PI3K-Akt-SGF1-Dimm pathway mediates the nutritional regulation of silk protein synthesis in Bombyx mori.

The efficient synthesis of silk protein is heavily reliant on the ingestion of massive nutrients during the peak growth phase in the silkworm. However, the molecular mechanism of nutritional regulation of silk protein synthesis remains unknown. In this study, we investigated the impact of nutrient deficiency on the synthesis of silk protein. Nutritional deficiency led to a reduction in silk yield, accompanied by decreased levels of silk proteins and fibroin heavy chain (FibH)-activating transcription factors SGF1 and Dimm. Furthermore, insulin enhanced the protein levels of SGF1 and Dimm, which can be attenuated by specific inhibitors of PI3K. Co-immunoprecipitation analysis showed that the nutrient pathway factor protein kinase B (Akt) could interact with SGF1 protein. Knockdown of Akt reduced the phosphorylation level of SGF1 and impedes its nuclear translocation. Further studies revealed that SGF1 was directly bound to Fkh site in the 22-43 region upstream of ATG of Dimm gene to activate its transcription. In conclusion, during the peak growth phase, nutrition promotes the massive synthesis of silk protein through the PI3K-Akt-SGF1-Dimm pathway. This study offers valuable insights into the efficient synthesis of silk proteins and establishes a theoretical foundation for improving silk yield.

Broad Complex-Z2 directly activates BmMBF2 to inhibit the silk protein synthesis in the silkworm, Bombyx mori.

Silk proteins, as natural macromolecules, have extensive applications in biomaterials and biomedicine. In the silkworm, the expression of silk protein genes is negatively associated with ecdysone during the molt stage, while it is positively correlated with juvenile hormone during the intermolt stage. In our previous study, overexpression of an isoform Z2 of Broad Complex (BmBrC-Z2), an ecdysone early response factor, significantly reduced the expression of silk protein genes. However, the underlying regulatory mechanism remains unclear. In this study, we conducted transcriptomic analysis and found that overexpressing BmBrC-Z2 significantly upregulated the expression level of multiprotein bridging factor 2 (BmMBF2), an inhibitor of fibroin heavy chain (FibH). Further investigations revealed that BmBrC-Z2 directly regulated BmMBF2 by binding to cis-regulatory elements, as demonstrated using Dual-Luciferase Reporter Gene Assay, EMSA, and ChIP-PCR assay. Additionally, when using the CRISPR/Cas9 system to knock out BmMBF2, silk protein genes were significantly upregulated during the molt stage of mutant larvae. These findings uncover the negative regulation of silk protein synthesis by the ecdysone signaling cascade, specifically through the manipulation of BmMBF2 expression during the molt stage. This study enhances to our understanding of the temporal regulatory mechanism governing silk protein synthesis and offers a potential strategy for improving silk yield.

Machine-Learning-Based Prediction of Plant Cuticle-Air Partition Coefficients for Organic Pollutants: Revealing Mechanisms from a Molecular Structure Perspective.

Accurately predicting plant cuticle-air partition coefficients (Kca) is essential for assessing the ecological risk of organic pollutants and elucidating their partitioning mechanisms. The current work collected 255 measured Kca values from 25 plant species and 106 compounds (dataset (I)) and averaged them to establish a dataset (dataset (II)) containing Kca values for 106 compounds. Machine-learning algorithms (multiple linear regression (MLR), multi-layer perceptron (MLP), k-nearest neighbors (KNN), and gradient-boosting decision tree (GBDT)) were applied to develop eight QSPR models for predicting Kca. The results showed that the developed models had a high goodness of fit, as well as good robustness and predictive performance. The GBDT-2 model (Radj2 = 0.925, QLOO2 = 0.756, QBOOT2 = 0.864, Rext2 = 0.837, Qext2 = 0.811, and CCC = 0.891) is recommended as the best model for predicting Kca due to its superior performance. Moreover, interpreting the GBDT-1 and GBDT-2 models based on the Shapley additive explanations (SHAP) method elucidated how molecular properties, such as molecular size, polarizability, and molecular complexity, affected the capacity of plant cuticles to adsorb organic pollutants in the air. The satisfactory performance of the developed models suggests that they have the potential for extensive applications in guiding the environmental fate of organic pollutants and promoting the progress of eco-friendly and sustainable chemical engineering.

Integrating molecular descriptors for enhanced prediction: Shedding light on the potential of pH to model hydrated electron reaction rates for organic compounds.

Hydrated electron reaction rate constant (ke-aq) is an important parameter to determine reductive degradation efficiency and to mitigate the ecological risk of organic compounds (OCs). However, OC species morphology and the concentration of hydrated electrons (e-aq) in water vary with pH, complicating OC fate assessment. This study introduced the environmental variable of pH, to develop models for ke-aq for 701 data points using 3 descriptor types: (i) molecular descriptors (MD), (ii) quantum chemical descriptors (QCD), and (iii) the combination of both (MD + QCD). Models were screened using 2 descriptor screening methods (MLR and RF) and 14 machine learning (ML) algorithms. The introduction of QCDs that characterized the electronic structure of OCs greatly improved the performance of models while ensuring the need for fewer descriptors. The optimal model MLR-XGBoost(MD + QCD), which included pH, achieved the most satisfactory prediction: R2tra = 0.988, Q2boot = 0.861, R2test = 0.875 and Q2test = 0.873. The mechanistic interpretation using the SHAP method further revealed that QCDs, polarizability, volume, and pH had a great influence on the reductive degradation of OCs by e-aq. Overall, the electrochemical parameters (QCDs, pH) related to the solvent and solute are of significance and should be considered in any future ML modeling that assesses the fate of OCs in aquatic environment.

Contribution of molecular structures and quantum chemistry technique to root concentration factor: An innovative application of interpretable machine learning.

Root concentration factor (RCF) is a significant parameter to characterize uptake and accumulation of hazardous organic contaminants (HOCs) by plant roots. However, complex interactions among chemicals, plant roots and soil make it challenging to identify underlying mechanisms of uptake and accumulation of HOCs. Here, nine machine learning techniques were applied to investigate major factors controlling RCF based on variable combinations of molecular descriptors (MD), MACCS fingerprints, quantum chemistry descriptors (QCD) and three physicochemical properties related to chemical-soil-plant system. Compared to models with variables including MACCS fingerprints or solitary physicochemical properties, the XGBoost-6 model developed by the variable combination of MD, QCD and three physicochemical properties achieved the most remarkable performance, with R2 of 0.977. Model interpretation achieved by permutation variable importance and partial dependence plots revealed the vital importance of HOCs lipophilicity, lipid content of plant roots, soil organic matter content, the overall deformability and the molecular dispersive ability of HOCs for regulating RCF. The integration of MD and QCD with physicochemical properties could improve our knowledge of underlying mechanisms regarding HOCs accumulation in plant roots from innovative structural perspectives. Multiple variables combination-oriented performance improvement of model can be extended to other parameters prediction in environmental risk assessment field.

A new perspective on predicting the reaction rate constants of hydrated electrons for organic contaminants: Exploring molecular structure characterization methods and ambient conditions.

Hydrated electrons (eaq-) exhibit rapid degradation of diverse persistent organic contaminants (OCs) and hold great promise as a formidable reducing agent in water treatment. However, the diverse structures of compounds exert different influences on the second-order rate constant of hydrated electron reactions (keaq-), while the same OCs demonstrate notable discrepancies in keaq- values across different pH levels. This study aims to develop machine learning (ML) models that can effectively simulate the intricate reaction kinetics between eaq- and OCs. Furthermore, the introduction of the pH variable enables a comprehensive investigation into the impact of ambient conditions on this process, thereby improving the practicality of the model. A dataset encompassing 701 keaq- values derived from 351 peer-reviewed publications was compiled. To comprehensively investigate compound properties, this study introduced molecular descriptor (MD), molecular fingerprint (MF), and the integration of both (MD + MF) as model variables. Furthermore, 60 sets of predictive models were established utilizing two variable screening methodologies (MLR and RF) and ten prominent algorithms. Through statistical parameter analysis, it was determined that descriptors combined with MD and MF, the RF screening method, and the symbolism algorithm exhibited the best predictive efficacy. Importantly, the combination of descriptor models exhibited significantly superior performance compared to individual MF and MD models. Notably, the optimal model, denoted as RF - (MF + MD) - LGB, exhibited highly satisfactory predictive results (R2tra = 0.967, Q2tra = 0.840, R2ext = 0.761). The mechanistic explanation study based on Shapley Additive Explanations (SHAP) values further elucidated the crucial influences of polarity, pH, molecular weight, electronegativity, carbon-carbon double bonds, and molecular topology on the degradation of OCs by eaq-. The proposed modeling approach, particularly the integration of MF and MD, alongside the introduction of pH, may furnish innovative ideas for advanced reduction or oxidation processes (ARPs/AOPs) and machine learning applications in other domains.

Multiple machine learning algorithms assisted QSPR models for aqueous solubility: Comprehensive assessment with CRITIC-TOPSIS.

As an essential environmental property, the aqueous solubility quantifies the hydrophobicity of a compound. It could be further utilized to evaluate the ecological risk and toxicity of organic pollutants. Concerned about the proliferation of organic contaminants in water and the associated technical burden, researchers have developed QSPR models to predict aqueous solubility. However, there are no standard procedures or best practices on how to comprehensively evaluate models. Hence, the CRITIC-TOPSIS comprehensive assessment method was first-ever proposed according to a variety of statistical parameters in the environmental model research field. 39 models based on 13 ML algorithms (belonged to 4 tribes) and 3 descriptor screening methods, were developed to calculate aqueous solubility values (log Kws) for organic chemicals reliably and verify the effectiveness of the comprehensive assessment method. The evaluations were carried out for exhibiting better predictive accuracy and external competitiveness of the MLR-1, XGB-1, DNN-1, and kNN-1 models in contrast to other prediction models in each tribe. Further, XGB model based on SRM (XGB-1, C = 0.599) was selected as an optimal pathway for prediction of aqueous solubility. We hope that the proposed comprehensive evaluation approach could act as a promising tool for selecting the optimum environmental property prediction methods.

Prediction of organic contaminant rejection by nanofiltration and reverse osmosis membranes using interpretable machine learning models.

Efficiency improvement in contaminant removal by nanofiltration (NF) and reverse osmosis (RO) membranes is a multidimensional process involving membrane material selection and experimental condition optimization. It is unrealistic to explore the contributions of diverse influencing factors to the removal rate by trial-and-error experimentation. However, the advanced machine learning (ML) method is a powerful tool to simulate this complex decision-making process. Here, 4 traditional learning algorithms (MLR, SVM, ANN, kNN) and 4 ensemble learning algorithms (RF, GBDT, XGBoost, LightGBM) were applied to predict the removal efficiency of contaminants. Results reported here demonstrate that ensemble models showed significantly better predictive performance than traditional models. More importantly, this study achieved a compelling tradeoff between accuracy and interpretability for ensemble models with an effective model interpretation approach, which revealed the mutual interaction mechanism between the membrane material, contaminants and experimental conditions in membrane separation. Additionally, feature selection was for the first time achieved based on the aforementioned model interpretation method to determine the most important variable influencing the contaminant removal rate. Ultimately, the four ensemble models retrained by the selected variables achieved distinguished prediction performance (R2adj = 92.4 %-99.5 %). MWCO (membrane molecular weight cut-off), McGowan volume of solute (V) and molecular weight (MW) of the compound were demonstrated to be the most important influencing factors in contaminant removal by the NF and RO processes. Overall, the proposed methods in this study can facilitate versatile complex decision-making processes in the environmental field, particularly in contaminant removal by advanced physicochemical separation processes.

Versatile in silico modeling of XAD-air partition coefficients for POPs based on abraham descriptor and temperature.

The concentration of persistent organic pollutants (POPs) makes remarkable difference to environmental fate. In the field of passive sampling, the partition coefficients between polystyrene-divinylbenzene resin (XAD) and air (i.e., KXAD-A) are indispensable to obtain POPs concentration, and the KXAD-A is generally thought to be governed by temperature and molecular structure of POPs. However, experimental determination of KXAD-A is unrealistic for countless and novel chemicals. Herein, the Abraham solute descriptors of poly parameter linear free energy relationship (pp-LFER) and temperature were utilized to develop models, namely pp-LFER-T, for predicting KXAD-A values. Two linear (MLR and LASSO) and four nonlinear (ANN, SVM, kNN and RF) machine learning algorithms were employed to develop models based on a data set of 307 sample points. For the aforementioned six models, R2adj and Q2ext were both beyond 0.90, indicating distinguished goodness-of-fit and robust generalization ability. By comparing the established models, the best model was observed as the RF model with R2adj = 0.991, Q2ext = 0.935, RMSEtra = 0.271 and RMSEext = 0.868. The mechanism interpretation revealed that the temperature, size of molecules and dipole-type interactions were the predominant factors affecting KXAD-A values. Concurrently, the developed models with the broad applicability domain provide available tools to fill the experimental data gap for untested chemicals. In addition, the developed models were helpful to preliminarily evaluate the environmental ecological risk and understand the adsorption behavior of POPs between XAD membrane and air.

Versatile in silico modelling of microplastics adsorption capacity in aqueous environment based on molecular descriptor and machine learning.

To comprehensively evaluate the hazards of microplastics and their coexisting organic pollutants, the sorption capacity of microplastics is a major issue that is quantified through the microplastic-aqueous sorption coefficient (Kd). Almost all quantitative structure-property relationship (QSPR) models that describe Kd apply only to narrow, relatively homogeneous groups of reactants. Herein, non-hybrid QSPR-based models were developed to predict PE-water (KPE-w), PE-seawater (KPE-sw), PVC-water (KPVC-w) and PP-seawater (KPP-sw) sorption coefficients at different temperatures, with eight machine learning algorithms. Moreover, novel hybrid intelligent models for predicting Kd more accurately were innovatively developed by applying GA, PSO and AdaBoost algorithms to optimize MLP and ELM models. The results indicated that all three optimization algorithms could improve the robustness and predictability of the standalone MLP and ELM models. In all models trained with KPE-w, KPE-sw, KPVC-w and KPP-sw data sets, GBDT-1 and XGBoost-1 models, MLP-GA-2 and MLP-PSO-2 models, MLR-3 and MLR-4 models performed better in terms of goodness of fit (Radj2: 0.907-0.999), robustness (QBOOT2: 0.900-0.937) and predictability (Rext2: 0.889-0.970), respectively. Analyzing the descriptors revealed that temperature, lipophilicity, ionization potential and molecular size were correlated closely with the adsorption capacity of microplastics to organic pollutants. The proposed QSPR models may assist in initial environmental exposure assessments without imposing heavy costs in the early experimental phase.

Prediction models with multiple machine learning algorithms for POPs: The calculation of PDMS-air partition coefficient from molecular descriptor.

Polydimethylsiloxane-air partition coefficient (KPDMS-air) is a key parameter for passive sampling to measure POPs concentrations. In this study, 13 QSPR models were developed to predict KPDMS-air, with two descriptor selection methods (MLR and RF) and seven algorithms (MLR, LASSO, ANN, SVM, kNN, RF and GBDT). All models were based on a data set of 244 POPs from 13 different categories. The diverse model evaluation parameters calculated from training and test set were used for internal and external verification. Notably, the Radj2, QBOOT2 and Qext2 are 0.995, 0.980 and 0.951 respectively for GBDT model, showing remarkable superiority in fitting, robustness and predictability compared with other models. The discovery that molecular size, branches and types of the bonds were the main internal factors affecting the partition process was revealed by mechanism explanation. Different from the existing QSPR models based on single category compounds, the models developed herein considered multiple classes compounds, so that its application domain was more comprehensive. Therefore, the obtained models can fill the data gap of missing experimental KPDMS-air values for compounds in the application range, and help researchers better understand the distribution behavior of POPs from the perspective of molecular structure.

Sage controls silk gland development by regulating Dfd in Bombyx mori.

Silk gland is an organ that produces and secretes silk proteins. The development of the silk gland is essential for high silk production yield and silk quality. Although Sage reportedly plays a pivotal role in embryonic silk gland development, the mechanism underlying its action remains unclear. Our study aimed to determine the genes downstream of Sage through which it regulates the development of the silk gland. After chromatin immunoprecipitation and sequencing, Dfd was identified as a downstream target gene of Sage and it was confirmed that Sage could inhibit Dfd expression by competing with SGF1. When Dfd was knocked down through RNA interference (RNAi), the number of cells in the middle silk gland decreased, and the posterior silk gland was straightened. Simultaneously, the expression of Ser1 and silk fibroin genes was no longer strictly regional. These changes eventually led to an alteration in the composition of the Dfd RNAi cocoon. In conclusion, our research contributes to a deeper understanding of the development of silk glands.

Transgenic Ectopic Overexpression of Broad Complex (BrC-Z2) in the Silk Gland Inhibits the Expression of Silk Fibroin Genes of Bombyx mori.

Bombyx mori silk protein genes are strictly turned on and off in different developmental stages under the hormone periodically change. The broad complex (BrC) is a transcription factor mediating 20-hydroxyecdysone action, which plays important roles during metamorphosis. Here, we observed that two isoforms of BmBrC (BmBrC-Z2 and BmBrC-Z4) exhibited contrasting expression patterns with fibroin genes (FibH, FibL and P25) in the posterior silk gland (PSG), suggesting that BmBrC may negatively regulate fibroin genes. Transgenic lines were constructed to ectopically overexpress BmBrC-Z2 in the PSG. The silk protein genes in the transgenic line were decreased to almost half of that in the wild type. The silk yield was decreased significantly. In addition, the expression levels of regulatory factors (BmKr-h1 and BmDimm) response to juvenile hormone (JH) signal were inhibited significantly. Then exogenous JH in the BmBrC-Z2 overexpressed lines can inhibit the expression of BmBrC-Z2 and activate the expression of silk protein genes and restore the silk yield to the level of the wild type. These results indicated that BmBrC may inhibit fibroin genes by repressing the JH signal pathway, which would assist in deciphering the comprehensive regulation mechanism of silk protein genes.