The EN-TEx resource of multi-tissue personal epigenomes & variant-impact models.

Understanding how genetic variants impact molecular phenotypes is a key goal of functional genomics, currently hindered by reliance on a single haploid reference genome. Here, we present the EN-TEx resource of 1,635 open-access datasets from four donors (∼30 tissues × âˆ¼15 assays). The datasets are mapped to matched, diploid genomes with long-read phasing and structural variants, instantiating a catalog of >1 million allele-specific loci. These loci exhibit coordinated activity along haplotypes and are less conserved than corresponding, non-allele-specific ones. Surprisingly, a deep-learning transformer model can predict the allele-specific activity based only on local nucleotide-sequence context, highlighting the importance of transcription-factor-binding motifs particularly sensitive to variants. Furthermore, combining EN-TEx with existing genome annotations reveals strong associations between allele-specific and GWAS loci. It also enables models for transferring known eQTLs to difficult-to-profile tissues (e.g., from skin to heart). Overall, EN-TEx provides rich data and generalizable models for more accurate personal functional genomics.

When the economy falters, hearts suffer: Economic recessions as a social determinant of health in cardiovascular emergencies.

INTRODUCTION: While the relationships between cardiovascular disease (CVD), stress, and financial strain are well studied, the association between recessionary periods and macroeconomic conditions on incidence of disease-specific CVD emergency department (ED) visits is not well established. OBJECTIVES: This retrospective observational study aimed to assess the relationship between macroeconomic trends and CVD ED visits. METHODS: This study uses data from the National Hospital Ambulatory Care Survey (NHAMCS), Federal Reserve Economic Database (FRED), National Bureau of Economic Research (NBER), and CVD groupings from National Vital Statistics (NVS) and Center for Medicare and Medicaid Services (CMS) from 1999 to 2020 to analyze ED visits in relation to macroeconomic indicators and NBER defined recessions and expansions. RESULTS: CVD ED visits grew by 79.7% from 1999 to 2020, significantly more than total ED visits (27.8%, p < 0.001). A national estimate of 213.2 million CVD ED visits, with 22.9 million visits in economic recessions were analyzed. A secondary group including a 6-month period before and after each recession (defined as a "broadened recession") was also analyzed to account for potential leading and lagging effects of the recession, with a total of 50.0 million visits. A significantly higher proportion of CVD ED visits related to heart failure (HF) and other acute ischemic heart diseases (IHD) was observed during recessionary time periods both directly and with a 6-month lead and lag (p < 0.05). The proportion of aortic aneurysm and dissection (AAA) and atherosclerosis (ASVD) ED visits was significantly higher (p = 0.024) in the recession period with a 6-month lead and lag. When controlled for common demographic factors, economic approximations of recession such as the CPI, federal funds rate, and real disposable income were significantly associated with increased CVD ED visits. CONCLUSION: Macroeconomic trends have a significant relationship with the overall mix of CVD ED visits and represent an understudied social determinant of health.

Controllable Synthesis of N2-Intercalated WO3 Nanorod Photoanode Harvesting a Wide Range of Visible Light for Photoelectrochemical Water Oxidation.

A highly efficient visible-light-driven photoanode, N2-intercalated tungsten trioxide (WO3) nanorod, has been controllably synthesized by using the dual role of hydrazine (N2H4), which functioned simultaneously as a structure directing agent and as a nitrogen source for N2 intercalation. The SEM results indicated that the controllable formation of WO3 nanorod by changing the amount of N2H4. The ß values of lattice parameters of the monoclinic phase and the lattice volume changed significantly with the nW: nN2H4 ratio. This is consistent with the addition of N2H4 dependence of the N content, clarifying the intercalation of N2 in the WO3 lattice. The UV-visible diffuse reflectance spectra (DRS) of N2-intercalated exhibited a significant redshift in the absorption edge with new shoulders appearing at 470-600 nm, which became more intense as the nW:nN2H4 ratio increased from 1:1.2 and then decreased up to 1:5 through the maximum at 1:2.5. This addition of N2H4 dependence is consistent with the case of the N contents. This suggests that N2 intercalating into the WO3 lattice is responsible for the considerable red shift in the absorption edge, with a new shoulder appearing at 470-600 nm owing to formation of an intra-bandgap above the VB edges and a dopant energy level below the CB of WO3. The N2 intercalated WO3 photoanode generated a photoanodic current under visible light irradiation below 530 nm due to the photoelectrochemical (PEC) water oxidation, compared with pure WO3 doing so below 470 nm. The high incident photon-to-current conversion efficiency (IPCE) of the WO3-2.5 photoanode is due to efficient electron transport through the WO3 nanorod film.

SCAN-ATAC-Sim: a scalable and efficient method for simulating single-cell ATAC-seq data from bulk-tissue experiments.

SUMMARY: scATAC-seq is a powerful approach for characterizing cell-type-specific regulatory landscapes. However, it is difficult to benchmark the performance of various scATAC-seq analysis techniques (such as clustering and deconvolution) without having a priori a known set of gold-standard cell types. To simulate scATAC-seq experiments with known cell-type labels, we introduce an efficient and scalable scATAC-seq simulation method (SCAN-ATAC-Sim) that down-samples bulk ATAC-seq data (e.g. from representative cell lines or tissues). Our protocol uses a consistent but tunable signal-to-noise ratio across cell types in a scATAC-seq simulation for integrating bulk experiments with different levels of background noise, and it independently samples twice without replacement to account for the diploid genome. Because it uses an efficient weighted reservoir sampling algorithm and is highly parallelizable with OpenMP, our implementation in C++ allows millions of cells to be simulated in less than an hour on a laptop computer. AVAILABILITY AND IMPLEMENTATION: SCAN-ATAC-Sim is available at scan-atac-sim.gersteinlab.org. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

DECODE: a Deep-learning framework for Condensing enhancers and refining boundaries with large-scale functional assays.

MOTIVATION: Mapping distal regulatory elements, such as enhancers, is a cornerstone for elucidating how genetic variations may influence diseases. Previous enhancer-prediction methods have used either unsupervised approaches or supervised methods with limited training data. Moreover, past approaches have implemented enhancer discovery as a binary classification problem without accurate boundary detection, producing low-resolution annotations with superfluous regions and reducing the statistical power for downstream analyses (e.g. causal variant mapping and functional validations). Here, we addressed these challenges via a two-step model called Deep-learning framework for Condensing enhancers and refining boundaries with large-scale functional assays (DECODE). First, we employed direct enhancer-activity readouts from novel functional characterization assays, such as STARR-seq, to train a deep neural network for accurate cell-type-specific enhancer prediction. Second, to improve the annotation resolution, we implemented a weakly supervised object detection framework for enhancer localization with precise boundary detection (to a 10 bp resolution) using Gradient-weighted Class Activation Mapping. RESULTS: Our DECODE binary classifier outperformed a state-of-the-art enhancer prediction method by 24% in transgenic mouse validation. Furthermore, the object detection framework can condense enhancer annotations to only 13% of their original size, and these compact annotations have significantly higher conservation scores and genome-wide association study variant enrichments than the original predictions. Overall, DECODE is an effective tool for enhancer classification and precise localization. AVAILABILITY AND IMPLEMENTATION: DECODE source code and pre-processing scripts are available at decode.gersteinlab.org. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

DiNeR: a Differential graphical model for analysis of co-regulation Network Rewiring.

BACKGROUND: During transcription, numerous transcription factors (TFs) bind to targets in a highly coordinated manner to control the gene expression. Alterations in groups of TF-binding profiles (i.e. "co-binding changes") can affect the co-regulating associations between TFs (i.e. "rewiring the co-regulator network"). This, in turn, can potentially drive downstream expression changes, phenotypic variation, and even disease. However, quantification of co-regulatory network rewiring has not been comprehensively studied. RESULTS: To address this, we propose DiNeR, a computational method to directly construct a differential TF co-regulation network from paired disease-to-normal ChIP-seq data. Specifically, DiNeR uses a graphical model to capture the gained and lost edges in the co-regulation network. Then, it adopts a stability-based, sparsity-tuning criterion -- by sub-sampling the complete binding profiles to remove spurious edges -- to report only significant co-regulation alterations. Finally, DiNeR highlights hubs in the resultant differential network as key TFs associated with disease. We assembled genome-wide binding profiles of 104 TFs in the K562 and GM12878 cell lines, which loosely model the transition between normal and cancerous states in chronic myeloid leukemia (CML). In total, we identified 351 significantly altered TF co-regulation pairs. In particular, we found that the co-binding of the tumor suppressor BRCA1 and RNA polymerase II, a well-known transcriptional pair in healthy cells, was disrupted in tumors. Thus, DiNeR successfully extracted hub regulators and discovered well-known risk genes. CONCLUSIONS: Our method DiNeR makes it possible to quantify changes in co-regulatory networks and identify alterations to TF co-binding patterns, highlighting key disease regulators. Our method DiNeR makes it possible to quantify changes in co-regulatory networks and identify alterations to TF co-binding patterns, highlighting key disease regulators.

Validation of Enhancer Regions in Primary Human Neural Progenitor Cells using Capture STARR-seq.

Genome-wide association studies (GWAS) and expression analyses implicate noncoding regulatory regions as harboring risk factors for psychiatric disease, but functional characterization of these regions remains limited. We performed capture STARR-sequencing of over 78,000 candidate regions to identify active enhancers in primary human neural progenitor cells (phNPCs). We selected candidate regions by integrating data from NPCs, prefrontal cortex, developmental timepoints, and GWAS. Over 8,000 regions demonstrated enhancer activity in the phNPCs, and we linked these regions to over 2,200 predicted target genes. These genes are involved in neuronal and psychiatric disease-associated pathways, including dopaminergic synapse, axon guidance, and schizophrenia. We functionally validated a subset of these enhancers using mutation STARR-sequencing and CRISPR deletions, demonstrating the effects of genetic variation on enhancer activity and enhancer deletion on gene expression. Overall, we identified thousands of highly active enhancers and functionally validated a subset of these enhancers, improving our understanding of regulatory networks underlying brain function and disease.

DeepVelo: Single-cell transcriptomic deep velocity field learning with neural ordinary differential equations.

Recent advances in single-cell sequencing technologies have provided unprecedented opportunities to measure the gene expression profile and RNA velocity of individual cells. However, modeling transcriptional dynamics is computationally challenging because of the high-dimensional, sparse nature of the single-cell gene expression measurements and the nonlinear regulatory relationships. Here, we present DeepVelo, a neural network-based ordinary differential equation that can model complex transcriptome dynamics by describing continuous-time gene expression changes within individual cells. We apply DeepVelo to public datasets from different sequencing platforms to (i) formulate transcriptome dynamics on different time scales, (ii) measure the instability of cell states, and (iii) identify developmental driver genes via perturbation analysis. Benchmarking against the state-of-the-art methods shows that DeepVelo can learn a more accurate representation of the velocity field. Furthermore, our perturbation studies reveal that single-cell dynamical systems could exhibit chaotic properties. In summary, DeepVelo allows data-driven discoveries of differential equations that delineate single-cell transcriptome dynamics.

Fabrication of an Efficient N, S Co-Doped WO3 Operated in Wide-Range of Visible-Light for Photoelectrochemical Water Oxidation.

In this work, a highly efficient wide-visible-light-driven photoanode, namely, nitrogen and sulfur co-doped tungsten trioxide (S-N-WO3), was synthesized using tungstic acid (H2WO4) as W source and ammonium sulfide ((NH4)2S), which functioned simultaneously as a sulfur source and as a nitrogen source for the co-doping of nitrogen and sulfur. The EDS and XPS results indicated that the controllable formation of either N-doped WO3 (N-WO3) or S-N-WO3 by changing the nW:n(NH4)2S ratio below or above 1:5. Both N and S contents increased when increasing the nW:n(NH4)2S ratio from 1:0 to 1:15 and thereafter decreased up to 1:25. The UV-visible diffuse reflectance spectra (DRS) of S-N-WO3 exhibited a significant redshift of the absorption edge with new shoulders appearing at 470-650 nm, which became more intense as the nW:n(NH4)2S ratio increased from 1:5 and then decreased up to 1:25, with the maximum at 1:15. The values of nW:n(NH4)2S ratio dependence is consistent with the cases of the S and N contents. This suggests that S and N co-doped into the WO3 lattice are responsible for the considerable redshift in the absorption edge, with a new shoulder appearing at 470-650 nm owing to the intrabandgap formation above the valence band (VB) edge and a dopant energy level below the conduction band (CB) of WO3. Therefore, benefiting from the S and N co-doping, the S-N-WO3 photoanode generated a photoanodic current under visible light irradiation below 580 nm due to the photoelectrochemical (PEC) water oxidation, compared with pure WO3 doing so below 470 nm.

Forest Fire Clustering for single-cell sequencing combines iterative label propagation with parallelized Monte Carlo simulations.

In the era of single-cell sequencing, there is a growing need to extract insights from data with clustering methods. Here, we introduce Forest Fire Clustering, an efficient and interpretable method for cell-type discovery from single-cell data. Forest Fire Clustering makes minimal prior assumptions and, different from current approaches, calculates a non-parametric posterior probability that each cell is assigned a cell-type label. These posterior distributions allow for the evaluation of a label confidence for each cell and enable the computation of "label entropies", highlighting transitions along developmental trajectories. Furthermore, we show that Forest Fire Clustering can make robust, inductive inferences in an online-learning context and can readily scale to millions of cells. Finally, we demonstrate that our method outperforms state-of-the-art clustering approaches on diverse benchmarks of simulated and experimental data. Overall, Forest Fire Clustering is a useful tool for rare cell type discovery in large-scale single-cell analysis.

Power of PTEN/AKT: Molecular switch between tumor suppressors and oncogenes.

An increasing amount of evidence has shown that tumor suppressors can become oncogenes, or vice versa, but the mechanism behind this is unclear. Recent findings have suggested that phosphatase and tensin homolog (PTEN) is one of the powerful switches for the conversion between tumor suppressors and oncogenes. PTEN regulates a number of cellular processes, including cell death and proliferation, through the phosphoinositide 3-kinase/protein kinase B/mammalian target of rapamycin (PI3K/AKT/mTOR) pathway. Furthermore, a number of studies have suggested that PTEN deletions may alter various functions of certain tumor suppressor and oncogenic proteins. The aim of the present review was to analyze specific cases driven by PTEN loss/AKT activation, including aberrant signaling pathways and novel drug targets for clinical application in personalized medicine. The findings illustrate how PTEN loss and/or AKT activation switches MDM2-dependent p53 downregulation, and induces conversion between oncogene and tumor suppressor in enhancer of zeste homolog 2, BTB domain-containing 7A, alternative reading frame 2, p27 and breast cancer 1, early onset, through multiple mechanisms. This review highlights the genetic basis of complex drug targets and provides insights into the rationale of precision cancer therapy.

nMET, A New Target in Recurrent Cancer.

Membranous Met is classically identified with its role in cancer metastases, while nuclear Met is associated with a more invasive, aggressive and proliferative form of cancer. Full-length Met or N-terminal transmembrane domain cleaved Met can translocate into nucleus in a cell growth and pH dependent but both ligand-dependent (full length Met) and -independent (cleaved Met) manner. nMET may play greater essential roles in cancer recurrence than membranous Met. For example, in prostate cancer, it has been found that androgen receptor (AR) may inhibit the expression of membranous Met so anti-androgen based prostate cancer therapy may promote the expression of nuclear Met (nMET). We recently found a novel nMET/SOX9/ ß-Catenin/AR pathway in relapsed prostate cancer which may contribute to the formation of the feedback loop of AR reactivation via MET/nMET. Emerging evidence suggests the possibility of nMET as a prognostic marker in relapsed cancer. This review summarizes recent findings about nMET and its unique role in recurrent cancer.

[Detection of peripheral blood lymphocyte subsets in treated syphilis patients with persistent positivity for rapid plasma reagin].

OBJECTIVE: To observe the changes in peripheral blood lymphocyte subsets in patients with latent syphilis after treatment, who had persistent positive results of test for rapid plasma reagin (RPR) and remained infectious. METHODS: T-lymphocyte subsets and natural killer (NK) cells in peripheral blood were measured with flow cytometry (FCM) in these 43 patients and 30 normal subjects served as controls. RESULTS: Peripheral blood CD3, CD4 and NK cells exhibited no significant difference between the latent patients and the controls (P>0.05), but CD8 cells were higher in these patients (P<0.05). The treated patients with persistent positive RPR within two years had elevated levels of CD3, CD4 and CD8 lymphocytes (P<0.05), but NK cells appeared to be lowered (P<0.05); in patients with positive RPR for over two years, CD3, CD4 and NK cells were comparable with those in the controls (P>0.05), but CD8 cells was elevated (P<0.05). Patients with RPR positivity within two years had higher CD3 and CD4 lymphocytes, but lower NK cells in comparison with the patients with more than two years' of positivity (P<0.05); CD8 cells were comparable between the two groups (P>0.05). CONCLUSIONS: Cellular immunity imbalance and immune suppression can be present in treated syphilis patients with persistent positive RPR and the risk to transmission, which may lower the host ability to resist and clear Treponema pallidum and is associated with the difficulty in treating syphilis patients and the persistence of positive RPR even after treatment.