Oral administration of pentachlorophenol induces interferon signaling mRNAs in C57BL/6 male mouse liver

DTox: A deep neural network-based in visio lens for large scale toxicogenomics data

With the advancement of large-scale omics technologies, particularly transcriptomics data sets on drug and treatment response repositories available in public domain, toxicogenomics has emerged as a key field in safety pharmacology and chemical risk assessment. Traditional statistics-based bioinformatics analysis poses challenges in its application across multidimensional toxicogenomic data, including administration time, dosage, and gene expression levels. Motivated by the visual inspection workflow of field experts to augment their efficiency of screening significant genes to derive meaningful insights, together with the ability of deep neural architectures to learn the image signals, we developed DTox, a deep neural network-based in visio approach. Using the Percellome toxicogenomics database, instead of utilizing the numerical gene expression values of the transcripts (gene probes of the microarray) for dose-time combinations, DTox learned the image representation of 3D surface plots of distinct time and dosage data points to train the classifier on the experts' labels of gene probe significance. DTox outperformed statistical threshold-based bioinformatics and machine learning approaches based on numerical expression values. This result shows the ability of image-driven neural networks to overcome the limitations of classical numeric value-based approaches. Further, by augmenting the model with explainability modules, our study showed the potential to reveal the visual analysis process of human experts in toxicogenomics through the model weights. While the current work demonstrates the application of the DTox model in toxicogenomic studies, it can be further generalized as an in visio approach for multi-dimensional numeric data with applications in various fields in medical data sciences.

[Percellome Project: research on molecular mechanisms of toxicological responses based on transcriptomics and epigenetics]

We are constructing the "Percellome Database" containing many transcriptomes of mice exposed to a series of chemicals to elucidate the molecular mechanism of toxicity and to develop toxicity prediction technology. Acute toxicity of a chemical can be predicted to a certain extent by searching the similarity of the transcriptomes obtained by the single-dose exposure experiments. In addition, we are analyzing the relation between the transcriptome and the epigenome i.e. histone modification and genomic DNA methylation to understand the molecular mechanism of the repeated dose toxicity. We are attempting to expand the scale and improve the efficiency of the analysis by introducing artificial intelligence technologies. This approach should maximize the use of toxicogenomics technology for optimizing the experimental protocols for repeated dose toxicity studies towards 3Rs principle, and optimizing the process of in silico toxicity prediction by combining the available big data.

Toxicity Analysis of Pentachlorophenol Data with a Bioinformatics Tool Set

Rapid progress in technologies opened the new era of computer-leaded analytics, leaving humans more space for experimental design and decision making. Here we demonstrate the machine learning analysis workflow represented by spectral clustering, elucidation of evolutionary conserved transcription regulation, and network analysis using reverse engineering. Analysis of genes induced by the Pentachlorophenol toxic chemical revealed two subnetworks, one orchestrated by Interferon and another by Nuclear receptor factor 2 (NRF2) gene. Furthermore, network-inference based analysis identified a gene network module composed of genes associated with interferon signaling and their regulatory interaction with downstream genes, especially TRIM family proteins involved in responses of innate immune systems.

A Geometric Clustering Tool (AGCT) to robustly unravel the inner cluster structures of time-series gene expressions

Systems biology aims at holistically understanding the complexity of biological systems. In particular, nowadays with the broad availability of gene expression measurements, systems biology challenges the deciphering of the genetic cell machinery from them. In order to help researchers, reverse engineer the genetic cell machinery from these noisy datasets, interactive exploratory clustering methods, pipelines and gene clustering tools have to be specifically developed. Prior methods/tools for time series data, however, do not have the following four major ingredients in analytic and methodological view point: (i) principled time-series feature extraction methods, (ii) variety of manifold learning methods for capturing high-level view of the dataset, (iii) high-end automatic structure extraction, and (iv) friendliness to the biological user community. With a view to meet the requirements, we present AGCT (A Geometric Clustering Tool), a software package used to unravel the complex architecture of large-scale, non-necessarily synchronized time-series gene expression data. AGCT capture signals on exhaustive wavelet expansions of the data, which are then embedded on a low-dimensional non-linear map using manifold learning algorithms, where geometric proximity captures potential interactions. Post-processing techniques, including hard and soft information geometric clustering algorithms, facilitate the summarizing of the complete map as a smaller number of principal factors which can then be formally identified using embedded statistical inference techniques. Three-dimension interactive visualization and scenario recording over the processing helps to reproduce data analysis results without additional time. Analysis of the whole-cell Yeast Metabolic Cycle (YMC) moreover, Yeast Cell Cycle (YCC) datasets demonstrate AGCT's ability to accurately dissect all stages of metabolism and the cell cycle progression, independently of the time course and the number of patterns related to the signal. Analysis of Pentachlorophenol iduced dataset demonstrat how AGCT dissects data to identify two networks: Interferon signaling and NRF2-signaling networks.

Tetrabromobisphenol A activates the hepatic interferon pathway in rats

Tetrabromobisphenol A (TBBPA) is a widely used flame retardant in printed circuit boards, paper, and textiles. In a two-year study, TBBPA showed evidence of uterine tumors in female Wistar-Han rats and liver and colon tumors in B6C3F1 mice. In order to gain further insight into early gene and pathway changes leading to cancer, we exposed female Wistar Han rats to TBBPA at 0, 25, 250, or 1000mg/kg (oral gavage in corn oil, 5×/week) for 13 weeks. Because at the end of the TBBPA exposure period, there were no treatment-related effects on body weights, liver or uterus lesions, and liver and uterine organ weights were within 10% of controls, only the high dose animals were analyzed. Analysis of the hepatic and uterine transcriptomes showed TBBPA-induced changes primarily in the liver (1000mg/kg), with 159 transcripts corresponding to 132 genes differentially expressed compared to controls (FDR=0.05). Pathway analysis showed activation of interferon (IFN) and metabolic networks. TBBPA induced few molecular changes in the uterus. Activation of the interferon pathway in the liver occurred after 13-weeks of TBBPA exposure, and with longer term TBBPA exposure this may lead to immunomodulatory changes that contribute to carcinogenic processes.

Tetrachlorobenzoquinone exhibits neurotoxicity by inducing inflammatory responses through ROS-mediated IKK/IκB/NF-κB signaling

Tetrachlorobenzoquinone (TCBQ) is a joint metabolite of persistent organic pollutants (POPs), hexachlorobenzene (HCB) and pentachlorophenol (PCP). Previous studies have been reported that TCBQ contributes to acute hepatic damage due to its pro-oxidative nature. In the current study, TCBQ showed the highest capacity on the cytotoxicity, ROS formation and inflammatory cytokines release among four compounds, i.e., HCB, PCP, tetrachlorohydroquinone (TCHQ, reduced form of TCBQ) and TCBQ, in PC 12 cells. Further mechanistic study illustrated TCBQ activates nuclear factor-kappa B (NF-κB) signaling. The activation of NF-κB was identified by measuring the protein expressions of inhibitor of nuclear factor kappa-B kinase (IKK) α/β, p-IKKα/β, an inhibitor of NF-κB (IκB) α, p-IκBα, NF-κB (p65) and p-p65. The translocation of NF-κB was assessed by Western blotting of p65 in nuclear/cytosolic fractions, electrophoretic mobility shift assay (EMSA) and luciferase reporter gene assay. In addition, TCBQ significantly induced protein and mRNA expressions of inflammatory cytokines and mediators, such as interleukin-1 beta (IL-1β), IL-6, tumor necrosis factor-alpha (TNF-α), inducible nitric oxide synthase (iNOS), cyclooxygenase-2 (COX-2) and the production of nitric oxide (NO) and prostaglandin E2 (PGE2). Pyrrolidine dithiocarbamate (PDTC), a specific NF-κB inhibitor inhibited these effects efficiently, further suggested TCBQ-induced inflammatory responses involve NF-κB signaling. Moreover, antioxidants, i.e., N-acetyl-l-cysteine (NAC), Vitamin E and curcumin, ameliorated TCBQ-induced ROS generation as well as the activation of NF-κB, which implied that ROS serve as the upstream molecule of NF-κB signaling. In summary, TCBQ exhibits a neurotoxic effect by inducing oxidative stress-mediated inflammatory responses via the activation of IKK/IκB/NF-κB pathway in PC12 cells.

Gene expression response to EWS-FLI1 in mouse embryonic cartilage

Ewing's sarcoma is a rare bone tumor that affects children and adolescents. We have recently succeeded to induce Ewing's sarcoma-like small round cell tumor in mice by expression of EWS-ETS fusion genes in murine embryonic osteochondrogenic progenitors. The Ewing's sarcoma precursors are enriched in embryonic superficial zone (eSZ) cells of long bone. To get insights into the mechanisms of Ewing's sarcoma development, gene expression profiles between EWS-FLI1-sensitive eSZ cells and EWS-FLI1-resistant embryonic growth plate (eGP) cells were compared using DNA microarrays. Gene expression of eSZ and eGP cells (total, 30 samples) was evaluated with or without EWS-FLI1 expression 0, 8 or 48 h after gene transduction. Our data provide useful information for gene expression responses to fusion oncogenes in human sarcoma.