MHESMMR: a multilevel model for predicting the regulation of miRNAs expression by small molecules.

According to the expression of miRNA in pathological processes, miRNAs can be divided into oncogenes or tumor suppressors. Prediction of the regulation relations between miRNAs and small molecules (SMs) becomes a vital goal for miRNA-target therapy. But traditional biological approaches are laborious and expensive. Thus, there is an urgent need to develop a computational model. In this study, we proposed a computational model to predict whether the regulatory relationship between miRNAs and SMs is up-regulated or down-regulated. Specifically, we first use the Large-scale Information Network Embedding (LINE) algorithm to construct the node features from the self-similarity networks, then use the General Attributed Multiplex Heterogeneous Network Embedding (GATNE) algorithm to extract the topological information from the attribute network, and finally utilize the Light Gradient Boosting Machine (LightGBM) algorithm to predict the regulatory relationship between miRNAs and SMs. In the fivefold cross-validation experiment, the average accuracies of the proposed model on the SM2miR dataset reached 79.59% and 80.37% for up-regulation pairs and down-regulation pairs, respectively. In addition, we compared our model with another published model. Moreover, in the case study for 5-FU, 7 of 10 candidate miRNAs are confirmed by related literature. Therefore, we believe that our model can promote the research of miRNA-targeted therapy.

Identification of key regulatory molecules in the early development stage of Alzheimer's disease.

Alzheimer's disease (AD) is one of the most common neurodegenerative diseases, the incidence of which increases with age, and the pathological changes in the brain are irreversible. Recent studies have highlighted the essential role of long noncoding RNAs (lncRNAs) in AD by acting as competing endogenous RNAs (ceRNAs). Our aim was to construct lncRNA-associated ceRNA regulatory networks composed of potential biomarkers for the early stage of AD. AD related datasets come from AlzData and GEO databases. The R package 'Limma' identifies differentially expressed genes (DEGs), Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) databases for functional enrichment analysis. Protein-protein interactions (PPIs) in DEGs were constructed in the STRING database, and Cytoscape software identified DEGs. Convergent functional genomics (CFG) analysis of differentially expressed hub genes (referred to as early-DEGs) in the brain before the development of AD pathology. The AlzData database analyses the expression levels of early-DEGs in different nerve cells. The lncRNA-miRNA-mRNA regulatory network was established according to the ceRNA hypothesis. We identified four lncRNAs (XIST, NEAT1, KCNQ1OT1 and HCG18) and four miRNAs (hsa-let-7c-5p, hsa-miR-107, hsa-miR-129-2-3p and hsa-miR-214-3p) were preliminarily identified as potential biomarkers for early AD, competitively regulating Atp6v0b, Atp6v1e1 Atp6v1f and Syt1. This study indicates that NEAT1, XIST, HCG18 and KCNQ1OT1 act as ceRNAs in competitive binding with miRNAs to regulate the expression of Atp6v0b, Atp6v1e1, Atp6v1f and Syt1 before the occurrence of pathological changes in AD.

Neuregulin 4 (Nrg4) cooperates with melatonin to regulate the PRL expression via ErbB4/Erk signaling pathway as a potential prolactin (PRL) regulator.

Neuregulin-4 (Nrg4) and melatonin play vital roles in endocrine diseases. However, there is little discussion about the function and potential mechanism of Nrg4 and melatonin in prolactin (PRL) regulation. The human normal pituitary data from Gene Expression Profiling Interactive Analysis (GEPIA) database was used to explore the correlation between NRG4 and PRL. The expression and correlation of NRG4 and PRL were determined by Immunofluorescence staining (IF) and human normal pituitary tissue microarray. Western Blot (WB) was used to detect the expression of PRL, p-ErbB2/3/4, ErbB2/3/4, p-Erk1/2, Erk1/2, p-Akt and Akt in PRL-secreting pituitary GH3 and RC-4B/C cells treated by Nrg4, Nrg4-small interfering RNA, Erk1/2 inhibitor FR180204 and melatonin. The expression of NRG4 was significantly positively correlated with that of PRL in the GEPIA database and normal human pituitary tissues. Nrg4 significantly increased the expression and secretion of PRL and p-Erk1/2 expression in GH3 cells and RC-4B/C cells. Inhibition of Nrg4 significantly inhibited PRL expression. The increased levels of p-Erk1/2 and PRL induced by Nrg4 were abolished significantly in response to FR180204 in GH3 and RC-4B/C cells. Additionally, Melatonin promotes the expression of Nrg4, p-ErbB4, p-Erk1/2, and PRL and can further promote the expression of p-Erk1/2 and PRL in combination with Nrg4. Further investigation into the function of Nrg4 and melatonin on PRL expression and secretion may provide new clues to advance the clinical control of prolactinomas and hyperprolactinemia.

A biomedical knowledge graph-based method for drug-drug interactions prediction through combining local and global features with deep neural networks.

Drug-drug interactions (DDIs) prediction is a challenging task in drug development and clinical application. Due to the extremely large complete set of all possible DDIs, computer-aided DDIs prediction methods are getting lots of attention in the pharmaceutical industry and academia. However, most existing computational methods only use single perspective information and few of them conduct the task based on the biomedical knowledge graph (BKG), which can provide more detailed and comprehensive drug lateral side information flow. To this end, a deep learning framework, namely DeepLGF, is proposed to fully exploit BKG fusing local-global information to improve the performance of DDIs prediction. More specifically, DeepLGF first obtains chemical local information on drug sequence semantics through a natural language processing algorithm. Then a model of BFGNN based on graph neural network is proposed to extract biological local information on drug through learning embedding vector from different biological functional spaces. The global feature information is extracted from the BKG by our knowledge graph embedding method. In DeepLGF, for fusing local-global features well, we designed four aggregating methods to explore the most suitable ones. Finally, the advanced fusing feature vectors are fed into deep neural network to train and predict. To evaluate the prediction performance of DeepLGF, we tested our method in three prediction tasks and compared it with state-of-the-art models. In addition, case studies of three cancer-related and COVID-19-related drugs further demonstrated DeepLGF's superior ability for potential DDIs prediction. The webserver of the DeepLGF predictor is freely available at http://120.77.11.78/DeepLGF/.

Chromium(VI) Adsorption and Reduction in Soils under Anoxic Conditions: The Relative Roles of Iron (oxyhr)oxides, Iron(II), Organic Matters, and Microbes.

Chromium (Cr) transformation in soils mediated by iron (Fe) (oxyhr)oxides, Fe(II), organic matter (OM), and microbes is largely unexplored. Here, their coupling processes and mechanisms were investigated during anoxic incubation experiments of four Cr(VI) spiked soil samples with distinct physicochemical properties from the tropical and subtropical regions of China. It demonstrates that easily oxidizable organic carbon (EOC, 55-84%) and microbes (16-48%) drive Cr(VI) reduction in soils enriched with goethite and/or hematite, among which in dryland soils microbial sulfate reduction may also be involved. In contrast, EOC (38 ± 1%), microbes (33 ± 1%), and exchangeable and poorly crystalline Fe (oxyhr)oxide-associated Fe(II) (29 ± 3%) contribute to Cr(VI) reduction in paddy soils enriched with ferrihydrite. Additionally, exogenous Fe(II) and microbes significantly enhance Cr(VI) reduction in ferrihydrite- and goethite-rich soils, and Fe(II) greatly promotes but microbes slightly inhibit Cr passivation. Both Fe(II) and microbes, especially the latter, promote OM mineralization and result in the most substantial OM loss in ferrihydrite-rich paddy soils. During the incubation, part of the ferrihydrite converts to goethite but microbes may hinder the transformation. These results provide deep insights into the geochemical fates of redox-sensitive heavy metals mediated by the complicated effects of Fe, OM, and microbes in natural and engineered environments.

Protein Corona-Directed Cellular Recognition and Uptake of Polyethylene Nanoplastics by Macrophages.

The widespread use of plastic products in daily life has raised concerns about the health hazards associated with nanoplastics (NPs). When exposed, NPs are likely to infiltrate the bloodstream, interact with plasma proteins, and trigger macrophage recognition and clearance. In this study, we focused on establishing a correlation between the unique protein coronal signatures of high-density (HDPE) and low-density (LDPE) polyethylene (PE) NPs with their ultimate impact on macrophage recognition and cytotoxicity. We observed that low-density and high-density lipoprotein receptors (LDLR and SR-B1), facilitated by apolipoproteins, played an essential role in PE-NP recognition. Consequently, PE-NPs activated the caspase-3/GSDME pathway and ultimately led to pyroptosis. Advanced imaging techniques, including label-free scattered light confocal imaging and cryo-soft X-ray transmission microscopy with 3D-tomographic reconstruction (nano-CT), provided powerful insights into visualizing NPs-cell interactions. These findings underscore the potential risks of NPs to macrophages and introduce analytical methods for studying the behavior of NPs in biological systems.

Vascular smooth muscle-specific LRRC8A knockout ameliorates angiotensin II-induced cerebrovascular remodeling by inhibiting the WNK1/FOXO3a/MMP signaling pathway.

Hypertensive cerebrovascular remodeling involves the enlargement of vascular smooth muscle cells (VSMCs), which activates volume-regulated Cl- channels (VRCCs). The leucine-rich repeat-containing family 8 A (LRRC8A) has been shown to be the molecular identity of VRCCs. However, its role in vascular remodeling during hypertension is unclear. In this study, we used vascular smooth muscle-specific LRRC8A knockout (CKO) mice and an angiotensin II (Ang II)-induced hypertension model. The results showed that cerebrovascular remodeling during hypertension was ameliorated in CKO mice, and extracellular matrix (ECM) deposition was reduced. Based on the RNA-sequencing analysis of aortic tissues, the level of matrix metalloproteinases (MMPs), such as MMP-9 and MMP-14, were reduced in CKO mice with hypertension, which was further verified in vivo by qPCR and immunofluorescence analysis. Knockdown of LRRC8A in VSMCs inhibited the Ang II-induced upregulation of collagen I, fibronectin, and matrix metalloproteinases (MMPs), and overexpression of LRRC8A had the opposite effect. Further experiments revealed an interaction between with-no-lysine (K)-1 (WNK1), which is a "Cl--sensitive kinase", and Forkhead transcription factor O3a (FOXO3a), which is a transcription factor that regulates MMP expression. Ang II induced the phosphorylation of WNK1 and downstream FOXO3a, which then increased the expression of MMP-2 and MMP-9. This process was inhibited or potentiated when LRRC8A was knocked down or overexpressed, respectively. Overall, these results demonstrate that LRRC8A knockout in vascular smooth muscle protects against cerebrovascular remodeling during hypertension by reducing ECM deposition and inhibiting the WNK1/FOXO3a/MMP signaling pathway, demonstrating that LRRC8A is a potential therapeutic target for vascular remodeling-associated diseases such as stroke.

Associations between immune cell phenotypes and lung cancer subtypes: insights from mendelian randomization analysis.

INTRODUCTION: Lung cancer is a common malignant tumor, and different types of immune cells may have different effects on the occurrence and development of lung cancer subtypes, including lung squamous cell carcinoma (LUSC) and lung adenocarcinoma (LUAD). However, the causal relationship between immune phenotype and lung cancer is still unclear. METHODS: This study utilized a comprehensive dataset containing 731 immune phenotypes from the European Bioinformatics Institute (EBI) to evaluate the potential causal relationship between immune phenotypes and LUSC and LUAD using the inverse variance weighted (IVW) method in Mendelian randomization (MR). Sensitivity analyses, including MR-Egger intercept, Cochran Q test, and others, were conducted for the robustness of the results. The study results were further validated through meta-analysis using data from the Transdisciplinary Research Into Cancer of the Lung (TRICL) data. Additionally, confounding factors were excluded to ensure the robustness of the findings. RESULTS: Among the final selection of 729 immune cell phenotypes, three immune phenotypes exhibited statistically significant effects with LUSC. CD28 expression on resting CD4 regulatory T cells (OR 1.0980, 95% CI: 1.0627-1.1344, p < 0.0001) and CD45RA + CD28- CD8 + T cell %T cell (OR 1.0011, 95% CI: 1.0007; 1.0015, p < 0.0001) were associated with increased susceptibility to LUSC. Conversely, CCR2 expression on monocytes (OR 0.9399, 95% CI: 0.9177-0.9625, p < 0.0001) was correlated with a decreased risk of LUSC. However, no significant causal relationships were established between any immune cell phenotypes and LUAD. CONCLUSION: This study demonstrates that specific immune cell types are associated with the risk of LUSC but not with LUAD. While these findings are derived solely from European populations, they still provide clues for a deeper understanding of the immunological mechanisms underlying lung cancer and may offer new directions for future therapeutic strategies and preventive measures.

Mono-2-ethylhexyl phthalate-induced downregulation of MMP11 in foreskin fibroblasts contributes to the pathogenesis of hypospadias.

Hypospadias is one of the most common congenital anomalies of the male urogenital system, and di(2-ethylhexyl) phthalate (DEHP), a widely used endocrine-disrupting chemical (EDC), is considered a significant risk factor for this condition. Mono-2-ethylhexyl phthalate (MEHP), the toxic active metabolite of DEHP, has been proven to affect penile development and ultimately result in the hypospadias phenotype. However, while it is acknowledged that hypospadias arises from the aberrant development of multiple penile tissues, the specific impact of MEHP on human foreskin tissue development and its underlying molecular mechanisms of action remain unclear. In this study, we constructed an in vitro toxicity assay for MEHP using human foreskin fibroblasts and employed high-throughput RNA sequencing to investigate the molecular mechanisms subserving the defects in cellular function. We subsequently conducted multi-omics data analysis using public databases to analyze key target genes, and identified MMP11 as a chief downstream gene responsible for the effects of MEHP on HFF-1 cell migration. Through molecular docking analysis and molecular biology experiments, we further demonstrated that the nuclear receptor PPAR-gamma was activated upon binding with MEHP, leading to the suppression of MMP11 expression. Additionally, we found that epigenetic modifications induced by MEHP were also involved in its pathogenic effects on hypospadias. Our research highlights the crucial role of impaired cellular proliferation and migration in MEHP-induced hypospadias. We identified the MEHP/PPAR-gamma/MMP11 pathway as a novel pathogenic mechanism, providing important potential targets for future preventive strategies with respect to hypospadias.

A Robust Monocular and Binocular Visual Ranging Fusion Method Based on an Adaptive UKF.

Visual ranging technology holds great promise in various fields such as unmanned driving and robot navigation. However, complex dynamic environments pose significant challenges to its accuracy and robustness. Existing monocular visual ranging methods are susceptible to scale uncertainty, while binocular visual ranging is sensitive to changes in lighting and texture. To overcome the limitations of single visual ranging, this paper proposes a fusion method for monocular and binocular visual ranging based on an adaptive Unscented Kalman Filter (AUKF). The proposed method first utilizes a monocular camera to estimate the initial distance based on the pixel size, and then employs the triangulation principle with a binocular camera to obtain accurate depth. Building upon this foundation, a probabilistic fusion framework is constructed to dynamically fuse monocular and binocular ranging using the AUKF. The AUKF employs nonlinear recursive filtering to estimate the optimal distance and its uncertainty, and introduces an adaptive noise-adjustment mechanism to dynamically update the observation noise based on fusion residuals, thus suppressing outlier interference. Additionally, an adaptive fusion strategy based on depth hypothesis propagation is designed to autonomously adjust the noise prior of the AUKF by combining current environmental features and historical measurement information, further enhancing the algorithm's adaptability to complex scenes. To validate the effectiveness of the proposed method, comprehensive evaluations were conducted on large-scale public datasets such as KITTI and complex scene data collected in real-world scenarios. The quantitative results demonstrate that the fusion method significantly improves the overall accuracy and stability of visual ranging, reducing the average relative error within an 8 m range by 43.1% and 40.9% compared to monocular and binocular ranging, respectively. Compared to traditional methods, the proposed method significantly enhances ranging accuracy and exhibits stronger robustness against factors such as lighting changes and dynamic targets. The sensitivity analysis further confirmed the effectiveness of the AUKF framework and adaptive noise strategy. In summary, the proposed fusion method effectively combines the advantages of monocular and binocular vision, significantly expanding the application range of visual ranging technology in intelligent driving, robotics, and other fields while ensuring accuracy, robustness, and real-time performance.