xQTLatlas: a comprehensive resource for human cellular-resolution multi-omics genetic regulatory landscape.

Understanding how genetic variants influence molecular phenotypes in different cellular contexts is crucial for elucidating the molecular and cellular mechanisms behind complex traits, which in turn has spurred significant advances in research into molecular quantitative trait locus (xQTL) at the cellular level. With the rapid proliferation of data, there is a critical need for a comprehensive and accessible platform to integrate this information. To meet this need, we developed xQTLatlas (http://www.hitxqtl.org.cn/), a database that provides a multi-omics genetic regulatory landscape at cellular resolution. xQTLatlas compiles xQTL summary statistics from 151 cell types and 339 cell states across 55 human tissues. It organizes these data into 20 xQTL types, based on four distinct discovery strategies, and spans 13 molecular phenotypes. Each entry in xQTLatlas is meticulously annotated with comprehensive metadata, including the origin of the tissue, cell type, cell state and the QTL discovery strategies utilized. Additionally, xQTLatlas features multiscale data exploration tools and a suite of interactive visualizations, facilitating in-depth analysis of cell-level xQTL. xQTLatlas provides a valuable resource for deepening our understanding of the impact of functional variants on molecular phenotypes in different cellular environments, thereby facilitating extensive research efforts.

Leveraging explainable deep learning methodologies to elucidate the biological underpinnings of Huntington's disease using single-cell RNA sequencing data.

BACKGROUND: Huntington's disease (HD) is a hereditary neurological disorder caused by mutations in HTT, leading to neuronal degeneration. Traditionally, HD is associated with the misfolding and aggregation of mutant huntingtin due to an extended polyglutamine domain encoded by an expanded CAG tract. However, recent research has also highlighted the role of global transcriptional dysregulation in HD pathology. However, understanding the intricate relationship between mRNA expression and HD at the cellular level remains challenging. Our study aimed to elucidate the underlying mechanisms of HD pathology using single-cell sequencing data. RESULTS: We used single-cell RNA sequencing analysis to determine differential gene expression patterns between healthy and HD cells. HD cells were effectively modeled using a residual neural network (ResNet), which outperformed traditional and convolutional neural networks. Despite the efficacy of our approach, the F1 score for the test set was 96.53%. Using the SHapley Additive exPlanations (SHAP) algorithm, we identified genes influencing HD prediction and revealed their roles in HD pathobiology, such as in the regulation of cellular iron metabolism and mitochondrial function. SHAP analysis also revealed low-abundance genes that were overlooked by traditional differential expression analysis, emphasizing its effectiveness in identifying biologically relevant genes for distinguishing between healthy and HD cells. Overall, the integration of single-cell RNA sequencing data and deep learning models provides valuable insights into HD pathology. CONCLUSION: We developed the model capable of analyzing HD at single-cell transcriptomic level.

Spatially exploring RNA biology in archival formalin-fixed paraffin-embedded tissues.

The capability to spatially explore RNA biology in formalin-fixed paraffin-embedded (FFPE) tissues holds transformative potential for histopathology research. Here, we present pathology-compatible deterministic barcoding in tissue (Patho-DBiT) by combining in situ polyadenylation and computational innovation for spatial whole transcriptome sequencing, tailored to probe the diverse RNA species in clinically archived FFPE samples. It permits spatial co-profiling of gene expression and RNA processing, unveiling region-specific splicing isoforms, and high-sensitivity transcriptomic mapping of clinical tumor FFPE tissues stored for 5 years. Furthermore, genome-wide single-nucleotide RNA variants can be captured to distinguish malignant subclones from non-malignant cells in human lymphomas. Patho-DBiT also maps microRNA regulatory networks and RNA splicing dynamics, decoding their roles in spatial tumorigenesis. Single-cell level Patho-DBiT dissects the spatiotemporal cellular dynamics driving tumor clonal architecture and progression. Patho-DBiT stands poised as a valuable platform to unravel rich RNA biology in FFPE tissues to aid in clinical pathology evaluation.

Role of respiratory system microbiota in development of lung cancer and clinical application.

Microbes play a significant role in human tumor development and profoundly impact treatment efficacy, particularly in immunotherapy. The respiratory tract extensively interacts with the external environment and possesses a mucosal immune system. This prompts consideration of the relationship between respiratory microbiota and lung cancer. Advancements in culture-independent techniques have revealed unique communities within the lower respiratory tract. Here, we provide an overview of the respiratory microbiota composition, dysbiosis characteristics in lung cancer patients, and microbiota profiles within lung cancer. We delve into how the lung microbiota contributes to lung cancer onset and progression through direct functions, sustained immune activation, and immunosuppressive mechanisms. Furthermore, we emphasize the clinical utility of respiratory microbiota in prognosis and treatment optimization for lung cancer.

Super multiple primary lung cancers harbor high-frequency BRAF and low-frequency EGFR mutations in the MAPK pathway.

The incidence of multiple primary lung cancer (MPLC) is increasing, with some of our surgical patients exhibiting numerous lesions. We defined lung cancer with five or more primary lesions as super MPLCs. Elucidating the genomic characteristics of this special MPLC subtype can help reduce disease burden and understand tumor evolution. In our cohort of synchronous super early-stage MPLCs (PUMCH-ssesMPLC), whole-exome sequencing on 130 resected malignant specimens from 18 patients provided comprehensive super-MPLC genomic landscapes. Mutations are enriched in PI3k-Akt and MAPK pathways. Their BRAF mutation frequency (31.5%) is significantly higher than MPLC with fewer lesions and early-stage single-lesion cancer, while EGFR mutations are significantly fewer (13.8%). As lesion counts increase, BRAF mutations gradually become dominant. Also, invasive lesions more tend to have classic super-MPLC mutation patterns. High-frequency BRAF mutations, especially Class II, and low-frequency EGFR mutations could be a reason for the limited effectiveness of targeted therapy in super-MPLC patients.

Numerical model for solid-like and fluid-like behavior of granular flows.

We propose a constitutive model for both the solid-like and fluid-like behavior of granular materials by decomposing the stress tensor into quasi-static and collisional components. A hypoplastic model is adopted for the solid-like behavior in the quasi-static regime, while the viscous and dilatant behavior in the fluid-like regime is represented by a modified µ ( I ) rheology model. This model effectively captures the transition between solid-like and fluid-like flows. Performance and validation of the proposed model are demonstrated through numerical simulations of element tests.

Microfluidic Chip-Based Automatic System for Sequencing Patient-Derived Organoids at the Single-Cell Level.

Genetically sequencing patient-derived organoids (PDOs) at the single-cell level has emerged as a promising method to infer cell-level heterogeneity of original organs and improve cancer precision medicine. Unfortunately, because of the limited starting quantity and uncontrolled establishing process of PDOs, the existing single-cell sequencing technologies, either manual-operation-based or microfluid-based, are inefficient in processing PDOs originating from clinical tissue samples. To address such issues, this study presents a microfluidic chip-based automatic system for sequencing organoids at the single-cell level, named as MASSO. By performing all required procedures, including PDO establishment/culturing/digesting and single-cell isolation/lysis/whole-genome amplification, in a single microfluidic chip, the possible loss of precious PDO is avoided, and the high quality of on-chip whole-genome amplification of a single PDO cell is ensured. By automating the entire operation process, possible human error is eliminated, and the data repeatability is improved, therefore bridging the technical gap between laboratorial proof-of-concept studies and clinical practices. After characterizing the organoid single-cell whole-genome amplification chip (named as OSA-Chip) and the MASSO, the first successful attempt, to the best of our knowledge, on whole-genome sequencing lung cancer PDO at the single-cell level was performed by MASSO. The results reveal that the MASSO is capable of not only identifying common cancer-related mutations but also discovering specific mutations that affect drug responses, therefore laying the technical foundation for efficiently understanding the cell-level heterogeneities of PDOs and corresponding original organs.

Neutrophil to lymphocyte ratio and five-year mortality in patients with acute ischemic stroke.

Background: Previous studies linked neutrophil to lymphocyte ratio (NLR) with short-term mortality after acute ischemic stroke (AIS), but its relationship with long-term mortality remains unclear. This study investigates the association between NLR and five-year mortality in AIS patients. Method: We analyzed 416 AIS patients from April 2012 to January 2016 at Zhangjiagang TCM Hospital. Admission NLR was divided into quartiles: Q1 (<2.00), Q2 (2.00-3.05), Q3 (3.06-5.46), and Q4 (≥5.46). We assessed 5-year all-cause and vascular mortality using Kaplan-Meier, Cox regression, and receiver operating characteristic (ROC) curve analyses. Results: Over five years, 134 (32.2 %) all-cause deaths and 114 (27.4 %) vascular deaths occurred. Elevated NLR was significantly associated with increased risks of all-cause and vascular mortality. Multivariate Cox analysis identified stroke history (HR: 1.57, 95 % CI 1.08-2.30), baseline National Institutes of Health Stroke Scale (NIHSS) score (HR: 1.09, 95 % CI 1.05-1.12), and NLR (HR: 1.09, 95 % CI 1.05-1.12) as independent risk factors for all-cause mortality. These factors also predicted 5-year vascular mortality: stroke history (HR: 1.65, 95 % CI 1.10-2.49), NIHSS score (HR: 1.10, 95 % CI 1.06-1.13), and NLR (HR: 1.08, 95 % CI 1.05-1.10). NLR quartiles were significantly linked to both outcomes: all-cause mortality HRs were Q2 (1.87, 95 % CI 1.00-3.51), Q3 (2.40, 95 % CI 1.31-4.39), Q4 (2.77, 95 % CI 1.47-5.24), P for trend = 0.001; vascular mortality HRs were Q2 (1.76, 95 % CI 0.88-3.55), Q3 (2.34, 95 % CI 1.14-4.40), Q4 (2.57, 95 % CI 1.28-5.16), P for trend = 0.002. Kaplan-Meier survival analysis revealed significantly higher mortality rates in higher NLR quartiles (log-rank p < 0.001). ROC analysis identified optimal NLR cutoff values of 3.42 for predicting 5-year all-cause mortality (AUC 0.689) and 3.51 for vascular-cause mortality (AUC 0.700), with moderate sensitivity and specificity. Conclusions: Higher NLR at admission was linked with five-year all-cause mortality and mortality attributed explicitly to vascular causes in AIS patients.

Protocol for detecting glycoRNAs using metabolic labeling and northwestern blot.

GlycoRNAs are glycosylated RNAs that can be detected in many cell types and often partly reside on the outer cell surface, with a recently demonstrated role in mediating neutrophil-endothelium interaction. Here, we present a protocol for glycoRNA detection based on metabolic tracing and northwestern blot. We describe steps for metabolic labeling of cells, extraction and purification of RNA, biotin labeling of RNA, and northwestern blot for glycoRNA detection. We also incorporate optimized conditions for biotin labeling, RNA dye, and membrane blocking. For complete details on the use and execution of this protocol, please refer to Zhang et al.1.

miniSNV: accurate and fast single nucleotide variant calling from nanopore sequencing data.

Nanopore sequence technology has demonstrated a longer read length and enabled to potentially address the limitations of short-read sequencing including long-range haplotype phasing and accurate variant calling. However, there is still room for improvement in terms of the performance of single nucleotide variant (SNV) identification and computing resource usage for the state-of-the-art approaches. In this work, we introduce miniSNV, a lightweight SNV calling algorithm that simultaneously achieves high performance and yield. miniSNV utilizes known common variants in populations as variation backgrounds and leverages read pileup, read-based phasing, and consensus generation to identify and genotype SNVs for Oxford Nanopore Technologies (ONT) long reads. Benchmarks on real and simulated ONT data under various error profiles demonstrate that miniSNV has superior sensitivity and comparable accuracy on SNV detection and runs faster with outstanding scalability and lower memory than most state-of-the-art variant callers. miniSNV is available from https://github.com/CuiMiao-HIT/miniSNV.

SCancerRNA: Expression at the Single-cell Level and Interaction Resource of Non-coding RNA Biomarkers for Cancers.

Non-coding RNAs (ncRNAs) participate in multiple biological processes associated with cancers as tumor suppressors or oncogenic drivers. Due to their high stability in plasma, urine, and many other fluids, ncRNAs have the potential to serve as key biomarkers for early diagnosis and screening of cancers. During cancer progression, tumor heterogeneity plays a crucial role, and it is particularly important to understand the gene expression patterns of individual cells. With the development of single-cell RNA sequencing (scRNA-seq) technologies, uncovering gene expression in different cell types for human cancers has become feasible by profiling transcriptomes at the cellular level. However, a well-organized and comprehensive online resource that provides access to the expression of genes corresponding to ncRNA biomarkers in different cell types at the single-cell level is not available yet. Therefore, we developed the SCancerRNA database to summarize experimentally supported data on long ncRNA, microRNA, PIWI-interacting RNA, small nucleolar RNA, and circular RNA biomarkers, as well as data on their differential expression at the cellular level. Furthermore, we collected biological functions and clinical applications of biomarkers to facilitate the application of ncRNA biomarkers to cancer diagnosis, as well as the monitoring of progression and targeted therapies. SCancerRNA also allows users to explore interaction networks of different types of ncRNAs, and build computational models in the future. SCancerRNA is freely accessible at http://www.scancerrna.com/BioMarker.

MEHunter: transformer-based mobile element variant detection from long reads.

SUMMARY: Mobile genetic elements (MEs) are heritable mutagens that significantly contribute to genetic diseases. The advent of long-read sequencing technologies, capable of resolving large DNA fragments, offers promising prospects for the comprehensive detection of ME variants (MEVs). However, achieving high precision while maintaining recall performance remains challenging mainly brought by the variable length and similar content of MEV signatures, which are often obscured by the noise in long reads. Here, we propose MEHunter, a high-performance MEV detection approach utilizing a fine-tuned transformer model adept at identifying potential MEVs with fragmented features. Benchmark experiments on both simulated and real datasets demonstrate that MEHunter consistently achieves higher accuracy and sensitivity than the state-of-the-art tools. Furthermore, it is capable of detecting novel potentially individual-specific MEVs that have been overlooked in published population projects. AVAILABILITY AND IMPLEMENTATION: MEHunter is available from https://github.com/120L021101/MEHunter.

Full bio-based flame retardant towards multifunctional polylactic acid: Crystallization, flame retardant, antibacterial and enhanced mechanical properties.

The preparation of flame-retardant high-performance polylactic acid (PLA) composites by adding green flame retardants have always been a challenge. In this work, an intumescent flame-retardant PCR based on phytic acid, chitosan, and resveratrol was successfully designed. Adding 4 wt% of PCR, the PLA-PCR composite was classified as UL-94 V-0 grade, with a LOI value increasing from 19.7 % (pure PLA) to 26.0 %, a peak heat release rate decreasing from 433 to 344 kW/m2. Owing to excellent compatibility of PCR, the mechanical strength and toughness of PLA-PCR composites have been improved, as reflected by a ~ 16 % increase in tensile strength, a ~ 73 % increase in impact strength and a ~ 57 % increase in toughness. In addition, PCR also presented plasticization effect on PLA, making it easier to process. This work provided a highly efficient and environmental-friendly modification approach for the development of multifunctional polymers.

Automatically detecting anchor cells and clustering for scRNA-seq data using scTSNN.

Advancing in single-cell RNA sequencing techniques enhances the resolution of cell heterogeneity study. Density-based unsupervised clustering has the potential to detect the representative anchor points and the number of clusters automatically. Meanwhile, discovering the true cell type of scRNA-seq data in the unsupervised scenario is still challenging. To this end, we proposed a tensor shared nearest neighbor anchor clustering for scRNA-seq data, named scTSNN, which first makes use of the tensor affinity learning module to mine the local-global balanced topological structures among cells, next designs density-based shared nearest neighbor measurement method to automatically detect anchor cells, finally partitions the non-anchor cells to obtain the clustering results. Validated on synthetic datasets and scRNA-seq datasets, scTSNN not only exactly detects the complicated structures but also has better performance in accuracy and robustness compared with the state-of-the-art methods. Moreover, case studies on mammalian cells and cervical cancer tumor cells demonstrate the selected anchor cells of scTSNN benefit the cell pseudotime inference and rare cell identification, which show good application and research value of scTSNN.

Integrating network pharmacology and experimental validation to decipher the pharmacological mechanism of DXXK in treating diabetic kidney injury.

Diabetes mellitus (DM) is a chronic metabolic disease that is highly susceptible to kidney injury. Di'ao XinXueKang capsules (DXXK) is a novel Chinese herbal medicine that has been used in clinical trials for the therapy of DM and kidney disease, but the underlying pharmacological mechanism remains unclear. This study aims to integrate network pharmacology, molecular docking and in vivo experiments to explore the potential mechanisms of DXXK in the treatment of diabetic kidney injury. The chemical constituents of DXXK were extracted from the ETCM and Batman-TCM databases, and then evaluated for their pharmacological activity via the Swiss ADME platform. Multiple disease databases were searched and integrated for DM-related targets. Overlapping targets were then collected to construct a protein-protein interaction (PPI) network. KEGG and GO enrichment analyses were performed based on the Metascape database, and molecular docking was performed using AutoDock Vina software. The main components in DXXK were analyzed by HPLC. The results of network pharmacology and molecular docking were validated in an animal model of DM induced by the combination of a high-fat diet (HFD) and streptozotocin (STZ). We screened and obtained 7 ingredients and identified dioscin, protodioscin, and pseudoprotodioscin as the major components of DXXK by HPLC. A total of 2,216 DM-related pathogenic genes were obtained from DrugBank, GeneCards, OMIM, and DisGeNET databases. KEGG and GO enrichment analyses indicated that the TGF-beta signaling pathway is a critical pathway associated with DM therapy. Molecular docking revealed that the ingredients in DXXK bind to the pivotal targets TGFß1, Smad2, and Smad3. In diabetic mice, we found that DXXK alleviated diabetic symptoms, lowered blood glucose, improved insulin tolerance, and modulated lipid metabolism. Furthermore, DXXK attenuated renal lesions and fibrosis by downregulating TGFß1, Smad2, and Smad3. Collectively, our results suggest that DXXK has the potential to regulate glucolipid metabolism in DM, and it may serve as a viable therapeutic option for renoprotection by inhibiting of the TGF-ß1/Smad2/3 pathway.

Effect of factor VIII and FVIII/PC ratio on portal vein thrombosis in liver cirrhosis: a systematic review and meta­analysis.

BACKGROUND: To date, there is an ongoing debate regarding the ability to predict PVT development using markers of FVIII or FVIII/PC ratio. This study presents evidence-based medical findings on the influence of FVIII activity levels and FVIII/PC values in the formation of PVT in cirrhosis. METHODS: The search for original studies on risk factors for portal vein thrombosis (PVT) associated with cirrhosis was conducted, which primarily focused on comparing circulating FVIII activity levels or FVIII/PC ratio in cirrhotic patients with and without PVT. The quality of evidence from each study was assessed using the Newcastle-Ottawa Scale. RESULTS: The meta-analysis included a total of 10 original studies. In total, 2250 cirrhotic patients were included, with 414 having PVT and 1836 without PVT. The pooled analysis using a random-effects model showed no significant difference in standardized mean difference (SMD) for FVIII activity levels in cirrhotic patients with or without PVT (SMD = 0.12, 95% CI=-0.46 to 0.70, P = 0.68), but there was significant heterogeneity (I2 = 95.52%, P = 0.00). Meta-regression analysis indicated that differences in mean FVIII activity levels in the PVT group, the number of cases in the non-PVT group, and the study design methods partially contributed to the heterogeneity (P < 0.05). However, compared to the non-PVT group, the PVT group had higher FVIII/PC ratio with a statistically significant difference (SMD = 0.39, 95% CI: 0.15 to 0.63, P = 0.00), and there was no significant heterogeneity (I2 = 28.62%). CONCLUSION: In conclusion, the FVIII/PC ratio not only reflects the severity of liver disease, but also can be used as one of the predictors of PVT development.

AFSC: A self-supervised augmentation-free spatial clustering method based on contrastive learning for identifying spatial domains.

Recent research in spatial transcriptomics allows researchers to analyze gene expression without losing spatial information. Spatial information can assist in cell communication, identification of new cell subtypes, which provides important research methods for multiple fields such as microenvironment interactions and pathological processes of diseases. Identifying spatial domains is an important step in spatial transcriptomics analysis, and improving spatial clustering methods can benefit for identifying spatial domains. In addition to eliminating noise in original gene expression, how to use spatial information to assist clustering has also become a new problem. A variety of calculating methods have been applied to spatial clustering, including contrastive learning methods. However, existing spatial clustering methods based on contrastive learning use data augmentation to generate positive and negative pairs, which will inevitably destroy the biological meaning of the data. We propose a new self-supervised spatial clustering method based on contrastive learning, Augmentation-Free Spatial Clustering (AFSC), which integrates spatial information and gene expression to learn latent representations. We construct a contrastive learning module without negative pairs or data augmentation by designing Teacher and Student Encoder. We also design an unsupervised clustering module to make clustering and contrastive learning be trained together. Experiments on multiple spatial transcriptomics datasets at different resolutions demonstrate that our method performs well in self-supervised spatial clustering tasks. Furthermore, the learned representations can be used for various downstream tasks including visualization and trajectory inference.

Recent advances in the use of resveratrol against Staphylococcus aureus infections (Review).

As a notorious bacterial pathogen, Staphylococcus aureus (S. aureus) can readily induce infections in the community and hospital, causing significant morbidity and mortality. With the extensive rise of multiple resistance, conventional antibiotic therapy has rapidly become ineffective for related infections. Resveratrol is a naturally occurring polyphenolic substance that has been demonstrated to have effective antimicrobial activity against S. aureus. Resveratrol at sub-inhibitory doses can suppress the expression of virulence factors, contributing to attenuated biofilm formation, interference with quorum sensing and the inhibition of the production of toxins. As a promising efflux pump inhibitor, resveratrol enhances antibiotic susceptibility to a certain extent. In conjunction with conventional antibiotics, resveratrol displays unique synergistic effects with norfloxacin and aminoglycoside on S. aureus, yet antagonizes the lethal effects of daptomycin, oxacillin, moxifloxacin and levofloxacin. Nevertheless, given the low oral bioavailability of resveratrol, advanced formulations need to be developed to delay the rapid metabolism conversion to low or inactive conjugates. The present review discusses the antibacterial properties of resveratrol against S. aureus, in an aim to provide in-depth insight for researchers to address the challenges of antimicrobial resistance.

Advances in antibacterial activity of zinc oxide nanoparticles against Staphylococcus aureus (Review).

Nanoparticles (NPs) are one of the promising strategies to deal with bacterial infections. As the main subset of NPs, metal and metal oxide NPs show destructive power against bacteria by releasing metal ions, direct contact of cell membranes and antibiotic delivery. Recently, a number of researchers have focused on the antibacterial activity of zinc oxide nanoparticles (ZnO NPs) against Staphylococcus aureus (S. aureus). Currently, there is a lack of a comprehensive review on ZnO NPs against S. aureus. Therefore, in this review, the antibacterial activity against S. aureus of ZnO NPs made by various synthetic methods was summarized, particularly the green synthetic ZnO NPs. The synergistic antibacterial effect against S. aureus of ZnO NPs with antibiotics was also summarized. Furthermore, the present review also emphasized the enhanced activities against S. aureus of ZnO nanocomposites, nano-hybrids and functional ZnO NPs.

Hollow Structure Derived Phosphide Nanosheets for Water Oxidation.

Avoiding the stacking of active sites in catalyst structural design is a promising route for realizing active oxygen evolution reaction (OER). Herein, using a CoFe Prussian blue analoge cube with hollow structure (C-CoFe PBA) as a derived support, a highly effective Ni2P-FeP4-Co2P catalyst with a larger specific surface area is reported. Benefiting from the abundant active sites and fast charge transfer capability of the phosphide nanosheets, the Ni2P-FeP4-Co2P catalyst in 1 m KOH requires only overpotentials of 248 and 277 mV to reach current density of 10 and 50 mA cm-2 and outperforms the commercial catalyst RuO2 and most reported non-noble metal OER catalysts. In addition, the two-electrode system consisting of Ni2P-FeP4-Co2P and Pt/C is able to achieve a current density of 10 and 50 mA cm-2 at 1.529 and 1.65 V. This work provides more ideas and directions for synthesizing transition metal catalysts for efficient OER performance.