Effect of external electric field on the electronic properties of the AlAs/SiC van der Waals heterostructure.

Type-II van der Waals (vdW) heterostructures are regarded as the optimum candidates for unipolar electronic device applications due to their capacity for spontaneous electron-hole separation. Here, we studied the electronic properties of the AlAs/SiC vdW heterostructure via density functional theory calculations. Results show that the conduction band minimum (CBM) and valence band maximum (VBM) of this heterostructure are mainly contributed by different materials, illustrating that the AlAs/SiC heterostructure has a type-II band alignment. Interestingly, this heterostructure possesses flat valence bands near the Fermi level. In addition, under the modulation of external electric field ranging between -1 V Å-1∼0.8 V Å-1, the band gap of the heterostructure can be tuned continuously, while the band structure maintains a stable type-II band alignment with flat top valence bands. When the electric field exceeds -1 or 0.8 V Å-1, the heterostructure transitions from semiconductor material to metal, indicating the tunability of electronic properties under external fields. These results indicate that the AlAs/SiC heterostructure shows great potential for application in high-performance optoelectronic devices and a strong correlation may exist in this system.

Nuclear PTEN Regulates Thymidylate Biosynthesis in Human Prostate Cancer Cell Lines.

The phosphatase and tensin homologue deleted on chromosome 10 (PTEN) tumor suppressor governs a variety of biological processes, including metabolism, by acting on distinct molecular targets in different subcellular compartments. In the cytosol, inactive PTEN can be recruited to the plasma membrane where it dimerizes and functions as a lipid phosphatase to regulate metabolic processes mediated by the phosphatidylinositol 3-kinase (PI3K)/AKT/mammalian target of rapamycin complex 1 (mTORC1) pathway. However, the metabolic regulation of PTEN in the nucleus remains undefined. Here, using a gain-of-function approach to targeting PTEN to the plasma membrane and nucleus, we show that nuclear PTEN contributes to pyrimidine metabolism, in particular de novo thymidylate (dTMP) biosynthesis. PTEN appears to regulate dTMP biosynthesis through interaction with methylenetetrahydrofolate dehydrogenase 1 (MTHFD1), a key enzyme that generates 5,10-methylenetetrahydrofolate, a cofactor required for thymidylate synthase (TYMS) to catalyze deoxyuridylate (dUMP) into dTMP. Our findings reveal a nuclear function for PTEN in controlling dTMP biosynthesis and may also have implications for targeting nuclear-excluded PTEN prostate cancer cells with antifolate drugs.

The crescent-like Golgi ribbon is shaped by the Ajuba/PRMT5/Aurora-A complex-modified HURP.

BACKGROUND: Golgi apparatus (GA) is assembled as a crescent-like ribbon in mammalian cells under immunofluorescence microscope without knowing the shaping mechanisms. It is estimated that roughly 1/5 of the genes encoding kinases or phosphatases in human genome participate in the assembly of Golgi ribbon, reflecting protein modifications play major roles in building Golgi ribbon. METHODS: To explore how Golgi ribbon is shaped as a crescent-like structure under the guidance of protein modifications, we identified a protein complex containing the scaffold proteins Ajuba, two known GA regulators including the protein kinase Aurora-A and the protein arginine methyltransferase PRMT5, and the common substrate of Aurora-A and PRMT5, HURP. Mutual modifications and activation of PRMT5 and Aurora-A in the complex leads to methylation and in turn phosphorylation of HURP, thereby producing HURP p725. The HURP p725 localizes to GA vicinity and its distribution pattern looks like GA morphology. Correlation study of the HURP p725 statuses and GA structure, site-directed mutagenesis and knockdown-rescue experiments were employed to identify the modified HURP as a key regulator assembling GA as a crescent ribbon. RESULTS: The cells containing no or extended distribution of HURP p725 have dispersed GA membranes or longer GA. Knockdown of HURP fragmentized GA and HURP wild type could, while its phosphorylation deficiency mutant 725A could not, restore crescent Golgi ribbon in HURP depleted cells, collectively indicating a crescent GA-constructing activity of HURP p725. HURP p725 is transported, by GA membrane-associated ARF1, Dynein and its cargo adaptor Golgin-160, to cell center where HURP p725 forms crescent fibers, binds and stabilizes Golgi assembly factors (GAFs) including TRIP11, GRASP65 and GM130, thereby dictating the formation of crescent Golgi ribbon at nuclear periphery. CONCLUSIONS: The Ajuba/PRMT5/Aurora-A complex integrates the signals of protein methylation and phosphorylation to HURP, and the HURP p725 organizes GA by stabilizing and recruiting GAFs to its crescent-like structure, therefore shaping GA as a crescent ribbon. Therefore, the HURP p725 fiber serves a template to construct GA according to its shape. Video Abstract.

MetaTiME integrates single-cell gene expression to characterize the meta-components of the tumor immune microenvironment.

Recent advances in single-cell RNA sequencing have shown heterogeneous cell types and gene expression states in the non-cancerous cells in tumors. The integration of multiple scRNA-seq datasets across tumors can indicate common cell types and states in the tumor microenvironment (TME). We develop a data driven framework, MetaTiME, to overcome the limitations in resolution and consistency that result from manual labelling using known gene markers. Using millions of TME single cells, MetaTiME learns meta-components that encode independent components of gene expression observed across cancer types. The meta-components are biologically interpretable as cell types, cell states, and signaling activities. By projecting onto the MetaTiME space, we provide a tool to annotate cell states and signature continuums for TME scRNA-seq data. Leveraging epigenetics data, MetaTiME reveals critical transcriptional regulators for the cell states. Overall, MetaTiME learns data-driven meta-components that depict cellular states and gene regulators for tumor immunity and cancer immunotherapy.

Integrating multiple single-cell multi-omics samples with Smmit.

Multi-sample single-cell multi-omics datasets, which simultaneously measure multiple data modalities in the same cells and in multiple samples, facilitate the study of gene expression and gene regulatory activities on a population scale. Existing integration methods can integrate either multiple samples or multiple modalities, but not both simultaneously. To address this limitation, we developed Smmit, a computational pipeline that leverages existing integration methods to simultaneously integrate both samples and modalities and produces a unified representation of reduced dimensions. We demonstrate Smmit's capability to integrate information across samples and modalities while preserving cell type differences in two real datasets. Smmit is an R software package that is freely available at Github: https://github.com/zji90/Smmit.

Enhanced Efficacy of Simultaneous PD-1 and PD-L1 Immune Checkpoint Blockade in High-Grade Serous Ovarian Cancer.

Immune therapies have had limited efficacy in high-grade serous ovarian cancer (HGSC), as the cellular targets and mechanism(s) of action of these agents in HGSC are unknown. Here we performed immune functional and single-cell RNA sequencing transcriptional profiling on novel HGSC organoid/immune cell co-cultures treated with a unique bispecific anti-programmed cell death protein 1 (PD-1)/programmed death-ligand 1 (PD-L1) antibody compared with monospecific anti-PD-1 or anti-PD-L1 controls. Comparing the functions of these agents across all immune cell types in real time identified key immune checkpoint blockade (ICB) targets that have eluded currently available monospecific therapies. The bispecific antibody induced superior cellular state changes in both T and natural killer (NK) cells. It uniquely induced NK cells to transition from inert to more active and cytotoxic phenotypes, implicating NK cells as a key missing component of the current ICB-induced immune response in HGSC. It also induced a subset of CD8 T cells to transition from naïve to more active and cytotoxic progenitor-exhausted phenotypes post-treatment, revealing the small, previously uncharacterized population of CD8 T cells responding to ICB in HGSC. These state changes were driven partially through bispecific antibody-induced downregulation of the bromodomain-containing protein BRD1. Small-molecule inhibition of BRD1 induced similar state changes in vitro and demonstrated efficacy in vivo, validating the co-culture results. Our results demonstrate that state changes in both NK and a subset of T cells may be critical in inducing an effective anti-tumor immune response and suggest that immune therapies able to induce such cellular state changes, such as BRD1 inhibitors, may have increased efficacy in HGSC. SIGNIFICANCE: This study indicates that increased efficacy of immune therapies in ovarian cancer is driven by state changes of NK and small subsets of CD8 T cells into active and cytotoxic states.

Integrative analyses of single-cell transcriptome and regulome using MAESTRO.

We present Model-based AnalysEs of Transcriptome and RegulOme (MAESTRO), a comprehensive open-source computational workflow ( http://github.com/liulab-dfci/MAESTRO ) for the integrative analyses of single-cell RNA-seq (scRNA-seq) and ATAC-seq (scATAC-seq) data from multiple platforms. MAESTRO provides functions for pre-processing, alignment, quality control, expression and chromatin accessibility quantification, clustering, differential analysis, and annotation. By modeling gene regulatory potential from chromatin accessibilities at the single-cell level, MAESTRO outperforms the existing methods for integrating the cell clusters between scRNA-seq and scATAC-seq. Furthermore, MAESTRO supports automatic cell-type annotation using predefined cell type marker genes and identifies driver regulators from differential scRNA-seq genes and scATAC-seq peaks.

Determinants of transcription factor regulatory range.

Characterization of the genomic distances over which transcription factor (TF) binding influences gene expression is important for inferring target genes from TF chromatin immunoprecipitation followed by sequencing (ChIP-seq) data. Here we systematically examine the relationship between thousands of TF and histone modification ChIP-seq data sets with thousands of gene expression profiles. We develop a model for integrating these data, which reveals two classes of TFs with distinct ranges of regulatory influence, chromatin-binding preferences, and auto-regulatory properties. We find that the regulatory range of the same TF bound within different topologically associating domains (TADs) depend on intrinsic TAD properties such as local gene density and G/C content, but also on the TAD chromatin states. Our results suggest that considering TF type, binding distance to gene locus, as well as chromatin context is important in identifying implicated TFs from GWAS SNPs.

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

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

Large-scale public data reuse to model immunotherapy response and resistance.

Despite growing numbers of immune checkpoint blockade (ICB) trials with available omics data, it remains challenging to evaluate the robustness of ICB response and immune evasion mechanisms comprehensively. To address these challenges, we integrated large-scale omics data and biomarkers on published ICB trials, non-immunotherapy tumor profiles, and CRISPR screens on a web platform TIDE (http://tide.dfci.harvard.edu). We processed the omics data for over 33K samples in 188 tumor cohorts from public databases, 998 tumors from 12 ICB clinical studies, and eight CRISPR screens that identified gene modulators of the anticancer immune response. Integrating these data on the TIDE web platform with three interactive analysis modules, we demonstrate the utility of public data reuse in hypothesis generation, biomarker optimization, and patient stratification.

Overexpression of Aurora-A bypasses cytokinesis through phosphorylation of suppressed in lung cancer.

Mitosis is a complicated process by which eukaryotic cells segregate duplicated genomes into two daughter cells. To achieve the goal, numerous regulators have been revealed to control mitosis. The oncogenic Aurora-A is a versatile kinase responsible for the regulation of mitosis including chromosome condensation, spindle assembly, and centrosome maturation through phosphorylating a range of substrates. However, overexpression of Aurora-A bypasses cytokinesis, thereby generating multiple nuclei by unknown the mechanisms. To explore the underlying mechanisms, we found that SLAN, a potential tumor suppressor, served as a substrate of Aurora-A and knockdown of SLAN induced immature cytokinesis. Aurora-A phosphorylates SLAN at T573 under the help of the scaffold protein 14-3-3η. The SLAN phosphorylation-mimicking mutants T573D or T573E, in contrast to the phosphorylation-deficiency mutant T573A, induced higher level of multinucleated cells, and the endogenous SLAN p573 resided at spindle midzone and midbody with the help of the microtubule motor MKLP1. The Aurora-A- or SLAN-induced multiple nuclei was prevented by the knockdown of 14-3-3η or Aurora-A respectively, thereby revealing a 14-3-3η/Aurora-A/SLAN cascade negatively controlling cytokinesis. Intriguingly, SLAN T573D or T573E inactivated and T573A activated the key cytokinesis regulator RhoA. RhoA interacted with SLAN np573, i.e., the nonphosphorylated form of SLAN at T573, which localized to the spindle midzone dictated by RhoA and ECT2. Therefore, we report here that SLAN mediates the Aurora-A-triggered cytokinesis bypass and SLAN plays dual roles in that process depending on its phosphorylation status.

Cistrome-GO: a web server for functional enrichment analysis of transcription factor ChIP-seq peaks.

Characterizing the ontologies of genes directly regulated by a transcription factor (TF), can help to elucidate the TF's biological role. Previously, we developed a widely used method, BETA, to integrate TF ChIP-seq peaks with differential gene expression (DGE) data to infer direct target genes. Here, we provide Cistrome-GO, a website implementation of this method with enhanced features to conduct ontology analyses of gene regulation by TFs in human and mouse. Cistrome-GO has two working modes: solo mode for ChIP-seq peak analysis; and ensemble mode, which integrates ChIP-seq peaks with DGE data. Cistrome-GO is freely available at http://go.cistrome.org/.

Cistrome Data Browser: expanded datasets and new tools for gene regulatory analysis.

The Cistrome Data Browser (DB) is a resource of human and mouse cis-regulatory information derived from ChIP-seq, DNase-seq and ATAC-seq chromatin profiling assays, which map the genome-wide locations of transcription factor binding sites, histone post-translational modifications and regions of chromatin accessible to endonuclease activity. Currently, the Cistrome DB contains approximately 47,000 human and mouse samples with about 24,000 newly collected datasets compared to the previous release two years ago. Furthermore, the Cistrome DB has a new Toolkit module with several features that allow users to better utilize the large-scale ChIP-seq, DNase-seq, and ATAC-seq data. First, users can query the factors which are likely to regulate a specific gene of interest. Second, the Cistrome DB Toolkit facilitates searches for factor binding, histone modifications, and chromatin accessibility in any given genomic interval shorter than 2Mb. Third, the Toolkit can determine the most similar ChIP-seq, DNase-seq, and ATAC-seq samples in terms of genomic interval overlaps with user-provided genomic interval sets. The Cistrome DB is a user-friendly, up-to-date, and well maintained resource, and the new tools will greatly benefit the biomedical research community. The database is freely available at http://cistrome.org/db, and the Toolkit is at http://dbtoolkit.cistrome.org.