scENCORE: leveraging single-cell epigenetic data to predict chromatin conformation using graph embedding.

Dynamic compartmentalization of eukaryotic DNA into active and repressed states enables diverse transcriptional programs to arise from a single genetic blueprint, whereas its dysregulation can be strongly linked to a broad spectrum of diseases. While single-cell Hi-C experiments allow for chromosome conformation profiling across many cells, they are still expensive and not widely available for most labs. Here, we propose an alternate approach, scENCORE, to computationally reconstruct chromatin compartments from the more affordable and widely accessible single-cell epigenetic data. First, scENCORE constructs a long-range epigenetic correlation graph to mimic chromatin interaction frequencies, where nodes and edges represent genome bins and their correlations. Then, it learns the node embeddings to cluster genome regions into A/B compartments and aligns different graphs to quantify chromatin conformation changes across conditions. Benchmarking using cell-type-matched Hi-C experiments demonstrates that scENCORE can robustly reconstruct A/B compartments in a cell-type-specific manner. Furthermore, our chromatin confirmation switching studies highlight substantial compartment-switching events that may introduce substantial regulatory and transcriptional changes in psychiatric disease. In summary, scENCORE allows accurate and cost-effective A/B compartment reconstruction to delineate higher-order chromatin structure heterogeneity in complex tissues.

The single-cell opioid responses in the context of HIV (SCORCH) consortium.

Substance use disorders (SUD) and drug addiction are major threats to public health, impacting not only the millions of individuals struggling with SUD, but also surrounding families and communities. One of the seminal challenges in treating and studying addiction in human populations is the high prevalence of co-morbid conditions, including an increased risk of contracting a human immunodeficiency virus (HIV) infection. Of the ~15 million people who inject drugs globally, 17% are persons with HIV. Conversely, HIV is a risk factor for SUD because chronic pain syndromes, often encountered in persons with HIV, can lead to an increased use of opioid pain medications that in turn can increase the risk for opioid addiction. We hypothesize that SUD and HIV exert shared effects on brain cell types, including adaptations related to neuroplasticity, neurodegeneration, and neuroinflammation. Basic research is needed to refine our understanding of these affected cell types and adaptations. Studying the effects of SUD in the context of HIV at the single-cell level represents a compelling strategy to understand the reciprocal interactions among both conditions, made feasible by the availability of large, extensively-phenotyped human brain tissue collections that have been amassed by the Neuro-HIV research community. In addition, sophisticated animal models that have been developed for both conditions provide a means to precisely evaluate specific exposures and stages of disease. We propose that single-cell genomics is a uniquely powerful technology to characterize the effects of SUD and HIV in the brain, integrating data from human cohorts and animal models. We have formed the Single-Cell Opioid Responses in the Context of HIV (SCORCH) consortium to carry out this strategy.

The Effect of Age on the Regenerative Potential of Adipose Stem-cell-derived Apoptotic Extracellular Vesicles in Rat Skin Wound Healing.

Introduction: Skin, being the body's largest organ, is susceptible to injuries. Despite the adoption of common treatments such as debridement, wound dressing, and infection control measures for skin injuries, the outcomes remain unsatisfactory, especially in diabetic patients or elderly patients. The use of adipose stem cell-derived apoptotic extracellular vesicles (apoEVs-ASCs) has been shown great therapeutic potential in wound repair. The effect of the donor age on the biological properties and functions of apoEVs-ASCs has not been reported. Methods: In this study, we isolated apoEVs-ASCs from young and aged rats. Transmission electron microscopy (TEM) and nanoparticle tracking analysis (NTA) were applied for the characteristics of apoEVs-ASCs. For aged and young apoEVs-ASCs groups, the proliferative and migration abilities in vitro, and wound healing function in vivo were contrastively evaluated and quantified for statistical analysis. Results: Our results showed that both young and aged apoEVs-ASCs induced skin healing and reduced scar formation. In addition, young apoEVs-ASCs had significantly higher proliferation, migration of fibroblasts and endothelial cells, and increased neo-angiogenesis ability, when compared with that of aged apoEVs-ASCs. Conclusion: Young apoEVs-ASCs should be employed for wound repair, which is associated with its superior promoting effect on wound healing.

Bulk Passivation Enables Hundredfold-Enhanced Electroluminescence of Monophosphine Cu4 I4 Cubes.

Electroluminescent (EL) clusters emerged rapidly, owing to their organic-inorganic hybrid character useful for comprehensive performance integration and the potential for large-scale display and lighting applications. However, despite their good photoluminescent (PL) properties, until present, no efficient EL monodentate ligand-based clusters were reported due to structural variation during processing and excitation and exciton confinement on cluster-centered quenching states. Here we demonstrate an effective bulky passivation strategy for efficient cluster light-emitting diodes with a monophosphine Cu4 I4 cube named [TMeOPP]4 Cu4 I4 . With terminal pyridine groups, an active matrix named TmPyPB supports an effective host-cluster interplay for configuration fixation, structural stabilization, and exciton-confinement optimization. Compared to common inactive hosts, the passivation effects of TmPyPB markedly reduce trap-state densities by 24-40 % to suppress nonradiative decay, resulting in state-of-the-art PL and EL quantum yields reaching 99 % and 15.6 %, respectively, which are significantly improved by about 7-fold. TmPyPB simultaneously increases EL luminance to 104 nits, which is ≈100-fold that of the non-doped analogue.

Integrating longitudinal clinical and microbiome data to predict growth faltering in preterm infants.

Preterm birth affects more than 10% of all births worldwide. Such infants are much more prone to Growth Faltering (GF), an issue that has been unsolved despite the implementation of numerous interventions aimed at optimizing preterm infant nutrition. To improve the ability for early prediction of GF risk for preterm infants we collected a comprehensive, large, and unique clinical and microbiome dataset from 3 different sites in the US and the UK. We use and extend machine learning methods for GF prediction from clinical data. We next extend graphical models to integrate time series clinical and microbiome data. A model that integrates clinical and microbiome data improves on the ability to predict GF when compared to models using clinical data only. Information on a small subset of the taxa is enough to help improve model accuracy and to predict interventions that can improve outcome. We show that a hierarchical classifier that only uses a subset of the taxa for a subset of the infants is both the most accurate and cost-effective method for GF prediction. Further analysis of the best classifiers enables the prediction of interventions that can improve outcome.

Expediting knowledge acquisition by a web framework for Knowledge Graph Exploration and Visualization (KGEV): case studies on COVID-19 and Human Phenotype Ontology.

BACKGROUND: Knowledges graphs (KGs) serve as a convenient framework for structuring knowledge. A number of computational methods have been developed to generate KGs from biomedical literature and use them for downstream tasks such as link prediction and question answering. However, there is a lack of computational tools or web frameworks to support the exploration and visualization of the KG themselves, which would facilitate interactive knowledge discovery and formulation of novel biological hypotheses. METHOD: We developed a web framework for Knowledge Graph Exploration and Visualization (KGEV), to construct and visualize KGs in five stages: triple extraction, triple filtration, metadata preparation, knowledge integration, and graph database preparation. The application has convenient user interface tools, such as node and edge search and filtering, data source filtering, neighborhood retrieval, and shortest path calculation, that work by querying a backend graph database. Unlike other KGs, our framework allows fast retrieval of relevant texts supporting the relationships in the KG, thus allowing human reviewers to judge the reliability of the knowledge extracted. RESULTS: We demonstrated a case study of using the KGEV framework to perform research on COVID-19. The COVID-19 pandemic resulted in an explosion of relevant literature, making it challenging to make full use of the vast and heterogenous sources of information. We generated a COVID-19 KG with heterogenous information, including literature information from the CORD-19 dataset, as well as other existing knowledge from eight data sources. We showed the utility of KGEV in three intuitive case studies to explore and query knowledge on COVID-19. A demo of this web application can be accessed at http://covid19nlp.wglab.org . Finally, we also demonstrated a turn-key adaption of the KGEV framework to study clinical phenotypic presentation of human diseases by Human Phenotype Ontology (HPO), illustrating the versatility of the framework. CONCLUSION: In an era of literature explosion, the KGEV framework can be applied to many emerging diseases to support structured navigation of the vast amount of newly published biomedical literature and other existing biological knowledge in various databases. It can be also used as a general-purpose tool to explore and query gene-phenotype-disease-drug relationships interactively.

Leaching and In Vivo Bioavailability of Antimony in PET Bottled Beverages.

Antimony (Sb) may leach from polyethylene terephthalate (PET) materials into bottled water under improper storage conditions, particularly at high temperatures, leading to potential Sb chronic exposure and adverse health effects. However, Sb leaching may be promoted by various beverage constituents, which has received limited attention to date. In addition, few studies have considered Sb bioavailability in beverages and the influence of the beverage matrix on Sb bioavailability. In this study, PET-bottled beverages (n = 50) covering six categories (namely, carbonated, fruit juices, tea, sports, protein, and coffee beverages) were explored. Antimony leaching was assessed following the incubation of beverages at 60 °C for 7 days, which resulted in Sb concentrations 1.10-10.9 times greater than concentrations observed pre-incubation. Although regulatory standards vary internationally, a total of 21 beverages exceeded the Japanese Sb drinking water standard of 2 µg/L (up to 4.08 ± 0.11 µg/L) following incubation at 60 °C. pH significantly influenced Sb leaching (r = -0.38, p = 0.007) with beverages displaying lower pH (e.g., carbonated drinks) exhibiting higher Sb concentrations. An in vivo mouse model, using the liver as the biological endpoint, was adopted to assess Sb relative bioavailability (RBA) in bottled beverages. Sb RBA ranged from 1.97-58.7% with coffee beverages exhibiting the lowest Sb RBA (1.97-13.7%) and protein drinks the highest (41.1-58.7%). Linear regression revealed that Sb RBA in beverages was negatively influenced by Fe (r = -0.69, p = 0.02) and P (r = -0.73, p = 0.01) concentrations but positively correlated with tartaric acid (r = 0.59, p = 0.02). When an exposure assessment was undertaken using data generated in this study, carbonated and protein-rich beverages exhibited a higher exposure risk due to elevated Sb leaching and high Sb RBA compared to other beverage categories.

Feasibility of Distal Radial Access for Coronary Angiography and Percutaneous Coronary Intervention: A Single Center Experience.

OBJECTIVE: Asymptomatic radial artery occlusion remains the most common complication in transradial coronary interventional procedure. To prevent radial artery occlusion, distal transradial access (dTRA) has been suggested recently. In this article, we aim to describe our experience and to assess feasibility and safety of this new access site for routine coronary angiography (CAG) and percutaneous coronary intervention (PCI). METHODS: We retrospective analyzed 1,063 consecutive patients who were assigned to undergo CAG or procedural PCI through dTRA between 1 January 2018 and 31 December 2019 at Affiliated Zhongshan Hospital of Dalian University. The size of radial sheath used was 5 or 6 French. The sheath was removed at procedure termination, and hemostasis was obtained by compression bandage with gauze. The success rate of dTRA access defined by successful radial artery cannulation on the first dTRA side attempted, the cause of access failure, the hemostasis duration, the incidence of post-catheterization radial artery occlusion, and the other access-related complications including hematoma of forearm and thumb numbness were assessed. RESULTS: Radial artery cannulation via dTRA was successful in 953 of 1,063 patients with a success rate of 89.7%. Mean age of successful cases was 64.6 ± 11.2 years (26-94 years) with 339 (35.6%) women. A total of 363 (38.1%) cases were PCI. Among them, 95 cases (10%) underwent urgent PCI, including primary PCI in 64 patients with ST-segment elevation myocardial infarction and immediate PCI (<2 h from hospital admission) in 31 patients with very high-risk non-ST-segment elevation acute coronary syndrome. A total of 269 (28.2%) cases were via left dTRA. The 6 French sheath was used in 602 (63.2%) cases. Hemostasis was obtained within 2 h in 853 (89.5%) patients. There were 110 (10.3%) procedural failures: 59 (5.6%) cases of artery puncture failure, 49 (4.9%) cases of guide wire insertion failure, and 2 (0.2%) cases of sheath insertion failure. Complications potentially related to distal radial access included radial artery occlusion at the access site (13 cases, 1.4%), forearm radial artery occlusion (4 cases, 0.4%), hematoma of forearm (5 cases, 0.5%), and transient thumb numbness (2 cases, 0.2%). CONCLUSION: dTRA is a feasible and safe access and can be used as a rational alternative to traditional radial access for routine coronary interventional procedure.

Inhibition of the NLRP3 inflammasome attenuates foam cell formation of THP-1 macrophages by suppressing ox-LDL uptake and promoting cholesterol efflux.

The NOD-like receptor family, pyrin domain-containing protein 3 (NLRP3) inflammasome plays an important role in the development of atherosclerosis. The activated NLRP3 inflammasome has been reported to promote macrophage foam cell formation, but not all studies have obtained the same result, and how NLRP3 inflammasome is involved in the formation of foam cells remains elusive. We used selective NLRP3 inflammasome inhibitors and NLRP3-deficient THP-1 cells to assess the effect of NLRP3 inflammasome inhibition on macrophage foam cell formation, oxidized low-density lipoprotein (ox-LDL) uptake, esterification, and cholesterol efflux, as well as the expression of associated proteins. Inhibition of the NLRP3 inflammasome attenuated foam cell formation, diminished ox-LDL uptake, and promoted cholesterol efflux from THP-1 macrophages. Moreover, it downregulated CD36, acyl coenzyme A: cholesterol acyltransferase-1 and neutral cholesterol ester hydrolase expression; upregulated ATP-binding cassette transporter A1 (ABCA1) and scavenger receptor class B type I (SR-BI) expression; but had no effect on the expression of scavenger receptor class A and ATP-binding cassette transporter G1. Collectively, our findings show that inhibition of the NLRP3 inflammasome decreases foam cell formation of THP-1 macrophages via suppression of ox-LDL uptake and enhancement of cholesterol efflux, which may be due to downregulation of CD36 expression and upregulation of ABCA1 and SR-BI expression, respectively.

Mitigating Effect of Matricin Against Benzo(a)pyrene-induced Lung Carcinogenesis in Experimental Mice Model.

BACKGROUND: Lung cancer is a life-threatening disease that is still prevalent worldwide. This study aims to evaluate the effects of matricin, a sesquiterpene, on the carcinogenic agent benzo(a)pyrene [B(a)P]-induced lung cancer in Swiss albino mice. METHODS: Lung cancer was induced by oral administration of B(a)P at 50 mg/kg b. wt. in model Swiss-albino mice (group II) as well in experimental group III, and treated with matricin (100 mg/kg b. wt.) in group III. Upon completion of treatment for 18 weeks, the changes in body weight, tumor formation, enzymatic and non-enzymatic antioxidant levels (GSH, SOD, GPx, GR, QR, CAT), lipid peroxidation (LPO) level, pro-inflammatory cytokines (TNF-α, IL-6, IL-1ß), immunoglobulin levels (IgG, IgM), apoptosis markers (Bax, Bcl-xL), tumor markers (carcinoembryogenic antigen (CEA), neuron-specific enolase (NSE)), and histopathological (H&E) alterations were determined. RESULTS: The results indicate that B(a)P caused a significant increase of tumor formation in the lungs, increased tumor markers and inflammatory cytokines in serum, and depletion of enzymatic/ non-enzymatic antioxidants and immunoglobulins, compared to the untreated control group. Matricin treatment significantly reversed the changes caused by B(a)P as evidenced by the biochemical and histopathological assays. CONCLUSION: The changes caused by matricin clearly indicate the cancer-preventive effects of matricin against B(a)P-induced lung cancer in animal models, which can be attributed to the antioxidant activity, immunomodulation, and mitigation of the NF-kß pathway.

Prognostic role of CRABP2 in lung cancer: a meta-analysis.

BACKGROUND: The prognostic value of cellular retinoic acid-binding protein 2 (CRABP2), in lung cancer patients remains to be uncertained. Therefore, our research attempted to assess the relationship between CRABP2 and survival analysis in lung cancer patients through meta-analysis. METHOD: Related literature retrieved from Cochrane Library, Ovid, Embase, PubMed, the CNKI, and the Web of Science. The latest update of the search was May 1, 2023. The outcome indicators included as effective measures in the study were hazard ratio (HR), and 95% confidence interval (CI). The Stata 12.0 software was used to analyze the data. RESULTS: A total of4 studies were finally enrolled in our meta-analysis. The increased plasma level of CRABP2 predicted poor OS in lung cancer patient with a combined HR of 1.14 (95% CI: 1.00-1.30), and were not associated with poor PFS with combined HR: 1.15% CI: 0.63-2.09) in lung cancer patients. CONCLUSIONS: Our meta-analysis found the increased plasma level of CRABP2 was associated with poor OS independently in NSCLC patients. The plasma CRABP2 level may be an indicator of biological aggressiveness of the tumor. Our research was promising regarding the feasibility and utility of plasma CRABP2 as a novel prognostic biomarker in NSCLC, and the findings warrant further investigation.

A right convergence area of the prefrontal lobe is involved in the improvement of semantic fluency in patients with post-stroke aphasia.

OBJECTIVES: This study aimed to longitudinally observe the improvement mechanism of semantic fluency in subacute post-stroke aphasia (PSA) patients using resting-state functional magnetic resonance imaging (rs-fMRI). METHODS: Twelve PSA patients, about one month after onset, were enrolled in this study and received speech-language therapy (SLT) for one month. Auditory comprehension and semantic fluency were evaluated using the Western Aphasia Battery (WAB) and the Animal Fluency Test. Before and after treatment, rs-fMRI data were collected, and the dice similarity coefficient was used to measure the spatial similarity between each patient's lesion and a reference lesion. The left posterior inferior temporal gyrus (pITG) was used as a seed to calculate the normalized functional connectivity in whole-brain voxel analysis using DPABI software for statistical analysis. RESULTS: The dice similarity coefficient between each patient's lesion and the reference lesion showed moderate to high intensity (0.57 ± 0.14) in the Montreal Neurological Institute space. After treatment, we found a significant increase in functional connectivity between the left pITG and the right prefrontal lobe convergence area (peak t = 8.219, Gaussian random field multiple comparison correction, voxel p < 0.001, cluster p < 0.05). The increase in functional connectivity was negatively correlated with the improvement in auditory comprehension (r =-0.707, p = 0.033) and positively correlated with the improvement in semantic fluency (r = 0.79, p = 0.02). CONCLUSION: The improvement of semantic fluency in subacute PSA patients may require the participation of the right convergence area of the prefrontal lobe.

KLRB1 expression is associated with lung adenocarcinoma prognosis and immune infiltration and regulates lung adenocarcinoma cell proliferation and metastasis through the MAPK/ERK pathway.

Background: Lung cancer is the most common primary malignant tumor of the lung, and as one of the malignant tumors that pose the greatest threat to the health of the population, the incidence rate has remained high in recent years. Previous studies have shown that KLRB1 is transcriptionally repressed in lung adenocarcinoma and correlates with lung adenocarcinoma prognosis. The objective of this study is to investigate the intrinsic mechanisms by which KLRB1 affects the malignant phenotypes of lung adenocarcinoma such as immune infiltration, proliferation, growth and metastasis. Methods: We assessed the expression levels of KLRB1 in publicly available databases and investigated its associations with clinical and pathological variables. Enrichment analysis was subsequently conducted to investigate possible signaling pathways and their associated biological functions. Statistical analysis, including Spearman correlation and the application of multigene prediction models, was utilized to assess the relationship between the expression of KLRB1 and the infiltration of immune cells. The diagnostic and prognostic value of KLRB1 was evaluated using Kaplan-Meier survival curves, diagnostic receptor operating characteristic (ROC) curves, histogram models, and Cox regression analysis. Specimens from lung adenocarcinoma (LUAD) patients were collected, the expression level of KLRB1 was detected by protein blotting analysis, and the expression level of KLRB1 was detected at the mRNA level by real-time quantitative reverse transcription polymerase chain reaction (RT-qPCR). Small interfering RNA (siRNA) was used to silence gene expression, and Transwell, Cell Counting Kit-8 (CCK-8) and colony formation assays were subsequently performed to analyze the effects of KLRB1 on LUAD cell migration, invasion and proliferation. Results: KLRB1 expression was lower in lung cancer tissue than in surrounding healthy tissue. Genes differentially expressed in the low and high KLRB1 expression groups were found to be significantly enriched in pathways related to immunity. KLRB1 exerted an impact on the MAPK/ERK signaling pathway, thereby modulating the growth and proliferation of LUAD cells. KLRB1 expression is linked to prognosis, immune infiltration, and cell migration and proliferation in LUAD. Conclusions: The evidence revealed a correlation between KLRB1 and both prognosis and immune infiltration in LUAD patients.

ScISOr-ATAC reveals convergent and divergent splicing and chromatin specificities between matched cell types across cortical regions, evolution, and in Alzheimer's Disease.

Multimodal measurements have become widespread in genomics, however measuring open chromatin accessibility and splicing simultaneously in frozen brain tissues remains unconquered. Hence, we devised Single-Cell-ISOform-RNA sequencing coupled with the Assay-for-Transposase-Accessible-Chromatin (ScISOr-ATAC). We utilized ScISOr-ATAC to assess whether chromatin and splicing alterations in the brain convergently affect the same cell types or divergently different ones. We applied ScISOr-ATAC to three major conditions: comparing (i) the Rhesus macaque (Macaca mulatta) prefrontal cortex (PFC) and visual cortex (VIS), (ii) cross species divergence of Rhesus macaque versus human PFC, as well as (iii) dysregulation in Alzheimer's disease in human PFC. We found that among cortical-layer biased excitatory neuron subtypes, splicing is highly brain-region specific for L3-5/L6 IT_RORB neurons, moderately specific in L2-3 IT_CUX2.RORB neurons and unspecific in L2-3 IT_CUX2 neurons. In contrast, at the chromatin level, L2-3 IT_CUX2.RORB neurons show the highest brain-region specificity compared to other subtypes. Likewise, when comparing human and macaque PFC, strong evolutionary divergence on one molecular modality does not necessarily imply strong such divergence on another molecular level in the same cell type. Finally, in Alzheimer's disease, oligodendrocytes show convergently high dysregulation in both chromatin and splicing. However, chromatin and splicing dysregulation most strongly affect distinct oligodendrocyte subtypes. Overall, these results indicate that chromatin and splicing can show convergent or divergent results depending on the performed comparison, justifying the need for their concurrent measurement to investigate complex systems. Taken together, ScISOr-ATAC allows for the characterization of single-cell splicing and chromatin patterns and the comparison of sample groups in frozen brain samples.

Single-cell genomics and regulatory networks for 388 human brains.

Single-cell genomics is a powerful tool for studying heterogeneous tissues such as the brain. Yet, little is understood about how genetic variants influence cell-level gene expression. Addressing this, we uniformly processed single-nuclei, multi-omics datasets into a resource comprising >2.8M nuclei from the prefrontal cortex across 388 individuals. For 28 cell types, we assessed population-level variation in expression and chromatin across gene families and drug targets. We identified >550K cell-type-specific regulatory elements and >1.4M single-cell expression-quantitative-trait loci, which we used to build cell-type regulatory and cell-to-cell communication networks. These networks manifest cellular changes in aging and neuropsychiatric disorders. We further constructed an integrative model accurately imputing single-cell expression and simulating perturbations; the model prioritized ~250 disease-risk genes and drug targets with associated cell types.

Single-cell genomics and regulatory networks for 388 human brains.

Single-cell genomics is a powerful tool for studying heterogeneous tissues such as the brain. Yet little is understood about how genetic variants influence cell-level gene expression. Addressing this, we uniformly processed single-nuclei, multiomics datasets into a resource comprising >2.8 million nuclei from the prefrontal cortex across 388 individuals. For 28 cell types, we assessed population-level variation in expression and chromatin across gene families and drug targets. We identified >550,000 cell type-specific regulatory elements and >1.4 million single-cell expression quantitative trait loci, which we used to build cell-type regulatory and cell-to-cell communication networks. These networks manifest cellular changes in aging and neuropsychiatric disorders. We further constructed an integrative model accurately imputing single-cell expression and simulating perturbations; the model prioritized ~250 disease-risk genes and drug targets with associated cell types.

The emotion prediction of college students with attention LSTM during the COVID19 epidemic.

During the COVID19 pandemic, there is a pronounced collective mental health issue among college students. Forecasting the trend of emotional changes in on-campus students is crucial to effectively address this issue. This study proposes an Attention-LSTM neural network model that performs deep learning on key input sequence information, so as to predict the distribution of emotional states in college students. By testing 60 consecutive days of emotional data, the model successfully predicts students' emotional distribution, triggers and resolution strategies, with an accuracy rate of no less than 99%. Compared with models such as ARIMA, SARIMA and VAR, this model shows significant advantages in accuracy, operational efficiency, and data collection requirements. The integration of deep learning technology with student management in this study offers a novel approach to address emotional issues among students under exceptional circumstances.

Effect of microRNA-181b on the biological characteristics and clinical drug resistance of small-cell lung cancer by targeting angiotensin converting enzyme 2.

OBJECTIVE: To clarify the effect of miR-181b on the biological function of small-cell lung cancer (SCLC) and explore the effect of clinical resistance on SCLC. METHODS: Blood samples were collected from 30 SCLC patients and 30 non-SCLC patients in our department from 2017 to 2019 to detect the expression level of miR-181b.The expression level of miR-181b was detected in SCLC cells by RT-PCR, and screening of downstream target genes by gene chip, verification with luciferase, and Western blotting. In addition, collect the general data of 30 SCLC patients and 30 non-SCLC patients (control group), the patients were diagnosed by pathology and undergoing EC protocol in the Department of Thoracic Surgery and Oncology of our hospital to detect the expression level of mir-181b in different periods. Furthermore, in the SCLC cell line, EC chemotherapy was administered to detect the sensitivity of drug resistance and nondrug resistance. RESULTS: miR-181b in SCLC patients was lower than in normal people as well as the drug-sensitive cell line. ACE2 was verified as a downstream target of miR-181b by gene chip screening. First-line chemotherapy can promote the recovery of miR-181b, but cannot repair to normal levels. miR-181b can enhance the drug sensitivity of SCLC drug-resistant cells. CONCLUSION: miR-181b directly targets ACE2 to affect the biological characteristics of SCLC. The expression level of miR-181b is highly related to the drug resistance of SCLC, which suggests that miR-181b could be a potential biomarker candidate for treatment efficacy of SCLC.

Translator: A Transfer Learning Approach to Facilitate Single-Cell ATAC-Seq Data Analysis from Reference Dataset.

Recent advances in single-cell sequencing assay for transposase-accessible chromatin (scATAC-seq) have allowed simultaneous epigenetic profiling over thousands of individual cells to dissect the cellular heterogeneity and elucidate regulatory mechanisms at the finest possible resolution. However, scATAC-seq is challenging to model computationally due to the ultra-high dimensionality, low signal-to-noise ratio, complex feature interactions, and high vulnerability to various confounding factors. In this study, we present Translator, an efficient transfer learning approach to capture generalizable chromatin interactions from high-quality (HQ) reference scATAC-seq data to obtain robust cell representations in low-to-moderate quality target scATAC-seq data. We applied Translator on various simulated and real scATAC-seq datasets and demonstrated that Translator could learn more biologically meaningful cell representations than other methods by incorporating information learned from the reference data, thus facilitating various downstream analyses such as clustering and motif enrichment measurements. Moreover, Translator's block-wise deep learning framework can handle nonlinear relationships with restricted connections using fewer parameters to boost computational efficiency through Graphics Processing Unit (GPU) parallelism. Finally, we have implemented Translator as a free software package available for the community to leverage large-scale, HQ reference data to study target scATAC-seq data.

InsuLock: A Weakly Supervised Learning Approach for Accurate Insulator Prediction, and Variant Impact Quantification.

Mapping chromatin insulator loops is crucial to investigating genome evolution, elucidating critical biological functions, and ultimately quantifying variant impact in diseases. However, chromatin conformation profiling assays are usually expensive, time-consuming, and may report fuzzy insulator annotations with low resolution. Therefore, we propose a weakly supervised deep learning method, InsuLock, to address these challenges. Specifically, InsuLock first utilizes a Siamese neural network to predict the existence of insulators within a given region (up to 2000 bp). Then, it uses an object detection module for precise insulator boundary localization via gradient-weighted class activation mapping (~40 bp resolution). Finally, it quantifies variant impacts by comparing the insulator score differences between the wild-type and mutant alleles. We applied InsuLock on various bulk and single-cell datasets for performance testing and benchmarking. We showed that it outperformed existing methods with an AUROC of ~0.96 and condensed insulator annotations to ~2.5% of their original size while still demonstrating higher conservation scores and better motif enrichments. Finally, we utilized InsuLock to make cell-type-specific variant impacts from brain scATAC-seq data and identified a schizophrenia GWAS variant disrupting an insulator loop proximal to a known risk gene, indicating a possible new mechanism of action for the disease.