Unveiling the association between HMG-CoA reductase inhibitors and bladder cancer: a comprehensive analysis using Mendelian randomization, animal models, and transcriptomics.

This study utilized Mendelian randomization (MR) analysis and genome-wide association study (GWAS) data to investigate the association between commonly prescribed drugs and bladder cancer (BLCA) risk. Our results revealed that HMG CoA reductase (HMGCR) inhibitors, specifically simvastatin, are significantly associated with reduced BLCA risk. We further showed that simvastatin could significantly inhibit BLCA proliferation and epithelial-mesenchymal transition in animal models, with transcriptomic data identifying several pathways associated with these processes. Higher expression of HMGCR were linked with BLCA development and progression, and certain blood lipids, such as lipoprotein particles and very low density lipoprotein (VLDL) cholesterol, might influence BLCA risk. These findings suggested that HMGCR inhibitors, particularly simvastatin, could be potential treatment options or adjuvant therapies for BLCA.

GCHN-DTI: Predicting drug-target interactions by graph convolution on heterogeneous networks.

Determining the interaction of drug and target plays a key role in the process of drug development and discovery. The calculation methods can predict new interactions and speed up the process of drug development. In recent studies, the network-based approaches have been proposed to predict drug-target interactions. However, these methods cannot fully utilize the node information from heterogeneous networks. Therefore, we propose a method based on heterogeneous graph convolutional neural network for drug-target interaction prediction, GCHN-DTI (Predicting drug-target interactions by graph convolution on heterogeneous net-works), to predict potential DTIs. GCHN-DTI integrates network information from drug-target interactions, drug-drug interactions, drug-similarities, target-target interactions, and target-similarities. Then, the graph convolution operation is used in the heterogeneous network to obtain the node embedding of the drugs and the targets. Furthermore, we incorporate an attention mechanism between graph convolutional layers to combine node embedding from each layer. Finally, the drug-target interaction score is predicted based on the node embedding of the drugs and the targets. Our model uses fewer network types and achieves higher prediction performance. In addition, the prediction performance of the model will be significantly improved on the dataset with a higher proportion of positive samples. The experimental evaluations show that GCHN-DTI outperforms several state-of-the-art prediction methods.

Deacetylation of Notch1 by SIRT1 contributes to HBsAg- and HBeAg-mediated M2 macrophage polarization.

Hepatic macrophages are involved in both pathogen clearance and immunopathogenesis. Emerging evidence demonstrates that macrophage polarization plays a critical role in hepatitis B virus (HBV)-induced immune impairment and liver pathology. However, it remains largely unknown as to how HBV infection facilitates M2 macrophage polarization. Here, a mouse HBV infection model was established by hydrodynamic injection with a vector containing 1.3-fold overlength HBV genome via the tail vein. Coculture experiments with HBV-producing HepG2.2.15 cells and macrophages were established in vitro. We found that HBV-inhibited M1 while enhancing M2 markers, which was accompanied by decreased proinflammatory tumor necrosis factor-α (TNF-α) and augmented anti-inflammatory IL-10 expression. Furthermore, both hepatitis B surface antigen (HBsAg) and hepatitis B e antigen (HBeAg) secretion contributed to HBV-triggered macrophage polarization from M1 toward M2 phenotype. Mechanistically, HBsAg and HBeAg could upregulate the sirtuins 1 (SIRT1) deacetylase expression, which in turn promote deacetylation of the Notch1 intracellular domain (NICD), leading to increased Akt phosphorylation and decreased NF-κB nuclear translocation in macrophages. Our findings suggest that NICD deacetylation by SIRT1 contributes to HBsAg- and HBeAg-mediated M2 macrophage polarization, raising the possibility of targeting SIRT1/Notch1 pathway in macrophages to treat HBV immune evasion and chronic HBV infection.NEW & NOTEWORTHY This study identified a previously unrecognized molecular mechanism of HBV-mediated suppression of innate immune responses. We demonstrate that deacetylation of NICD by SIRT1 contributes to HBsAg- and HBeAg-mediated M2 macrophage polarization, which may aid in the development of new macrophage-based immunotherapy for chronic HBV infection and related diseases.

Botrytis cinerea BcSSP2 protein is a late infection phase, cytotoxic effector.

Botrytis cinerea is a broad-host-range necrotrophic phytopathogen responsible for serious diseases in leading crops. To facilitate infection, B. cinerea secretes a large number of effectors that induce plant cell death. In screening secretome data of B. cinerea during infection stage, we identified a phytotoxic protein (BcSSP2) that can also induce immune resistance in plants. BcSSP2 is a small, cysteine-rich protein without any known domains. Transient expression of BcSSP2 in leaves caused chlorosis that intensifies with time and eventually leads to death. Point mutations in eight of 10 cysteine residues abolished phytotoxicity, but residual toxic activity remained after heating treatment, suggesting contribution of unknown epitopes to protein phytotoxicity. The expression of bcssp2 was low during the first 36 h after inoculation and increased sharply upon transition to late infection stage. Deletion of bcssp2 did not cause statistically significant changes in lesions size on bean and tobacco leaves. Further analyses indicated that the phytotoxicity of BcSSP2 is negatively regulated by the receptor-like kinases BAK1 and SOBIR1. Collectively, our findings show that BcSSP2 is an effector protein that toxifies the host cells, but is also recognized by the plant immune system.

The immune-related biomarker TEK inhibits the development of clear cell renal cell carcinoma (ccRCC) by regulating AKT phosphorylation.

BACKGROUND: High immunogenicity is an important feature of ccRCC, but its underlying immune-related molecular mechanisms remain unclear. This study aimed to investigate the effect of immune-related gene TEK on ccRCC and its prognostic value. METHODS: The immune-related differentially expressed genes (DEGs) and transcription factors (TFs) in ccRCC were screened based on The Cancer Genome Atlas (TCGA) database, and a regulatory network of TF was constructed. Prognostic-related immune genes were screened by univariate Cox regression analysis and functional annotation was performed. Univariate and multivariate Cox regression analyses were performed to construct the immune gene risk model and identify the hub gene TEK that independently affected the prognosis of ccRCC. The effectiveness of the TEK was verified by external microarray datasets. The relationship between TEK and immune cells in ccRCC was evaluated based on Tumor Immune Estimation Resource (TIMER). The expression of TEK in clinical specimens was verified by qRT-PCR and immunohistochemical (IHC) staining. MTT and cloning formation assay were used to evaluate cell proliferation. Transwell assays were used to assess cell migration. Apoptosis was assessed by flow cytometry, and the expression of related proteins was detected by Western blot and immunofluorescence. RESULTS: We constructed a prognostic model consisting of 12 hub genes and performed risk scores to determine the relationship between these scores and prognosis. Through Cox regression analysis and survival analysis, TEK, an immune marker highly related to survival prognosis, was obtained and validated. In vitro experiments showed that knockdown of TEK promoted the proliferation and migration of ccRCC cells, and we found that TEK promoted apoptosis by regulating the phosphorylation of AKT, thereby inhibiting cell proliferation. CONCLUSIONS: TEK plays an important role in risk assessment and survival prediction for ccRCC patients as a new immune gene and maybe an emerging target for immunotherapy for ccRCC patients.

The inhibitory effect of melatonin on human prostate cancer.

Prostate cancer (PCa) is one of the most commonly diagnosed human cancers in males. Nearly 191,930 new cases and 33,330 new deaths of PCa are estimated in 2020. Androgen and androgen receptor pathways played essential roles in the pathogenesis of PCa. Androgen depletion therapy is the most used therapies for primary PCa patients. However, due to the high relapse and mortality of PCa, developing novel noninvasive therapies have become the focus of research. Melatonin is an indole-like neurohormone mainly produced in the human pineal gland with a prominent anti-oxidant property. The anti-tumor ability of melatonin has been substantially confirmed and several related articles have also reported the inhibitory effect of melatonin on PCa, while reviews of this inhibitory effect of melatonin on PCa in recent 10 years are absent. Therefore, we systematically discuss the relationship between melatonin disruption and the risk of PCa, the mechanism of how melatonin inhibited PCa, and the synergistic benefits of melatonin and other drugs to summarize current understandings about the function of melatonin in suppressing human prostate cancer. We also raise several unsolved issues that need to be resolved to translate currently non-clinical trials of melatonin for clinic use. We hope this literature review could provide a solid theoretical basis for the future utilization of melatonin in preventing, diagnosing and treating human prostate cancer. Video abstract.

Hepatitis B virus-triggered PTEN/ß-catenin/c-Myc signaling enhances PD-L1 expression to promote immune evasion.

Hepatitis B virus (HBV) exploits multiple strategies to evade host immune surveillance. Programmed cell death 1 (PD-1)/programmed death ligand 1 (PD-L1) signaling plays a critical role in regulating T cell homeostasis. However, it remains largely unknown as to how HBV infection elevates PD-L1 expression in hepatocytes. A mouse model of HBV infection was established by hydrodynamic injection with a vector containing 1.3-fold overlength HBV genome (pHBV1.3) via the tail vein. Coculture experiments with HBV-expressing hepatoma cells and Jurkat T cells were established in vitro. We observed significant decrease in the expression of phosphatase and tensin homolog deleted on chromosome 10 (PTEN) and increase in ß-catenin/PD-L1 expression in liver tissues from patients with chronic hepatitis B and mice subjected to pHBV1.3 hydrodynamic injection. Mechanistically, decrease in PTEN enhanced ß-catenin/c-Myc signaling and PD-L1 expression in HBV-expressing hepatoma cells, which in turn augmented PD-1 expression, lowered IL-2 secretion, and induced T cell apoptosis. However, ß-catenin disruption inhibited PTEN-mediated PD-L1 expression, which was accompanied by decreased PD-1 expression, and increased IL-2 production in T cells. Luciferase reporter assays revealed that c-Myc stimulated transcriptional activity of PD-L1. In addition, HBV X protein (HBx) and HBV polymerase (HBp) contributed to PTEN downregulation and ß-catenin/PD-L1 upregulation. Strikingly, PTEN overexpression in hepatocytes inhibited ß-catenin/PD-L1 signaling and promoted HBV clearance in vivo. Our findings suggest that HBV-triggered PTEN/ß-catenin/c-Myc signaling via HBx and HBp enhances PD-L1 expression, leading to inhibition of T cell response, and promotes HBV immune evasion.NEW & NOTEWORTHY This study demonstrates that during HBV infection, HBV can increase PD-L1 expression via PTEN/ß-catenin/c-Myc signaling pathway, which in turn inhibits T cell response and ultimately promotes HBV immune evasion. Targeting this signaling pathway is a potential strategy for immunotherapy of chronic hepatitis B.

Systematic analyses reveal long non-coding RNA (PTAF)-mediated promotion of EMT and invasion-metastasis in serous ovarian cancer.

BACKGROUND: A deeper mechanistic understanding of epithelial-to-mesenchymal transition (EMT) regulation is needed to improve current anti-metastasis strategies in ovarian cancer (OvCa). This study was designed to investigate the role of lncRNAs in EMT regulation during process of invasion-metastasis in serous OvCa to improve current anti-metastasis strategies for OvCa. METHODS: We systematically analyzes high-throughput gene expression profiles of both lncRNAs and protein-coding genes in OvCa samples with integrated epithelial (iE) subtype and integrated mesenchymal (iM) subtype labels. Mouse models, cytobiology, molecular biology assays and clinical samples were performed to elucidate the function and underlying mechanisms of lncRNA PTAF-mediated promotion of EMT and invasion-metastasis in serous OvCa. RESULTS: We constructed a lncRNA-mediated competing endogenous RNA (ceRNA) regulatory network that affects the expression of many EMT-related protein-coding genes in mesenchymal OvCa. Using a combination of in vitro and in vivo studies, we provided evidence that the lncRNA PTAF-miR-25-SNAI2 axis controlled EMT in OvCa. Our results revealed that up-regulated PTAF induced elevated SNAI2 expression by competitively binding to miR-25, which in turn promoted OvCa cell EMT and invasion. Moreover, we found that silencing of PTAF inhibited tumor progression and metastasis in an orthotopic mouse model of OvCa. We then observed a significant correlation between PTAF expression and EMT markers in OvCa patients. CONCLUSIONS: The lncRNA PTAF, a mediator of TGF-ß signaling, can predispose OvCa patients to metastases and may serve as a potential target for anti-metastatic therapies for mesenchymal OvCa patients.

SMGCN: Multiple Similarity and Multiple Kernel Fusion Based Graph Convolutional Neural Network for Drug-Target Interactions Prediction.

Accurately identifying potential drug-target interactions (DTIs) is a critical step in accelerating drug discovery. Despite many studies that have been conducted over the past decades, detecting DTIs remains a highly challenging and complicated process. Therefore, we propose a novel method called SMGCN, which combines multiple similarity and multiple kernel fusion based on Graph Convolutional Network (GCN) to predict DTIs. In order to capture the features of the network structure and fully explore direct or indirect relationships between nodes, we propose the method of multiple similarity, which combines similarity fusion matrices with Random Walk with Restart (RWR) and cosine similarity. Then, we use GCN to extract multi-layer low-dimensional embedding features. Unlike traditional GCN methods, we incorporate Multiple Kernel Learning (MKL). Finally, we use the Dual Laplace Regularized Least Squares method to predict novel DTIs through combinatorial kernels in drug and target spaces. We conduct experiments on a golden standard dataset, and demonstrate the effectiveness of our proposed model in predicting DTIs through showing significant improvements in Area Under the Curve (AUC) and Area Under the Precision-Recall Curve (AUPR). In addition, our model can also discover some new DTIs, which can be verified by the KEGG BRITE Database and relevant literature.

Investigation on synergistic inhibition of protein-bound heterocyclic amines in sarcoplasmic and myofibrillar model systems by amino acid combinations.

The inhibition of amino acids on the formation of protein-bound HAs was assessed in both model systems and roast beef patties, and the synergism between these amino acids was also investigated. The amino acids can promote the formation of protein-bound HAs at low addition amount, and the total content of protein-bound HAs increased from 444.05 ± 4.98 ng/g of the control group to 517.36 ± 16.51 ng/g when 0.05 % cysteine was added. Amino acid combinations exhibited stable inhibitory effects, with the maximum inhibitory rate of 64 % in the treatment with histidine-proline combination (1:4). The synergistic inhibition may be caused by simultaneously scavenging intermediates and competing for the binding sites of muscle proteins, and the reaction with protein-bound HAs to form adduct can serve as supporting factors to co-mitigate the promotion in protein-bound HAs from increased protein solubility. These findings proposed the potential mitigation strategies against protein-bound HAs formation.

Strategies for the Enhancement of Secondary Metabolite Production via Biosynthesis Gene Cluster Regulation in Aspergillus oryzae.

The filamentous fungus Aspergillus oryzae (A. oryzae) has been extensively used for the biosynthesis of numerous secondary metabolites with significant applications in agriculture and food and medical industries, among others. However, the identification and functional prediction of metabolites through genome mining in A. oryzae are hindered by the complex regulatory mechanisms of secondary metabolite biosynthesis and the inactivity of most of the biosynthetic gene clusters involved. The global regulatory factors, pathway-specific regulatory factors, epigenetics, and environmental signals significantly impact the production of secondary metabolites, indicating that appropriate gene-level modulations are expected to promote the biosynthesis of secondary metabolites in A. oryzae. This review mainly focuses on illuminating the molecular regulatory mechanisms for the activation of potentially unexpressed pathways, possibly revealing the effects of transcriptional, epigenetic, and environmental signal regulation. By gaining a comprehensive understanding of the regulatory mechanisms of secondary metabolite biosynthesis, strategies can be developed to enhance the production and utilization of these metabolites, and potential functions can be fully exploited.

Tracking single-cell evolution using clock-like chromatin accessibility loci.

Single-cell chromatin accessibility sequencing (scATAC-seq) reconstructs developmental trajectory by phenotypic similarity. However, inferring the exact developmental trajectory is challenging. Previous studies showed age-associated DNA methylation (DNAm) changes in specific genomic regions, termed clock-like differential methylation loci (ClockDML). Age-associated DNAm could either result from or result in chromatin accessibility changes at ClockDML. As cells undergo mitosis, the heterogeneity of chromatin accessibility on clock-like loci is reduced, providing a measure of mitotic age. In this study, we developed a method, called EpiTrace, that counts the fraction of opened clock-like loci from scATAC-seq data to determine cell age and perform lineage tracing in various cell lineages and animal species. It shows concordance with known developmental hierarchies, correlates well with DNAm-based clocks and is complementary with mutation-based lineage tracing, RNA velocity and stemness predictions. Applying EpiTrace to scATAC-seq data reveals biological insights with clinically relevant implications, ranging from hematopoiesis, organ development, tumor biology and immunity to cortical gyrification.

Genomic analysis of Alteromonas sp. M12 isolated from the Mariana Trench reveals its role in dimethylsulfoniopropionate cycling.

Dimethylsulfoniopropionate (DMSP) is a ubiquitous organosulfur molecule in marine environments with important roles in stress tolerance, global carbon and sulfur cycling, and chemotaxis. It is the main precursor of the climate active gas dimethyl sulfide (DMS), which is the greatest natural source of bio­sulfur transferred from ocean to atmosphere. Alteromonas sp. M12, a Gram-negative and aerobic bacterium, was isolated from the seawater samples collected from the Mariana Trench at the depth of 2500 m. Here, we report the complete genome sequence of strain M12 and its genomic characteristics to import and utilize DMSP. The genome of strain M12 contains one circular chromosome (5,012,782 bp) with the GC content of 40.88%. Alteromonas sp. M12 can grow with DMSP as a sole carbon source, and produced DMS with DMSP as a precursor. Genomic analysis showed that strain M12 contained a set of genes involved in the downstream steps of DMSP cleavage, but no known genes encoding DMSP transporters or DMSP lyases. The results indicated that this strain contained novel DMSP transport and cleavage genes in its genome which warrants further investigation. The import of DMSP into cells may be a strategy of strain M12 to adapt the hydrostatic pressure environment in the Mariana Trench, as DMSP can be used as a hydrostatic pressure protectant. This study sheds light on the catabolism of DMSP by deep-sea bacteria.

Causal association of smoking, blood lipids, and bladder cancer: Insights from a multivariable and mediation mendelian randomization investigation.

We used Mendelian randomization (MR) to examine the relationship between smoking, various categories of blood lipids, and bladder cancer (BLCA). Data for this study were drawn from the genome-wide association studies of the GSCAN consortium (~1.2 million participants), a subset of the UK Biobank (~120,000 participants), and the FinnGen consortium (2,072 cases and 307,082 controls). Initially, we utilized inverse variance weighted (IVW), complementary and sensitivity analyses, multivariable MR, and meta-analysis to confirm the association between blood lipids and BLCA. We then performed mediation MR to elucidate the relationship between smoking, blood lipids, and BLCA. Our analysis identified five lipids, including triglycerides in very large HDL, cholesterol in small VLDL, free cholesterol in very large HDL, total free cholesterol, and apolipoprotein B, as having strong and inverse associations with BLCA. These lipids demonstrated no heterogeneity or pleiotropy and exhibited consistent direction and magnitude across IVW, weighted median, and MR-Egger analyses. Our mediation MR further revealed that triglycerides in very large HDL and cholesterol in small VLDL could reduce the impact of smoking on BLCA, mediating -4.3% and -4.5% of the effect, respectively. In conclusion, our study identified five lipids exhibiting a robust inverse relationship with BLCA, two of which can buffer the impact of smoking on BLCA.

ACAT1 promotes proliferation and metastasis of bladder cancer via AKT/GSK3ß/c-Myc signaling pathway.

Acetyl-CoA acetyltransferase 1 (ACAT1) plays a significant role in the regulation of gene expression and tumorigenesis. However, the biological role of ACAT1 in bladder cancer (BLCA) has yet to be elucidated. This research aimed to elucidate the bioinformatics features and biological functions of ACAT1 in BLCA. Here, we demonstrate that ACAT1 is elevated in BLCA tissues and is correlated with specific clinicopathological features and an unfavorable prognosis for survival in BLCA patients. ACAT1 was identified as an independent risk factor in BLCA. Phenotypically, both in vitro and in vivo, ACAT1 knockdown suppressed BLCA cell proliferation and migration, while ACAT1 overexpression had the opposite effect. Mechanistic assays revealed that ACAT1 enhances BLCA cell proliferation and metastasis through the AKT/GSK3ß/c-Myc signaling pathway by modulating the cell cycle and EMT. Taken together, the results of our study reveal that ACAT1 is an oncogenic driver in BLCA that enhances tumor proliferation and metastasis, indicating its potential as a diagnostic and therapeutic target for this disease.

Histone methyltransferase SETD2: An epigenetic driver in clear cell renal cell carcinoma.

SET domain-containing 2 (SETD2) is a lysine methyltransferase that catalyzes histone H3 lysine36 trimethylation (H3K36me3) and has been revealed to play important roles in the regulation of transcriptional elongation, RNA splicing, and DNA damage repair. SETD2 mutations have been documented in several cancers, including clear cell renal cell carcinoma (ccRCC). SETD2 deficiency is associated with cancer occurrence and progression by regulating autophagy flux, general metabolic activity, and replication fork speed. Therefore, SETD2 is considered a potential epigenetic therapeutic target and is the subject of ongoing research on cancer-related diagnosis and treatment. This review presents an overview of the molecular functions of SETD2 in H3K36me3 regulation and its relationship with ccRCC, providing a theoretical basis for subsequent antitumor therapy based on SETD2 or H3K36me3 targets.

GraphPLBR: Protein-Ligand Binding Residue Prediction With Deep Graph Convolution Network.

The intermolecular interactions between proteins and ligands occur through site-specific amino acid residues in the proteins, and the identification of these key residues plays a critical role in both interpreting protein function and facilitating drug design based on virtual screening. In general, the information about the ligands-binding residues on proteins is unknown, and the detection of the binding residues by the biological wet experiments is time consuming. Therefore, many computational methods have been developed to identify the protein-ligand binding residues in recent years. We propose GraphPLBR, a framework based on Graph Convolutional Neural (GCN) networks, to predict protein-ligand binding residues (PLBR). The proteins are represented as a graph with residues as nodes through 3D protein structure data, such that the PLBR prediction task is transformed into a graph node classification task. A deep graph convolutional network is applied to extract information from higher-order neighbors, and initial residue connection with identity mapping is applied to cope with the over-smoothing problem caused by increasing the number of graph convolutional layers. To the best of our knowledge, this is a more unique and innovative perspective that utilizes the idea of graph node classification for protein-ligand binding residues prediction. By comparing with some state-of-the-art methods, our method performs better on several metrics.

Targeting Notch1-YAP Circuit Reprograms Macrophage Polarization and Alleviates Acute Liver Injury in Mice.

BACKGROUND & AIMS: Hepatic immune system disorder plays a critical role in the pathogenesis of acute liver injury. The intrinsic signaling mechanisms responsible for dampening excessive activation of liver macrophages are not completely understood. The Notch and Hippo-YAP signaling pathways have been implicated in immune homeostasis. In this study, we investigated the interactive cell signaling networks of Notch1/YAP pathway during acute liver injury. METHODS: Myeloid-specific Notch1 knockout (Notch1M-KO) mice and the floxed Notch1 (Notch1FL/FL) mice were subjected to lipopolysaccharide/D-galactosamine toxicity. Some mice were injected via the tail vein with bone marrow-derived macrophages transfected with lentivirus-expressing YAP. Some mice were injected with YAP siRNA using an in vivo mannose-mediated delivery system. RESULTS: We found that the activated Notch1 and YAP signaling in liver macrophages were closely related to lipopolysaccharide/D-galactosamine-induced acute liver injury. Macrophage/neutrophil infiltration, proinflammatory mediators, and hepatocellular apoptosis were markedly ameliorated in Notch1M-KO mice. Importantly, myeloid Notch1 deficiency depressed YAP signaling and facilitated M2 macrophage polarization in the injured liver. Furthermore, YAP overexpression in Notch1M-KO livers exacerbated liver damage and shifted macrophage polarization toward the M1 phenotype. Mechanistically, macrophage Notch1 signaling could transcriptionally activate YAP gene expression. Reciprocally, YAP transcriptionally upregulated the Notch ligand Jagged1 gene expression and was essential for Notch1-mediated macrophage polarization. Finally, dual inhibition of Notch1 and YAP in macrophages further promoted M2 polarization and alleviated liver damage. CONCLUSIONS: Our findings underscore a novel molecular insight into the Notch1-YAP circuit for controlling macrophage polarization in acute liver injury, raising the possibility of targeting macrophage Notch1-YAP circuit as an effective strategy for liver inflammation-related diseases.