Lifelong Exposure to PCBs in the Remote Norwegian Arctic Disrupts

Publication Date (Web): December 13, 2017 ... Lake Ellasjøen on the remote Norwegian island of Bjørnøya is populated by Arctic charr (Salvelinus al...
0 downloads 0 Views 947KB Size
Subscriber access provided by READING UNIV

Article

Lifelong exposure to PCBs in the remote Norwegian Arctic disrupts the plasma stress metabolome in Arctic charr Patrick T. Gauthier, Anita Evenset, Guttorm N. Christensen, Even H. Jørgensen, and Mathilakath M. Vijayan Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.7b05272 • Publication Date (Web): 13 Dec 2017 Downloaded from http://pubs.acs.org on December 21, 2017

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 free service to the research community to expedite the dissemination of scientific material as soon as possible after acceptance. “Just Accepted” manuscripts appear in full 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 accessible to all readers and 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. After a manuscript is technically edited and formatted, it will be removed from the “Just Accepted” Web site 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 and ethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errors or consequences arising from the use of information contained in these “Just Accepted” manuscripts.

Environmental Science & Technology 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 course of their duties.

Page 1 of 30

Environmental Science & Technology

Gauthier et al.

1 2

Lifelong exposure to PCBs in the remote Norwegian Arctic disrupts the plasma stress metabolome in Arctic charr

3

Patrick T. Gauthier1, Anita Evenset2, Guttorm N. Christensen2, Even H. Jorgensen3, and Mathilakath M. Vijayan1,*

4 5 6 7 8 9 10 11 12 13 14 15

1

Department of Biological Sciences, University of Calgary, Calgary, Alberta, Canada T2N1N4 Akvaplan-niva AS, Fram Centre-High North Research Centre for Climate and the Environment, Hjalmar Johansens Gate 14, 9007 Tromsø, Norway 3 Department of Arctic and Marine Biology, UiT the Arctic University of Norway, NO-9037, Tormsø, Norway 2

*Corresponding author: [email protected]

16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

Manuscript summary Manuscript Format: Abstract word count: Manuscript word count: Small figures (300 words): Large figures (600 words): Small tables (300 words): Large tables (600 words): Word-equivalent: References: Supporting information:

Research Article – 7,000 word-equivalent limit. 192 4,100 Figure 1-3 – 300 x 3 = 900 Figure 4-5 – 600 x 2 = 1,200 Table 1-2 – 300 x 2 = 600 na – 600 x 0 = 0 192 + 4,100 + 900 + 1,200 + 600 = 6992 44 Metabolite library, Tables S1, S2, and S3

31

32

33

34

35

ACS Paragon Plus Environment

Environmental Science & Technology

Gauthier et al. 36 37

Abstract Lake Ellasjøen on the remote Norwegian island of Bjørnøya is populated by Arctic charr

38

(Salvelinus alpinus) having 20-fold higher body burdens of polychlorinated biphenyls (PCB)

39

compared to charr from the neighbouring Lake Laksvatn. This provides a natural setting to test

40

the hypothesis that lifelong exposure to PCBs compromises the energy metabolism in this

41

northernmost living salmonid. To test this, blood was sampled from charr from both lakes

42

immediately after capture and following a 1 h handling and confinement stressor to assess

43

possible differences in their energy metabolism and energy substrate mobilization, respectively.

44

The plasma metabolome of charr was assessed by metabolite detection/separation with LC-MS.

45

Plasma metabolite profiles revealed differences in key pathways involved in amino acid

46

metabolism between charr from each lake, underscoring an impact of PCBs on energy

47

metabolism in Arctic charr residing in Lake Ellasjøen. Subjecting charr from either lake to an

48

acute stressor altered the plasma metabolite profiles and revealed distinct stress metabolome in

49

Lake Ellasjøen charr, suggesting a reduced metabolic capacity. Taken together, lifelong exposure

50

to PCBs in Ellasjøen charr disrupts the plasma metabolome, and may impair the adaptive

51

metabolic response to stressors, leading to a reduced fitness.

52 53

KEYWORDS: Arctic, energy metabolism, metabolomics, PCBs, salmonid, stress performance,

54

wildlife

2

ACS Paragon Plus Environment

Page 2 of 30

Page 3 of 30

Environmental Science & Technology

Gauthier et al. 55

1.0. Introduction On the remote island of Bjørnøya (74° 30′N, 19° 00′E) in the Norwegian arctic, an

56 57

interesting case of environmental contamination occurs. Lake Ellasjøen is frequented by

58

migratory seabirds that breed in cliffs along the coast of the island and use the lake as a resting

59

area during summer months,1 during which a large amount of seabird guano is deposited directly

60

into the lake. This seabird guano is enriched with organohalogenated compounds, including

61

polychlorinated biphenyls (PCBs), which contribute up to 80% of reported PCBs within the

62

lake.1 Several other lakes on Bjørnøya, including Lake Laksvatn, are not visited by seabirds, and

63

thus have no contributions of PCB-rich guano.1 Lake Ellasjøen and Lake Laksvatn are

64

oligotrophic lakes with no point-source of pollution, located within ca. 15 km of each other, and

65

contain only one species of fish, the Arctic charr (Salvelinus alpinus), which are land-locked.2

66

Therefore, these island lakes of Bjørnøya provide an excellent opportunity to study the

67

ecological effects of life-long exposure to PCBs on a high-latitude freshwater fish in a natural

68

setting.

69

Exposure to PCBs can have adverse toxicological effects in fish, including the

70

modulation of stress performances via disruption in the functioning of the hypothalamus-

71

pituitary-interrenal (HPI) axis.3 It has already been shown that PCBs bioaccumulate in Arctic

72

charr that inhabit Lake Ellasjøen, and disrupt the molecular mechanisms involved in the

73

activation of the HPI axis in this species.4,5 Activation of the HPI axis initiates a cascade of

74

events eventually leading to the release of cortisol, the primary glucocorticoid in teleosts,6

75

triggering the mobilization of energy reserves to cope with the stressor. A key role for cortisol

76

during stress adaptation involves an increase in the intermediary metabolism, including enhanced

77

activity of alanine aminotransferase, aspartate aminotransferase, glutamate dehydrogenase and

3

ACS Paragon Plus Environment

Environmental Science & Technology

Gauthier et al. 78

glutamine synthetase, which facilitates the mobilization of amino acids substrates for oxidation

79

and/or gluconeogenesis.6

80

The magnitude of plasma cortisol levels in response to an acute stressor exposure is

81

widely applied as a biomarker of stress performance in fish.3 Yet, despite changes in HPI

82

transcript abundance observed in charr from Lake Ellasjøen when compared to charr from Lake

83

Laksvatn, PCB exposure did not modify the plasma cortisol response to a stressor.5 However,

84

whether the lifelong exposure to PCBs may have downstream metabolic effects, including

85

disruption in mobilization of energy reserves to cope with stressor insults, are far from clear.7,8

86

For instance, exposure to PCBs has been shown to modulate the activities of enzymes involved

87

in amino acid metabolism, including alanine aminotransferase in rainbow trout (Oncorhynchus

88

mykiss) and Arctic charr liver.9-11 Also, lifelong exposure to PCBs increases the liver transcript

89

abundance of the glucocorticoid receptor, a key protein involved in cortisol signalling and

90

mediating the metabolic response to stress,12 and this could potentially make the animal more

91

sensitive to corticosteroid action.5 Consequently, the lower body mass observed in charr from

92

Lake Ellasjøen compared to Lake Laksvatn may suggest an increased metabolic demand and

93

reduced anabolic capacity in response to lifelong PCBs exposure, but this was not tested

94

previously.5

95

In order to better understand the metabolic consequences of lifelong PCB exposure on

96

feral charr, we assessed the whole plasma metabolome, as well as plasma lactate and glucose

97

levels, of charr caught from Lake Ellasjøen and Lake Laksvatn. Our hypothesis was that charr

98

from the contaminated lake have a lower metabolic capacity and this will be reflected in the

99

altered plasma metabolome in response to an acute secondary stressor. We sampled plasma from

100

charr from Lake Ellasjøen and Lake Laksvatn before and after they had been subjected to an

4

ACS Paragon Plus Environment

Page 4 of 30

Page 5 of 30

Environmental Science & Technology

Gauthier et al. 101

acute handling/confinement stressor in situ. Plasma metabolomes were quantified with LC-MS,

102

and the data processed with a non-metric multidimensional scaling (NMDS)-permutational

103

multivariate analysis of variance (PERMANOVA) to determine the pre-stress differences in

104

metabolite profiles between charr from the two lakes, as well as their response to an acute

105

stressor exposure. Metabolite set enrichment and pathway topology analyses were utilized to

106

identify metabolic pathways impacted due to lifelong PCB exposure and modulated by stressor

107

exposure.

108 109

2.0. Methods

110

2.1. Animal and plasma sampling

111

Arctic charr sampling and the stress protocol have been described previously.5 Briefly,

112

charr were caught from Lake Ellasjøen and Lake Laksvatn by hook and line in September 2014

113

(Table 1). Only large (> 400 g) immature fish were used for sampling, as this size class has

114

historically had the highest level of accumulated PCBs.12 Fish were anaesthetized with 60 mg L-1

115

benzocaine and a maximum of 1 mL of blood was drawn from the caudal vein within 4 min of

116

hooking by Li-heparinized Vacutainers. Sampled fish were then tagged with Floy FTF-69

117

fingerling tags (MGF, Seattle, WA, USA) to identify individuals following a confinement

118

stressor that involved fish being contained in a holding container filled with ca. 50 L of 5 °C lake

119

water. After 1 h, fish were anaesthetised in 120 mg L-1 benzocaine and again sampled for blood

120

for metabolome analysis. Blood samples were centrifuged at × 4000 g for 5 min and plasma was

121

collected and stored at -80 °C for later metabolite and metabolome analyses. Permission for

122

fieldwork was granted by the Governor of Svalbard and the experimental work was approved by

123

the Norwegian animal research authority (Norwegian Food Safety Authority).

5

ACS Paragon Plus Environment

Environmental Science & Technology

Gauthier et al. 124 125

2.2. Glucose and lactate levels Plasma glucose and lactate concentrations were measured in plasma samples prior to the

126

acute stressor and 1 h after stressor exposure according to protocols described previously.14,15

127

2.3. Plasma metabolome analysis

128

We used a methanol extraction for detection of polar metabolites by hydrophilic

129

interaction liquid chemistry (HILIC)16. Plasma samples were centrifuged at ×17,000 g for 1 min

130

and 50 µL of supernatant was transferred to 450 µL of 50% MeOH in clean 1 mL centrifuge

131

tubes and vortexed. The diluted samples were centrifuged at ×17,000 g for 1 min and then frozen

132

at -80 °C for ca. 12 h prior to analysis. An additional centrifuge step was included if there was

133

visible precipitate in the supernatant. The supernatant was used for mass spectrometry (MS)

134

analysis at the Calgary Metabolomics Research Facility (CMRF), University of Calgary.

135

Metabolites were detected by liquid chromatography mass spectrometry (LC-MS) with a

136

Vanquish™ UHPLC system (Thermo-Fisher, Waltham, MA, USA) and Q Exactive™ HF Hybrid

137

Quadrupole-Orbitrap™ mass spectrometer (Thermo-Fisher). Metabolites were separated with a

138

Syncronis HILIC 1.7 µm 2.1 × 100 mm column (Thermo-Fisher).

139

2.4. MS data processing

140

Spectral intensity data were matched to an in-house metabolite library within

141

MAVEN17,18 provided by the CMRF (see supporting information). A minimum peak intensity of

142

100,000 ions excluded low intensity metabolite matches, which were further screened for quality

143

of peak alignment according to Clasquin et al.18 Data were exported from MAVEN and imported

144

in R19 for subsequent processing. Metabolite names were matched to the Kyoto Encyclopedia of

145

Genes and Genomes (KEGG) compound database. In cases where KEGG compound accession

146

identifiers were unavailable, the compound was removed from the dataset.

6

ACS Paragon Plus Environment

Page 6 of 30

Page 7 of 30

Environmental Science & Technology

Gauthier et al. 147 148

2.5. Statistics The effects of lake, stress, sex and their interaction on glucose and lactate were tested

149

using a linear mixed-model to account for repeated measurements of pre- and post-stress

150

samplings with the ‘lme’ function of the ‘nlme’ package in R version 3.3.2.19,20 Results from the

151

glucose and lactate analyses are presented as means ± standard errors.

152

Non-metric multidimensional scaling was carried out using the ‘metaMDS’ function from

153

the ‘vegan’ package21 to ordinate similarities among treatments and metabolites. Spectral

154

intensity data were square root transformed and Wisconsin double standardization was

155

performed prior to calculating the Euclidean distance matrix for NMDS. Ordination results were

156

centre-scaled and axes were rotated to maximally represent variation in the first dimension.

157

Ellipses were drawn around the four treatment groups, excluding sex (i.e., Ellasjøen pre- and

158

post-stressor, and Laksvatn pre- and post-stressor) using the ‘ordiellipse’ function of the ‘vegan’

159

package to illustrate standard deviations of NMDS ordinations scores based on replicates (i.e.,

160

plasma samples) within each treatment group. Following NMDS, the effects of lake, stress, sex

161

and their interactions on metabolite spectral intensity data were analyzed with a PERMANOVA

162

using the Euclidean distance matrix.22 The PERMANOVA was performed with the ‘adonis’

163

function of the ‘vegan’ package.

164

When no interaction was detected from the PERMANOVA, subsequent reporting of main

165

effects represent changes exclusive to that treatment (i.e., lake effect independent of stressor

166

effect and stressor effect independent of lake effect).

167

Metabolite set enrichment analysis was carried out to determine metabolite pathways that

168

were active in fish from Lake Ellasjøen and Lake Laksvatn. An over-representation analysis

169

(ORA) was applied according to Xia and Wishart.23 We obtained the KEGG metabolite pathway

170

database for zebrafish (Danio rerio) in R using the ‘keggGet’ function from the ‘pathview’ 7

ACS Paragon Plus Environment

Environmental Science & Technology

Gauthier et al. 171

package.24 We applied a hypergeometric test using the ‘phyper’ function from the ‘stats’

172

package20 to determine the probability of randomly matching the metabolites present in the charr

173

plasma samples to those of each pathway. The ORA was carried out independent of treatment

174

effects as metabolites occurred ubiquitously across all plasma samples, despite any potential

175

differences in their spectral intensities. A false discovery rate correction was applied using the

176

‘p.adjust’ function of ‘stats’ package to reduce the risk of type 1 error associated with

177

independent ORAs for each pathway.

178

Metabolite pathway topology was analyzed to determine the relative impact of

179

metabolites in each pathway. Pathway topology assists in objectively measuring the importance

180

of over-represented pathways in terms of metabolites present in plasma samples. We applied

181

relative betweenness centrality (RBC) as our centrality measure for topology analyses. Briefly,

182

RBC first determines the shortest paths between all metabolite pairs in the pathway, and then

183

quantifies the number of shortest paths that intersect with a given metabolite, and divides that

184

number by the total number of shortest paths in the pathway.25 Metabolites that have a greater

185

number of intersecting shortest paths will have a higher RBC. Pathway maps were downloaded

186

as .xml files from the KEGG database and imported into R using the ‘parseKGML’ function of

187

the ‘KEGGgraph’ package.26 Metabolite information was then translated into graph objects using

188

the ‘KEGGpathway2reactionGraph’ function of the ‘KEGGgraph’ package. Graph objects

189

contained the necessary metabolite (i.e., nodes) and linkage (i.e., edges) information to determine

190

RBC using the ‘brandes.betweenness.centrality’ function of the ‘RBGL’ package.27 After

191

determining the RBC for each metabolite, total pathway impact was calculated by dividing the

192

summed RBCs of matched metabolites (i.e., metabolites within each pathway that were present

193

in charr plasma samples) by the total RBC score from all metabolites in the pathway.

8

ACS Paragon Plus Environment

Page 8 of 30

Page 9 of 30

Environmental Science & Technology

Gauthier et al. 194

Pathways were plotted according to their log p-values from ORA and pathway impact.

195

We chose to focus on a subset of pathways based on a threshold of their combined ORA log p-

196

values and pathway impact. Because total pathway impact always has a maximum of 1, a line

197

having a negative slope of minimum log p-value connects the maximum values of each axis.

198

Pathways that were on the origin-side of this line were excluded from further analysis. This

199

conservative threshold allowed us to focus only on the most important pathways. Pathways that

200

were deemed important were mapped using the ‘Rgraphviz’ package28 with log2 fold-changes in

201

metabolite spectral peak intensities to illustrate patterns among treatment groups. Spectral

202

intensity data were median-normalized prior to log2 fold-change calculations.

203

We recognize the growing concern of biases and errors associated with metabolomics

204

data.29 Biological variance from selection bias was reduced by sampling similarly aged fish with

205

a near-equal sex ratio during the same sampling period.5 Analytical variance was reduced by

206

having samples prepared for LC-MS by the same user during a 2 h window, with all samples

207

being analyzed by LC-MS the following day. Once data were obtained, only strong peaks were

208

retained within the dataset. The NMDS and PERMANOVA analyses, both non-parametric tests,

209

avoided assumptions on the distribution of errors in the dataset. The biases associated with ORA

210

were in part reduced by imposing strict and objective criteria to screen metabolic pathways

211

having the greatest likelihood of being relevant to the charr metabolome. For example, 29

212

pathways were identified as being over-represented, yet after combining the ORA with a

213

pathway impact analysis to develop a selection threshold, only 7 over-represented pathways were

214

retained for further analysis. Our prediction was that lifelong exposure to PCBs would alter the

215

metabolome in terms of stress performance and energy substrate metabolism, and irrespective of

216

our selection threshold, the identified pathways corroborated this expectation.

9

ACS Paragon Plus Environment

Environmental Science & Technology

Gauthier et al. 217

3.0. Results

218

3.1. Plasma glucose and lactate analysis

219

Plasma glucose (t(16) = 3.54; p = 0.0027) and lactate (t(16) = 8.57; p < 0.0001) levels

220

increased by 1.97 ± 0.55 and 5.23 ± 0.61 mM, respectively, following the handling/confinement

221

stressor. There was no effect of sex, lake, or interactive effect of lake, stressor, and sex on

222

plasma glucose and lactate concentrations in charr plasma (Figure 1).

223

3.2 Metabolome analysis

224

Analysis and screening of spectral intensity data in MAVEN identified 165 metabolites

225

present in the KEGG database (Tables S1 and S2). The ordination by NMDS separated

226

metabolites by lake and stressor treatments (Figure 2). The ORA identified 27 pathways in which

227

metabolites were over-represented (Table 1). Within these over-represented pathways, total

228

pathway impact varied from 0 to 1, indicating that some identified pathways had none or all of

229

the metabolites with RBC scores greater than 0 present (Table 1). Pathways that surpassed the

230

threshold ratio of log(p-values) from ORA and pathway impact were aminoacyl-tRNA

231

biosynthesis (KEGGpid 00970), alanine, aspartate, and glutamate metabolism (KEGGpid

232

00250), glycine, serine and threonine metabolism (KEGGpid 00260), arginine biosynthesis

233

(KEGGpid 00220), phenylalanine metabolism (KEGGpid 00360), caffeine metabolism

234

(KEGGpid 00232), and D-glutamine and D-glutamate metabolism (KEGGpid 00471; Figure 3).

235

As no sex-related effects were observed on the charr metabolome and plasma glucose and lactate

236

concentrations, we omitted sex from our final analyses of the plasma metabolome.

237

3.2.1 Lake effect

238 239

The PERMANOVA revealed metabolites varied between lakes (F(1,36) = 5.1; p = 0.011), with log2 fold-changes of metabolites ranging from -5.33 to 6.67 between the lakes. The majority

10

ACS Paragon Plus Environment

Page 10 of 30

Page 11 of 30

Environmental Science & Technology

Gauthier et al. 240

of metabolites measured were lower in Lake Ellasjøen charr compared to Lake Laksvatn charr,

241

with 32% and 68% of metabolites being up- and down-regulated, respectively (Table S1).

242

Metabolites detected within the phenylalanine metabolism pathway were all up-regulated in

243

Lake Ellasjøen charr, whereas metabolites detected within the caffeine, and D-glutamine and D-

244

glutamate metabolism pathways were all down-regulated in Lake Ellasjøen compared to

245

Laksvatn charr (Figure 4). For alanine, aspartate, and glutamate metabolism, glycine, serine, and

246

threonine metabolism, arginine biosynthesis, and aminoacyl t-RNA biosynthesis, 63.6%, 45.5%,

247

50%, and 52.9% of detected plasma metabolites were down-regulated respectively in Lake

248

Ellasjøen charr compared to Lake Laksvatn charr (Figure 4).

249

3.2.2 Stressor effect

250

The PERMANOVA revealed that metabolites varied prior to and in response to stressor

251

exposure (F(1,36) = 14.5; p = 0.0009), with log2 fold-changes of metabolites ranging from -1.47 to

252

3.21 pre-and post-stressor. The majority of metabolites were down-regulated following the

253

confinement stressor, with 39% and 61% of metabolites being up- and down-regulated

254

respectively (Table S2). In comparison with metabolite log2 fold-changes from the effect of lake,

255

61% of metabolites had opposite changes in response to the confinement stressor. Metabolites

256

detected within the phenylalanine metabolism pathway were all down-regulated following the

257

confinement stressor, whereas metabolites detected within the caffeine metabolism pathway

258

were all up-regulated following the confinement stressor (Figure 5). For alanine, aspartate, and

259

glutamate metabolism, glycine, serine, and threonine metabolism, arginine biosynthesis, D-

260

glutamine and D-glutamate metabolism, and aminoacyl t-RNA biosynthesis, 36.4%, 54.5%,

261

62.5%, 66.6% and 82.4% of detected metabolites were down-regulated post-stressor,

262

respectively (Figure 5). The PERMANOVA did not detect an interactive effect of lake and

11

ACS Paragon Plus Environment

Environmental Science & Technology

Gauthier et al. 263

stressor exposure (F(1,36) = 1.23; p = 0.26). However, there were differences in metabolites

264

between post-stressed fish from each lake, with log2-fold changes ranged between -5.9 to 7.4

265

(Table S3).

266 267 268

4.0. Discussion Although the Arctic environment lacks a point-source for PCB contamination, studies

269

have clearly shown that animals residing in this pristine environment are exposed to

270

contaminants from different off-target sources.13,30 Our companion study recently demonstrated

271

that charr from Lake Ellasjøen exhibited altered gene expressions suggestive of endocrine

272

disruption of the stress axis.5 Using a metabolomics approach, our results suggest for the first

273

time that lifelong exposure to PCBs may also affect energy metabolism in Arctic charr, leading

274

to disruption in energy substrate mobilization that is critical for coping with additional stressors.

275

4.1. Effect of PCBs on charr plasma metabolome

276

The levels of PCB contamination in Lake Ellasjøen and bioaccumulation in charr within

277

the lake have been monitored for over two decades.2 Sediment concentrations of PCBs in Lake

278

Ellasjøen have ranged from 2 to 600 times higher than sediments from other arctic lakes around

279

the world.13 When compared with charr we caught from Lake Laksvatn, muscle PCB

280

concentrations in charr from Lake Ellasjøen were 29 ng g-1 ww, approximately 750% higher.5

281

Also, previous surveys have reported muscle PCB concentrations as high as 5175 ng g-1 ww in

282

charr from Lake Ellasjøen.13 Exposure to these levels of PCBs is sufficient to induce

283

reproductive toxicity in rainbow trout.2 Thus, it is expected that charr from Lake Ellasjøen have

284

been experiencing toxicological effects with life-long exposure to PCBs at these concentrations.

285 286

The continual exposure to PCBs throughout their lifetime may subject charr to a higher metabolic cost as indicated by the strong (10-fold) up-regulation of cytochrome P450 1A, a key

12

ACS Paragon Plus Environment

Page 12 of 30

Page 13 of 30

Environmental Science & Technology

Gauthier et al. 287

protein involved in PCB detoxification.31 This increased energy demand associated with critical

288

protein synthesis for biotransformation may, at least partly, explain the lower body mass of charr

289

from Lake Ellasjøen compared to the less contaminated charr of the same age from Lake

290

Laksvatn.5,31 Along with this, the distinct plasma metabolome observed in charr from Lake

291

Ellasjøen further supports an enhanced metabolic demand due to PCB-exposure compared to

292

charr from Lake Laksvatn. The most significant differences in charr plasma metabolome from

293

Lake Ellasjøen compared to Lake Laksvatn were related to amino acid metabolism, with the

294

majority (i.e., 87.8%) of metabolites related to alanine, aspartate, and glutamate metabolism,

295

caffeine metabolism, and D-glutamine and D-glutamate metabolism being lower in Lake

296

Ellasjøen charr compared to Lake Laksvatn charr. A decrease in plasma amino acid

297

concentrations (Tables S1 and S2), including alanine, and lysine, glutamine, and glutamate

298

suggests a lowering of oxidative and gluconeogenic substrates in the plasma in response to PCB

299

contamination. The lowering of plasma amino acid concentration is normally associated with

300

extended fasting,32 and we propose that fish in the contaminated lake may have a lower feeding

301

or food-conversion efficiency supporting a reduced anabolic capacity due to PCB contamination.

302

However, a reduction in plasma amino acids may also be indicative of an increased utilization

303

within various tissues due to increased metabolic demand.33 Specifically, pyruvate and 2-

304

oxoglutarate, two metabolites critical to energy metabolism,34 were lower in plasma from charr

305

inhabiting Lake Ellasjøen. This along with an up-regulation of cocarboxylase, a coenzyme

306

fundamental to energy metabolism via the decarboxylation of pyruvate and 2-oxoglutarate,35

307

suggests an increased tissue utilization of pyruvate and 2-oxoglutarate supporting an increase in

308

metabolic demand. The combination of higher liver cytochrome P450 1a mRNA,5 a plasma

309

metabolome profile indicative of disruption of energy metabolism, and the observed reduced

13

ACS Paragon Plus Environment

Environmental Science & Technology

Gauthier et al. 310

growth in charr from Lake Ellasjøen,5 suggests that lifelong exposure to PCB may increase the

311

metabolic demand and curtail the anabolic capacity in Lake Ellasjøen charr.

312

4.2. Effect of PCBs on charr stress metabolome

313

The response of fish to a stressor, including handling and confinement, involves the

314

activation of the sympathetic system and the HPI axis initiating a cascade of events, including

315

hormone release, ultimately leading to the mobilization of energy reserves to cope with the

316

increased energy demand.4-6,8 While the sympathetic system activation is essential for the rapid

317

response to stressors, the HPI axis activation and the associated release of cortisol, the main

318

glucocorticoid in teleosts, plays a key role in the mobilization and reallocation of energy

319

substrates to cope with the stressor, as well as re-establish homeostasis.4,8,36 Glucose is the main

320

fuel to meet the increased energy demand during stress, and this is produced mainly in the liver

321

in response to stress hormone stimulation.4,6,36,37 In addition to glucose, lactate and amino acids

322

have also been used as substrates for oxidation and gluconeogenesis in fish hepatocytes in

323

response to stress and cortisol stimulation.9 However, the plasma metabolite changes during an

324

acute stressor exposure are far from clear in fishes. In the present study, plasma glucose and

325

lactate levels were elevated in response to an acute stressor in charr from both lakes, supporting

326

enhanced energy substrate mobilization.6

327

The metabolomics approach allowed us to identify pathways that may be important in

328

affecting stress performance and energy metabolism in Arctic charr. In the present study,

329

stressor-mediated changes in plasma metabolome points to increases in mobilization of energy

330

substrates, including pyruvate and 2-oxoglutarate. In general, the majority of pathways we

331

identified through ORA and total pathway impact are involved in the production of amino acids

332

that are either substrates for oxidation and/or gluconeogenesis.6 We propose the acute stress

14

ACS Paragon Plus Environment

Page 14 of 30

Page 15 of 30

Environmental Science & Technology

Gauthier et al. 333

plasma metabolome is most likely indicative of a mobilization of energy substrates from

334

muscles, for use by target tissues, including liver, for oxidation and also for gluconeogenesis.6,8,36

335

The elevation in plasma cortisol levels in response to a stressor, as was seen in the present

336

study,5 may be playing a key role in the energy substrate mobilization. For instance, activation of

337

the HPI axis has been shown to increase the activity of glutamine synthetase, aspartate

338

aminotransferase, tyrosine aminotransferase, and glutamate dehydrogenase in the muscle and

339

liver,6 all of which are critical enzymes involved in amino acid metabolism and may be involved

340

in the altered plasma amino acid patterns in the post-stressed charr plasma (i.e., arginine

341

biosynthesis, alanine, aspartate, and glutamate metabolism, and phenylalanine metabolism).

342

However, most of these enzyme changes were reported after several hours of cortisol treatment

343

in fishes,6,8 leading to the proposal that the rapid changes in plasma amino acids after an acute

344

stress may be due to other metabolic hormones stimulation and/or a nongenomic action of

345

cortisol, but remains to be determined.8

346

In general, there was a lowering of plasma metabolites following the confinement

347

stressor, regardless of the lakes. However, because the baseline plasma metabolome (prior to

348

stress) was different between the two lakes (see section 4.1), the post-stressor plasma metabolites

349

profile of post-stressed fish from Lake Ellasjøen was also distinct from that of Lake Laksvatn

350

(Figure 2). In fact, metabolite log2 fold-changes were greatest when comparing post-stressed fish

351

between the two lakes (Tables S1-S3). These changes in plasma stress metabolite profiles

352

between the two lakes suggest that lifelong PCB-exposure may compromise the metabolic

353

adjustments essential for coping with stressors in Arctic charr. Moreover, 61% of metabolites

354

that showed log2 fold-changes in the pre-stress lake comparisons (Table S1) also differed in

355

response to stressor exposure, but in the opposite direction (Tables S2; Figures 4 and 5). This

15

ACS Paragon Plus Environment

Environmental Science & Technology

Gauthier et al. 356

would further support our notion that the capacity for charr from Lake Ellasjøen to mobilize

357

energy substrates to subsequent stressors may be impaired to a greater extent due to their already

358

compromised pre-stress metabolic capacity.

359

Although predominant, PCBs are not the only contaminants present in Lake Ellasjøen38

360

and in the resident charr tissues.1 However, historic records from Lake Ellasjøen indicate low

361

levels of trace metals.39 Moreover, the effects of PCBs on the physiological and cellular stress

362

responses in charr have already been demonstrated in controlled laboratory studies40,41 and

363

support our findings of compromised amino acid metabolism.42,43,44 As both lakes are

364

oligotrophic,2 guano contributions to Lake Ellasjøen have likely increased nutrient loading in the

365

lake, which would influence primary productivity and growth rates in charr.13 However, charr

366

from Lake Ellasjøen were smaller in size compared to Lake Laksvatn, leading us to propose that

367

changes in energy repartitioning associated with biotransformation of contaminants may in part

368

play a role in reducing the anabolic capacity of charr in the contaminated lake. The combination

369

of altered metabolic capacity, including a lower body mass and an impaired metabolite response

370

to stress may reduce the fitness of charr in Lake Ellasjøen, but this needs to be ascertained.

371

Future studies involving early life-stages may also greatly aid in our understanding of the

372

molecular mechanisms leading to the development of PCB-related health dysfunction in the wild

373

populations of this northernmost teleost species.

374 375 376 377

Supporting information Supporting information includes a list of all metabolites within the CMRF library, and tables S1, S2, and S3, which describe log2 fold-changes in metabolites.

16

ACS Paragon Plus Environment

Page 16 of 30

Page 17 of 30

Environmental Science & Technology

Gauthier et al. 378 379 380

Acknowledgements The study was funded by The Research Council of Norway (project no. 221371/E40) and

381

the Norwegian Ministry of Environment through the Fram Centre flagship (Tromsø, Norway)

382

"Hazardous substances – effects on ecosystems and human health", and by the Natural Sciences

383

and Engineering Research Council (NSERC) of Canada Discovery Grant. We thank Ryan

384

Groves at the Calgary Metabolomics Research Facility for developing the LC-MS protocol and

385

running our samples, Nawamaka Merah for plasma glucose and lactate analysis, and Jenny

386

Bytingsvik and Marianne Frantzen for valuable assistance with fieldwork.

387 388

References

389 390 391

(1) Evenset, A., Carroll, J., Christensen, G.N., Kallenborn, R., Gregor, D., Gabrielsen, G.W. Seabird guano is an efficient conveyer of persistent organic pollutants (POPs) to arctic lake ecosystems. Environ. Sci. Technol. 2007, 41: 1173-1179.

392 393 394 395

(2) Bytingsvik, J., Frantzen, M., Gotsch, A., Heimstad, E.S., Christensen, G., Evenset, A. Current status, between-year comparisons and maternal transfer of organohalogenated compounds (OHCs) in Arctic char (Salvelinus alpinus) from Bjørnøya, Svalbard (Norway). Sci. Total Environ. 2015, 521-522: 421-430.

396 397

(3) Hontela, A., Vijayan, M.M. In Adrenal Toxicology; Harvey, P.W., Everett, D., Springall, C., Eds.; CRC Press; Boca Raton, 2008; pp 233-256.

398 399

(4) Vijayan, M.M., Aluru, B., Leatherland, J.F. 2010. In Fish Disease and Disorders; Leatherland, J.F., Woo, P.T.K., Eds.; CAB International; Wallingsford, 2010; pp 182-201.

400 401 402 403

(5) Jørgensen, E.H., Maule, A.G., Evenset, A., Christensen, G., Bytningsvik, J., Frantzen, M., Nikiforov, V., Faught, E., Vijayan, M.M. Biomarker response and hypothalamus-pituitaryinterrenal axis functioning in Arctic charr from Bjørnøya (73°30’N), Norway, with high levels of organohalogenated compounds. Aquat. Toxicol. 2017, 187: 64-71.

404 405

(6) Mommsen, T.P., Vijayan, M.M., Moon, T.W. Cortisol in teleosts: dynamics, mechanisms of action, and metabolic regulation. Rev. Fish Biol. Fish. 1999, 9: 211-268.

406 407 408

(7) Trenzado, C.E., Carrick, T.R., Pottinger, T.G. Divergence of endocrine and metabolic responses to stress in two rainbow trout lines selected for differing cortisol responsiveness to stress. Gen. Comp. Endocrinol. 2003, 133: 332-340.

409 410

(8) Faught, E., Vijayan, M.M. 2016. Mechanisms of cortisol action in fish hepatocytes. Comp. Biochem. Physiol. B Biochem. Mol. Biol. 2016, 199: 136-145.

17

ACS Paragon Plus Environment

Environmental Science & Technology

Gauthier et al. 411 412 413

(9) Braunbeck, R., Strmac, M. Assessment of water and sediment contamination in small streams by means of cytological and biochemical alterations in isolated rainbow (Oncorhynchus mykiss) through hepatocytes. J. Aquat. Ecosys. Stress Recov. 2001, 8: 337-354.

414 415 416

(10) Řehulka, J., Minařík, B. Effect of polychlorinated biphenyls (Declor 103) on haematological and enzyme parameters of the rainbow trout Oncorhynchus mykiss. Dis. Aquat. Organ. 2004, 62: 147-153.

417 418

(11) Vijayan, M.M., Aluru, N., Maule, A.G., Jorgensen, E.H. Fasting augments PCB impact on liver metabolism in anadromous arctic char. Toxicol. Sci. 2006, 91: 431-439.

419 420 421

(12) Wiseman, S., Jørgensen, E.H., Maule, A.G., Vijayan, M.M. Contaminant loading in remote Arctic lakes impact cellular stress-related proteins expression in feral charr. Polar Biol. 2011, 34: 933-937.

422 423 424

(13) Evenset, A., Christensen, G.N., Skotvold, T., Fjeld, E., Schlabach, M., Wartena, E., Gregor, D. A comparison of organic contaminants in two high Arctic lake ecosystems, Bjørnøya (Bear Island), Norway. Sci. Total Environ. 2004, 318:125-141.

425 426

(14) Bergmeyer,

427 428 429

(15) Gutmann, I., Wahlefeld, A.W. L-(+)-Lactate. Determination with lactate dehydrogenase and NAD. In Methods of Enzymatic Analysis, 2nd English ed., Vol. 3. H. U. Bergmeyer, ed. Academic Press, New York, 1974, pp. 1464.

430 431

(16) Cai, X., Li, R. Concurrent profiling of polar metabolites and lipids in human plasma using HILIC-FTMS. Sci. Rep. 2016, 6: 36490.

432 433

(17) Melamud, E., Vastag, L., Rabinowitz, J.D. Metabolomic analysis and visualization engine for LC-MS data. Anal. Chem. 2010, 82: 9818-9826.

434 435 436

(18) Clasquin, M.F., Melamud, E., Rabinowitz, J.D. LC-MS processing with MAVEN: a metabolomics analysis and visualization engine. Curr. Protoc. Bioinformatics 2012, 37: 14.11.1-14.11.23.

437 438

(19) R: A Language and Environment for Statistical Computing, version 3.4.1; R Foundation for Statistical Computing: Vienna, 2017.

439 440

(20) Pinheiro, J., Bates, D., DebRoy, S., Sarkar, D, R Core Team. nlme: linear and nonlinear mixed effects model, version 3.1-131; 2017.

441 442 443

(21) Oksanen, J., Blanchet, F.G., Friendly, M., Kindt, R., Legendre, P., McGlinn, D., Minchin, P.R., O’Hara, R.B., Simpson, G.L., Solymos, P., Stevens, M.H.H., Szoecs, E., Wagner, H. vegan: community Ecology Package, version 2.4-2.; 2017/

444 445

(22) Anderson, M.J. A new method for non-parametric multivariate analysis of variance. Austral Ecol. 2001, 26: 32-46.

446 447

(23) Xia, J., Wishart, D.S. MSEA: a web-based tool to identify biologically meaningful patterns in quantitative metabolomic data. Nucleic Acids Res. 2010, 38: W71W77.

448 449

(24) Luo, W., Brouwer, C. Pathview: an R/Bioconductor package for pathway-based data integration and visualization. Bioinformatics 2013, 29: 1830-1831.

H. U. UV-Methods with hexokinase and glucose-6- phosphate dehydrogenase. In Methods of Enzymatic Analysis; Verlag Chemie: Weinheim, FR Germany, 1984; pp 163.

18

ACS Paragon Plus Environment

Page 18 of 30

Page 19 of 30

Environmental Science & Technology

Gauthier et al. 450 451

(25) Barthélemy, M. Betweenness centrality in large complex networks. Eur. Phys. J. B 2004, 38: 163-168.

452 453

(26) Zhang, J.D., Wiemann, S. KEGGgraph: a graph approach to KEGG PAHWAY in R and Bioconductor. Bioinformatics 2009, 25: 1470-1471.

454 455

(27) Carey, V., Long, L., Gentleman, R. RBGL: an interface to the BOOST graph library, version 1.50.0; 2016.

456 457

(28) Hansen, K.D., Gentry, J., Long, L., Gentleman, R., Falcon, S., Hahne, F., Sarkar, D. Rgraphviz: provides plotting capabilities for R graph objects, version 2.18.0.; 2016.

458 459

(29) Moseley, H.N.B. Error analysis and propagation in metabolomics data analysis. Comput. Struct. Biotechnol. J. 2013, 4: e201301006.

460 461 462 463

(30) Letcher, R.J., Bustnes, J.O., Dietz, R., Jenssen, B.M., Jørgensen, E.H., Sonne, C., Verreault, J., Vijayan, M.M., Gabrielsen, G.W. Exposure and effects assessment of persistent organohalogen contaminants in arctic wildlife and fish. Sci. Total Environ. 2010, 408: 29953043.

464 465 466

(31) Jørgensen, E.H., Vijayan, M.M., Killie, J.-E.A., Aluru, N., Aas-Hansen, Ø., Maule, A. Toxicokinetics and effects of PCBs in Arctic fish: a review of studies on Arctic charr. J. Toxicol. Environ. Health A 2016, 9:37-52.

467 468 469

(32) Pottinger, T.G., Rand-Weaver, M., Sumpter, J.P. Overwinter fasting and re-feeding in rainbow trout: plasma growth hormone and cortisol levels in relation to energy mobilisation. Comp. Biochem. Physiol. B Biochem. Mol. Biol. 2003, 136: 403-417.

470 471 472

(33) Hori, T.S.F., Avilez, I.M., Inoue, L.K., Moreas, G. 2006. Metabolic changes induced by chronic phenol exposure in matrinxã Brycon cephalus (teleostei: characidae) juveniles. Comp. Biochem. Physiol. C Toxicol. Pharmacol. 2006, 143: 67-72.

473 474

(34) Enes, P., Panserat, S., Kaushik, S., Oliva-Telesm A. Nutritional regulation of hepatic glucose metabolism in fish. Fish Physiol. Biochem. 2009, 35: 519-539.

475 476

(35) Longsdale, D. A Review of the biochemistry, metabolism and clinical benefits of thiamin(e) and its derivatives. Evid. Based Complement. Alternat. Med. 2006, 3: 49-59.

477 478

(36) Aluru, N., Vijayan, M.M. Stress transcriptomics in fish: a role for genomic cortisol signalling. Gen. Comp. Endocrinol. 2009, 164: 312-329.

479 480 481

(37) Faught, E., Aluru, N., Vijayan, M.M. In Biology of Stress in Fish: Fish Physiology 35; Schreck, C.B., Tort, L., Farrell, A.P., Brauner, C.J., Eds.; Academic Press/Elsevier Inc.; New York, 2016: pp 113-166.

482 483 484

(38) Evenset, A., Christensen, G.N., Carroll, J., Zaborska, A., Berger, U., Herzke, D., Gregor, D. Historical trends in persistent organic pollutants and metals recorded in sediment from Lake Ellasjøen, Bjørnøya, Norwegian Arctic. Environ. Pollut. 2007, 146: 196-205.

485 486 487

(39) Nasjonal innsjøundersøkelse 2004 – 2006, Del I: Vannkjemi. Status for forsuring, næringssalter og metaller; Akvaplan NIVA as: Oslo, 2008; http://www.miljodirektoratet.no/old/klif/publikasjoner/2361/ta2361.pdf

19

ACS Paragon Plus Environment

Environmental Science & Technology

Gauthier et al. 488 489 490

(40) Jørgensen, E.H., Vijayan, M.M. Aluru, N., Maule, A.G. Fasting modifies Aroclor 1254 impact on plasma cortisol: glucose and lactate responses to handling disturbance in Arctic charr. Comp. Biochem. Physiol. C Toxicol. Pharmacol. 2002b, 132: 235-245.

491 492 493

(41) Aluru, N., Jørgensen, E.H., Maule, A., Vijayan, M.M. PCB disruption of the hypothalamuspituitary-interrenal axis involves brain glucocorticoid receptor downregulation in anadromous Arctic charr. Am. J. Physiol. Regul. Integr. Comp. Physiol. 2004, 287: R787-R793.

494 495

(42) Vijayan, M.M., Aluru, N., Maule, A.G., Jørgensen, E.H. Fasting augments PCB impact on liver metabolism in anadromous Arctic char. Toxicol. Sci. 2006, 91: 431-439.

496 497 498

(43) Bellehumeur, K., Lapointe, D., Cooke, S.J., Moon, T.W. Exposure to sublethal levels of PCB-126 impacts fuel metabolism and swimming performance in rainbow trout. Comp. Biochem. Physiol. B Biochem. Mol. Biol. 2016, 199: 97-104.

499 500 501

(44) Nault, R., Al-Hameedi, S., Moon, T.W. Effects of polychlorinated biphenyls on whole animal energy mobilization and hepatic cellular respiration in rainbow trout, Oncorhyncus mykiss. Chemosphere 2012, 87: 1057-1062.

502

20

ACS Paragon Plus Environment

Page 20 of 30

Page 21 of 30

Environmental Science & Technology

Gauthier et al. 503

Figure captions

504

Figure 1. Plasma glucose and lactate levels in charr from Lake Ellasjøen or Lake Laksvatn

505

sampled prior to and after a 1 h of handling/confinement stressor. Values represent means ±

506

SEM (n = 18). Glucose (t(16) = 3.54; p = 0.0027) and lactate (t(16) = 8.57; p < 0.0001) increased

507

following the handling/confinement stressor, while there was no effect of lake or interactive

508

effect of lake and stressor. Different letters indicate significant differences in glucose and lactate

509

between pre- and post-stressed fish.

510 511

Figure 2. Nonmetric multidimensional scaling (NMDS) of Arctic charr plasma metabolome by

512

site and stressor. Axes represent arbitrary distances calculated through NMDS. Objects that are

513

closer together on the plot are more closely associated with one another. Black circles represent

514

metabolites. Red and blue circles represent fish plasma samples from Lake Laksvatn and Lake

515

Ellasjøen respectively. Red and blue shaded and striped ellipses represent standard deviations of

516

charr plasma sample scores for Lake Laksvatn and Lake Ellasjøen pre-and post-stressed

517

respectively. A permutational multivariate analysis of variance (PERMANOVA) detected that

518

the charr plasma metabolome changed following the handling/confinement stress (F(1,36) = 14.6;

519

p = 0.0009) and differed between lakes (F(1,36) = 5.1; p = 0.011).

520 521

Figure 3. Metabolomics pathway enrichment analysis. The y-axis represents results from over-

522

representation analysis (ORA; see section 2.5.) reported as log(p-values). The x-axis represents

523

results from the metabolite pathway topology analysis reported as total pathway impact. Closed

524

black circles represent pathways. The dashed blue line represents our threshold for further

525

pathway analysis. Pathways that exceeded the threshold are identified with text.

526

21

ACS Paragon Plus Environment

Environmental Science & Technology

Gauthier et al. 527

Figure 4. Metabolomics pathway topology analysis, including log2 fold-change differences in

528

charr from Ellasjøen in reference to charr from Laksvatn. Circles represent metabolites. Arrows

529

represent reactions. Circles coloured gray indicate metabolites within the pathway that were

530

absent in the charr plasma samples. The size of each circle indicates its relative betweenness

531

centrality (RBC) within the pathway. Changes observed for metabolites having a higher RBC

532

(i.e., larger circle) are likely to reflect a greater impact on the pathway as a whole compared to

533

metabolites with lower RBC scores.

534

535

Figure 5. Metabolomics pathway topology analysis, including log2 fold-change differences post-

536

stressor (bottom half of circles). Circles represent metabolites. Arrows represent reactions.

537

Circles coloured gray indicate metabolites within the pathway that were absent in the charr

538

plasma samples. The size of each circle indicates its relative betweenness centrality (RBC)

539

within the pathway. Changes observed for metabolites having a higher RBC (i.e., larger circle)

540

are likely to reflect a greater impact on the pathway as a whole compared to metabolites with

541

lower RBC scores.

22

ACS Paragon Plus Environment

Page 22 of 30

Page 23 of 30

Environmental Science & Technology

Gauthier et al.

Table 1. Summary of characteristics of fish sampled from Lake Laksvatn and Lake Ellasjøen modified from Jørgensen et al.5. Sex ratio [Female (F):male (M)], age, fork length (FL), body mass (BM), condition factor (CF), and muscle fat (MF) contents are represented as means ± SEM. n F:M

Lake Laksvatn Ellasjøen

10 10

1:1 2:3

age (yr)

FL (cm)

BM (g)

CF

MF (%)

10.2 ± 0.4 11.3 ± 0.5

48.9 ± 1.1 40.6 ± 0.8

1046 ± 52.9 595 ± 31.1

0.89 ± 0.03 0.88 ± 0.01

0.9 ± 0.18 0.36 ± 0.04

23

ACS Paragon Plus Environment

Environmental Science & Technology

Page 24 of 30

Gauthier et al. Table 2. Over-representation analysis (ORA) and pathway impact of Arctic charr metabolome from lakes Ellasjøen and Laksvatn, Norway. ORA and pathway impact calculations are carried out irrespective metabolite levels (i.e., treatment effects), and identify pathways within the plasma samples of all sampled fish. p-values indicate results from over-representation analysis. Pathway impact is calculated as the summed relative-betweenness centrality (RBC) scores for metabolites present in plasma samples proportional to the total RBC score from all metabolites in the pathway. pathway

KEGGpid

p

Impact

Aminoacyl-tRNA biosynthesis Alanine, aspartate and glutamate metabolism Glycine, serine and threonine metabolism Arginine biosynthesis Tyrosine metabolism Butanoate metabolism Phenylalanine metabolism Pantothenate and CoA biosynthesis Taurine and hypotaurine metabolism Propanoate metabolism Arginine and proline metabolism Pyrimidine metabolism Caffeine metabolism Cysteine and methionine metabolism beta-Alanine metabolism Nicotinate and nicotinamide metabolism Galactose metabolism Purine metabolism Ascorbate and aldarate metabolism Glutathione metabolism Citrate cycle (TCA cycle) Pyruvate metabolism Valine, leucine and isoleucine degradation D-Glutamine and D-glutamate metabolism Sulfur metabolism Lysine biosynthesis Synthesis and degradation of ketone bodies Histidine metabolism Glyoxylate and dicarboxylate metabolism

00970 00250 00260 00220 00350 00650 00360 00770 00430 00640 00330 00240 00232 00270 00410 00760 00052 00230 00053 00480 00020 00620 00280 00471 00920 00300 00072 00340 00630

5.22 × 10-10 7.79 × 10-8 1.75 × 10-7 1.48× 10-5 5.05 × 10-5 5.18 × 10-5 8.88 × 10-5 5.26 × 10-4 8.11 × 10-4 8.40 × 10-4 2.15 × 10-3 3.02 × 10-3 4.30 × 10-3 5.30 × 10-3 6.29 × 10-3 8.34 × 10-3 9.30 × 10-3 9.53 × 10-3 1.18 × 10-2 1.47 × 10-2 2.00 × 10-2 3.32 × 10-2 2.33 × 10-2 2.36 × 10-2 2.64 × 10-2 3.74 × 10-2 3.75 × 10-2 3.83 × 10-2 4.35 × 10-2

0.17 0.78 0.63 0.48 0.09 0.14 0.62 0.01 0.60 0.04 0.34 0.21 1.00 0.24 0.45 0.00 0.42 0.12 0.00 0.13 0.17 0.24 0.02 1.00 0.00 0.00 0.60 0.36 0.15

24

ACS Paragon Plus Environment

Page 25 of 30

Environmental Science & Technology

Gauthier et al. FIGURE 1

25

ACS Paragon Plus Environment

Environmental Science & Technology

26

FIGURE 2

26

ACS Paragon Plus Environment

Page 26 of 30

Page 27 of 30

Environmental Science & Technology

27

FIGURE 3

27

ACS Paragon Plus Environment

Environmental Science & Technology

Page 28 of 30

28

FIGURE 4

Downregulated Alanine, aspartate, and glutamate metabolism

Glycine, serine and threonine Glycine, serine and threonine metabolism metabolism

Alanine, aspartate and glutamate metabolism

2-Oxoglutarate L-Glutamate

L-Threonine Betaine L-Glutamate Glycine

Pyruvate

L-Aspartate

Arginine biosynthesis Arginine biosynthesis

Pyruvaldehyde

L-Alanine

N-Acetyl-Laspartate

Upregulated

2-Oxobutanoate L-Glutamine Sarcosine

L-Serine

Fumarate

Pyruvate

Fumarate

Succinate semialdehyde

Creatine 5-Aminolevulinate

2-Oxoglutarate

L-Asparagine

L-Glutamine

Aspartate Citruline

Succinate

L-Arginine L-Ornithine

D-Glutamine and D-glutamate metabolism

Caffeine metabolism Caffeine metabolism

Phenylalanine metabolism

Phenylalanine metabolism

D-Glutamine and D-glutamate metabolism

L-Glutamine

L-Phenylalanine Theobromine Paraxanthine Phenylpyruvate

L-Glutamate

2-hydroxyphenylacetate

2-Oxoglutarate

Phenylacetate

Aminoacyl t-RNA biosynthesis Aminoacyl-tRNA biosynthesis L-Serine

L-Methionine L-Glutamate

L-Tryptophan

L-Threonine

L-Leucine

L-Isoleucine

L-Lysine

L-Alanine

L-Valine

L-Aspartate

Glycine

L-Glutamine

L-Arginine

L-Phenylalanine

L-Histidine

L-Asparagine

28

ACS Paragon Plus Environment

Page 29 of 30

Environmental Science & Technology

29

FIGURE 5 Down regulated Alanine, aspartate, and glutamate metabolism

Glycine, serine and threonine Glycine, serine and threonine metabolism metabolism

Alanine, aspartate and glutamate metabolism

2-Oxoglutarate L-Glutamate

L-Threonine Betaine L-Glutamate Glycine

Pyruvate

L-Aspartate

Arginine biosynthesis Arginine biosynthesis

Pyruvaldehyde

L-Alanine

N-Acetyl-Laspartate

Up regulated

2-Oxobutanoate L-Glutamine Sarcosine

L-Serine

Fumarate

Pyruvate

Fumarate

Succinate semialdehyde

Creatine 5-Aminolevulinate

2-Oxoglutarate

L-Asparagine

L-Glutamine

Aspartate Citruline

Succinate

L-Arginine L-Ornithine

D-Glutamine and D-glutamate metabolism D-Glutamine and D-glutamate metabolism

Caffeine metabolism Caffeine metabolism

Phenylalanine Phenylalanine metabolism metabolism

L-Glutamine

L-Phenylalanine Theobromine Paraxanthine Phenylpyruvate

L-Glutamate

2-hydroxyphenylacetate

2-Oxoglutarate

Phenylacetate

Aminoacyl t-RNA biosynthesis Aminoacyl-tRNA biosynthesis L-Serine

L-Methionine L-Glutamate

L-Tryptophan

L-Threonine

L-Leucine

L-Isoleucine

L-Lysine

L-Alanine

L-Valine

L-Aspartate

Glycine

L-Glutamine

L-Arginine

L-Phenylalanine

L-Histidine

L-Asparagine

29

ACS Paragon Plus Environment

Environmental Science & Technology

30

ABSTRACT TOC

30

ACS Paragon Plus Environment

Page 30 of 30