Mechanistic and predictive studies on the oxidation of furans by cytochrome P450: A DFT study.

Furan-containing compounds distribute widely in food, herbal medicines, industrial synthetic products, and environmental media. These compounds can undergo oxidative metabolism catalyzed by cytochrome P450 enzymes (CYP450) within organisms, which may produce reactive products, possibly reacting with biomolecules to induce toxic effects. In this work, we performed DFT calculations to investigate the CYP450-mediated metabolic mechanism of furan-ring oxidation using 2-methylfuran as a model substrate, meanwhile, we studied the regioselective competition of another hydroxylation reaction involving methyl group of 2-methylfuran. As a result, we found the toxicological-relevant cis-enedione product can be produced from O-addition directly via a concerted manner without formation of an epoxide intermediate as traditionally believed. Moreover, our calculations demonstrate the kinetic and thermodynamic feasibility of both furan-ring oxidation and methyl hydroxylation pathways, although the former pathway is a bit more favorable. We then constructed a linear model to predict the rate-limiting activation energies (ΔE*) of O-addition with 11 diverse furan substates based on their adiabatic ionization potentials (AIPs) and condensation Fukui functions (CFFs). The results show a good predictive ability (R2=0.94, Q2CV=0.87). Therefore, AIP and CFF with clear physichem meanings relevant to the mechanism, emerge as pivotal molecular descriptors to enable the fast prediction of furan-ring oxidation reactivities for quick insight into the toxicological risk of furans, using just ground-state calculations.

Insight into chemically reactive metabolites of aliphatic amine pollutants: A de novo prediction strategy and case study of sertraline.

The uncommon metabolic pathways of organic pollutants are easily overlooked, potentially leading to idiosyncratic toxicity. Prediction of their biotransformation associated with the toxic effects is the very purpose that this work focuses, to develop a de novo method to mechanistically predict the reactive toxicity pathways of uncommon metabolites from start aliphatic amine molecules, which employed sertraline triggered by CYP450 enzymes as a model system, as there are growing concerns about the effects on human health posed by antidepressants in the aquatic environment. This de novo prediction strategy combines computational and experimental methods, involving DFT calculations upon sequential growth, in vitro and in vivo assays, dissecting chemically reactive mechanism relevant to toxicity, and rationalizing the fundamental factors. Significantly, desaturation and debenzylation-aromatization as the emerging metabolic pathways of sertraline have been elucidated, with the detection of DNA adducts of oxaziridine metabolite in mice, highlighting the potential reactive toxicity. Molecular orbital analysis supports the reactivity preference for toxicological-relevant C-N desaturation over N-hydroxylation of sertraline, possibly extended to several other aliphatic amines based on the Bell-Evans-Polanyi principle. It was further validated toward some other wide-concerned aliphatic amine pollutants involving atrazine, ε-caprolactam, 6PPD via in silico and in vitro assays, thereby constituting a complete path for de novo prediction from case study to general applications.

Molecular Evolutionary Mechanisms of CYP6ER1vA-Type Variant Associated with Resistance to Neonicotinoid Insecticides in Field Populations of Nilaparvata lugens.

The evolution of insecticide resistance has threatened the control of Nilaparvata lugens. Research on mechanisms behind neonicotinoid resistance in N. lugens remains incomplete. This study examined P450-mediated resistance to neonicotinoids in a resistant N. lugens strain (XA-2017-3G). The overexpression of CYP6ER1 in the XA-2017-3G strain plays a role in neonicotinoid resistance, as confirmed by RNA interference. Phenotypic analyses of CYP6ER1-mediated resistance in strains, including laboratory-susceptible, field-collected, and imidacloprid-laboratory further-selected strains, revealed that the vA-type/vL-type genotype exhibited greater resistance to neonicotinoids compared to the vA-type/vA-type genotype. The mRNA expression levels of CYP6ER1vA-type were closely correlated with the levels of neonicotinoid resistance in N. lugens strains, in which CYP6ER1vA-type overexpression is in part attributed to increased copy numbers of CYP6ER1. CYP6ER1vA-type-mediated neonicotinoid resistance was further confirmed by a CYP6ER1vA-type transgenic Drosophila melanogaster line. Taken together, our findings strongly suggest that the overexpression of CYP6ER1vA-type, which can be partially attributed to copy number variations, plays a crucial role in N. lugens resistance to neonicotinoids.

Large-Scale Biomedical Relation Extraction Across Diverse Relation Types: Model Development and Usability Study on COVID-19.

BACKGROUND: Biomedical relation extraction (RE) is of great importance for researchers to conduct systematic biomedical studies. It not only helps knowledge mining, such as knowledge graphs and novel knowledge discovery, but also promotes translational applications, such as clinical diagnosis, decision-making, and precision medicine. However, the relations between biomedical entities are complex and diverse, and comprehensive biomedical RE is not yet well established. OBJECTIVE: We aimed to investigate and improve large-scale RE with diverse relation types and conduct usability studies with application scenarios to optimize biomedical text mining. METHODS: Data sets containing 125 relation types with different entity semantic levels were constructed to evaluate the impact of entity semantic information on RE, and performance analysis was conducted on different model architectures and domain models. This study also proposed a continued pretraining strategy and integrated models with scripts into a tool. Furthermore, this study applied RE to the COVID-19 corpus with article topics and application scenarios of clinical interest to assess and demonstrate its biological interpretability and usability. RESULTS: The performance analysis revealed that RE achieves the best performance when the detailed semantic type is provided. For a single model, PubMedBERT with continued pretraining performed the best, with an F1-score of 0.8998. Usability studies on COVID-19 demonstrated the interpretability and usability of RE, and a relation graph database was constructed, which was used to reveal existing and novel drug paths with edge explanations. The models (including pretrained and fine-tuned models), integrated tool (Docker), and generated data (including the COVID-19 relation graph database and drug paths) have been made publicly available to the biomedical text mining community and clinical researchers. CONCLUSIONS: This study provided a comprehensive analysis of RE with diverse relation types. Optimized RE models and tools for diverse relation types were developed, which can be widely used in biomedical text mining. Our usability studies provided a proof-of-concept demonstration of how large-scale RE can be leveraged to facilitate novel research.

A knowledge empowered explainable gene ontology fingerprint approach to improve gene functional explication and prediction.

Functional explication of genes is of great scientific value. However, conventional methods have challenges for those genes that may affect biological processes but are not annotated in public databases. Here, we developed a novel explainable gene ontology fingerprint (XGOF) method to automatically produce knowledge networks on biomedical literature in a given field which quantitatively characterizes the association between genes and ontologies. XGOF provides systematic knowledge for the potential function of genes and ontologically compares similarities and discrepancies in different disease-XGOFs integrating omics data. More importantly, XGOF can not only help to infer major cellular components in a disease microenvironment but also reveal novel gene panels or functions for in-depth experimental research where few explicit connections to diseases have previously been described in the literature. The reliability of XGOF is validated in four application scenarios, indicating a unique perspective of integrating text and data mining, with the potential to accelerate scientific discovery.

ViMRT: a text-mining tool and search engine for automated virus mutation recognition.

MOTIVATION: Virus mutation is one of the most important research issues which plays a critical role in disease progression and has prompted substantial scientific publications. Mutation extraction from published literature has become an increasingly important task, benefiting many downstream applications such as vaccine design and drug usage. However, most existing approaches have low performances in extracting virus mutation due to both lack of precise virus mutation information and their development based on human gene mutations. RESULTS: We developed ViMRT, a text-mining tool and search engine for automated virus mutation recognition using natural language processing. ViMRT mainly developed 8 optimized rules and 12 regular expressions based on a development dataset comprising 830 papers of 5 human severe disease-related viruses. It achieved higher performance than other tools in a test dataset (1662 papers, 99.17% in F1-score) and has been applied well to two other viruses, influenza virus and severe acute respiratory syndrome coronavirus-2 (212 papers, 96.99% in F1-score). These results indicate that ViMRT is a high-performance method for the extraction of virus mutation from the biomedical literature. Besides, we present a search engine for researchers to quickly find and accurately search virus mutation-related information including virus genes and related diseases. AVAILABILITY AND IMPLEMENTATION: ViMRT software is freely available at http://bmtongji.cn:1225/mutation/index.

Occurrence and translocation of ustiloxins in rice false smut-occurred paddy fields, Hubei, China.

Ustiloxin A (UA) and ustiloxin B (UB), two major mycotoxins produced by the pathogen of rice false smut (RFS) during rice cultivation, have attracted increasing attentions due to their potential health risks. However, limited data are available about their occurrence and fate in paddy fields and contamination profiles in rice. In this study, a field study was performed to investigate the occurrence and translocation of UA and UB in RFS-occurred paddies. For the first time to our knowledge, we reported a ubiquitous occurrence of the two ustiloxins in the paddy water (range: 0.01-3.46 µg/L for UA and <0.02-1.15 µg/L for UB) and brown rice (range: 0.09-154.08 µg/kg for UA and <0.09-23.57 µg/kg for UB). A significant positive correlation was observed between ustiloxin levels in paddy water and brown rice (rs = 0.48-0.79, p < 0.01). The occurrence of ustiloxin uptake in water-rice system was also evidenced by the rice exposure experiment, suggesting paddy water might be an important source for ustiloxin accumulation in rice. These results suggested that the contamination of ustiloxins in rice might occur widely, which was supported by the significantly high detection frequencies of UA (96.6%) and UB (62.4%) in polished rice (149 samples) from Hubei Province, China. The total concentrations of ustiloxins in the polished rice samples collected from Hubei Province ranged from <20.7 ng/kg (LOD) to 55.1 µg/kg (dry weight). Further studies are needed to evaluate the potential risks of ustiloxin exposure in the environment and humans.

Global distribution of ustiloxins in rice and their male-biased hepatotoxicity.

Ustiloxins, a group of bioactive metabolites produced by the pathogen of rice false smut (RFS), have emerged as ubiquitous contaminants in RFS-occurred paddy fields and could accumulate in rice. Nevertheless, the prevalence of ustiloxins in rice and exposure risks of humans are limited. In this study, concentrations of ustiloxin A (UA) and ustiloxins B (UB), which are two predominant ustiloxins, were measured in 240 rice samples from China and 72 rice samples from 12 other counties. The detection rates (DRs) of UA and UB were 82.1% and 49.3%, respectively, and their concentrations in rice ranged from below detection limit (LOD: 0.22 µg/kg) to 85.96 µg/kg dw. Furthermore, for the first time, we reported the occurrence of UA (DR = 22.8%) in urine collected from residues of Enshi city, China. Urinary UA were significantly correlated with the activities of alanine aminotransferase in male, and this male-biased hepatotoxicity was further confirmed in mice exposure experiment. This study for the first time reported the widespread geographical distribution of ustiloxins in rice, as well as emphasized the occurrence of internal exposure and potential health risk in humans.

Uptake, excretion and toxicity of titanate nanotubes in three stains of free-living ciliates of the genus Tetrahymena.

The potential exposure of titanate nanotubes (TNTs) to wildlife and humans may occur as a result of increased use and application as functional nanomaterials. However, there is a dearth of knowledge regarding the pathways of uptake and excretion of TNTs and their toxicity in cells. In this study, three strains of the Tetrahymena genus of free-living ciliates, including a wild type strain (SB210) and two mutant strains (SB255: mucocyst-deficient; NP1: temperature-sensitive "mouthless''), were used to study the pathways of uptake and excretion and evaluate the cytotoxicity of TNTs. The three Tetrahymena strains were separately exposed to 0, 0.01, 0.1, 1 or 10 mg/L of TNTs, and cells were collected at different time points for quantification of intracellular TNTs (e.g., 5, 10, 20, 40, 60, 90 and 120 min) and evaluation of cytotoxicity (12 and 24 h). TNT contents in NP1 and SB255 were greater or comparable to the contents in SB210 while exposure to 10 mg/L TNTs in 120 min. Furthermore, exposure to 10 mg/L TNTs for 24 h caused greater decreases in cell density of NP1 (38.2 %) and SB255 (36.8 %) compared with SB210 (26.5 %) and upregulated the expression of caspase 15 in SB210. Taken together, our results suggested that TNT uptake by pinocytosis and excretion by exocytosis in Tetrahymena, and the exposure could cause cytotoxicity which can offer novel insights into the accumulation kinetics of nanotubes and even nanomaterials in single cell.

LAIR-1 overexpression inhibits epithelial-mesenchymal transition in osteosarcoma via GLUT1-related energy metabolism.

BACKGROUND: Leukocyte-associated immunoglobulin-like receptor-1 (LAIR-1) is a collagen receptor belonging to the immunoglobulin superfamily. Although previous studies have evaluated the biological role of LAIR in solid tumors, the precise mechanisms underlying the functions of LAIR-1 as a regulator of tumor biological functions remain unclear. METHODS: LAIR-1 expression was evaluated by immunohistochemical analysis using an osteosarcoma (OS) tissue microarray. Wound healing and transwell migration assays were performed to evaluate tumor cell migration. Quantitative real-time polymerase chain reaction (qPCR) and western blotting were conducted to detect the expression of epithelial-mesenchymal transition (EMT)-related molecules. RNA-sequencing (RNA-seq) was conducted to evaluate the mRNA expression profiles after overexpressing LAIR-1 in OS cells. Glucose transporter (Glut)1 expression in OS cells was evaluated by western blotting. RESULTS: LAIR-1 expression was significantly different between the T1 and T2 stages of OS tumors, and it inhibited OS cell migration. LAIR-1 expression was inversely correlated with the expression of Twist1, an EMT-associated transcription factor, via the Forkhead box O1 signal transduction pathway. Furthermore, RNA-seq and qPCR demonstrated that the expression of EMT energy metabolism-related molecules was significantly reduced after LAIR-1 overexpression. CONCLUSIONS: LAIR-1 overexpression decreased the expression of Glut1 and inhibited the expression of EMT-related molecules in OS cells. These findings provide new insights into the molecular mechanism underlying OS progression.

Effects of triphenyl phosphate on ciliate protozoa Tetrahymena thermophila following acute exposure and sub-chronic exposure.

Triphenyl phosphate (TPHP) is one of the most widely used organophosphate flame retardants (OPFRs) and is frequently detected in a variety of environmental media. Previous studies reported that TPHP had toxic effects on vertebrates, but little toxic information was available in lower trophic aquatic organisms which were more sensitive to the exposure of many toxic substances. In this study, protozoa Tetrahymena thermophila (T. thermophila) were exposed to 0, 0.01, 0.17 or 2.35 mg/L TPHP for 5 days to study the effects of sub-chronic exposure on theoretical population, cell viability, cell size and number of cilia. Additionally, the effects of TPHP on gene transcription were assessed by transcriptome sequencing technique (RNA-Seq). Cell viability and number of cilia were significantly reduced in all TPHP exposure groups compared with the control. In addition, exposure to 0.17 or 2.35 mg/L TPHP significantly reduced the theoretical population, circumference and body width, and there was a significant decrease in body length in the 2.35 mg/L exposure group. Comparative transcriptome sequencing identified a total of 4105 up- and 4487 down-regulated genes after exposure to 2.35 mg/L TPHP for 5 days compared with the control. KEGG analysis showed that dysfunction of pathways associated with ribosome, spliceosome, phagosome, proteasome and protein processing in endoplasmic reticulum in this study might be responsible for the toxicity of T. thermophila caused by TPHP. In general, the results indicated that TPHP had an adverse effect on the protozoa T. thermophila.

Effects of tris (2-chloroethyl) phosphate (TCEP) on growth, reproduction and gene transcription in the protozoan Tetrahymena thermophila.

As a typical organophosphorus flame retardant, tris (2-chloroethyl) phosphate (TCEP) has been widely detected in various environmental media. Toxicity of TCEP to vertebrates have been investigated, but potential effects on lower trophic level species were unknown to date. In this study, toxic effects and molecular mechanisms of toxic actions of TCEP on the aquatic protozoan Tetrahymena thermophila were evaluated by use of phenotypic observations, transcriptome sequencing analysis and real-time quantitative PCR detection. Exposure to 0.044, 0.411 or 4.26 mg/L TCEP for 5 days decreased the theoretical population, cell viability, number of cilia and cell size of Tetrahymena thermophila in a time- and dose-dependent manner. Meanwhile, RNA-Seq analysis indicated that exposure to 4.26 mg/L TCEP significantly changed expression of 2932 genes (up-regulation: 1228; down-regulation: 1704). Of these, expressions of 9, 10 and 17 genes that were enriched in soluble N-ethylmaleimide-sensitive factor attachment protein receptor (SNARE) interaction in vesicular transport, proteasome and endocytosis pathway respectively were down-regulated. Data collected during this study suggested that exposure to high concentrations of TCEP might affect growth and reproduction of Tetrahymena thermophila through down-regulating transcriptional levels of genes encoding proteins associated with vesicle trafficking, proteasome and endocytosis.

Occurrence of the fungus mycotoxin, ustiloxin A, in surface waters of paddy fields in Enshi, Hubei, China, and toxicity in Tetrahymena thermophila.

There has been an increasing incidence rate of rice false smut in global rice cultivation areas. However, there is a dearth of studies on the environmental concentrations and hazards of ustiloxin A (UA), which is the major mycotoxin produced by a pathogenic fungus of the rice false smut. Here, the concentrations of UA in the surface waters of two paddy fields located in Enshi city, Hubei province, China, were measured, and its toxicity in T. Thermophila was evaluated. This is the first study to detect UA in the surface waters of the two paddy fields, and the measured mean concentrations were 2.82 and 0.26 µg/L, respectively. Exposure to 2.19, 19.01 or 187.13 µg/L UA for 5 days significantly reduced the theoretical population and cell size of T. thermophila. Furthermore, treatment with 187.13 µg/L UA changed the percentages of T. thermophila cells in different cell-cycle stages, and with an increased malformation rate compared with the control, suggesting the disruption of the cell cycle. The expressions of 30 genes involved in the enriched proteasome pathway, 7 cyclin genes (cyc9, cyc10, cyc16, cyc22, cyc23, cyc26, cyc33) and 2 histone genes (mlh1 and hho1) were significantly down-regulated, which might be the modes of action responsible for the disruption of cell cycling due to UA exposure.

Side Information for Face Completion: A Robust PCA Approach.

Robust principal component analysis (RPCA) is a powerful method for learning low-rank feature representation of various visual data. However, for certain types as well as significant amount of error corruption, it fails to yield satisfactory results; a drawback that can be alleviated by exploiting domain-dependent prior knowledge or information. In this paper, we propose two models for the RPCA that take into account such side information, even in the presence of missing values. We apply this framework to the task of UV completion which is widely used in pose-invariant face recognition. Moreover, we construct a generative adversarial network (GAN) to extract side information as well as subspaces. These subspaces not only assist in the recovery but also speed up the process in case of large-scale data. We quantitatively and qualitatively evaluate the proposed approaches through both synthetic data and eight real-world datasets to verify their effectiveness.

Silencing LAIR-1 in human THP-1 macrophage increases foam cell formation by modulating PPARγ and M2 polarization.

Formation of macrophage-derived foam cells may mark the initial stages of atherosclerosis. We investigated the association between the expression of the leukocyte-associated immunoglobulin-like receptor 1 (LAIR-1) in macrophages and foam cell formation. A foam cell model was established by incubating THP-1-derived macrophages and bone marrow macrophages (BMMs) with oxidized low-density lipoprotein (ox-LDL). The role of LAIR-1 in foam cell formation was evaluated via Oil Red O staining and Dil-ox-LDL fluorescence intensities. Peroxisome proliferator-activated receptor gamma (PPARγ), cholesterol metabolism-related genes, and the role of LAIR-1 in activating classically activated (M1) and alternatively activated (M2) macrophages were evaluated by qPCR. Additionally, activation of protein-tyrosine phosphatase-1 (SHP-1) and cAMP-response element binding protein (CREB) were detected by western blotting. Results indicated that silencing LAIR-1 in macrophages modulated the SHP-1/CREB/PPARγ pathway, thereby promoting M2 macrophage polarization and increasing foam cell formation. Therefore, Inhibition of LAIR-1 in macrophages may promote foam cell formation and atherosclerosis.

Electrochemically Scavenging the Silica Impurities at the Ni-YSZ Triple Phase Boundary of Solid Oxide Cells.

Silica impurity originated from the sealing or raw materials of the solid oxide cells (SOCs) accumulating at the Ni-YSZ triple phase boundaries (TPBs) is known as one major reason for electrode passivation. Here we report nanosilica precipitates inside Ni grains instead of blocking the TPBs when operating the SOCs at |i| ≥ 1.5 A cm(-2) for electrolysis of H2O/CO2. An electrochemical scavenging mechanism was proposed to explain this unique behavior: the removal of silica proceeded through the reduction of the silica to Si under strong cathodic polarization, followed by bulk diffusion of Si into Ni and reoxidation of Si in the Ni grain.

High-Performance Microchanneled Asymmetric Gd(0.1)Ce(0.9)O(1.95-δ)-La(0.6)Sr(0.4)FeO(3-δ)-Based Membranes for Oxygen Separation.

A microchanneled asymmetric dual phase composite membrane of 70 vol % Gd(0.1)Ce(0.9)O(1.95-δ)-30 vol % La(0.6)Sr(0.4)FeO(3-δ) (CGO-LSF) was fabricated by a "one step" phase-inversion tape casting. The sample consists of a thin dense membrane (100 µm) and a porous substrate including "finger-like" microchannels. The oxygen permeation flux through the membrane with and without catalytic surface layers was investigated under a variety of oxygen partial pressure gradients. At 900 °C, the oxygen permeation flux of the bare membrane was 1.6 (STP) ml cm(-2) min(-1) for the air/He-case and 10.10 (STP) ml cm(-2) min(-1) for the air/CO-case. Oxygen flux measurements as well as electrical conductivity relaxation show that the oxygen flux through the bare membrane without catalyst is limited by the oxygen surface exchange. The surface exchange can be enhanced by introduction of catalyst on the membrane surface. An increase of the oxygen flux of ∼1.49 (STP) mL cm(-2) min(-1) at 900 °C was observed when catalyst is added for the air/He-case. Mass transfer polarization through the finger-like support was confirmed to be negligible, which benefits the overall performance. A stable flux of 7.00 (STP) ml cm(-2) min(-1) was observed between air/CO/CO2 over 200 h at 850 °C. Partial surface decomposition was observed on the permeate side exposed to CO, in line with predictions from thermodynamic calculations. In a mixture of CO, CO2, H2, and H2O at similar oxygen activity the material will according to the calculation not decompose. The microchanneled asymmetric CGO-LSF membranes show high oxygen permeability and chemical stability under a range of technologically relevant oxygen potential gradients.

From Pixels to Response Maps: Discriminative Image Filtering for Face Alignment in the Wild.

We propose a face alignment framework that relies on the texture model generated by the responses of discriminatively trained part-based filters. Unlike standard texture models built from pixel intensities or responses generated by generic filters (e.g. Gabor), our framework has two important advantages. First, by virtue of discriminative training, invariance to external variations (like identity, pose, illumination and expression) is achieved. Second, we show that the responses generated by discriminatively trained filters (or patch-experts) are sparse and can be modeled using a very small number of parameters. As a result, the optimization methods based on the proposed texture model can better cope with unseen variations. We illustrate this point by formulating both part-based and holistic approaches for generic face alignment and show that our framework outperforms the state-of-the-art on multiple "wild" databases. The code and dataset annotations are available for research purposes from http://ibug.doc.ic.ac.uk/resources.