Identify organic contaminants of high-concern based on non-targeted toxicity testing and non-targeted LC-HRMS analysis in tap water and source water along the Yangtze River.

Many organic pollutants were detected in tap water (TW) and source water (SW) along the Yangtze River. However, the potential toxic effects and the high-concern organics (HCOs) which drive the effect are still unknown. Here, a non-targeted toxicity testing method based on the concentration-dependent transcriptome and non-targeted LC-HRMS analysis combining tiered filtering were used to reveal the overall biological effects and chemical information. Subsequently, we developed a qualitative pathway-structure relationship (QPSR) model to effectively match the biological and chemical information and successfully identified HCOs in TW and SW along the Yangtze River by potential substructures of HCOs. Non-targeted toxicity testing found that the biological potency of both TW and SW was stronger in the downstream of the Yangtze River, and disruption of the endocrine system and cancer were the main drivers of the effect. In addition, non-targeted LC-HRMS analysis combined with retention time prediction results identified 3220 and 631 high-confidence compound structures in positive and negative ion modes, respectively. Then, QPSR model was further implied and identified a total of 103 HCOs, containing 35 industrial chemicals, 30 PPCPs, 26 pesticides, and 12 hormones in TW and SW, respectively. Among them, the neuroactive and hormonal compounds oxoamide, 8-iso-16-cyclohexyl-tetranor prostaglandin E2, E Keppra, and Tocris-0788 showed the highest frequency of detection, which were identified in more than 1/3 of the samples. The strategy of combining non-targeted toxicity testing and non-targeted LC-HRMS analysis will support comprehensive biological effect assessment, identification of HCOs, and risk control of mixtures.

Construction of diagnosis system and gene regulatory networks based on microarray analysis.

A microarray analysis generally contains expression data of thousands of genes, but most of them are irrelevant to the disease of interest, making analyzing the genes concerning specific diseases complicated. Therefore, filtering out a few essential genes as well as their regulatory networks is critical, and a disease can be easily diagnosed just depending on the expression profiles of a few critical genes. In this study, a target gene screening (TGS) system, which is a microarray-based information system that integrates F-statistics, pattern recognition matching, a two-layer K-means classifier, a Parameter Detection Genetic Algorithm (PDGA), a genetic-based gene selector (GBG selector) and the association rule, was developed to screen out a small subset of genes that can discriminate malignant stages of cancers. During the first stage, F-statistic, pattern recognition matching, and a two-layer K-means classifier were applied in the system to filter out the 20 critical genes most relevant to ovarian cancer from 9600 genes, and the PDGA was used to decide the fittest values of the parameters for these critical genes. Among the 20 critical genes, 15 are associated with cancer progression. In the second stage, we further employed a GBG selector and the association rule to screen out seven target gene sets, each with only four to six genes, and each of which can precisely identify the malignancy stage of ovarian cancer based on their expression profiles. We further deduced the gene regulatory networks of the 20 critical genes by applying the Pearson correlation coefficient to evaluate the correlationship between the expression of each gene at the same stages and at different stages. Correlationships between gene pairs were calculated, and then, three regulatory networks were deduced. Their correlationships were further confirmed by the Ingenuity pathway analysis. The prognostic significances of the genes identified via regulatory networks were examined using online tools, and most represented biomarker candidates. In summary, our proposed system provides a new strategy to identify critical genes or biomarkers, as well as their regulatory networks, from microarray data.