BART: a transcription factor prediction tool with query gene sets or epigenomic profiles

UniBind: maps of high-confidence direct TF-DNA interactions across nine species

BACKGROUND

Transcription factors (TFs) bind specifically to TF binding sites (TFBSs) at cis-regulatory regions to control transcription. It is critical to locate these TF-DNA interactions to understand transcriptional regulation. Efforts to predict bona fide TFBSs benefit from the availability of experimental data mapping DNA binding regions of TFs (chromatin immunoprecipitation followed by sequencing - ChIP-seq).

RESULTS

In this study, we processed ~ 10,000 public ChIP-seq datasets from nine species to provide high-quality TFBS predictions. After quality control, it culminated with the prediction of ~ 56 million TFBSs with experimental and computational support for direct TF-DNA interactions for 644 TFs in > 1000 cell lines and tissues. These TFBSs were used to predict > 197,000 cis-regulatory modules representing clusters of binding events in the corresponding genomes. The high-quality of the TFBSs was reinforced by their evolutionary conservation, enrichment at active cis-regulatory regions, and capacity to predict combinatorial binding of TFs. Further, we confirmed that the cell type and tissue specificity of enhancer activity was correlated with the number of TFs with binding sites predicted in these regions. All the data is provided to the community through the UniBind database that can be accessed through its web-interface ( https://unibind.uio.no/ ), a dedicated RESTful API, and as genomic tracks. Finally, we provide an enrichment tool, available as a web-service and an R package, for users to find TFs with enriched TFBSs in a set of provided genomic regions.

CONCLUSIONS

UniBind is the first resource of its kind, providing the largest collection of high-confidence direct TF-DNA interactions in nine species.

BARTweb: a web server for transcriptional regulator association analysis

Identifying active transcriptional regulators (TRs) associating with cis-regulatory elements in the genome to regulate gene expression is a key task in gene regulation research. TR binding profiles from numerous public ChIP-seq data can be utilized for association analysis with query data for TR identification, as an alternative to DNA sequence motif analysis. However, integration of the massive ChIP-seq datasets has been a major challenge in such approaches. Here we present BARTweb, an interactive web server for identifying TRs whose genomic binding patterns associate with input genomic features, by leveraging over 13 000 public ChIP-seq datasets for human and mouse. Using an updated binding analysis for regulation of transcription (BART) algorithm, BARTweb can identify functional TRs that regulate a gene set, have a binding profile correlated with a ChIP-seq profile or are enriched in a genomic region set, without a priori information of the cell type. BARTweb can be a useful web server for performing functional analysis of gene regulation. BARTweb is freely available at http://bartweb.org and the source code is available at https://github.com/zanglab/bart2.

Mutations in cis that affect mRNA synthesis, processing and translation

Genetic mutations that cause hereditary diseases usually affect the composition of the transcribed mRNA and its encoded protein, leading to instability of the mRNA and/or the protein. Sometimes, however, such mutations affect the synthesis, the processing or the translation of the mRNA, with similar disastrous effects. We here present an overview of mRNA synthesis, its posttranscriptional modification and its translation into protein. We then indicate which elements in these processes are known to be affected by pathogenic mutations, but we restrict our review to mutations in cis, in the DNA of the gene that encodes the affected protein. These mutations can be in enhancer or promoter regions of the gene, which act as binding sites for transcription factors involved in pre-mRNA synthesis. We also describe mutations in polyadenylation sequences and in splice site regions, exonic and intronic, involved in intron removal. Finally, we include mutations in the Kozak sequence in mRNA, which is involved in protein synthesis. We provide examples of genetic diseases caused by mutations in these DNA regions and refer to databases to help identify these regions. The over-all knowledge of mRNA synthesis, processing and translation is essential for improvement of the diagnosis of patients with genetic diseases.

A pan-cancer transcriptome analysis of exitron splicing identifies novel cancer driver genes and neoepitopes

Exitron splicing (EIS) creates a cryptic intron (called an exitron) within a protein-coding exon to increase proteome diversity. EIS is poorly characterized, but emerging evidence suggests a role for EIS in cancer. Through a systematic investigation of EIS across 33 cancers from 9,599 tumor transcriptomes, we discovered that EIS affected 63% of human coding genes and that 95% of those events were tumor specific. Notably, we observed a mutually exclusive pattern between EIS and somatic mutations in their affected genes. Functionally, we discovered that EIS altered known and novel cancer driver genes for causing gain- or loss-of-function, which promotes tumor progression. Importantly, we identified EIS-derived neoepitopes that bind to major histocompatibility complex (MHC) class I or II. Analysis of clinical data from a clear cell renal cell carcinoma cohort revealed an association between EIS-derived neoantigen load and checkpoint inhibitor response. Our findings establish the importance of considering EIS alterations when nominating cancer driver events and neoantigens.

Auto-aggressive CXCR6+ CD8 T cells cause liver immune pathology in NASH

Nonalcoholic steatohepatitis (NASH) is a manifestation of systemic metabolic disease related to obesity, and causes liver disease and cancer^1,2. The accumulation of metabolites leads to cell stress and inflammation in the liver³, but mechanistic understandings of liver damage in NASH are incomplete. Here, using a preclinical mouse model that displays key features of human NASH (hereafter, NASH mice), we found an indispensable role for T cells in liver immunopathology. We detected the hepatic accumulation of CD8 T cells with phenotypes that combined tissue residency (CXCR6) with effector (granzyme) and exhaustion (PD1) characteristics. Liver CXCR6⁺ CD8 T cells were characterized by low activity of the FOXO1 transcription factor, and were abundant in NASH mice and in patients with NASH. Mechanistically, IL-15 induced FOXO1 downregulation and CXCR6 upregulation, which together rendered liver-resident CXCR6⁺ CD8 T cells susceptible to metabolic stimuli (including acetate and extracellular ATP) and collectively triggered auto-aggression. CXCR6⁺ CD8 T cells from the livers of NASH mice or of patients with NASH had similar transcriptional signatures, and showed auto-aggressive killing of cells in an MHC-class-I-independent fashion after signalling through P2X7 purinergic receptors. This killing by auto-aggressive CD8 T cells fundamentally differed from that by antigen-specific cells, which mechanistically distinguishes auto-aggressive and protective T cell immunity.

BART3D: inferring transcriptional regulators associated with differential chromatin interactions from Hi-C data

SUMMARY

Identification of functional transcriptional regulators (TRs) associated with chromatin interactions is an important problem in studies of 3-dimensional genome organization and gene regulation. Direct inference of TR binding has been limited by the resolution of Hi-C data. Here, we present BART3D, a computational method for inferring TRs associated with genome-wide differential chromatin interactions by comparing Hi-C maps from two states, leveraging public ChIP-seq data for human and mouse. We demonstrate that BART3D can detect relevant TRs from dynamic Hi-C profiles with TR perturbation or cell differentiation. BART3D can be a useful tool in 3D genome data analysis and functional genomics research.

AVAILABILITY AND IMPLEMENTATION

BART3D is implemented in Python and the source code is available at https://github.com/zanglab/bart3d.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

DNA methylation predicts age and provides insight into exceptional longevity of bats

Exceptionally long-lived species, including many bats, rarely show overt signs of aging, making it difficult to determine why species differ in lifespan. Here, we use DNA methylation (DNAm) profiles from 712 known-age bats, representing 26 species, to identify epigenetic changes associated with age and longevity. We demonstrate that DNAm accurately predicts chronological age. Across species, longevity is negatively associated with the rate of DNAm change at age-associated sites. Furthermore, analysis of several bat genomes reveals that hypermethylated age- and longevity-associated sites are disproportionately located in promoter regions of key transcription factors (TF) and enriched for histone and chromatin features associated with transcriptional regulation. Predicted TF binding site motifs and enrichment analyses indicate that age-related methylation change is influenced by developmental processes, while longevity-related DNAm change is associated with innate immunity or tumorigenesis genes, suggesting that bat longevity results from augmented immune response and cancer suppression.

BART Cancer: a web resource for transcriptional regulators in cancer genomes

Dysregulation of gene expression plays an important role in cancer development. Identifying transcriptional regulators, including transcription factors and chromatin regulators, that drive the oncogenic gene expression program is a critical task in cancer research. Genomic profiles of active transcriptional regulators from primary cancer samples are limited in the public domain. Here we present BART Cancer (bartcancer.org), an interactive web resource database to display the putative transcriptional regulators that are responsible for differentially regulated genes in 15 different cancer types in The Cancer Genome Atlas (TCGA). BART Cancer integrates over 10000 gene expression profiling RNA-seq datasets from TCGA with over 7000 ChIP-seq datasets from the Cistrome Data Browser database and the Gene Expression Omnibus (GEO). BART Cancer uses Binding Analysis for Regulation of Transcription (BART) for predicting the transcriptional regulators from the differentially expressed genes in cancer samples compared to normal samples. BART Cancer also displays the activities of over 900 transcriptional regulators across cancer types, by integrating computational prediction results from BART and the Cistrome Cancer database. Focusing on transcriptional regulator activities in human cancers, BART Cancer can provide unique insights into epigenetics and transcriptional regulation in cancer, and is a useful data resource for genomics and cancer research communities.

RAD: a web application to identify region associated differentially expressed genes

With the advance of genomic sequencing techniques, chromatin accessible regions, transcription factor binding sites and epigenetic modifications can be identified at genome-wide scale. Conventional analyses focus on the gene regulation at proximal regions; however, distal regions are usually less focused, largely due to the lack of reliable tools to link these regions to coding genes. In this study, we introduce RAD (Region Associated Differentially expressed genes), a user-friendly web tool to identify both proximal and distal region associated differentially expressed genes (DEGs). With DEGs and genomic regions of interest (gROI) as input, RAD maps the up- and down-regulated genes associated with any gROI and helps researchers to infer the regulatory function of these regions based on the distance of gROI to differentially expressed genes. RAD includes visualization of the results and statistical inference for significance.

AVAILABILITY

RAD is implemented with Python 3.7 and run on a Nginx server. RAD is freely available at http://labw.org/rad as online web service.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Sex differences in human adipose tissue gene expression and genetic regulation involve adipogenesis

Sex differences in adipose tissue distribution and function are associated with sex differences in cardiometabolic disease. While many studies have revealed sex differences in adipocyte cell signaling and physiology, there is a relative dearth of information regarding sex differences in transcript abundance and regulation. We investigated sex differences in subcutaneous adipose tissue transcriptional regulation using omic-scale data from ∼3000 geographically and ethnically diverse human samples. We identified 162 genes with robust sex differences in expression. Differentially expressed genes were implicated in oxidative phosphorylation and adipogenesis. We further determined that sex differences in gene expression levels could be related to sex differences in the genetics of gene expression regulation. Our analyses revealed sex-specific genetic associations, and this finding was replicated in a study of 98 inbred mouse strains. The genes under genetic regulation in human and mouse were enriched for oxidative phosphorylation and adipogenesis. Enrichment analysis showed that the associated genetic loci resided within binding motifs for adipogenic transcription factors (e.g., PPARG and EGR1). We demonstrated that sex differences in gene expression could be influenced by sex differences in genetic regulation for six genes (e.g., FADS1 and MAP1B). These genes exhibited dynamic expression patterns during adipogenesis and robust expression in mature human adipocytes. Our results support a role for adipogenesis-related genes in subcutaneous adipose tissue sex differences in the genetic and environmental regulation of gene expression.

YY1 is a cis-regulator in the organoid models of high mammographic density

MOTIVATION

Our previous study has shown that ERBB2 is overexpressed in the organoid model of MCF10A when the stiffness of the microenvironment is increased to that of high mammographic density (MD). We now aim to identify key transcription factors (TFs) and functional enhancers that regulate processes associated with increased stiffness of the microenvironment in the organoid models of premalignant human mammary cell lines.

RESULTS

3D colony organizations and the cis-regulatory networks of two human mammary epithelial cell lines (184A1 and MCF10A) are investigated as a function of the increased stiffness of the microenvironment within the range of MD. The 3D colonies are imaged using confocal microscopy, and the morphometries of colony organizations and heterogeneity are quantified as a function of the stiffness of the microenvironment using BioSig3D. In a surrogate assay, colony organizations are profiled by transcriptomics. Transcriptome data are enriched by correlative analysis with the computed morphometric indices. Next, a subset of enriched data are processed against publicly available ChIP-Seq data using Model-based Analysis of Regulation of Gene Expression to predict regulatory transcription factors. This integrative analysis of morphometric and transcriptomic data predicted YY1 as one of the cis-regulators in both cell lines as a result of the increased stiffness of the microenvironment. Subsequent experiments validated that YY1 is expressed at protein and mRNA levels for MCF10A and 184A1, respectively. Also, there is a causal relationship between activation of YY1 and ERBB2 when YY1 is overexpressed at the protein level in MCF10A.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Cancer-specific CTCF binding facilitates oncogenic transcriptional dysregulation

BACKGROUND

The three-dimensional genome organization is critical for gene regulation and can malfunction in diseases like cancer. As a key regulator of genome organization, CCCTC-binding factor (CTCF) has been characterized as a DNA-binding protein with important functions in maintaining the topological structure of chromatin and inducing DNA looping. Among the prolific binding sites in the genome, several events with altered CTCF occupancy have been reported as associated with effects in physiology or disease. However, hitherto there is no comprehensive survey of genome-wide CTCF binding patterns across different human cancers.

RESULTS

To dissect functions of CTCF binding, we systematically analyze over 700 CTCF ChIP-seq profiles across human tissues and cancers and identify cancer-specific CTCF binding patterns in six cancer types. We show that cancer-specific lost and gained CTCF binding events are associated with altered chromatin interactions, partially with DNA methylation changes, and rarely with sequence mutations. While lost bindings primarily occur near gene promoters, most gained CTCF binding events exhibit enhancer activities and are induced by oncogenic transcription factors. We validate these findings in T cell acute lymphoblastic leukemia cell lines and patient samples and show that oncogenic NOTCH1 induces specific CTCF binding and they cooperatively activate expression of target genes, indicating transcriptional condensation phenomena.

CONCLUSIONS

Specific CTCF binding events occur in human cancers. Cancer-specific CTCF binding can be induced by other transcription factors to regulate oncogenic gene expression. Our results substantiate CTCF binding alteration as a functional epigenomic signature of cancer.

Oncogenic Features in Histologically Normal Mucosa: Novel Insights Into Field Effect From a Mega-Analysis of Colorectal Transcriptomes

INTRODUCTION:

Colorectal cancer is a common malignancy that can be cured when detected early, but recurrence among survivors is a persistent risk. A field effect of cancer in the colon has been reported and could have implications for surveillance, but studies to date have been limited. A joint analysis of pooled transcriptomic data from all available bulk RNA-sequencing data sets of healthy, histologically normal tumor-adjacent, and tumor tissues was performed to provide an unbiased assessment of field effect.

METHODS:

A novel bulk RNA-sequencing data set from biopsies of nondiseased colon from screening colonoscopy along with published data sets from the Genomic Data Commons and Sequence Read Archive were considered for inclusion. Analyses were limited to samples with a quantified read depth of at least 10 million reads. Transcript abundance was estimated with Salmon, and downstream analysis was performed in R.

RESULTS:

A total of 1,139 samples were analyzed in 3 cohorts. The primary cohort consisted of 834 independent samples from 8 independent data sets, including 462 healthy, 61 tumor-adjacent, and 311 tumor samples. Tumor-adjacent gene expression was found to represent an intermediate state between healthy and tumor expression. Among differentially expressed genes in tumor-adjacent samples, 1,143 were expressed in patterns similar to tumor samples, and these genes were enriched for cancer-associated pathways.

DISCUSSION:

Novel insights into the field effect in colorectal cancer were generated in this mega-analysis of the colorectal transcriptome. Oncogenic features that might help explain metachronous lesions in cancer survivors and could be used for surveillance and risk stratification were identified.

Predicting Preference of Transcription Factors for Methylated DNA Using Sequence Information

Transcription factors play key roles in cell-fate decisions by regulating 3D genome conformation and gene expression. The traditional view is that methylation of DNA hinders transcription factors binding to them, but recent research has shown that many transcription factors prefer to bind to methylated DNA. Therefore, identifying such transcription factors and understanding their functions is a stepping-stone for studying methylation-mediated biological processes. In this paper, a two-step discriminated method was proposed to recognize transcription factors and their preference for methylated DNA based only on sequences information. In the first step, the proposed model was used to discriminate transcription factors from non-transcription factors. The areas under the curve (AUCs) are 0.9183 and 0.9116, respectively, for the 5-fold cross-validation test and independent dataset test. Subsequently, for the classification of transcription factors that prefer methylated DNA and transcription factors that prefer non-methylated DNA, our model could produce the AUCs of 0.7744 and 0.7356, respectively, for the 5-fold cross-validation test and independent dataset test. Based on the proposed model, a user-friendly web server called TFPred was built, which can be freely accessed at http://lin-group.cn/server/TFPred/.

A cytoskeleton regulator AVIL drives tumorigenesis in glioblastoma

Glioblastoma is a deadly cancer, with no effective therapies. Better understanding and identification of selective targets are urgently needed. We found that advillin (AVIL) is overexpressed in all the glioblastomas we tested including glioblastoma stem/initiating cells, but hardly detectable in non-neoplastic astrocytes, neural stem cells or normal brain. Glioma patients with increased AVIL expression have a worse prognosis. Silencing AVIL nearly eradicated glioblastoma cells in culture, and dramatically inhibited in vivo xenografts in mice, but had no effect on normal control cells. Conversely, overexpressing AVIL promoted cell proliferation and migration, enabled fibroblasts to escape contact inhibition, and transformed immortalized astrocytes, supporting AVIL being a bona fide oncogene. We provide evidence that the tumorigenic effect of AVIL is partly mediated by FOXM1, which regulates LIN28B, whose expression also correlates with clinical prognosis. AVIL regulates the cytoskeleton through modulating F-actin, while mutants disrupting F-actin binding are defective in its tumorigenic capabilities.

Genes that modulate the cytoskeleton have been associated with increased cell proliferation and migration. Here, the authors show that AVIL, an actin regulatory protein, is overexpressed in glioblastomas and mediates oncogenic effects through regulation of FOXM1 stability and LIN28B expression.

TFEA.ChIP: a tool kit for transcription factor binding site enrichment analysis capitalizing on ChIP-seq datasets

SUMMARY

The computational identification of the transcription factors (TFs) [more generally, transcription regulators, (TR)] responsible for the co-regulation of a specific set of genes is a common problem found in genomic analysis. Herein, we describe TFEA.ChIP, a tool that makes use of ChIP-seq datasets to estimate and visualize TR enrichment in gene lists representing transcriptional profiles. We validated TFEA.ChIP using a wide variety of gene sets representing signatures of genetic and chemical perturbations as input and found that the relevant TR was correctly identified in 126 of a total of 174 analyzed. Comparison with other TR enrichment tools demonstrates that TFEA.ChIP is an highly customizable package with an outstanding performance.

AVAILABILITY AND IMPLEMENTATION

TFEA.ChIP is implemented as an R package available at Bioconductor https://www.bioconductor.org/packages/devel/bioc/html/TFEA.ChIP.html and github https://github.com/LauraPS1/TFEA.ChIP_downloads. A web-based GUI to the package is also available at https://www.iib.uam.es/TFEA.ChIP/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

MAGIC: A tool for predicting transcription factors and cofactors driving gene sets using ENCODE data

Transcriptomic profiling is an immensely powerful hypothesis generating tool. However, accurately predicting the transcription factors (TFs) and cofactors that drive transcriptomic differences between samples is challenging. A number of algorithms draw on ChIP-seq tracks to define TFs and cofactors behind gene changes. These approaches assign TFs and cofactors to genes via a binary designation of 'target', or 'non-target' followed by Fisher Exact Tests to assess enrichment of TFs and cofactors. ENCODE archives 2314 ChIP-seq tracks of 684 TFs and cofactors assayed across a 117 human cell lines under a multitude of growth and maintenance conditions. The algorithm presented herein, Mining Algorithm for GenetIc Controllers (MAGIC), uses ENCODE ChIP-seq data to look for statistical enrichment of TFs and cofactors in gene bodies and flanking regions in gene lists without an a priori binary classification of genes as targets or non-targets. When compared to other TF mining resources, MAGIC displayed favourable performance in predicting TFs and cofactors that drive gene changes in 4 settings: 1) A cell line expressing or lacking single TF, 2) Breast tumors divided along PAM50 designations 3) Whole brain samples from WT mice or mice lacking a single TF in a particular neuronal subtype 4) Single cell RNAseq analysis of neurons divided by Immediate Early Gene expression levels. In summary, MAGIC is a standalone application that produces meaningful predictions of TFs and cofactors in transcriptomic experiments.

Lisa: inferring transcriptional regulators through integrative modeling of public chromatin accessibility and ChIP-seq data

We developed Lisa (http://lisa.cistrome.org/) to predict the transcriptional regulators (TRs) of differentially expressed or co-expressed gene sets. Based on the input gene sets, Lisa first uses histone mark ChIP-seq and chromatin accessibility profiles to construct a chromatin model related to the regulation of these genes. Using TR ChIP-seq peaks or imputed TR binding sites, Lisa probes the chromatin models using in silico deletion to find the most relevant TRs. Applied to gene sets derived from targeted TF perturbation experiments, Lisa boosted the performance of imputed TR cistromes and outperformed alternative methods in identifying the perturbed TRs.

Nickel-induced transcriptional changes persist post exposure through epigenetic reprogramming

Background

Nickel is an occupational and environmental toxicant associated with a number of diseases in humans including pulmonary fibrosis, bronchitis and lung and nasal cancers. Our earlier studies showed that the nickel-exposure-induced genome-wide transcriptional changes, which persist even after the termination of exposure may underlie nickel pathogenesis. However, the mechanisms that drive nickel-induced persistent changes to the transcriptome remain elusive.

Results

To elucidate the mechanisms that underlie nickel-induced long-term transcriptional changes, in this study, we examined the transcriptome and the epigenome of human lung epithelial cells during nickel exposure and after the termination of exposure. We identified two categories of persistently differentially expressed genes: (i) the genes that were differentially expressed during nickel exposure; and (ii) the genes that were differentially expressed only after the termination of exposure. Interestingly, > 85% of the nickel-induced gene expression changes occurred only after the termination of exposure. We also found extensive genome-wide alterations to the activating histone modification, H3K4me3, after the termination of nickel exposure, which coincided with the post-exposure gene expression changes. In addition, we found significant post-exposure alterations to the repressive histone modification, H3K27me3.

Conclusion

Our results suggest that while modest first wave of transcriptional changes occurred during nickel exposure, extensive transcriptional changes occurred during a second wave of transcription for which removal of nickel ions was essential. By uncovering a new category of transcriptional and epigenetic changes, which occur only after the termination of exposure, this study provides a novel understanding of the long-term deleterious consequences of nickel exposure on human health.

Using transcription factors for direct reprogramming of neurons in vitro

Cell therapy offers great promises in replacing the neurons lost due to neurodegenerative diseases or injuries. However, a key challenge is the cellular source for transplantation which is often limited by donor availability. Direct reprogramming provides an exciting avenue to generate specialized neuron subtypes in vitro, which have the potential to be used for autologous transplantation, as well as generation of patient-specific disease models in the lab for drug discovery and testing gene therapy. Here we present a detailed review on transcription factors that promote direct reprogramming of specific neuronal subtypes with particular focus on glutamatergic, GABAergic, dopaminergic, sensory and retinal neurons. We will discuss the developmental role of master transcriptional regulators and specification factors for neuronal subtypes, and summarize their use in promoting direct reprogramming into different neuronal subtypes. Furthermore, we will discuss up-and-coming technologies that advance the cell reprogramming field, including the use of computational prediction of reprogramming factors, opportunity of cellular reprogramming using small chemicals and microRNA, as well as the exciting potential for applying direct reprogramming in vivo as a novel approach to promote neuro-regeneration within the body. Finally, we will highlight the clinical potential of direct reprogramming and discuss the hurdles that need to be overcome for clinical translation.

ChEA3: transcription factor enrichment analysis by orthogonal omics integration

Identifying the transcription factors (TFs) responsible for observed changes in gene expression is an important step in understanding gene regulatory networks. ChIP-X Enrichment Analysis 3 (ChEA3) is a transcription factor enrichment analysis tool that ranks TFs associated with user-submitted gene sets. The ChEA3 background database contains a collection of gene set libraries generated from multiple sources including TF-gene co-expression from RNA-seq studies, TF-target associations from ChIP-seq experiments, and TF-gene co-occurrence computed from crowd-submitted gene lists. Enrichment results from these distinct sources are integrated to generate a composite rank that improves the prediction of the correct upstream TF compared to ranks produced by individual libraries. We compare ChEA3 with existing TF prediction tools and show that ChEA3 performs better. By integrating the ChEA3 libraries, we illuminate general transcription factor properties such as whether the TF behaves as an activator or a repressor. The ChEA3 web-server is available from https://amp.pharm.mssm.edu/ChEA3.

Specific inhibition of DPY30 activity by ASH2L-derived peptides suppresses blood cancer cell growth

DPY30 facilitates H3K4 methylation by directly binding to ASH2L in the SET1/MLL complexes and plays an important role in hematologic malignancies. However, the domain on DPY30 that regulates cancer growth is not evident, and the potential of pharmacologically targeting this chromatin modulator to inhibit cancer has not been explored. Here we have developed a peptide-based strategy to specifically target DPY30 activity. We have designed cell-penetrating peptides derived from ASH2L that can either bind to DPY30 or show defective or enhanced binding to DPY30. The DPY30-binding peptides specifically inhibit DPY30's activity in interacting with ASH2L and enhancing H3K4 methylation. Treatment with the DPY30-binding peptides significantly inhibited the growth of MLL-rearranged leukemia and other MYC-dependent hematologic cancer cells. We also revealed subsets of genes that may mediate the effect of the peptides on cancer cell growth, and showed that the DPY30-binding peptide sensitized leukemia to other types of epigenetic inhibitors. These results strongly support a critical role of the ASH2L-binding groove of DPY30 in promoting blood cancers, and demonstrate a proof-of-principle for the feasibility of pharmacologically targeting the ASH2L-binding groove of DPY30 for potential cancer inhibition.

Distinct structural classes of activating FOXA1 alterations in advanced prostate cancer

Forkhead box A1 (FOXA1) is a pioneer transcription factor that is essential for the normal development of several endoderm-derived organs, including the prostate gland^1,2. FOXA1 is frequently mutated in the hormone-receptor driven prostate, breast, bladder, and salivary gland tumors^3–8. However, how FOXA1 alterations affect cancer development is unclear, with FOXA1 previously ascribed both tumor suppressive^9–11 and oncogenic^12–14 roles. Here we assemble an aggregate cohort of 1546 prostate cancers (PCa) and show that FOXA1 alterations fall into three distinct structural classes that diverge in clinical incidence and genetic co-alteration profiles, with a collective prevalence of 35%. Class1 activating mutations originate in early PCa without ETS/SPOP alterations, selectively recur within the Wing2-region of the DNA-binding Forkhead domain (FKHD), enable enhanced chromatin mobility and binding frequency, and strongly transactivate a luminal androgen receptor (AR) program of prostate oncogenesis. By contrast, class2 activating mutations are acquired in metastatic PCa, truncate the C-terminal domain of FOXA1, enable dominant chromatin binding by increasing DNA affinity, and through TLE3 inactivation promote WNT-pathway driven metastasis. Finally, class3 genomic rearrangements are enriched in metastatic PCa, comprise of duplications and translocations within the FOXA1 locus, and structurally reposition a conserved regulatory element, herein denoted FOXA1 Mastermind (FOXMIND), to drive overexpression of FOXA1 or other oncogenes. Our study reaffirms the central role of FOXA1 in mediating AR-driven oncogenesis, and provides mechanistic insights into how different classes of FOXA1 alterations uniquely promote PCa initiation and/or metastatic progression. Furthermore, these results have direct implications in understanding the pathobiology of other hormone-receptor driven cancers and rationalize therapeutic co-targeting of FOXA1 activity.

Epigenetic Remodeling through Downregulation of Polycomb Repressive Complex 2 Mediates Chemotherapy Resistance in Testicular Germ Cell Tumors

A greater understanding of the hypersensitivity and curability of testicular germ cell tumors (TGCTs) has the potential to inform strategies to sensitize other solid tumors to conventional chemotherapies. The mechanisms of cisplatin hypersensitivity and resistance in embryonal carcinoma (EC), the stem cells of TGCTs, remain largely undefined. To study the mechanisms of cisplatin resistance we generated a large panel of independently derived, acquired resistant clones from three distinct parental EC models employing a protocol designed to match standard of care regimens of TGCT patients. Transcriptomics revealed highly significant expression changes shared between resistant cells regardless of their parental origin. This was dominated by a highly significant enrichment of genes normally repressed by H3K27 methylation and the polycomb repressive complex 2 (PRC2) which correlated with a substantial decrease in global H3K27me3, H2AK119 ubiquitination, and expression of BMI1. Importantly, repression of H3K27 methylation with the EZH2 inhibitor GSK-126 conferred cisplatin resistance to parental cells while induction of H3K27 methylation with the histone lysine demethylase inhibitor GSK-J4 resulted in increased cisplatin sensitivity to resistant cells. A gene signature based on H3K27me gene enrichment was associated with an increased rate of recurrent/progressive disease in testicular cancer patients. Our data indicates that repression of H3K27 methylation is a mechanism of cisplatin acquired resistance in TGCTs and that restoration of PRC2 complex function is a viable approach to overcome treatment failure.

Building Transcription Factor Binding Site Models to Understand Gene Regulation in Plants

Transcription factors (TFs) are key cellular components that control gene expression. They recognize specific DNA sequences, the TF binding sites (TFBSs), and thus are targeted to specific regions of the genome where they can recruit transcriptional co-factors and/or chromatin regulators to fine-tune spatiotemporal gene regulation. Therefore, the identification of TFBSs in genomic sequences and their subsequent quantitative modeling is of crucial importance for understanding and predicting gene expression. Here, we review how TFBSs can be determined experimentally, how the TFBS models can be constructed in silico, and how they can be optimized by taking into account features such as position interdependence within TFBSs, DNA shape, and/or by introducing state-of-the-art computational algorithms such as deep learning methods. In addition, we discuss the integration of context variables into the TFBS modeling, including nucleosome positioning, chromatin states, methylation patterns, 3D genome architectures, and TF cooperative binding, in order to better predict TF binding under cellular contexts. Finally, we explore the possibilities of combining the optimized TFBS model with technological advances, such as targeted TFBS perturbation by CRISPR, to better understand gene regulation, evolution, and plant diversity.