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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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Environmental Science & Technology

1

Title:

2

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

3

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]

23 24

ACS Paragon Plus Environment


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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

34

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.

43

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

44

evaluation and identification in whole water samples.

45

KEYWORDS:

46

identification, estrogenic activity, whole environmental water

In conclusion, the image-based multi-parameters phenotypic

multi-parameter

phenotypic

analysis,

47 48


toxicity

evaluation

and

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TOC

50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67



68

INTRODUCTION

69

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

70

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

71

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

72

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

74

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

75

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

76

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

77

for toxicity identification in whole wastewater samples.

78

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

79

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

80

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

81

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

82

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

83

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

84

therefore performed targeting various endpoints for toxicity evaluation and

85

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

86

profiling and protein profiling were employed for toxicity evaluation and identification

87

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

89

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

13

But the methods have various

90

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

91

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

94

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

95

of a morphological database of compounds illustrated the relevance between mechanism

96

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

97

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

98

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

99

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

105

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

107

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

108

biological pathways through exposure to 30 reference compounds and complex

109

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

110

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

112

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

115

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.

119 120

MATERIAL AND METHODS

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

122

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

123

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

124

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

125

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

126

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

127

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

128

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;

131

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

132

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

133

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.

135

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

136

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

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modified

138

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

139

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

Eagle’s

medium

and

Nutrient Mixture


F-12

Ham (DMEM-F12)

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

141

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

142

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

143

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

144

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

145

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

146

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

147

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

148

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

149

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

150

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

151

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

152

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

153

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

154

immunosorbent

155

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

assays

(ELISA)

and

high-performance

liquid

156

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

157

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

158

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

159

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

160

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

161

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

162

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

163

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



164

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

165

Image Acquisition. Typical microphotographs were obtained using an Olympus LV1200

166

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

167

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

168

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

169

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

170

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

171

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

172

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

173

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

174

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

175

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

176

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

177

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

178

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

179

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

180

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

181

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

182

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

183

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

184

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

185

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

186

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

188

provided in Supporting Information Text S1.

189

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

190

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

191

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

192

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

193

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

194

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

195

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

196

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

197

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

198

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

199

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

200

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

201

including hierarchical clustering and PCA.

202

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

203

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

204

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

205

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

206

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

207

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

208

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

209

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

210

significant at P < 0.05.



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RESULTS AND DISCUSSION

213

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

214

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

215

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

216

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

217

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

218

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

219

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

220

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

221

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

222

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

223

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

224

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

225

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

226

elicited

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

228

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

229

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

230

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

231

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

232

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

233

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

234

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

by

exposure

to

the

well-known

EDCs


bisphenol

A

(BPA)

and

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

236

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

237

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

238

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

239

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

240

osmotic pressure (Figure 1 and Figure S10B).

241

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

242

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

243

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

244

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

245

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

246

or chemical identification in complex mixtures. The compound mixtures induced

247

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

248

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

249

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

250

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

251

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

252

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

253

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

254

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

255

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

256

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

257

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

258

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



259

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

260

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

261

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

262

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

263

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

264

cells, and 3) rounded cells.

265

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

266

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

267

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

268

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

269

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

270

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

271

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

273

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

274

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

275

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

276

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

277

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

278

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

279

exposure.

280

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

281

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

284

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

285

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

286

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

287

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

288

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

289

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

290

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

291

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

292

NMDR,

293

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

we

hypothesized

that

a

multi-parameter

phenotypic

analysis

for

294

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

296

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

297

with different mechanisms of action induced diverse phenotypic effects and clustered

298

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

299

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

300

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

301

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

302

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

303

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

304

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

305

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

306

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



307

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

308

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

309

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

310

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

311

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

312

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

313

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

314

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

315

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

316

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

317

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

318

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

319

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

320

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

321

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

322

phenotypic parameters exhibited relation to biological processes and provided

323

information-rich endpoints for whole water toxicity analyses.

324

Linking phenotypic responses and toxicity pathways for toxicity identification.

325

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

326

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

327

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

328

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

329

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

330

37

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


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331

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

332

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

333

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

334

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

335

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

336

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

337

toxicity characterization. The mixture effects demonstrated that the phenotypic effects

338

(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

340

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

341

identification. The phenotypic effects brought about by the toxicity pathways

342

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

343

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

344

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

345

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

346

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

347

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

348

Comparison of the phenotypic analysis, estrogenic activity assays, and

349

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

350

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

351

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

352

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

353

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

354

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



355

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

356

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

357

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

358

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

359

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

360

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

361

samples.

362

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

363

different phenotypic variation, we characterized the overall chemicals profiles using

364

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

365

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

366

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

367

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

368

phenotypic variation. This hypothesis and our mixture toxicity evaluation together

369

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

370

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

371

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

372

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

373

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

374

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

376

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

377 378

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

382

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

383

interactions and the constructing of a cell morphology database for environmental

384

contaminants.

385 386

ASSOCIATED CONTENT

387

Supporting Information

388

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

389

AUTHOR INFORMATION

390

Corresponding Author

391

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

392

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

393

Notes

394

The authors declare no competing financial interest.

395

ACKNOWLEDGMENTS

396

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

397

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

398

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

399

procedures of cell cultures.

400 401 402 403

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

539

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

540

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

541

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

542 543 544 545 546 547 548 549



550 551

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

552

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

553

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

554

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

555

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

563

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

564

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

565

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

566 567 568 569 570



571 572

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

573

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

574

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

575

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

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