Identification of Key Toxic Substances Considering Metabolic Activation: A Combination of Transcriptome and Nontarget Analysis.

There have been numerous studies using effect-directed analysis (EDA) to identify key toxic substances present in source and drinking water, but none of these studies have considered the effects of metabolic activation. This study developed a comprehensive method including a pretreatment process based on an in vitro metabolic activation system, a comprehensive biological effect evaluation based on concentration-dependent transcriptome (CDT), and a chemical feature identification based on nontarget chemical analysis (NTA), to evaluate the changes in the toxic effects and differences in the chemical composition after metabolism. Models for matching metabolites and precursors as well as data-driven identification methods were further constructed to identify toxic metabolites and key toxic precursor substances in drinking water samples from the Yangtze River. After metabolism, the metabolic samples showed a general trend of reduced toxicity in terms of overall biological potency (mean: 3.2-fold). However, metabolic activation led to an increase in some types of toxic effects, including pathways such as excision repair, mismatch repair, protein processing in endoplasmic reticulum, nucleotide excision repair, and DNA replication. Meanwhile, metabolic samples showed a decrease (17.8%) in the number of peaks and average peak area after metabolism, while overall polarity, hydrophilicity, and average molecular weight increased slightly (10.3%). Based on the models for matching of metabolites and precursors and the data-driven identification methods, 32 chemicals were efficiently identified as key toxic substances as main contributors to explain the different transcriptome biological effects such as cellular component, development, and DNA damage related, including 15 industrial compounds, 7 PPCPs, 6 pesticides, and 4 natural products. This study avoids the process of structure elucidation of toxic metabolites and can trace them directly to the precursors based on MS spectra, providing a new idea for the identification of key toxic pollutants of metabolites.

Methodology for Effect-Based Identification of Bioconcentratable Endocrine Disrupting Chemicals (EDCs) in Water: Establishment, Validation, and Application.

Since the wide occurrence of endocrine disrupting chemicals (EDCs) in water is associated with various adverse effects in aquatic organisms, it is urgent to identify key bioconcentratable EDCs. Currently, bioconcentration is generally ignored during the identification of key EDCs. Thus, a methodology for effect-based identification of bioconcentratable EDCs was established in Microcosm, validated in the field, and applied to typical surface water in Taihu Lake. In Microcosm, an inverted U-shaped relationship between logBCFs and logKows was observed for typical EDCs, with medium hydrophobic EDCs (3 ≤ logKow ≤ 7) exhibiting the greatest bioconcentration potentials. On this basis, enrichment methods for bioconcentratable EDCs were established using POM and LDPE, which better fitted the bioconcentration characteristics and enabled the enrichment of 71 ± 8% and 69 ± 6% bioconcentratable compounds. The enrichment methods were validated in the field, where LDPE exhibited a more significant correlation with the bioconcentration characteristics than POM, with mean correlation coefficients of 0.36 and 0.15, respectively, which was selected for further application. By application of the new methodology in Taihu Lake, 7 EDCs were prioritized from 79 identified EDCs as key bioconcentratable EDCs on consideration of their great abundance, bioconcentration potentials, and anti-androgenic potencies. The established methodology could support the evaluation and identification of bioconcentratable contaminants.

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.

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.

Deep mining of reported emerging contaminants in China's surface water in the past decade: Exposure, ecological effects and risk assessment.

The identification and management of high-risk contaminants have raised great concern from governments. Facing the growing amount of data on the occurrence of emerging contaminants (ECs) in surface water, a deep mining and quality control strategy was developed to integrate data on all reported ECs in Chinese surface water over the past decade, and an exposure and effect database was further built. In addition, multilevel risk characterization was carried out to prioritize high-risk areas, contaminants and endpoints. A total of 1038 ECs, mainly pharmaceutical and personal care products (PPCPs) and industrial chemicals, were curated, with concentrations ranging from 0.02 pg/L to 533 µg/L. For individual risk, all the provinces had acceptable risks except for Henan, which was characterized with a medium chronic risk. Nine ECs, including 4-nonylphenol and estrone, dominated individual risks. Conversely, for multisubstance risk, 76.20% and 73.87% of aquatic organisms were affected acutely and chronically at the national level, with acute and chronic risks exceeding the safety threshold of 5% in 11 and 19 provinces, respectively. Nineteen ECs, including sitosterol and chyfluthrin, dominated the multisubstance risk. In addition, 9 MoAs mainly inducing electron transfer inhibition, neurotoxicity and narcosis toxicity are high-risk endpoints. The study revealed the ecological risk status and key risk entities of Chinese surface waters, which provided the latest data to support the control of ECs in China.

Effects of Cane Use and Position on Performance of the Sit-to-Stand Task in Stroke Patients.

OBJECTIVE: The aim of the study was to examine the effect of cane use and cane positions on the sit-to-stand performance of stroke patients. DESIGN: In a crossover study, 30 stroke patients performed sit-to-stand test in seven situations in a random sequence: without a cane, three positions with a regular cane (parallel to the ankle, parallel to the 5th toe, and 10 cm in front of the 5th toe), and the same three positions with a quad cane. The peak vertical ground reaction force and maximum vertical cane support force during sit-to-stand were recorded. RESULTS: Using a cane significantly reduced the peak ground reaction force by 3% to 9% of body weight compared with that without a cane (P = 0.000-0.023). Different cane positions strongly influenced the maximum cane support force and peak ground reaction force. When the cane was closer to the ankle, the maximal cane support force increased by 6.7% to 8.6% of body weight, which resulted in a decrease in the peak ground reaction force. CONCLUSIONS: Both types of cane reduced lower limbs' load during sit to stand. When the cane was closer to the trunk, the load on legs was lessened, whereas the load on the upper limb increased. Upper limb's load during sit to stand was greater when using quad cane than using regular cane.