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

Benchmarking tools for transcription factor prioritization

Spatiotemporal regulation of gene expression is controlled by transcription factor (TF) binding to regulatory elements, resulting in a plethora of cell types and cell states from the same genetic information. Due to the importance of regulatory elements, various sequencing methods have been developed to localise them in genomes, for example using ChIP-seq profiling of the histone mark H3K27ac that marks active regulatory regions. Moreover, multiple tools have been developed to predict TF binding to these regulatory elements based on DNA sequence. As altered gene expression is a hallmark of disease phenotypes, identifying TFs driving such gene expression programs is critical for the identification of novel drug targets. In this study, we curated 84 chromatin profiling experiments (H3K27ac ChIP-seq) where TFs were perturbed through e.g., genetic knockout or overexpression. We ran nine published tools to prioritize TFs using these real-world datasets and evaluated the performance of the methods in identifying the perturbed TFs. This allowed the nomination of three frontrunner tools, namely RcisTarget, MEIRLOP and monaLisa. Our analyses revealed opportunities and commonalities of tools that will help to guide further improvements and developments in the field.

Assessing next-generation sequencing-based computational methods for predicting transcriptional regulators with query gene sets

This article provides an in-depth review of computational methods for predicting transcriptional regulators (TRs) with query gene sets. Identification of TRs is of utmost importance in many biological applications, including but not limited to elucidating biological development mechanisms, identifying key disease genes, and predicting therapeutic targets. Various computational methods based on next-generation sequencing (NGS) data have been developed in the past decade, yet no systematic evaluation of NGS-based methods has been offered. We classified these methods into two categories based on shared characteristics, namely library-based and region-based methods. We further conducted benchmark studies to evaluate the accuracy, sensitivity, coverage, and usability of NGS-based methods with molecular experimental datasets. Results show that BART, ChIP-Atlas, and Lisa have relatively better performance. Besides, we point out the limitations of NGS-based methods and explore potential directions for further improvement.

A hybrid approach for predicting transcription factors

Transcription factors are essential DNA-binding proteins that regulate the transcription rate of several genes and control the expression of genes inside a cell. The prediction of transcription factors with high precision is important for understanding biological processes such as cell differentiation, intracellular signaling, and cell-cycle control. In this study, we developed a hybrid method that combines alignment-based and alignment-free methods for predicting transcription factors with higher accuracy. All models have been trained, tested, and evaluated on a large dataset that contains 19,406 transcription factors and 523,560 non-transcription factor protein sequences. To avoid biases in evaluation, the datasets were divided into training and validation/independent datasets, where 80% of the data was used for training, and the remaining 20% was used for external validation. In the case of alignment-free methods, models were developed using machine learning techniques and the composition-based features of a protein. Our best alignment-free model obtained an AUC of 0.97 on an independent dataset. In the case of the alignment-based method, we used BLAST at different cut-offs to predict the transcription factors. Although the alignment-based method demonstrated excellent performance, it was unable to cover all transcription factors due to instances of no hits. To combine the strengths of both methods, we developed a hybrid method that combines alignment-free and alignment-based methods. In the hybrid method, we added the scores of the alignment-free and alignment-based methods and achieved a maximum AUC of 0.99 on the independent dataset. The method proposed in this study performs better than existing methods. We incorporated the best models in the webserver/Python Package Index/standalone package of "TransFacPred" (https://webs.iiitd.edu.in/raghava/transfacpred).

BIT: Bayesian Identification of Transcriptional Regulators

BIT is a novel Bayesian hierarchical model capable of predicting transcriptional regulators (TRs) from the input of user-provided epigenomic regions. TRs are critical molecules in transcriptional regulation. Many diseases and cancers are linked to the dysfunction of TRs. Knowing TRs in certain biological process can help find new biomarkers or therapeutic targets. Thus, BIT formulates a novel Bayesian hierarchical model with the Pólya-gamma data augmentation strategy. Based on collected ChIP-seq datasets, BIT can identify TRs responsible for the genome-wide binding pattern within the user-provided epigenomic regions. BIT has been validated by using a simulation study and three applications.

Integrative analysis of DNA replication origins and ORC-/MCM-binding sites in human cells reveals a lack of overlap

Based on experimentally determined average inter-origin distances of ~100 kb, DNA replication initiates from ~50,000 origins on human chromosomes in each cell cycle. The origins are believed to be specified by binding of factors like the origin recognition complex (ORC) or CTCF or other features like G-quadruplexes. We have performed an integrative analysis of 113 genome-wide human origin profiles (from five different techniques) and five ORC-binding profiles to critically evaluate whether the most reproducible origins are specified by these features. Out of ~7.5 million union origins identified by all datasets, only 0.27% (20,250 shared origins) were reproducibly obtained in at least 20 independent SNS-seq datasets and contained in initiation zones identified by each of three other techniques, suggesting extensive variability in origin usage and identification. Also, 21% of the shared origins overlap with transcriptional promoters, posing a conundrum. Although the shared origins overlap more than union origins with constitutive CTCF-binding sites, G-quadruplex sites, and activating histone marks, these overlaps are comparable or less than that of known transcription start sites, so that these features could be enriched in origins because of the overlap of origins with epigenetically open, promoter-like sequences. Only 6.4% of the 20,250 shared origins were within 1 kb from any of the ~13,000 reproducible ORC-binding sites in human cancer cells, and only 4.5% were within 1 kb of the ~11,000 union MCM2-7-binding sites in contrast to the nearly 100% overlap in the two comparisons in the yeast, Saccharomyces cerevisiae. Thus, in human cancer cell lines, replication origins appear to be specified by highly variable stochastic events dependent on the high epigenetic accessibility around promoters, without extensive overlap between the most reproducible origins and currently known ORC- or MCM-binding sites.

PAD4 controls tumor immunity via restraining the MHC class II machinery in macrophages

Tumor-associated macrophages (TAMs) shape tumor immunity and therapeutic efficacy. However, it is poorly understood whether and how post-translational modifications (PTMs) intrinsically affect the phenotype and function of TAMs. Here, we reveal that peptidylarginine deiminase 4 (PAD4) exhibits the highest expression among common PTM enzymes in TAMs and negatively correlates with the clinical response to immune checkpoint blockade. Genetic and pharmacological inhibition of PAD4 in macrophages prevents tumor progression in tumor-bearing mouse models, accompanied by an increase in macrophage major histocompatibility complex (MHC) class II expression and T cell effector function. Mechanistically, PAD4 citrullinates STAT1 at arginine 121, thereby promoting the interaction between STAT1 and protein inhibitor of activated STAT1 (PIAS1), and the loss of PAD4 abolishes this interaction, ablating the inhibitory role of PIAS1 in the expression of MHC class II machinery in macrophages and enhancing T cell activation. Thus, the PAD4-STAT1-PIAS1 axis is an immune restriction mechanism in macrophages and may serve as a cancer immunotherapy target.

Assessing NGS-based computational methods for predicting transcriptional regulators with query gene sets

This article provides an in-depth review of computational methods for predicting transcriptional regulators with query gene sets. Identification of transcriptional regulators is of utmost importance in many biological applications, including but not limited to elucidating biological development mechanisms, identifying key disease genes, and predicting therapeutic targets. Various computational methods based on next-generation sequencing (NGS) data have been developed in the past decade, yet no systematic evaluation of NGS-based methods has been offered. We classified these methods into two categories based on shared characteristics, namely library-based and region-based methods. We further conducted benchmark studies to evaluate the accuracy, sensitivity, coverage, and usability of NGS-based methods with molecular experimental datasets. Results show that BART, ChIP-Atlas, and Lisa have relatively better performance. Besides, we point out the limitations of NGS-based methods and explore potential directions for further improvement.

Key points

An introduction to available computational methods for predicting functional TRs from a query gene set.A detailed walk-through along with practical concerns and limitations.A systematic benchmark of NGS-based methods in terms of accuracy, sensitivity, coverage, and usability, using 570 TR perturbation-derived gene sets.NGS-based methods outperform motif-based methods. Among NGS methods, those utilizing larger databases and adopting region-centric approaches demonstrate favorable performance. BART, ChIP-Atlas, and Lisa are recommended as these methods have overall better performance in evaluated scenarios.

Small Cajal Body-Specific RNA12 Promotes Carcinogenesis through Modulating Extracellular Matrix Signaling in Bladder Cancer

Background: Small Cajal body-specific RNAs (scaRNAs) are a specific subset of small nucleolar RNAs (snoRNAs) that have recently emerged as pivotal contributors in diverse physiological and pathological processes. However, their defined roles in carcinogenesis remain largely elusive. This study aims to explore the potential function and mechanism of SCARNA12 in bladder cancer (BLCA) and to provide a theoretical basis for further investigations into the biological functionalities of scaRNAs. Materials and Methods: TCGA, GEO and GTEx data sets were used to analyze the expression of SCARNA12 and its clinicopathological significance in BLCA. Quantitative real-time PCR (qPCR) and in situ hybridization were applied to validate the expression of SCARNA12 in both BLCA cell lines and tissues. RNA sequencing (RNA-seq) combined with bioinformatics analyses were conducted to reveal the changes in gene expression patterns and functional pathways in BLCA patients with different expressions of SCARNA12 and T24 cell lines upon SCARNA12 knockdown. Single-cell mass cytometry (CyTOF) was then used to evaluate the tumor-related cell cluster affected by SCARNA12. Moreover, SCARNA12 was stably knocked down in T24 and UMUC3 cell lines by lentivirus-mediated CRISPR/Cas9 approach. The biological effects of SCARNA12 on the proliferation, clonogenic, migration, invasion, cell apoptosis, cell cycle, and tumor growth were assessed by in vitro MTT, colony formation, wound healing, transwell, flow cytometry assays, and in vivo nude mice xenograft models, respectively. Finally, a chromatin isolation by RNA purification (ChIRP) experiment was further conducted to delineate the potential mechanisms of SCARNA12 in BLCA. Results: The expression of SCARNA12 was significantly up-regulated in both BLCA tissues and cell lines. RNA-seq data elucidated that SCARAN12 may play a potential role in cell adhesion and extracellular matrix (ECM) related signaling pathways. CyTOF results further showed that an ECM-related cell cluster with vimentin+, CD13+, CD44+, and CD47+ was enriched in BLCA patients with high SCARNA12 expression. Additionally, SCARNA12 knockdown significantly inhibited the proliferation, colony formation, migration, and invasion abilities in T24 and UMUC3 cell lines. SCARNA12 knockdown prompted cell arrest in the G0/G1 and G2/M phase and promoted apoptosis in T24 and UMUC3 cell lines. Furthermore, SCARNA12 knockdown could suppress the in vivo tumor growth in nude mice. A ChIRP experiment further suggested that SCARNA12 may combine transcription factors H2AFZ to modulate the transcription program and then affect BLCA progression. Conclusions: Our study is the first to propose aberrant alteration of SCARNA12 and elucidate its potential oncogenic roles in BLCA via the modulation of ECM signaling. The interaction of SCARNA12 with the transcriptional factor H2AFZ emerges as a key contributor to the carcinogenesis and progression of BLCA. These findings suggest SCARNA12 may serve as a diagnostic biomarker and potential therapeutic target for the treatment of BLCA.

DARDN: A Deep-Learning Approach for CTCF Binding Sequence Classification and Oncogenic Regulatory Feature Discovery

Characterization of gene regulatory mechanisms in cancer is a key task in cancer genomics. CCCTC-binding factor (CTCF), a DNA binding protein, exhibits specific binding patterns in the genome of cancer cells and has a non-canonical function to facilitate oncogenic transcription programs by cooperating with transcription factors bound at flanking distal regions. Identification of DNA sequence features from a broad genomic region that distinguish cancer-specific CTCF binding sites from regular CTCF binding sites can help find oncogenic transcription factors in a cancer type. However, the presence of long DNA sequences without localization information makes it difficult to perform conventional motif analysis. Here, we present DNAResDualNet (DARDN), a computational method that utilizes convolutional neural networks (CNNs) for predicting cancer-specific CTCF binding sites from long DNA sequences and employs DeepLIFT, a method for interpretability of deep learning models that explains the model's output in terms of the contributions of its input features. The method is used for identifying DNA sequence features associated with cancer-specific CTCF binding. Evaluation on DNA sequences associated with CTCF binding sites in T-cell acute lymphoblastic leukemia (T-ALL) and other cancer types demonstrates DARDN's ability in classifying DNA sequences surrounding cancer-specific CTCF binding from control constitutive CTCF binding and identifying sequence motifs for transcription factors potentially active in each specific cancer type. We identify potential oncogenic transcription factors in T-ALL, acute myeloid leukemia (AML), breast cancer (BRCA), colorectal cancer (CRC), lung adenocarcinoma (LUAD), and prostate cancer (PRAD). Our work demonstrates the power of advanced machine learning and feature discovery approach in finding biologically meaningful information from complex high-throughput sequencing data.

epidecodeR: a functional exploration tool for epigenetic and epitranscriptomic regulation

Recent technological advances in sequencing DNA and RNA modifications using high-throughput platforms have generated vast epigenomic and epitranscriptomic datasets whose power in transforming life science is yet fully unleashed. Currently available in silico methods have facilitated the identification, positioning and quantitative comparisons of individual modification sites. However, the essential challenge to link specific 'epi-marks' to gene expression in the particular context of cellular and biological processes is unmet. To fast-track exploration, we generated epidecodeR implemented in R, which allows biologists to quickly survey whether an epigenomic or epitranscriptomic status of their interest potentially influences gene expression responses. The evaluation is based on the cumulative distribution function and the statistical significance in differential expression of genes grouped by the number of 'epi-marks'. This tool proves useful in predicting the role of H3K9ac and H3K27ac in associated gene expression after knocking down deacetylases FAM60A and SDS3 and N6-methyl-adenosine-associated gene expression after knocking out the reader proteins. We further used epidecodeR to explore the effectiveness of demethylase FTO inhibitors and histone-associated modifications in drug abuse in animals. epidecodeR is available for downloading as an R package at https://bioconductor.riken.jp/packages/3.13/bioc/html/epidecodeR.html.

KnockTF 2.0: a comprehensive gene expression profile database with knockdown/knockout of transcription (co-)factors in multiple species

Transcription factors (TFs), transcription co-factors (TcoFs) and their target genes perform essential functions in diseases and biological processes. KnockTF 2.0 (http://www.licpathway.net/KnockTF/index.html) aims to provide comprehensive gene expression profile datasets before/after T(co)F knockdown/knockout across multiple tissue/cell types of different species. Compared with KnockTF 1.0, KnockTF 2.0 has the following improvements: (i) Newly added T(co)F knockdown/knockout datasets in mice, Arabidopsis thaliana and Zea mays and also an expanded scale of datasets in humans. Currently, KnockTF 2.0 stores 1468 manually curated RNA-seq and microarray datasets associated with 612 TFs and 172 TcoFs disrupted by different knockdown/knockout techniques, which are 2.5 times larger than those of KnockTF 1.0. (ii) Newly added (epi)genetic annotations for T(co)F target genes in humans and mice, such as super-enhancers, common SNPs, methylation sites and chromatin interactions. (iii) Newly embedded and updated search and analysis tools, including T(co)F Enrichment (GSEA), Pathway Downstream Analysis and Search by Target Gene (BLAST). KnockTF 2.0 is a comprehensive update of KnockTF 1.0, which provides more T(co)F knockdown/knockout datasets and (epi)genetic annotations across multiple species than KnockTF 1.0. KnockTF 2.0 facilitates not only the identification of functional T(co)Fs and target genes but also the investigation of their roles in the physiological and pathological processes.

Genome binding properties of Zic transcription factors underlie their changing functions during neuronal maturation

Background

The Zic family of transcription factors (TFs) promote both proliferation and maturation of cerebellar granule neurons (CGNs), raising the question of how a single, constitutively expressed TF family can support distinct developmental processes. Here we use an integrative experimental and bioinformatic approach to discover the regulatory relationship between Zic TF binding and changing programs of gene transcription during CGN differentiation.

Results

We first established a bioinformatic pipeline to integrate Zic ChIP-seq data from the developing mouse cerebellum with other genomic datasets from the same tissue. In newborn CGNs, Zic TF binding predominates at active enhancers that are co-bound by developmentally-regulated TFs including Atoh1, whereas in mature CGNs, Zic TF binding consolidates toward promoters where it co-localizes with activity-regulated TFs. We then performed CUT&RUN-seq in differentiating CGNs to define both the time course of developmental shifts in Zic TF binding and their relationship to gene expression. Mapping Zic TF binding sites to genes using chromatin looping, we identified the set of Zic target genes that have altered expression in RNA-seq from Zic1 or Zic2 knockdown CGNs.

Conclusion

Our data show that Zic TFs are required for both induction and repression of distinct, developmentally regulated target genes through a mechanism that is largely independent of changes in Zic TF binding. We suggest that the differential collaboration of Zic TFs with other TF families underlies the shift in their biological functions across CGN development.

Development of new binary expression systems for plant synthetic biology

KEY MESSAGE

A novel plant binary expression system was developed from the compactin biosynthetic pathway 27 of Penicillium citrinum ML-236B. The system achieved >fivefold activation of gene expression in 28 transgenic tobacco. A diverse and well-characterized genetic toolset is fundamental to achieve the overall goals of plant synthetic biology. To properly coordinate expression of a multigene pathway, this toolset should include binary systems that control gene expression at the level of transcription. In plants, few highly functional, orthogonal transcriptional regulators have been identified. Here, we describe the process of developing synthetic plant transcription factors using regulatory elements from the Penicillium citrinum ML-236B (compactin) pathway. This pathway contains several genes including mlcA and mlcC that are transcriptionally regulated in a dose-dependent manner by the activator mlcR. In Nicotiana benthamiana, we first expressed mlcR with several cognate synthetic promoters driving expression of GFP. Synthetic promoters contained operator sequences from the compactin gene cluster. Following identification of the most active synthetic promoter, the DNA-binding domain from mlcR was used to generate chimeric transcription factors containing variable activation domains, including QF from the Neurospora crassa Q-system. Activity was measured at both protein and RNA levels which correlated with an R2 value of 0.94. A synthetic transcription factor with a QF activation domain increased gene expression from its synthetic promoter up to sixfold in N. benthamiana. Two systems were characterized in transgenic tobacco plants. The QF-based plants maintained high expression in tobacco, increasing expression from the cognate synthetic promoter by fivefold. Transgenic plants and non-transgenic plants were morphologically indistinguishable. The framework of this study can easily be adopted for other putative transcription factors to continue improvement of the plant synthetic biology toolbox.

MORA and EnsembleTFpredictor: An ensemble approach to reveal functional transcription factor regulatory networks

MOTIVATION

Our study aimed to identify biologically relevant transcription factors (TFs) that control the expression of a set of co-expressed or co-regulated genes.

RESULTS

We developed a fully automated pipeline, Motif Over Representation Analysis (MORA), to detect enrichment of known TF binding motifs in any query sequences. MORA performed better than or comparable to five other TF-prediction tools as evaluated using hundreds of differentially expressed gene sets and ChIP-seq datasets derived from known TFs. Additionally, we developed EnsembleTFpredictor to harness the power of multiple TF-prediction tools to provide a list of functional TFs ranked by prediction confidence. When applied to the test datasets, EnsembleTFpredictor not only identified the target TF but also revealed many TFs known to cooperate with the target TF in the corresponding biological systems. MORA and EnsembleTFpredictor have been used in two publications, demonstrating their power in guiding experimental design and in revealing novel biological insights.

Integrative analysis of DNA replication origins and ORC/MCM binding sites in human cells reveals a lack of overlap

Based on experimentally determined average inter-origin distances of ∼100 kb, DNA replication initiates from ∼50,000 origins on human chromosomes in each cell cycle. The origins are believed to be specified by binding of factors like the Origin Recognition Complex (ORC) or CTCF or other features like G-quadruplexes. We have performed an integrative analysis of 113 genome-wide human origin profiles (from five different techniques) and 5 ORC-binding profiles to critically evaluate whether the most reproducible origins are specified by these features. Out of ∼7.5 million union origins identified by all datasets, only 0.27% were reproducibly obtained in at least 20 independent SNS-seq datasets and contained in initiation zones identified by each of three other techniques (20,250 shared origins), suggesting extensive variability in origin usage and identification. 21% of the shared origins overlap with transcriptional promoters, posing a conundrum. Although the shared origins overlap more than union origins with constitutive CTCF binding sites, G-quadruplex sites and activating histone marks, these overlaps are comparable or less than that of known Transcription Start Sites, so that these features could be enriched in origins because of the overlap of origins with epigenetically open, promoter-like sequences. Only 6.4% of the 20,250 shared origins were within 1 kb from any of the ∼13,000 reproducible ORC binding sites in human cancer cells, and only 4.5% were within 1 kb of the ∼11,000 union MCM2-7 binding sites in contrast to the nearly 100% overlap in the two comparisons in the yeast, S. cerevisiae . Thus, in human cancer cell lines, replication origins appear to be specified by highly variable stochastic events dependent on the high epigenetic accessibility around promoters, without extensive overlap between the most reproducible origins and currently known ORC- or MCM-binding sites.

Characterization of tumor microenvironment in glioblastoma multiforme identifies ITGB2 as a key immune and stromal related regulator in glial cell types

Glioblastoma multiforme (GBM) is the most aggressive form of brain tumor characterized by inter and intra-tumor heterogeneity and complex tumor microenvironment. To uncover the molecular targets in this milieu, we systematically identified immune and stromal interactions at the glial cell type level that leverages on RNA-sequencing data of GBM patients from The Cancer Genome Atlas. The perturbed genes between the high vs low immune and stromal scored patients were subjected to weighted gene co-expression network analysis to identify the glial cell type specific networks in immune and stromal infiltrated patients. The intramodular connectivity analysis identified the highly connected genes in each module. Combining it with univariable and multivariable prognostic analysis revealed common vital gene ITGB2, between the immune and stromal infiltrated patients enriched in microglia and newly formed oligodendrocytes. We found following unique hub genes in immune infiltrated patients; COL6A3 (microglia), ITGAM (oligodendrocyte precursor cells), TNFSF9 (microglia), and in stromal infiltrated patients, SERPINE1 (microglia) and THBS1 (newly formed oligodendrocytes, oligodendrocyte precursor cells). To validate these hub genes, we used external GBM patient single cell RNA-sequencing dataset and this identified ITGB2 to be significantly enriched in microglia, newly formed oligodendrocytes, T-cells, macrophages and adipocyte cell types in both immune and stromal datasets. The tumor infiltration analysis of ITGB2 showed that it is correlated with myeloid dendritic cells, macrophages, monocytes, neutrophils, B-cells, fibroblasts and adipocytes. Overall, the systematic screening of tumor microenvironment components at glial cell types uncovered ITGB2 as a potential target in primary GBM.

Transcriptomic and proteomic profiling reveals distinct pathogenic features of peripheral non-classical monocytes in systemic lupus erythematosus

Peripheral blood monocytes propagate inflammation in systemic lupus erythematosus (SLE). Three major populations of monocytes have been recognized namely classical (CM), intermediate (IM) and non-classical monocytes (NCM). Herein, we performed a comprehensive transcriptomic, proteomic and functional characterization of the three peripheral monocytic subsets from active SLE patients and healthy individuals. Our data demonstrate extensive molecular disruptions in circulating SLE NCM, characterized by enhanced inflammatory features such as deregulated DNA repair, cell cycle and heightened IFN signaling combined with differentiation and developmental cues. Enhanced DNA damage, elevated expression of p53, G0 arrest of cell cycle and increased autophagy stress the differentiation potential of NCM in SLE. This immunogenic profile is associated with an activated macrophage phenotype of NCM exhibiting M1 characteristics in the circulation, fueling the inflammatory response. Together, these findings identify circulating SLE NCM as a pathogenic cell type in the disease that could represent an additional therapeutic target.

Chromatin accessibility dynamics in colorectal cancer liver metastasis: Uncovering the liver tropism at single cell resolution

Tumor metastasis causes over 90% of cancer related death and no currently available therapies target it. However, there is limited understanding regarding the epigenetic regulation of genes during this complex process. Here by integrating single-cell ATAC-seq (scATAC-seq), single-cell RNA-seq (scRNA-seq), microarray, bulk RNA-seq, immunohistochemistry (IHC) staining, as well as proteomics datasets from paired primary and liver metastatic colorectal cancer (CRC) patient-derived xenograft (PDX) model and patients, we discovered that liver metastatic CRC cells lose their colon-specific chromatin accessible sites yet gain liver-specific ones. Importantly, we observed elevated accessibility of HNF4A, a liver-specific transcription factor, in liver metastatic CRC cells. Subsequently, we performed clustering analysis of liver metastatic CRC cells together with cells involved in liver development, revealing significant heterogeneity among the liver metastatic CRC cells. Over 50% of the liver metastatic CRC cells exhibited characteristics similar to those of erythroid progenitors and hepatocytes, showing increased expression of genes involved in oxidative phosphorylation and glycolysis. Moreover, our discovery further revealed that the MHC and IFN response genes in these cells exhibit moderate epigenetic activity, which is significantly associated with the low objective response rates in checkpoint blockade immunotherapy. Our findings uncovered the critical roles of HNF4A and the cell populations within liver metastatic CRC cells might serve as crucial therapeutic targets for addressing liver metastasis and improving the immunotherapy response in patients with CRC.

A genome-wide single-cell 3D genome atlas of lung cancer progression

Alterations in three-dimensional (3D) genome structures are associated with cancer1-5. However, how genome folding evolves and diversifies during subclonal cancer progression in the native tissue environment remains unknown. Here, we leveraged a genome-wide chromatin tracing technology to directly visualize 3D genome folding in situ in a faithful Kras-driven mouse model of lung adenocarcinoma (LUAD)6, generating the first single-cell 3D genome atlas of any cancer. We discovered stereotypical 3D genome alterations during cancer development, including a striking structural bottleneck in preinvasive adenomas prior to progression to LUAD, indicating a stringent selection on the 3D genome early in cancer progression. We further showed that the 3D genome precisely encodes cancer states in single cells, despite considerable cell-to-cell heterogeneity. Finally, evolutionary changes in 3D genome compartmentalization - partially regulated by polycomb group protein Rnf2 through its ubiquitin ligase-independent activity - reveal novel genetic drivers and suppressors of LUAD progression. Our results demonstrate the importance of mapping the single-cell cancer 3D genome and the potential to identify new diagnostic and therapeutic biomarkers from 3D genomic architectures.

Therapeutic targeting of metabolic vulnerabilities in cancers with MLL3/4-COMPASS epigenetic regulator mutations

Epigenetic status-altering mutations in chromatin-modifying enzymes are a feature of human diseases, including many cancers. However, the functional outcomes and cellular dependencies arising from these mutations remain unresolved. In this study, we investigated cellular dependencies, or vulnerabilities, that arise when enhancer function is compromised by loss of the frequently mutated COMPASS family members MLL3 and MLL4. CRISPR dropout screens in MLL3/4-depleted mouse embryonic stem cells (mESCs) revealed synthetic lethality upon suppression of purine and pyrimidine nucleotide synthesis pathways. Consistently, we observed a shift in metabolic activity toward increased purine synthesis in MLL3/4-KO mESCs. These cells also exhibited enhanced sensitivity to the purine synthesis inhibitor lometrexol, which induced a unique gene expression signature. RNA-Seq identified the top MLL3/4 target genes coinciding with suppression of purine metabolism, and tandem mass tag proteomic profiling further confirmed upregulation of purine synthesis in MLL3/4-KO cells. Mechanistically, we demonstrated that compensation by MLL1/COMPASS was underlying these effects. Finally, we demonstrated that tumors with MLL3 and/or MLL4 mutations were highly sensitive to lometrexol in vitro and in vivo, both in culture and in animal models of cancer. Our results depicted a targetable metabolic dependency arising from epigenetic factor deficiency, providing molecular insight to inform therapy for cancers with epigenetic alterations secondary to MLL3/4 COMPASS dysfunction.

Reduced mitochondrial calcium uptake in macrophages is a major driver of inflammaging

Mitochondrial dysfunction is linked to age-associated inflammation or inflammaging, but underlying mechanisms are not understood. Analyses of 700 human blood transcriptomes revealed clear signs of age-associated low-grade inflammation. Among changes in mitochondrial components, we found that the expression of mitochondrial calcium uniporter (MCU) and its regulatory subunit MICU1, genes central to mitochondrial Ca²⁺ (mCa²⁺) signaling, correlated inversely with age. Indeed, mCa²⁺ uptake capacity of mouse macrophages decreased significantly with age. We show that in both human and mouse macrophages, reduced mCa²⁺ uptake amplifies cytosolic Ca²⁺ oscillations and potentiates downstream nuclear factor kappa B activation, which is central to inflammation. Our findings pinpoint the mitochondrial calcium uniporter complex as a keystone molecular apparatus that links age-related changes in mitochondrial physiology to systemic macrophage-mediated age-associated inflammation. The findings raise the exciting possibility that restoring mCa²⁺ uptake capacity in tissue-resident macrophages may decrease inflammaging of specific organs and alleviate age-associated conditions such as neurodegenerative and cardiometabolic diseases.

Convergence of YAP/TAZ, TEAD and TP63 activity is associated with bronchial premalignant severity and progression

BACKGROUND

Bronchial premalignant lesions (PMLs) are composed primarily of cells resembling basal epithelial cells of the airways, which through poorly understood mechanisms have the potential to progress to lung squamous cell carcinoma (LUSC). Despite ongoing efforts that have mapped gene expression and cell diversity across bronchial PML pathologies, signaling and transcriptional events driving malignancy are poorly understood. Evidence has suggested key roles for the Hippo pathway effectors YAP and TAZ and associated TEAD and TP63 transcription factor families in bronchial basal cell biology and LUSC. In this study we examine the functional association of YAP/TAZ, TEADs and TP63 in bronchial epithelial cells and PMLs.

METHODS

We performed RNA-seq in primary human bronchial epithelial cells following small interfering RNA (siRNA)-mediated depletion of YAP/TAZ, TEADs or TP63, and combined these data with ChIP-seq analysis of these factors. Directly activated or repressed genes were identified and overlapping genes were profiled across gene expression data obtained from progressive or regressive human PMLs and across lung single cell RNA-seq data sets.

RESULTS

Analysis of genes regulated by YAP/TAZ, TEADs, and TP63 in human bronchial epithelial cells revealed a converged transcriptional network that is strongly associated with the pathological progression of bronchial PMLs. Our observations suggest that YAP/TAZ-TEAD-TP63 associate to cooperatively promote basal epithelial cell proliferation and repress signals associated with interferon responses and immune cell communication. Directly repressed targets we identified include the MHC Class II transactivator CIITA, which is repressed in progressive PMLs and associates with adaptive immune responses in the lung. Our findings provide molecular insight into the control of gene expression events driving PML progression, including those contributing to immune evasion, offering potential new avenues for lung cancer interception.

CONCLUSIONS

Our study identifies important gene regulatory functions for YAP/TAZ-TEAD-TP63 in the early stages of lung cancer development, which notably includes immune-suppressive roles, and suggest that an assessment of the activity of this transcriptional complex may offer a means to identify immune evasive bronchial PMLs and serve as a potential therapeutic target.

The NFIA-ETO2 fusion blocks erythroid maturation and induces pure erythroid leukemia in cooperation with mutant TP53

The NFIA-ETO2 fusion is the product of a t(1;16)(p31;q24) chromosomal translocation, so far, exclusively found in pediatric patients with pure erythroid leukemia (PEL). To address the role for the pathogenesis of the disease, we facilitated the expression of the NFIA-ETO2 fusion in murine erythroblasts (EBs). We observed that NFIA-ETO2 significantly increased proliferation and impaired erythroid differentiation of murine erythroleukemia cells and of primary fetal liver-derived EBs. However, NFIA-ETO2-expressing EBs acquired neither aberrant in vitro clonogenic activity nor disease-inducing potential upon transplantation into irradiated syngenic mice. In contrast, in the presence of 1 of the most prevalent erythroleukemia-associated mutations, TP53R248Q, expression of NFIA-ETO2 resulted in aberrant clonogenic activity and induced a fully penetrant transplantable PEL-like disease in mice. Molecular studies support that NFIA-ETO2 interferes with erythroid differentiation by preferentially binding and repressing erythroid genes that contain NFI binding sites and/or are decorated by ETO2, resulting in a activity shift from GATA- to ETS-motif-containing target genes. In contrast, TP53R248Q does not affect erythroid differentiation but provides self-renewal and survival potential, mostly via downregulation of known TP53 targets. Collectively, our work indicates that NFIA-ETO2 initiates PEL by suppressing gene expression programs of terminal erythroid differentiation and cooperates with TP53 mutation to induce erythroleukemia.

Bidirectional regulation of postmitotic H3K27me3 distributions underlie cerebellar granule neuron maturation dynamics

The functional maturation of neurons is a prolonged process that extends past the mitotic exit and is mediated by the chromatin-dependent orchestration of gene transcription programs. We find that expression of this maturation gene program in mouse cerebellar granule neurons (CGNs) requires dynamic changes in the genomic distribution of histone H3 lysine 27 trimethylation (H3K27me3), demonstrating a function for this chromatin modification beyond its role in cell fate specification. The developmental loss of H3K27me3 at promoters of genes activated as CGNs mature is facilitated by the lysine demethylase and ASD-risk gene, Kdm6b. Interestingly, inhibition of the H3K27 methyltransferase EZH2 in newborn CGNs not only blocks the repression of progenitor genes but also impairs the induction of mature CGN genes, showing the importance of bidirectional H3K27me3 regulation across the genome. These data demonstrate that H3K27me3 turnover in developing postmitotic neurons regulates the temporal coordination of gene expression programs that underlie functional neuronal maturation.

Chromatin-bound protein colocalization analysis using bedGraph2Cluster and PanChIP

Computational pipelines for chromatin immunoprecipitation sequencing analysis can neglect colocalization events that occur in a mere subset of the genome. Here, we detail a streamlined approach for assessing colocalization of chromatin-bound proteins using the bedGraph2Cluster and PanChIP algorithms. Using histone modifications as an example, bedGraph2Cluster performs clustering analysis on chromatin binding patterns of target proteins. PanChIP then compares these clusters with a reference library of chromatin binding patterns and measures the overlap in peaks, capturing the heterogeneity in chromatin binding and colocalization patterns. For complete details on the use and execution of this protocol, please refer to Sanidas et al. (2022).1.

Transcription factor-based biosensors for screening and dynamic regulation

Advances in synthetic biology and genetic engineering are bringing into the spotlight a wide range of bio-based applications that demand better sensing and control of biological behaviours. Transcription factor (TF)-based biosensors are promising tools that can be used to detect several types of chemical compounds and elicit a response according to the desired application. However, the wider use of this type of device is still hindered by several challenges, which can be addressed by increasing the current metabolite-activated transcription factor knowledge base, developing better methods to identify new transcription factors, and improving the overall workflow for the design of novel biosensor circuits. These improvements are particularly important in the bioproduction field, where researchers need better biosensor-based approaches for screening production-strains and precise dynamic regulation strategies. In this work, we summarize what is currently known about transcription factor-based biosensors, discuss recent experimental and computational approaches targeted at their modification and improvement, and suggest possible future research directions based on two applications: bioproduction screening and dynamic regulation of genetic circuits.

Decoding transcriptional regulation via a human gene expression predictor

Transcription factors (TFs) regulate cellular activities by controlling gene expression, but a predictive model describing how TFs quantitatively modulate human transcriptomes is lacking. We construct a universal human gene expression predictor and utilize it to decode transcriptional regulation. Using the expression of 1613 TFs, the predictor reconstitutes highly accurate transcriptomes for samples derived from a wide range of tissues and conditions. The broad applicability of the predictor indicates that it recapitulates the quantitative relationships between TFs and target genes ubiquitous across tissues. Significant interacting TF-target gene pairs are extracted from the predictor and enable downstream inference of TF regulators for diverse pathways involved in development, immunity, metabolism, and stress response. A detailed analysis of the hematopoiesis process reveals an atlas of key TFs regulating the development of different hematopoietic cell lineages, and a portion of these TFs are conserved between humans and mice. The results demonstrate that our method is capable of delineating the TFs responsible for fate determination. Compared to other existing tools, our approach shows better performance in recovering the correct TF regulators. Thus, we present a novel approach that can be used to study human transcriptional regulation in general.

Inactivation of LATS1/2 drives luminal-basal plasticity to initiate basal-like mammary carcinomas

Basal-like breast cancers, an aggressive breast cancer subtype that has poor treatment options, are thought to arise from luminal mammary epithelial cells that undergo basal plasticity through poorly understood mechanisms. Using genetic mouse models and ex vivo primary organoid cultures, we show that conditional co-deletion of the LATS1 and LATS2 kinases, key effectors of Hippo pathway signaling, in mature mammary luminal epithelial cells promotes the development of Krt14 and Sox9-expressing basal-like carcinomas that metastasize over time. Genetic co-deletion experiments revealed that phenotypes resulting from the loss of LATS1/2 activity are dependent on the transcriptional regulators YAP/TAZ. Gene expression analyses of LATS1/2-deleted mammary epithelial cells notably revealed a transcriptional program that associates with human basal-like breast cancers. Our study demonstrates in vivo roles for the LATS1/2 kinases in mammary epithelial homeostasis and luminal-basal fate control and implicates signaling networks induced upon the loss of LATS1/2 activity in the development of basal-like breast cancer.

Identification of transcription factors dictating blood cell development using a bidirectional transcription network-based computational framework

Advanced computational methods exploit gene expression and epigenetic datasets to predict gene regulatory networks controlled by transcription factors (TFs). These methods have identified cell fate determining TFs but require large amounts of reference data and experimental expertise. Here, we present an easy to use network-based computational framework that exploits enhancers defined by bidirectional transcription, using as sole input CAGE sequencing data to correctly predict TFs key to various human cell types. Next, we applied this Analysis Algorithm for Networks Specified by Enhancers based on CAGE (ANANSE-CAGE) to predict TFs driving red and white blood cell development, and THP-1 leukemia cell immortalization. Further, we predicted TFs that are differentially important to either cell line- or primary- associated MLL-AF9-driven gene programs, and in primary MLL-AF9 acute leukemia. Our approach identified experimentally validated as well as thus far unexplored TFs in these processes. ANANSE-CAGE will be useful to identify transcription factors that are key to any cell fate change using only CAGE-seq data as input.

Chromatin-bound RB targets promoters, enhancers, and CTCF-bound loci and is redistributed by cell-cycle progression

The interaction of RB with chromatin is key to understanding its molecular functions. Here, for first time, we identify the full spectrum of chromatin-bound RB. Rather than exclusively binding promoters, as is often described, RB targets three fundamentally different types of loci (promoters, enhancers, and insulators), which are largely distinguishable by the mutually exclusive presence of E2F1, c-Jun, and CTCF. While E2F/DP facilitates RB association with promoters, AP-1 recruits RB to enhancers. Although phosphorylation in CDK sites is often portrayed as releasing RB from chromatin, we show that the cell cycle redistributes RB so that it enriches at promoters in G1 and at non-promoter sites in cycling cells. RB-bound promoters include the classic E2F-targets and are similar between lineages, but RB-bound enhancers associate with different categories of genes and vary between cell types. Thus, RB has a well-preserved role controlling E2F in G1, and it targets cell-type-specific enhancers and CTCF sites when cells enter S-phase.

WhichTF is functionally important in your open chromatin data?

We present WhichTF, a computational method to identify functionally important transcription factors (TFs) from chromatin accessibility measurements. To rank TFs, WhichTF applies an ontology-guided functional approach to compute novel enrichment by integrating accessibility measurements, high-confidence pre-computed conservation-aware TF binding sites, and putative gene-regulatory models. Comparison with prior sheer abundance-based methods reveals the unique ability of WhichTF to identify context-specific TFs with functional relevance, including NF-κB family members in lymphocytes and GATA factors in cardiac cells. To distinguish the transcriptional regulatory landscape in closely related samples, we apply differential analysis and demonstrate its utility in lymphocyte, mesoderm developmental, and disease cells. We find suggestive, under-characterized TFs, such as RUNX3 in mesoderm development and GLI1 in systemic lupus erythematosus. We also find TFs known for stress response, suggesting routine experimental caveats that warrant careful consideration. WhichTF yields biological insight into known and novel molecular mechanisms of TF-mediated transcriptional regulation in diverse contexts, including human and mouse cell types, cell fate trajectories, and disease-associated cells.

Transcription factors (TFs), a class of DNA binding proteins, regulate tissue- and cell-type-specific expression of genes. Identifying the critical TFs in a given cellular context leads to investigating molecular regulatory mechanisms in development, differentiation, and disease. Because there are more than 1,500 human TFs, experimental measurements of genome-wide occupancy across all TFs have been challenging. While computational approaches play pivotal roles, most existing methods rely on statistical enrichment, focusing either on sequence motif similarity recognized by TFs or the similarity of the genomic region of interest with the previously characterized TF occupancy profile. Here we propose WhichTF as an alternative, incorporating curated biomedical knowledge from ontology and integrating it with the high-confidence prediction of conserved TF binding sites in user-provided genomic regions of interest. We develop a new WhichTF score to rank TFs and demonstrate its applicability across human and mouse cell types, cellular differentiation trajectories, and disease-associated cells.

Single-Cell Analysis Reveals the Range of Transcriptional States of Circulating Human Neutrophils

Neutrophils are the most abundant leukocytes in human blood and are essential components of innate immunity. Until recently, neutrophils were considered homogeneous and transcriptionally inactive cells, but both concepts are being challenged. Single-cell RNA sequencing (scRNA-seq) offers an unbiased view of cells along a continuum of transcriptional states. However, the use of scRNA-seq to characterize neutrophils has proven technically difficult, explaining in part the paucity of published single-cell data on neutrophils. We have found that modifications to the data analysis pipeline, rather than to the existing scRNA-seq chemistries, can significantly increase the detection of human neutrophils in scRNA-seq. We have then applied a modified pipeline to the study of human peripheral blood neutrophils. Our findings indicate that circulating human neutrophils are transcriptionally heterogeneous cells, which can be classified into one of four transcriptional clusters that are reproducible among healthy human subjects. We demonstrate that peripheral blood neutrophils shift from relatively immature (Nh0) cells, through a transitional phenotype (Nh1), into one of two end points defined by either relative transcriptional inactivity (Nh2) or high expression of type I IFN-inducible genes (Nh3). Transitions among states are characterized by the expression of specific transcription factors. By simultaneously measuring surface proteins and intracellular transcripts at the single-cell level, we show that these transcriptional subsets are independent of the canonical surface proteins that are commonly used to define and characterize human neutrophils. These findings provide a new view of human neutrophil heterogeneity, with potential implications for the characterization of neutrophils in health and disease.

Myeloid cell-specific topoisomerase 1 inhibition using DNA origami mitigates neuroinflammation

Targeting myeloid cells, especially microglia, for the treatment of neuroinflammatory diseases such as multiple sclerosis (MS), is underappreciated. Our in silico drug screening reveals topoisomerase 1 (TOP1) inhibitors as promising drug candidates for microglial modulation. We show that TOP1 is highly expressed in neuroinflammatory conditions, and TOP1 inhibition using camptothecin (CPT) and its FDA-approved analog topotecan (TPT) reduces inflammatory responses in microglia/macrophages and ameliorates neuroinflammation in vivo. Transcriptomic analyses of sorted microglia from LPS-challenged mice reveal an altered transcriptional phenotype following TPT treatment. To target myeloid cells, we design a nanosystem using β-glucan-coated DNA origami (MyloGami) loaded with TPT (TopoGami). MyloGami shows enhanced specificity to myeloid cells while preventing the degradation of the DNA origami scaffold. Myeloid-specific TOP1 inhibition using TopoGami significantly suppresses the inflammatory response in microglia and mitigates MS-like disease progression. Our findings suggest that TOP1 inhibition in myeloid cells represents a therapeutic strategy for neuroinflammatory diseases and that the myeloid-specific nanosystems we designed may also benefit the treatment of other diseases with dysfunctional myeloid cells.

Dnmt3a knockout in excitatory neurons impairs postnatal synapse maturation and increases the repressive histone modification H3K27me3

Two epigenetic pathways of transcriptional repression, DNA methylation and polycomb repressive complex 2 (PRC2), are known to regulate neuronal development and function. However, their respective contributions to brain maturation are unknown. We found that conditional loss of the de novo DNA methyltransferase Dnmt3a in mouse excitatory neurons altered expression of synapse-related genes, stunted synapse maturation, and impaired working memory and social interest. At the genomic level, loss of Dnmt3a abolished postnatal accumulation of CG and non-CG DNA methylation, leaving adult neurons with an unmethylated, fetal-like epigenomic pattern at ~222,000 genomic regions. The PRC2-associated histone modification, H3K27me3, increased at many of these sites. Our data support a dynamic interaction between two fundamental modes of epigenetic repression during postnatal maturation of excitatory neurons, which together confer robustness on neuronal regulation.

Relative contributions of sex hormones, sex chromosomes, and gonads to sex differences in tissue gene regulation

Sex differences in physiology and disease in mammals result from the effects of three classes of factors that are inherently unequal in males and females: reversible (activational) effects of gonadal hormones, permanent (organizational) effects of gonadal hormones, and cell-autonomous effects of sex chromosomes, as well as genes driven by these classes of factors. Often, these factors act together to cause sex differences in specific phenotypes, but the relative contribution of each and the interactions among them remain unclear. Here, we used the four core genotypes (FCG) mouse model with or without hormone replacement to distinguish the effects of each class of sex-biasing factors on transcriptome regulation in liver and adipose tissues. We found that the activational hormone levels have the strongest influence on gene expression, followed by the organizational gonadal sex effect, and last, sex chromosomal effect, along with interactions among the three factors. Tissue specificity was prominent, with a major impact of estradiol on adipose tissue gene regulation and of testosterone on the liver transcriptome. The networks affected by the three sex-biasing factors include development, immunity and metabolism, and tissue-specific regulators were identified for these networks. Furthermore, the genes affected by individual sex-biasing factors and interactions among factors are associated with human disease traits such as coronary artery disease, diabetes, and inflammatory bowel disease. Our study offers a tissue-specific account of the individual and interactive contributions of major sex-biasing factors to gene regulation that have broad impact on systemic metabolic, endocrine, and immune functions.

FindIT2: an R/Bioconductor package to identify influential transcription factor and targets based on multi-omics data

BACKGROUND

Transcription factors (TFs) play central roles in regulating gene expression. With the rapid growth in the use of high-throughput sequencing methods, there is a need to develop a comprehensive data processing and analyzing framework for inferring influential TFs based on ChIP-seq/ATAC-seq datasets.

RESULTS

Here, we introduce FindIT2 (Find Influential TFs and Targets), an R/Bioconductor package for annotating and processing high-throughput multi-omics data. FindIT2 supports a complete framework for annotating ChIP-seq/ATAC-seq peaks, identifying TF targets by the combination of ChIP-seq and RNA-seq datasets, and inferring influential TFs based on different types of data input. Moreover, benefited from the annotation framework based on Bioconductor, FindIT2 can be applied to any species with genomic annotations, which is particularly useful for the non-model species that are less well-studied.

CONCLUSION

FindIT2 provides a user-friendly and flexible framework to generate results at different levels according to the richness of the annotation information of user's species. FindIT2 is compatible with all the operating systems and is released under Artistic-2.0 License. The source code and documents are freely available through Bioconductor ( https://bioconductor.org/packages/devel/bioc/html/FindIT2.html ).

Metabolism drives macrophage heterogeneity in the tumor microenvironment

Tumor-associated macrophages (TAMs) are a major cellular component in the tumor microenvironment (TME). However, the relationship between the phenotype and metabolic pattern of TAMs remains poorly understood. We performed single-cell transcriptome profiling on hepatic TAMs from mice bearing liver metastatic tumors. We find that TAMs manifest high heterogeneity at the levels of transcription, development, metabolism, and function. Integrative analyses and validation experiments indicate that increased purine metabolism is a feature of TAMs with pro-tumor and terminal differentiation phenotypes. Like mouse TAMs, human TAMs are highly heterogeneous. Human TAMs with increased purine metabolism exhibit a pro-tumor phenotype and correlate with poor therapeutic efficacy to immune checkpoint blockade. Altogether, our work demonstrates that TAMs are developmentally, metabolically, and functionally heterogeneous and purine metabolism may be a key metabolic feature of a pro-tumor macrophage population.

Distinct MUNC lncRNA structural domains regulate transcription of different promyogenic factors

Many lncRNAs have been discovered using transcriptomic data; however, it is unclear what fraction of lncRNAs is functional and what structural properties affect their phenotype. MUNC lncRNA (also known as ^DRReRNA) acts as an enhancer RNA for the Myod1 gene in cis and stimulates the expression of other promyogenic genes in trans by recruiting the cohesin complex. Here, experimental probing of the RNA structure revealed that MUNC contains multiple structural domains not detected by prediction algorithms in the absence of experimental information. We show that these specific and structurally distinct domains are required for induction of promyogenic genes, for binding genomic sites and gene expression regulation, and for binding the cohesin complex. Myod1 induction and cohesin interaction comprise only a subset of MUNC phenotype. Our study reveals unexpectedly complex, structure-driven functions for the MUNC lncRNA and emphasizes the importance of experimentally determined structures for understanding structure-function relationships in lncRNAs.

The transcriptional elongation factor CTR9 demarcates PRC2-mediated H3K27me3 domains by altering PRC2 subtype equilibrium

CTR9 is the scaffold subunit in polymerase-associated factor complex (PAFc), a multifunctional complex employed in multiple steps of RNA Polymerase II (RNAPII)-mediated transcription. CTR9/PAFc is well known as an evolutionarily conserved elongation factor that regulates gene activation via coupling with histone modifications enzymes. However, little is known about its function to restrain repressive histone markers. Using inducible and stable CTR9 knockdown breast cancer cell lines, we discovered that the H3K27me3 levels are strictly controlled by CTR9. Quantitative profiling of histone modifications revealed a striking increase of H3K27me3 levels upon loss of CTR9. Moreover, loss of CTR9 leads to genome-wide expansion of H3K27me3, as well as increased recruitment of PRC2 on chromatin, which can be reversed by CTR9 restoration. Further, CTR9 depletion triggers a PRC2 subtype switch from the less active PRC2.2, to the more active PRC2.1 with higher methyltransferase activity. As a consequence, CTR9 depletion generates vulnerability that renders breast cancer cells hypersensitive to PRC2 inhibitors. Our findings that CTR9 demarcates PRC2-mediated H3K27me3 levels and genomic distribution provide a unique mechanism that explains the transition from transcriptionally active chromatin states to repressive chromatin states and sheds light on the biological functions of CTR9 in development and cancer.

RegEnrich gene regulator enrichment analysis reveals a key role of the ETS transcription factor family in interferon signaling

Changes in a few key transcriptional regulators can lead to different biological states. Extracting the key gene regulators governing a biological state allows us to gain mechanistic insights. Most current tools perform pathway/GO enrichment analysis to identify key genes and regulators but tend to overlook the gene/protein regulatory interactions. Here we present RegEnrich, an open-source Bioconductor R package, which combines differential expression analysis, data-driven gene regulatory network inference, enrichment analysis, and gene regulator ranking to identify key regulators using gene/protein expression profiling data. By benchmarking using multiple gene expression datasets of gene silencing studies, we found that RegEnrich using the GSEA method to rank the regulators performed the best. Further, RegEnrich was applied to 21 publicly available datasets on in vitro interferon-stimulation of different cell types. Collectively, RegEnrich can accurately identify key gene regulators from the cells under different biological states, which can be valuable in mechanistically studying cell differentiation, cell response to drug stimulation, disease development, and ultimately drug development.

Detection of transcription factors binding to methylated DNA by deep recurrent neural network

Transcription factors (TFs) are proteins specifically involved in gene expression regulation. It is generally accepted in epigenetics that methylated nucleotides could prevent the TFs from binding to DNA fragments. However, recent studies have confirmed that some TFs have capability to interact with methylated DNA fragments to further regulate gene expression. Although biochemical experiments could recognize TFs binding to methylated DNA sequences, these wet experimental methods are time-consuming and expensive. Machine learning methods provide a good choice for quickly identifying these TFs without experimental materials. Thus, this study aims to design a robust predictor to detect methylated DNA-bound TFs. We firstly proposed using tripeptide word vector feature to formulate protein samples. Subsequently, based on recurrent neural network with long short-term memory, a two-step computational model was designed. The first step predictor was utilized to discriminate transcription factors from non-transcription factors. Once proteins were predicted as TFs, the second step predictor was employed to judge whether the TFs can bind to methylated DNA. Through the independent dataset test, the accuracies of the first step and the second step are 86.63% and 73.59%, respectively. In addition, the statistical analysis of the distribution of tripeptides in training samples showed that the position and number of some tripeptides in the sequence could affect the binding of TFs to methylated DNA. Finally, on the basis of our model, a free web server was established based on the proposed model, which can be available at https://bioinfor.nefu.edu.cn/TFPM/.

Assessing acute myeloid leukemia susceptibility in rearrangement-driven patients by DNA breakage at topoisomerase II and CCCTC-binding factor/cohesin binding sites

An initiating DNA double strand break (DSB) event precedes the formation of cancer-driven chromosomal abnormalities, such as gene rearrangements. Therefore, measuring DNA breaks at rearrangement-participating regions can provide a unique tool to identify and characterize susceptible individuals. Here, we developed a highly sensitive and low-input DNA break mapping method, the first of its kind for patient samples. We then measured genome-wide DNA breakage in normal cells of acute myeloid leukemia (AML) patients with KMT2A (previously MLL) rearrangements, compared to that of nonfusion AML individuals, as a means to evaluate individual susceptibility to gene rearrangements. DNA breakage at the KMT2A gene region was significantly greater in fusion-driven remission individuals, as compared to nonfusion individuals. Moreover, we identified select topoisomerase II (TOP2)-sensitive and CCCTC-binding factor (CTCF)/cohesin-binding sites with preferential DNA breakage in fusion-driven patients. Importantly, measuring DSBs at these sites, in addition to the KMT2A gene region, provided greater predictive power when assessing individual break susceptibility. We also demonstrated that low-dose etoposide exposure further elevated DNA breakage at these regions in fusion-driven AML patients, but not in nonfusion patients, indicating that these sites are preferentially sensitive to TOP2 activity in fusion-driven AML patients. These results support that mapping of DSBs in patients enables discovery of novel break-prone regions and monitoring of individuals susceptible to chromosomal abnormalities, and thus cancer. This will build the foundation for early detection of cancer-susceptible individuals, as well as those preferentially susceptible to therapy-related malignancies caused by treatment with TOP2 poisons.

ATRX regulates glial identity and the tumor microenvironment in IDH-mutant glioma

BACKGROUND

Recent single-cell transcriptomic studies report that IDH-mutant gliomas share a common hierarchy of cellular phenotypes, independent of genetic subtype. However, the genetic differences between IDH-mutant glioma subtypes are prognostic, predictive of response to chemotherapy, and correlate with distinct tumor microenvironments.

RESULTS

To reconcile these findings, we profile 22 human IDH-mutant gliomas using scATAC-seq and scRNA-seq. We determine the cell-type-specific differences in transcription factor expression and associated regulatory grammars between IDH-mutant glioma subtypes. We find that while IDH-mutant gliomas do share a common distribution of cell types, there are significant differences in the expression and targeting of transcription factors that regulate glial identity and cytokine elaboration. We knock out the chromatin remodeler ATRX, which suffers loss-of-function alterations in most IDH-mutant astrocytomas, in an IDH-mutant immunocompetent intracranial murine model. We find that both human ATRX-mutant gliomas and murine ATRX-knockout gliomas are more heavily infiltrated by immunosuppressive monocytic-lineage cells derived from circulation than ATRX-intact gliomas, in an IDH-mutant background. ATRX knockout in murine glioma recapitulates gene expression and open chromatin signatures that are specific to human ATRX-mutant astrocytomas, including drivers of astrocytic lineage and immune-cell chemotaxis. Through single-cell cleavage under targets and tagmentation assays and meta-analysis of public data, we show that ATRX loss leads to a global depletion in CCCTC-binding factor association with DNA, gene dysregulation along associated chromatin loops, and protection from therapy-induced senescence.

CONCLUSIONS

These studies explain how IDH-mutant gliomas from different subtypes maintain distinct phenotypes and tumor microenvironments despite a common lineage hierarchy.

Integrating single-cell genomics pipelines to discover mechanisms of stem cell differentiation

Pluripotent stem cells underpin a growing sector that leverages their differentiation potential for research, industry, and clinical applications. This review evaluates the landscape of methods in single-cell transcriptomics that are enabling accelerated discovery in stem cell science. We focus on strategies for scaling stem cell differentiation through multiplexed single-cell analyses, for evaluating molecular regulation of cell differentiation using new analysis algorithms, and methods for integration and projection analysis to classify and benchmark stem cell derivatives against in vivo cell types. By discussing the available methods, comparing their strengths, and illustrating strategies for developing integrated analysis pipelines, we provide user considerations to inform their implementation and interpretation.

Mapping the evolving landscape of super-enhancers during cell differentiation

BACKGROUND

Super-enhancers are clusters of enhancer elements that play critical roles in the maintenance of cell identity. Current investigations on super-enhancers are centered on the established ones in static cell types. How super-enhancers are established during cell differentiation remains obscure.

RESULTS

Here, by developing an unbiased approach to systematically analyze the evolving landscape of super-enhancers during cell differentiation in multiple lineages, we discover a general trend where super-enhancers emerge through three distinct temporal patterns: conserved, temporally hierarchical, and de novo. The three types of super-enhancers differ further in association patterns in target gene expression, functional enrichment, and 3D chromatin organization, suggesting they may represent distinct structural and functional subtypes. Furthermore, we dissect the enhancer repertoire within temporally hierarchical super-enhancers, and find enhancers that emerge at early and late stages are enriched with distinct transcription factors, suggesting that the temporal order of establishment of elements within super-enhancers may be directed by underlying DNA sequence. CRISPR-mediated deletion of individual enhancers in differentiated cells shows that both the early- and late-emerged enhancers are indispensable for target gene expression, while in undifferentiated cells early enhancers are involved in the regulation of target genes.

CONCLUSIONS

In summary, our analysis highlights the heterogeneity of the super-enhancer population and provides new insights to enhancer functions within super-enhancers.

Protein Ligands in the Secretome of CD36+ Fibroblasts Induce Growth Suppression in a Subset of Breast Cancer Cell Lines

Simple Summary

Human breast cancers are not fully autonomous. They are dependent on nutrients and growth-promoting signals provided by stromal cells. In order to instruct the surrounding cells to provide essential growth factors, cancer cells co-opt normal signaling molecules and mechanisms. To inhibit or potentially reverse tumor growth, our goal is to emulate this signaling and reprogram the microenvironment. For example, in a healthy mammary gland, fibroblasts (FBs) overexpress CD36; and the downregulation of CD36 is one of the hallmarks of cancer-associated FBs. Therefore, in this project, we hypothesized that signaling from CD36⁺ FBs could cause growth suppression in a subset of breast cancer cell lines. We then designed a series of experiments to validate this growth suppression and identified responsible secreted factors by the CD36⁺ FBs. These experiments suggested that three protein ligands are primarily responsible for growth suppression in a subset of breast cancer cell lines.

Abstract

Reprogramming the tumor stroma is an emerging approach to circumventing the challenges of conventional cancer therapies. This strategy, however, is hampered by the lack of a specific molecular target. We previously reported that stromal fibroblasts (FBs) with high expression of CD36 could be utilized for this purpose. These studies are now expanded to identify the secreted factors responsible for tumor suppression. Methodologies included 3D colonies, fluorescent microscopy coupled with quantitative techniques, proteomics profiling, and bioinformatics analysis. The results indicated that the conditioned medium (CM) of the CD36⁺ FBs caused growth suppression via apoptosis in the triple-negative cell lines of MDA-MB-231, BT549, and Hs578T, but not in the ERBB2⁺ SKBR3. Following the proteomics and bioinformatic analysis of the CM of CD36⁺ versus CD36⁻ FBs, we determined KLF10 as one of the transcription factors responsible for growth suppression. We also identified FBLN1, SLIT3, and PENK as active ligands, where their minimum effective concentrations were determined. Finally, in MDA-MB-231, we showed that a mixture of FBLN1, SLIT3, and PENK could induce an amount of growth suppression similar to the CM of CD36⁺ FBs. In conclusion, our findings suggest that these ligands, secreted by CD36⁺ FBs, can be targeted for breast cancer treatment.

ANANSE: an enhancer network-based computational approach for predicting key transcription factors in cell fate determination

Proper cell fate determination is largely orchestrated by complex gene regulatory networks centered around transcription factors. However, experimental elucidation of key transcription factors that drive cellular identity is currently often intractable. Here, we present ANANSE (ANalysis Algorithm for Networks Specified by Enhancers), a network-based method that exploits enhancer-encoded regulatory information to identify the key transcription factors in cell fate determination. As cell type-specific transcription factors predominantly bind to enhancers, we use regulatory networks based on enhancer properties to prioritize transcription factors. First, we predict genome-wide binding profiles of transcription factors in various cell types using enhancer activity and transcription factor binding motifs. Subsequently, applying these inferred binding profiles, we construct cell type-specific gene regulatory networks, and then predict key transcription factors controlling cell fate transitions using differential networks between cell types. This method outperforms existing approaches in correctly predicting major transcription factors previously identified to be sufficient for trans-differentiation. Finally, we apply ANANSE to define an atlas of key transcription factors in 18 normal human tissues. In conclusion, we present a ready-to-implement computational tool for efficient prediction of transcription factors in cell fate determination and to study transcription factor-mediated regulatory mechanisms. ANANSE is freely available at https://github.com/vanheeringen-lab/ANANSE.

AnnoMiner is a new web-tool to integrate epigenetics, transcription factor occupancy and transcriptomics data to predict transcriptional regulators

Gene expression regulation requires precise transcriptional programs, led by transcription factors in combination with epigenetic events. Recent advances in epigenomic and transcriptomic techniques provided insight into different gene regulation mechanisms. However, to date it remains challenging to understand how combinations of transcription factors together with epigenetic events control cell-type specific gene expression. We have developed the AnnoMiner web-server, an innovative and flexible tool to annotate and integrate epigenetic, and transcription factor occupancy data. First, AnnoMiner annotates user-provided peaks with gene features. Second, AnnoMiner can integrate genome binding data from two different transcriptional regulators together with gene features. Third, AnnoMiner offers to explore the transcriptional deregulation of genes nearby, or within a specified genomic region surrounding a user-provided peak. AnnoMiner’s fourth function performs transcription factor or histone modification enrichment analysis for user-provided gene lists by utilizing hundreds of public, high-quality datasets from ENCODE for the model organisms human, mouse, Drosophila and C. elegans. Thus, AnnoMiner can predict transcriptional regulators for a studied process without the strict need for chromatin data from the same process. We compared AnnoMiner to existing tools and experimentally validated several transcriptional regulators predicted by AnnoMiner to indeed contribute to muscle morphogenesis in Drosophila. AnnoMiner is freely available at http://chimborazo.ibdm.univ-mrs.fr/AnnoMiner/.

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.