Circ_0008657 regulates lung DNA damage induced by hexavalent chromium through the miR-203a-3p/ATM axis.

Hexavalent chromium [Cr (VI)] is an important environmental pollutant and may cause lung injury when inhaled into the human body. Cr (VI) is genotoxic and can cause DNA damage, although the underlying epigenetic mechanisms remain unclear. To simulate the real-life workplace exposure to Cr (VI), we used a novel exposure dose calculation method. We evaluated the effect of Cr (VI) on DNA damage in human bronchial epithelial cells (16HBE and BEAS-2B) by calculating the equivalent real-time exposure dose of Cr (VI) (0 to 10 µM) in an environmental population. Comet experiments and olive tail moment measurements revealed increased DNA damage in cells exposed to Cr (VI). Cr (VI) treatment increased nuclear γ-H2AX foci and γ-H2AX protein expression, and caused DNA damage in the lung tissues of mice. An effective Cr (VI) dose (6 µM) was determined and used for cell treatment. Cr (VI) exposure upregulated circ_0008657, and knockdown of circ_0008657 decreased Cr (VI)-induced DNA damage, whereas circ_0008657 overexpression had the opposite effect. Mechanistically, we found that circ_0008657 binds to microRNA (miR)-203a-3p and subsequently regulates ATM serine/threonine kinase (ATM), a key protein involved in homologous recombination repair downstream of miR-203a-3p, thereby regulating DNA damage induced by Cr (VI). The present findings suggest that circ_0008657 competitively binds to miR-203a-3p to activate the ATM pathway and regulate the DNA damage response after environmental chemical exposure in vivo and in vitro.

Circ0087385 promotes DNA damage in benzo(a)pyrene-induced lung cancer development by upregulating CYP1A1.

Increasing environmental genotoxic chemicals have been shown to induce epigenetic alterations. However, the interaction between genetics and epigenetics in chemical carcinogenesis is still not fully understood. Here, we constructed an in vitro human lung carcinogenesis model (16HBE-T) by treating human bronchial epithelial cells with a typical significant carcinogen benzo(a)pyrene (BaP). We identified a novel circular RNA, circ0087385, which was overexpressed in 16HBE-T and human lung cancer cell lines, as well as in lung cancer tissues and serum exosomes from lung cancer patients. The upregulated circ0087385 after exposure to BaP promoted DNA damage in the early stage of chemical carcinogenesis and affected the cell cycle, proliferation, and apoptosis of the malignantly transformed cells. Overexpression of circ0087385 enhanced the expression of cytochrome P450 1A1 (CYP1A1), which is crucial for metabolically activating BaP. Interfering with circ0087385 or CYP1A1 reduced the levels of ultimate carcinogen benzo(a)pyrene diol epoxide (BPDE) and BPDE-DNA adducts. Interfering with CYP1A1 partially reversed the DNA damage induced by high expression of circ0087385, as well as decreased the level of BPDE and BPDE-DNA adducts. These findings provide novel insights into the interaction between epigenetics and genetics in chemical carcinogenesis which are crucial for understanding the epigenetic and genetic toxicity of chemicals.

Carbon black nanoparticles and cadmium co-exposure aggravates bronchial epithelial cells inflammation via autophagy-lysosome pathway.

Carbon black nanoparticles (CBNPs) and cadmium (Cd) are major components of various air pollutants and cigarette smoke. Autophagy and inflammation both play critical roles in understanding the toxicity of particles and their components, as well as maintaining body homeostasis. However, the effects and mechanisms of CBNPs and Cd (CBNPs-Cd) co-exposure on the human respiratory system remain unclear. In this study, a CBNPs-Cd exposure model was constructed to explore the respiratory toxicity and combined mechanism of these chemicals on the autophagy-lysosome pathway in the context of respiratory inflammation. Co-exposure of CBNPs and Cd significantly increased the number of autophagosomes and lysosomes in human bronchial epithelial cells (16HBE) and mouse lung tissues compared to the control group, as well as the groups exposed to CBNPs and Cd alone. Autophagic markers, LC3II and P62 proteins, were up-regulated in 16HBE cells and mouse lung tissues after CBNPs-Cd co-exposure. However, treatment with Cq inhibitor (an indicator of lysosomal acid environment) resulted in a substantial decreased co-localization fluorescence of LC3 and lysosomes in the CBNPs-Cd combination group compared with the CBNPs-Cd single and control groups. No difference in LAMP1 protein expression was observed among the exposed groups. Adding 3 MA alleviated inflammatory responses, while applying the Baf-A1 inhibitor aggravated inflammation both in vitro and in vivo following CBNPs-Cd co-exposure. Factorial analysis showed no interaction between CBNPs and Cd in their effects on 16HBE cells. We demonstrated that co-exposure to CBNPs-Cd increases the synthesis of autophagosomes and regulates the acidic environment of lysosomes, thereby inhibiting autophagy-lysosome fusion and enhancing the inflammatory response in both 16HBE cells and mouse lung. These findings provide evidence for a comprehensive understanding of the interaction between CBNPs and Cd in mixed pollutants, as well as for the prevention and control of occupational exposure to these two chemicals.

Benzo[a]pyrene-induced up-regulation of circ_0003552 via ALKBH5-mediated m6A modification promotes DNA damage in human bronchial epithelial cells.

Benzo [a]pyrene (B [a]P) is a widespread environmental chemical pollutant that has been linked to the development of various diseases. However, the specific mechanism of action remains unclear. In this study, human bronchial epithelial 16HBE and BEAS-2B cells were exposed to B [a]P at 0-32 µM to assess the DNA-damaging effects. B [a]P exposure resulted in elevated expression of γ-H2AX, a marker of DNA damage. The m6A RNA methylation assay showed that B [a]P exposure increased the extent of m6A modification and the demethylase ALKBH5 played an integral role in this process. Moreover, the results of the comet assay and Western blot analysis showed an increase in m6A modification mediated by ALKBH5 that promoted DNA damage. Furthermore, the participation of a novel circular RNA, circ_0003552, was assessed by high-throughput sequencing under the condition of high m6A modification induced by B [a]P exposure. In subsequent functional studies, an interference/overexpression system was created to confirm that circ_0003552 participated in regulation of DNA damage. Mechanistically, circ_0003552 had an m6A binding site that could regulate its generation. This study is the first to report that B [a]P upregulated circ_0003552 through m6A modification, thereby promoting DNA damage. These findings revealed that epigenetics played a key role in environmental carcinogen-induced DNA damage, and the quantitative changes it brought might provide an early biomarker for future medical studies of genetic-related diseases and a new platform for investigations of the interaction between epigenetics and genetics.

Circ_0089282 inhibits carbon black nanoparticle-induced DNA damage by promoting DNA repair protein in the lung.

Inhalation of carbon black nanoparticles (CBNPs) can impair lung tissue and cause DNA damage, but the epigenetic mechanism responsible for these effects is still unclear. We explored the role of circular RNAs (circRNAs) in DNA damage induced by CBNPs in the lung. Human bronchial epithelial cell lines (16HBE and BEAS-2B) were treated with 0, 5, 10, 20, 40, or 80 µg/ml CBNPs for 24, 48, and 72 h, and BALB/c mice were exposed to 8 and 80 µg/d CBNPs for 14 days to establish in vitro and vivo models of CBNP exposure, respectively. We found that CBNPs caused DNA double-strand breaks in the lung. Using high-throughput sequencing and quantitative real-time PCR to identify CBNP-related circRNAs, we identified a novel circRNA (circ_0089282) that was overexpressed in the CBNP-exposed group. We used gain-/loss-of-function approaches, RNA pulldown assays, and silver staining to explore the regulatory function of circ_0089282 and its interactions with targeted proteins. We found that circ_0089282 interference could increase CBNP-induced DNA damage, whereas overexpression resulted in the opposite. Circ_0089282 could directly bind to the fused in sarcoma (FUS) protein and positively regulate downstream DNA repair protein DNA ligase 4 (LIG4) through FUS. This regulatory effect of circRNA on DNA damage via promotion of LIG4 illustrated the interactions between genetics and epigenetics in toxicology.

DNMT3A-mediated high expression of circ_0057504 promotes benzo[a]pyrene-induced DNA damage via the NONO-SFPQ complex in human bronchial epithelial cells.

Benzo[a]pyrene (B[a]P) is a class I carcinogen and hazardous environmental pollutant with genetic toxicity. Understanding the molecular mechanisms underlying genetic deterioration and epigenetic alterations induced by environmental contaminants may contribute to the early detection and prevention of cancer. However, the role and regulatory mechanisms of circular RNAs (circRNAs) in the B[a]P-induced DNA damage response (DDR) have not been elucidated. In this study, human bronchial epithelial cell lines (16HBE and BEAS-2B) were exposed to various concentrations of B[a]P, and BALB/c mice were treated with B[a]P intranasally. B[a]P exposure was found to induce DNA damage and upregulate circular RNA hsa_circ_0057504 (circ_0057504) expression in vitro and in vivo. In addition, B[a]P upregulated TMEM194B mRNA and circ_0057504 expression through inhibition of DNA methyltransferase 3 alpha (DNMT3A) expression in vitro. Modulation (overexpression or knockdown) of circ_0057504 expression levels using a lentiviral system in human bronchial epithelial cells revealed that circ_0057504 promoted B[a]P-induced DNA damage. RNA pull-down and western blot assays showed that circ_0057504 interacted with non-POU domain-containing octamer-binding (NONO) and splicing factor proline and glutamine rich (SFPQ) proteins and regulated formation of the NONO-SFPQ protein complex. Thus, our findings indicate that circ_0057504 acts as a novel regulator of DNA damage in human bronchial epithelial cells exposed to B[a]P. The current study reveals novel insights into the role of circRNAs in the regulation of genetic damage, and describes the effect and regulatory mechanisms of circ_0057504 on B[a]P genotoxicity.

The involvement of copper, circular RNAs, and inflammatory cytokines in chronic respiratory disease.

Exposure to high concentrations of copper is associated with pulmonary inflammation and chronic respiratory disease (CRD). Epigenetic modulation of noncoding RNAs contributes to the development of several CRDs. It is unknown whether epigenetic modulation is involved in copper mediated pulmonary inflammation and CRD. We conducted a case-control study of 101 CRD cases and 161 control subjects in Shijiazhuang, China, and evaluated circRNAs and cytokine levels (IL-6 and IL-8) by qPCR and ELISA. Urinary copper concentration was determined by inductively coupled plasma mass spectrometry. Linear mixed models and generalized linear mixed models were used to assess the associations of circRNAs with CRD, urinary copper, and cytokines. We exposed the human bronchial epithelial cell line, 16HBE, to copper and assessed the functional role of a circRNA, circ_0008882, by RNA overexpression. Cellular location of circ_0008882 was assessed by separation of nuclear and cytoplasmic RNAs. Nine circRNAs were associated with an increased risk for CRDs, while the relative expression of circ_0008882 was decreased after copper exposure in vitro and in vivo. Copper exposure stimulated 16HBE cells to release proinflammatory IL-6 and IL-8. The release of the cytokines was inhibited by overexpression of circ_0008882. These results suggest a role for circ_0008882 in the regulation of CRD associated inflammation following copper exposure.

Experimental and Molecular Dynamics Simulation Study on Sol-Gel Conversion Process of Aluminum Carboxylate System.

Due to the lack of relevant in situ characterization techniques, the investigation of aluminum sol-gel progress is lacking. In this study, combined with molecular dynamics simulation and conventional experimental methods, the microstructures, rheological properties, and gelation process of the carboxylic aluminum sol system were studied. The experimental results showed that, with the increase in solid content, the microstructure of the colloid developed from a loose and porous framework to a homogeneous and compact structure. The viscosity of aluminum sol decreased significantly with the increase in temperature, and a temperature above 318 k was more conducive to improving the fluidity. The simulation results show that the increase in free volume and the connectivity of pores in colloidal framework structure were the key factors to improve fluidity. In addition, free water molecules had a higher migration rate, which could assist the rotation and rearrangement of macromolecular chains and also played an essential role in improving fluidity. The Molecular dynamics simulation (MD) results were consistent with experimental results and broaden the scope of experimental research, providing necessary theoretical guidance for enhancing the spinning properties of aluminum sol.

DeepGraphGO: graph neural network for large-scale, multispecies protein function prediction.

MOTIVATION: Automated function prediction (AFP) of proteins is a large-scale multi-label classification problem. Two limitations of most network-based methods for AFP are (i) a single model must be trained for each species and (ii) protein sequence information is totally ignored. These limitations cause weaker performance than sequence-based methods. Thus, the challenge is how to develop a powerful network-based method for AFP to overcome these limitations. RESULTS: We propose DeepGraphGO, an end-to-end, multispecies graph neural network-based method for AFP, which makes the most of both protein sequence and high-order protein network information. Our multispecies strategy allows one single model to be trained for all species, indicating a larger number of training samples than existing methods. Extensive experiments with a large-scale dataset show that DeepGraphGO outperforms a number of competing state-of-the-art methods significantly, including DeepGOPlus and three representative network-based methods: GeneMANIA, deepNF and clusDCA. We further confirm the effectiveness of our multispecies strategy and the advantage of DeepGraphGO over so-called difficult proteins. Finally, we integrate DeepGraphGO into the state-of-the-art ensemble method, NetGO, as a component and achieve a further performance improvement. AVAILABILITY AND IMPLEMENTATION: https://github.com/yourh/DeepGraphGO. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

NetGO 2.0: improving large-scale protein function prediction with massive sequence, text, domain, family and network information.

With the explosive growth of protein sequences, large-scale automated protein function prediction (AFP) is becoming challenging. A protein is usually associated with dozens of gene ontology (GO) terms. Therefore, AFP is regarded as a problem of large-scale multi-label classification. Under the learning to rank (LTR) framework, our previous NetGO tool integrated massive networks and multi-type information about protein sequences to achieve good performance by dealing with all possible GO terms (>44 000). In this work, we propose the updated version as NetGO 2.0, which further improves the performance of large-scale AFP. NetGO 2.0 also incorporates literature information by logistic regression and deep sequence information by recurrent neural network (RNN) into the framework. We generate datasets following the critical assessment of functional annotation (CAFA) protocol. Experiment results show that NetGO 2.0 outperformed NetGO significantly in biological process ontology (BPO) and cellular component ontology (CCO). In particular, NetGO 2.0 achieved a 12.6% improvement over NetGO in terms of area under precision-recall curve (AUPR) in BPO and around 2.6% in terms of $\mathbf {F_{max}}$ in CCO. These results demonstrate the benefits of incorporating text and deep sequence information for the functional annotation of BPO and CCO. The NetGO 2.0 web server is freely available at http://issubmission.sjtu.edu.cn/ng2/.

Preparation of Continuous Alumina Fiber with Nano Grains by the Addition of Iron Sol.

Continuous alumina fiber exhibits excellent mechanical properties owing to its dense microstructure with fine grains. In this study, alumina fiber was prepared by the sol-gel method using iron sol as a nucleating agent. It was proposed that the α-Al2O3 grain size be adjusted based on the modification of colloidal particle size. The effect of holding temperature and reaction material ratio on the iron colloidal particle size was studied. The microstructure of alumina fiber was characterized by scanning electron microscopy (SEM). The experiment results indicated that iron colloidal particle size increases with the holding temperature and the NH4HCO3/Fe(NO3)3·9H2O ratio. The alumina fiber with uniform nano α-Al2O3 grains was obtained by calcination at 1400 °C for 5 min. The mean grain size tends to rise with the mean colloidal particle size. Using the iron sol as a nucleating agent, the fiber with a mean grain size of 22.5 nm could be formed. The tensile strength of fibers increased with the decrease of grain size.

HPOAnnotator: improving large-scale prediction of HPO annotations by low-rank approximation with HPO semantic similarities and multiple PPI networks.

BACKGROUND: As a standardized vocabulary of phenotypic abnormalities associated with human diseases, the Human Phenotype Ontology (HPO) has been widely used by researchers to annotate phenotypes of genes/proteins. For saving the cost and time spent on experiments, many computational approaches have been proposed. They are able to alleviate the problem to some extent, but their performances are still far from satisfactory. METHOD: For inferring large-scale protein-phenotype associations, we propose HPOAnnotator that incorporates multiple Protein-Protein Interaction (PPI) information and the hierarchical structure of HPO. Specifically, we use a dual graph to regularize Non-negative Matrix Factorization (NMF) in a way that the information from different sources can be seamlessly integrated. In essence, HPOAnnotator solves the sparsity problem of a protein-phenotype association matrix by using a low-rank approximation. RESULTS: By combining the hierarchical structure of HPO and co-annotations of proteins, our model can well capture the HPO semantic similarities. Moreover, graph Laplacian regularizations are imposed in the latent space so as to utilize multiple PPI networks. The performance of HPOAnnotator has been validated under cross-validation and independent test. Experimental results have shown that HPOAnnotator outperforms the competing methods significantly. CONCLUSIONS: Through extensive comparisons with the state-of-the-art methods, we conclude that the proposed HPOAnnotator is able to achieve the superior performance as a result of using a low-rank approximation with a graph regularization. It is promising in that our approach can be considered as a starting point to study more efficient matrix factorization-based algorithms.

NetGO: improving large-scale protein function prediction with massive network information.

Automated function prediction (AFP) of proteins is of great significance in biology. AFP can be regarded as a problem of the large-scale multi-label classification where a protein can be associated with multiple gene ontology terms as its labels. Based on our GOLabeler-a state-of-the-art method for the third critical assessment of functional annotation (CAFA3), in this paper we propose NetGO, a web server that is able to further improve the performance of the large-scale AFP by incorporating massive protein-protein network information. Specifically, the advantages of NetGO are threefold in using network information: (i) NetGO relies on a powerful learning to rank framework from machine learning to effectively integrate both sequence and network information of proteins; (ii) NetGO uses the massive network information of all species (>2000) in STRING (other than only some specific species) and (iii) NetGO still can use network information to annotate a protein by homology transfer, even if it is not contained in STRING. Separating training and testing data with the same time-delayed settings of CAFA, we comprehensively examined the performance of NetGO. Experimental results have clearly demonstrated that NetGO significantly outperforms GOLabeler and other competing methods. The NetGO web server is freely available at http://issubmission.sjtu.edu.cn/netgo/.