Multiparameter Phenotypic Profiling in MCF‑7 ... - ACS Publications

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Environmental Measurements Methods

Multi-parameter phenotypic profiling in MCF-7 cells for assessing the toxicity and estrogenic activity of whole environmental water Wenlong Wang, Mitsuru Tada, Daisuke Nakajima, Manabu Sakai, Minoru Yoneda, and Hideko Sone Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.8b01696 • Publication Date (Web): 20 Jul 2018 Downloaded from http://pubs.acs.org on July 22, 2018

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Title:

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Multi-parameter phenotypic profiling in MCF-7 cells for assessing the toxicity and

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estrogenic activity of whole environmental water

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Author names: Wenlong Wang1,2, Mitsuru Tada3, Daisuke Nakajima1, Manabu Sakai4,

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Minoru Yoneda2, Hideko Sone1*

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Author affiliations: 1. Center for Environmental Risk Research, National Institute for

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Environmental Studies, 16-2 Onogawa, Tsukuba, Ibaraki 305-8606, Japan;

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2. Department of Environmental Engineering, Graduate School of Engineering, Kyoto

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University, Katsura, Nishikyo-ku, 615-8540, Kyoto, Japan

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3. Center for Health and Environmental Risk Research, National Institute for

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Environmental Studies, Tsukuba, Ibaraki, Japan

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4. Yokohama Environmental Research Institute, 1-2-15, Takigashira, Isogo Ward,

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Yokohama City 235-0012, Japan

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Corresponding Author’s Address: Hideko Sone, Research Center for Environmental

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Risk, National Institute for Environmental Studies, 16-2 Onogawa, Tsukuba, Ibaraki

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305-8506, Phone: +81-29-850-2464, FAX: +81-29-850-2546; e-mail: [email protected]

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ABSTRACT

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Multi-parameter phenotypic profiling of small molecules is a powerful approach to their

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toxicity assessment and identifying potential mechanisms of actions. The present study

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demonstrates the application of image-based multi-parameter phenotypic profiling in

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MCF-7 cells to assess the overall toxicity and estrogenic activity of whole

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environmental water. Phenotypic profiling of 30 reference compounds and their

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complex mixtures was evaluated to investigate the cellular morphological outcomes to

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targeted biological pathways. Overall toxicity and estrogenic activity of environmental

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water samples were then evaluated by phenotypic analysis comparing with conventional

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bioassays and chemical analysis by multivariate analysis. The phenotypic analysis for

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reference compounds demonstrated that size and structure of cells related to biological

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processes like cell growth, death, and communication. The phenotypic alteration and

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nuclei intensity were selected as potential biomarkers to evaluate overall toxicity and

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estrogenic activities, respectively. The phenotypic profiles were associated with the

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chemical structure profiles in environmental water samples.Since the phenotypic

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parameters revealed multiple toxicity endpoints, it could provide more information that

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is relevant to assessing the toxicity of environmental water samples in compare with

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conventional bioassays.

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analysis with MCF-7 cells provides a rapid and information-rich tool for toxicity

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evaluation and identification in whole water samples.

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KEYWORDS:

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identification, estrogenic activity, whole environmental water

In conclusion, the image-based multi-parameters phenotypic

multi-parameter

phenotypic

analysis,

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toxicity

evaluation

and

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TOC

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INTRODUCTION

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Environmental contaminants, covering vast categories of chemicals such as estrogens,

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pharmaceuticals, pesticides, and heavy metals, are ubiquitous in aquatic environments,

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posing high risks for ecosystems and human health.1-3 Wastewater effluent is a major

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source of the contaminants and induces adverse health effects through acting on broad

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biological pathways, including G protein-coupled receptor signaling,4 DNA damage,5

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neurotoxicities,6 and endocrinologies/hormones.7 The complex toxicity mechanisms of

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the pollutants and their uncertain impacts have increased public concern for effluent

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toxicity evaluation as a whole. To clarify, the toxicity mechanisms make it challenging

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for toxicity identification in whole wastewater samples.

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Various bioassays have been applied for overall toxicity evaluation, assaying the

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lethal effects on mammal cell lines such as HepG2 cells8 and whole organisms such as

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microbes,9 microalgae,10 Magna,11 and fish.12,

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limitations such as: (i) ambiguity between toxic effects and mechanisms of actions, (ii)

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middle or low-screening throughput capacity, and (iii) poor portability to humans,

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which limits understanding of the mechanisms of toxicity. A battery of bioassays was

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therefore performed targeting various endpoints for toxicity evaluation and

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identification but this tends to be time-consuming.14,15 Multi-parameter transcription

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profiling and protein profiling were employed for toxicity evaluation and identification

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of environmental samples, however, the significant costs limited large-scale screening.

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Therefore, biological pathway-abundant and cost-efficient approaches are clearly

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required for the evaluation and identification of toxicity in environmental samples.

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But the methods have various

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Image-based phenotypic analysis is a robust and cost-efficient approach for

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identifying small molecules through cellular morphology modifications related to

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specific mechanisms of action.16-18 Evidence has indicated correlations between

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morphological changes and mechanisms of compounds, thus making it feasible to

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predict chemical mechanisms or toxicity using phenotypic similarity.19 The construction

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of a morphological database of compounds illustrated the relevance between mechanism

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similarity and phenotypic similarity and in turn, suggested a novel method to identify

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mechanisms of action or toxicity of compounds using morphological profiles. Yet this

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technology has been restricted to drug discovery owing to the small scale of the

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chemicals database and a lack of clear biological relevance.20 Cellular receptors (e.g.,

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estrogen receptors, G protein-coupled receptors ) or growth processes (e.g., apoptosis

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and cellular proliferation) are frequently used as endpoints for toxicity assessment of

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environmental contaminants and water samples,21,

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correlations with morphological effects.19 On this premise, we hypothesized that

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environmental contaminants with different biological mechanisms would exhibit

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specific phenotypic effects and their toxicity in environmental water samples can be

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preliminarily screened and identified by multi-parameter phenotypic analysis.

22

and were reported to exhibit

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To test our hypothesis, we investigated the phenotypic responses to targeted

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biological pathways through exposure to 30 reference compounds and complex

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mixtures (e.g., pharmaceuticals, pesticides, and estrogens), and then aimed to assess the

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overall toxicity and estrogenic activity of whole water samples through the phenotypic

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analysis. The MCF-7 cell line was selected because it has been widely applied to

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evaluations of estrogenic activities such as E-screen.23 Morphological parameters were

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selected as endpoints to assess the overall water toxicity and estrogenic activity of

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whole water samples compared with conventional toxicity bioassays (MTT assay, direct

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nuclei count) and estrogenic activity evaluation methods (E-screen, ELISA,

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LC-qTOF-MS). The image-based multi-parameters phenotypic analysis with the cells

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provides a rapid and information-rich tool for toxicity evaluation and identification in

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whole water samples.

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MATERIAL AND METHODS

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Chemicals. Table 1 shows the pharmaceuticals, pesticides, estrogens, and ions

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selected for this study based on biological pathways. All the pharmaceuticals were

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purchased from the SCREEN-WELL® Cardiotoxicity library (BML-2850, ENZO Life

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Sciences, Farmingdale, USA). The other chemicals were obtained from Wako Pure

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Chemical Industries, Ltd. (Osaka, Japan). Dimethyl sulfoxide (DMSO) was used as the

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primary solvent, with solutions further diluted in cell culture media before use. The final

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concentration of DMSO in the medium did not exceed 0.1% (v/v). The compounds were

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mixed at the average ratios as following: M1, a mixture of chemicals acting on the

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neurotoxicity pathway including IMI, ACE, CAR, CPS, THD, DDT; M2, a mixture of

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chemicals acting on the endocrinology/hormones pathway including E2, BPA, TAM;

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M3, a mixture of chemicals acting on membrane transporter/ion channel pathway

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including AMI, BEP, FLE, CARB; M4, a mixture of environmental chemicals; M5, a

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mixture of pharmaceuticals; M6, a mixture of all chemicals; M7, a mixture of

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pharmaceuticals excluding DGT; M8, a mixture of all chemicals excluding DGT.

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Cell culture. Human breast cancer cells (MCF-7) were obtained from the American

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Type Culture Collection (Manassas, VA, USA). The cells were cultured in Dulbecco’s

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modified

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(Sigma-Aldrich, Japan) supplemented with 10% fetal bovine serum (FBS) (HyClone,

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USA). The cells cultures were maintained at 37°C in a 5% CO2 humidified incubator.

Eagle’s

medium

and

Nutrient Mixture

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F-12

Ham (DMEM-F12)

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Sampling preparation and treatment. Water samples (500 mL) were collected at 8

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river locations and 6 wastewater treatment plants (WWTP) within one river basin in the

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Kanto area of Japan from July 3 to August 7, 2014. The 15 water samples included

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wastewater treatment plant effluents (W1-Eff, W2-Eff, W3-Eff), wastewater treatment

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influents (W1-Inf, W2-Inf, W3-Inf), upstream river water (T2, T7), middle stream river

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water (T3, T8, T9), downstream river water (T4), and river water from an estuary (T5,

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T6) (Figure S1). For cell exposure experiments, 50 mL of each sample was filtered

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through a 0.22 µm polyethersulfone (PES) membrane (Millipore, Germany) and stored

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at −20 °C before use. The filtered water samples were mixed with 10× concentrated

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DMEM-F12 medium (1:9), and adjusted to a pH of 7.2–7.4 with a sodium bicarbonate

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solution [7.5% w/v] after adding 10% fetal bovine serum (FBS). The remaining water in

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each sample (400 mL) was extracted by Autoprep EDS-1 (SHOWA DENKO, Japan)

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and eluted with methanol. After the eluate was evaporated to dryness with a nitrogen

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stream, the residue was dissolved with 1mL of DMSO for enzyme-linked

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immunosorbent

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chromatography/quadrupole time-of-flight mass spectrometry (LC-qTOF-MS).

assays

(ELISA)

and

high-performance

liquid

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Image-based phenotypic analysis. 1) Exposure setup. MCF-7 cells (1000 cells/well

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in 200 µL DMEM-F12) were plated in 96-well plates. After 24 h, triplicate samples of

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cells were exposed to single compounds ranging from 10-12 M to 10-6 M, mixtures or

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whole water medium for 6 days in preparation for the staining. 2) Immunofluorescence.

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After a 6-day exposure, the cells were fixed with 4% paraformaldehyde (Wako, Japan)

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for 15 min, treated with 0.1% TritonX-100 (Wako, Japan) for 30 min, and incubated

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with 1% BSA-PBS for 30 minutes at room temperature. The samples were stained with

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2% Phalloidin (546 A2228; Red) (Life Technologies, USA) for 1 h and 2 µg/ml

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Hoechst (33342; Blue) (DOJINDO, Japan) for 15 minutes at room temperature. 3)

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Image Acquisition. Typical microphotographs were obtained using an Olympus LV1200

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High-Performance Laser Scanning Microscope (Olympus, Japan). For image-based

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analysis, immunofluorescence images (9 fields per well of a 96-well plate) were

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acquired automatically on an IN Cell Analyzer 1000 (GE Healthcare, UK) using a 4× or

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10× objective. A laser autofocus system analyzed at least 1000 cells in each well.

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Hoechst-positive nuclei and phalloidin-positive cell cytoskeletons were recognized

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using IN Cell Developer Software (GE Healthcare, UK). 4) Data processing. To

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characterize the phenotypic responses of the cells, 13 phenotypic parameters of the cells

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and their nuclei (intensity, perimeter, major, minor, minor/major, area, and form factor)

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were quantified with the IN Cell Developer Tool Box 1.7 (GE Healthcare, UK). The

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morphological parameters of the cells (n > 1000) were averaged and normalized to

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those of a negative control. The normalized multi-parameter data were then determined

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with a PCA, and the resulting principal components were displayed on a 2D score plot.

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The “Euclidean distance” of each treatment from the control was calculated using a

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distance formula. A detailed description of the procedure is provided in Figure S2.

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Conventional bioassays. Two methods, an MTT (3-(4, 5-dimethyl thiazolyl-2)-2,

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5-diphenyltetrazolium bromide) assay and direct cell counting, were used to assess cell

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viability. An E-screen assay based on the protocol developed by Soto (1995) was used

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to calculate the estrogen equivalent concentration (EEQ) in river water and wastewater

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during a 6-day exposure to MCF-7 cells.24 The 17ß-estradiol (E2) concentration was

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measured with an estradiol enzyme immunoassay (EIA) kits (Cayman Chemical, USA).

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Five-hundred-microliters samples of a concentrated solution equivalent to 200 mL

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water samples were used for triplicate ELISAs. The detailed bioassay protocols are

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provided in Supporting Information Text S1.

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Non-targeted LC-qTOF-MS analysis. A non-target chemical analysis was

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completed with an LC (Agilent 1200 series, USA) coupled to a Q-TOF-MS (Agilent

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6540 UHD Accurate-Mass, USA) and electrospray ionization (ESI) source with positive

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and negative modes. Chromatography was performed with a reversed-phase column

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(ZORBAX Extend-C18, 5 µm, 2.1 × 150 mm, Agilent, USA) with the injection volume,

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flow rate, and temperature set to 5 µL, 0.2 mL/min, and 40°C, respectively. The mass

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spectra were collected in full-scale mode from 50–2000 m/z. The data were assessed

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with MassHunter Qualitative Analysis software (MPP, version 12.0, Agilent, USA)

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software for the detection of molecular features followed by Agilent Mass Profiler

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Professional Software. The raw data were normalized with a quantile algorithm using

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GeneSpring v14.5 (Agilent, USA), and chemicals with a significant difference (fold

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change > 2, P < 0.05, n=500) were selected for multivariate statistical analyses,

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including hierarchical clustering and PCA.

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Statistical Analysis. The data from phenotypic and non-target analyses were used in

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a PCA to evaluate the differences in phenotypic and chemical profiles among the

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samples. An OPLS-DA was performed to identify chemical candidates that exhibited

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high correlations with the phenotypic parameters. The PCA and OPLS-DA were

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performed using SIMCA 13 software (Umetrics, Sweden). Quantitative data were

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expressed as the fold change vs. the control value ± standard deviation (SD). Statistical

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significance was determined using a one-way analysis of variance (ANOVA) followed

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by the Dunnett’s test for pairwise comparisons. Differences were considered statistically

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significant at P < 0.05.

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RESULTS AND DISCUSSION

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Phenotypic profiling of MCF-7 cells treated with single compounds. The

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dose-dependent effects on the cellular phenotype were measured for 30 compounds

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acting on diverse biological pathways (Table 1). Endocrine disrupting chemicals

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(EDCs) normally induce non-monotonic dose-responses (NMDRs) because they bind

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mainly to estrogenic receptors (ERs) at low doses but may interact with other receptors

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at high doses.25 17ß-estradiol (E2) at a low-dose range (10-14 M to 10-9 M) was used as a

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model compound for assessing the morphological relevance of estrogenic activities, by

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merit of its sensitivity to ER in MCF-7 cells.26 E2 induced a dose-independent response

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in phenotypic parameters (Figure S3B), and a correlation between cellular phenotype

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(intensity and area) and cell proliferation (Figure S4) revealed the strong potential of

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these parameters for the evaluation of an estrogenic effect. The phenotypic responses to

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numerous biological pathways were investigated by exposing the cells to 26 selected

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organic compounds at a wide range of doses from 10-12 M to 10-6 M. NMDRs were

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elicited

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di(2-ethylhexyl)phthalate (DEHP); the pharmaceuticals lisinopril (LIS), quinidine

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(QUI), propranolol (PH), bepridil (BEP), and prednisone (PSE); and the pesticides

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acetamiprid (ACE) and thalidomide (THD) (Figure S5 and S6). Exposure to the

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compounds LIS, QUI, AMI, PSE, and THD elicited phenotypic characteristics similar

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to those observed after E2 exposure (increased intensity but decreased areas of both the

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nuclei and cell). The diverse phenotypic effects of ACE (increased cell area), BPA

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(decreased nuclei intensity), and DEHP (decreased nuclei intensity and increased cell

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area) may have stemmed from interactions with other toxicity pathways. Digitoxin

by

exposure

to

the

well-known

EDCs

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bisphenol

A

(BPA)

and

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(DGT) at a high dose (10-7 M) induced abnormal cells (synaptic cells) (Figure S10A)

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exhibiting a significantly decreased cell area but larger cell major (Figure 1). The

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influence of inorganic chemicals (K, Ca, Na, Mg) and osmotic pressure (PBS) were

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considered owing to the downstream whole water sample exposure, and the

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PBS-induced remarkable larger cell shape may have resulted from change in the

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osmotic pressure (Figure 1 and Figure S10B).

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Phenotypic profiling of MCF-7 cells treated with chemical mixtures. Similar

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acting compounds were mixed (M1 to M3) at the average ratios for phenotypic analysis

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to investigate the feasibility of the analysis to characterize the toxicity of complex

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mixtures. Otherwise, cells were exposed to mixtures including or excluding the

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phenotype-active compound DGT (M5, M6 or M4, M7, M8, respectively) for toxicity

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or chemical identification in complex mixtures. The compound mixtures induced

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diverse phenotypic variations (Figure 2). M2 with estrogenic-active compounds induced

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phenotypic effects similar to those brought about by the previous E2 exposure

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(increased nuclei intensity, decreased cell area), which proved the feasibility of the

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phenotypic analysis for characterizing estrogenic effects. We were interested to find that

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mixtures with DGT (M5 and M6) also induced cellular abnormalities (echinoid spikes),

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while the cells exposed to mixtures excluding DGT (M7 and M8) did not (Figure 2A).

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These findings indicate that phenotype-active compounds or toxicities induce similar

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phenotypic effects in complex mixtures, as well. In reverse, the compounds or toxicities

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can be identified on the basis of phenotypic similarity in phenotypic analyses.

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Phenotypic effects of whole water samples on MCF-7 cells. The MCF-7 phenotype

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showed several variations after exposure to wastewater and river water (Figure 3A). The

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vehicle control cells were round and equal-side angle in shape (Figure 3A-a), whereas

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the cells exposed to wastewater or river water were malformed into shrunk and

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aggregated cells (W1-Eff and W3-Inf exposure) (Figure 3A-b, e), synaptic cells

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(W2-Eff, T1, and T3 exposure) (Figure 3A-d, f, g), and cells with enlarged nuclei and

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cell bodies (T6 exposure) (Figure 3A-h). The morphological changes of MCF-7 cells

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were classified into 3 categories: 1) shrunk or enlarged cells; 2) synaptic or polygonal

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cells, and 3) rounded cells.

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Most of the wastewater samples brought about increases in the nuclei intensity of

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MCF-7 cells but negatively affected other parameters. The morphological changes in

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the cells exposed to the different river water samples differed markedly: increased

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nuclei intensity (T1-T3) and cell area (T4), increased nuclei area (T6), and decreased

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nuclei form factor (T8) (Figure 3B). Nuclei intensity has been well applied as the

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endpoint for cell cycle analysis, and evidence indicates that the G2 phase of cell cycle

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induces increased nuclei intensity,27 which verifies the positive correlations of cell

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proliferation and nuclei intensity. The nuclei intensity variation was higher in cells

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exposed to whole water samples than in cells with single compounds exposure, which

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may result from the induced greater cell proliferation after whole water exposure

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(Figure 1 and 3B). Moreover, cellular apoptosis also induces nuclei intensity

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increasing,28 and may happen after W1-Inf exposure (Figure 3C). The cells exposed to

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T6, the sample taken from the estuary region, showed increases in both nuclei area and

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cell area, the same changes brought about by osmotic pressure effect from PBS

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exposure.

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Phenotypic alteration as a biomarker for toxicity evaluation. The U.S ToxCast

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Chemical Prioritization Program demonstrated that environmental toxicants act on

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molecular targets (e.g., kinases, cellular receptors, DNA) affecting biological processes

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such as cell cycle, apoptosis, and DNA recombination.29 Biological processes induce

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morphological variation (Table S2) and are applied in cell-based biosensors for toxicity

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evaluation in water.30 As such, multiple phenotypic parameters related to various

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biological processes can be expected to provide rich mechanistic information (e.g., cell

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cycle, apoptosis, cell death) (Figure S9) that can be retained through multivariate

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statistical analyses in the lower dimension to assessing the toxicity and mechanisms.31

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In this study, we characterized phenotypic alteration by Euclidean distance in a PCA for

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the toxicity evaluation of compounds and river waters. Speculating that EDCs binding

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to other toxicity receptors posed toxicological complexity at a high-dose range in

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NMDR,

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high-concentration compounds or water samples may yield superior results.

we

hypothesized

that

a

multi-parameter

phenotypic

analysis

for

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To characterize the phenotypic effects of compounds and whole water samples, we

295

investigated the phenotypic results by PCA (Figure 4) and calculated the Euclidean

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distance for comparison with the cell viability (Figure 5 and Figure S5-8). Compounds

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with different mechanisms of action induced diverse phenotypic effects and clustered

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together in the PCA score plot. DGT, PH, TAM, DEHP, and TCDD at high doses (10-7

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M or 10-6 M) were plotted outside the cluster, indicating remarkable phenotypic effects

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(Figure 4A). DGT, PH, and TAM at 10-6 M induced reduced cell viability along with a

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large phenotypic alteration. The cell viability was not altered after exposure to BEP,

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BUS, PSE, CAR, DDT, DEHP, and TCDD at 10-6 M, whereas the alterations of the

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cellular phenotype dramatically increased (Figure S5-6). Cell death by cell apoptosis

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and necrosis has been typically measured for cytotoxicity analysis.32 These compounds

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mainly induced enlarged cell areas and longer minor/major axes at 10-6 M, bringing

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about a large phenotypic alteration that may result from necrosis -induced cell swelling

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(Figure 1).33 Yet the changes in cell viability at high doses diverged too little from

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control to reflect the toxicity such as with exposure to BEP, BUS (Figure S5-6). Our

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results suggest that multi-parameter phenotypic analysis was more accurate in profiling

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the toxicity-based cellular outcome than cell viability, and therefore was more suitable

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for toxicity evaluation in high-dose ranges comparing with conventional toxicity assays.

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After we applied the phenotypic approach for whole water toxicity analysis, the

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phenotypic responses of WWTP influent, WWTP effluent and river water were well

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clustered and separated in the PCA score plot (Figure 4C). The Euclidean distance from

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control was calculated and compared with the results of the cell viability analysis

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(Figure 5). The phenotypic alteration was significant (W1-Inf, W3-Inf) and was reduced

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after wastewater treatment processes (Figure 5A), however, the cell viability by MTT

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assay and nuclei count analysis was inconsistent, with the former showing no effect and

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the last showing dramatically increased cell viability (W1-Inf, W3-Inf) (Figure 5B). The

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conventional cell viability analyses is difficult to distinguish cell death, cell survival and

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cell proliferation with a single bioassay.34 Our results indicated that the multiple

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phenotypic parameters exhibited relation to biological processes and provided

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information-rich endpoints for whole water toxicity analyses.

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Linking phenotypic responses and toxicity pathways for toxicity identification.

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Image-based phenotypic analyses, are highly automatic, provide abundant information,

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and are well applied to predict the potential mechanism of small molecules based on

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phenotypic profiles.35, 36 Investigations of the phenotypic effects underlying toxicity

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pathways, therefore, allow the prediction and characterization of toxicities. Among the

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EDCs, the well-known androgen receptor antagonist and estrogen receptor agonist BPA

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37

induced estrogenic actions such as decreased nuclei area and cell area, but the

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decreased nuclei intensity may result from coexisting anti-androgenic mechanisms.38

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Evidence has shown that DGT elicits anti-proliferation activities39 and that the Na+/K+

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ATPase-inhibition activities of DGT may induce both cell migration and cell adhesion40

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characterizing synaptic cell membranes (Table S2). BPS exposure led to enlarged cell

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shape, a change brought about by cell necrosis via increased osmotic pressure.41 The

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results demonstrated the viability of multi-parameter phenotypic analysis for in-depth

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toxicity characterization. The mixture effects demonstrated that the phenotypic effects

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(increased nuclei intensity and decreased area) could serve as markers for estrogenic

339

activities. This result, together with the phenotypic similarity exposed to DGT and its

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complex mixture, indicated the high-potential of phenotypic analysis for toxicity

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identification. The phenotypic effects brought about by the toxicity pathways

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demonstrated that the increased nuclei intensity and decreased area by water exposure

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(all wastewater, T1-T3) could characterize the estrogenic activities. T3 exposure

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induced synaptic cells by acting on cell communication, and the enlarged cell shape

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induced by T4 and T6 exposure may have resulted from cell necrosis. Interestingly, T6

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was sampled near an estuary (Figure S1) with high electrical conductivity (Table S3),

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resulting in a high tolerance of MCF-7 osmotic stress in phenotypic profiles.42

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Comparison of the phenotypic analysis, estrogenic activity assays, and

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non-target chemical analysis for estrogenic activity prediction. We measured the

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estrogenic activity of whole with conventional bioassays (E-screen assay and ELISA

351

assay), and analyzed the correlations among chemical profiles, estrogenic activities and

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phenotypic parameters using an OPLS-DA model (Figure 6). W1-Inf, the influent with

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the highest E2 concentration at 13.5 ng/L (equal to 5.5×10-11 M) (Figure 6A), induced

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the highest increased nuclei intensity (fold change=1.47, Figure 3B). Otherwise, the

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nuclei intensity in the phenotypic parameters exhibited well clustered with EEQ and E2

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in the plot of the scores, indicating that the nuclei intensity had relatively higher

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correlations to the estrogenic effects (Figure 6B). Although we cannot exclude the

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effects of inorganic chemicals on phenotype and cell proliferation (Figure S7) in the

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present study, the results demonstrate the potential of applying phenotypic parameters

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(nuclei intensity and area) for estrogenic activity evaluation in complex whole water

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samples.

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For contaminants in 6 water samples (W1-Inf, W1-Eff, W3-Inf, T2, T8, T9) with

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different phenotypic variation, we characterized the overall chemicals profiles using

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PCA (Figure 6C). The results showed that W1-Eff, W3-Inf, and T2 clustered whereas

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T9, T8, and W1-Inf were distinct in the PCA plot. We also were interested to find a

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similar distribution in the multivariate analysis of the cell phenotypic variation (Figure

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4C). This similarity suggests that the chemical profiles may be correlated with the

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phenotypic variation. This hypothesis and our mixture toxicity evaluation together

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suggest that the phenotypic effects may have stemmed from unique chemicals in the

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water samples. T8, for example, induced a dramatic decrease in the nuclei formfactor

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(fold change=0.66) that may have been associated with the DNA-damaging effects of

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one or more of 43 unique chemicals in this sample (Figure S11).43

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In conclusion, we report that our in vitro multiple phenotypic analysis was correlated

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with the toxicity pathways and provided rich information for integrated toxicity

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evaluation and characterization in environmental water samples. The following study

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should be performed to improve this phenotypic analysis in the future research:

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1) A more massive morphological database to investigate phenotypic effects of toxicity pathways.

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2) Mechanism research to understand the correlations between phenotypic effects

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and functional genes.

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We believe that this method is a promising approach for environmental toxicity

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evaluation and identification, as well as for the study of toxicity-phenotype-chemical

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interactions and the constructing of a cell morphology database for environmental

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contaminants.

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ASSOCIATED CONTENT

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Supporting Information

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This material is available free of charge via the Internet at http://pubs.acs.org.

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AUTHOR INFORMATION

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Corresponding Author

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*Phone/fax: +81-029-850-2464;

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E-mail: [email protected]; [email protected]

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Notes

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The authors declare no competing financial interest.

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ACKNOWLEDGMENTS

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This study was supported by a Grant-in-Aid for Scientific Research (A) 15H01749 to

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HS. We thank Ms. Miyuki Yoneyama for performing the analyses of LC-qTOFMS and

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Ms. Hiroko Nansai and Qin Zeng for carrying out the experimental operation

399

procedures of cell cultures.

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Figure 1. Phenotypic effects of single compounds on MCF-7 cells. 13 multiple phenotypic

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parameters were normalized to the control for cells treated with thirty compounds at varying

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concentration. The fold change values are shown by numbers on the bar and the color scale

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shows increase (red) and decrease (blue), per dose from 10-12 M to 10-6 M within each grid.

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Figure 2. Phenotypic effects of chemical mixtures on MCF-7 cells. Confocal images of

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MCF-7 cells exposed to chemical mixture (A) and a phenotypic analysis using 13 parameters

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(B). 13 multiple phenotypic parameters were normalized by control and the fold change values

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are shown by numbers on the bar and the color scale shows increase (red) and decrease (blue),

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per dose from 10-12 M to 10-6 M within each grid

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Figure 3. Phenotypic analysis of MCF-7 cells after exposure to whole water

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samples. Confocal images of MCF-7 cells exposed to wastewater and river water (A)

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and phenotypic analysis using 13 parameters (B). The arrows indicate (1) control cells,

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(2) condensed cells, (3) scattering cells, (4) echinoid spike cells, and (5) enlarged nuclei.

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Figure 4. Principal components analysis (PCA) plot of phenotypic variation in cells

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exposed to single compounds (A), chemical mixtures (B), and whole water(C).

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W1-Inf, W1-Eff, W3-Inf, T2, T8, and T9 (underlined) were selected for non-target

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chemical analysis.

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Figure 5. Comparison of the phenotypic analysis (A) and conventional cell viability

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analyses (B) for whole water toxicity evaluation. * P