Advances in functional lipid nanoparticles: from drug delivery platforms to clinical applications.

Since Doxil's first clinical approval in 1995, lipid nanoparticles have garnered great interest and shown exceptional therapeutic efficacy. It is clear from the licensure of two RNA treatments and the mRNA-COVID-19 vaccination that lipid nanoparticles have immense potential for delivering nucleic acids. The review begins with a list of lipid nanoparticle types, such as liposomes and solid lipid nanoparticles. Then it moves on to the earliest lipid nanoparticle forms, outlining how lipid is used in a variety of industries and how it is used as a versatile nanocarrier platform. Lipid nanoparticles must then be functionally modified. Various approaches have been proposed for the synthesis of lipid nanoparticles, such as High-Pressure Homogenization (HPH), microemulsion methods, solvent-based emulsification techniques, solvent injection, phase reversal, and membrane contractors. High-pressure homogenization is the most commonly used method. All of the methods listed above follow four basic steps, as depicted in the flowchart below. Out of these four steps, the process of dispersing lipids in an aqueous medium to produce liposomes is the most unpredictable step. A short outline of the characterization of lipid nanoparticles follows discussions of applications for the trapping and transporting of various small molecules. It highlights the use of rapamycin-coated lipid nanoparticles in glioblastoma and how lipid nanoparticles function as a conjugator in the delivery of anticancer-targeting nucleic acids. High biocompatibility, ease of production, scalability, non-toxicity, and tailored distribution are just a meager of the enticing allowances of using lipid nanoparticles as drug delivery vehicles. Due to the present constraints in drug delivery, more research is required to utterly realize the potential of lipid nanoparticles for possible clinical and therapeutic purposes.

DegronMD: Leveraging Evolutionary and Structural Features for Deciphering Protein-Targeted Degradation, Mutations, and Drug Response to Degrons.

Protein-targeted degradation is an emerging and promising therapeutic approach. The specificity of degradation and the maintenance of cellular homeostasis are determined by the interactions between E3 ubiquitin ligase and degradation signals, known as degrons. The human genome encodes over 600 E3 ligases; however, only a small number of targeted degron instances have been identified so far. In this study, we introduced DegronMD, an open knowledgebase designed for the investigation of degrons, their associated dysfunctional events, and drug responses. We revealed that degrons are evolutionarily conserved and tend to occur near the sites of protein translational modifications, particularly in the regions of disordered structure and higher solvent accessibility. Through pattern recognition and machine learning techniques, we constructed the degrome landscape across the human proteome, yielding over 18,000 new degrons for targeted protein degradation. Furthermore, dysfunction of degrons disrupts the degradation process and leads to the abnormal accumulation of proteins; this process is associated with various types of human cancers. Based on the estimated phenotypic changes induced by somatic mutations, we systematically quantified and assessed the impact of mutations on degron function in pan-cancers; these results helped to build a global mutational map on human degrome, including 89,318 actionable mutations that may induce the dysfunction of degrons and disrupt protein degradation pathways. Multiomics integrative analysis unveiled over 400 drug resistance events associated with the mutations in functional degrons. DegronMD, accessible at https://bioinfo.uth.edu/degronmd, is a useful resource to explore the biological mechanisms, infer protein degradation, and assist with drug discovery and design on degrons.

A Framework for Detecting Thyroid Cancer from Ultrasound and Histopathological Images Using Deep Learning, Meta-Heuristics, and MCDM Algorithms.

Computer-assisted diagnostic systems have been developed to aid doctors in diagnosing thyroid-related abnormalities. The aim of this research is to improve the diagnosis accuracy of thyroid abnormality detection models that can be utilized to alleviate undue pressure on healthcare professionals. In this research, we proposed deep learning, metaheuristics, and a MCDM algorithms-based framework to detect thyroid-related abnormalities from ultrasound and histopathological images. The proposed method uses three recently developed deep learning techniques (DeiT, Swin Transformer, and Mixer-MLP) to extract features from the thyroid image datasets. The feature extraction techniques are based on the Image Transformer and MLP models. There is a large number of redundant features that can overfit the classifiers and reduce the generalization capabilities of the classifiers. In order to avoid the overfitting problem, six feature transformation techniques (PCA, TSVD, FastICA, ISOMAP, LLE, and UMP) are analyzed to reduce the dimensionality of the data. There are five different classifiers (LR, NB, SVC, KNN, and RF) evaluated using the 5-fold stratified cross-validation technique on the transformed dataset. Both datasets exhibit large class imbalances and hence, the stratified cross-validation technique is used to evaluate the performance. The MEREC-TOPSIS MCDM technique is used for ranking the evaluated models at different analysis stages. In the first stage, the best feature extraction and classification techniques are chosen, whereas, in the second stage, the best dimensionality reduction method is evaluated in wrapper feature selection mode. Two best-ranked models are further selected for the weighted average ensemble learning and features selection using the recently proposed meta-heuristics FOX-optimization algorithm. The PCA+FOX optimization-based feature selection + random forest model achieved the highest TOPSIS score and performed exceptionally well with an accuracy of 99.13%, F2-score of 98.82%, and AUC-ROC score of 99.13% on the ultrasound dataset. Similarly, the model achieved an accuracy score of 90.65%, an F2-score of 92.01%, and an AUC-ROC score of 95.48% on the histopathological dataset. This study exploits the combination novelty of different algorithms in order to improve the thyroid cancer diagnosis capabilities. This proposed framework outperforms the current state-of-the-art diagnostic methods for thyroid-related abnormalities in ultrasound and histopathological datasets and can significantly aid medical professionals by reducing the excessive burden on the medical fraternity.

Rewired m6A epitranscriptomic networks link mutant p53 to neoplastic transformation.

N6-methyladenosine (m6A), one of the most prevalent mRNA modifications in eukaryotes, plays a critical role in modulating both biological and pathological processes. However, it is unknown whether mutant p53 neomorphic oncogenic functions exploit dysregulation of m6A epitranscriptomic networks. Here, we investigate Li-Fraumeni syndrome (LFS)-associated neoplastic transformation driven by mutant p53 in iPSC-derived astrocytes, the cell-of-origin of gliomas. We find that mutant p53 but not wild-type (WT) p53 physically interacts with SVIL to recruit the H3K4me3 methyltransferase MLL1 to activate the expression of m6A reader YTHDF2, culminating in an oncogenic phenotype. Aberrant YTHDF2 upregulation markedly hampers expression of multiple m6A-marked tumor-suppressing transcripts, including CDKN2B and SPOCK2, and induces oncogenic reprogramming. Mutant p53 neoplastic behaviors are significantly impaired by genetic depletion of YTHDF2 or by pharmacological inhibition using MLL1 complex inhibitors. Our study reveals how mutant p53 hijacks epigenetic and epitranscriptomic machinery to initiate gliomagenesis and suggests potential treatment strategies for LFS gliomas.

Benchmark of embedding-based methods for accurate and transferable prediction of drug response.

Prediction of therapy response has been a major challenge in cancer precision medicine due to the extensive tumor heterogeneity. Recently, several deep learning methods have been developed to predict drug response by utilizing various omics data. Most of them train models by using the drug-response screening data generated from cell lines and then use these models to predict response in cancer patient data. In this study, we focus on and evaluate deep learning methods using transcriptome data for the long-standing question of personalized drug-response prediction. We developed an embedding-based approach for drug-response prediction and benchmarked similar methods for their performance. For all methods, we used pretreatment transcriptome data to train models and then conducted a comprehensive evaluation and comparison of the models using cross-panels, cross-datasets and target genes. We further validated the methods using three independent datasets assessing multiple compounds for their predictive capability of drug response, survival outcome and cell line status. As a result, the methods building on gene embeddings had an overall competitive performance with reduced overfitting when we applied evaluation parameters for model fitting as well as the correlation with clinical outcomes in the validation data. We further developed an ensemble model to combine the results from the three most competitive methods for an overall prediction. Finally, we developed DrVAEN (https://bioinfo.uth.edu/drvaen), a user-friendly and easy-accessible web-server that hosts all these methods for drug-response prediction and model comparison for broad use in cancer research, method evaluation and drug development.

Hereditary retinoblastoma iPSC model reveals aberrant spliceosome function driving bone malignancies.

The RB1 gene is frequently mutated in human cancers but its role in tumorigenesis remains incompletely defined. Using an induced pluripotent stem cell (iPSC) model of hereditary retinoblastoma (RB), we report that the spliceosome is an up-regulated target responding to oncogenic stress in RB1-mutant cells. By investigating transcriptomes and genome occupancies in RB iPSC­derived osteoblasts (OBs), we discover that both E2F3a, which mediates spliceosomal gene expression, and pRB, which antagonizes E2F3a, coregulate more than one-third of spliceosomal genes by cobinding to their promoters or enhancers. Pharmacological inhibition of the spliceosome in RB1-mutant cells leads to global intron retention, decreased cell proliferation, and impaired tumorigenesis. Tumor specimen studies and genome-wide TCGA (The Cancer Genome Atlas) expression profile analyses support the clinical relevance of pRB and E2F3a in modulating spliceosomal gene expression in multiple cancer types including osteosarcoma (OS). High levels of pRB/E2F3a­regulated spliceosomal genes are associated with poor OS patient survival. Collectively, these findings reveal an undiscovered connection between pRB, E2F3a, the spliceosome, and tumorigenesis, pointing to the spliceosomal machinery as a potentially widespread therapeutic vulnerability of pRB-deficient cancers.

PRMT1-mediated H4R3me2a recruits SMARCA4 to promote colorectal cancer progression by enhancing EGFR signaling.

BACKGROUND: Aberrant changes in epigenetic mechanisms such as histone modifications play an important role in cancer progression. PRMT1 which triggers asymmetric dimethylation of histone H4 on arginine 3 (H4R3me2a) is upregulated in human colorectal cancer (CRC) and is essential for cell proliferation. However, how this dysregulated modification might contribute to malignant transitions of CRC remains poorly understood. METHODS: In this study, we integrated biochemical assays including protein interaction studies and chromatin immunoprecipitation (ChIP), cellular analysis including cell viability, proliferation, colony formation, and migration assays, clinical sample analysis, microarray experiments, and ChIP-Seq data to investigate the potential genomic recognition pattern of H4R3me2s in CRC cells and its effect on CRC progression. RESULTS: We show that PRMT1 and SMARCA4, an ATPase subunit of the SWI/SNF chromatin remodeling complex, act cooperatively to promote colorectal cancer (CRC) progression. We find that SMARCA4 is a novel effector molecule of PRMT1-mediated H4R3me2a. Mechanistically, we show that H4R3me2a directly recruited SMARCA4 to promote the proliferative, colony-formative, and migratory abilities of CRC cells by enhancing EGFR signaling. We found that EGFR and TNS4 were major direct downstream transcriptional targets of PRMT1 and SMARCA4 in colon cells, and acted in a PRMT1 methyltransferase activity-dependent manner to promote CRC cell proliferation. In vivo, knockdown or inhibition of PRMT1 profoundly attenuated the growth of CRC cells in the C57BL/6 J-ApcMin/+ CRC mice model. Importantly, elevated expression of PRMT1 or SMARCA4 in CRC patients were positively correlated with expression of EGFR and TNS4, and CRC patients had shorter overall survival. These findings reveal a critical interplay between epigenetic and transcriptional control during CRC progression, suggesting that SMARCA4 is a novel key epigenetic modulator of CRC. Our findings thus highlight PRMT1/SMARCA4 inhibition as a potential therapeutic intervention strategy for CRC. CONCLUSION: PRMT1-mediated H4R3me2a recruits SMARCA4, which promotes colorectal cancer progression by enhancing EGFR signaling.

Deep generative neural network for accurate drug response imputation.

Drug response differs substantially in cancer patients due to inter- and intra-tumor heterogeneity. Particularly, transcriptome context, especially tumor microenvironment, has been shown playing a significant role in shaping the actual treatment outcome. In this study, we develop a deep variational autoencoder (VAE) model to compress thousands of genes into latent vectors in a low-dimensional space. We then demonstrate that these encoded vectors could accurately impute drug response, outperform standard signature-gene based approaches, and appropriately control the overfitting problem. We apply rigorous quality assessment and validation, including assessing the impact of cell line lineage, cross-validation, cross-panel evaluation, and application in independent clinical data sets, to warrant the accuracy of the imputed drug response in both cell lines and cancer samples. Specifically, the expression-regulated component (EReX) of the observed drug response achieves high correlation across panels. Using the well-trained models, we impute drug response of The Cancer Genome Atlas data and investigate the features and signatures associated with the imputed drug response, including cell line origins, somatic mutations and tumor mutation burdens, tumor microenvironment, and confounding factors. In summary, our deep learning method and the results are useful for the study of signatures and markers of drug response.

Decoding regulatory structures and features from epigenomics profiles: A Roadmap-ENCODE Variational Auto-Encoder (RE-VAE) model.

The development of chromatin immunoprecipitation (ChIP) with massively parallel DNA sequencing (ChIP-seq) technologies has promoted generation of large-scale epigenomics data, providing us unprecedented opportunities to explore the landscape of epigenomic profiles at scales across both histone marks and tissue types. In addition to many tools directly for data analysis, advanced computational approaches, such as deep learning, have recently become promising to deeply mine the data structures and identify important regulators from complex functional genomics data. We implemented a neural network framework, a Variational Auto-Encoder (VAE) model, to explore the epigenomic data from the Roadmap Epigenomics Project and the Encyclopedia of DNA Elements (ENCODE) project. Our model is applied to 935 reference samples, covering 28 tissues and 12 histone marks. We used the enhancer and promoter regions as the annotation features and ChIP-seq signal values in these regions as the feature values. Through a parameter sweep process, we identified the suitable hyperparameter values and built a VAE model to represent the epigenomics data and to further explore the biological regulation. The resultant Roadmap-ENCODE VAE (RE-VAE) model contained data compression and feature representation. Using the compressed data in the latent space, we found that the majority of histone marks were well clustered but not for tissues or cell types. Tissue or cell specificity was observed only in some histone marks (e.g., H3K4me3 and H3K27ac) and could be characterized when the number of tissue samples is large (e.g., blood and brain). In blood, the contributive regions and genes identified by RE-VAE model were confirmed by tissue-specificity enrichment analysis with an independent tissue expression panel. Finally, we demonstrated that RE-VAE model could detect cancer cell lines with similar epigenomics profiles. In conclusion, we introduced and implemented a VAE model to represent large-scale epigenomics data. The model could be used to explore classifications of histone modifications and tissue/cell specificity and to classify new data with unknown sources.

KinaseMD: kinase mutations and drug response database.

Mutations in kinases are abundant and critical to study signaling pathways and regulatory roles in human disease, especially in cancer. Somatic mutations in kinase genes can affect drug treatment, both sensitivity and resistance, to clinically used kinase inhibitors. Here, we present a newly constructed database, KinaseMD (kinase mutations and drug response), to structurally and functionally annotate kinase mutations. KinaseMD integrates 679 374 somatic mutations, 251 522 network-rewiring events, and 390 460 drug response records curated from various sources for 547 kinases. We uniquely annotate the mutations and kinase inhibitor response in four types of protein substructures (gatekeeper, A-loop, G-loop and αC-helix) that are linked to kinase inhibitor resistance in literature. In addition, we annotate functional mutations that may rewire kinase regulatory network and report four phosphorylation signals (gain, loss, up-regulation and down-regulation). Overall, KinaseMD provides the most updated information on mutations, unique annotations of drug response especially drug resistance and functional sites of kinases. KinaseMD is accessible at https://bioinfo.uth.edu/kmd/, having functions for searching, browsing and downloading data. To our knowledge, there has been no systematic annotation of these structural mutations linking to kinase inhibitor response. In summary, KinaseMD is a centralized database for kinase mutations and drug response.

Decoding whole-genome mutational signatures in 37 human pan-cancers by denoising sparse autoencoder neural network.

Millions of somatic mutations have recently been discovered in cancer genomes. These mutations in cancer genomes occur due to internal and external mutagenesis forces. Decoding the mutational processes by examining their unique patterns has successfully revealed many known and novel signatures from whole exome data, but many still remain undiscovered. Here, we developed a deep learning approach, DeepMS, to decompose mutational signatures using 52,671,908 somatic mutations from 2780 highly curated cancer genomes with whole genome sequencing (WGS) in 37 cancer types/subtypes. With rigorous model training and comparison, we characterized 54 signatures for single base substitutions (SBSs), 11 for doublet base substitutions (DBSs) and 16 for small insertions and deletions (Indels). Compared to the previous methods, DeepMS could discover 37 SBS, 5 DBS, and 9 Indel new signatures, many of which represent associations with DNA mismatch or base excision repair and cisplatin therapy mechanisms. We further developed a regression-based model to estimate the correlation between signatures and clinical and demographical phenotypes. The first deep learning model DeepMS on WGS somatic mutational profiles enable us identify more comprehensive context-based mutational signatures than traditional NMF approaches. Our work substantially expands the landscape of the naturally occurring mutational signatures in cancer genomes, and provides new insights into cancer biology.

PRMT5-dependent transcriptional repression of c-Myc target genes promotes gastric cancer progression.

The proto-oncogene c-Myc regulates multiple biological processes mainly through selectively activating gene expression. However, the mechanisms underlying c-Myc-mediated gene repression in the context of cancer remain less clear. This study aimed to clarify the role of PRMT5 in the transcriptional repression of c-Myc target genes in gastric cancer. Methods: Immunohistochemistry was used to evaluate the expression of PRMT5, c-Myc and target genes in gastric cancer patients. PRMT5 and c-Myc interaction was assessed by immunofluorescence, co-immunoprecipitation and GST pull-down assays. Bioinformatics analysis, immunoblotting, real-time PCR, chromatin immunoprecipitation, and rescue experiments were used to evaluate the mechanism. Results: We found that c-Myc directly interacts with protein arginine methyltransferase 5 (PRMT5) to transcriptionally repress the expression of a cohort of genes, including PTEN, CDKN2C (p18INK4C), CDKN1A (p21CIP1/WAF1), CDKN1C (p57KIP2) and p63, to promote gastric cancer cell growth. Specifically, we found that PRMT5 was required to promote gastric cancer cell growth in vitro and in vivo, and for transcriptional repression of this cohort of genes, which was dependent on its methyltransferase activity. Consistently, the promoters of this gene cohort were enriched for both PRMT5-mediated symmetric di-methylation of histone H4 on Arg 3 (H4R3me2s) and c-Myc, and c-Myc depletion also upregulated their expression. H4R3me2s also colocalized with the c-Myc-binding E-box motif (CANNTG) on these genes. We show that PRMT5 directly binds to c-Myc, and this binding is required for transcriptional repression of the target genes. Both c-Myc and PRMT5 expression levels were upregulated in primary human gastric cancer tissues, and their expression levels inversely correlated with clinical outcomes. Conclusions: Taken together, our study reveals a novel mechanism by which PRMT5-dependent transcriptional repression of c-Myc target genes is required for gastric cancer progression, and provides a potential new strategy for therapeutic targeting of gastric cancer.

6mA-Finder: a novel online tool for predicting DNA N6-methyladenine sites in genomes.

MOTIVATION: DNA N6-methyladenine (6 mA) has recently been found as an essential epigenetic modification, playing its roles in a variety of cellular processes. The abnormal status of DNA 6 mA modification has been reported in cancer and other disease. The annotation of 6 mA marks in genome is the first crucial step to explore the underlying molecular mechanisms including its regulatory roles. RESULTS: We present a novel online DNA 6 mA site tool, 6 mA-Finder, by incorporating seven sequence-derived information and three physicochemical-based features through recursive feature elimination strategy. Our multiple cross-validations indicate the promising accuracy and robustness of our model. 6 mA-Finder outperforms its peer tools in general and species-specific 6 mA site prediction, suggesting it can provide a useful resource for further experimental investigation of DNA 6 mA modification. AVAILABILITY AND IMPLEMENTATION: https://bioinfo.uth.edu/6mA_Finder. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

VISDB: a manually curated database of viral integration sites in the human genome.

Virus integration into the human genome occurs frequently and represents a key driving event in human disease. Many studies have reported viral integration sites (VISs) proximal to structural or functional regions of the human genome. Here, we systematically collected and manually curated all VISs reported in the literature and publicly available data resources to construct the Viral Integration Site DataBase (VISDB, https://bioinfo.uth.edu/VISDB). Genomic information including target genes, nearby genes, nearest transcription start site, chromosome fragile sites, CpG islands, viral sequences and target sequences were integrated to annotate VISs. We further curated VIS-involved oncogenes and tumor suppressor genes, virus-host interactions involved in non-coding RNA (ncRNA), target gene and microRNA expression in five cancers, among others. Moreover, we developed tools to visualize single integration events, VIS clusters, DNA elements proximal to VISs and virus-host interactions involved in ncRNA. The current version of VISDB contains a total of 77 632 integration sites of five DNA viruses and four RNA retroviruses. VISDB is currently the only active comprehensive VIS database, which provides broad usability for the study of disease, virus related pathophysiology, virus biology, host-pathogen interactions, sequence motif discovery and pattern recognition, molecular evolution and adaption, among others.

A panel of miRNAs derived from plasma extracellular vesicles as novel diagnostic biomarkers of lung adenocarcinoma.

Lung cancer is the leading cause of cancer-related morbidity and mortality worldwide, with lung adenocarcinoma (LUAD) being the most common histological subtype (approximately 40%). In the absence of reliable screening biomarkers for early diagnosis, most patients with LUAD are inevitably diagnosed at an advanced stage. MicroRNAs (miRNAs) encapsulated within plasma-derived extracellular vesicles (EVs) may be suitable for use as noninvasive diagnostic biomarkers for aggressive malignancies, including LUAD. In this study, we first investigated the miRNA profiles of plasma-derived EVs from LUAD patients and healthy donors, and then systematically evaluated the expression patterns of selected plasma-derived EV miRNAs in a large cohort of patients with LUAD and healthy controls. Notably, we observed that miR-451a, miR-194-5p, and miR-486-5p were significantly increased in EVs from LUAD patients, compared to healthy controls. The area under the curve values for the three miRNAs were 0.9040 (95% confidence interval [CI], 0.8633-0.9447) for miR-451a, 0.7492 (95% CI, 0.6992-0.7992) for miR-194-5p, and 0.9574 (95% CI, 0.9378-0.9769) for miR-486-5p, while the AUC of the combination of these three miRNAs was 0.9650. Thus, these results suggest that these EV miRNAs may be promising candidates for the development of highly effective, noninvasive biomarkers for early LUAD diagnosis.

Delivery of platelet TPM3 mRNA into breast cancer cells via microvesicles enhances metastasis.

Platelets are implicated in the pathophysiology of breast and other cancers through their role in exchanging biomolecules with tumor cells in the tumor microenvironment. Such exchange results in tumor-educated platelets with altered RNA expression profiles. Multiple lines of evidence indicate that platelet RNA profiles may be suitable as diagnostic biomarkers for cancer-related biological processes. In this study, we characterized the gene expression signatures of platelets in breast cancer (BC) by high-throughput sequencing and quantitative real-time RT-PCR. Our results indicate that the expression of TPM3 (tropomyosin 3) mRNA is significantly elevated in platelets from patients with BC compared with age-matched healthy control subjects. Furthermore, up-regulation of TPM3 mRNA in platelets was found to be significantly correlated with metastasis in patients with BC. Finally, we report that platelet TPM3 mRNA is delivered into BC cells through microvesicles and leads to enhanced migrative phenotype of BC cells. In summary, our findings suggest that the transfer of platelet TPM3 mRNA into cancer cells via microvesicles promotes cancer cell migration, and thus platelet-derived TPM3 mRNA may be a suitable biomarker for early diagnosis of metastatic BC.

ANCO-GeneDB: annotations and comprehensive analysis of candidate genes for alcohol, nicotine, cocaine and opioid dependence.

Studies have shown that genetic factors play an important role in the risk to substance addiction and abuse. So far, various genetic and genomic studies have reported the related evidence. These rich, but highly heterogeneous, data provide us an unprecedented opportunity to systematically collect, curate and assess the genetic and genomic signals from published studies and to perform a comprehensive analysis of their features, functional roles and druggability. Such genetic data resources have been made available for other disease or phenotypes but not for major substance dependence yet. Here, we report comprehensive data collection and secondary analyses of four phenotypes of dependence: alcohol dependence, nicotine dependence, cocaine dependence and opioid dependence, collectively named as Alcohol, Nicotine, Cocaine and Opioid (ANCO) dependence. We built the ANCO-GeneDB, an ANCO-dependence-associated gene resource database. ANCO-GeneDB includes resources from genome-wide association studies and candidate gene-based studies, transcriptomic studies, methylation studies, literature mining and drug-target data, as well as the derived data such as spatial-temporal gene expression, promoters, enhancers and expression quantitative trait loci. All associated genes and genetic variants are well annotated by using the collected evidence. Based on the collected data, we performed integrative, secondary analyses to prioritize genes, pathways, eQTLs and tissues that are significantly enriched in ANCO-related phenotypes.

Salvianolic Acid B and Ginsenoside Re Synergistically Protect Against Ox-LDL-Induced Endothelial Apoptosis Through the Antioxidative and Antiinflammatory Mechanisms.

Salvianolic acid B (SalB) and ginsenoside Re (Re) protect endotheliocytes against apoptosis through different mechanisms. However, whether both compounds could synergistically protect endothelial cells against oxidized low-density lipoprotein (Ox-LDL)-induced apoptosis is unclear. This study aimed to assess the protective effect of combined SalB and Re (SR) treatment on Ox-LDL-induced endothelial apoptosis and to explore the mechanism underlying this effect. Results showed that SalB, Re, or SR could protect against Ox-LDL-induced endothelial apoptosis. Furthermore, the composition of SR was optimized through central composite design with response surface methodology. SR with a composition of 60 µg/mL of SalB and 120 µg/mL of Re exerted the optimal protective effect. Network pharmacology research revealed that SalB and Re in SR synergistically protect against Ox-LDL-induced endothelial apoptosis by regulating oxidative stress and phlogistic pathways. In vitro experiments confirmed these results. Compared with the same dose of SalB or Re alone, SR significantly decreased the contents of inflammatory mediators and increased the activities of antioxidant enzymes. SR could synergistically restore the balanced redox state of the cells and inhibit the activation of nuclear transcription factor kappa B and the caspase cascade by activating the phosphatidylinositol 3 kinase/protein kinase B pathway and inhibiting the phosphorylation of p38 mitogen-activated protein kinase. These pathways are regulated by down-regulating the expression of lectin-like Ox-LDL receptor-1 and NADPH oxidase and up-regulating the expression of estrogen receptor alpha. Therefore, SR effectively prevents Ox-LDL-induced endothelial apoptosis through antioxidative and antiinflammatory mechanisms.

Angiotensin II promotes the anticoagulant effects of rivaroxaban via angiotensin type 2 receptor signaling in mice.

Rivaroxaban is an oral direct factor Xa inhibitor approved for the treatment of stroke and systemic thromboembolism in patients with non-valvular atrial fibrillation. Despite its efficacy, rivaroxaban therapy results in adverse effects and complications, such as bleeding. Angiotensin II (AngII) is implicated in many cardiovascular conditions, such as hypertension and heart failure. In this study, we investigate whether AngII influences anticoagulant effects of rivaroxaban by using an experimental mouse model with type 2 diabetes mellitus and advanced glycation end product (AGE)-exposed human umbilical vein endothelial cells (HUVECs). We found that AngII promoted the anticoagulant effects of rivaroxaban in KKAy mice. The combination of rivaroxaban and AngII enhanced in vivo tissue factor pathway inhibitor (TFPI) activity and induced TFPI expression and activity in AGE-exposed HUVECs. Angiotensin type 2 receptor (AT2R) and Mas antagonists attenuated the AngII-enhanced anticoagulant action of rivaroxaban in vivo, and abolished the increased endothelial TFPI expression and activity. However, angiotensin type 1 receptor (AT1R) antagonist exerted no effects. Additionally, combination of rivaroxaban and AngII induced aortic AT2R and Mas expression. Our data suggest that the anticoagulant effects of rivaroxaban are promoted by AngII via AT2R and Mas signaling. These findings are significant for the clinical administration of rivaroxaban.