Glucose-induced CRL4COP1-p53 axis amplifies glycometabolism to drive tumorigenesis.

The diabetes-cancer association remains underexplained. Here, we describe a glucose-signaling axis that reinforces glucose uptake and glycolysis to consolidate the Warburg effect and overcome tumor suppression. Specifically, glucose-dependent CK2 O-GlcNAcylation impedes its phosphorylation of CSN2, a modification required for the deneddylase CSN to sequester Cullin RING ligase 4 (CRL4). Glucose, therefore, elicits CSN-CRL4 dissociation to assemble the CRL4COP1 E3 ligase, which targets p53 to derepress glycolytic enzymes. A genetic or pharmacologic disruption of the O-GlcNAc-CK2-CSN2-CRL4COP1 axis abrogates glucose-induced p53 degradation and cancer cell proliferation. Diet-induced overnutrition upregulates the CRL4COP1-p53 axis to promote PyMT-induced mammary tumorigenesis in wild type but not in mammary-gland-specific p53 knockout mice. These effects of overnutrition are reversed by P28, an investigational peptide inhibitor of COP1-p53 interaction. Thus, glycometabolism self-amplifies via a glucose-induced post-translational modification cascade culminating in CRL4COP1-mediated p53 degradation. Such mutation-independent p53 checkpoint bypass may represent the carcinogenic origin and targetable vulnerability of hyperglycemia-driven cancer.

Transformation of Accessible Chromatin and 3D Nucleome Underlies Lineage Commitment of Early T Cells.

How chromatin reorganization coordinates differentiation and lineage commitment from hematopoietic stem and progenitor cells (HSPCs) to mature immune cells has not been well understood. Here, we carried out an integrative analysis of chromatin accessibility, topologically associating domains, AB compartments, and gene expression from HSPCs to CD4+CD8+ T cells. We found that abrupt genome-wide changes at all three levels of chromatin organization occur during the transition from double-negative stage 2 (DN2) to DN3, accompanying the T lineage commitment. The transcription factor BCL11B, a critical regulator of T cell commitment, is associated with increased chromatin interaction, and Bcl11b deletion compromised chromatin interaction at its target genes. We propose that these large-scale and concerted changes in chromatin organization present an energy barrier to prevent the cell from reversing its fate to earlier stages or redirecting to alternatives and thus lock the cell fate into the T lineages.

The chromatin accessibility dynamics during cell fate specifications in zebrafish early embryogenesis.

Chromatin accessibility plays a critical role in the regulation of cell fate decisions. Although gene expression changes have been extensively profiled at the single-cell level during early embryogenesis, the dynamics of chromatin accessibility at cis-regulatory elements remain poorly studied. Here, we used a plate-based single-cell ATAC-seq method to profile the chromatin accessibility dynamics of over 10 000 nuclei from zebrafish embryos. We investigated several important time points immediately after zygotic genome activation (ZGA), covering key developmental stages up to dome. The results revealed key chromatin signatures in the first cell fate specifications when cells start to differentiate into enveloping layer (EVL) and yolk syncytial layer (YSL) cells. Finally, we uncovered many potential cell-type specific enhancers and transcription factor motifs that are important for the cell fate specifications.

ISSAAC-seq enables sensitive and flexible multimodal profiling of chromatin accessibility and gene expression in single cells.

Joint profiling of chromatin accessibility and gene expression from the same single cell provides critical information about cell types in a tissue and cell states during a dynamic process. Here, we develop in situ sequencing hetero RNA-DNA-hybrid after assay for transposase-accessible chromatin-sequencing (ISSAAC-seq), a highly sensitive and flexible single-cell multi-omics method to interrogate chromatin accessibility and gene expression from the same single nucleus. We demonstrated that ISSAAC-seq is sensitive and provides high quality data with orders of magnitude more features than existing methods. Using the joint profiles from over 10,000 nuclei from the mouse cerebral cortex, we uncovered major and rare cell types and cell-type specific regulatory elements and identified heterogeneity at the chromatin level within established cell types defined by gene expression. Finally, we revealed distinct dynamics and relationships of gene expression and chromatin accessibility during an oligodendrocyte maturation trajectory.

A fast and globally optimal solution for RNA-seq quantification.

Alignment-based RNA-seq quantification methods typically involve a time-consuming alignment process prior to estimating transcript abundances. In contrast, alignment-free RNA-seq quantification methods bypass this step, resulting in significant speed improvements. Existing alignment-free methods rely on the Expectation-Maximization (EM) algorithm for estimating transcript abundances. However, EM algorithms only guarantee locally optimal solutions, leaving room for further accuracy improvement by finding a globally optimal solution. In this study, we present TQSLE, the first alignment-free RNA-seq quantification method that provides a globally optimal solution for transcript abundances estimation. TQSLE adopts a two-step approach: first, it constructs a k-mer frequency matrix A for the reference transcriptome and a k-mer frequency vector b for the RNA-seq reads; then, it directly estimates transcript abundances by solving the linear equation ATAx = ATb. We evaluated the performance of TQSLE using simulated and real RNA-seq data sets and observed that, despite comparable speed to other alignment-free methods, TQSLE outperforms them in terms of accuracy. TQSLE is freely available at https://github.com/yhg926/TQSLE.

CTCF-Mediated Enhancer-Promoter Interaction Is a Critical Regulator of Cell-to-Cell Variation of Gene Expression.

Recent studies indicate that even a homogeneous population of cells display heterogeneity in gene expression and response to environmental stimuli. Although promoter structure critically influences the cell-to-cell variation of gene expression in bacteria and lower eukaryotes, it remains unclear what controls the gene expression noise in mammals. Here we report that CTCF decreases cell-to-cell variation of expression by stabilizing enhancer-promoter interaction. We show that CTCF binding sites are interwoven with enhancers within topologically associated domains (TADs) and a positive correlation is found between CTCF binding and the activity of the associated enhancers. Deletion of CTCF sites compromises enhancer-promoter interactions. Using single-cell flow cytometry and single-molecule RNA-FISH assays, we demonstrate that knocking down of CTCF or deletion of a CTCF binding site results in increased cell-to-cell variation of gene expression, indicating that long-range promoter-enhancer interaction mediated by CTCF plays important roles in controlling the cell-to-cell variation of gene expression in mammalian cells.

Comprehensive mapping of alternative polyadenylation site usage and its dynamics at single-cell resolution.

Alternative polyadenylation (APA) plays an important role in posttranscriptional gene regulation such as transcript stability and translation efficiency. However, our knowledge about APA dynamics at the single-cell level is largely unexplored. Here, we developed single-cell polyadenylation sequencing, a strand-specific approach for sequencing the 3' end of transcripts, to investigate the landscape of APA at the single-cell level. By analyzing several cell lines, we found many genes using multiple polyA sites in bulk data are prone to use only one polyA site in each single cell. Interestingly, cell cycle genes were significantly enriched in genes with high variation in polyA site usages. Furthermore, the 414 genes showing a polyA site usage switch after cell synchronization enriched cell cycle genes, while the differentially expressed genes after cell synchronization did not enrich cell cycle genes. We further identified 812 genes showing polyA site usage changes between neighboring cell cycles, which were grouped into six clusters, with cell phase-specific functional categories enriched in each cluster. Deletion of one polyA site in MSL1 and SCCPDH results in slower and faster cell cycle progression, respectively, supporting polyA site usage switch played an important role in cell cycle. These results indicate that APA is an important layer for cell cycle regulation.

Reinvestigation of Classic T Cell Subsets and Identification of Novel Cell Subpopulations by Single-Cell RNA Sequencing.

Classic T cell subsets are defined by a small set of cell surface markers, while single-cell RNA sequencing (scRNA-seq) clusters cells using genome-wide gene expression profiles. The relationship between scRNA-seq clustered populations (scCPops) and cell surface marker-defined classic T cell subsets remains unclear. In this article, we integrated six bead-enriched T cell subsets with 62,235 single-cell transcriptomes from human PBMCs and clustered them into nine scCPops. Bead-enriched CD4+/CD45RA+/CD25- naive T and CD8+/CD45RA+ naive T cells were mainly clustered into their scCPop counterparts, while cells from the other T cell subsets were assigned to multiple scCPops, including mucosal-associated invariant T cells and NKT cells. The multiple T cell subsets forming one scCPop exhibit similar expression patterns, but not vice versa, indicating scCPop is a more homogeneous cell population with similar cell states. Interestingly, we discovered and named IFN signaling-associated gene (ISAG) high T (ISAGhi T) cells, a T cell subpopulation that highly expressed ISAGs. We further enriched ISAGhi T cells from human PBMCs by FACS of BST2 for scRNA-seq analyses. The ISAGhi T cell cluster disappeared on t-distributed stochastic neighbor embedding plot after removing ISAGs, whereas the ISAGhi T cell cluster showed up by analysis of ISAGs alone, indicating ISAGs are the major contributor of the ISAGhi T cell cluster. BST2+ and BST2- T cells showing different efficiencies of T cell activation indicate that a high level of ISAGs may contribute to quick immune responses.

Principles of nucleosome organization revealed by single-cell micrococcal nuclease sequencing.

Nucleosome positioning is critical to chromatin accessibility and is associated with gene expression programs in cells1-3. Previous nucleosome mapping methods assemble profiles from cell populations and reveal a cell-averaged pattern: nucleosomes are positioned and form a phased array that surrounds the transcription start sites of active genes3-6 and DNase I hypersensitive sites7. However, even in a homogenous population of cells, cells exhibit heterogeneity in expression in response to active signalling8,9 that may be related to heterogeneity in chromatin accessibility10-12. Here we report a technique, termed single-cell micrococcal nuclease sequencing (scMNase-seq), that can be used to simultaneously measure genome-wide nucleosome positioning and chromatin accessibility in single cells. Application of scMNase-seq to NIH3T3 cells, mouse primary naive CD4 T cells and mouse embryonic stem cells reveals two principles of nucleosome organization: first, nucleosomes in heterochromatin regions, or that surround the transcription start sites of silent genes, show large variation in positioning across different cells but are highly uniformly spaced along the nucleosome array; and second, nucleosomes that surround the transcription start sites of active genes and DNase I hypersensitive sites show little variation in positioning across different cells but are relatively heterogeneously spaced along the nucleosome array. We found a bimodal distribution of nucleosome spacing at DNase I hypersensitive sites, which corresponds to inaccessible and accessible states and is associated with nucleosome variation and variation in accessibility across cells. Nucleosome variation is smaller within single cells than across cells, and smaller within the same cell type than across cell types. A large fraction of naive CD4 T cells and mouse embryonic stem cells shows depleted nucleosome occupancy at the de novo enhancers detected in their respective differentiated lineages, revealing the existence of cells primed for differentiation to specific lineages in undifferentiated cell populations.

Publisher Correction: Principles of nucleosome organization revealed by single-cell micrococcal nuclease sequencing.

Change history: In Fig. 1c of this Letter, the two graphs were duplicates. The right panel of Fig. 1c has been corrected online.

Diploid genome architecture revealed by multi-omic data of hybrid mice.

Although mammalian genomes are diploid, previous studies extensively investigated the average chromatin architectures without considering the differences between homologous chromosomes. We generated Hi-C, ChIP-seq, and RNA-seq data sets from CD4 T cells of B6, Cast, and hybrid mice, to investigate the diploid chromatin organization and epigenetic regulation. Our data indicate that inter-chromosomal interaction patterns between homologous chromosomes are similar, and the similarity is highly correlated with their allelic coexpression levels. Reconstruction of the 3D nucleus revealed that distances of the homologous chromosomes to the center of nucleus are almost the same. The inter-chromosomal interactions at centromere ends are significantly weaker than those at telomere ends, suggesting that they are located in different regions within the chromosome territories. The majority of A|B compartments or topologically associated domains (TADs) are consistent between B6 and Cast. We found 58% of the haploids in hybrids maintain their parental compartment status at B6/Cast divergent compartments owing to cis effect. About 95% of the trans-effected B6/Cast divergent compartments converge to the same compartment status potentially because of a shared cellular environment. We showed the differentially expressed genes between the two haploids in hybrid were associated with either genetic or epigenetic effects. In summary, our multi-omics data from the hybrid mice provided haploid-specific information on the 3D nuclear architecture and a rich resource for further understanding the epigenetic regulation of haploid-specific gene expression.

H4K20me3 methyltransferase SUV420H2 shapes the chromatin landscape of pluripotent embryonic stem cells.

Heterochromatin, a densely packed chromatin state that is transcriptionally silent, is a critical regulator of gene expression. However, it is unclear how the repressive histone modification H4K20me3 or the histone methyltransferase SUV420H2 regulates embryonic stem (ES) cell fate by patterning the epigenetic landscape. Here, we report that depletion of SUV420H2 leads to a near-complete loss of H4K20me3 genome wide, dysregulated gene expression and delayed ES cell differentiation. SUV420H2-bound regions are enriched with repetitive DNA elements, which are de-repressed in SUV420H2 knockout ES cells. Moreover, SUV420H2 regulation of H4K20me3-marked heterochromatin controls chromatin architecture, including fine-scale chromatin interactions in pluripotent ES cells. Our results indicate that SUV420H2 plays a crucial role in stabilizing the three-dimensional chromatin landscape of ES cells, as loss of SUV420H2 resulted in A/B compartment switching, perturbed chromatin insulation, and altered chromatin interactions of pericentric heterochromatin and surrounding regions, indicative of localized decondensation. In addition, depletion of SUV420H2 resulted in compromised interactions between H4K20me3 and gene-regulatory regions. Together, these findings describe a new role for SUV420H2 in regulating the chromatin landscape of ES cells.

Inhibition of Glycolysis in Pathogenic TH17 Cells through Targeting a miR -21-Peli1-c-Rel Pathway Prevents Autoimmunity.

It is well known that some pathogenic cells have enhanced glycolysis; the regulatory network leading to increased glycolysis are not well characterized. In this study, we show that CNS-infiltrated pathogenic TH17 cells from diseased mice specifically upregulate glycolytic pathway genes compared with homeostatic intestinal TH17 cells. Bioenergetic assay and metabolomics analyses indicate that in vitro-derived pathogenic TH17 cells are highly glycolytic compared with nonpathogenic TH17 cells. Chromatin landscape analyses demonstrate TH17 cells in vivo that show distinct chromatin states, and pathogenic TH17 cells show enhanced chromatin accessibility at glycolytic genes with NF-κB binding sites. Mechanistic studies reveal that miR-21 targets the E3 ubiquitin ligase Peli1-c-Rel pathway to promote glucose metabolism of pathogenic TH17 cells. Therapeutic targeting c-Rel-mediated glycolysis in pathogenic TH17 cells represses autoimmune diseases. These findings extend our understanding of the regulation TH17 cell glycolysis in vivo and provide insights for future therapeutic intervention to TH17 cell-mediated autoimmune diseases.

Trac-looping measures genome structure and chromatin accessibility.

Long-range chromatin interactions play critical roles in genome organization and regulation of transcription. We now report transposase-mediated analysis of chromatin looping (Trac-looping) for simultaneous detection of multiscale genome-wide chromatin interactions among regulatory elements and chromatin accessibility. With this technique, a bivalent oligonucleotide linker is inserted between two interacting regions such that the chromatin interactions are captured without prior chromatin fragmentation and proximity-based ligation. Application of Trac-looping to human CD4+ T cells revealed substantial reorganization of enhancer-promoter interactions associated with changes in gene expression after T cell receptor stimulation.

Genome-wide detection of DNase I hypersensitive sites in single cells and FFPE tissue samples.

DNase I hypersensitive sites (DHSs) provide important information on the presence of transcriptional regulatory elements and the state of chromatin in mammalian cells. Conventional DNase sequencing (DNase-seq) for genome-wide DHSs profiling is limited by the requirement of millions of cells. Here we report an ultrasensitive strategy, called single-cell DNase sequencing (scDNase-seq) for detection of genome-wide DHSs in single cells. We show that DHS patterns at the single-cell level are highly reproducible among individual cells. Among different single cells, highly expressed gene promoters and enhancers associated with multiple active histone modifications display constitutive DHS whereas chromatin regions with fewer histone modifications exhibit high variation of DHS. Furthermore, the single-cell DHSs predict enhancers that regulate cell-specific gene expression programs and the cell-to-cell variations of DHS are predictive of gene expression. Finally, we apply scDNase-seq to pools of tumour cells and pools of normal cells, dissected from formalin-fixed paraffin-embedded tissue slides from patients with thyroid cancer, and detect thousands of tumour-specific DHSs. Many of these DHSs are associated with promoters and enhancers critically involved in cancer development. Analysis of the DHS sequences uncovers one mutation (chr18: 52417839G>C) in the tumour cells of a patient with follicular thyroid carcinoma, which affects the binding of the tumour suppressor protein p53 and correlates with decreased expression of its target gene TXNL1. In conclusion, scDNase-seq can reliably detect DHSs in single cells, greatly extending the range of applications of DHS analysis both for basic and for translational research, and may provide critical information for personalized medicine.

Exploring the changing landscape of cell-to-cell variation after CTCF knockdown via single cell RNA-seq.

BACKGROUND: CCCTC-Binding Factor (CTCF), also known as 11-zinc finger protein, participates in many cellular processes, including insulator activity, transcriptional regulation and organization of chromatin architecture. Based on single cell flow cytometry and single cell RNA-FISH analyses, our previous study showed that deletion of CTCF binding site led to a significantly increase of cellular variation of its target gene. However, the effect of CTCF on genome-wide landscape of cell-to-cell variation remains unclear. RESULTS: We knocked down CTCF in EL4 cells using shRNA, and conducted single cell RNA-seq on both wild type (WT) cells and CTCF-Knockdown (CTCF-KD) cells using Fluidigm C1 system. Principal component analysis of single cell RNA-seq data showed that WT and CTCF-KD cells concentrated in two different clusters on PC1, indicating that gene expression profiles of WT and CTCF-KD cells were systematically different. Interestingly, GO terms including regulation of transcription, DNA binding, zinc finger and transcription factor binding were significantly enriched in CTCF-KD-specific highly variable genes, implying tissue-specific genes such as transcription factors were highly sensitive to CTCF level. The dysregulation of transcription factors potentially explains why knockdown of CTCF leads to systematic change of gene expression. In contrast, housekeeping genes such as rRNA processing, DNA repair and tRNA processing were significantly enriched in WT-specific highly variable genes, potentially due to a higher cellular variation of cell activity in WT cells compared to CTCF-KD cells. We further found that cellular variation-increased genes were significantly enriched in down-regulated genes, indicating CTCF knockdown simultaneously reduced the expression levels and increased the expression noise of its regulated genes. CONCLUSIONS: To our knowledge, this is the first attempt to explore genome-wide landscape of cellular variation after CTCF knockdown. Our study not only advances our understanding of CTCF function in maintaining gene expression and reducing expression noise, but also provides a framework for examining gene function.

Exploring population admixture dynamics via empirical and simulated genome-wide distribution of ancestral chromosomal segments.

The processes of genetic admixture determine the haplotype structure and linkage disequilibrium patterns of the admixed population, which is important for medical and evolutionary studies. However, most previous studies do not consider the inherent complexity of admixture processes. Here we proposed two approaches to explore population admixture dynamics, and we demonstrated, by analyzing genome-wide empirical and simulated data, that the approach based on the distribution of chromosomal segments of distinct ancestry (CSDAs) was more powerful than that based on the distribution of individual ancestry proportions. Analysis of 1,890 African Americans showed that a continuous gene flow model, in which the African American population continuously received gene flow from European populations over about 14 generations, best explained the admixture dynamics of African Americans among several putative models. Interestingly, we observed that some African Americans had much more European ancestry than the simulated samples, indicating substructures of local ancestries in African Americans that could have been caused by individuals from some particular lineages having repeatedly admixed with people of European ancestry. In contrast, the admixture dynamics of Mexicans could be explained by a gradual admixture model in which the Mexican population continuously received gene flow from both European and Amerindian populations over about 24 generations. Our results also indicated that recent gene flows from Sub-Saharan Africans have contributed to the gene pool of Middle Eastern populations such as Mozabite, Bedouin, and Palestinian. In summary, this study not only provides approaches to explore population admixture dynamics, but also advances our understanding on population history of African Americans, Mexicans, and Middle Eastern populations.

Genome-wide detection of natural selection in African Americans pre- and post-admixture.

It is particularly meaningful to investigate natural selection in African Americans (AfA) due to the high mortality their African ancestry has experienced in history. In this study, we examined 491,526 autosomal single nucleotide polymorphisms (SNPs) genotyped in 5210 individuals and conducted a genome-wide search for selection signals in 1890 AfA. Several genomic regions showing an excess of African or European ancestry, which were considered the footprints of selection since population admixture, were detected based on a commonly used approach. However, we also developed a new strategy to detect natural selection both pre- and post-admixture by reconstructing an ancestral African population (AAF) from inferred African components of ancestry in AfA and comparing it with indigenous African populations (IAF). Interestingly, many selection-candidate genes identified by the new approach were associated with AfA-specific high-risk diseases such as prostate cancer and hypertension, suggesting an important role these disease-related genes might have played in adapting to a new environment. CD36 and HBB, whose mutations confer a degree of protection against malaria, were also located in the highly differentiated regions between AAF and IAF. Further analysis showed that the frequencies of alleles protecting against malaria in AAF were lower than those in IAF, which is consistent with the relaxed selection pressure of malaria in the New World. There is no overlap between the top candidate genes detected by the two approaches, indicating the different environmental pressures AfA experienced pre- and post-population admixture. We suggest that the new approach is reasonably powerful and can also be applied to other admixed populations such as Latinos and Uyghurs.

A systematic characterization of genes underlying both complex and Mendelian diseases.

Traditionally, genetic disorders have been classified as either Mendelian diseases or complex diseases. This nosology has greatly benefited genetic counseling and the development of gene mapping strategies. However, based on two well-established databases, we identified that 54% (524 of 968) of the Mendelian disease genes were also involved in complex diseases, and this kind of genes has not been systematically analyzed. Here, we classified human genes into five categories: Mendelian and complex disease (MC) genes, Mendelian but not complex disease (MNC) genes, complex but not Mendelian disease (CNM) genes, essential genes and OTHER genes. First, we found that MC genes were associated with more diseases and phenotypes, and were involved in more complex protein-protein interaction network than MNC or CNM genes on average. Secondly, MC genes encoded the longest proteins and had the highest transcript count among all gene categories. Especially, tissue specificity of MC genes was much higher than that of any other gene categories (P < 7.5 × 10(-5)), although their expression level was similar to that of essential genes. Thirdly, evidences from different aspects supported that MC genes have been subjected to both purifying and positive selection. Interestingly, functions of some human disease genes might be different from those of their orthologous genes in non-primate mammalians since they were even less conserved than OTHER genes. The significant over-representation of copy number variations (CNVs) in CNM genes suggested the important roles of CNVs in complex diseases. In brief, our study not only revealed the characteristics of MC genes, but also provided new insights into the other four gene categories.

Progress in single-cell multimodal sequencing and multi-omics data integration.

With the rapid advance of single-cell sequencing technology, cell heterogeneity in various biological processes was dissected at different omics levels. However, single-cell mono-omics results in fragmentation of information and could not provide complete cell states. In the past several years, a variety of single-cell multimodal omics technologies have been developed to jointly profile multiple molecular modalities, including genome, transcriptome, epigenome, and proteome, from the same single cell. With the availability of single-cell multimodal omics data, we can simultaneously investigate the effects of genomic mutation or epigenetic modification on transcription and translation, and reveal the potential mechanisms underlying disease pathogenesis. Driven by the massive single-cell omics data, the integration method of single-cell multi-omics data has rapidly developed. Integration of the massive multi-omics single-cell data in public databases in the future will make it possible to construct a cell atlas of multi-omics, enabling us to comprehensively understand cell state and gene regulation at single-cell resolution. In this review, we summarized the experimental methods for single-cell multimodal omics data and computational methods for multi-omics data integration. We also discussed the future development of this field.