High-Resolution Mass Spectrometry Combined with Reactive Oxygen Species Reveals Differences in Photoreactivity of Dissolved Organic Matter from Microplastic Sources in Aqueous Environments.

Microplastics (MPs)-derived dissolved organic matter (MPs-DOM) is becoming a non-negligible source of DOM pools in aquatic systems, but there is limited understanding about the photoreactivity of different MPs-DOM. Herein, MPs-DOM from polystyrene (PS), polyethylene terephthalate (PET), poly(butylene adipate-co-terephthalate) (PBAT), PE, and polypropylene (PP), representing aromatic, biodegradable, and aliphatic plastics, were prepared to examine their photoreactivity. Spectral and high-resolution mass spectrometry analyses revealed that PS/PET/PBAT-DOM contained more unsaturated aromatic components, whereas PE/PP-DOM was richer in saturated aliphatic components. Photodegradation experiments observed that unsaturated aromatic molecules were prone to be degraded compared to saturated aliphatic molecules, leading to a higher degradation of PS/PET/PBAT-DOM than PE/PP-DOM. PS/PET/PBAT-DOM was mainly degraded by hydroxyl (•OH) via attacking unsaturated aromatic structures, whereas PE/PP-DOM by singlet oxygen (1O2) through oxidizing aliphatic side chains. The [•OH]ss was 1.21-1.60 × 10-4 M in PS/PET/PBAT-DOM and 0.97-1.14 × 10-4 M in PE/PP-DOM, while the [1O2]ss was 0.90-1.35 × 10-12 and 0.33-0.44 × 10-12 M, respectively. This contributes to the stronger photoreactivity of PS/PET/PBAT-DOM with a higher unsaturated aromatic degree than PE/PP-DOM. The photodegradation of MPs-DOM reflected a decreasing tendency from aromatic-unsaturated molecules to aliphatic-saturated molecules. Special attention should be paid to the photoreactivity and environmental impacts associated with MPs-DOM containing highly unsaturated aromatic compounds.

Identification of a lncRNA/circRNA-miRNA-mRNA network in Nasopharyngeal Carcinoma by deep sequencing and bioinformatics analysis.

Background: Accumulating evidence indicates that non-coding RNAs (ncRNA), including long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs), can function as competitive endogenous RNAs (ceRNAs) by binding to microRNAs (miRNAs) and regulating host gene expression at the transcriptional or post-transcriptional level. Dysregulation in ceRNA network regulation has been implicated in the occurrence and development of cancer. However, the lncRNA/circRNA-miRNA-mRNA regulatory network is still lacking in nasopharyngeal carcinoma (NPC). Methods: Differentially expressed genes (DEGs) were obtained from our previous sequencing data and Gene Expression Omnibus (GEO). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) were used to explore the biological functions of these common DEGs. Through a series of bioinformatic analyses, the lncRNA/circRNA-miRNA-mRNA network was established. In additional, the external data GSE102349 was used to test the prognostic value of the hub mRNAs through the Kaplan-Meier method. Results: We successfully constructed a lncRNA/circRNA-miRNA-mRNA network in NPC, consisting of 16 lncRNAs, 6 miRNAs, 3 circRNAs and 10 mRNAs and found that three genes (TOP2A, ZWINT, TTK) were significantly associated with overall survival time (OS) in patients. Conclusion: The regulatory network revealed in this study may help comprehensively elucidate the ceRNA mechanisms driving NPC, and provide novel candidate biomarkers for evaluating the prognosis of NPC.

Structural characterization of the DNA binding mechanism of retinoic acid-related orphan receptor gamma.

Retinoic acid-related orphan receptor gamma (RORγ) plays critical roles in regulating various biological processes and has been linked to immunodeficiency disorders and cancers. DNA recognition is essential for RORγ to exert its functions. However, the underlying mechanism of the DNA binding by RORγ remains unclear. In this study, we present the crystal structure of the complex of RORγ1 DNA-binding domain (RORγ1-DBD)/direct repeat DNA element DR2 at 2.3 Å resolution. We demonstrate that RORγ1-DBD binds the DR2 motif as a homodimer, with the C-terminal extension (CTE) region of RORγ1-DBD contributing to the DNA recognition and the formation of dimeric interface. Further studies reveal that REV-ERB-DBD and RXR-DBD, also bind the DR2 site as a homodimer, while NR4A2-DBD binds DR2 as a monomer. Our research uncovers a binding mechanism of RORγ1 to the DR2 site and provides insights into the biological functions of RORγ1 and the broader RORs subfamily.

Microbial metabolism influences microplastic perturbation of dissolved organic matter in agricultural soils.

An estimated 258 million tons of plastic enter the soil annually. Joining persistent types of microplastic (MP), there will be an increasing demand for biodegradable plastics. There are still many unknowns about plastic pollution by either type, and one large gap is the fate and composition of dissolved organic matter (DOM) released from MPs as well as how they interact with soil microbiomes in agricultural systems. In this study, polyethylene MPs, photoaged to different degrees, and virgin polylactic acid MPs were added to agricultural soil at different levels and incubated for 100 days to address this knowledge gap. We find that, upon MP addition, labile components of low aromaticity were degraded and transformed, resulting in increased aromaticity and oxidation degree, reduced molecular diversity, and changed nitrogen and sulfur contents of soil DOM. Terephthalate, acetate, oxalate, and L-lactate in DOM released by polylactic acid MPs and 4-nitrophenol, propanoate, and nitrate in DOM released by polyethylene MPs were the major molecules available to the soil microbiomes. The bacteria involved in the metabolism of DOM released by MPs are mainly concentrated in Proteobacteria, Actinobacteriota, and Bacteroidota, and fungi are mainly in Ascomycota and Basidiomycota. Our study provides an in-depth understanding of the microbial transformation of DOM released by MPs and its effects of DOM evolution in agricultural soils.

Significant contribution of different sources of particulate organic matter to the photoaging of microplastics.

Particulate organic matter (POM), as an important component of organic matter, can act as a redox mediator and thus intervene in the environmental behavior of microplastics (MPs). However, quantitative information on the role of POM in the photoaging of MPs under ultraviolet (UV) light is still lacking. To raise the knowledge gap, through environmental simulation experiments and qualitative/quantitative experiments of active substances, we found that POM from peat soil has stronger oxidation capacity than POM from sediment, and the involvement of POM at high water content makes the aging of MPs more obvious. This is because the persistent radicals and electron-absorbing groups on the surface of POM indirectly generate reactive oxygen species (ROS) by promoting electron transfer, and the dissolved organic matter (DOM) released from POM under UV light (POM-DOM) is further excited to generate triplet-state photochemistry of DOM (3DOM*) to promote the aging of MPs. Theoretical calculations revealed that the benzene ring, mainly C = C, and C = O in the main chain in the plastic macromolecule structure are more susceptible to ROS attack, and the differences in the vulnerable sites contained in different plastic structures as well as the differences in the energy band gaps lead to differences in their aging processes. This study firstly elucidates the key role and intrinsic mechanism of POM in the photoaging of MPs, providing a theoretical basis for a comprehensive assessment of the effect of POM on MPs in the environment.

Metabolomics reveals how spinach plants reprogram metabolites to cope with intense stress responses induced by photoaged polystyrene nanoplastics (PSNPs).

While land-based sources have been recognized as significant long-term sinks for micro- and nanoplastics, there is limited knowledge about the uptake, translocation, and phytotoxicity of nanoplastics (NPs) in terrestrial environments, especially aged NPs. In this study, we investigated the impact of aged polystyrene nanoplastics (PSNPs) on the uptake, physiology, and metabolism of spinach. Our findings revealed that both pristine and aged PSNPs can accumulate in the roots and subsequently translocate to the aboveground tissues, thereby influencing numerous key growth indicators in spinach plants. A more pronounced impact was observed in the treatment of aged PSNPs, triggering more significant and extensive changes in metabolite levels. Furthermore, alterations in targeted pathways, specifically aminoacyl-tRNA biosynthesis and phenylpropanoid biosynthesis, were induced by aged PSNPs, while pristine PSNPs influenced pathways related to sulfur metabolism, biosynthesis of unsaturated fatty acids, and tryptophan metabolism. Additionally, tissue-specific responses were observed at the metabolomics level in both roots and leaves. These results highlight the existence of diverse and tissue-specific metabolic responses in spinach plants exposed to pristine and aged PSNPs, providing insights into the mechanisms of defense and detoxification against NP-induced stress.

Predicting miRNA-Disease Associations by Combining Graph and Hypergraph Convolutional Network.

miRNAs are important regulators for many crucial biological processes. Many recent studies have shown that miRNAs are closely related to various human diseases and can be potential biomarkers or therapeutic targets for some diseases, such as cancers. Therefore, accurately predicting miRNA-disease associations is of great importance for understanding and curing diseases. However, how to efficiently utilize the characteristics of miRNAs and diseases and the information on known miRNA-disease associations for prediction is still not fully explored. In this study, we propose a novel computational method for predicting miRNA-disease associations. The proposed method combines the graph convolutional network and the hypergraph convolutional network. The graph convolutional network is utilized to extract the information from miRNA-similarity data as well as disease-similarity data. Based on the representations of miRNAs and diseases learned by the graph convolutional network, we further use the hypergraph convolutional network to capture the complex high-order interactions in the known miRNA-disease associations. We conduct comprehensive experiments with different datasets and predictive tasks. The results show that the proposed method consistently outperforms several other state-of-the-art methods. We also discuss the influence of hyper-parameters and model structures on the performance of our method. Some case studies also demonstrate that the predictive results of the method can be verified by independent experiments.

Insight into interactions between microplastics and fulvic acid: Mechanisms affected by microplastics type.

Microplastics (MPs) can interact with dissolved organic matter (DOM), a common component found in the environment. However, the effect of MPs type on its interaction with DOM has not been systematically studied. Therefore, the binding properties of different MPs with fulvic acid (FA) were explored in this study. The results showed that polypropylene (PP) and polyethylene (PE) had higher adsorption affinity for FA than polystyrene (PS) and polyvinyl chloride (PVC). The interaction between MPs and FA conformed to the pseudo-first-order model and Freundlich model (except PS). The interaction mechanisms between various MPs tested in this paper and FA are considered to be different. PP, PE and PS interacted with the aromatic structure of FA and were entrapped in the FA polymers by the carboxyl groups and CO bonds, resulting in a highly conjugated co-polymer, suggesting that oxygen-containing functional groups played a key role. However, it was assumed that the interaction between PVC and FA was more likely to be caused by hydrophobic interaction. This research will help to enhance our comprehension of the environmental behavior of MPs and their interaction with the DOM specifically.

Identification of a Difluorinated Alkoxy Sulfonyl Chloride as a Novel Antitumor Agent for Hepatocellular Carcinoma through Activating Fumarate Hydratase Activity.

Fenofibrate is known as a lipid-lowering drug. Although previous studies have reported that fenofibrate exhibits potential antitumor activities, IC50 values of fenofibrate could be as high as 200 µM. Therefore, we investigated the antitumor activities of six synthesized fenofibrate derivatives. We discovered that one compound, SIOC-XJC-SF02, showed significant antiproliferative activity on human hepatocellular carcinoma (HCC) HCCLM3 cells and HepG2 cells (the IC50 values were 4.011 µM and 10.908 µM, respectively). We also found this compound could inhibit the migration of human HCC cells. Transmission electron microscope and flow cytometry assays demonstrated that this compound could induce apoptosis of human HCC cells. The potential binding sites of this compound acting on human HCC cells were identified by mass spectrometry-cellular thermal shift assay (MS-CETSA). Molecular docking, Western blot, and enzyme activity assay-validated binding sites in human HCC cells. The results showed that fumarate hydratase may be a potential binding site of this compound, exerting antitumor effects. A xenograft model in nude mice demonstrated the anti-liver cancer activity and the mechanism of action of this compound. These findings indicated that the antitumor effect of this compound may act via activating fumarate hydratase, and this compound may be a promising antitumor candidate for further investigation.

Structural basis of the farnesoid X receptor/retinoid X receptor heterodimer on inverted repeat DNA.

Farnesoid X receptor (FXR) is a ligand-activated transcription factor known as bile acid receptor (BAR). FXR plays critical roles in various biological processes, including metabolism, immune inflammation, liver regeneration and liver carcinogenesis. FXR forms a heterodimer with the retinoid X receptor (RXR) and binds to diverse FXR response elements (FXREs) to exert its various biological functions. However, the mechanism by which the FXR/RXR heterodimer binds the DNA elements remains unclear. In this study, we aimed to use structural, biochemical and bioinformatics analyses to study the mechanism of FXR binding to the typical FXRE, such as the IR1 site, and the heterodimer interactions in the FXR-DBD/RXR-DBD complex. Further biochemical assays showed that RAR, THR and NR4A2 do not form heterodimers with RXR when bound to the IR1 sites, which indicates that IR1 may be a unique binding site for the FXR/RXR heterodimer. Our studies may provide a further understanding of the dimerization specificity of nuclear receptors.

Lactylation-Related Gene Signature Effectively Predicts Prognosis and Treatment Responsiveness in Hepatocellular Carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) is a malignant tumor associated with high morbidity and mortality. Therefore, it is of great importance to develop effective prognostic models and guide clinical treatment in HCC. Protein lactylation is found in HCC tumors and is associated with HCC progression. METHODS: The expression levels of lactylation-related genes were identified from the TCGA database. A lactylation-related gene signature was constructed using LASSO regression. The prognostic value of the model was assessed and further validated in the ICGC cohort, with the patients split into two groups based on risk score. Glycolysis and immune pathways, treatment responsiveness, and the mutation of signature genes were analyzed. The correlation between PKM2 expression and the clinical characteristics was investigated. RESULTS: Sixteen prognostic differentially expressed lactylation-related genes were identified. An 8-gene signature was constructed and validated. Patients with higher risk scores had poorer clinical outcomes. The two groups were different in immune cell abundance. The high-risk group patients were more sensitive to most chemical drugs and sorafenib, while the low-risk group patients were more sensitive to some targeted drugs such as lapatinib and FH535. Moreover, the low-risk group had a higher TIDE score and was more sensitive to immunotherapy. PKM2 expression correlated with clinical characteristics and immune cell abundance in the HCC samples. CONCLUSIONS: The lactylation-related model exhibited robust predictive efficiency in HCC. The glycolysis pathway was enriched in the HCC tumor samples. A low-risk score indicated better treatment response to most targeted drugs and immunotherapy. The lactylation-related gene signature could be used as a biomarker for the effective clinical treatment of HCC.

Adsorption and intracellular uptake of mercuric mercury and methylmercury by methanotrophs and methylating bacteria.

The cell surface adsorption and intracellular uptake of mercuric mercury Hg(II) and methylmercury (MeHg) are important in determining the fate and transformation of Hg in the environment. However, current information is limited about their interactions with two important groups of microorganisms, i.e., methanotrophs and Hg(II)-methylating bacteria, in aquatic systems. This study investigated the adsorption and uptake dynamics of Hg(II) and MeHg by three strains of methanotrophs, Methylomonas sp. strain EFPC3, Methylosinus trichosporium OB3b, and Methylococcus capsulatus Bath, and two Hg(II)-methylating bacteria, Pseudodesulfovibrio mercurii ND132 and Geobacter sulfurreducens PCA. Distinctive behaviors of these microorganisms towards Hg(II) and MeHg adsorption and intracellular uptake were observed. The methanotrophs took up 55-80% of inorganic Hg(II) inside cells after 24 h incubation, lower than methylating bacteria (>90%). Approximately 80-95% of MeHg was rapidly taken up by all the tested methanotrophs within 24 h. In contrast, after the same time, G. sulfurreducens PCA adsorbed 70% but took up <20% of MeHg, while P. mercurii ND132 adsorbed <20% but took up negligible amounts of MeHg. These results suggest that microbial surface adsorption and intracellular uptake of Hg(II) and MeHg depend on the specific types of microbes and appear to be related to microbial physiology that requires further detailed investigation. Despite being incapable of methylating Hg(II), methanotrophs play important roles in immobilizing both Hg(II) and MeHg, potentially influencing their bioavailability and trophic transfer. Therefore, methanotrophs are not only important sinks for methane but also for Hg(II) and MeHg and can influence the global cycling of C and Hg.

Proteomics Profiling Reveals the Molecular Signatures and Potential Therapeutic Targets of Human Nasopharyngeal Carcinoma.

Nasopharyngeal carcinoma (NPC), a malignant tumor distinctly characterized by ethnic and geographic distribution, is highly prevalent in Southern China and Southeast Asia. However, the molecular mechanisms of NPC have not been fully revealed at the proteomic level. In this study, 30 primary NPC samples and 22 normal nasopharyngeal epithelial tissues were collected for proteomics analysis, and a relatively complete proteomics landscape of NPC was depicted for the first time. By combining differential expression analysis, differential co-expression analysis, and network analysis, potential biomarkers and therapeutic targets were identified. Some identified targets were verified by biological experiments. We found that 17-AAG, a specific inhibitor of the identified target heat shock protein 90 (HSP90), could be a potential therapeutic drug for NPC. Finally, consensus clustering identified two NPC subtypes with specific molecular features. The subtypes and the related molecules were verified by an independent data set and may have different progression-free survival. The results of this study provide a comprehensive understanding of the proteomics molecular signatures of NPC and provide new perspectives and inspiration for prognostic determination and treatment of NPC.

Corn stalk pith-based hydrophobic aerogel for efficient oil sorption.

Bio-based aerogel has become an attractive sorbent for spilled oil and organic pollutants because of its light weight, high porosity and strong sorption capacity. However, the current fabrication process is mainly "bottom-up" technology, which is cost-expensive, time-consuming, and energy-intensive. Herein, we report a top-down, green, efficient and selective sorbent prepared from corn stalk pith (CSP) using the deep eutectic solvent (DES) treatment, followed by TEMPO/NaClO/NaClO2 oxidization and microfibrillation, and then hexamethyldisilazane coating. Such chemical treatments selectively removed lignin and hemicellulose, broke the thin cell walls of natural CSP, forming an aligned porous structure with capillary channels. The resultant aerogels had a density of 29.3 mg/g, a porosity of 98.13%, and a water contact angle of 130.5◦, exhibiting excellent oil/organic solvents sorption performance, with a high sorption capacity in the range of 25.4-36.5 g/g, approximately 5-16-fold higher than CSP, and with fast absorption speed and good reusability.

Inhibition of Methylmercury and Methane Formation by Nitrous Oxide in Arctic Tundra Soil Microcosms.

Climate warming causes permafrost thaw predicted to increase toxic methylmercury (MeHg) and greenhouse gas [i.e., methane (CH4), carbon dioxide (CO2), and nitrous oxide (N2O)] formation. A microcosm incubation study with Arctic tundra soil over 145 days demonstrates that N2O at 0.1 and 1 mM markedly inhibited microbial MeHg formation, methanogenesis, and sulfate reduction, while it slightly promoted CO2 production. Microbial community analyses indicate that N2O decreased the relative abundances of methanogenic archaea and microbial clades implicated in sulfate reduction and MeHg formation. Following depletion of N2O, both MeHg formation and sulfate reduction rapidly resumed, whereas CH4 production remained low, suggesting that N2O affected susceptible microbial guilds differently. MeHg formation strongly coincided with sulfate reduction, supporting prior reports linking sulfate-reducing bacteria to MeHg formation in the Arctic soil. This research highlights complex biogeochemical interactions in governing MeHg and CH4 formation and lays the foundation for future mechanistic studies for improved predictive understanding of MeHg and greenhouse gas fluxes from thawing permafrost ecosystems.

Prediction of drug side effects with transductive matrix co-completion.

MOTIVATION: Side effects of drugs could cause severe health problems and the failure of drug development. Drug-target interactions are the basis for side effect production and are important for side effect prediction. However, the information on the known targets of drugs is incomplete. Furthermore, there could be also some missing data in the existing side effect profile of drugs. As a result, new methods are needed to deal with the missing features and missing labels in the problem of side effect prediction. RESULTS: We propose a novel computational method based on transductive matrix co-completion and leverage the low-rank structure in the side effects and drug-target data. Positive-unlabelled learning is incorporated into the model to handle the impact of unobserved data. We also introduce graph regularization to integrate the drug chemical information for side effect prediction. We collect the data on side effects, drug targets, drug-associated proteins and drug chemical structures to train our model and test its performance for side effect prediction. The experiment results show that our method outperforms several other state-of-the-art methods under different scenarios. The case study and additional analysis illustrate that the proposed method could not only predict the side effects of drugs but also could infer the missing targets of drugs. AVAILABILITY AND IMPLEMENTATION: The data and the code for the proposed method are available at https://github.com/LiangXujun/GTMCC. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

DNA methylation-mediated silencing of Neuronatin promotes hepatocellular carcinoma proliferation through the PI3K-Akt signaling pathway.

AIMS: To explore the methylation status, function, and underlying mechanism of the imprinted gene Neuronatin (NNAT) in hepatocellular carcinoma (HCC) progression. MAIN METHODS: Immunohistochemistry (IHC) was performed to evaluate the expression of NNAT in HCC samples. Bisulfite genomic sequencing PCR (BSP) was applied to examine the methylation status of the NNAT promoter. In addition, colony formation, 5-Ethynyl-20-deoxyuridine (EdU) assays and subcutaneous xenograft nude models were used to explore the roles of NNAT in HCC cell proliferation. Furthermore, RNA-seq and phospho-specific protein microarray assays were conducted to illustrate the underlying mechanism by which NNAT regulates HCC progression. KEY FINDINGS: NNAT was obviously downregulated in HCC tissues, and its expression level was closely associated with tumor growth and patient prognosis. The downregulation of NNAT in HCC was induced by hypermethylation of CpG islands in the promoter region, and hypermethylation was correlated with overall survival of HCC. Moreover, the enforced expression of NNAT significantly inhibited HCC cell proliferation in vitro and in vivo. Transcriptome analysis showed that the alteration of NNAT expression was mainly related to dysregulation of the PI3K-Akt signaling pathway. Finally, phospho-specific antibody microarray detection further revealed that overexpressed NNAT can increase the phosphorylation levels of LKB1, Met, and elF4E and decrease the phosphorylation levels of PTEN, which are all involved in the PI3K-Akt signaling pathway. SIGNIFICANCE: Our research provides new insights into the epigenetic regulation of imprinted genes in tumorigenesis and implies that the imprinted gene NNAT may act as a prognostic biomarker and tumor suppressor in HCC.

Enzymatic preparation of hydrophobic biomass with one-pot synthesis and the oil removal performance.

Oil pollution is causing deleterious damage to aquatic ecosystems and human health. The utilization of agricultural waste such as corn stalk (CS) to produce biosorbents has been considered an ecofriendly and efficient approach for removing oil. However, most previous studies focused on the modification of the whole CS, which is inefficient due to the heterogeneity of CS. In this study, corn stalk pith (CP), which has excellent amphipathic characteristics, was selected to prepare a high-efficiency oil sorbent by grafting dodecyl gallate (DG, a long-chain alkyl) onto CP surface lignin via laccase mediation. The modified biomass (DGCP) shows high hydrophobicity (water contact angle = 140.2°) and superoleophilicity (oil contact angle = 0°) and exhibits a high oil sorption capacity (46.43 g/g). In addition, DGCP has good stability and reusability for adsorbing oil from the aqueous phase. Kinetic and isotherm models and two-dimensional correlation spectroscopy integrated with FTIR analyses revealed that the main sorption mechanism involves the H-bond effect, hydrophobic effect and van der Waals force. This work provides an ecofriendly method to prepare oil sorbents and new insights into the mechanisms underlying the removal of spilled oil from wastewater.

CapG promoted nasopharyngeal carcinoma cell motility involving Rho motility pathway independent of ROCK.

BACKGROUND: Gelsolin-like capping actin protein (CapG) modulates actin dynamics and actin-based motility with a debatable role in tumorigenic progression. The motility-associated functions and potential molecular mechanisms of CapG in nasopharyngeal carcinoma (NPC) remain unclear. METHODS: CapG expression was detected by immunohistochemistry in a cohort of NPC tissue specimens and by Western blotting assay in a variety of NPC cell lines. Loss of function and gain of function of CapG in scratch wound-healing and transwell assays were performed. Inactivation of Rac1 and ROCK with the specific small molecular inhibitors was applied to evaluate CapG's role in NPC cell motility. GTP-bound Rac1 and phosphorylated-myosin light chain 2 (p-MLC2) were measured in the ectopic CapG overexpressing cells. Finally, CapG-related gene set enrichment analysis was conducted to figure out the significant CapG-associated pathways in NPC. RESULTS: CapG disclosed increased level in the poorly differentiated NPC tissues and highly metastatic cells. Knockdown of CapG reduced NPC cell migration and invasion in vitro, while ectopic CapG overexpression showed the opposite effect. Ectopic overexpression of CapG compensated for the cell motility loss caused by simultaneous inactivation of ROCK and Rac1 or inactivation of ROCK alone. GTP-bound Rac1 weakened, and p-MLC2 increased in the CapG overexpressing cells. Bioinformatics analysis validated a positive correlation of CapG with Rho motility signaling, while Rac1 motility pathway showed no significant relationship. CONCLUSIONS: The present findings highlight the contribution of CapG to NPC cell motility independent of ROCK and Rac1. CapG promotes NPC cell motility at least partly through MLC2 phosphorylation and contradicts with Rac1 activation.

A novel machine learning model based on sparse structure learning with adaptive graph regularization for predicting drug side effects.

Drug side effects are closely related to the success and failure of drug development. Here we present a novel machine learning method for side effect prediction. The proposed method treats side effect prediction as a multi-label learning problem and uses sparse structure learning to model the relationships between side effects. Additionally, the proposed method adopts the adaptive graph regularization strategy to explore the local structure in drug data and fuse multiple types of drug features. An alternating optimization algorithm is proposed to solve the optimization problem. We collected chemical structures and biological pathway features of drugs as the inputs of our method to predict drug side effects. The results of the cross-validation experiment showed that our method could significantly improve the prediction performance compared to the other state-of-the-art methods. Besides, our model is highly interpretable. It could learn the drug neighbourhood relationships, side effect relationships, and drug features related to side effects. We systematically validated the information extracted by the model with independent data. Some prediction results could also be supported by literature reports. The proposed method could be applied to integrate both chemical and biological data to predict side effects and helps improve drug safety.