Deciphering exogenous chemical carcinogenicity through interpretable deep learning: A novel approach for evaluating atmospheric pollutant hazards.

Cancer remains a significant global health concern, with millions of deaths attributed to it annually. Environmental pollutants play a pivotal role in cancer etiology and contribute to the growing prevalence of this disease. The carcinogenic assessment of these pollutants is crucial for chemical health evaluation and environmental risk assessments. Traditional experimental methods are expensive and time-consuming, prompting the development of alternative approaches such as in silico methods. In this regard, deep learning (DL) has shown potential but lacks optimal performance and interpretability. This study introduces an interpretable DL model called CarcGC for chemical carcinogenicity prediction, utilizing a graph convolutional neural network (GCN) that employs molecular structural graphs as inputs. Compared to existing models, CarcGC demonstrated enhanced performance, with the area under the receiver operating characteristic curve (AUCROC) reaching 0.808 on the test set. Due to air pollution is closely related to the incidence of lung cancers, we applied the CarcGC to predict the potential carcinogenicity of chemicals listed in the United States Environmental Protection Agency's Hazardous Air Pollutants (HAPs) inventory, offering a foundation for environmental carcinogenicity screening. This study highlights the potential of artificially intelligent methods in carcinogenicity prediction and underscores the value of CarcGC interpretability in revealing the structural basis and molecular mechanisms underlying chemical carcinogenicity.

CatNet: Sequence-based deep learning with cross-attention mechanism for identifying endocrine-disrupting chemicals.

Endocrine-disrupting chemicals (EDCs) pose significant environmental and health risks due to their potential to interfere with nuclear receptors (NRs), key regulators of physiological processes. Despite the evident risks, the majority of existing research narrows its focus on the interaction between compounds and the individual NR target, neglecting a comprehensive assessment across the entire NR family. In response, this study assembled a comprehensive human NR dataset, capturing 49,244 interactions between 35,467 unique compounds and 42 NRs. We introduced a cross-attention network framework, "CatNet", innovatively integrating compound and protein representations through cross-attention mechanisms. The results showed that CatNet model achieved excellent performance with an area under the receiver operating characteristic curve (AUCROC) = 0.916 on the test set, and exhibited reliable generalization on unseen compound-NR pairs. A distinguishing feature of our research is its capacity to expand to novel targets. Beyond its predictive accuracy, CatNet offers a valuable mechanistic perspective on compound-NR interactions through feature visualization. Augmenting the utility of our research, we have also developed a graphical user interface, empowering researchers to predict chemical binding to diverse NRs. Our model enables the prediction of human NR-related EDCs and shows the potential to identify EDCs related to other targets.

Harmonization of multi-site functional MRI data with dual-projection based ICA model.

Modern neuroimaging studies frequently merge magnetic resonance imaging (MRI) data from multiple sites. A larger and more diverse group of participants can increase the statistical power, enhance the reliability and reproducibility of neuroimaging research, and obtain findings more representative of the general population. However, measurement biases caused by site differences in scanners represent a barrier when pooling data collected from different sites. The existence of site effects can mask biological effects and lead to spurious findings. We recently proposed a powerful denoising strategy that implements dual-projection (DP) theory based on ICA to remove site-related effects from pooled data, demonstrating the method for simulated and in vivo structural MRI data. This study investigates the use of our DP-based ICA denoising method for harmonizing functional MRI (fMRI) data collected from the Autism Brain Imaging Data Exchange II. After frequency-domain and regional homogeneity analyses, two modalities, including amplitude of low frequency fluctuation (ALFF) and regional homogeneity (ReHo), were used to validate our method. The results indicate that DP-based ICA denoising method removes unwanted site effects for both two fMRI modalities, with increases in the significance of the associations between non-imaging variables (age, sex, etc.) and fMRI measures. In conclusion, our DP method can be applied to fMRI data in multi-site studies, enabling more accurate and reliable neuroimaging research findings.

Removal of site effects and enhancement of signal using dual projection independent component analysis for pooling multi-site MRI data.

Combining magnetic resonance imaging (MRI) data from multi-site studies is a popular approach for constructing larger datasets to greatly enhance the reliability and reproducibility of neuroscience research. However, the scanner/site variability is a significant confound that complicates the interpretation of the results, so effective and complete removal of the scanner/site variability is necessary to realise the full advantages of pooling multi-site datasets. Independent component analysis (ICA) and general linear model (GLM) based harmonisation methods are the two primary methods used to eliminate scanner/site effects. Unfortunately, there are challenges with both ICA-based and GLM-based harmonisation methods to remove site effects completely when the signals of interest and scanner/site effects-related variables are correlated, which may occur in neuroscience studies. In this study, we propose an effective and powerful harmonisation strategy that implements dual projection (DP) theory based on ICA to remove the scanner/site effects more completely. This method can separate the signal effects correlated with site variables from the identified site effects for removal without losing signals of interest. Both simulations and vivo structural MRI datasets, including a dataset from Autism Brain Imaging Data Exchange II and a travelling subject dataset from the Strategic Research Program for Brain Sciences, were used to test the performance of a DP-based ICA harmonisation method. Results show that DP-based ICA harmonisation has superior performance for removing site effects and enhancing the sensitivity to detect signals of interest as compared with GLM-based and conventional ICA harmonisation methods.

Environmental toxicity risk evaluation of nitroaromatic compounds: Machine learning driven binary/multiple classification and design of safe alternatives.

Nitroaromatic compounds (NACs) represent a significant source of organic pollutants in the environment. In this study, a well-rounded dataset containing 371 NACs with rat oral median lethal doses (LD50s) was developed. Based on the dataset, binary and multiple classification models were established. Seven machine learning algorithms were used to establish the prediction models in combination with six fingerprints. In the binary classification models, the overall predictive accuracy of 10-fold cross-validation for training set in the top ten models ranged from 0.823 to 0.874. In the multiple classification models, the combination of graph fingerprint and random forest (Graph-RF) yielded the best predictive effects with AUC values of 0.929 and 0.956 for the training set and the test set, respectively. Model prediction performance was further evaluated using the true external set comprising 1366 NACs, including 96.6% belonging to the applicability domain. Further, we determined the structural features influencing the acute oral toxicity based on information gain and substructure frequency analysis. Finally, we identified highly toxic compounds based on the structural alerts and successfully transformed a representative highly toxic compound into low-toxic alternatives via structural modification. Overall, the models constructed facilitate environmental risk assessment and the design of green and safe chemicals.

Discovering hidden brain network responses to naturalistic stimuli via tensor component analysis of multi-subject fMRI data.

The study of brain network interactions during naturalistic stimuli facilitates a deeper understanding of human brain function. To estimate large-scale brain networks evoked with naturalistic stimuli, a tensor component analysis (TCA) based framework was used to characterize shared spatio-temporal patterns across subjects in a purely data-driven manner. In this framework, a third-order tensor is constructed from the timeseries extracted from all brain regions from a given parcellation, for all participants, with modes of the tensor corresponding to spatial distribution, time series and participants. TCA then reveals spatially and temporally shared components, i.e., evoked networks with the naturalistic stimuli, their time courses of activity and subject loadings of each component. To enhance the reproducibility of the estimation with the adaptive TCA algorithm, a novel spectral clustering method, tensor spectral clustering, was proposed and applied to evaluate the stability of the TCA algorithm. We demonstrated the effectiveness of the proposed framework via simulations and real fMRI data collected during a motor task with a traditional fMRI study design. We also applied the proposed framework to fMRI data collected during passive movie watching to illustrate how reproducible brain networks are evoked by naturalistic movie viewing.

An efficient functional magnetic resonance imaging data reduction strategy using neighborhood preserving embedding algorithm.

High dimensionality data have become common in neuroimaging fields, especially group-level functional magnetic resonance imaging (fMRI) datasets. fMRI connectivity analysis is a widely used, powerful technique for studying functional brain networks to probe underlying mechanisms of brain function and neuropsychological disorders. However, data-driven technique like independent components analysis (ICA), can yield unstable and inconsistent results, confounding the true effects of interest and hindering the understanding of brain functionality and connectivity. A key contributing factor to this instability is the information loss that occurs during fMRI data reduction. Data reduction of high dimensionality fMRI data in the temporal domain to identify the important information within group datasets is necessary for such analyses and is crucial to ensure the accuracy and stability of the outputs. In this study, we describe an fMRI data reduction strategy based on an adapted neighborhood preserving embedding (NPE) algorithm. Both simulated and real data results indicate that, compared with the widely used data reduction method, principal component analysis, the NPE-based data reduction method (a) shows superior performance on efficient data reduction, while enhancing group-level information, (b) develops a unique stratagem for selecting components based on an adjacency graph of eigenvectors, (c) generates more reliable and reproducible brain networks under different model orders when the outputs of NPE are used for ICA, (d) is more sensitive to revealing task-evoked activation for task fMRI, and (e) is extremely attractive and powerful for the increasingly popular fast fMRI and very large datasets.

Frequency specific co-activation pattern analysis via sparse nonnegative tensor decomposition.

BACKGROUND: Traditionally, the diagnosis of Parkinson's disease (PD) has been made based on symptoms. Extensive studies have demonstrated that PD may lead to variation of brain activity in different frequency bands. However, frequency specific dynamic alterations of PD have not yet been explored. NEW METHOD: In order to address this gap, a novel sparse nonnegative tensor decomposition (SNTD) method was used to estimate frequency specific co-activation patterns (CAP). The difference between PD and healthy controls (HC) are investigated with the proposed framework. RESULT: The difference between PD and HC mainly exists at frequency band 0.04-0.1 Hz in basal ganglia. We also found that the average intensity of PD in this frequency band is significantly correlated with the Hoehn and Yahr scale. COMPARISON WITH EXISTING METHODS: Compared with conventional CAP approach, SNTD estimates frequency specific CAPs that show alterations in PD patients. CONCLUSION: SNTD provides an alternative to K-means clustering used in conventional CAP analysis. With the proposed framework, frequency specific CAPs are extracted, and alterations in PD patients are also successfully discovered.

Consistency of independent component analysis for FMRI.

BACKGROUND: Independent component analysis (ICA) has been widely used for blind source separation in the field of medical imaging. However, despite of previous substantial efforts, the stability of ICA components remains a critical issue which has not been adequately addressed, despite numerous previous efforts. Most critical is the inconsistency of some of the extracted components when ICA is run with different model orders (MOs). NEW METHOD: In this study, a novel method of determining the consistency of component analysis (CoCA) is proposed to evaluate the consistency of extracted components with different model orders. In the method, "consistent components" (CCs) are defined as those which can be extracted repeatably over a range of model orders. RESULT: The efficacy of the method was evaluated with simulation data and fMRI datasets. With our method, the simulation result showed a clear difference of consistency between ground truths and noise. COMPARISON WITH EXISTING METHODS: The information criteria were implemented to provide suggestions for the optimal model order, where some of the ICs were revealed inconsistent in our proposed method. CONCLUSIONS: This method provided an objective protocol for choosing CCs of an ICA decomposition of a data matrix, independent of model order. This is especially useful with high model orders, where noise or other disturbances could possibly lead to an instability of the components.

In vivo toxicity of nitroaromatic compounds to rats: QSTR modelling and interspecies toxicity relationship with mouse.

Nitroaromatic compounds (NACs) in the environment can cause serious public health and environmental problems due to their potential toxicity. This study established quantitative structure-toxicity relationship (QSTR) models for the acute oral toxicity of NACs towards rats following the stringent OECD principles for QSTR modelling. All models were assessed by various internationally accepted validation metrics and the OECD criteria. The best QSTR model contains seven simple and interpretable 2D descriptors with defined physicochemical meaning. Mechanistic interpretation indicated that van der Waals surface area, presence of C-F at topological distance 6, heteroatom content and frequency of C-N at topological distance 9 are main factors responsible for the toxicity of NACs. This proposed model was successfully applied to a true external set (295 compounds), and prediction reliability was analysed and discussed. Moreover, the rat-mouse and mouse-rat interspecies quantitative toxicity-toxicity relationship (iQTTR) models were also constructed, validated and employed in toxicity prediction for true external sets consisting of 67 and 265 compounds, respectively. These models showed good external predictivity that can be used to rapidly predict the rat oral acute toxicity of new or untested NACs falling within the applicability domain of the models, thus being beneficial in environmental risk assessment and regulatory purposes.

3-Bromopyruvate regulates the status of glycolysis and BCNU sensitivity in human hepatocellular carcinoma cells.

Chloroethylnitrosoureas (CENUs) are bifunctional antitumor alkylating agents, which exert their antitumor activity through inducing the formation of dG-dC interstrand crosslinks (ICLs) within DNA double strand. However, the complex process of tumor biology enables tumor cells to escape the killing triggered by CENUs, as for instance with the detoxifying activity of O6-methylguanine DNA methyltransferase (MGMT) to accomplish DNA damage repair. Considering the fact that most tumor cells highly depend on aerobic glycolysis to provide energy for survival even in the presence of oxygen (Warburg effect), inhibition of aerobic glycolysis may be an attractive strategy to overcome the resistance and improve the chemotherapeutic effects of CENUs. Especially, 3-bromopyruvate (3-BrPA), a small molecule alkylating agent, has been emerged as an effective glycolytic inhibitor (energy blocker) in cancer treatment. In view of its tumor specificity and inhibition on cellular multiple targets, it is likely to reduce the chemoresistance when chemotherapeutic drugs are combined with 3-BrPA. In this study, we investigated the effects of 3-BrPA on the chemosensitivity of two human hepatocellular carcinoma (HCC) cell lines to the cytotoxic effects of l,3-bis(2-chloroethyl)-1-nitrosourea (BCNU) and the underlying molecular mechanism. The sensitivity of SMMC-7721 and HepG2 cells to BCNU was significantly increased by 2 h pretreatment with micromolar dosage of 3-BrPA. Moreover, 3-BrPA decreased the cellular ATP and GSH levels, and extracellular lactate excreted by tumor cells, and the effects were more effective when 3-BrPA was combined with BCNU. Cellular hexokinase-II (HK-II) activity was also reduced after exposure to the treatment of 3-BrPA plus BCNU. Based on the above results, the effects of 3-BrPA on the formation of dG-dC ICLs induced by BCNU was investigated by stable isotope dilution high-performance liquid chromatography electrospray ionization tandem mass spectrometry (HPLC-ESI-MS/MS). The results indicated that BCNU produced higher levels of dG-dC ICLs in SMMC-7721 and HepG2 cells pretreated with 3-BrPA compared to that without 3-BrPA pretreatment. Notably, in MGMT-deficient HepG2 cells, the levels of dG-dC ICLs were significantly higher than MGMT-proficient SMMC-7721 cells. In general, these findings revealed that 3-BrPA, as an effective glycolytic inhibitor, may be considered as a potential clinical chemosensitizer to optimize the therapeutic index of CENUs.

Prediction on the mutagenicity of nitroaromatic compounds using quantum chemistry descriptors based QSAR and machine learning derived classification methods.

Nitroaromatic compounds (NACs) are an important type of environmental organic pollutants. However, it is lack of sufficient information relating to their potential adverse effects on human health and the environment due to the limited resources. Thus, using in silico technologies to assess their potential hazardous effects is urgent and promising. In this study, quantitative structure activity relationship (QSAR) and classification models were constructed using a set of NACs based on their mutagenicity against Salmonella typhimurium TA100 strain. For QSAR studies, DRAGON descriptors together with quantum chemistry descriptors were calculated for characterizing the detailed molecular information. Based on genetic algorithm (GA) and multiple linear regression (MLR) analyses, we screened descriptors and developed QSAR models. For classification studies, seven machine learning methods along with six molecular fingerprints were applied to develop qualitative classification models. The goodness of fitting, reliability, robustness and predictive performance of all developed models were measured by rigorous statistical validation criteria, then the best QSAR and classification models were chosen. Moreover, the QSAR models with quantum chemistry descriptors were compared to that without quantum chemistry descriptors and previously reported models. Notably, we also obtained some specific molecular properties or privileged substructures responsible for the high mutagenicity of NACs. Overall, the developed QSAR and classification models can be utilized as potential tools for rapidly predicting the mutagenicity of new or untested NACs for environmental hazard assessment and regulatory purposes, and may provide insights into the in vivo toxicity mechanisms of NACs and related compounds.

Neuroprotective effect of glycosides in Buyang Huanwu Decoction on pyroptosis following cerebral ischemia-reperfusion injury in rats.

ETHNOPHARMACOLOGICAL RELEVANCE: Buyang Huanwu Decoction (BYHWD) is used in classical traditional Chinese medicine to prevent and treat cerebral ischemia. Glycosides, which are effective components extracted from BYHWD, mainly include astragaloside IV, paeoniflorin, and amygdalin. These glycosides are the primary pharmacologically effective constituents of BYHWD that act against cerebral ischemic nerve injury; however, the mechanism of action of BYHWD is still unclear. AIM OF THE STUDY: The present study aimed to determine the effect of BYHWD glycosides on pyroptosis after cerebral ischemia reperfusion injury and explore whether its mechanism involves the classical pyroptosis pathway mediated by NLRP3. MATERIAL AND METHODS: Adult male Sprague-Dawley rats (n = 140) were randomly divided into seven groups: sham, cerebral ischemia and reperfusion (I/R), glycosides (0.064 g/kg, 0.128 g/kg, and 0.256 g/kg), BYHWD, and AC-YVAD-CMK (caspase-1 inhibitor). A rat model of cerebral I/R was established via classic middle cerebral artery occlusion (MCAO) for 2 h, followed by 24-h reperfusion. Neurological function was estimated using neurological defect scores. Brain infarct volumes were determined by 2,3,5-triphenyltetrazolium chloride (TTC) staining, and nerve cell damage was evaluated by Nissl staining. Pyroptosis was detected using TUNEL and caspase-1 immunofluorescence double staining. Protein expression of NLRP3, ASC, caspase-1, pro-caspase-1, and IL-1ß was analyzed using Western blot analysis. RESULTS: Glycosides improved neurological dysfunction, alleviated neuronal damage, and inhibited neuronal pyroptosis. The 0.128 g/kg glycosides group showed the most significant effects. Furthermore, we observed that this group showed significant inhibition of the expression of NLRP3, ASC, pro-caspase-1, caspase-1, and IL-1ß proteins of the NLRP3-mediated classical pathway of pyroptosis. CONCLUSIONS: Glycosides exert neuroprotective effects by inhibiting pyroptosis of neurons after cerebral I/R injury. The underlying mechanism of action is closely related to the regulation of the classical pyroptosis pathway by NLRP3.

In Silico Prediction of O6-Methylguanine-DNA Methyltransferase Inhibitory Potency of Base Analogs with QSAR and Machine Learning Methods.

O6-methylguanine-DNA methyltransferase (MGMT), a unique DNA repair enzyme, can confer resistance to DNA anticancer alkylating agents that modify the O6-position of guanine. Thus, inhibition of MGMT activity in tumors has a great interest for cancer researchers because it can significantly improve the anticancer efficacy of such alkylating agents. In this study, we performed a quantitative structure activity relationship (QSAR) and classification study based on a total of 134 base analogs related to their ED50 values (50% inhibitory concentration) against MGMT. Molecular information of all compounds were described by quantum chemical descriptors and Dragon descriptors. Genetic algorithm (GA) and multiple linear regression (MLR) analysis were combined to develop QSAR models. Classification models were generated by seven machine-learning methods based on six types of molecular fingerprints. Performances of all developed models were assessed by internal and external validation techniques. The best QSAR model was obtained with Q²Loo = 0.83, R² = 0.87, Q²ext = 0.67, and R²ext = 0.69 based on 84 compounds. The results from QSAR studies indicated topological charge indices, polarizability, ionization potential (IP), and number of primary aromatic amines are main contributors for MGMT inhibition of base analogs. For classification studies, the accuracies of 10-fold cross-validation ranged from 0.750 to 0.885 for top ten models. The range of accuracy for the external test set ranged from 0.800 to 0.880 except for PubChem-Tree model, suggesting a satisfactory predictive ability. Three models (Ext-SVM, Ext-Tree and Graph-RF) showed high and reliable predictive accuracy for both training and external test sets. In addition, several representative substructures for characterizing MGMT inhibitors were identified by information gain and substructure frequency analysis method. Our studies might be useful for further study to design and rapidly identify potential MGMT inhibitors.