A genetic disorder reveals a hematopoietic stem cell regulatory network co-opted in leukemia.

The molecular regulation of human hematopoietic stem cell (HSC) maintenance is therapeutically important, but limitations in experimental systems and interspecies variation have constrained our knowledge of this process. Here, we have studied a rare genetic disorder due to MECOM haploinsufficiency, characterized by an early-onset absence of HSCs in vivo. By generating a faithful model of this disorder in primary human HSCs and coupling functional studies with integrative single-cell genomic analyses, we uncover a key transcriptional network involving hundreds of genes that is required for HSC maintenance. Through our analyses, we nominate cooperating transcriptional regulators and identify how MECOM prevents the CTCF-dependent genome reorganization that occurs as HSCs differentiate. We show that this transcriptional network is co-opted in high-risk leukemias, thereby enabling these cancers to acquire stem cell properties. Collectively, we illuminate a regulatory network necessary for HSC self-renewal through the study of a rare experiment of nature.

Deciphering cell states and genealogies of human haematopoiesis.

The human blood system is maintained through the differentiation and massive amplification of a limited number of long-lived haematopoietic stem cells (HSCs)1. Perturbations to this process underlie diverse diseases, but the clonal contributions to human haematopoiesis and how this changes with age remain incompletely understood. Although recent insights have emerged from barcoding studies in model systems2-5, simultaneous detection of cell states and phylogenies from natural barcodes in humans remains challenging. Here we introduce an improved, single-cell lineage-tracing system based on deep detection of naturally occurring mitochondrial DNA mutations with simultaneous readout of transcriptional states and chromatin accessibility. We use this system to define the clonal architecture of HSCs and map the physiological state and output of clones. We uncover functional heterogeneity in HSC clones, which is stable over months and manifests as both differences in total HSC output and biases towards the production of different mature cell types. We also find that the diversity of HSC clones decreases markedly with age, leading to an oligoclonal structure with multiple distinct clonal expansions. Our study thus provides a clonally resolved and cell-state-aware atlas of human haematopoiesis at single-cell resolution, showing an unappreciated functional diversity of human HSC clones and, more broadly, paving the way for refined studies of clonal dynamics across a range of tissues in human health and disease.

Lysine demethylase 5C inhibits transcription of prefoldin subunit 5 to activate c-Myc signal transduction and colorectal cancer progression.

BACKGROUND: Lysine demethylase 5C (KDM5C) has been implicated in the development of several human cancers. This study aims to investigate the role of KDM5C in the progression of colorectal cancer (CRC) and explore the associated molecular mechanism. METHODS: Bioinformatics tools were employed to predict the target genes of KDM5C in CRC. The expression levels of KDM5C and prefoldin subunit 5 (PFDN5) in CRC cells were determined by RT-qPCR and western blot assays. The interaction between KDM5C, H3K4me3, and PFDN5 was validated by chromatin immunoprecipitation. Expression and prognostic values of KDM5C and PFDN5 in CRC were analyzed in a cohort of 72 patients. The function of KDM5C/PFDN5 in c-Myc signal transduction was analyzed by luciferase assay. Silencing of KDM5C and PFDN5 was induced in CRC cell lines to analyze the cell malignant phenotype in vitro and tumorigenic activity in nude mice. RESULTS: KDM5C exhibited high expression, while PFDN5 displayed low expression in CRC cells and clinical CRC samples. High KDM5C levels correlated with poor survival and unfavorable clinical presentation, whereas elevated PFDN5 correlated with improved patient outcomes. KDM5C mediated demethylation of H3K4me3 on the PFDN5 promoter, suppressing its transcription and thereby enhancing the transcriptional activity of c-Myc. KDM5C knockdown in CRC cells suppressed cell proliferation, migration and invasion, epithelial-mesenchymal transition, and tumorigenic activity while increasing autophagy and apoptosis rates. However, the malignant behavior of cells was restored by the further silencing of PFDN5. CONCLUSION: This study demonstrates that KDM5C inhibits PFDN5 transcription, thereby activating c-Myc signal transduction and promoting CRC progression.

Human embryonic stem cell-derived organoid retinoblastoma reveals a cancerous origin.

Retinoblastoma (Rb) is the most prevalent intraocular malignancy in children, with a worldwide survival rate <30%. We have developed a cancerous model of Rb in retinal organoids derived from genetically engineered human embryonic stem cells (hESCs) with a biallelic mutagenesis of the RB1 gene. These organoid Rbs exhibit properties highly consistent with Rb tumorigenesis, transcriptome, and genome-wide methylation. Single-cell sequencing analysis suggests that Rb originated from ARR3-positive maturing cone precursors during development, which was further validated by immunostaining. Notably, we found that the PI3K-Akt pathway was aberrantly deregulated and its activator spleen tyrosine kinase (SYK) was significantly up-regulated. In addition, SYK inhibitors led to remarkable cell apoptosis in cancerous organoids. In conclusion, we have established an organoid Rb model derived from genetically engineered hESCs in a dish that has enabled us to trace the cell of origin and to test novel candidate therapeutic agents for human Rb, shedding light on the development and therapeutics of other malignancies.

CUT&RUNTools 2.0: a pipeline for single-cell and bulk-level CUT&RUN and CUT&Tag data analysis.

MOTIVATION: Genome-wide profiling of transcription factor binding and chromatin states is a widely-used approach for mechanistic understanding of gene regulation. Recent technology development has enabled such profiling at single-cell resolution. However, an end-to-end computational pipeline for analyzing such data is still lacking. RESULTS: Here, we have developed a flexible pipeline for analysis and visualization of single-cell CUT&Tag and CUT&RUN data, which provides functions for sequence alignment, quality control, dimensionality reduction, cell clustering, data aggregation and visualization. Furthermore, it is also seamlessly integrated with the functions in original CUT&RUNTools for population-level analyses. As such, this provides a valuable toolbox for the community. AVAILABILITY AND IMPLEMENTATION: https://github.com/fl-yu/CUT-RUNTools-2.0. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

CFEA: a cell-free epigenome atlas in human diseases.

Epigenetic alterations, including 5-methylcytosine (5mC), 5-hydroxymethylcytosine (5hmC) and nucleosome positioning (NP), in cell-free DNA (cfDNA) have been widely observed in human diseases, and many available cfDNA-based epigenome-wide profiles exhibit high sensitivity and specificity in disease detection and classification. However, due to the lack of efficient collection, standardized quality control, and analysis procedures, efficiently integrating and reusing these data remain considerable challenges. Here, we introduce CFEA (http://www.bio-data.cn/CFEA), a cell-free epigenome database dedicated to three types of widely adopted epigenetic modifications (5mC, 5hmC and NP) involved in 27 human diseases. We developed bioinformatic pipelines for quality control and standard data processing and an easy-to-use web interface to facilitate the query, visualization and download of these cell-free epigenome data. We also manually curated related biological and clinical information for each profile, allowing users to better browse and compare cfDNA epigenomes at a specific stage (such as early- or metastasis-stage) of cancer development. CFEA provides a comprehensive and timely resource to the scientific community and supports the development of liquid biopsy-based biomarkers for various human diseases.

Breast cancer prognosis signature: linking risk stratification to disease subtypes.

Breast cancer is a very complex and heterogeneous disease with variable molecular mechanisms of carcinogenesis and clinical behaviors. The identification of prognostic risk factors may enable effective diagnosis and treatment of breast cancer. In particular, numerous gene-expression-based prognostic signatures were developed and some of them have already been applied into clinical trials and practice. In this study, we summarized several representative gene-expression-based signatures with significant prognostic value and separately assessed their ability of prognosis prediction in their originally targeted populations of breast cancer. Notably, many of the collected signatures were originally designed to predict the outcomes of estrogen receptor positive (ER+) patients or the whole breast cancer cohort; there are no typical signatures used for the prognostic prediction in a specific population of patients with the intrinsic subtype. We thus attempted to identify subtype-specific prognostic signatures via a computational framework for analyzing multi-omics profiles and patient survival. For both the discovery and an independent data set, we confirmed that subtype-specific signature is a strong and significant independent prognostic factor in the corresponding cohort. These results indicate that the subtype-specific prognostic signature has a much higher resolution in the risk stratification, which may lead to improved therapies and precision medicine for patients with breast cancer.

scTPA: a web tool for single-cell transcriptome analysis of pathway activation signatures.

MOTIVATION: At present, a fundamental challenge in single-cell RNA-sequencing data analysis is functional interpretation and annotation of cell clusters. Biological pathways in distinct cell types have different activation patterns, which facilitates the understanding of cell functions using single-cell transcriptomics. However, no effective web tool has been implemented for single-cell transcriptome data analysis based on prior biological pathway knowledge. RESULTS: Here, we present scTPA, a web-based platform for pathway-based analysis of single-cell RNA-seq data in human and mouse. scTPA incorporates four widely-used gene set enrichment methods to estimate the pathway activation scores of single cells based on a collection of available biological pathways with different functional and taxonomic classifications. The clustering analysis and cell-type-specific activation pathway identification were provided for the functional interpretation of cell types from a pathway-oriented perspective. An intuitive interface allows users to conveniently visualize and download single-cell pathway signatures. Overall, scTPA is a comprehensive tool for the identification of pathway activation signatures for the analysis of single cell heterogeneity. AVAILABILITY AND IMPLEMENTATION: http://sctpa.bio-data.cn/sctpa. CONTACT: sujz@wmu.edu.cn or yufulong421@gmail.com or zgj@zjut.edu.cn. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Application of low dose pancreas perfusion CT combined with enhancement scanning in diagnosis of pancreatic neuroendocrine tumors.

PURPOSE: To explore the diagnostic value of pancreatic perfusion CT combined with contrast-enhanced CT in one-time scanning (PCECT) in pancreatic neuroendocrine tumors (PNETs) and to evaluate the difference of perfusion parameters between different grades of PNETs. MATERIALS AND METHODS: From October 2016 to December 2018, forty consecutive patients with histopathological-proven PNETs were identified retrospectively that received PCECT for the preoperative PNETs evaluation. Two board certified radiologists who were blinded to the clinical data evaluated the images independently. The image characters of PNETs vs. tumor-free pancreatic parenchymal and different grades of PNETs were analyzed. RESULTS: One-time PCECT scanning had a detection rate of 89.1% for PNETs, which was higher than the detection accuracy of the perfusion CT only (83.6%). The perfusion parameters of PNETs including blood volume (BV), blood flow (BF), mean slope of increase (MSI), and capillary surface permeability (PS) were significantly increased than those of tumor-free pancreatic parenchyma (p < 0.05, respectively). For differential comparison between grade I (G1) and grade II (G2) tumors, the parameters of BF and impulse residue function (IRF) of tumor tissue were significantly higher in the G2 tumors (p < 0.05, for both). In this study, the total radiation dose of the whole PCECT scan was 16.241 ± 2.289 mSv. CONCLUSION: The one-time PCECT scan may improve the detection of PNETs according to morphological features and perfusion parameters with a relative small radiation dose. The perfusion parameters of BF and IRF may be used to help distinguish G1 and G2 tumors in the preoperative evaluation.

SEECancer: a resource for somatic events in evolution of cancer genome.

Cancer cells progressively evolve from a premalignant to a malignant state, which is driven by accumulating somatic alterations that confer normal cells a fitness advantage. Improvements in high-throughput sequencing techniques have led to an increase in construction of tumor phylogenetics and identification of somatic driver events that specifically occurred in different tumor progression stages. Here, we developed the SEECancer database (http://biocc.hrbmu.edu.cn/SEECancer), which aims to present the comprehensive cancer evolutionary stage-specific somatic events (including early-specific, late-specific, relapse-specific, metastasis-specific, drug-resistant and drug-induced genomic events) and their temporal orders. By manually curating over 10 000 published articles, 1231 evolutionary stage-specific genomic events and 5772 temporal orders involving 82 human cancers and 23 tissue origins were collected and deposited in the SEECancer database. Each entry contains the somatic event, evolutionary stage, cancer type, detection approach and relevant evidence. SEECancer provides a user-friendly interface for browsing, searching and downloading evolutionary stage-specific somatic events and temporal relationships in various cancers. With increasing attention on cancer genome evolution, the necessary information in SEECancer will facilitate understanding of cancer etiology and development of evolutionary therapeutics, and help clinicians to discover biomarkers for monitoring tumor progression.

A comprehensive overview of lncRNA annotation resources.

Long noncoding RNAs (lncRNAs) are emerging as a class of important regulators participating in various biological functions and disease processes. With the widespread application of next-generation sequencing technologies, large numbers of lncRNAs have been identified, producing plenty of lncRNA annotation resources in different contexts. However, at present, we lack a comprehensive overview of these lncRNA annotation resources. In this study, we reviewed 24 currently available lncRNA annotation resources referring to > 205 000 lncRNAs in over 50 tissues and cell lines. We characterized these annotation resources from different aspects, including exon structure, expression, histone modification and function. We found many distinct properties among these annotation resources. Especially, these resources showed diverse chromatin signatures, remarkable tissue and cell type dependence and functional specificity. Our results suggested the incompleteness and complementarity of current lncRNA annotations and the necessity of integration of multiple resources to comprehensively characterize lncRNAs. Finally, we developed 'LNCat' (lncRNA atlas, freely available at http://biocc.hrbmu.edu.cn/LNCat/), a user-friendly database that provides a genome browser of lncRNA structures, visualization of different resources from multiple angles and download of different combinations of lncRNA annotations, and supports rapid exploration, comparison and integration of lncRNA annotation resources. Overall, our study provides a comprehensive comparison of numerous lncRNA annotations, and can facilitate understanding of lncRNAs in human disease.

'Traffic light rules': Chromatin states direct miRNA-mediated network motifs running by integrating epigenome and regulatome.

BACKGROUND: Epigenetic marks can cooperatively regulate chromatin accessibility and in turn facilitate or impede the binding of regulatory factors to various elements, suggesting their important roles in regulatory circuits. However, it remains elusive as to how epigenetic marks cooperate in the operations of regulatory network. METHODS: Here, we systematically characterized chromatin states of 26 epigenetic marks on different elements of protein-coding genes and miRNAs. We comprehensively analyzed, by using an integrative regulatory network, how cooperation among epigenetic, transcriptional, and post-transcriptional regulations came about. RESULTS: We observed extensive cooperation of epigenetic marks on local functional elements and complex epigenetic patterns corresponding to different biological functions. By identifying the significantly epigenetic state-modified motifs, we found that multiple combinations of epigenetic states were associated with a specific type of motif. Interestingly, miRNA-mediated motifs were linked to stable epigenetic states of downstream targets. Changes in epigenetic states of downstream targets in miRNA-mediated motifs can buffer the effects of upstream regulator on target genes, suggesting that miRNA-mediated motifs require the cooperation of epigenetic marks. CONCLUSIONS: Overall, epigenetic marks are involved in the running of regulatory motifs in the way traffic lights control traffic flows and hence should be part of the architecture of complex regulatory circuits. GENERAL SIGNIFICANCE: We demonstrated a detailed analysis of the cooperation of multiple epigenetic marks and how epigenetic regulation was organized into a human regulatory network. The findings form a basis for further understanding of the complicated roles of epigenetic marks on regulatory circuits.

Identifying core gene modules in glioblastoma based on multilayer factor-mediated dysfunctional regulatory networks through integrating multi-dimensional genomic data.

The driver genetic aberrations collectively regulate core cellular processes underlying cancer development. However, identifying the modules of driver genetic alterations and characterizing their functional mechanisms are still major challenges for cancer studies. Here, we developed an integrative multi-omics method CMDD to identify the driver modules and their affecting dysregulated genes through characterizing genetic alteration-induced dysregulated networks. Applied to glioblastoma (GBM), the CMDD identified a core gene module of 17 genes, including seven known GBM drivers, and their dysregulated genes. The module showed significant association with shorter survival of GBM. When classifying driver genes in the module into two gene sets according to their genetic alteration patterns, we found that one gene set directly participated in the glioma pathway, while the other indirectly regulated the glioma pathway, mostly, via their dysregulated genes. Both of the two gene sets were significant contributors to survival and helpful for classifying GBM subtypes, suggesting their critical roles in GBM pathogenesis. Also, by applying the CMDD to other six cancers, we identified some novel core modules associated with overall survival of patients. Together, these results demonstrate integrative multi-omics data can identify driver modules and uncover their dysregulated genes, which is useful for interpreting cancer genome.

Characterizing and optimizing human anticancer drug targets based on topological properties in the context of biological pathways.

One of the challenging problems in drug discovery is to identify the novel targets for drugs. Most of the traditional methods for drug targets optimization focused on identifying the particular families of "druggable targets", but ignored their topological properties based on the biological pathways. In this study, we characterized the topological properties of human anticancer drug targets (ADTs) in the context of biological pathways. We found that the ADTs tended to present the following seven topological properties: influence the number of the pathways related to cancer, be localized at the start or end of the pathways, interact with cancer related genes, exhibit higher connectivity, vulnerability, betweenness, and closeness than other genes. We first ranked ADTs based on their topological property values respectively, then fused them into one global-rank using the joint cumulative distribution of an N-dimensional order statistic to optimize human ADTs. We applied the optimization method to 13 anticancer drugs, respectively. Results demonstrated that over 70% of known ADTs were ranked in the top 20%. Furthermore, the performance for mercaptopurine was significant: 6 known targets (ADSL, GMPR2, GMPR, HPRT1, AMPD3, AMPD2) were ranked in the top 15 and other four out of the top 15 (MAT2A, CDKN1A, AREG, JUN) have the potentialities to become new targets for cancer therapy.

Context-aware single-cell multiomics approach identifies cell-type-specific lung cancer susceptibility genes.

Genome-wide association studies (GWAS) identified over fifty loci associated with lung cancer risk. However, underlying mechanisms and target genes are largely unknown, as most risk-associated variants might regulate gene expression in a context-specific manner. Here, we generate a barcode-shared transcriptome and chromatin accessibility map of 117,911 human lung cells from age/sex-matched ever- and never-smokers to profile context-specific gene regulation. Identified candidate cis-regulatory elements (cCREs) are largely cell type-specific, with 37% detected in one cell type. Colocalization of lung cancer candidate causal variants (CCVs) with these cCREs combined with transcription factor footprinting prioritize the variants for 68% of the GWAS loci. CCV-colocalization and trait relevance score indicate that epithelial and immune cell categories, including rare cell types, contribute to lung cancer susceptibility the most. A multi-level cCRE-gene linking system identifies candidate susceptibility genes from 57% of the loci, where most loci display cell-category-specific target genes, suggesting context-specific susceptibility gene function.

A noncoding regulatory variant in IKZF1 increases acute lymphoblastic leukemia risk in Hispanic/Latino children.

Hispanic/Latino children have the highest risk of acute lymphoblastic leukemia (ALL) in the US compared to other racial/ethnic groups, yet the basis of this remains incompletely understood. Through genetic fine-mapping analyses, we identified a new independent childhood ALL risk signal near IKZF1 in self-reported Hispanic/Latino individuals, but not in non-Hispanic White individuals, with an effect size of ∼1.44 (95% confidence interval = 1.33-1.55) and a risk allele frequency of ∼18% in Hispanic/Latino populations and <0.5% in European populations. This risk allele was positively associated with Indigenous American ancestry, showed evidence of selection in human history, and was associated with reduced IKZF1 expression. We identified a putative causal variant in a downstream enhancer that is most active in pro-B cells and interacts with the IKZF1 promoter. This variant disrupts IKZF1 autoregulation at this enhancer and results in reduced enhancer activity in B cell progenitors. Our study reveals a genetic basis for the increased ALL risk in Hispanic/Latino children.

Genomic and phenotypic correlates of mosaic loss of chromosome Y in blood.

Mosaic loss of Y (mLOY) is the most common somatic chromosomal alteration detected in human blood. The presence of mLOY is associated with altered blood cell counts and increased risk of Alzheimer's disease, solid tumors, and other age-related diseases. We sought to gain a better understanding of genetic drivers and associated phenotypes of mLOY through analyses of whole genome sequencing of a large set of genetically diverse males from the Trans-Omics for Precision Medicine (TOPMed) program. This approach enabled us to identify differences in mLOY frequencies across populations defined by genetic similarity, revealing a higher frequency of mLOY in the European American (EA) ancestry group compared to those of Hispanic American (HA), African American (AA), and East Asian (EAS) ancestry. Further, we identified two genes ( CFHR1 and LRP6 ) that harbor multiple rare, putatively deleterious variants associated with mLOY susceptibility, show that subsets of human hematopoietic stem cells are enriched for activity of mLOY susceptibility variants, and that certain alleles on chromosome Y are more likely to be lost than others.

Systematic investigation of mitochondrial transfer between cancer cells and T cells at single-cell resolution.

Mitochondria (MT) participate in most metabolic activities of mammalian cells. A near-unidirectional mitochondrial transfer from T cells to cancer cells was recently observed to "metabolically empower" cancer cells while "depleting immune cells," providing new insights into tumor-T cell interaction and immune evasion. Here, we leverage single-cell RNA-seq technology and introduce MERCI, a statistical deconvolution method for tracing and quantifying mitochondrial trafficking between cancer and T cells. Through rigorous benchmarking and validation, MERCI accurately predicts the recipient cells and their relative mitochondrial compositions. Application of MERCI to human cancer samples identifies a reproducible MT transfer phenotype, with its signature genes involved in cytoskeleton remodeling, energy production, and TNF-α signaling pathways. Moreover, MT transfer is associated with increased cell cycle activity and poor clinical outcome across different cancer types. In summary, MERCI enables systematic investigation of an understudied aspect of tumor-T cell interactions that may lead to the development of therapeutic opportunities.

Context-aware single-cell multiome approach identified cell-type specific lung cancer susceptibility genes.

Genome-wide association studies (GWAS) identified over fifty loci associated with lung cancer risk. However, the genetic mechanisms and target genes underlying these loci are largely unknown, as most risk-associated-variants might regulate gene expression in a context-specific manner. Here, we generated a barcode-shared transcriptome and chromatin accessibility map of 117,911 human lung cells from age/sex-matched ever- and never-smokers to profile context-specific gene regulation. Accessible chromatin peak detection identified cell-type-specific candidate cis-regulatory elements (cCREs) from each lung cell type. Colocalization of lung cancer candidate causal variants (CCVs) with these cCREs prioritized the variants for 68% of the GWAS loci, a subset of which was also supported by transcription factor abundance and footprinting. cCRE colocalization and single-cell based trait relevance score nominated epithelial and immune cells as the main cell groups contributing to lung cancer susceptibility. Notably, cCREs of rare proliferating epithelial cell types, such as AT2-proliferating (0.13%) and basal cells (1.8%), overlapped with CCVs, including those in TERT. A multi-level cCRE-gene linking system identified candidate susceptibility genes from 57% of lung cancer loci, including those not detected in tissue- or cell-line-based approaches. cCRE-gene linkage uncovered that adjacent genes expressed in different cell types are correlated with distinct subsets of coinherited CCVs, including JAML and MPZL3 at the 11q23.3 locus. Our data revealed the cell types and contexts where the lung cancer susceptibility genes are functional.

Genetic regulation of fetal hemoglobin across global populations.

Human genetic variation has enabled the identification of several key regulators of fetal-to-adult hemoglobin switching, including BCL11A, resulting in therapeutic advances. However, despite the progress made, limited further insights have been obtained to provide a fuller accounting of how genetic variation contributes to the global mechanisms of fetal hemoglobin (HbF) gene regulation. Here, we have conducted a multi-ancestry genome-wide association study of 28,279 individuals from several cohorts spanning 5 continents to define the architecture of human genetic variation impacting HbF. We have identified a total of 178 conditionally independent genome-wide significant or suggestive variants across 14 genomic windows. Importantly, these new data enable us to better define the mechanisms by which HbF switching occurs in vivo. We conduct targeted perturbations to define BACH2 as a new genetically-nominated regulator of hemoglobin switching. We define putative causal variants and underlying mechanisms at the well-studied BCL11A and HBS1L-MYB loci, illuminating the complex variant-driven regulation present at these loci. We additionally show how rare large-effect deletions in the HBB locus can interact with polygenic variation to influence HbF levels. Our study paves the way for the next generation of therapies to more effectively induce HbF in sickle cell disease and ß-thalassemia.