The complete genome sequence of a Paenibacillus tundrae strain MLY93 isolated from spot disease-infected tobacco leaves in China.

This study presents the complete gene sequence of a Paenibacillus tundrae strain isolated from tobacco spot disease leaves in Xingyi, Guizhou Province, China. The genetic understanding of P. tundrae is advanced by this research.

Recognizing high-priority disinfection byproducts based on experimental and predicted endocrine disrupting data: Virtual screening and in vitro study.

So far, about 130 disinfection by-products (DBPs) and several DBPs-groups have had their potential endocrine-disrupting effects tested on some endocrine endpoints. However, it is still not clear which specific DBPs, DBPs-groups/subgroups may be the most toxic substances or groups/subgroups for any given endocrine endpoint. In this study, we attempt to address this issue. First, a list of relevant DBPs was updated, and 1187 DBPs belonging to 4 main-groups (aliphatic, aromatic, alicyclic, heterocyclic) and 84 subgroups were described. Then, the high-priority endocrine endpoints, DBPs-groups/subgroups, and specific DBPs were determined from 18 endpoints, 4 main-groups, 84 subgroups, and 1187 specific DBPs by a virtual-screening method. The results demonstrate that most of DBPs could not disturb the endocrine endpoints in question because the proportion of active compounds associated with the endocrine endpoints ranged from 0 (human thyroid receptor beta) to 32% (human transthyretin (hTTR)). All the endpoints with a proportion of active compounds greater than 10% belonged to the thyroid system, highlighting that the potential disrupting effects of DBPs on the thyroid system should be given more attention. The aromatic and alicyclic DBPs may have higher priority than that of aliphatic and heterocyclic DBPs by considering the activity rate and potential for disrupting effects. There were 2 (halophenols and estrogen DBPs), 12, and 24 subgroups that belonged to high, moderate, and low priority classes, respectively. For individual DBPs, there were 23 (2%), 193 (16%), and 971 (82%) DBPs belonging to the high, moderate, and low priority groups, respectively. Lastly, the hTTR binding affinity of 4 DBPs was determined by an in vitro assay and all the tested DBPs exhibited dose-dependent binding potency with hTTR, which was consistent with the predicted result. Thus, more efforts should be performed to reveal the potential endocrine disruption of those high research-priority main-groups, subgroups, and individual DBPs.

Endocrine disrupting effect and reproductive toxicity of the separate exposure and co-exposure of nano-polystyrene and diethylstilbestrol to zebrafish.

The co-occurrence of nanoplastics and other pollutants in the environment has gotten a lot of attention, but information on the biological toxicity of their co-exposure was limited. This study aims to reveal the endocrine disrupting effect and reproductive toxicity of nano-polystyrene (NPS) and diethylstilbestrol (DES) to zebrafish under separate and combined exposure. Results indicated that NPS and DES exposure in isolation reduced the hepatosomatic index and gonadosomatic index, and altered the cell maturity in gonads in both cases. Even worse, the co-exposure of NPS and DES exacerbated the damage to the liver and gonads of fish. The two pollutants individually inhibited the secretion of sex hormones and vitellogenin. The inhibition effect of DES was especially dose-dependent, while NPS had weaker effect than DES. Their combined action on the secretion of sex hormones and vitellogenin exhibited additive effect. However, NPS did not affect the content of thyroid hormones in fish, and also had no significant effect on the reduction of thyroid hormone caused by DES exposure. Furthermore, their co-exposure decreased the cumulative eggs from 1031 to 306, and the spawning number from 12 to 8. The fertilization rate and hatchability rete of eggs were reduced by 30.9% and 40.4%, respectively. The abnormality rate of embryos was 65.0%, significantly higher than in separate DES and NPS groups (55.7% and 30.8% respectively). The abnormal development of offspring was mainly pericardial cyst, spinal curvature, and growth retardation.

Prediction of Surface Roughness in Gas-Solid Two-Phase Abrasive Flow Machining Based on Multivariate Linear Equation.

The main purpose of this study is to explore a surface roughness prediction model of Gas-Solid Two-Phase Abrasive Flow Machining. In order to achieve the above purpose, an orthogonal experiment was carried out. Q235 steel as processing material and white corundum with different particle sizes as abrasive particles were used in the experiment. Shape and spindle speed were the main reference factors. The range method and factor trend graph are used to comprehensively analyze the experimental results of different processing stages of the detection point, and the optimal parameter combination of A3B2C1D2 was obtained. According to the experimental results, a multiple linear regression equation was established to predict the surface roughness, and the experimental results were solved and significantly analyzed by software to obtain a highly reliable prediction model. Through experiments, modeling and verification, it is known that the maximum error between the obtained model and the actual value is 0.339 µm and the average error is 0.00844 µm, which can better predict the surface roughness of the gas-solid two-phase flow abrasive pool.

Research Progress of the Endocrine-Disrupting Effects of Disinfection Byproducts.

Since 1974, more than 800 disinfection byproducts (DBPs) have been identified from disinfected drinking water, swimming pool water, wastewaters, etc. Some DBPs are recognized as contaminants of high environmental concern because they may induce many detrimental health (e.g., cancer, cytotoxicity, and genotoxicity) and/or ecological (e.g., acute toxicity and development toxicity on alga, crustacean, and fish) effects. However, the information on whether DBPs may elicit potential endocrine-disrupting effects in human and wildlife is scarce. It is the major objective of this paper to summarize the reported potential endocrine-disrupting effects of the identified DBPs in the view of adverse outcome pathways (AOPs). In this regard, we introduce the potential molecular initiating events (MIEs), key events (KEs), and adverse outcomes (AOs) associated with exposure to specific DBPs. The present evidence indicates that the endocrine system of organism can be perturbed by certain DBPs through some MIEs, including hormone receptor-mediated mechanisms and non-receptor-mediated mechanisms (e.g., hormone transport protein). Lastly, the gaps in our knowledge of the endocrine-disrupting effects of DBPs are highlighted, and critical directions for future studies are proposed.

Polarizability and aromaticity index govern AhR-mediated potencies of PAHs: A QSAR with consideration of freely dissolved concentrations.

Polycyclic aromatic hydrocarbons (PAHs) are ubiquitous environmental pollutants associated with adverse human effects including cancer, and the aryl hydrocarbon receptor (AhR) is a key ligand-activated transcription factor mediating their toxicity. However, there is presently a lack of data on AhR potencies of PAHs. Simple, transparent, interpretable and predictive quantitative structure-activity relationship (QSAR) models are helpful, especially with the consideration of freely dissolved concentrations linked to bioavailability. Here, QSAR models on AhR-mediated luciferase activity of PAHs were developed with nominal median effect concentrations (EC50, nom) and freely dissolved concentration (EC50, free) as endpoints, and quantum chemical and Dragon descriptors as predictor variables. Results indicated that only the EC50, free model met the acceptable criteria of QSAR model (determination coefficient (R2) > 0.600, leave-one-out cross validation (QLOO2) > 0.500, and external validation coefficient (QEXT2) > 0.500), implying that it has good goodness-of-fit, robustness and external predictive power. Molecular polarizability and aromaticity index reflecting the partition behavior and intermolecular interactions can effectively predict AhR-mediated potencies of PAHs. The results highlight the necessity of adoption of the freely dissolved concentration in the QSAR modeling and more in silico models need to be further developed for different animal models (in vivo or in vitro).

Cutting Performance Evaluation of the Coated Tools in High-Speed Milling of AISI 4340 Steel.

The cutting performance of cutting tools in high-speed machining (HSM) is an important factor restricting the machined surface integrity of the workpiece. The HSM of AISI 4340 is carried out by using coated tools with TiN/TiCN/TiAlN multi-coating, TiAlN + TiN coating, TiCN + NbC coating, and AlTiN coating, respectively. The cutting performance evaluation of the coated tools is revealed by the chip morphology, cutting force, cutting temperature, and tool wear. The results show that the serration and shear slip of the chips become more clear with the cutting speed. The lower cutting force and cutting temperature are achieved by the TiN/TiCN/TiAlN multi-coated tool. The flank wear was the dominant wear form in the milling process of AISI 4340. The dominant wear mechanisms of the coated tools include the crater wear, coating chipping, adhesion, abrasion, and diffusion. In general, a TiN/TiCN/TiAlN multi-coated tool is the most suitable tool for high-speed milling of AISI 4340, due to the lower cutting force, the lower cutting temperature, and the high resistance of the element diffusion.

Development of QSAR models for predicting the binding affinity of endocrine disrupting chemicals to eight fish estrogen receptor.

Endocrine disrupting effect has become a central point of concern, and various biological mechanisms involve in the disruption of endocrine system. Recently, we have explored the mechanism of disrupting hormonal transport protein, through the binding affinity of sex hormone-binding globulin in different fish species. This study, serving as a companion article, focused on the mechanism of activating/inhibiting hormone receptor, by investigating the binding interaction of chemicals with the estrogen receptor (ER) of different fish species. We collected the relative binding affinity (RBA) of chemicals with 17ß-estradiol binding to the ER of eight fish species. With this parameter as the endpoints, quantitative structure-activity relationship (QSAR) models were established using DRAGON descriptors. Statistical results indicated that the developed models had satisfactory goodness of fit, robustness and predictive ability. The Euclidean distance and Williams plot verified that these models had wide application domains, which covered a large number of structurally diverse chemicals. Based on the screened descriptors, we proposed an appropriate mechanism interpretation for the binding potency. Additionally, even though the same chemical had different affinities for ER from different fish species, the affinity of ER exhibited a high correlation for fish species within the same Order (i.e., Salmoniformes, Cypriniformes, Perciformes), which consistent with that in our previous study. Hence, when performing the endocrine disrupting effect assessment, the species diversity should be taken into account, but maybe the fish species in the same Order can be grouped together.

Development of predictive models for predicting binding affinity of endocrine disrupting chemicals to fish sex hormone-binding globulin.

Disturbing the transport process is a crucial pathway for endocrine disrupting chemicals (EDCs) exerting disrupting endocrine function. However, this mechanism has not received enough attention compared with that of hormones receptors and synthetase. Recently, we have explored the interaction between EDCs and sex hormone-binding globulin of human (hSHBG). In this study, interactions between EDCs and sex hormone-binding globulin of eight fish species (fSHBG) were investigated by employing classification methods and quantitative structure-activity relationships (QSAR). In the modeling, the relative binding affinity (RBA) of a chemical with 17ß-estradiol binding to fSHBG was selected as the endpoint. Classification models were developed for two fish species, while QSAR models were established for the other six fish species. Statistical results indicated that the models had satisfactory goodness of fit, robustness and predictive ability, and that application domain covered a large number of endogenous and exogenous steroidal and non-steroidal chemicals. Additionally, by comparing the log RBA values, it was found that the same chemical may have different affinities for fSHBG from different fish species, thus species diversity should be taken into account. However, the affinity of fSHBG showed a high correlation for fishes within the same Order (i.e., Salmoniformes, Cypriniformes, Perciformes and Siluriformes), thus the fSHBG binding data for one fish species could be used to extrapolate other fish species in the same Order.

Rational Selection of the 3D Structure of Biomacromolecules for Molecular Docking Studies on the Mechanism of Endocrine Disruptor Action.

Molecular modeling has become an essential tool in predicting and simulating endocrine disrupting effects of chemicals. A key prerequisite for successful application of molecular modeling lies in the correctness of 3D structure for biomacromolecules to be simulated. To date, there are several databases that can provide the experimentally-determined 3D structures. However, commonly, there are many challenges or disadvantageous factors, e.g., (a) lots of 3D structures for a given biomacromolecular target in the protein database; (b) the quality variability for those structures; (c) belonging to different species; (d) mutant amino acid residue in key positions, and so on. Once an inappropriate 3D structure of a target biomacromolecule was selected in molecular modeling, the accuracy and scientific nature of the modeling results could be inevitably affected. In this article, based on literature survey and an analysis of the 3D structure characterization of biomacromolecular targets belonging to the endocrine system in protein databases, six principles were proposed to guide the selection of the appropriate 3D structure of biomacromolecules. The principles include considering the species diversity, the mechanism of action, whether there are mutant amino acid residues, whether the number of protein chains is correct, the degree of structural similarity between the ligand in 3D structure and the target compounds, and other factors, e.g., the experimental pH conditions of the structure determined process and resolution.

Development of classification model and QSAR model for predicting binding affinity of endocrine disrupting chemicals to human sex hormone-binding globulin.

Disturbing the transport process is a crucial pathway for endocrine disrupting chemicals (EDCs) to disrupt endocrine function. However, this mechanism has not gotten enough attention, compared with that of hormone receptors and synthetase up to now, especially for the sex hormone transport process. In this study, we selected sex hormone-binding globulin (SHBG) and EDCs as a model system and the relative competing potency of a chemical with testosterone binding to SHBG (log RBA) as the endpoints, to develop classification models and quantitative structure-activity relationship (QSAR) models. With the classification model, a satisfactory model with nR09, nR10 and RDF155v as the most relevant variables was screened. Statistic results indicated that the model had the sensitivity, specificity, accuracy of 86.4%, 80.0%, 84.4% and 85.7%, 87.5%, 86.2% for the training set and validation set, respectively, highlighting a high classification performance of the model. With the QSAR model, a satisfactory model with statistical parameters, specifically, an adjusted determination coefficient (Radj(2)) of 0.810, a root mean square error (RMSE) of 0.616, a leave-one-out cross-validation squared correlation coefficient (QLOO(2)) of 0.777, a bootstrap method (QBOOT(2)) of 0.756, an external validation coefficient (Qext(2)) of 0.544 and a RMSEext of 0.859, were obtained, which implied satisfactory goodness of fit, robustness and predictive ability. The applicability domain of the current model covers a large number of structurally diverse chemicals, especially a few classes of nonsteroidal compounds.

Determination and prediction of the binding interaction between organophosphate flame retardants and p53.

Organophosphate flame retardants (OPFRs) have caused widespread concern because of the harm to the environment. In this study, to better explain the mechanism for the binding of OPFRs with the tumor suppressor gene p53, an integrated experimental and in silico approach was used. The binding constants of 10 OPFRs were measured by surface plasmon resonance technology (SPR). The effect of OPFRs on p53 gene and protein expression in ZF4 cells was determined by quantitative real-time PCR and Western blotting. Molecular docking and dynamics simulation were explored to find that the H-bonds and hydrophobic interactions were the dominant interaction between OPFRs and p53. On the basis of the observed interactions, proper molecular structural descriptors were used to build the quantitative structure-activity relationship (QSAR) model. The current QSAR model provided robustness, predictive ability, and mechanism interpretability. The applicability domain of the QSAR was discussed by the Williams plot. The results showed that H-bonds and electrostatic interaction governed the binding affinities between OPFRs and p53.

The interaction between organic phosphate ester and p53: an integrated experimental and in silico approach.

Concerns have been raised in regards to the environmental impact of the more used organophosphate flame retardants (OPFRs). In this study, to better understand the relationship between molecular structural features of OPFRs and binding affinity for the tumor suppressor p53, an integrated experimental and in silico approach was used. From docking analysis, hydrogen bonding and hydrophobic interactions were found to be the dominant interactions, which implied the binding affinities of the compounds. The binding constants of 5 OPFRs were determined by surface plasmon resonance technology (SPR). Based on the observed interactions, appropriate molecular structural parameters were adopted to develop a quantitative structure-activity relationship (QSAR) model. The developed QSAR model had good robustness, predictive ability and mechanism interpretability. The interactions between the OPFRs and p53 (Ebinding) and the partition ability of the OPFRs into the bio-phase are main factors governing the binding affinities.