Fipronil disturbs the antigen-specific immune responses and GABAergic gene expression in the ovalbumin-immunized BALB/c mice.

BACKGROUND: Fipronil (FPN) is a broad-spectrum pesticide and commonly known as low toxicity to vertebrates. However, increasing evidence suggests that exposure to FPN might induce unexpected adverse effects in the liver, reproductive, and nervous systems. Until now, the influence of FPN on immune responses, especially T-cell responses has not been well examined. Our study is designed to investigate the immunotoxicity of FPN in ovalbumin (OVA)-sensitized mice. The mice were administered with FPN by oral gavage and immunized with OVA. Primary splenocytes were prepared to examine the viability and functionality of antigen-specific T cells ex vivo. The expression of T cell cytokines, upstream transcription factors, and GABAergic signaling genes was detected by qPCR. RESULTS: Intragastric administration of FPN (1-10 mg/kg) for 11 doses did not show any significant clinical symptoms. The viability of antigen-stimulated splenocytes, the production of IL-2, IL-4, and IFN-γ by OVA-specific T cells, and the serum levels of OVA-specific IgG1 and IgG2a were significantly increased in FPN-treated groups. The expression of the GABAergic signaling genes was notably altered by FPN. The GAD67 gene was significantly decreased, while the GABAR ß2 and GABAR δ were increased. CONCLUSION: FPN disturbed antigen-specific immune responses by affecting GABAergic genes in vivo. We propose that the immunotoxic effects of FPN may enhance antigen-specific immunity by dysregulation of the negative regulation of GABAergic signaling on T cell immunity.

Decrypting orphan GPCR drug discovery via multitask learning.

The drug discovery of G protein-coupled receptors (GPCRs) superfamily using computational models is often limited by the availability of protein three-dimensional (3D) structures and chemicals with experimentally measured bioactivities. Orphan GPCRs without known ligands further complicate the process. To enable drug discovery for human orphan GPCRs, multitask models were proposed for predicting half maximal effective concentrations (EC50) of the pairs of chemicals and GPCRs. Protein multiple sequence alignment features, and physicochemical properties and fingerprints of chemicals were utilized to encode the protein and chemical information, respectively. The protein features enabled the transfer of data-rich GPCRs to orphan receptors and the transferability based on the similarity of protein features. The final model was trained using both agonist and antagonist data from 200 GPCRs and showed an excellent mean squared error (MSE) of 0.24 in the validation dataset. An independent test using the orphan dataset consisting of 16 receptors associated with less than 8 bioactivities showed a reasonably good MSE of 1.51 that can be further improved to 0.53 by considering the transferability based on protein features. The informative features were identified and mapped to corresponding 3D structures to gain insights into the mechanism of GPCR-ligand interactions across the GPCR family. The proposed method provides a novel perspective on learning ligand bioactivity within the diverse human GPCR superfamily and can potentially accelerate the discovery of therapeutic agents for orphan GPCRs.

Exploring the Relationship between Gut Microbiome Composition and Blood Indole-3-acetic Acid in Hemodialysis Patients.

Indole-3-acetic acid (IAA), a protein-bound uremic toxin resulting from gut microbiota-driven tryptophan metabolism, increases in hemodialysis (HD) patients. IAA may induce endothelial dysfunction, inflammation, and oxidative stress, elevating cardiovascular and cognitive risk in HD patients. However, research on the microbiome-IAA association is limited. This study aimed to explore the gut microbiome's relationship with plasma IAA levels in 72 chronic HD patients aged over 18 (August 2016-January 2017). IAA levels were measured using tandem mass spectrometry, and gut microbiome analysis utilized 16s rRNA next-generation sequencing. Linear discriminative analysis effect size and random forest analysis distinguished microbial species linked to IAA levels. Patients with higher IAA levels had reduced microbial diversity. Six microbial species significantly associated with IAA levels were identified; Bacteroides clarus, Bacteroides coprocola, Bacteroides massiliensi, and Alisteps shahii were enriched in low-IAA individuals, while Bacteroides thetaiotaomicron and Fusobacterium varium were enriched in high-IAA individuals. This study sheds light on specific gut microbiota species influencing IAA levels, enhancing our understanding of the intricate interactions between the gut microbiota and IAA metabolism.

Probing the hair detectability of prohibited substances in sports: an in vivo-in silico-clinical approach and analytical implications compared with plasma, urine, and faeces.

Hair analysis is a crucial method in forensic toxicology with potential applications in revealing doping histories in sports. Despite its widespread use, knowledge about detectable substances in hair is limited. This study systematically assessed the detectability of prohibited substances in sports using a multifaceted approach. Initially, an animal model received a subset of 17 model drugs to compare dose dependencies and detection windows across different matrices. Subsequently, hair incorporation data from the animal experiment were extrapolated to all substances on the World Anti-Doping Agency's List through in-silico prediction. The detectability of substances in hair was further validated in a proof-of-concept human study involving the consumption of diuretics and masking agents. Semi-quantitative analysis of substances in specimens was performed using ultra-performance liquid chromatography-tandem mass spectrometry. Results showed plasma had optimal dose dependencies with limited detection windows, while urine, faeces, and hair exhibited a reasonable relationship with the administered dose. Notably, hair displayed the highest detection probability (14 out of 17) for compounds, including anabolic agents, hormones, and diuretics, with beta-2 agonists undetected. Diuretics such as furosemide, canrenone, and hydrochlorothiazide showed the highest hair incorporation. Authentic human hair confirmed diuretic detectability, and their use duration was determined via segmental analysis. Noteworthy is the first-time reporting of canrenone in human hair. Anabolic agents were expected in hair, whereas undetectable compounds, such as peptide hormones and beta-2 agonists, were likely due to large molecular mass or high polarity. This study enhances understanding of hair analysis in doping investigations, providing insights into substance detectability.

Multitask learning for predicting pulmonary absorption of chemicals.

Pulmonary absorption is an important route for drug delivery and chemical exposure. To streamline the chemical assessment process for the reduction of animal experiments, several animal-free models were developed for pulmonary absorption research. While Calu-3 and Caco-2 cells and their derived computational models were used in estimating pulmonary permeability, the ex vivo isolated perfused lung (IPL) models are considered more clinically relevant measurements. However, the IPL experiments are resource-consuming making it infeasible for the large-scale screening of potential inhaled toxicants and drugs. In silico models are desirable for estimating pulmonary absorption. This study presented a novel machine learning method that employed an extratrees-based multitask learning approach to predict the IPL absorption rate constant (kaIPL) of various chemicals. The shared permeability knowledge was extracted by simultaneously learning three relevant tasks of Caco-2 and Calu-3 cell permeability and IPL absorption rate. Seven informative physicochemical descriptors were identified. A rigorous evaluation of the developed prediction model showed good performance with a high correlation between predictions and observations (r = 0.84) in the independent test dataset. Two case studies of inhalation drugs and respiratory sensitizers revealed the potential application of this model, which may serve as a valuable tool for predicting pulmonary absorption of chemicals.

Canine diffuse large b-cell lymphoma downregulates the activity of CD8 + T-cells through tumor-derived extracellular vesicles.

BACKGROUND: Tumor-derived extracellular vesicles (EVs) have been proposed as the essential mediator between host immunity and cancer development. These EVs conduct cellular communication to facilitate tumor growth, enable invasion and metastasis, and shape the favorable tumor microenvironment. Lymphoma is one of the most common hematological malignancies in humans and dogs. Effective T-cell responses are required for the control of these malignancies. However, the immune crosstalk between CD8 + T-cells, which dominates anti-tumor responses, and canine lymphoma has rarely been described. METHODS: This study investigates the immune manipulating effects of EVs, produced from the clinical cases and cell line of canine B cell lymphoma, on CD8 + T-cells isolated from canine donors. RESULTS: Lymphoma-derived EVs lead to the apoptosis of CD8 + T-cells. Furthermore, EVs trigger the overexpression of CTLA-4 on CD8 + T-cells, which indicates that EV blockade could serve as a potential therapeutic strategy for lymphoma patients. Notably, EVs transform the CD8 + T-cells into regulatory phenotypes by upregulating their PD-1, PD-L1, and FoxP3 mRNA expression. The regulatory CD8 + T-cells secret the panel of inhibitory cytokines and angiogenic factors and thus create a pro-tumorigenic microenvironment. CONCLUSION: In summary, the current study demonstrated that the EVs derived from canine B cell lymphoma impaired the anti-tumor activity of CD8 + T-cells and manipulated the possible induction of regulatory CD8 + T-cells to fail the activation of host cellular immunity.

Analysis of structure-activity relationship of indol-3-yl-N-phenylcarbamic amides as potent STING inhibitors.

A structure-activity relationship (SAR) study of stimulator of interferon gene (STING) inhibition was performed using a series of indol-3-yl-N-phenylcarbamic amides and indol-2-yl-N-phenylcarbamic amides. Among these analogs, compounds 10, 13, 15, 19, and 21 inhibited the phosphorylation of STING and interferon regulatory factor 3 (IRF3) to a greater extent than the reference compound, H-151. All five analogs showed stronger STING inhibition than H-151 on the 2',3'-cyclic GMP-AMP-induced expression of interferon regulatory factors (IRFs) in a STINGR232 knock-in THP-1 reporter cell line. The half-maximal inhibitory concentration of the most potent compound, 21, was 11.5 nM. The molecular docking analysis of compound 21 and STING combined with the SAR study suggested that the meta- and para-positions of the benzene ring of the phenylcarbamic amide moiety could be structurally modified by introducing halides or alkyl substituents.

Marine diterpenoid targets STING palmitoylation in mammalian cells.

Natural products are important sources of therapeutic agents and useful drug discovery tools. The fused macrocycles and multiple stereocenters of briarane-type diterpenoids pose a major challenge to total synthesis and efforts to characterize their biological activities. Harnessing a scalable source of excavatolide B (excB) from cultured soft coral Briareum stechei, we generated analogs by late-stage diversification and performed structure-activity analysis, which was critical for the development of functional excB probes. We further used these probes in a chemoproteomic strategy to identify Stimulator of Interferon Genes (STING) as a direct target of excB in mammalian cells. We showed that the epoxylactone warhead of excB is required to covalently engage STING at its membrane-proximal Cys91, inhibiting STING palmitoylation and signaling. This study reveals a possible mechanism-of-action of excB, and expands the repertoire of covalent STING inhibitors.

Machine learning for predicting chemical migration from food packaging materials to foods.

Food contact chemicals (FCCs) can migrate from packaging materials to food posing an issue of exposure to FCCs of toxicity concern. Compared to costly experiments, computational methods can be utilized to assess the migration potentials for various migration scenarios for further experimental investigation that can potentially accelerate the migration assessment. This study developed a nonlinear machine learning method utilizing chemical properties, material type, food type and temperature to predict chemical migration from package to food. Nine nonlinear algorithms were evaluated for their prediction performance. The ensemble model leveraging multiple algorithms provides state-of-the-art performance that is much better than previous linear regression models. The developed prediction models were subsequently applied to profile the migration potential of FCCs of high toxicity concern. The models are expected to be useful for accelerating the assessment of migration of FCCs from package to foods.

Using random forest to predict antimicrobial minimum inhibitory concentrations of nontyphoidal Salmonella in Taiwan.

Antimicrobial resistance (AMR) is a global health issue and surveillance of AMR can be useful for understanding AMR trends and planning intervention strategies. Salmonella, widely distributed in food-producing animals, has been considered the first priority for inclusion in the AMR surveillance program by the World Health Organization (WHO). Recent advances in rapid and affordable whole-genome sequencing (WGS) techniques lead to the emergence of WGS as a one-stop test to predict the antimicrobial susceptibility. Since the variation of sequencing and minimum inhibitory concentration (MIC) measurement methods could result in different results, this study aimed to develop WGS-based random forest models for predicting MIC values of 24 drugs using data generated from the same laboratories in Taiwan. The WGS data have been transformed as a feature vector of 10-mers for machine learning. Based on rigorous validation and independent tests, a good performance was obtained with an average mean absolute error (MAE) less than 1 for both validation and independent test. Feature selection was then applied to identify top-ranked 10-mers that can further improve the prediction performance. For surveillance purposes, the genome sequence-based machine learning methods could be utilized to monitor the difference between predicted and experimental MIC, where a large difference might be worthy of investigation on the emerging genomic determinants.

Reconfiguring the online tool of SkinSensPred for predicting skin sensitization of pesticides.

Adverse outcome pathway (AOP)-based computational models provide state-of-the-art prediction for human skin sensitizers and are promising alternatives to animal testing. However, little is known about their applicability to pesticides due to scarce pesticide data for evaluation. Moreover, pesticides traditionally have been tested on animals without human data, making validation difficult. Direct application of AOP-based models to pesticides may be inappropriate since their original applicability domains were designed to maximize reliability for human response prediction on diverse chemicals but not pesticides. This study proposed to identify a consensus chemical space with concordant human responses predicted by the SkinSensPred online tool and animal testing data to reduce animal testing. The identified consensus chemical space for non-sensitizers achieved high concordance of 85% and 100% for the cross-validation and independent test, respectively. The reconfigured SkinSensPred can be applied as the first-tier tool for identifying non-sensitizers to reduce. animal testing for pesticides by 19.6%.

SkinSensPred as a Promising in Silico Tool for Integrated Testing Strategy on Skin Sensitization.

Skin sensitization is an important regulatory endpoint associated with allergic contact dermatitis. Recently, several adverse outcome pathway (AOP)-based alternative methods were developed to replace animal testing for evaluating skin sensitizers. The AOP-based assays were further integrated as a two-out-of-three method with good predictivity. However, the acquisition of experimental data is resource-intensive. In contrast, an integrated testing strategy (ITS) capable of maximizing the usage of laboratory data from AOP-based and in silico methods was developed as defined approaches (DAs) to both hazard and potency assessment. There are currently two in silico models, namely Derek Nexus and OECD QSAR Toolbox, evaluated in the OECD Testing Guideline No. 497. Since more advanced machine learning algorithms have been proposed for skin sensitization prediction, it is therefore desirable to evaluate their performance under the ITS framework. This study evaluated the performance of a new ITS DA (ITS-SkinSensPred) adopting a transfer learning-based SkinSensPred model. Results showed that the ITS-SkinSensPred has similar or slightly better performance compared to the other ITS models. SkinSensPred-based ITS is expected to be a promising method for assessing skin sensitization.

Computational prediction of Calu-3-based in vitro pulmonary permeability of chemicals.

Pulmonary is a potential route for drug delivery and exposure to toxic chemicals. The human bronchial epithelial cell line Calu-3 is generally considered to be a useful in vitro model of pulmonary permeability by calculating the apparent permeability coefficient (Papp) values. Since in vitro experiments are time-consuming and labor-intensive, computational models for pulmonary permeability are desirable for accelerating drug design and toxic chemical assessment. This study presents the first attempt for developing quantitative structure-activity relationship (QSAR) models for addressing this goal. A total of 57 chemicals with Papp values based on Calu-3 experiments was first curated from literature for model development and testing. Subsequently, eleven descriptors were identified by a sequential forward feature selection algorithm to maximize the cross-validation performance of a voting regression model integrating linear regression and nonlinear random forest algorithms. With applicability domain adjustment, the developed model achieved high performance with correlation coefficient values of 0.935 and 0.824 for cross-validation and independent test, respectively. The preliminary results showed that computational models could be helpful for predicting Calu-3-based in vitro pulmonary permeability of chemicals. Future works include the collection of more data for further validating and improving the model.

In silico prediction of parkinsonian motor deficits-related neurotoxicants based on the adverse outcome pathway concept.

Exposure to neurotoxicants has been associated with Parkinson's disease (PD). Limited by the clinical variation in the signs and symptoms as well as the slow disease progression, the identification of parkinsonian neurotoxicants relies on animal models. Here, we propose an innovative in silico model for the prediction of parkinsonian neurotoxicants. The model was designed based on a validated adverse outcome pathway (AOP) for parkinsonian motor deficits initiated from the inhibition of mitochondrial complex I. The model consists of a molecular docking model for mitochondrial complex I protein to predict the molecular initiating event and a neuronal cytotoxicity Quantitative Structure-Activity Relationships (QSAR) model to predict the cellular outcome of the AOP. Four known PD-related complex I inhibitors and four non-neurotoxic chemicals were utilized to develop the threshold of the models and to validate the model, respectively. The integrated model showed 100% specificity in ruling out the non-neurotoxic chemicals. The screening of 41 neurotoxicants and complex I inhibitors with the model resulted in 16 chemicals predicted to induce parkinsonian disorder through the molecular initiating event of mitochondrial complex I inhibition. Five of them, namely cyhalothrin, deguelin, deltamethrin, diazepam, and permethrin, are cases with direct evidence linking them to parkinsonian motor deficit-related signs and symptoms. The neurotoxicant prediction model for parkinsonian motor deficits based on the AOP concept may be useful in prioritizing chemicals for further evaluations on PD potential.

Aryl hydrocarbon receptor deficiency augments dysregulated microangiogenesis and diabetic retinopathy.

Diabetic retinopathy (DR) is a pathophysiologic vasculopathic process with obscure mechanisms and limited effective therapeutic strategies. Aryl hydrocarbon receptor (AhR) is an important regulator of xenobiotic metabolism and an environmental sensor. The aim of the present study was to investigate the role of AhR in the development of DR and elucidate the molecular mechanism of its downregulation. DR was evaluated in diabetes-induced retinal injury in wild type and AhR knockout (AhR-/-) mice. Retinal expression of AhR was determined in human donor and mice eyes by immunofluorescence since AhR activity was examined in diabetes. AhR knockout (AhRKO) mice were used to induce diabetes with streptozotocin, high-fat diet, or genetic double knockout with diabetes spontaneous mutation (Leprdb) (DKO; AhR-/-×Leprdb/db) for investigating structural, functional, and metabolic abnormalities in vascular and epithelial retina. Structural molecular docking simulation was used to survey the pharmacologic AhR agonists targeting phosphorylated AhR (Tyr245). Compared to diabetic control mice, diabetic AhRKO mice had aggravated alterations in retinal vasculature that amplified hallmark features of DR like vasopermeability, vascular leakage, inflammation, blood-retinal barrier breakdown, capillary degeneration, and neovascularization. AhR agonists effectively inhibited inflammasome formation and promoted AhR activity in human retinal microvascular endothelial cells and pigment epithelial cells. AhR activity and protein expression was downregulated, resulting in a decrease in DNA promoter binding site of pigment epithelium-derived factor (PEDF) by gene regulation in transcriptional cascade. This was reversed by AhR agonists. Our study identified a novel of DR model that target the protective AhR/PEDF axis can potentially maintain retinal vascular homeostasis, providing opportunities to delay the development of DR.

Ensemble learning for predicting ex vivo human placental barrier permeability.

BACKGROUND: The placental barrier protects the fetus from exposure to some toxicants and is vital for drug development and risk assessment of environmental chemicals. However, in vivo experiments for assessing the placental barrier permeability of chemicals is not ethically acceptable. Although ex vivo placental perfusion methods provide good alternatives for the assessment of placental barrier permeability, the application to a large number of test chemicals could be time- and resource-consuming. Computational prediction models for ex vivo placental barrier permeability are therefore desirable. METHODS: A total of 87 chemicals and corresponding 1444 physicochemical properties were divided into training and test datasets. Three types of algorithms including linear regression, random forest, and ensemble models were applied to develop prediction models for ex vivo placental barrier permeability. RESULTS: Among the tested models, the ensemble model integrating the previous two methods performed best for predicting ex vivo human placental barrier permeability with correlation coefficients of 0.887 and 0.825 when considering the applicability domain. An additional test on seven newly curated chemicals from the literature showed a good correlation coefficient of 0.879 which was further improved to 0.921 by considering the variation of experiments. CONCLUSION: In this study, the first valid predicting model for ex vivo human placental barrier permeability was developed following the OECD guideline. The model is expected to be useful for assessing the human placental barrier permeability and can be integrated with developmental toxicity prediction models for investigating the toxic effects of chemicals on the fetus.

Chlorogenic Acid Inhibition of Esophageal Squamous Cell Carcinoma Metastasis via EGFR/p-Akt/Snail Signaling Pathways.

BACKGROUND/AIM: Chlorogenic acid (CGA) is a polyphenol compound found in a variety of foods, including coffee, tea, cherries, and apples. It has been found by a number of studies to affect the viability of human cancer cells. No study has investigated its effect on esophageal squamous cell carcinoma (ESCC) metastasis or the molecular mechanism underlying its effect on this disease. MATERIALS AND METHODS: We first used the Taiwanese ESCC cell line CE81T/VGH to create CE81T-M4 cells. Treatment of higher motility cells with chlorogenic acid for 24 h led to inhibition of cell migration and invasion as shown by scratch migration and transwell assays. RESULTS: Western blotting showed that chlorogenic acid halted the activation of EGFR/p-Akt/Snail pathway and suppressed the expression of MMP-2 and MMP-9. Knockdown of either EGFR or Akt inhibited Snail, MMP2, and MMP9 activity as well as cell migration and invasion. CONCLUSION: Chlorogenic acid inhibited cancer cell motility via the EGFR/p-Akt/Snail pathway and could potentially be used to develop an antimetastatic agent for ESCC in the future.

A Machine Learning Classifier for Predicting Stable MCI Patients Using Gene Biomarkers.

Alzheimer's disease (AD) is a neurodegenerative disorder with an insidious onset and irreversible condition. Patients with mild cognitive impairment (MCI) are at high risk of converting to AD. Early diagnosis of unstable MCI patients is therefore vital for slowing the progression to AD. However, current diagnostic methods are either highly invasive or expensive, preventing their wide applications. Developing low-invasive and cost-efficient screening methods is desirable as the first-tier approach for identifying unstable MCI patients or excluding stable MCI patients. This study developed feature selection and machine learning algorithms to identify blood-sample gene biomarkers for predicting stable MCI patients. Two datasets obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database were utilized to conclude 29 genes biomarkers (31 probes) for predicting stable MCI patients. A random forest-based classifier performed well with area under the receiver operating characteristic curve (AUC) values of 0.841 and 0.775 for cross-validation and test datasets, respectively. For patients with a prediction score greater than 0.9, an excellent concordance of 97% was obtained, showing the usefulness of the proposed method for identifying stable MCI patients. In the context of precision medicine, the proposed prediction model is expected to be useful for identifying stable MCI patients and providing medical doctors and patients with new first-tier diagnosis options.

A machine learning-driven approach for prioritizing food contact chemicals of carcinogenic concern based on complementary in silico methods.

Carcinogenicity is one of the most critical endpoints for the risk assessment of food contact chemicals (FCCs). However, the carcinogenicity of FCCs remains insufficiently investigated. To fill the data gap, the application of standard experimental methods for identifying chemicals of carcinogenic concerns from a large set of FCCs is impractical due to their resource-intensive nature. In contrast, computational methods provide an efficient way to quickly screen chemicals with carcinogenic potential for subsequent experimental validation. Since every model was developed based on a limited number of training samples, the use of single models for carcinogenicity assessment may not cover the complex mechanisms of carcinogenesis. This study proposed a novel machine learning-based weight-of-evidence (WoE) model for prioritizing chemical carcinogenesis. The WoE model can nonlinearly integrate complementary computational methods of structural alerts, quantitative structure-activity relationship models and in silico toxicogenomics models into a WoE-score. Compared to the best single method, the WoE model gained 8% and 19.7% improvement in the area under the receiver operating characteristic curve (AUC) value and chemical coverage, respectively. The prioritization of 1623 FCCs concludes 44 chemicals of high carcinogenic concern. The machine learning-based WoE approach provides a fast and comprehensive way for prioritizing chemicals of carcinogenic concern.

Computational identification of preservatives with potential neuronal cytotoxicity.

Preservatives play a vital role in cosmetics by preventing microbiological contamination for keeping products safe to use. However, a few commonly used preservatives have been suggested to be neurotoxic. Cytotoxicity to neuronal cells is commonly used as the first-tier assay for assessing chemical-induced neurotoxicity. Given the time and resources required for chemical screening, computational methods are attractive alternatives over experimental approaches in prioritizing chemicals prior to further experimental evaluations. In this study, we developed a Quantitative Structure-Activity Relationships (QSAR) model for the identification of potential neurotoxicants. A set of 681 chemicals was utilized to construct a robust prediction model using oversampling and Random Forest algorithms. Within a defined applicability domain, the independent test on 452 chemicals showed a high accuracy of 87.7%. The application of the model to 157 preservatives identified 15 chemicals potentially toxic to neuronal cells. Three of them were further validated by in vitro experiments. The results suggested that further experiments are desirable for assessing the neurotoxicity of the identified preservatives with potential neuronal cytotoxicity.