CORE

Metadata, citation and similar papers at core.ac.uk

Provided by Ghent University Academic Bibliography

Subscriber access provided by Ghent University Library

Ecotoxicology and Human Environmental Health

Deciphering the Combined Effects of Environmental
Stressors on Gene Transcription: a Conceptual Approach
You Song, Jana Asselman, Karel AC De Schamphelaere, Brit Salbu, and Knut Erik Tollefsen
Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.8b00749 • Publication Date (Web): 11 Apr 2018
Downloaded from http://pubs.acs.org on April 17, 2018

Just Accepted“Just Accepted” manuscripts have been peer-reviewed and accepted for publication. They are posted
online prior to technical editing, formatting for publication and author proofing. The American Chemical
Society provides “Just Accepted” as a service to the research community to expedite the disseminationof scientific material as soon as possible after acceptance. “Just Accepted” manuscripts appear infull in PDF format accompanied by an HTML abstract. “Just Accepted” manuscripts have been fully
peer reviewed, but should not be considered the official version of record. They are citable by the
Digital Object Identifier (DOI®). “Just Accepted” is an optional service offered to authors. Therefore,
the “Just Accepted” Web site may not include all articles that will be published in the journal. Aftera manuscript is technically edited and formatted, it will be removed from the “Just Accepted” Website and published as an ASAP article. Note that technical editing may introduce minor changes
to the manuscript text and/or graphics which could affect content, and all legal disclaimers andethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errors orconsequences arising from the use of information contained in these “Just Accepted” manuscripts.

is published by the American Chemical Society. 1155 Sixteenth Street N.W.,
Washington, DC 20036
Published by American Chemical Society. Copyright © American Chemical Society.
However, no copyright claim is made to original U.S. Government works, or works
produced by employees of any Commonwealth realm Crown government in the courseof their duties.

Page 1 of 23

Environmental Science & Technology

1

Deciphering the Combined Effects of Environmental Stressors on

2

Gene Transcription: a Conceptual Approach

3
4

You Song*,†,‡, Jana Asselman§, Karel A. C. De Schamphelaere§, Brit Salbu‡, Knut Erik Tollefsen†,‡

5
6

†

7

Gaustadalléen 21, N-0349 Oslo, Norway

8

‡

9

P.O. Box 5003, N-1432 Ås, Norway

Norwegian Institute for Water Research (NIVA), Section of Ecotoxicology and Risk Assessment,

Norwegian University of Life Sciences (NMBU), Centre for Environmental Radioactivity (CERAD).

10

§

11

Aquatic Ecology (GhEnToxLab). Campus Coupure Building F – 2nd Floor, Coupure Links 653, B9000

12

Ghent, Belgium.

Ghent University, Faculty of Bioscience Engineering, Laboratory of Environmental Toxicology and

13
14
15

TOC

16
17
1
ACS Paragon Plus Environment

Environmental Science & Technology

Page 2 of 23

18

 ABSTRACT19

Use of classical mixture toxicity models to predict the combined effects of environmental stressors based on

20

toxicogenomics (OMICS) data is still in its infancy. Although several studies have made attempts to implement

21

mixture modeling in OMICS analysis to understand the low-dose interactions of stressors, it is not clear how

22interactions occur at the molecular level and how results generated from such approaches can be better used to23

inform future studies and cumulative hazard assessment of multiple stressors. The present work was therefore

24

conducted to propose a conceptual approach for combined effect assessment using global gene expression data, as

25

illustrated by a case study on assessment of combined effects of gamma radiation and depleted uranium (DU) on

26

Atlantic salmon (Salmo salar). Implementation of the independent action (IA) model in re-analysis of a previously

27

published microarray gene expression data was performed to describe gene expression patterns of combined effects

28

and identify key gene sets and pathways that were relevant for understanding the interactive effects of these stressors.

29

By using this approach, 3120 differentially expressed genes (DEGs) were caused by additive effects, whereas 279

30(273 synergistic, 6 antagonistic) were found to deviate from additivity. Functional analysis further revealed that31

multiple toxicity pathways, such as oxidative stress responses, cell cycle regulation, lipid metabolism and immune

32

responses were enriched by DEGs showing synergistic gene expression. A key toxicity pathway of excessive

33

reactive oxygen species (ROS) formation leading to enhanced tumorigenesis signaling is highlighted and discussed

34

in detail as an example of how to take advance of the approach. Furthermore, a conceptual workflow describing the35

integration of combined effect modeling, OMICS analysis and bioinformatics is proposed. The present study

36

presents a conceptual framework for utilizing OMICS data in combined effect assessment and may provide novel

37

strategies for dealing with data analysis and interpretation of molecular responses of multiple stressors.

38
39

Key Words: Multiple stressor, Mixture modeling, Gene expression, Independent action, Synergy

40
41

2
ACS Paragon Plus Environment

Page 3 of 23

Environmental Science & Technology

42

 INTRODUCTION43

A multitude of environmental stressors (multiple stressors) may co-exist in the environment, thus creating complex

44

exposure scenarios and potentially causing cumulative hazard and risk to organisms. Studies on multiple stressors

45

have been increasing rapidly in the past decades (reviewed in ref1-3). Development of prediction models for46

combined (joint) toxicity has facilitated the assessment of multiple stressor effects, especially for mixtures of47chemical contaminants.4, 5 Prediction models such as concentration addition (CA), which often assumes two or more48

stressors having similar mode of action (MoA) and affecting common biological targets,6, 7 or independent action

49(IA), which assumes dissimilar MoA of stressors, and multiplicative responses at the target sites,8 have been

50

successfully implemented in the hazard assessment of chemical mixtures utilizing both in vitro and in vivo

51

experimental approaches.9-11 The CA model often requires extensive data support derived from dose/concentration-

52

response relationships, whereas the IA model can be applied based on effects observed from each single stressor

53

without full knowledge on the dose/concentration-response relationships.12 Therefore, the IA model is usually54

suitable for predicting the combined effects of stressors with distinct toxicological properties.

55

In the past decades, ecotoxicological research on multiple stressors and cumulative risk has shifted the focus more

56

towards effects occurring at environmentally realistic low-exposure levels and long-term ecosystem impacts.13 In

57

concordance with this, inclusion of sensitive toxicological endpoints at lower levels of biological organization (e.g.

58molecular/cellular level) in routine toxicity testing and better mechanistic understanding are becoming increasingly

59

important. Use of toxicogenomics (OMICS) approaches (e.g. transcriptomics, proteomics, metabolomics and60

epigenomics) in combination with advanced biostatistics/bioinformatics for identifying key molecular/cellular

61

events and toxicity pathways fits this purpose well. Among all OMICS approaches, transcriptomics is the most62

frequently used in various multiple stressor studies and has proven to be a powerful tool for MoA characterization

63

and toxicity pathway identification (e.g. ref14, 15). Altenburger and co-workers12 critically reviewed the use of

64

OMICS in 41 mixture toxicity studies in the period of 2002 to 2011 and reported that half of the studies employed65

transcriptomics for elucidating the combined toxicity at the molecular level. However, they12 also pointed out that66

most of the studies only used qualitative assessment (i.e. comparison between single stressors and the mixture based67

on the presence or absence of a gene or pathway in order to demonstrate the differences in toxic mechanisms),

68

whereas only a small portion of the studies attempted to apply quantitative mixture modeling (i.e. comparison based69

on a combined effect prediction model) to the OMICS data (e.g. ref16-19). It has become increasingly evident that3
ACS Paragon Plus Environment

Environmental Science & Technology

Page 4 of 23

70

lack of quantitative assessment in such mixture studies are predominantly due to the high number of single data71

generated, the complexity of the response patterns observed and the lack of ability to interpret the responses at the72

functional level. First, the OMICS technologies typically generate thousands of data points, where the sheer

73

handling of statistical treatment and correction for potential errors (e.g. type I and II errors)20 may introduce bias in74

identifying the relevance of single responses. Second, difficulties in determining the maximal level of a molecular75

response, bi-directional regulation (e.g. up- or down-regulation), and presence of non-monotonic concentration76(dose)-response relationships may challenge the generation of comparable thresholds across different molecular77

responses. Third, the integration and interpretation of multiple responses into functional understanding with78

relevance to a given biological, biochemical or toxicity pathway may not be straight forward to identify and is79

furthermore complicated by temporal changes often occurring dramatically at the molecular level. Although several

80

attempts have been made in recent years to address these issues, such as critically evaluating different biostatistical81

approaches21, developing high-throughput concentration-response analysis of OMICS data21, using various

82

functional and pathway analyses22 and performing analyses using the IA model for predicting transcriptional

83changes after binary exposure to stressors,18, 23 a clear strategy to maximize the output from such types of studies to84

inform hazard assessment of multiple stressors is still lacking.

85

The present work was therefore conducted as a case study to illustrate a conceptual approach for integrating86

mixture modeling, transcriptomics and bioinformatics in combined effect assessment of multiple stressors. This

87

study re-analyzed the transcriptomic data generated from a previously published study on combined effects of88

gamma radiation and depleted uranium (DU) in Atlantic salmon (Salmo salar).14 The two stressors studied herein89

may co-occur in the environment naturally or after anthropogenic activities such as uranium mining and nuclear

90

accidents (e.g. nuclear power plant accident in Chernobyl),24 thus representing a realistic exposure scenario for91

combined effects of radionuclides such as uranium (e.g. metal properties and alpha radiation) and external ionizing

92

radiation. Gamma radiation and uranium (i.e. DU in this case) are known to induce reactive oxygen species (ROS)93

and cause oxidative damage to macromolecules as a common MoA.14, 25-29 However, these stressors have distinct

94

properties and display differences in their response at the molecular scale. Previous studies also suggest that gamma

95

radiation and DU may have multiple MoAs and affect the same endpoint in salmon through dissimilar toxicity

96

mechanisms.14, 27-29 In addition, transcriptomic analysis is a relatively untargeted analysis which investigates global

97

gene expression responses without presumption of the MoAs of a stressor. Therefore, the IA model is considered

4
ACS Paragon Plus Environment

Page 5 of 23

Environmental Science & Technology

98

more appropriate in this case. The objectives of the current study were to: 1) characterize different types of99

transcriptional responses as consequences of additive, synergistic and antagonistic responses of the stressors using

100

the IA prediction model; 2) identify key toxicity pathways associated with differentially expressed genes (DEGs)

101

displaying synergistic effects; 3) propose a conceptual workflow for quantitative mixture modeling with the102transcriptomic data.

103
104
105

 MATERIALS AND METHODS106

Design and Data Acquisition. The detailed exposure experiment has been published elsewhere.14 A simple

107“a+b” design (i.e. same concentration/dose of single stressors as used in the mixture) was used in the binary108

exposure. Briefly, juvenile (parr) Atlantic salmon were exposed to 14 mGy/h gamma radiation from a cobalt-60

109source (FIGARO, NMBU, Ås, Norway) for the first 5h (total dose: 70 mGy) of a 48h period (referred to as Gamma),

1100.25 mg/L waterborne DU (uptake: 5.5 µg U/kg in liver) for a continuous period of 48h (referred to as DU) and the111

combination of these (referred to as Combined). Single-color microarray gene expression analysis was performed

112

using total RNA isolated from dissected fish liver (n=4), as previously described.14 The microarray data was

113

deposited in Gene Expression Omnibus (GEO, accession number: GSE74012) and re-analyzed in the present study.

114

Combined Effect Modeling. The raw microarray data was downloaded from GEO and corrected for115

background signal, flagged for low quality and missing features and log2 transformed for normalization (quantiles)

116

using GeneSpring GX v11.0 (Agilent Technologies) prior to combined effect modeling.

117

Differentially expressed genes were determined using the linear models implemented in the LIMMA package

118(Bioconductor, R statistical environment),30 with modifications.31 Contrasts were defined over the linear model in119

the statistical test to identify transcriptional responses as a consequence of single and/or combined exposure to the120

stressors by two-way analysis of variance (two-way ANOVA), as previously described.18, 23 The two-way ANOVA

121

examines the effect of each independent variable (Gamma and DU) and the interaction between them, on basis of122

variance between treatment replicates. No multiple testing correction was applied to avoid loss of biologically123

relevant genes for the functional analyses.

5
ACS Paragon Plus Environment

Environmental Science & Technology

Page 6 of 23

124

To assess the combined effects of Gamma and DU, the IA model8, 32 was adapted to the gene expression data to125

determine whether the observed transcriptional responses were in agreement or deviated from the assumption of126

additivity, as previously described:18, 23

127 () =  ( ) ×   ()  ()

(1)

128
Where  (

129
130

) is the predicted absolute gene expression in Combined (i.e. Gamma + DU) under theassumption of no interaction,   () is the measured absolute gene expression after exposure to Gamma alone,131  () is the measured absolute gene expression after exposure to DU alone. Gene expression is defined as an M-

132

value, in which a treatment is expressed relative to a control treatment, referring to up- or down-regulation.

133

Therefore, equation (1) can be transformed to (2), in which all observations are normalized relative to the control

134

treatment, (i.e.,   () , the measured absolute gene expression in the control). Equation (1) can be transformed

135

to:

136
2 

 (  ( )  ()  ( )  ()) = Log2 × = Log2  + Log2  (2)  ()  ()  ()  ()  ()

137
138
139

M-value is defined as the log2 value of the absolute gene expression in each treatment relative to the control.
Therefore, each component in equation can be rewritten as follows:140# (

) =

#

 (

#

 () =) =2 

2 

2 

 ()  ()  ( )  ()  ()  ()

141
142

Equation (2) can then be written as:

6
ACS Paragon Plus Environment

Page 7 of 23

Environmental Science & Technology# (

) =

#

 (

) + #(3) ()

143
144

Therefore, if # () = # (

) =

#

 (

) + #

 () ,the combined effect on gene145

transcription is considered additive. Then the transcriptional interactive effect (#%  ) that deviates from additivity

146

can be defined as:147#%  = # () −#

 () = #

 () + #

 () −#

 (

(4)

)

148
149

Based on equation (4), genes regulated as consequence of interaction (referred to as Interact) were defined as

150

genes whose M-values of interaction (#%  ) were significantly different from zero (p-value<0.05) and when no151

overlap of the 95% confidence intervals of the predicted M-value ( # (

152

(#

153

considered the consequence of interactions between the stressors. Venn diagram analysis was performed using

154

Venny (http://bioinfogp.cnb.csic.es/tools/venny/) to classify gene sets with different response patterns.

 (

) )

and observed M-value) ). The expression of genes displaying synergistic (#%  >0) or antagonistic (#%  <0) patterns were

155

Functional Enrichment Analysis. To understand the toxicological functions of the gene sets, gene ontology

156

enrichment (GO, hypergeometric test, p<0.05) and pathway enrichment (Fisher’s Exact test, p<0.05) analyses were157

performed using Bingo v2.433 in Cytoscape v334 and Ingenuity® Pathway Analysis (IPA®, QIAGEN Redwood City,

158

www.qiagen.com/ingenuity), respectively. No multiple testing correction was applied to avoid loss of biologically159

relevant functions. As IPA is predominantly based on mammalian centric gene and pathway knowledge, ortholog

160

genes between Atlantic salmon and mammalian species were used for pathway analysis. Orthologs were identified

161

using a two-pass BLAST approach in Inparanoid 4.1,35 as previously described.14

162
163
164

 RESULTS AND DISCUSSION

165

Response Classification. A total of 3460 (1484 up- and 1976 down-regulated) genes were identified as DEGs

166

in Atlantic salmon after combined exposure, of which 3124 were initially predicted as additive, 323 as synergistic167

and 13 as antagonistic by the IA model (SI, Table S1). To get more insight into different types of joint actions,

7
ACS Paragon Plus Environment

Environmental Science & Technology

Page 8 of 23

168

DEGs were categorized into two major groups on basis of the direction of transcriptional regulation compared to the169

control (i.e. up- or down-regulation). Genes that were monotonically up-regulated or down-regulated in all groups

170(i.e. Gamma, DU and Combined) were considered one-directional, whereas DEGs that were non-monotonically171

regulated (e.g. up-regulated by Gamma, down-regulated by DU, and up-regulated by Combined, etc.) were172

considered bi-directional. The one-directional group (Type 1) had a total of 2934 DEGs, of which 2847 were173

predicted to be consequences of additive, 82 as synergistic and 5 as antagonistic effects of the stressors (Table 1).

174

The Type 1 joint actions are similar to that observed in combined effect assessment using conventional toxicological175

endpoints, such as survival, reproduction and growth. The bi-directional group (Type 2) had a total of 526 DEGs, of

176

which 273 were predicted as consequences of additive, 191 as synergistic, 1 as antagonistic effects of the stressors

177(Table 1). It is also interesting to note that in the bi-directional group, the responses of 61 DEGs contradict the basic

178

assumption of the IA prediction model (e.g. up-regulated in Gamma and DU but down-regulated in Combined, or179

vice versa) (SI, Table S1). The contradicting responses have also been frequently observed in multiple stressor

180

studies based on individual (e.g. mortality and reproduction) and ecological endpoints.36 It is not clear how this “two181

negatives make a positive” type of response (or vice versa) occurred. However, several known factors may

182

potentially affect the model predictions as well as combined effect classification, such as appropriate mixture design

183(e.g. a+b, n×n, ray or surface design), types of OMICS technology employed (e.g. qPCR, microarray or RNA

184

sequencing), statistical analysis (e.g. t-test, LIMMA, ANOVA, with or without multiple testing correction) and185

mechanistic understanding (e.g. gene functions and regulatory networks). In this case, the fourth type of joint action

186(i.e. contradicted) observed may likely be due to activation of feedback loops to upstream regulators upon exceeding187

certain gene transcription thresholds,37 which ultimately cause modulation of downstream transcriptional regulation

188(e.g. from up-regulation to down-regulation, or vice versa). This is likely an adaptive response (compensatory

189

mechanism) which has been commonly observed in organisms exposed to oxidative stressors.38 If this is the case,190

the assumption of the IA model is breached and improvement of the IA model parametrization may therefore be191

required (e.g. by adding a random variable to the model to capture the variation of data that fails to meet the192

assumption of IA). Although many factors can affect the data quality and interpretation, the current case study has

193

successfully demonstrated the usefulness of this conceptual approach for classification of gene sets according to the194conventional types of joint action (e.g. majority of DEGs reasonably predicted as additive), and the ability to detect

195

unexpected (or novel) types of combined effects (e.g. contradicted action).

8
ACS Paragon Plus Environment

Page 9 of 23

Environmental Science & Technology

196
197
198

Table 1. Types of combined effects on gene/pathway regulation.
Direction of transcriptional regulation

Type of joint action

Sub-type of joint action

Illustration

No. of DEG

Additive up-regulation (34.74%)(1)+(1)=2

1202

Additive down-regulation (47.57%)(-1)+(-1)=-2

1645

Synergistic up-regulation (1.3%)(1)+(1)>2

45

Synergistic down-regulation (1.07%)(-1)+(-1)<-2

37

Antagonistic up-regulation (0%)0<(1)+(1)<20Antagonistic down-regulation (0.14%)-2<(-1)+(-1)<0

5Counteracted up-regulation (4.45%)(-1)+(2)=1

154

Counteracted down-regulation (3.44%)(-2)+(1)=-1

119

Enhanced up-regulation (2.37%)(-1)+(1)>1

82

Enhanced down-regulation (3.15%)(-1)+(1)<-1

109

Reduced up-regulation (0.03%)0<(-1)+(1)<11

Reduced down-regulation (0%)-1<(-1)+(1)<00

Reversed up-regulation (1.01%)(-1)+(-1)>035

Reversed down-regulation (0.75%)(1)+(1)<026Type 1 Additivity (82.28%)

One-directional (84.8%)

Type 1 Synergy (2.37%)

Type 1 Antagonism (0.14%)

Type 2 Additivity (7.89%)

Type 2 Synergy (5.52%)

Bi-directional (15.2%)

Type 2 Antagonism (0.03%)

Contradicted (1.76%)

199
200

Function Analysis. To further understand the toxicological functions of the DEGs displaying different types of

201

joint actions, enrichment analyses were performed with the three DEG sets (Type 1 & 2 merged to avoid loss of202

biologically significant information) displaying additive, synergistic and antagonistic effects. Both GO (Figure 1A)

203

and pathway (Figure 1B) analysis showed that the majority of the enriched functions were unique when comparing

204

different types of interactions. A relatively lower number of GO functions and pathways were found to be common

205

between different types of joint action, indicating that genes in the same functional cluster may have dissimilar

9
ACS Paragon Plus Environment

Environmental Science & Technology

Page 10 of 23

206

patterns of response to combined exposure, possibly due to their multiple roles in toxicological responses to207

different types of stressors and pathway cross-talks. For example, for the same GO function “cellular responses to208oxidative stress”, one set of supporting DEGs such as reactive oxygen species modulator 1 (c20orf52/romo1) and209

aryl hydrocarbon receptor nuclear translocator (arnt) were down-regulated and displayed Type 1 additivity, whereas

210

another set of supporting DEGs such as peroxiredoxin 2 (prdx2) and Paxillin (pxn) were up-regulated by combined

211

exposure and displayed Type 2 synergy. These findings suggest another level of gene set classification which may

212

require substantial mechanistic understanding of individual gene functions and gene regulatory network.

213

Differentially expressed genes displaying additive responses were mainly enriched in functions/pathways

214

directly relevant for several main MoAs of Gamma and DU in salmon14, 26-28 and zebrafish (Danio rerio),25, 26 such215

as induction of oxidative stress responses, DNA damage responses, mitochondrial energetic dysfunctions and216

immune responses. Although similar pathways were also identified in the previous publication using MoA

217

comparison-based qualitative approach, the comparative (qualitative) approach was not able to differentiate

218

supporting DEGs displaying interactive or non-interactive (additive) actions of the stressors in the pathway.14 The

219

results obtained from the current quantitative approach thus clearly suggests added benefits of using the prediction

220

model to classify gene sets with the same type of joint action without losing the resolution of mechanistic

221

understanding.

222

The six DEGs displaying antagonistic effects were involved in a high number of functions mainly associated223with metabolic processes, membrane integrity and DNA damage responses, which may also be relevant for the224

toxicity mechanisms of the stressors.14, 28, 29 Genes such as GRIP and coiled-coil domain-containing protein 2

225(gcc2/gcc185, Type 1 antagonism), PTPRF interacting protein binding protein 1 isoform 1 (ppfibp1, Type 1

226

antagonism), protein PXR1 (pxr1, Type 1 antagonism) were down-regulated by both single and combined stressors,

227

whereas neuroligin 3 (nlgn3, Type 2 antagonism) was down-regulated by DU, up-regulated by Gamma and down-

228

regulated by Combined. These are essential genes that are common for diverse types of biological functions in229

higher vertebrates, such as transmembrane protein activities, neuron development, cell organelle organization and230

nucleosome assembly.39-42 Modulation of these genes by antagonistic action of Gamma and DU may potentially231

affect cellular signal transduction and development. However, due to the low number of DEGs in this category, it is232

difficult to obtain in-depth understanding of the MoAs and likely outcomes associated with the antagonistic action

233of the stressors.

10
ACS Paragon Plus Environment

Page 11 of 23

Environmental Science & Technology

234

The functional characterization was focused more on DEGs displaying apparent synergistic regulation, as these235

may potentially lead to synergistic responses along toxicity pathways relevant for adverse effects of the stressors. In

236line with this assumption, GO analysis revealed that these DEGs were mainly enriched in biological functions, such237

as oxidative stress responses, cell cycle regulation and immune responses (SI, Table S2), all being demonstrated to238

have high relevance for the toxicity of both Gamma and DU.14, 25, 26, 28, 29, 43 To further explore the toxicological239

functions based on curated pathways, the salmon DEGs were mapped to the mammalian orthologs (162 out of 275

240

mapped) and analyzed by IPA (SI, Table S1). Gene network analysis showed that these DEGs were grouped into 6241functional gene clusters, including 1) neurological disease, organismal injury and abnormalities, cancer; 2)242

developmental disorder, neurological disease, cell signaling; 3) cell death and survival, organ morphology,

243

reproductive system development and function. These gene clusters are directly associated with the synergistic244

effects of the stressors as predicted by the IA model and highly relevant for the known effects of Gamma and DU in

245

fish. 14, 25, 26, 28, 29, 43 Pathway analysis showed that DEGs displaying synergistic effects were exclusively involved in

246

the ATM signaling, p53 signaling, GADD45 signaling, SUMOylation pathway, calcium signaling, mTOR signaling247

and fatty acid β-oxidation III, thus highlighting the modulation of two major functions, DNA damage responses and248

cellular energy homeostasis (SI, Table S3 & S4) by the synergistic effects of the stressors. These pathways are249

relevant for the major MoAs of Gamma and DU in Atlantic salmon 14, 28, 29 and zebrafish.25, 26, indicating that the250

quantitative approach proposed herein is capable of capturing key mechanistic information based on small and251

highly related gene sets.

252

In addition, the 61 DEGs displaying apparent contradicting responses were mainly involved in the SUMOylation

253

pathway and several biosynthetic processes of sugar derivatives, pyrimidine nucleotide and reductants. Although the254roles of these pathways in Gamma- and DU-mediated toxicological responses in fish have not been well investigated,

255

evidence from the mammalian studies suggests that several of these pathways are likely involved in certain feedback256

loops to regulate physiological processes. For example, the SUMO proteases are involved in a negative feedback

257

loop to regulate cell survival in response to genotoxic stress.44 The biosynthesis of nucleotides is also considered258

strictly regulated by certain feedback inhibition mechanisms.45 Therefore, it is possible that genes displaying

259

contradicting responses in this study were regulated by certain feedback loops in response to different levels of260

stress induced by single and combined stressors. However, whether this leads to functional changes of relevance still261

needs to be investigated.

11
ACS Paragon Plus Environment

Environmental Science & Technology

Page 12 of 23

262
263

Figure 1. Venn diagram analysis of toxicologically relevant gene ontology (GO) functions (A) and canonical pathways (B) that264

were enriched by differentially expressed genes (DEGs) displaying additive, synergistic and antagonistic effects in Atlantic

265

salmon (Salmo salar) after combined exposure to gamma radiation and depleted uranium.

266

12
ACS Paragon Plus Environment

Page 13 of 23

Environmental Science & Technology

267

Putative Synergistic Pathway Characterization. A number of molecular toxicity pathways were enriched268

by DEGs displaying synergistic effects and highly relevant for the toxicity mechanisms of Gamma and DU in fish,

269

such as GADD45 signaling, nervous system and immune dysfunctions.14, 28, 29 To illustrate the quantitative aspect

270

and novelty of the current approach, a putative synergistic toxicity pathway representing the major MoA of gamma

271

radiation and DU was characterized in detail: excessive DNA damage leading to promoted cell cycle progression272

and carcinogenesis (Figure 2). This putative pathway was characterized as an illustration of using the results273

obtained from the proposed quantitative approach to guide follow-up studies on anchoring the effects at higher

274

levels of biological organization. In contrast to the previous qualitative assessment which also identified this key

275

toxicity pathway, the new approach described herein allows quantification and understanding of the changes and276

patterns of gene expression within the pathway. It is well-known that Gamma and DU can cause DNA damage in277

fish through direct actions, such as excitation and ionization of DNA molecules (Gamma) and formation of U-DNA

278

adducts (DU), or most likely indirect actions such as induction of ROS and causing oxidative DNA damage.46, 47

279

Peroxiredoxin-2 (prdx2), an antioxidant encoding gene against oxidative stress, was synergistically up-regulated,

280

potentially indicating excessive ROS formation and subsequent DNA damage.48 Between DNA damage and the281

activation of cancer signaling, the oncogene myc plays a key role. The myc gene was found to be up-regulated due to282

the synergistic effect of Gamma and DU in the present study. It is known that normal expression of this oncogene is283

involved in the cellular defensive mechanisms against DNA damage and tumorigenesis, whereas abnormal

284

regulation or mutation of this gene can lead to completely opposite consequences.49, 50 Overexpression of myc by

285

gamma radiation has been reported to suppress DNA repair, promote DNA damage and cell cycle progression from286

G1 to S phase, thus facilitating mutagenesis and tumorigenesis in mammals.51, 52 Studies on zebrafish (Danio rerio)

287

also showed that overexpression of myc resulted in increased proliferation of cancer cells, and induction of T-cell288

acute lymphoblastic leukemia and hepatoma.53, 54 Although detailed mechanism of myc overexpression leading to289

promoted cell cycle progression is not fully understood, recent mammalian studies suggested that myc may impede290

the function of tumor protein P53 (p53), a central transcription factor for activation of cell cycle arrest, DNA repair291

and programmed cell death, thus promoting cell cycle progression.55-57 The p53 gene per se was not identified as a

292

DEG after combined exposure, likely due to large variations between individual replicates and limited induction

293

potential.58 However, its downstream target, growth arrest and DNA-damage-inducible protein GADD45 gamma

294(gadd45g), an effector gene to mediate DNA damage associated S and G2/M cell cycle arrest,59 was highly down-

13
ACS Paragon Plus Environment

Environmental Science & Technology

Page 14 of 23295

regulated and displayed a synergistic response. This transition from no effect to significant effect between upstream296

and downstream genes potentially shows a good example that synergy may occur along a pathway. In addition,

297

another downstream target of p53, tumor protein p53-inducible nuclear protein 1 (tp53inp1) which triggers P53-

298

dependent apoptosis,60 was down-regulated but displaying additive effect of the stressors. The evidence taken

299

together suggest that p53 was likely suppressed in salmon liver after combined exposure to the two stressors. The

300

gadd45 gene is normally induced in response to low level of genotoxic stress to control cell cycle progression, DNA

301

repair and initiation of apoptosis to eliminate damaged cells.61 Repression of this gene promotes the expression of302

cyclin-dependent kinase inhibitors (e.g. cdkn1b), thus inhibiting the expression of cyclin-dependent kinases (e.g.

303

cdkl1), a gene responsible for progression of the cell cycle.62 The cdkn1b gene was found to be down-regulated,

304

whereas cdkl1 was up-regulated due to the combined effect in the present study, thus suggesting that cell cycle

305

progression was enhanced beyond the expectation of additivity by the combined exposure. The key regulatory role306

of gadd45 in this molecular pathway is likely dependent on the level of stress. However, lack of temporal and dose-307

response data in the current study limits the possibility to investigate the expression dynamics of this gene. In

308

mammals, deficiency in the GADD45 pathway has been associated with oncogenesis.59 Collectively, impaired DNA

309

repair, suppressed apoptosis and promoted cell cycle progression may potentially facilitate the accumulation of310

mutated cells and activation of various carcinogenic signaling pathways, which are highly associated with tumor

311

formation (Figure 2). Although it was not clear if the adverse outcome(s) of this toxicity pathway was also enhanced312

as result of combined exposure, due to lack of phenotypic anchoring, the illustrative analysis conducted herein

313

shows a strategy for extracting key information from the data and improved interpretation of the results for guiding314

follow-up studies.

315

14
ACS Paragon Plus Environment

Page 15 of 23

Environmental Science & Technology

316
317

Figure 2. An example illustrating synergistic toxicity pathways of DNA damage leading to reduced cell cycle arrest and enhanced

318

carcinogenesis signaling in the liver of Atlantic salmon (Salmo salar) after combined exposure to gamma radiation and depleted319

uranium (DU). ROS: reactive oxygen species; prdx: peroxiredoxin; myc: c-myc; atm: p53: tumor protein P53; gadd45: growth320

arrest and DNA-damage-inducible protein GADD45; cdk inhibitor: cyclin-dependent kinase inhibitor; cdk: cyclin-dependent321

kinase.

322
323

Applications and limitations of the conceptual approach. As illustrated by the case study, a conceptual324

workflow for combined effect assessment using transcriptomic data is proposed (Figure 3). This conceptual

325

approach integrates mechanistically-based comparative analysis (qualitative/descriptive), expression-based mixture

326

toxicity modeling (quantitative) and biological pathway-based functional analysis (bioinformatics) to understand the327

underlying mechanisms of combined effects in a toxicodynamics context and maximize the knowledge output from328

such high-content OMICS analysis. This approach complies with the adverse outcome pathway (AOP) concept in329

predictive ecotoxicology, which describes a conceptual framework that causally links the molecular initiating event

330(MIE), a series of key events (KE) and the adverse outcome (AO) into a linear relationship that is relevant for risk

331

assessment.63 By characterizing key molecular regulatory pathways, downstream KEs along an AOP potentially

15
ACS Paragon Plus Environment

Environmental Science & Technology

Page 16 of 23

332

leading to adversity relevant for cumulative risk can be targeted and anchored to well characterized toxicity

333

pathways using functional bioassays (tissue/organ level) or standardized toxicity tests (individual/population level).

334

The IA prediction model used in this conceptual approach is suitable for quantitatively assessing the combined335

effects of environmental stressors with distinct toxicological profiles and multiple MoAs, such as a combination of336

chemical contaminants and natural stressors (e.g. pH, temperature, UV, ionizing radiation). The IA model is also

337

considered appropriate for analyzing data generated from such high-content and hypothesis-generating OMICS

338

analysis which may lacks temporal and dose-response relationships due to relatively high costs of these technologies.

339

Nevertheless, this approach has both advantages and limitations. On one hand, classification of DEG sets by type of

340

interaction (e.g. additivity, synergy, antagonism) can reduce the complexity of high-dimensional OMICS data, thus

341

facilitating the identification of key gene sets relevant for understanding the joint actions of the stressors. On the342

other hand, grouping of genes according to the response (expression) patterns may potentially limit the343

characterization of their biological significance at the functional (e.g. gene clusters or pathways identified by the344

enrichment analyses) level of certain genes when classified into different types of interactions. Alternative to the345

currently proposed approach is to classify DEGs by their functional clusters (e.g. pathway functions) first, then346

group supporting DEGs in the same functional cluster (pathway) by type of interactions. However, complexity for347

interpretation may still exist, as one pathway may be enriched by DEGs displaying multiple types of joint actions

348(e.g. 50% DEGs showing synergy whereas the rest showing antagonism). Therefore, choice of classification349

approaches is highly dependent on a combination of whether the biological functions of DEG sets are relevant for350

the MoAs of the stressors and resulting perturbations of key toxic pathways, and whether DEGs in the same351

functional cluster uniformly display the same of type of joint action of the stressors. It would be interesting to try

352

both approaches described above to capture all information needed in future assessments.

353

As clearly illustrated by the present case study, the proposed conceptual approach may also be limited by several

354

key factors. First, mixture design is certainly an important aspect which may influence the overall conclusion.

355

Although the simple “a+b” design employed in this case study has reasonably captured most patterns of combined356

effects, it has limitations to provide complete information due to lack of sufficient data points (e.g. dose-response

357

relationships and temporal patterns of transcriptional responses) and may potentially introduce bias to the analysis.

358

Altenburger and coworkers have reviewed appropriate mixture design for specific purposes and pointed out that use

359

of dose-response and temporal gene expression data can refine the mixture design (e.g. by using appropriate

16
ACS Paragon Plus Environment

Page 17 of 23

Environmental Science & Technology

360

concentration/dose in the mixture) and reduce uncertainties in combined effect modeling.12 Second, the OMICS data361

quality may also be highly dependent on the analytical technologies. The microarray analysis used in this case study362

has been useful for identifying various types of transcriptional responses, but the technical limitations of this method363

may potentially introduce experimental artefacts (e.g. cross-hybridization),64 thus jeopardizing the identification of364

true DEGs. Nevertheless, the previously published qualitative assessment14 using the same dataset evaluated the365

responses of six biomarkers genes by quantitative real-time reverse transcriptional polymerase chain reaction (qPCR)

366

and verified that results were in general consistent with that measured by microarray, thus suggesting that367

experimental artefact due to the technology employed may not be the most important factor affecting the368

conclusions of this study. To reduce potential experimental artefacts, use of state-of-the-art techniques (e.g. RNA

369

sequencing) and inclusion of multiple analytical approaches verifying the transcriptional changes may increase data

370

confidence. Third, different statistical analyses (e.g. t-test, LIMMA, ANOVA, with or without multiple testing371

correction) for determining DEGs and data filtering methods (e.g. fold change cutoff, p-value cutoff) may lead to372

gain or loss of information on key genes being highly relevant for key toxicity pathways. No multiple testing373

correction was applied in this study to preserve the low-abundant transcripts and marginally regulated genes with374

potential biological significance. As a side-effect, the chance of identifying false positives may also increase and375

affect data interpretation. Standardized processing and reporting of OMICS data is therefore a prerequisite for376

reproducible output using the current approach and highly required for regulatory applications.65-68 Fourth,377

bioinformatics can also be a limiting factor for data interpretation which is highly required by the current approach.

378

Poor genome/transcriptome annotation (e.g. non-model species such as Atlantic salmon) and lack of sufficient379

knowledge on gene co-expression networks at the functional level (e.g. clusters and pathways) may thus become the380bottlenecks for identification of key toxicity pathways relevant for the combined toxicity of the stressors. Finally,381

lack of mechanistic knowledge at the molecular and functional level may limit the understanding and interpretation382

of unexpected (or novel) responses which may be highly relevant for assessing cumulative hazards. The IA model383

may also have limitations in capturing all types of combined effects at the molecular level. For instance, if not being

384

experimental artefacts or false positives, DEGs displaying contradicted type of joint action may violate the385

assumptions of the IA model and should be interpreted on a case-by-case basis. Although appropriate experimental

386

design, biostatistics/bioinformatics, technology and mechanistic knowledge are clearly required, the current case387

study has successfully demonstrated that a combination of quantitative combined effects modeling and functional

17
ACS Paragon Plus Environment

Environmental Science & Technology

Page 18 of 23388

analyses may increase the ability to decipher and classify relevant combined effects at the gene level and quantify

389

combined effects relevant for key toxicity pathways.

390

391
392

Figure 3. Proposed workflow for mechanistically-based assessment of low-dose interactive effects of combined stressors using

393

transcriptomics data. Qualitative comparison: Mode of action (MoA)-based assessment; Quantitative assessment: Prediction

394

model-based assessment; DEG: differentially expressed gene; CA: concentration addition; IA: independent action. qPCR:

395

quantitative real-time reverse-transcription polymerase chain reaction.

396
397

Future Perspectives. A key question raised from the present study is whether additivity, synergism and398

antagonism of gene expression and pathways at the molecular level can be used to predict the corresponding joint399

action at the organismal or population level. Recent advance in gene co-expression network modeling showed that it

400

is possible to quantitatively predict adverse effects at the organismal level by using gene expression data,19 which is401

a first step of extrapolation between different levels of biological organization. This is especially important as future

18
ACS Paragon Plus Environment

Page 19 of 23

Environmental Science & Technology

402

regulatory toxicology requires reduced animal testing, better extrapolations from low to high biological levels (e.g.

403

in vitro to in vivo), and increased predictability across taxa and stressors69 To answer this question, anchoring of404

combined effects at multiple biological levels along a defined AOP or network of AOPs is needed. Anchoring of405

relevant toxicity pathways being perturbed by a set of single and multiple stressor to key components in the AOP

406

continuum (i.e. the molecular initiating event and the key events) can help to identify more complex responses

407

involving multiple AOPs (i.e. network of AOPs) which may mutually interact to cause adverse outcomes of408

ecological relevance.12, 70 Another important question is whether the proposed approach can also be used for an

409

increased number of stressors. Although the principles outlined herein should ideally be applicable to an infinite

410

number of stressors, proof-of-concept studies to demonstrate the applicability and robustness for a number of411

stressors and extended dose-rate/concentration ranges reflecting ecologically-relevant exposure scenarios is highly

412

warranted. For different types of studies, the choice of appropriate model is also important. A recent study by

413

Schäfer and Piggott71 proposed a guideline for selecting the optimal null model (i.e. a prediction model assuming no414

interaction between the stressors) for prediction of multiple-stressor effect on individuals or populations, which may

415

also be adapted for modeling the effects at the molecular level. Other modeling approaches in combination with the416classical combined effect prediction models, such as machine learning-based classification techniques72 and417

advanced correlation/regression analysis73 may provide additional options for combined toxicity assessment of418

multiple stressors. Moreover, the complexity of biological responses (i.e. directional responses) as observed in the419

present study as well as other studies (reviewed in ref36) needs to be taken into account in the next generation of

420

cumulative hazard assessment of multiple stressors. Mechanistic knowledge on the MoAs of the stressors as well as421

molecular regulatory networks should be preferably obtained prior to conducting complex multiple stressor studies

422

using the OMICS tools. Reconceptualizing the definitions for additivity, synergy and antagonism by considering423

more complex biological responses may be required.36

424
425
426

 ASSOCIATED CONTENT

427

Supporting Information (SI)428

The Supporting Information is available free of charge on the ACS Publications website at DOI:

19
ACS Paragon Plus Environment

Environmental Science & Technology

429

Page 20 of 23

Table S1: DEGs, Table S2: GOs, Table S3: Tox lists, Table S4: Canonical pathways (XLSX)

430
431
432

 AUTHOR INFORMATION

433

Corresponding Author

434*You Song, Norwegian Institute for Water Research (NIVA), Gaustadalléen 21, NO-0349 Oslo, Norway. Tlf.:

435

02348, Fax: (+47) 22 18 52 00, E-mail: you.song@niva.no

436

Notes

437

The authors declare no competing financial interest.

438
439
440

 ACKNOWLEDGEMENTS

441

The present work was funded by the Research Council of Norway (RCN) through the Centre of Excellence (CoE)442

funding scheme “Centre for Environmental Radioactivity (CERAD, project No. 223268/F50)” and the MixTox

443

project (project No. 178621). Jana Asselman is a postdoctoral fellow of the FWO Science Foundation Flanders.

444
445
446

 References

447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468

1.
Holmstrup, M., Bindesbol, A. M., Oostingh, G. J., Duschl, A., Scheil, V., Kohler, H. R., Loureiro, S., Soares, A. M. V. M., Ferreira, A.
L. G., Kienle, C., Gerhardt, A., Laskowski, R., Kramarz, P. E., Bayley, M., Svendsen, C., Spurgeon, D. J., Interactions between effects ofenvironmental chemicals and natural stressors: A review. Sci. Total Environ. 2010, 408, (18), 3746-3762.
2.
Laskowski, R., Bednarska, A. J., Kramarz, P. E., Loureiro, S., Scheil, V., Kudlek, J., Holmstrup, M., Interactions between toxic
chemicals and natural environmental factors - A meta-analysis and case studies. Sci. Total Environ. 2010, 408, (18), 3763-3774.
3.
Vanhoudt, N.; Vandenhove, H.; Real, A.; Bradshaw, C.; Stark, K., A review of multiple stressor studies that include ionising radiation.
Environ. Pollut. 2012, 168, 177-192.
4.
Jonker, M. J.; Svendsen, C.; Bedaux, J. J. M.; Bongers, M.; Kammenga, J. E., Significance testing of synergistic/antagonistic, doselevel-dependent, or dose ratio-dependent effects in mixture dose-response analysis. Environ. Toxicol. Chem. 2005, 24, (10), 2701-2713.
5.
Plackett, R. L.; Hewlett, P. S., Quantal responses to mixtures of poisons. Journal of the Royal Statistical Society Series B-Statistical
Methodology 1952, 14, (2), 141-163.
6.
Loewe, S., Die Mischarznei. Versuch einer allgemeinen Pharmakologie der Arzneikombinationen. Klin. Wochenschr. 1927, 6, 10771085.
7.
Loewe, S.; Muischnek, H., Combinated effects I Announcement - Implements to the problem. Naunyn Schmiedebergs Arch. Exp.
Pathol. Pharmakol. 1926, 114, 313-326.
8.
Bliss, C. I., The toxicity of poisons applied jointly. Ann. Appl. Biol. 1939, 26, 585-615.
9.
Petersen, K.; Tollefsen, K. E., Assessing combined toxicity of estrogen receptor agonists in a primary culture of rainbow trout(Oncorhynchus mykiss) hepatocytes. Aquat. Toxicol. 2011, 101, (1), 186-195.
10.
Petersen, K.; Tollefsen, K. E., Combined effects of oestrogen receptor antagonists on in vitro vitellogenesis. Aquat. Toxicol. 2012, 112,
46-53.
11.
Tollefsen, K. E.; Petersen, K.; Rowland, S. J., Toxicity of Synthetic Naphthenic Acids and Mixtures of These to Fish Liver Cells.
Environ Sci Technol 2012, 46, (9), 5143-5150.

20
ACS Paragon Plus Environment

Page 21 of 23

469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536

Environmental Science & Technology

12.
Altenburger, R.; Scholz, S.; Schmitt-Jansen, M.; Busch, W.; Escher, B. I., Mixture toxicity revisited from a toxicogenomic perspective.
Environ Sci Technol 2012, 46, (5), 2508-22.
13.
Boobis, A.; Budinsky, R.; Collie, S.; Crofton, K.; Embry, M.; Felter, S.; Hertzberg, R.; Kopp, D.; Mihlan, G.; Mumtaz, M.; Price, P.;
Solomon, K.; Teuschler, L.; Yang, R.; Zaleski, R., Critical analysis of literature on low-dose synergy for use in screening chemical mixtures forrisk assessment. Crit. Rev. Toxicol. 2011, 41, (5), 369-383.
14.
Song, Y.; Salbu, B.; Teien, H. C.; Evensen, O.; Lind, O. C.; Rosseland, B. O.; Tollefsen, K. E., Hepatic transcriptional responses inAtlantic salmon (Salmo salar) exposed to gamma radiation and depleted uranium singly and in combination. Sci. Total Environ. 2016, 562, 270279.
15.
Jensen, L. K.; Halvorsen, E.; Song, Y.; Hallanger, I. G.; Hansen, E. L.; Brooks, S. J.; Hansen, B. H.; Tollefsen, K. E., Individual andmolecular level effects of produced water contaminants on nauplii and adult females of Calanus finmarchicus. J. Toxicol. Environ. Health A 2016,
79, (13-15), 585-601.
16.
Hutchins, C. M.; Simon, D. F.; Zerges, W.; Wilkinson, K. J., Transcriptomic signatures in Chlamydomonas reinhardtii as Cd
biomarkers in metal mixtures. Aquat. Toxicol. 2010, 100, (1), 120-127.
17.
Tilton, F. A.; Tilton, S. C.; Bammler, T. K.; Beyer, R. P.; Stapleton, P. L.; Scholz, N. L.; Gallagher, E. P., Transcriptional impact oforganophosphate and metal mixtures on olfaction: Copper dominates the chlorpyrifos-induced response in adult zebrafish. Aquat. Toxicol. 2011,
102, (3-4), 205-215.
18.
Asselman, J. Transcriptomic profiles and fitness of daphnia exposed to cyanobacteria and interactions with insecticides. PhD thesis,
Ghent University, Ghent, Belgium, 2014.
19.
Asselman, J.; Pfrender, M. E.; Lopez, J. A.; Shaw, J. R.; De Schamphelaere, K. A. C., Gene coexpression networks drive and predict
reproductive effects in Daphnia in response to environmental disturbances. Environ Sci Technol 2018, 52, (1), 317-326.
20.
Mudge, J. F.; Martyniuk, C. J.; Houlahan, J. E., Optimal alpha reduces error rates in gene expression studies: a meta-analysis approach.
BMC Bioinformatics 2017, 18, (1), 312.
21.
Smetanova, S.; Riedl, J.; Zitzkat, D.; Altenburger, R.; Busch, W., High-throughput concentration-response analysis for omics datasets.
Environ Toxicol Chem 2015, 34, (9), 2167-80.
22.
Khatri, P.; Sirota, M.; Butte, A. J., Ten years of pathway analysis: current approaches and outstanding challenges. PLoS Comput. Biol.
2012, 8, (2), e1002375.
23.
De Coninck, D. I. M.; Asselman, J.; Glaholt, S.; Janssen, C. R.; Colhourne, J. K.; Shaw, J. R.; De Schamphelaere, K. A. C., Genomewide transcription profiles reveal genotype-dependent responses of biological pathways and gene-families in Daphnia exposed to single andmixed stressors. Environ. Sci. Technol. 2014, 48, (6), 3513-3522.
24.
Salbu, B., Radioactive particles released from different nuclear sources. In Radioactive particles in the envrionment, Oughton, D. H.;
Kashparov, V., Eds. Springer: The Netherlands, 2009; p 279.
25.
Ng, C. Y.; Pereira, S.; Cheng, S. H.; Adam-Guillermin, C.; Garnier-Laplace, J.; Yu, K. N., Combined effects of depleted uranium andionising radiation on zebrafish embryos. Radiat Prot Dosimetry 2015, 167, (1-3), 311-5.
26.
Ng, C. Y.; Pereira, S.; Cheng, S. H.; Adam-Guillermin, C.; Garnier-Laplace, J.; Yu, K. N., Combined effects of alpha particles anddepleted uranium on Zebrafish (Danio rerio) embryos. J Radiat Res 2016, 57, (4), 343-55.
27.
Song, Y.; Salbu, B.; Heier, L. S.; Teien, H. C.; Lind, O. C.; Oughton, D.; Petersen, K.; Rosseland, B. O.; Skipperud, L.; Tollefsen, K.
E., Early stress responses in Atlantic salmon (Salmo salar) exposed to environmentally relevant concentrations of uranium. Aquat. Toxicol. 2012,
112, 62-71.
28.
Song, Y.; Salbu, B.; Teien, H. C.; Heier, L. S.; Rosseland, B. O.; Hogasen, T.; Tollefsen, K. E., Hepatic transcriptomic profilingreveals early toxicological mechanisms of uranium in Atlantic salmon (Salmo salar). BMC Genomics 2014, 15, 694.
29.
Song, Y.; Salbu, B.; Teien, H. C.; Heier, L. S.; Rosseland, B. O.; Tollefsen, K. E., Dose-dependent hepatic transcriptional responses inAtlantic salmon (Salmo salar) exposed to sublethal doses of gamma radiation. Aquat. Toxicol. 2014, 156, 52-64.
30.
Smyth, G. K., Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat. Appl.
Genet. Mol. Biol. 2004, 3, Article3.
31.
Asselman, J.; De Coninck, D. I.; Glaholt, S.; Colbourne, J. K.; Janssen, C. R.; Shaw, J. R.; De Schamphelaere, K. A., Identification ofpathways, gene networks, and paralogous gene families in Daphnia pulex responding to exposure to the toxic cyanobacterium Microcystis
aeruginosa. Environ Sci Technol 2012, 46, (15), 8448-57.
32.
Faust, M.; Altenburger, R.; Backhaus, T.; Blanck, H.; Boedeker, W.; Gramatica, P.; Hamer, V.; Scholze, M.; Vighi, M.; Grimme, L.
H., Joint algal toxicity of 16 dissimilarly acting chemicals is predictable by the concept of independent action. Aquat. Toxicol. 2003, 63, (1), 4363.
33.
Maere, S.; Heymans, K.; Kuiper, M., BiNGO: a Cytoscape plugin to assess overrepresentation of gene ontology categories inbiological networks. Bioinformatics 2005, 21, (16), 3448-9.
34.
Shannon, P.; Markiel, A.; Ozier, O.; Baliga, N. S.; Wang, J. T.; Ramage, D.; Amin, N.; Schwikowski, B.; Ideker, T., Cytoscape: asoftware environment for integrated models of biomolecular interaction networks. Genome Res. 2003, 13, (11), 2498-504.
35.
Ostlund, G.; Schmitt, T.; Forslund, K.; Kostler, T.; Messina, D. N.; Roopra, S.; Frings, O.; Sonnhammer, E. L., InParanoid 7: newalgorithms and tools for eukaryotic orthology analysis. Nucleic Acids Res 2010, 38, (Database issue), D196-203.
36.
Piggott, J. J.; Townsend, C. R.; Matthaei, C. D., Reconceptualizing synergism and antagonism among multiple stressors. Ecol. Evol.
2015, 5, (7), 1538-1547.
37.
Mitrophanov, A. Y.; Groisman, E. A., Positive feedback in cellular control systems. Bioessays 2008, 30, (6), 542-55.
38.
Lushchak, V. I., Adaptive response to oxidative stress: Bacteria, fungi, plants and animals. Comp. Biochem. Physiol. C Toxicol.
Pharmacol. 2011, 153, (2), 175-90.
39.
Derby, M. C.; Lieu, Z. Z.; Brown, D.; Stow, J. L.; Goud, B.; Gleeson, P. A., The trans-Golgi network golgin, GCC185, is required forendosome-to-Golgi transport and maintenance of Golgi structure. Traffic 2007, 8, (6), 758-773.
40.
Wei, Z.; Zheng, S.; Spangler, S. A.; Yu, C.; Hoogenraad, C. C.; Zhang, M., Liprin-mediated large signaling complex organization
revealed by the liprin-alpha/CASK and liprin-alpha/liprin-beta complex structures. Mol Cell 2011, 43, (4), 586-98.
41.
Venkatesh, H. S.; Johung, T. B.; Caretti, V.; Noll, A.; Tang, Y. J.; Nagaraja, S.; Gibson, E. M.; Mount, C. W.; Polepalli, J.; Mitra, S.
S.; Woo, P. J.; Malenka, R. C.; Vogel, H.; Bredel, M.; Mallick, P.; Monje, M., Neuronal Activity Promotes Glioma Growth through Neuroligin-3
Secretion. Cell 2015, 161, (4), 803-816.

21
ACS Paragon Plus Environment

Environmental Science & Technology

537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606

Page 22 of 23

42.
Yuan, K.; Li, N.; Jiang, K.; Zhu, T. G.; Huo, Y. D.; Wang, C.; Lu, J.; Shaw, A.; Thomas, K.; Zhang, J. C.; Mann, D.; Liao, J.; Jin, C.
J.; Yao, X. B., PinX1 Is a Novel Microtubule-binding Protein Essential for Accurate Chromosome Segregation. J. Biol. Chem. 2009, 284, (34),23072-23082.
43.
Hurem, S.; Martin, L. M.; Brede, D. A.; Skjerve, E.; Nourizadeh-Lillabadi, R.; Lind, O. C.; Christensen, T.; Berg, V.; Teien, H. C.;
Salbu, B.; Oughton, D. H.; Alestrom, P.; Lyche, J. L., Dose-dependent effects of gamma radiation on the early zebrafish development and gene
expression. PLoS One 2017, 12, (6), e0179259.
44.
Lee, M. H.; Mabb, A. M.; Gill, G. B.; Yeh, E. T.; Miyamoto, S., NF-kappaB induction of the SUMO protease SENP2: A negative
feedback loop to attenuate cell survival response to genotoxic stress. Mol Cell 2011, 43, (2), 180-91.
45.
Lane, A. N.; Fan, T. W., Regulation of mammalian nucleotide metabolism and biosynthesis. Nucleic Acids Res 2015, 43, (4), 2466-85.
46.
Stearns, D. M.; Yazzie, M.; Bradley, A. S.; Coryell, V. H.; Shelley, J. T.; Ashby, A.; Asplund, C. S.; Lantz, R. C., Uranyl acetate
induces hprt mutations and uranium-DNA adducts in Chinese hamster ovary EM9 cells. Mutagenesis 2005, 20, (6), 417-23.
47.
Lomax, M. E.; Folkes, L. K.; O'Neill, P., Biological consequences of radiation-induced DNA damage: relevance to radiotherapy. Clin.
Oncol. (R. Coll. Radiol.) 2013, 25, (10), 578-85.
48.
Valero, Y.; Martínez-Morcillo, F. J.; Esteban, M.; Chaves-Pozo, E.; Cuesta, A., Fish peroxiredoxins and their role in immunity.
Biology (Basel) 2015, 4, (4), 860-880.
49.
Campaner, S.; Amati, B., Two sides of the Myc-induced DNA damage response: from tumor suppression to tumor maintenance. Cell
Div. 2012, 7, (1), 6.
50.
Li, Z. Z.; Owonikoko, T. K.; Sun, S. Y.; Ramalingam, S. S.; Doetsch, P. W.; Xiao, Z. Q.; Khuri, F. R.; Curran, W. J.; Deng, X. M., cMyc suppression of DNA double-strand break repair. Neoplasia 2012, 14, (12), 1190-1202.
51.
Sheen, J. H.; Dickson, R. B., Overexpression of c-Myc alters G(1)/S arrest following ionizing radiation. Mol. Cell. Biol. 2002, 22, (6),1819-1833.
52.
Felsher, D. W.; Bishop, J. M., Transient excess of MYC activity can elicit genomic instability and tumorigenesis. Proc. Natl. Acad.
Sci. U. S. A. 1999, 96, (7), 3940-3944.
53.
Mione, M. C.; Trede, N. S., The zebrafish as a model for cancer. Dis. Model. Mech. 2010, 3, (9-10), 517-523.
54.
Zhao, S.; Huang, J.; Ye, J., A fresh look at zebrafish from the perspective of cancer research. J. Exp. Clin. Cancer Res. 2015, 34, (80),80.
55.
Aird, K. M.; Zhang, R., ATM in senescence. Oncotarget 2015, 6, (17), 14729-30.
56.
Evan, G. I.; Vousden, K. H., Proliferation, cell cycle and apoptosis in cancer. Nature 2001, 411, (6835), 342-348.
57.
Pei, D. S.; Strauss, P. R., Zebrafish as a model system to study DNA damage and repair. Mutat. Res.-Fundam. Mol. Mech. Mutag.
2013, 743, 151-159.
58.
Zhao, Y.; Chen, X. Q.; Du, J. Z., Cellular adaptation to hypoxia and p53 transcription regulation. J Zhejiang Univ Sci B 2009, 10, (5),404-10.
59.
Tamura, R. E.; de Vasconcellos, J. F.; Sarkar, D.; Libermann, T. A.; Fisher, P. B.; Zerbini, L. F., GADD45 proteins: central players intumorigenesis. Curr. Mol. Med. 2012, 12, (5), 634-51.
60.
Okamura, S.; Arakawa, H.; Tanaka, T.; Nakanishi, H.; Ng, C. C.; Taya, Y.; Monden, M.; Nakamura, Y., p53DINP1, a p53-inducible
gene, regulates p53-dependent apoptosis. Mol Cell 2001, 8, (1), 85-94.
61.
Salvador, J. M.; Brown-Clay, J. D.; Fornace, A. J., Jr., Gadd45 in stress signaling, cell cycle control, and apoptosis. Adv Exp Med Biol
2013, 793, 1-19.
62.
Hochegger, H.; Takeda, S.; Hunt, T., Cyclin-dependent kinases and cell-cycle transitions: does one fit all? Nature Reviews Molecular
Cell Biology 2008, 9, (11), 910-U26.
63.
Ankley, G. T.; Bennett, R. S.; Erickson, R. J.; Hoff, D. J.; Hornung, M. W.; Johnson, R. D.; Mount, D. R.; Nichols, J. W.; Russom, C.
L.; Schmieder, P. K.; Serrrano, J. A.; Tietge, J. E.; Villeneuve, D. L., Adverse outcome pathways: a conceptual framework to support
ecotoxicology research and risk assessment. Environ Toxicol Chem 2010, 29, (3), 730-41.
64.
Petri, T.; Berchtold, E.; Zimmer, R.; Friedel, C. C., Detection and correction of probe-level artefacts on microarrays. BMC
Bioinformatics 2012, 13, 114.
65.
Gant, T. W.; Sauer, U. G.; Zhang, S. D.; Chorley, B. N.; Hackermuller, J.; Perdichizzi, S.; Tollefsen, K. E.; Van Ravenzwaay, B.;
Yauk, C.; Tong, W. D.; Poole, A., A generic Transcriptomics Reporting Framework (TRF) for 'omics data processing and analysis. Regul.
Toxicol. Pharmacol. 2017, 91, S36-S45.
66.
Kauffmann, H. M.; Kamp, H.; Fuchs, R.; Chorley, B. N.; Deferme, L.; Ebbels, T.; Hackermuller, J.; Perdichizzi, S.; Poole, A.; Sauer,
U. G.; Tollefsen, K. E.; Tralau, T.; Yauk, C.; van Ravenzwaay, B., Framework for the quality assurance of 'omics technologies considering GLP
requirements. Regul. Toxicol. Pharmacol. 2017, 91, S27-S35.
67.
Bridges, J.; Sauer, U. G.; Buesen, R.; Deferme, L.; Tollefsen, K. E.; Tralau, T.; van Ravenzwaay, B.; Poole, A.; Pemberton, M.,
Framework for the quantitative weight-of-evidence analysis of 'omics data for regulatory purposes. Regul. Toxicol. Pharmacol. 2017, 91, S46S60.
68.
Sauer, U. G.; Deferme, L.; Gribaldo, L.; Hackermuller, J.; Tralau, T.; van Ravenzwaay, B.; Yauk, C.; Poole, A.; Tong, W. D.; Gant, T.
W., The challenge of the application of 'omics technologies in chemicals risk assessment: Background and outlook. Regul. Toxicol. Pharmacol.
2017, 91, S14-S26.
69.
Krewski, D.; Acosta, D., Jr.; Andersen, M.; Anderson, H.; Bailar, J. C., 3rd; Boekelheide, K.; Brent, R.; Charnley, G.; Cheung, V. G.;
Green, S., Jr.; Kelsey, K. T.; Kerkvliet, N. I.; Li, A. A.; McCray, L.; Meyer, O.; Patterson, R. D.; Pennie, W.; Scala, R. A.; Solomon, G. M.;
Stephens, M.; Yager, J.; Zeise, L., Toxicity testing in the 21st century: a vision and a strategy. J. Toxicol. Environ. Health. B Crit. Rev. 2010, 13,(2-4), 51-138.
70.
Beyer, J.; Petersen, K.; Song, Y.; Ruus, A.; Grung, M.; Bakke, T.; Tollefsen, K. E., Environmental risk assessment of combined
effects in aquatic ecotoxicology: A discussion paper. Mar. Environ. Res. 2014, 96, 81-91.
71.
Schäfer, R. B.; Piggott, J. J., Advancing understanding and prediction in multiple stressor research through a mechanistic basis for nullmodels. Glob. Chang. Biol. 2018, 1-10.
72.
de Boer, T. E.; Janssens, T. K. S.; Legler, J.; van Straalen, N. M.; Roelofs, D., Combined transcriptomics analysis for classification ofadverse effects as a potential end point in effect based screening. Environ. Sci. Technol. 2015, 49, (24), 14274-14281.
73.
Garcia-Reyero, N.; Escalon, B. L.; Loh, P. R.; Laird, J. G.; Kennedy, A. J.; Berger, B.; Perkins, E. J., Assessment of chemical
mixtures and groundwater effects on Daphnia magna transcriptomics. Environ Sci Technol 2012, 46, (1), 42-50.

22
ACS Paragon Plus Environment

Page 23 of 23

Environmental Science & Technology

607

23
ACS Paragon Plus Environment