Profiling transcriptomes of human SH-SY5Y neuroblastoma cells exposed to maleic acid

8-Hydroxydaidzein Induces Apoptosis and Inhibits AML-Associated Gene Expression in U-937 Cells: Potential Phytochemical for AML Treatment

BACKGROUND

8-hydroxydaidzein (8-OHD) is a compound derived from daidzein, known for its anti-inflammatory and anti-proliferative properties in K562 human chronic myeloid leukemia (CML) cells. However, its effects on acute myeloid leukemia (AML) cells have not been fully understood.

METHOD

To investigate its potential anti-AML mechanism, we employed an integrated in vitro-in silico approach.

RESULTS

Our findings demonstrate that 8-OHD suppresses the expression of CDK6 and CCND2 proteins and induces cell apoptosis in U-937 cells by activating Caspase-7 and cleaving PARP-1. Microarray analysis revealed that 8-OHD downregulates differentially expressed genes (DEGs) associated with rRNA processing and ribosome biogenesis pathways. Moreover, AML-target genes, including CCND2, MYC, NPM1, FLT3, and TERT, were downregulated by 8-OHD. Additionally, molecular docking software predicted that 8-OHD has the potential to interact with CDK6, FLT3, and TERT proteins, thereby reducing their activity and inhibiting cell proliferation. Notably, we discovered a synergic pharmacological interaction between 8-OHD and cytarabine (Ara-C).

CONCLUSIONS

Overall, this study provides insights into the therapeutic applications of 8-OHD in treating AML and elucidates its underlying mechanisms of action.

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.

8-Hydroxydaidzein Downregulates JAK/STAT, MMP, Oxidative Phosphorylation, and PI3K/AKT Pathways in K562 Cells

A metabolite isolated from fermented soybean, 8-hydroxydaidzein (8-OHD, 7,8,4′-trihydroxyisoflavone, NSC-678112), is widely used in ethnopharmacological research due to its anti-proliferative and anti-inflammatory effects. We reported previously that 8-OHD provoked reactive oxygen species (ROS) overproduction, and induced autophagy, apoptosis, breakpoint cluster region-Abelson murine leukemia viral oncogene (BCR-ABL) degradation, and differentiation in K562 human chronic myeloid leukemia (CML) cells. However, how 8-OHD regulates metabolism, the extracellular matrix during invasion and metastasis, and survival signaling pathways in CML remains largely unexplored. High-throughput technologies have been widely used to discover the therapeutic targets and pathways of drugs. Bioinformatics analysis of 8-OHD-downregulated differentially expressed genes (DEGs) revealed that Janus kinase/signal transducer and activator of transcription (JAK/STAT), matrix metalloproteinases (MMPs), c-Myc, phosphoinositide 3-kinase (PI3K)/AKT, and oxidative phosphorylation (OXPHOS) metabolic pathways were significantly altered by 8-OHD treatment. Western blot analyses validated that 8-OHD significantly downregulated cytosolic JAK2 and the expression and phosphorylation of STAT3 dose- and time-dependently in K562 cells. Zymography and transwell assays also confirmed that K562-secreted MMP9 and invasion activities were dose-dependently inhibited by 8-OHD after 24 h of treatment. RT-qPCR analyses verified that 8-OHD repressed metastasis and OXPHOS-related genes. In combination with DisGeNET, it was found that 8-OHD’s downregulation of PI3K/AKT is crucial for controlling CML development. A STRING protein–protein interaction analysis further revealed that AKT and MYC are hub proteins for cancer progression. Western blotting revealed that AKT phosphorylation and nuclear MYC expression were significantly inhibited by 8-OHD. Collectively, this systematic investigation revealed that 8-OHD exerts anti-CML effects by downregulating JAK/STAT, PI3K/AKT, MMP, and OXPHOS pathways, and MYC expression. These results could shed new light on the development of 8-OHD for CML therapy.

8-Hydroxydaidzein, an Isoflavone from Fermented Soybean, Induces Autophagy, Apoptosis, Differentiation, and Degradation of Oncoprotein BCR-ABL in K562 Cells

8-Hydroxydaidzein (8-OHD, 7,8,4'-trihydoxyisoflavone) is a hydroxylated derivative of daidzein isolated from fermented soybean products. The aim of this study is to investigate the anti-proliferative effects and the underlying mechanisms of 8-OHD in K562 human chronic myeloid leukemia (CML) cells. We found that 8-OHD induced reactive oxygen species (ROS) overproduction and cell cycle arrest at the S phase by upregulating p21Cip1 and downregulating cyclin D2 (CCND2) and cyclin-dependent kinase 6 (CDK6) expression. 8-OHD also induced autophagy, caspase-7-dependent apoptosis, and the degradation of BCR-ABL oncoprotein. 8-OHD promoted Early Growth Response 1 (EGR1)-mediated megakaryocytic differentiation as an increased expression of marker genes, CD61 and CD42b, and the formation of multi-lobulated nuclei in enlarged K562 cells. A microarray-based transcriptome analysis revealed a total of 3174 differentially expressed genes (DEGs) after 8-OHD (100 μM) treatment for 48 h. Bioinformatics analysis of DEGs showed that hemopoiesis, cell cycle regulation, nuclear factor-κB (NF-κB), and mitogen-activated protein kinase (MAPK) and Janus kinase/signal transducers and activators of transcription (JAK-STAT)-mediated apoptosis/anti-apoptosis networks were significantly regulated by 8-OHD. Western blot analysis confirmed that 8-OHD significantly induced the activation of MAPK and NF-κB signaling pathways, both of which may be responsible, at least in part, for the stimulation of apoptosis, autophagy, and differentiation in K562 cells. This is the first report on the anti-CML effects of 8-OHD and the combination of experimental and in silico analyses could provide a better understanding for the development of 8-OHD on CML therapy.

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.

Leveraging complementary computational models for prioritizing chemicals of developmental and reproductive toxicity concern: an example of food contact materials

The evaluation of developmental and reproductive toxicity of food contact materials (FCMs) is an important task for food safety. Since traditional experiments are both time-consuming and labor-intensive, only a small number of FCMs have sufficient toxicological data for evaluating their effects on human health. While computational methods such as structural alerts and quantitative structure-activity relationships can serve as first-line tools for the identification of chemicals of high toxicity concern, models with binary outputs and unsatisfied accuracy and coverage prevent the use of computational methods for prioritizing chemicals of high concern. This study proposed a genetic algorithm-based method to develop a weight-of-evidence (WoE) model leveraging complementary methods of structural alerts, quantitative structure-activity relationships and in silico toxicogenomics models for chemical prioritization. The WoE model was applied to evaluate 623 food contact chemicals and identify 26 chemicals of high toxicity concern, where 13 chemicals have been reported to be developmental or reproductive toxic and further experiments are suggested for the remaining 13 chemicals without toxicity data related to developmental and reproductive effects. The proposed WoE model is potentially useful for prioritizing chemicals of high toxicity concern and the methodology may be applied to toxicities other than developmental and reproductive toxicity.

ChemDIS 2: an update of chemical-disease inference system

Computational inference of affected functions, pathways and diseases for chemicals could largely accelerate the evaluation of potential effects of chemical exposure on human beings. Previously, we have developed a ChemDIS system utilizing information of interacting targets for chemical-disease inference. With the target information, testable hypotheses can be generated for experimental validation. In this work, we present an update of ChemDIS 2 system featured with more updated datasets and several new functions, including (i) custom enrichment analysis function for single omics data; (ii) multi-omics analysis function for joint analysis of multi-omics data; (iii) mixture analysis function for the identification of interaction and overall effects; (iv) web application programming interface (API) for programmed access to ChemDIS 2. The updated ChemDIS 2 system capable of analyzing more than 430 000 chemicals is expected to be useful for both drug development and risk assessment of environmental chemicals.

Database URL: ChemDIS 2 is freely accessible via https://cwtung.kmu.edu.tw/chemdis

ChemDIS-Mixture: an online tool for analyzing potential interaction effects of chemical mixtures

The assessment of bioactivity and toxicity for mixtures remains a challenging work. Although several computational models have been developed to accelerate the evaluation of chemical-chemical interaction, a specific biological endpoint should be defined before applying the models that usually relies on clinical and experimental data. The development of computational methods is desirable for identifying potential biological endpoints of mixture interactions. To facilitate the identification of potential effects of mixture interactions, a novel online system named ChemDIS-Mixture is proposed to analyze the shared target proteins, and common enriched functions, pathways, and diseases affected by multiple chemicals. Venn diagram tools have been implemented for easy analysis and visualization of interaction targets and effects. Case studies have been provided to demonstrate the capability of ChemDIS-Mixture for identifying potential effects of mixture interactions in clinical studies. ChemDIS-Mixture provides useful functions for the identification of potential effects of coexposure to multiple chemicals. ChemDIS-Mixture is freely accessible at http://cwtung.kmu.edu.tw/chemdis/mixture .