Exploring the molecular mechanisms by which per- and polyfluoroalkyl substances induce polycystic ovary syndrome through in silico toxicogenomic data mining.

The pathogeny of polycystic ovary syndrome (PCOS) is intricate, with endocrine disruptors (EDCs) being acknowledged as significant environmental factors. Research has shown a link between exposure to per- and polyfluoroalkyl substances (PFAS) and the development and progression of PCOS, although the precise mechanism is not fully understood. This study utilized toxicogenomics and comparative toxicogenomics databases to analyze data and investigate how PFAS mixtures may contribute to the development of PCOS. The results indicated that 74 genes are associated with both PFAS exposure and PCOS progression. Enrichment analysis suggested that cell cycle regulation and steroid hormone synthesis may be crucial pathways through which PFAS mixtures participate in the development of PCOS, involving important genes such as CCNB1 and SRD5A1. Furthermore, the study identified transcription factors (TFs) and miRNAs that may be involved in the onset and progression of PCOS, constructing regulatory networks encompassing TFs-mRNA interactions and miRNA-mRNA relationships to elucidate their regulatory roles in gene expression. By utilizing data mining techniques based on toxicogenomic databases, this study provides relatively comprehensive insights into the association between exposure factors and diseases compared to traditional toxicology studies. These findings offer new perspectives for further in vivo or in vitro investigations and contribute to understanding the pathogenesis of PCOS, thereby providing valuable references for identifying clinical treatment targets.

Toxicogenomics of the C-C chemokine receptor type 5 (CCR5): Exploring the potential impacts of chemical-CCR5 interactions on inflammation and human health.

This article explores the impact of environmental chemicals on CCR5 expression and related inflammatory responses based on curated data from the Comparative Toxicogenomics Database (CTD). A total of 143 CCR5-interacting chemicals was found, with 229 chemical interactions. Of note, 67 (29.3%) out of 229 interactions resulted in "increased expression" of CCR5 mRNA or CCR5 protein, and 42 (18.3%) chemical interactions resulted in "decreased expression". The top-5 CCR5-interacting chemicals were "Tetrachlorodibenzodioxin", "Lipopolysaccharides", "Benzo(a)pyrene", "Drugs, Chinese Herbal", and "Ethinyl Estradiol". Based on the number of interactions and importance as environmental contaminant, we then focused our analysis on Tetrachlorodibenzodioxin and Benzo(a)pyrene. There is some consistency in the data supporting an increase in CCR5 expression triggered by Tetrachlorodibenzodioxin; although data concerning CCR5-Benzo(a)pyrene interactions is limited. Considering the high linkage disequilibrium between CCR5 and CCR2 genes, we also search for chemicals that interact with both genes, which resulted in 72 interacting chemicals, representing 50.3% of the 143 CCR5-interacting chemicals and 37.5% of the 192 CCR2-interacting chemicals. In conclusion, CTD data showed that environmental contaminants indeed affect CCR5 expression, with a tendency towards increased expression. The interaction of environmental contaminants with other chemokine receptor genes may potentialize their toxic effects on the chemokine system, favoring inflammation.

Screening the critical protein subnetwork to delineate potential mechanisms and protective agents associated with arsenic-induced cutaneous squamous cell carcinoma: A toxicogenomic study.

Recent studies show that complex mechanisms are involved in arsenic-induced malignant transformation of cells. This study aimed to decipher molecular mechanisms associated with arsenic-induced cutaneous squamous cell carcinoma (cSCC) and suggest potential protective factors. RNA-seq-based differentially expressed genes between arsenic-exposed human keratinocytes (HaCaT) and controls were used to construct a protein-protein interaction (PPI) network and discover critical subnetwork-based mechanisms. Protective compounds against arsenic toxicity were determined and their target interactions in the core sub-network were identified by the comparative toxicogenomic database (CTD). The binding affinity between the effective factor and target was calculated by molecular docking. A total of 15 key proteins were screened out as critical arsenic-responsive subnetwork (FN1, IL-1A, CCN2, PECAM1, FGF5, EDN1, FGF1, PXDN, DNAJB9, XBP1, ERN1, PDIA4, DNAJB11, FOS, PDIA6) and 7 effective protective agents were identified (folic acid, quercetin, zinc, acetylcysteine, methionine, catechin, selenium). The GeneMANIA predicted detailed interactions of the subnetwork and revealed terms related to unfolded protein response as the main processes. FN1, IL1A and CCN2, as top significant genes, had good docking affinity with folic acid and quercetin, as selected key compounds. Integration of gene expression and protein-protein interaction related to arsenic exposure in cSCC explored the potential mechanisms and protective agents.

The alternation of halobenzoquinone disinfection byproduct on toxicogenomics of DNA damage and repair in uroepithelial cells.

Halobenzoquinones (HBQs) were recently discovered as an emerging class of drinking water disinfection byproducts with carcinogenic concern. However, the molecular mechanism underlying HBQs-induced DNA damage is not clear. In this study, we integrated in vitro genotoxicity, computational toxicology, and the quantitative toxicogenomic analysis of HBQs on DNA damage/repair pathways in human bladder epithelial cells SV-HUC-1. The results showed that HBQs could induce cytotoxicity with the descending order as 2,6-DIBQ > 2,6-DCBQ ≈ 2,6-DBBQ. Also, HBQs can increase DNA damage in SV-HUC-1 cells and thus generate genotoxicity. However, there is no significant difference in genotoxicity among the three HBQs. The results of molecular docking and molecular dynamics simulation further confirmed that HBQs had high binding fractions and stability to DNA. Toxicogenomic analysis indicated that HBQs interfered with DNA repair pathways, mainly affecting base excision repair, nucleotide excision repair and homologous recombination repair. These results have provided new insights into the underlying molecular mechanisms of HBQs-induced DNA damage, and contributed to the understanding of the relationship between exposure to DBPs and risks of developing bladder cancer.

Exposure to mixed chemicals elevated triiodothyronine (T3) and follicle-stimulating hormone (FSH) levels: epidemiology and in silico toxicogenomic involvement.

There is a dearth of evidence on the effects of a mixture of numerous different types of chemicals on hormone functions. We hypothesized that exposure to mixed chemicals may alter hormone levels. Thus, this study was to identify an association between the mixed chemicals (25 chemicals) and hormone levels (thyroxine (T4) and triiodothyronine (T3), thyroid-stimulating hormone (TSH), and follicle-stimulating hormone (FSH)) among 5687 Korean adults using four different statistical approaches. Furthermore, we elucidate the effects of the key chemicals on thyroid disease and infertility based on the findings from epidemiology data. The positive associations between mixed chemicals and T3 and between mixed chemicals and FSH were observed across different methods after adjusting for all possible confounders. In the weighted quantile sum regression models, there were positive associations between mixed chemicals and T3 (ß = 4.43, 95%CI: 2.81-5.88) and ln-transformed FSH (lnFSH) (ß = 0.15, 95%CI: 0.10-0.20). In the quantile g-computation models, positive associations were found between mixed chemicals and T3 (ß=2.15, 95%CI: 0.17-4.14) and lnFSH (ß=0.15, 95%CI: 0.07-0.22). In the Bayesian kernel machine regression models, culminative effects of mixed chemicals showed positive associations with T3 and lnFSH; mercury (group posterior inclusion probabilities (PIPs) = 0.557 and conditional PPI = 0.556) and lead (group PIP group = 0.815 and conditional PPI = 0.951) were the most important chemicals for T3 and FSH, respectively. The results obtained were partially robust when subjected to in silico toxicogenomic data. We identified several molecular mechanisms that were implicated in Hg-induced thyroid disease, including the selenium micronutrient network, oxidative stress response, IL-17 signaling pathway, poorly differentiated thyroid carcinoma, and primary hyperthyroidism. The molecular processes implicated in Pb-induced infertility were "response to nutrient levels," "gonad development," "male infertility," "female infertility," and "intrinsic pathway for apoptosis," with a particular focus on FSH. The present study investigated the threshold levels of the studied chemicals and their potential impact on the disruption of T3 and FSH hormones. Future research is warranted to determine the effects of mixed chemicals on various hormones because there have been few studies on the disruption of hormones caused by such mixed chemicals.

CTD tetramers: a new online tool that computationally links curated chemicals, genes, phenotypes, and diseases to inform molecular mechanisms for environmental health.

The molecular mechanisms connecting environmental exposures to adverse endpoints are often unknown, reflecting knowledge gaps. At the Comparative Toxicogenomics Database (CTD), we developed a bioinformatics approach that integrates manually curated, literature-based interactions from CTD to generate a "CGPD-tetramer": a 4-unit block of information organized as a step-wise molecular mechanism linking an initiating Chemical, an interacting Gene, a Phenotype, and a Disease outcome. Here, we describe a novel, user-friendly tool called CTD Tetramers that generates these evidence-based CGPD-tetramers for any curated chemical, gene, phenotype, or disease of interest. Tetramers offer potential solutions for the unknown underlying mechanisms and intermediary phenotypes connecting a chemical exposure to a disease. Additionally, multiple tetramers can be assembled to construct detailed modes-of-action for chemical-induced disease pathways. As well, tetramers can help inform environmental influences on adverse outcome pathways (AOPs). We demonstrate the tool's utility with relevant use cases for a variety of environmental chemicals (eg, perfluoroalkyl substances, bisphenol A), phenotypes (eg, apoptosis, spermatogenesis, inflammatory response), and diseases (eg, asthma, obesity, male infertility). Finally, we map AOP adverse outcome terms to corresponding CTD terms, allowing users to query for tetramers that can help augment AOP pathways with additional stressors, genes, and phenotypes, as well as formulate potential AOP disease networks (eg, liver cirrhosis and prostate cancer). This novel tool, as part of the complete suite of tools offered at CTD, provides users with computational datasets and their supporting evidence to potentially fill exposure knowledge gaps and develop testable hypotheses about environmental health.

Toxicogenomic analysis of physiologically important metals: An integrated in silico approach.

Biologically important metals regulate cellular homeostasis in living systems. Anthropogenic exposure to these metals can cause adverse effects, including an increased incidence of diseases in humans such as cancer, lung, and cardiovascular defects. However, the effects of metals and the common genes/signaling pathways involved in metal toxicity have not been elucidated. Hence, the present study used toxicogenomic data mining with the comparative toxicogenomics database to explore the impact of these metals. The metals were categorized into transition, alkali, and alkaline earth. The common genes were identified and subjected to functional enrichment analysis. Further, gene-gene and protein-protein interactions were assessed. Also, the top ten transcription factors and miRNAs that regulate the genes were identified. The phenotypes and diseases that have increased incidence upon alterations of these genes were detected. Overall, we were able to identify IL1B and SOD2 as the common genes, along with the AGE-RAGE signaling pathway in diabetic complications as the common pathway altered. Enriched genes and pathways specific to each metal category were also found. Further, we identified heart failure as the major disease that could have increase in the incidence upon these metals' exposure. In conclusion, exposure to essential metals might cause adverse effects via inflammation and oxidative stress.

Predicting thrombotic cardiovascular outcomes induced by waterpipe-associated chemicals using comparative toxicogenomic database: Genes, phenotypes, and pathways.

Hookah, or waterpipe, is a tobacco smoking device that has gained popularity in the United States. A growing body of evidence demonstrates that waterpipe smoke (WPS) is associated with various adverse effects on human health, including infectious diseases, cancer, and cardiovascular diseases (CVDs), particularly thrombotic events. However, the molecular mechanisms through which WPS contributes to disease development remain unclear. In this study, we utilized an analytical approach based on the Comparative Toxicogenomics Database (CTD) to integrate chemical, gene, phenotype, and disease data to predict potential molecular mechanisms underlying the effects of WPS, based on its chemical and toxicant profile. Our analysis revealed that CVDs were among the top disease categories with regard to the number of curated interactions with WPS chemicals. We identified 5674 genes common between those modulated by WPS chemicals and traditional tobacco smoking. The CVDs with the most curated interactions with WPS chemicals were hypertension, atherosclerosis, and myocardial infarction, whereas "particulate matter", "heavy metals", and "nicotine" showed the highest number of curated interactions with CVDs. Our analysis predicted that the potential mechanisms underlying WPS-induced thrombotic diseases involve common phenotypes, such as inflammation, apoptosis, and cell proliferation, which are shared across all thrombotic diseases and the three aforementioned chemicals. In terms of enriched signaling pathways, we identified several, including chemokine and MAPK signaling, with particulate matter exhibiting the most statistically significant association with all 12 significant signaling pathways related to WPS chemicals. Collectively, our predictive comprehensive analysis provides evidence that WPS negatively impacts health and offers insights into the potential mechanisms through which it exerts these effects. This information should guide further research to explore and better understand the WPS and other tobacco product-related health consequences.

Testing sulforaphane as a strategy against toxic chemicals of public health concern by toxicogenomic data analysis: Friend or foe at the gene level - Colorectal carcinoma case study.

Toxic metals (cadmium (Cd), lead (Pb), mercury (Hg) and arsenic (As)) and plastificators (bis (2 - ethylhexyl) phthalate (DEHP), dibutyl phthalate (DBP)) and bisphenol A (BPA)) have been suggested to aid in colorectal carcinoma (CRC) advancement. Sulforaphane (SFN), isothiocyanate from cruciferous vegetables, diminishes chemical carcinogenesis susceptibility, but has been shown to act as a friend or a foe depending on various factors. By conducting the mechanistic toxicogenomic data mining approach, this research aimed to determine if SFN can alleviate toxic-metal and/or phthalate/BPA mixture-induced CRC at the gene level. Comparative Toxicogenomics Database, ToppGene Suite portal, Cytoscape software, InteractiVenn and Gene Expression Omnibus (GEO) database (GEO2R tool) was used. Among the mutual genes for all the investigated substances, SFN had a protective impact only through PTGS2. Other proposed protective SFN-targets included ABCA1, ALDH2, BMP2, DPYD, MYC, SLCO2A1, and SOD2, only in the case of phthalates/BPA exposure. The only additional gene relevant for SFN protection against the toxic metal mixture-induced CRC was ABCB1. Additionally, the majority of the top 15 molecular pathways extracted for SFN impact on phthalate and BPA mixture-linked CRC development were directly linked with cancer development, which was not the case with the toxic metal mixture. The current research has indicated that SFN is a more effective chemoprotective agent against CRC induced by phthalates/BPA mixture than by toxic-metal mixture. It has also presented the value of computational methods as a simple tool for directing further research, selecting appropriate biomarkers and exploring the mechanisms of toxicity.

An in silico toxicogenomic approach in constructing the aflatoxin B1-mediated regulatory network of hub genes in hepatocellular carcinoma.

Aflatoxin B1 (AFB1) can cause hepatocellular carcinoma (HCC) through a mutagenic mode of action but can also lead to global changes in gene expression; however, the AFB1 network of molecular pathways involved in HCC is not known. Here, we used toxicogenomic data from human liver cells exposed to AFB1 to infer the network of AFB1-responsive molecular pathways involved in HCC. The following computational tools: STRING, MCODE, cytoHubba, iRegulon, kinase enrichment tool KEA3, and DAVID were used to identify protein-protein interaction network, hub genes, transcription factors (TFs), upstream kinases, and biological processes (BPs). Predicted molecular events were validated with an external dataset, whereas the hub genes in HCC were validated using the UALCAN database. The results revealed an association between AFB1 and the hub genes involved in the cell cycle. We identified TFs that regulate the hub genes and linked them with upstream kinases including cyclin-dependent kinases, mitogen-activated protein kinase 1, and AKT. This approach enabled the construction of the AFB1-mediated regulatory network consisting of upstream kinases, TFs, hub genes, and BPs, thus revealing the signaling hierarchy and information flow that may contribute to AFB1-induced HCC. This could be a useful tool in predicting the molecular mechanisms involved in chemical-induced diseases when available toxicogenomic data exist.

Toxicogenomics scoring system: TGSS, a novel integrated risk assessment model for chemical carcinogenicity prediction.

BACKGROUND: Given the increasing exposure of humans to environmental chemicals and the limitations of conventional toxicity test, there is an urgent need to develop next-generation risk assessment methods. OBJECTIVES: This study aims to establish a novel computational system named Toxicogenomics Scoring System (TGSS) to predict the carcinogenicity of chemicals coupling chemical-gene interactions with multiple cancer transcriptomic datasets. METHODS: Chemical-related gene signatures were derived from chemical-gene interaction data from the Comparative Toxicogenomics Database (CTD). For each cancer type in TCGA, genes were ranked by their effects on tumorigenesis, which is based on the differential expression between tumor and normal samples. Next, we developed carcinogenicity scores (C-scores) using pre-ranked GSEA to quantify the correlation between chemical-related gene signatures and ranked gene lists. Then we established TGSS by systematically evaluating the C-scores in multiple chemical-tumor pairs. Furthermore, we examined the performance of our approach by ROC curves or prognostic analyses in TCGA and multiple independent cancer cohorts. RESULTS: Forty-six environmental chemicals were finally included in the study. C-score was calculated for each chemical-tumor pair. The C-scores of IARC Group 3 chemicals were significantly lower than those of chemicals in Group 1 (P-value = 0.02) and Group 2 (P-values = 7.49 ×10-5). ROC curves analysis indicated that C-score could distinguish "high-risk chemicals" from the other compounds (AUC = 0.67) with a specificity and sensitivity of 0.86 and 0.57. The results of survival analysis were also in line with the assessed carcinogenicity in TGSS for the chemicals in Group 1. Finally, consistent results were further validated in independent cancer cohorts. CONCLUSION: TGSS highlighted the great potential of integrating chemical-gene interactions with gene-cancer relationships to predict the carcinogenic risk of chemicals, which would be valuable for systems toxicology.

Toxicogenomics Approaches to Address Toxicity and Carcinogenicity in the Liver.

Toxicogenomic technologies query the genome, transcriptome, proteome, and the epigenome in a variety of toxicological conditions. Due to practical considerations related to the dynamic range of the assays, sensitivity, cost, and technological limitations, transcriptomic approaches are predominantly used in toxicogenomics. Toxicogenomics is being used to understand the mechanisms of toxicity and carcinogenicity, evaluate the translational relevance of toxicological responses from in vivo and in vitro models, and identify predictive biomarkers of disease and exposure. In this session, a brief overview of various transcriptomic technologies and practical considerations related to experimental design was provided. The advantages of gene network analyses to define mechanisms were also discussed. An assessment of the utility of toxicogenomic technologies in the environmental and pharmaceutical space showed that these technologies are being increasingly used to gain mechanistic insights and determining the translational relevance of adverse findings. Within the environmental toxicology area, there is a broader regulatory consideration of benchmark doses derived from toxicogenomics data. In contrast, these approaches are mainly used for internal decision-making in pharmaceutical development. Finally, the development and application of toxicogenomic signatures for prediction of apical endpoints of regulatory concern continues to be area of intense research.

A transcriptome-wide association study of uterine fibroids to identify potential genetic markers and toxic chemicals.

Uterine fibroid is one of the most prevalent benign tumors in women, with high socioeconomic costs. Although genome-wide association studies (GWAS) have identified several loci associated with uterine fibroid risks, they could not successfully interpret the biological effects of genomic variants at the gene expression levels. To prioritize uterine fibroid susceptibility genes that are biologically interpretable, we conducted a transcriptome-wide association study (TWAS) by integrating GWAS data of uterine fibroid and expression quantitative loci data. We identified nine significant TWAS genes including two novel genes, RP11-282O18.3 and KBTBD7, which may be causal genes for uterine fibroid. We conducted functional enrichment network analyses using the TWAS results to investigate the biological pathways in which the overall TWAS genes were involved. The results demonstrated the immune system process to be a key pathway in uterine fibroid pathogenesis. Finally, we carried out chemical-gene interaction analyses using the TWAS results and the comparative toxicogenomics database to determine the potential risk chemicals for uterine fibroid. We identified five toxic chemicals that were significantly associated with uterine fibroid TWAS genes, suggesting that they may be implicated in the pathogenesis of uterine fibroid. In this study, we performed an integrative analysis covering the broad application of bioinformatics approaches. Our study may provide a deeper understanding of uterine fibroid etiologies and informative notifications about potential risk chemicals for uterine fibroid.

Toxicogenetic assessment of a pre-workout supplement: In vitro mutagenicity, cytotoxicity, genotoxicity and glutathione determination in liver cell lines and in silico ADMET approaches.

The benefits of practicing physical activity, such as weight loss and control, are commonly associated with caloric restriction diets and may be improved by the ingestion of thermogenic and ergogenic supplements. However, there is a lack of safety data on commonly marketed nutritional supplements. Therefore, this investigation aims to evaluate a pre-workout supplement for mutagenicity using the Ames test, hepatocytoxicity in HepG2 and F C3H cells after 24 h, 48 h and 72 h, genotoxicity using the CBMN assay, determination of gluthatione activity and computational prediction of the three major isolated compounds present in the supplement. The mutagenicity test showed a mutagenic response in TA98 His+ revertants of 5 mg/plate in the presence of metabolic activation, cytotoxicity in TA98 of 5 mg/plate in the absence of metabolic conditions, and in TA102 of 0.5 mg/plate both in the presence and absence of metabolic activation. In our in vitro eukaryotic cell viability, WST-1, LDH and alkaline phosphatase assays, the supplement showed hepatocytotoxicity both dose-dependently and time-dependently. In the cytokinesis blocking micronuclei assay, the supplement induced micronuclei, nuclear buds, nucleoplasmatic, bridge formation, and a decreased in nuclear division. In addition, the supplement decreased intra and extracellular GSH. Computational analysis showed that the three isolated compounds most present in the supplement have the potential to cause hepatotoxicity. In the present investigation, the pre-workout supplement induced mutagenic, genotoxic, and cytotoxic responses and GSH decrease. Thus, considering food safety and public health sanitary vigilance, the consumption of this pre-workout supplement may harm the health of its consumers.

Joint impact of key air pollutants on COVID-19 severity: prediction based on toxicogenomic data analysis.

Considering that some researchers point to a possible influence of air pollution on COVID-19 transmission, severity, and death rate, the aim of our in silico study was to determine the relationship between the key air pollutants [sulphur dioxide (SO), carbon monoxide (CO), 2particulate matter (PMx), nitrogen dioxide (NO2), and ozone (O3)] and COVID-19 complications using the publicly available toxicogenomic analytical and prediction tools: (i) Comparative Toxicogenomic Database (CTD) to identify genes common to air pollutants and COVID-19 complications; (ii) GeneMANIA to construct a network of these common and related genes; (iii) ToppGene Suite to extract the most important biological processes and molecular pathways; and (iv) DisGeNET to search for the top gene-disease pairs. SO2, CO, PMx, NO2, and O3 interacted with 6, 6, 18, 9, and 12 COVID-19-related genes, respectively. Four of these are common for all pollutants (IL10, IL6, IL1B, and TNF) and participate in most (77.64 %) physical interactions. Further analysis pointed to cytokine binding and cytokine-mediated signalling pathway as the most important molecular function and biological process, respectively. Other molecular functions and biological processes are mostly related to cytokine activity and inflammation, which might be connected to the cytokine storm and resulting COVID-19 complications. The final step singled out the link between the CEBPA gene and acute myelocytic leukaemia and between TNFRSF1A and TNF receptor-associated periodic fever syndrome. This indicates possible complications in COVID-19 patients suffering from these diseases, especially those living in urban areas with poor air quality.

Antimitotic and toxicogenetic action of Stevia urticifolia aerial parts on proliferating vegetal and mammalian cells: in vitro and in vivo traditional and replacement methods.

Stevia urticifolia Thunb. is an underexploited herb possessing bioactive flavonoids, saponins, and terpenoids. The aim of this study was to examine the antiproliferative and toxicogenetic properties of the ethyl acetate extract from Stevia urticifolia aerial parts (EtAcSur) upon Artemia salina, erythrocytes, Allium cepa and sarcoma 180 cells and fibroblasts, as well as in vivo studies on mice to determine systemic, macroscopic, and behavioral alterations and bone marrow chromosomal damage. The assessment using A. salina larvae and mouse blood cells revealed LC50 and EC50 values of 68.9 and 113.6 µg/ml, respectively. Root growth and mitosis were inhibited by EtAcSur, and chromosomal aberrations were detected only at 100 µg/ml. EtAcSur exhibited potent concentration-dependent viability reduction of S180 and L-929 cells and antioxidant capacity employing ABTS• and DPPH•. No previous in vivo studies were performed before with the EtAcSur. Signals of acute toxicity were not observed at 300 mg/kg. Physiological and toxicological investigations at 25 and 50 mg/mg/day i.p. for 8 days did not markedly change body or organ relative weights, nor patterns of spontaneous locomotor and exploratory activities. In contrast, clastogenic effects on bone marrow were found at 50 mg/mg/day. EtAcSur was found to (1) produce toxicity in microcrustaceans, (2) capacity as free radical scavenger, (3) antimitotic, cytotoxic and clastogenic activties upon vegetal and mammalian cells, and (4) lethality on both tumor and normal murine cells indistinctly. In vivo damage systemic effects were not remarkable and clinical signals of toxicity were not observed, suggesting the significant pharmacological potential of S. urticifolia for the development of antineoplastic agents.Abbreviations: ABTS: 2,2'-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid); DMSO: dimethylsulfoxide; DPPH: 1,1-diphenyl-2-picrylhydrazyl; EC50: effective concentration 50%; EtAcSur: ethyl acetate extract from Stevia urticifolia aerial parts; Hb, hemoglobin; IC50: inhibitory concentration 50%; LC50,: lethal concentration 50%; MI: mitotic index; RBC, red blood cells; Trolox: 6-hydroxy-2,5,7,8-tetramethylchroman-2-carboxylic acid.

Network-based integrated analysis for toxic effects of high-concentration formaldehyde inhalation exposure through the toxicogenomic approach.

Formaldehyde is a colorless, pungent, highly reactive, and toxic environmental pollutant used in various industries and products. Inhaled formaldehyde is a human and animal carcinogen that causes genotoxicity, such as reactive oxygen species formation and DNA damage. This study aimed to identify the toxic effects of inhaled formaldehyde through an integrated toxicogenomic approach utilizing database information. Microarray datasets (GSE7002 and GSE23179) were collected from the Gene Expression Omnibus database, and differentially expressed genes were identified. The network analyses led to the construction of the respiratory system-related biological network associated with formaldehyde exposure, and six upregulated hub genes (AREG, CXCL2, HMOX1, PLAUR, PTGS2, and TIMP1) were identified. The expression levels of these genes were verified via qRT-PCR in 3D reconstructed human airway tissues exposed to aerosolized formaldehyde. Furthermore, NRARP was newly found as a potential gene associated with the respiratory and carcinogenic effects of formaldehyde by comparison with human in vivo and in vitro formaldehyde-exposure data. This study improves the understanding of the toxic mechanism of formaldehyde and suggests a more applicable analytic pipeline for predicting the toxic effects of inhaled toxicants.

Latent Variables Capture Pathway-Level Points of Departure in High-Throughput Toxicogenomic Data.

Estimation of points of departure (PoDs) from high-throughput transcriptomic data (HTTr) represents a key step in the development of next-generation risk assessment (NGRA). Current approaches mainly rely on single key gene targets, which are constrained by the information currently available in the knowledge base and make interpretation challenging as scientists need to interpret PoDs for thousands of genes or hundreds of pathways. In this work, we aimed to address these issues by developing a computational workflow to investigate the pathway concentration-response relationships in a way that is not fully constrained by known biology and also facilitates interpretation. We employed the Pathway-Level Information ExtractoR (PLIER) to identify latent variables (LVs) describing biological activity and then investigated in vitro LVs' concentration-response relationships using the ToxCast pipeline. We applied this methodology to a published transcriptomic concentration-response data set for 44 chemicals in MCF-7 cells and showed that our workflow can capture known biological activity and discriminate between estrogenic and antiestrogenic compounds as well as activity not aligning with the existing knowledge base, which may be relevant in a risk assessment scenario. Moreover, we were able to identify the known estrogen activity in compounds that are not well-established ER agonists/antagonists supporting the use of the workflow in read-across. Next, we transferred its application to chemical compounds tested in HepG2, HepaRG, and MCF-7 cells and showed that PoD estimates are in strong agreement with those estimated using a recently developed Bayesian approach (cor = 0.89) and in weak agreement with those estimated using a well-established approach such as BMDExpress2 (cor = 0.57). These results demonstrate the effectiveness of using PLIER in a concentration-response scenario to investigate pathway activity in a way that is not fully constrained by the knowledge base and to ease the biological interpretation and support the development of an NGRA framework with the ability to improve current risk assessment strategies for chemicals using new approach methodologies.

Associating diethylhexyl phthalate to gestational diabetes mellitus via adverse outcome pathways using a network-based approach.

Gestational diabetes mellitus (GDM) is a common pregnancy complication that is harmful to both the woman and fetus. Several epidemiological studies have found that exposure to diethylhexyl phthalate (DEHP), an endocrine disruptor ubiquitous in the environment, may be associated with GDM. This study aims to investigate the mechanism between DEHP and GDM using the adverse outcome pathway (AOP) framework, which can integrate information from different sources to elucidate the causal pathways between chemicals and adverse outcomes. We applied a network-based workflow to integrate diverse information to generate computational AOPs and accelerate the AOP development. The interactions among DEHP, genes, phenotypes, and GDM were retrieved from several publicly available databases, including the Comparative Toxicogenomics Database (CTD), Computational Toxicology (CompTox) Chemicals Dashboard, DisGeNET, MalaCards, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG). Based on the above interactions, a DEHP-Gene-Phenotype-GDM network consisting of 52 nodes and 227 edges was formed to support AOP construction. The filtered genes and phenotypes were assembled as molecular initiating events (MIEs) and key events (KEs) according to the upstream and downstream relationships, generating a computational AOP (cAOP) network. Based on the Organization for Economic Co-operation and Development handbook of AOPs, a cAOP was assessed and applied to determine the effects of DEHP on GDM. DEHP could increase TNF-α, downregulate the glucose uptake process, and lead to GDM. Overall, this study revealed the utility of computational methods in integrating a variety of datasets, supporting AOP development, and facilitating a better understanding of the underlying mechanism of exposure to chemicals on human health.

Is Cadmium Toxicity Tissue-Specific? Toxicogenomics Studies Reveal Common and Specific Pathways in Pulmonary, Hepatic, and Neuronal Cell Models.

Several harmful modifications in different tissues-organs, leading to relevant diseases (e.g., liver and lung diseases, neurodegeneration) are reported after exposure to cadmium (Cd), a wide environmental contaminant. This arises the question whether any common molecular signatures and/or Cd-induced modifications might represent the building block in initiating or contributing to address the cells towards different pathological conditions. To unravel possible mechanisms of Cd tissue-specificity, we have analyzed transcriptomics data from cell models representative of three major Cd targets: pulmonary (A549), hepatic (HepG2), and neuronal (SH-SY-5Y) cells. Further, we compared common features to identify any non-specific molecular signatures. The functional analysis of dysregulated genes (gene ontology and KEGG) shows GO terms related to metabolic processes significantly enriched only in HepG2 cells. GO terms in common in the three cell models are related to metal ions stress response and detoxification processes. Results from KEGG analysis show that only one specific pathway is dysregulated in a significant way in all cell models: the mineral absorption pathway. Our data clearly indicate how the molecular mimicry of Cd and its ability to cause a general metal ions dyshomeostasis represent the initial common feature leading to different molecular signatures and alterations, possibly responsible for different pathological conditions.