupt the regulatory function of the region under study.An advancement in protein-based purification strategies came with the development of insertional chromatin immunoprecipitation (iChIP) . BuildinDrosophila embryos correlated chromatin conformation with gene expression in a cell type-specific manner . . IRF-1 lma cells . The trama cells . After itrometry . The dattrometry . The proSome researchers have utilized CRISPR-enChIP to interrogate repetitive genomic elements beyond telomeres and centromeres. More than half the genome is composed of repetitive elements, and these regions are a large source of human genetic variation and have contributions to health and disease ,157. In GAL1 promoter in yeast in a CRISPR-ChAP-MS approach . . 164]. Cem cells . Many RNSequence-defined chromatin purification by the CAPTURE technique can also reveal long-range DNA contacts by tying to a sequencing readout. These experiments are analogous to chromatin conformation capture (3C)  and relaResearchers have utilized proximity labeling to study chromatin in order to take advantage of the uniquely strong interaction between biotin and streptavidin . These t7 cells cultured for 24 h). Newly identified proteins were confirmed to interact with the region targeted by the sgRNA via super-resolution microscopy in live cells, orthogonally confirming the results from the CasID experiments. In an extension of the CasID work by a separate research group, the protein desmoplakin, previously known only for its role in cell-cell adhesion, was identified as a telomere-associated protein and confirmed through functional experiments to play a role in maintaining telomere length [A BirA biotin ligase mutated to promiscuously attach biotin to all nearby lysine residues was fused to dCas9 for the CasID technique . In the e length . Desmople length .An alternative proximity labeling strategy leverages the engineered peroxidase APEX2 . This apThe direct fusion of APEX2 to dCas9 has also been utilized for genetically targeted proximity labeling. In a technique termed dCas9-APEX2 biotinylation at genomic elements by restricted spatial tagging (C-BERST), U2OS cells were engineered to contain a dCas9-APEX2 fusion protein under the control of a Tet-On promoter . The conhTERT or MYC promoters in separate experiments. hTERT encodes the human telomerase protein whose function has been implicated in a multitude of cancers [MYC encodes an oncogene commonly expressed in cancers [In a concurrent report, a dCas9-APEX2 approach termed genetic locus proteomics (GLoPro) demonstrated the ability to target non-repetitive, single-copy loci . In this cancers ,190,191. cancers ,195,196.Control GLoPro experiments showed a labeling radius of ~400 bp (~3 nucleosomes). Key to success for targeting single-copy loci was the tiling approach used for sgRNAs—multiple separate engineered sgRNA cell lines targeting the promoter region allowed samples to be pooled to increase signal. Additionally, any interactions disrupted by the binding of dCas9-APEX2 in any individual sgRNA cell line can be picked up by the other cell lines. A TMT-based quantitative proteomics approach used four to five individual sgRNA cell lines targeting each promoter along with a no-sgRNA control. Highly abundant proteins, such as histones and subunits of RNA polymerase, were identified but not enriched (presumably due to high background), but several classic sequence-specific transcription factors were enriched and confirmed to interact with each locus through conventional ChIP experiments. This observation highlights an important consideration in reverse-ChIP experiments: proteins with high levels of background expression are more difficult to identify as locus-specific binders. In other words, necessary attempts to minimize false positives can often result in false negatives.Ripk3 locus in mouse epithelial cells by a separate research group [Ripk3 promoter by ChIP-qPCR. Knockdown of the factors also prevented the increase in RIPK3 transcription typically observed upon TNFα stimulation. Confirmation of putative transcriptional regulators by both ChIP and functional assays gives high confidence that GLoPro is a powerful tool for identifying novel contributors to locus-specific proteomes.The GLoPro technique was adapted to study the regulation of the ch group . RIPK3 ich group ,201,202.ch group . Three sSeveral key considerations in designing a dCas9-based reverse-ChIP experiment emerge from the numerous reports leveraging the modular sgRNA-targeted CRISPR system. As with all reverse-ChIP approaches, control experiments are necessary to demonstrate that expression and/or binding of dCas9 does not disrupt the regulatory function of the region under study. Unique to dCas9-based experiments is the need to carefully design sgRNAs to minimize off-target binding and the associated control experiments to determine the degree of enrichment for predicted off-target sites. Minimizing expression levels of dCas9 is crucial when sgRNAs target non-repetitive regions. For single-copy loci, only two dCas9 molecules can specifically bind per diploid cell; increased expression of dCas9 provides more background, which necessitates larger input volumes to increase signal. Tiling sgRNAs to target single-copy loci can increase the relevant signal and identify proteins for which binding is disrupted in any individual sgRNA cell line. Appropriate negative controls must be included to filter out nonspecific interactions. Important negative controls include not expressing dCas9 to identify background binders, expression of dCas9 with no sgRNA or a nonspecific sgRNA to identify off-target effects, and exclusion of chemicals necessary to induce biotinylation in proximity labeling experiments. Using a nonspecific sgRNA provides a cleaner control than not expressing a sgRNA since the dynamics of dCas9 interrogating the genome varies with the presence or absence of a sgRNA. In some cases, a control sgRNA targeting a specific region far from the gene under study can be used . Proximity labeling-based experiments also have special considerations. There is a differential abundance of surface-exposed lysine residues vs. surface-exposed tyrosine residues, so BirA vs. APEX2 experiments will have different biases for what proteins can be identified. Another important consideration is the difference in time scales for each method (hours for BirA vs. minutes or less for APEX2). Last, as with all reverse-ChIP approaches, any proteins identified must be confirmed to be functionally relevant via orthogonal experiments and/or confirmation from the literature.Understanding the vast complexity of gene regulation necessitates tools that enable the unbiased quantitative identification of molecular factors interacting with any arbitrarily selected genomic locus. Many labs have devoted an enormous amount of effort to developing strategies for the selective purification of any chosen sequence-defined region. Though these efforts have uncovered novel biological insights, they are often only performed by specialist laboratories that have the expertise to effectively engage in labor-intensive processes. The emergence of dCas9-based reverse-ChIP strategies provides a modular tool that shows high potential for more widespread adoption. Increasingly powerful and sophisticated mass spectrometry protocols coupled with chromatin purification construct a list of candidate regulators that must be confirmed through orthogonal experiments. Advancements in mass spectrometry techniques will continue to provide opportunities to advance locus-specific chromatin isolation techniques . ChromatBox 19–1011 cells in some protocols). The signal-boosting strategy from single cell proteomics provides a possible experimental paradigm to lessen the labor-intensive workflow in reverse-ChIP strategies. Individual cell lines used to isolate locus-specific chromatin could be labeled with TMTs using a smaller input volume, while the parent cell line can have ~109 or more cells labeled to serve as the signal-boosting channel. The large number of proteins present in the parent cell line could serve as a platform for ‘amplification’ of the proteins present in the purified target region in an analogy to PCR.A current frontier in quantitative mass spectrometry research is the development of techniques for single-cell proteomics ,205. TheReverse-ChIP strategies have evolved from proof-of-concept experiments targeting repetitive genetic elements requiring specialist knowledge to strategies that allow for the study of single-copy loci by laboratories that have not devoted their careers to developing the necessary technology. These approaches have matured to the point that identification of functionally relevant molecular regulators at an arbitrarily selected locus of interest can be achieved. A longer-term goal will be to advance the techniques to the point that the entire proteome of a specific locus, including the stoichiometry of all components and identification of protein post-translational modifications, becomes routine. As adoption of reverse-ChIP protocols becomes more widespread, especially with the use of dCas9, the fields of transcriptional regulation and epigenetics can be expected to tackle long-standing questions with unprecedented molecular resolution. Development of an allele-specific reverse-ChIP strategy has the potential to provide a crucial tool to study genomic imprinting . Expert Adoption of reverse-ChIP techniques by cancer biologists will be invaluable in developing personalized cancer therapeutics. The phenotype of excessive cell growth embodied by cancer provides a technical advantage for contemporary reverse-ChIP experiments, which require large initial sample inputs. A future aim for clinical application of these technologies would be to create patient-specific, tumor-derived cell lines. Genomic and transcriptomic analysis can identify utilization of cancer specific promoters and other aspects of the cancer ‘regulome’ that can then be targeted for locus-specific chromatin purification. Identification of the molecular players controlling regulation at these key cancer genes in a patient-centered way will provide an avenue to develop personalized therapeutic regimens.


Responses of cells to stimuli are increasingly discovered to involve the binding of sequence-specific transcription factors outside of known target genes. We wanted to determine to what extent the genome-wide binding and function of a transcription factor are shaped by the cell type versus the stimulus. To do so, we induced the Heat Shock Response pathway in two different cancer cell lines with two different stimuli and related the binding of its master regulator HSF1 to nascent RNA and chromatin accessibility. Here, we show that HSF1 binding patterns retain their identity between basal conditions and under different magnitudes of activation, so that common HSF1 binding is globally associated with distinct transcription outcomes. HSF1-induced increase in DNA accessibility was modest in scale, but occurred predominantly at remote genomic sites. Apart from regulating transcription at existing elements including promoters and enhancers, HSF1 binding amplified during responses to stimuli may engage inactive chromatin. The authors compare the heat shock response between different cell lines and stimuli and reveal the genome-wide binding of its master transcription factor HSF1 as a platform for context-specific transcription activation. Even though HSR is recognized to be a complex pathway involving numerous regulatory components11, the Heat Shock Factor  is considered its master regulator. HSF1 is normally found in the cytoplasm12, but during stress is phosphorylated to translocate into the nucleus and bind to its cognate Heat Shock Response DNA Elements (HREs)14. Foundational work in Drosophila demonstrated that HSR involves rapid appropriation of the RNA polymerase II (Pol II) machinery by a handful of Heat Shock Protein (HSP) genes, leading to their massive activation at the expense of the rest of the genome16. Binding to promoters of HSP genes to activate their transcription was presumed to be the function of HSF19. This paradigm has shaped our understanding of transcription and roles of DNA sequence-specific transcription factors in regulation21. However, recent studies revealed that the binding of HSF1 during HSR is not limited to HSP gene promoters, or to promoters at all, but also occurs at thousands of intergenic loci22. Moreover, HSF1 binding near promoters does not necessarily lead to transcription activation of nearby genes8. These findings challenged the established relationship between the binding of a transcription factor to its target sites and its function in transcription.Transcriptional responses to stimuli involve the binding of sequence-specific factors to DNA to regulate their target genes. The Heat Shock Response (HSR) is an evolutionarily conserved cellular stress defense mechanism that can be triggered by heat and some other stimuli23. In contrast, profiling using Chromatin-Immunoprecipitation Sequencing (ChIP-seq) in human24 or mouse cells8 finds only on the order of ten thousand peaks per dataset25, leaving ample room for transcription factor binding patterns to be flexible. Earlier work reported different HSF1 programs between steady-state cancer cells and noncancerous cells responding to heat4. Heat shock in cancer cells was also shown to induce widespread HSF1 binding22. However, experimental information on transcription factor binding between cell types or in response to different stimuli remains limited. We therefore performed a side-by-side comparison of HSR induced with different stimuli in distinct ground states. To do so, we subjected distant human cell lines, MCF-7 breast adenocarcinoma and K562 chronic myelogenous leukemia, to two different HSR-inducing stimuli, elevated temperature and arsenic, a toxic metalloid found in soil, air and water, at the ambient temperature26.Analysis of the human genome identifies over two hundred and eighty thousand HSF1 binding motifs in the hg19 assemblyHere, we show that global HSF1 binding patterns retain cell type specificity under different stimuli and magnitudes of activation. By following the binding of HSF1 along with nascent RNA and chromatin accessibility, we show that HSF1 favors pre-existing open chromatin sites across the genome and that its global binding patterns are more stable than transcription. Apart from context-specific transcription activation of promoters and enhancers, stimulus-induced HSF1 binding may engage inactive chromatin sites.HSPH1 gene promoter near the transcription start site (TSS)  and outside of annotated gene promoters  identified between independent biological replicates in HS, compared to up to ~2400 peaks before activation   showed 2288 genes upregulated in HS (p-adj < 0.05) (Supplementary Data 31.We began by examining MCF7 breast adenocarcinoma cells during temperature-induced Heat Shock Response (HSR) Fig. . A 60-miSS) Fig. , similarers Fig. , with ~11B) Fig. , similarHSP genes has been previously induced at the ambient temperature in the presence of inorganic arsenic26. This prompted us to ask whether arsenic would induce genome-wide HSF1 binding at the ambient temperature in these cells. Treatment of MCF-7 cells with sodium meta-arsenite (As) at 37 °C showed transcriptional activation of control HS genes, with the timing and magnitude of mRNA level increase resembling those induced by heat   Fig. , with th01) Fig. . Elevatep-adj < 0.05, Supplementary Data HSP genes   Fig.  and no d11) Fig. . Taken t22. Both MCF7 and K562 cells responded to HS or As by upregulating control genes all Fig.  and, simall Fig. . The proall Fig. , reinforTo understand how the genome-wide HSF1 binding may be established from the ground state, we compared HSF1 signal during HSR and at non-heat shock (NHS) conditions, considering two possibilities. First, HSF1 may occupy different sites before and during HSR. However, a vast majority of HSF1 peaks detected in NHS datasets were in common with HS samples Fig. . A smallAnother possibility is that HSF1 may bind to the same sites before and after HSR. In this case, low-intensity HSF1 signal should be evident in NHS cells at the locations of HSF1 peaks that would be newly acquired in HSR. The average HSF1 signal in NHS cells at HS peak locations, including HS-only peaks, was above that at randomly selected background regions Fig. . Unlike 35, we related HSF1 signal to chromatin accessibility by examining ATAC and HSF1 data. A positive control HSPH1 gene showed an HSR-dependent increase in ATAC signal along the gene body consistent with highly active transcription  mostly increased in HS and decreased in As, with HSF1 associated with an increase in ATAC signal in either treatment , but showed more prominent changes at intergenic regions    Fig. . Likewis01) Fig. . Accordi01) Fig. . The binLastly, we examined remote HSF1 peaks for changes in chromatin accessibility induced in HS. These sites showed an ATAC signal increase in HS at the exact locations of HSF1 peaks, but not in the immediate surrounding regions Fig. . This in4. However, despite the growing numbers of available TF binding datasets25, information on genome-wide binding of transcription factors between cell types, conditions and basal versus activated states in any system remains limited. Using HSR as a model, we show that cell type specific HSF1 patterns robustly retain their identity between basal conditions and various degrees of activation. In contrast to Drosophila, in which transcription in heat shock is globally inhibited in favor of few highly activated loci16, mammalian transcription continues across the genome. The numbers of peaks and upregulated genes in our data are consistent with observations for other TFs25 and are in line with other environmental and physiological responses39. Whether induced by heat or chemically, mammalian HSR is not quantitatively different from responses to other signals. TF binding patterns reflecting the activity of signaling pathways39 may serve as readouts of cellular identity.Challenging distant cell lines with different stimuli that activate a common transcription factor allowed us to characterize its genome-wide binding and connection to nearby transcription. Earlier work noted distinct HSF1 patterns between cancer cells and noncancer cells responding to heat shockHSP gene promoters prior to activation was previously noted on individual genes40 and in global datasets25. We find that despite the differences in signal intensity, genome-wide HSF1 patterns show similarity before and during HSR. It is conceivable that low-level basal HSF1 binding  and incubation at the ambient 37 °C under 5% CO226.All cells were purchased from the American Type Culture Collection and used within the first 15 passages. Cells were grown in 15 cm dishes as before326]-HSF1 (Enzo #ADI-SPA-902-D) antibodies and GAPDH (Millipore #ABS16) as control. All antibodies used for western blotting were at 1:1000 final dilution. Band sizes were verified using Precision Plus dual color protein ladder (Bio-Rad). For an example of presentation of full scan blots, see the Source Data file.Cells were scraped (MCF-7) or collected from suspension (K562), washed with ice-cold Phosphate-Buffered Saline (PBS), resuspended in lysis buffer , and protein concentration was measured on Qubit fluorometer using protein assay (Invitrogen). Approximately 30 μg of total protein was resolved on a 10% SDS-PAGE gel and transferred onto a PVDF membrane. Nonspecific binding was blocked with 5% nonfat milk diluted in 1x Tris-Buffered Saline pH 7.5 with 0.1% Tween-20 (TBS-T) followed by primary antibody incubation overnight and three TBS-T washes. Blots were developed using horseradish peroxidase-conjugated secondary antibody (GE #NA934) and imaged on a Li-COR Odyssey Fc imager. Western blotting was performed against HSF1 (Enzo #ADI-SPA-901-D) or [pSerApproximately 500 ng of total RNA was used to synthesize cDNA using random hexamers and SSRT III reverse transcriptase (Life Technologies). PCR primers were from IDTDNA and were synthesized in 25 nmol scale with no additional purification  in serum-free DMEM/F12 media at 25 °C for 10 min, quenched with 125 mM Glycine, and resuspended in 1 ml of Lysis buffer (1% Sodium-Dodecyl Sulfate (SDS), 50 mM Tris (pH 8.0), 10 mM EDTA, 1X PMSF and protease inhibitor cocktail) followed by disruption in the Covaris S2 sonicator for 6 min . ChIP-sequencing was performed with anti RPB1 NTD (D8L4Y) (CST #14958), HSF1 (Enzo #ADI-SPA-901-D) or H3K4me3 (Abcam #ab8580) antibodies. HSF1 antibody  was used for some controls as indicated. Each chromatin sample for ChIP-sequencing contained 150 μg’s worth of DNA  as extrapolated by extracting DNA from 1% of the sample. The sample volume was adjusted to 2 ml with Dilution buffer  and cOmplete protease inhibitor cocktail (Sigma), 1 tablet per 50 ml). Protein A and Protein G Dynabeads (Thermo) premixed at 1:1 ratio (50 μl) were added for pre-clearing for 1 h at 4 °C. After removing the beads on a magnetic rack, one percent of each precleared sample was taken as input. To the rest of the reaction, 5 μg of an antibody was added . After overnight incubation at 4 °C under slow rotation, 30 μl of Protein A + G bead slurry was added and reactions were incubated for an additional 2 h, followed by washing with high salt , LiCl , low salt  wash buffers, Tris-EDTA (TE) buffer pH 8.0, on a magnetic rack, and elution with two changes of Elution Buffer , 200 μl each, followed by ethanol-precipitation60. Four percent of DNA after ChIP was taken for validation by qPCR using positive and negative control primers  or centrifugation (for K562 cells), resuspending in 20 ml Permeabilization buffer  and RNaseIn RNAse inhibitor (Thermo)), washing in the same buffer and resuspending in the final volume 100 μl of Storage buffer  glycerol, 5 mM MgCl2, 0.1 mM EDTA, 5 mM DTT), flash-freezing in liquid N2 and storing at −80 °C until use. Run-on reactions were carried out in a 37 °C water bath by adding an equal volume (100 μl) of run-on mix  for 3 min. Reactions were immediately mixed by pipetting with a cut-off 200 μl pipette tip. Reactions were stopped with 300 μl Trizol-LS followed by extraction of RNA. Water phase was additionally extracted with 0.6 volumes of chloroform to remove traces of phenol before ethanol-precipitation. RNA was fragmented by incubating in 0.2 M NaOH on ice for 10 min, neutralized with an equal volume of 1 M Tris-HCl pH 7.0, purified on a Bio-Rad P-30 Micro BioSpin desalting column, and biotin-enriched using 30 μl of C1 Streptavidin magnetic beads (Thermo) in the final volume of 100 μl of Binding Buffer  Triton X-100), for 20 min at room temperature with gentle shaking. Beads were washed with Binding, High-Salt, Low Salt buffers and TE, and RNA was recovered by double extraction with regular Trizol reagent (300 μl each time), using an additional chloroform extraction of the combined aqueous phase to remove traces of phenol prior to adding GlycoBlue  and ethanol precipitation with 3 volumes of 96% ethanol and incubation for at least 10 min at room temperature. After microcentrifugation at max speed for 15 min at 4 °C and 70% ethanol wash, air-dried ethanol-free RNA pellet was resuspended in 4 μl of 2.5 mM VRA3 3’-small RNA adapter and ligated by adding a mixture containing 1 μl T4 RNA ligase buffer, 1 μl 10 mM ATP, 2 μl 50% PEG 8000, 1 μl T4 RNA ligase 1  and 1 μl RNAseIn RNAse inhibitor, followed by ligation at 20 °C for 6 h. Following ligation, the reaction was biotin-enriched as above, treated with T4 Polynucleotide kinase (PNK) (NEB) containing 1 mM ATP in 10 μl of 1x PNK buffer (NEB) for 30 min at 37 °C followed by addition of 90 μl of RNA decapping mix (containing 20 μl of 5x Thermopol buffer and 1 μl of RppH (both NEB) and 1 μl RNAseIn RNAse inhibitor) and incubation for an additional 60 min. Following Trizol extraction, RNA was resuspended in 4 μl of 2.5 mM VRA5 5’-adapter and ligated as above. After another biotin enrichment, RNA was reverse-transcribed using SuperScript III (Thermo) reverse transcriptase with RP1 primer, and reactions were amplified using real-time PCR in the presence of EvaGreen dye as above. PRO-seq oligonucleotides  placed inside a capless 2 ml microcentrifuge tube and spun at max speed to crush and extrude gel material. Elution was done by soaking the gel slurry in 400 μl of TE containing 300 μM NaCl for 2 h at room temperature with gentle shaking. The slurry was transferred into a 0.22 μm spin Filter for Gel Matrix (Agilent) and microcentrifugated at 1800 g for 2 min. DNA from the gel-depleted eluate containing the library was ethanol-precipitated and resuspended in 20 μl of TE, quantified and Illumina-sequenced .Precision Run-On sequencing (PRO-seq)2 and 0.1% Igepal)63. Incubation with Tn5 transposase enzyme  was done in a shaking heating block at 37 °C and 500 RPM for 30 min followed by real-time PCR amplification with Nextera Illumina-compatible dual index primers and NEBNext High-Fidelity 2x PCR mix with Evagreen dye to avoid overamplification.The Assay for Transposase Accessible Chromatin with high-throughput sequencing (ATAC-seq) was performed using 50,000 cells and the original Tn5 buffer  in either MCF7 or K562 cell lines from Cancer Cell Line Encyclopedia67, and UCSC blacklisted human genome regions68. After removing PCR duplicates with Picard’s MarkDuplicates, and peaks were called using macs269. UCSC genome browser tracks were generated using sequencing depth-normalized bigwig files, with each type of a track shown in the same numerical y-axis scale for all samples. Individual peak list replicates were merged by combining peaks present in both replicates (K562 cells) or either replicate (MCF7 cells). Differential signal was calculated using DiffBind70. PCA plots were generated using affinity values default function for PCA in DiffBind. The 23 K  gene list and exact genes and TSS definitions were as defined previously31, with promoter regions defined as TSS + /−1000 nt. Venn diagrams were drawn using R venneuler package. Motif search for HSF1 was done with findmotifs.pl (HOMER)23.Sequencing was done to the depth indicated . Heatmaps were generated using plotHeatmap from deepTools.Illumina small RNA TruSeq adapters were trimmed and reads shorter than 15 nt were discarded. Trimmed reads were used to remove ribosomal RNAs and remaining reads were aligned to hg19 using hisat2 using no-splicing option. Individual samples were normalized using 3’-ends of long genesp_adj < 0.05. ChIP-seq peaks were defined using macs2 based on q value cutoff of 0.01. Omics datasets were visualized as violin plots or box plots indicating top and bottom quartiles, and error bars showing 10–90 percentiles. Comparisons were done using nonparametric Mann–Whitney test calculating two-tailed p-values.Experiments are based on a minimum of two repeats with similar results, with quantitative PCR using three or more independent biological replicates. Differentially expressed genes were defined using DESeq2 based on Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8Reporting SummarySource Data