1
2
OPEN ACCESS DOCUMENT
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
Information of the Journal in which the present paper is
published:
 Environmental Science & Technology, http://pubs.acs.org/est
 dx.doi.org/10.1021/es4012299
20
1
21
TITLE PAGE:
22
Identification of metabolic pathways in Daphnia magna explaining hormetic effects of
23
selective serotonin reuptake inhibitors and 4-nonylphenol using transcriptomic and
24
phenotypic responses.
25
26
Bruno Campos1,Natàlia Garcia-Reyero2, Claudia Rivetti1, Lynn Escalon3, Tanwir
27
Habib4, Romà Tauler1, Stefan Tsakovski5, Benjamín Piña1, Carlos Barata 1*.
28
29
30
1
31
2
32
Starkville, MS 39759, USA
33
3
34
3909 Halls Ferry Road, Vicksburg, MS 39180, USA
35
4
36
78216, USA
37
5
38
Bourchier Blvd, 1164 Sofia, Bulgaria
39
40
41
*Address correspondence to Carlos Barata, Institute of Environmental Assessment
and Water Research (IDAEA-CSIC), Jordi Girona 18, 08034 Barcelona, Spain.
Telephone: +34-93-4006100. Fax: +34-93-2045904. E-mail: cbmqam@cid.csic.es
Institute of Environmental Assessment and Water Research (IDÆA-CSIC), Jordi
Girona 18, 08034, Barcelona, Spain.
Institute for Genomics, Biocomputing and Biotechnology, Mississippi State University,
U.S. Army Engineer Research and Development Center, Environmental Laboratory
Badger Technical Services, 12500 San Pedro Avenue, Suite 450, San Antonio, TX
Department of Analytical Chemistry, Faculty of Chemistry, Sofia University, James
42
2
43
ABSTRACT
44
The molecular mechanisms explaining hormetic effects of selective serotonin reuptake
45
inhibitors (SSRIs) and 4-nonylphenol in Daphnia magna reproduction were studied in
46
juveniles and adults. Transcriptome analyses showed changes in mRNA levels for 1,796
47
genes in juveniles and 1,214 genes in adults (out of 15,000 total probes) exposed to two
48
SSRIs (fluoxetine and fluvoxamine) or to 4-nonylphenol. Functional annotation of
49
affected genes was improved by assuming the annotations of putatively homologous
50
Drosophila genes. Self-Organizing Map analysis and Partial Least Square Regression
51
coupled with selectivity ratio procedures analyses allowed to define groups of genes
52
with specific responses to the different treatments. Differentially expressed genes were
53
analysed for functional enrichment using Gene Ontology and Kyoto Encyclopaedia of
54
Genes and Genomes databases. Serotonin metabolism, neuronal developmental
55
processes, and carbohydrates and lipid metabolism functional categories appeared as
56
selectively affected by SSRI treatment, whereas 4-nonylphenol de-regulated genes from
57
the carbohydrate metabolism and the ecdysone regulatory pathway. These changes in
58
functional and metabolic pathways are consistent with previously reported SSRI and 4-
59
nonylphenol hormetic effects in D. magna, including a decrease in reserve
60
carbohydrates and an increase in respiratory metabolism.
61
KEYWORDS: Daphnia magna, microarrays, mechanism of action, 4-nonylphenol,
62
SSRIs, fluoxetine, fluvoxamine
63
3
64
INTRODUCTION
65
There is increasing evidence that the presence of many emerging pollutants in aquatic
66
ecosystems may have detrimental effects on aquatic biota1. In the last decade substantial
67
research has been conducted evaluating toxic effects of pharmaceuticals and other
68
emerging pollutants across non-vertebrate species2, 3. Among emerging pollutants, those
69
that may act as putative endocrine disruptors in invertebrate species, for example
70
altering moulting and reproductive processes, are of special concern4. The industrial
71
detergent degradation product 4-nonylphenol and the pharmaceutical group of selective
72
serotonin reuptake inhibitors (SSRIs) had hormetic consistent effects (i.e. increased) on
73
offspring production and/or juvenile developmental rates in D. magna at low
74
concentrations (i.e. 3-15 µg/l), inhibiting those responses at higher concentrations (i.e.
75
100-125 µg/l) 5-7. Surveys in US have reported levels of 12-540 ng/L of fluoxetine in
76
surface waters and effluents 8 but total concentrations of SSRIs in aquatic systems were
77
measured in the range of 840 ng/ L 9 to 3.2 μg/L 10. Nonylphenol levels in surface
78
waters as high as 8 μg/L have been reported in surface waters 11. Therefore the above
79
mentioned hormetic effects occurred at environmental relevant concentrations.
80
SSRIs switch life-history responses towards higher food levels at limiting food
81
conditions: juveniles develop faster reach maturity earlier and females produce more
82
offspring12. Such hormetic effects, however, have a fitness costs: females exposed to
83
SSRIs have enhanced glycogen and aerobic metabolism, which translates into a lower
84
tolerance to starvation at low oxygen levels, a common situation in the field12.
85
Exposure of D. magna adult females to low concentrations of 4-nonylphenol increases
86
reproduction and decreases offspring size 5, 13. These effects are maladaptive in D.
87
magna, since smaller offspring take longer to grow, reach smaller adult size and
88
produce less eggs at maturity 14.
4
89
The mechanisms of action of SSRIs studying phenotypic responses of D. magna
90
exposed to SSRIs were analysed by studying effects on levels of lipids, carbohydrate,
91
proteins, oxygen consumption rates, survival, and offspring production after exposure to
92
SSRIs alone or with a serotonin antagonist
93
SSRIs act in a similar way in D. magna than in humans by increasing serotoninergic
94
activity, but in doing so they alter physiological processes such as increase glycogen
95
and aerobic metabolism. At high doses 4-nonylphenol causes embryo arrest in D.
96
magna, an effect that according to LeBlanc et al 13 may be related to altered metabolism
97
of endogenous steroids (such as ecdysone), resulting in perturbations in their provision
98
to the newly produced eggs.
99
The above mentioned studies support the view that 4-nonylphenol and SSRIs increase
12
. The results from this analysis show that
100
offspring production through different mechanisms of action5,
101
history responses of both, juveniles and adults, having important metabolic effects that
102
in adults result in increased carbohydrate and aerobic metabolism. Sublethal effects of
103
4-nonylphenol have only been observed in adults and probably are related to altered
104
metabolism of endogenous steroids like ecdysone13.
105
The water flea Daphnia magna is possibly the invertebrate system most used in
106
toxicology and experimental ecology and together with its close relative D. pulex is
107
used as a model for environmental genomics research
108
fully sequenced and about 50% of its genome is annotated16, thus that of its close
109
relative D. magna, despite of being incomplete, may benefit from the former.
110
Nevertheless, gene annotation available for the D. pulex genome was insufficient to aid
111
functional analysis, thus additional annotation for functional enrichment and
112
identification of gene networks and metabolic pathways can be performed using
15
12
. SSRIs affect life-
. D. pulex genome has been
5
113
available bioinformatic tools such as Blast, Gene Ontology (GO) and Kyoto
114
Encyclopedia of Genes and Genomes (KEGG) 17-20.
115
The main objective of this work is the study of molecular mechanisms explaining the
116
hormetic effects observed in our two previous studies5, 12 . This was accomplished using
117
transcriptional stress responses of D. magna juveniles and adults exposed to sublethal
118
doses of 4-nonylphenol and the SSRIs fluoxetine and fluvoxamine. We aimed to
119
identify gene pathways that characterize observed physiological and phenotypic
120
response to these contaminants. To this effect, we used a custom microarray that has
121
been successfully used to assess mechanisms of action of different pollutants in D.
122
magna21. Differentially transcribed genes were related to effects observed on offspring
123
production of females chronically exposed to these pollutants using Partial Least Square
124
regression coupled with selectivity ratio procedures. Existing information from D. pulex
125
and Drosophila melanogaster genomes was used to improve the currently poor
126
annotation of D. magna genes and to facilitate the functional analyses of the observed
127
transcriptomic changes.
128
EXPERIMENTAL SECTION
129
Chemicals
130
Analytical grade standards of 4-nonylphenol, fluvoxamine (FV) and fluoxetine (FX)
131
were used in this study. Details can be found in the Supporting Information,
132
experimental section.
133
Analytical Chemistry. Methods and measured concentrations of the studied chemicals
134
in adult reproduction tests were reported elsewhere 5 and in all cases were within 90%
135
of nominal values indicating that degradation/adsorption/uptake to experimental
136
vessels/animals were negligible.
6
137
Experimental animals. All experiments were performed using a well-characterized
138
single clone of D. magna (Clone F), maintained indefinitely as pure parthenogenetic
139
cultures
140
section.
141
Experimental design. Juveniles and adult females from two distinct assay-experiments
142
were considered for transcriptomic studies. Assays with adult females were conducted
143
in a previous study 5 and are briefly described in experiment 2. Selected exposure levels
144
of the studied compounds include those treatments that in a previous study5 affected
145
most reproduction responses: for SSRIs, 7 µg/L of FV and 40 µg/L of FX; for 4-
146
nonylphenol 20 and 60 µg/L.
147
Experiment 1: 72 h exposures of juveniles to the SSRIs: FV (7 µg/L), FX (40 µg/L)
148
and the industrial detergent 4-nonylphenol (20, 60 µg/L). Groups of twenty juveniles
149
(less than 24h old) were exposed to selected exposure levels in 0.5 L of ASTM hard
150
water with food present (5x105 cells/mL of C. vulgaris) during two consecutive moults
151
(about 3 days at 20oC and high food levels)23. Longer exposure periods were not
152
performed as juveniles enter the adolescent instar where ovaries start to develop24
153
Contaminants were dosed using acetone as a carrier (0.1 ml/L). A carrier control of 0.1
154
ml/L was also used to serve as a baseline comparison of transcriptomic responses.
155
Treatments were quadruplicated. At the end of exposure the 20 juveniles of each
156
treatment per replicate were collected, pooled in an eppendorf vial, flash- frozen in
157
liquid N2 and preserved at -80ºC until RNA extraction.
158
Experiment 2: Adult reproduction tests were performed to determine the effects of the
159
chemicals of interest on transcriptomic responses and reproduction rates on adult
160
stages12. Experiments started with 8-9 day old gravid females, which were exposed
161
during three consecutive broods to the studied chemicals (10-14 days). Just after
22
. Culture conditions are described in Supporting Information, experimental
7
162
releasing their fourth clutch into the brood pouch, eggs were gently flushed from the
163
brood pouch and females were immediately flash- frozen in liquid N2 and preserved at -
164
80oC until RNA extraction. As in Experiment 1, a carrier control of 0.1 ml/L was used
165
to serve as a baseline comparison of transcriptomic responses. Full description of these
166
experiments can be found in Experimental section, Supporting Information.
167
RNA extraction. Total RNA was isolated from the samples using Trizol reagent ®
168
(Invitrogen, Carlsbad, USA) following the manufacturer’s protocol and quantified in a
169
NanoDrop D-1000 Spectrophotometer (NanoDrop Technologies, Delaware, DE). RNA
170
quality was checked in an Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara
171
CA). The samples that did not show degradation were used for microarray analysis.
172
Microarrays. Custom D. magna 15,000 probe microarrays (GLP13761) were
173
purchased from Agilent (Palo Alto, CA, USA). A total of four replicates per treatment
174
were used for both juveniles and adult exposures. One µg of total RNA was used for all
175
hybridizations, and cDNA synthesis, cRNA labelling, amplification, and hybridizations
176
were performed following the manufacture’s kits and protocols (Quick Amp labelling
177
kit; Agilent, Palo Alto, CA). The Agilent one-color Microarray Based Gene Expression
178
Analysis v6.5 was used for microarray hybridizations according to the manufacturer’s
179
recommendations. An Agilent high-resolution C microarray scanner was used to scan
180
microarray images. Data were resolved from microarray images using Agilent Feature
181
Extraction software v10.7. Raw microarray data from this study have been deposited at
182
the Gene Expression Omnibus Web site (www.ncbi.nlm.nih.gov/geo/) with accession
183
number GSE45053.
184
Validation of Microarray results by qRT-PCR
185
Microarray results were validated with real-time quantitative polymerase chain reaction
186
(qPCR). We selected six significantly regulated genes from different pathways/gene
8
187
families: lipids metabolism (thiolase), Krebs cycle (aconitase-acon, isocytrate
188
dehydrogenase-idh, ATP citrate lyase-ATPCL), tryptophan metabolism (dopamine
189
decarboxylase -Ddc) and nucleotide metabolism (fumarilacetoacetase -Faa). The gene
190
G3PDH (glyceraldehyde 3-phosphate dehydrogenase) was used as internal control. For
191
each of these genes primers were designed with Primer Quest (IDT Technologies,
192
Coralville, IA) and are listed in Table S1 in Supporting Information. qPCR was
193
performed according to manufacturers protocols described in Experimental section,
194
Supporting information.
195
Gene Expression analysis. Microarray data were analyzed using GeneSpring GX v4.0
196
software (Agilent). Data were normalized using quantile normalization and baseline
197
transformation to the median of all samples. Differentially transcribed genes of D.
198
magna individuals exposed to 4-nonylphenol and SSRIs from that of carrier controls
199
(hereafter named as controls) were analyzed using one way ANOVA followed by
200
Dunnett’s pos-hoc comparison tests and filtered based on expression levels (p<0.05;
201
cut-off 1.5 fold change). A list of differentially transcribed genes for each of the
202
treatments can be found in Table S2 (Supporting Information).
203
Transcriptomic patterns of differentially expressed genes of juveniles and adults were
204
analysed using the Multi-Experiment viewer MeV4 software 25, both by hierarchical
205
clustering (sites) and Self Organizing Map (SOM) analysis (features), and the standard
206
normalization protocol. Furthermore, the assays performed with single adult females
207
(experiment 2), allowed to establish possible correlations between individual
208
transcriptomic profiles and the observed effects of both SSRIs and 4-nonylphenol
209
treatments on D. magna reproduction responses, and hence the identification of putative
210
gene regulatory pathways affecting these reproduction responses. Partial Least Squares
211
Regression (PLS) was used to investigate the correlations between transcriptome data
9
212
(X-variables) and total offspring production (y variable) under both SSRIs and 4-
213
nonylphenol treatments. Since both treatments affected fecundity similarly (increased
214
offspring production) but likely through different molecular mechanisms, SSRIs and 4-
215
nonylphenol treatments were analysed separately by PLS-SR. Genes contributing more
216
to the prediction of the reproduction responses by the proposed PLS model were
217
selected using the selectivity ratio (SR) 26. This variable selection method minimizes
218
the effect of uncorrelated X variance sources. Those genes (X-variables) with SR values
219
higher than the mean were considered to be the most relevant for explaining the Y
220
responses related to treatments. Prior to data analysis, X and Y variables were mean-
221
centred to correct for their offset. The number of PLS-SR components was finally set at
222
two, according to cross validation by the leaving-one-out prediction errors’ criteria 27.
223
PLS-SR analyses were performed using the PLS Toolbox version 6.5, Eigenvector
224
Research Inc. (Manson, WA) running under Matlab 7.4 computer environment,
225
(MathWorks, Natick, Massachusetts)
226
Genes included in the clusters identified by SOM and PLS-SR analyses were annotated
227
using the recent update of the custom microarray, which incorporates D. pulex sequence
228
annotations available at the Fleabase21. Further annotation was performed by identifying
229
putative homologous Drosophila melanogaster genes using BLAST at Flybase server
230
(http://flybase.org/blast/). Annotated genes were ascribed to functional Gene Ontology
231
(GO terms) and to metabolic pathways using the AmiGO! Term Enrichment tool
232
(http://www.geneontology.org/) and the Kyoto Encyclopaedia of Genes and Genomes
233
(KEGG, http://www.genome.jp/kegg/kegg2.html) databases, respectively.
234
RESULTS
235
Phenotypic responses
10
236
There was no mortality during juvenile exposures. Selected females for transcriptomic
237
analyses produced significantly (P<0.01; F
238
all treatments (offspring production results are depicted in Fig S1, Supporting
239
Information).
240
Transcriptome analysis
241
Transcriptome responses of juveniles and adults of experiments 1 and 2 were analysed
242
separately. A total of 1,796 and 1,214 genes were identified as differentially transcribed
243
in at least one of the chemical treatments, in juveniles and adults, respectively. From the
244
above-mentioned genes, only 221 were annotated (see Table S2, Supporting
245
Information for further information). Hierarchical clustering separated most biological
246
replicates of SRRIs, 4-nonylphenol and control treatments on both the juvenile and
247
adult transcriptomic data set (full clusters and heat maps are provided in Fig S2, S3 of
248
Supporting information). The SOM analysis performed on the differentially transcribed
249
genes in juveniles and adults revealed six clusters of significantly de-regulated genes.
250
For juveniles cluster 1 include 151 significantly (P<0.01) up-regulated genes by both
251
SRRI compounds (FX and FV). Clusters 2 and 3 included 210 and 456 FV down-
252
regulated (P<0.01) unique genes,, respectively. For adults, cluster 4 included 32 genes
253
significantly (P<0.01) down-regulated by both SSRIs (FV and FX treatments), cluster 5
254
included 86 genes significantly (P<0.01) down-regulated by 4-nonylphenol treatments
255
(20, 60 µg/L), whereas cluster 6 included 19 genes up-regulated by 4-nonylphenol
256
treatments. SOM graphs together with the de-regulated genes for each cluster are
257
depicted in Fig S4 and Table S3 (Supporting information). PLS analyses were
258
performed separately for the different treatments considered (control and SSRIs) and
259
(control and 4-nonylphenol treatments). In each data set the 1,214 de-regulated gene
4,15
= 5.02) more offspring than controls in
11
260
fragments were selected as predictors (X matrix of predictor variables) and the total
261
offspring production of the exposed females was used as predicted variable (Y vector of
262
predicted variables). The two components introduced in the PLS analysis explained
263
around 80% of the total Y variance (r2= 0.79) for the prediction of total offspring
264
production of females treated either with SSRIs (Fig 1A) or with 4-nonylphenol (Fig
265
1B). Following the established SR cut-off criteria, PLS analysis identified 272 and 331
266
genes differentially affected by SSRIs and 4-nonylphenol, respectively (Fig 1). These
267
predictor genes formed two differentiated groups: one in close proximity with SSRIs or
268
4-nonylphenol Y-treatment including genes that covaried positively with both
269
treatments, and a second one close to control treatments and including genes inversely
270
correlated with the two studied treatments. The final set of annotated genes
271
differentially affected ny SSRIs and 4-nonylphenol treatments (35 and 31 genes,
272
respectively) are identify by numbers in Fig 1. Correlation plots between the log2 of
273
fold change ratios of the selected annotated and non annotated genes with the total
274
offspring production are given in Table S4 of Supporting Information.
275
Results from RT-qPCR confirmed the fold change ratios of mRNA levels observed
276
with the microarray, both in magnitude and direction, for the six selected genes (Fig 2)
277
with a Pearson correlation coefficient of 0.83 (P<0.01, N=33).
278
GO analyses were performed on annotated genes from SOM and PLS-SR clusters
279
(Table 1, further details of GO terms and the name of involved genes are depicted in
280
Table S5, Supporting Information). Juveniles appeared as selectively more affected by
281
SSRIs treatments than adults, showing the highest amount of annotated genes grouped
282
into GO term (P<0.05; 151 genes in total, Table 1). ,Adult SOM and PLS-SR clusters
283
affected by SSRIs included 40 genes that could be adscribed to specific GO term
284
(Table 1). Nonylphenol only affected adult SOM and PLS-SR clusters, which included
12
285
38 genes with a specific GO term assignation (Table 1). The GO terms with the highest
286
number of de-regulated genes were related to three parental GO categories:
287
developmental processes, structural constituents and binding (Table 1). Developmental
288
processes were only affected by SSRIs. Other processes only affected by SSRIs were
289
synaptic vesicle transport, localization and response to stimuli. Structural constituents,
290
binding and catalytic activity related GO categories were shared by both SSRIs and 4-
291
nonylphenol treatments although most of the de-regulated genes and sub-GO categories
292
belonged to SSRIs treatments. The only unique GO category affected by 4-nonylphenol
293
was lipid binding.
294
Metabolic pathway analyses of de-regulated genes (>1.5 fold) in adult females exposed
295
chronically to SSRIs and 4-nonylphenol indicated that 20 of these genes regulated
296
metabolic pathways (Fig 3). More specifically, SRRIs and 4-nonylphenol de-regulated
297
12 and 7 genes from metabolism of carbohydrates and lipids and Krebs cycle,
298
respectively. Further details of KEGG analyses are in supplementary information (Table
299
S6, Supporting Information).
300
DISCUSSION
301
In this study we tested the hypothesis that low concentrations of SSRIs and 4-
302
nonylphenol affected offspring production and/or juvenile developmental rates though
303
different mechanisms of action. Our hypothesis was tested by combining two
304
developmental stages, juveniles and adults, exposed during two (3 days) and three (10
305
days) consecutive instars, respectively. Although in absolute terms juvenile exposures
306
were much shorter than that of adults, both schemes included physiological equivalent
307
periods (i.e. include 2-3 instars)28. Previous results also indicate that sublethal effects on
308
juvenile developmental rates of SSRIs occurred at similar concentrations than those of
13
309
adults12. Therefore, we consider that juvenile and adult stages were chronically exposed
310
to equivalent concentrations and periods in our experiments.
311
Hierarchical clustering clearly grouped replicated samples of SSRIs, 4-nonylphenol and
312
control by treatment in both juvenile and adult exposures, providing the first evidence
313
that these contaminant families induced specific and unique transcriptional patterns. The
314
number of de-regulated genes and significant GO terms obtained for juveniles exposed
315
to all three chemicals was higher than that of adults, which supports the hypothesis that
316
short term exposures may be more suited to assess differentially altered transcriptomic
317
responses. This was particularly evident for the SOM clusters obtained for adults that
318
only involved 17 annotated genes whereas those of juveniles include 151 ones (Table
319
1). Alternatively, the number of de-regulated genes could be a direct consequence of
320
previously reported phenotypic effects. Previous work indicated that at the studied
321
exposure levels FV affected juvenile developmental rates whereas FX and 4-
322
nonylphenol did not5. This is consistent with the apparent stronger effect of FV on the
323
juveniles' transcriptome, both in terms of number of de-regulated genes and of the
324
number of significantly enriched GO terms (Fig S2 and S3 of SI). Reproduction was
325
similarly affected by the three chemicals at all exposure levels, and so was the number
326
of annotated genes and enriched GO terms defined by both SOM and PLS-SR analyses
327
(7,10 genes in SOM clusters for SSRIs and 4-nonylphenol, respectively; 35 and 32
328
genes selected by PLS-SR for SSRIs and 4-nonylphenol, respectively; Table 1).
329
Relating molecular responses to phenotypic effects is crucial in environmental risk
330
assessment. A promising approach is the adverse outcome pathway (AOP) framework,
331
which is a conceptual construct that portrays existing knowledge concerning the linkage
332
between a direct molecular initiating event and an adverse outcome at a biological level
333
of organization relevant to risk assessment29. The application of AOP has been
14
334
restricted to model organisms (i.e. few fish species)30-32. In a previous study 5 we
335
showed that 4-nonylphenol increased offspring production but decreased at the same
336
time offspring size. According to several studies being smaller at birth bears a fitness
337
disadvantage since smaller neonates are more sensitive to environmental stress, and
338
have lower fitness than bigger ones14, 33. SSRIs also increased offspring production with
339
apparently no effects on offspring size but such effects were related with increasing
340
oxygen demand and being less tolerant to low oxygen levels 12. Thus in the two studied
341
chemical groups (4-nonylphenol and SSRIs), adverse effects were directly or indirectly
342
associated with increasing reproduction at the selected exposure levels. In the present
343
study the combination of further annotation establishing gene homologies with known
344
genomes like D. melanogaster improved the functional enrichment of de-regulated D.
345
magna gene sequences and hence it was possible to obtain reliable gene pathways using
346
GO and KEGG. Furthermore, the use of multivariate methods like PLS and SR to
347
identify de-regulated genes related with observed phenotypic effects in chronic
348
exposures performed with adult females identified 111 annotated genes related to
349
effects observed in reproduction. More standard clustering methods like SOM only
350
allowed to identify 17 de-regulated genes in adult females. PLS methods are already
351
being used to relate gene transcription patterns with toxicological effects34. The
352
combination of PLS with SR, however, offers the possibility to select de-regulated
353
genes highly correlated with phenotypic responses (see Table S4, Supporting
354
Information).
355
Juvenile SOM clusters included several differentially expressed genes involved in
356
developmental processes after SSRIs exposure, which can be linked to the reported
357
increased juvenile developmental rates5. The SSRIs exposures also de-regulated
358
ribosomal structural constituents, which could be linked to protein biosynthesis or cell
15
359
proliferation. Dom et al.34 reported the up-regulation of ribosomal constituents related
360
genes in D. magna exposed to polar narcotics as an intent to overcome toxicant stress
361
and /or to restore the damage elicited by exposure to those narcotics. Neuronal
362
pathways, including synaptic vesicle transport, establishment and maintenance of
363
localization, and response to stimuli were GO functional terms down-regulated in both
364
juvenile and adults. The down regulation of neuronal pathways may be associated to
365
an adaptive gene response to the presence of neuronal active compounds, as a similar
366
response has been observed for the neurotoxic insecticide methomyl35.
367
Three genes involved in the ecdysone regulatory pathway that controls morphogenesis
368
and integrin expression during Drosophila metamorphosis36 appeared specifically
369
down-regulated
370
phosphosulfate synthetase), got1 (Glutamate oxaloacetate transaminase), and btk29A
371
(Bruton's tyrosine kinase). This is consistent with the hypothesis that 4-nonylphenol can
372
affect D. magna by disrupting ecdysone metabolism
373
affected genes related to this pathway was too low to be considered significantly
374
enriched by the AmiGO! algorithm. Further research on ecdysone metabolism-related
375
genes in Daphnia is thus required to prove o refute this hypothesis.
376
Common pathways de-regulated by both, SSRIs and 4-nonylphenol, involved enzymes
377
that regulate morphogenesis and tissue differentiation probably involved in molting and
378
reproduction, which are essential processes for growth and energy supply (e.g. cuticle
379
formation, ribosomes, catabolism, transport and binding of essential macromolecules).
380
Similar de-regulated pathways were found to be altered in D. magna individuals
381
exposed to narcotic chemicals, pesticides, ammunitions constituents, silver
382
nanoparticles, metals and temperature changes 21, 34, 35, 37, 38 18, 19, 39-42. The simplest
383
explanation to this finding of similar responses to disparate substances is that these
by
4-nonylphenol
treatment:
papss
13
(3'-phosphoadenosine
5'-
. However, the number of
16
384
pathways are related to general stress responses and not to the observed specific
385
hormetic responses on juvenile developmental rates and female reproduction.
386
De-regulated metabolic pathways in D. magna adults chronically exposed to the studied
387
chemicals were further analysed using KEGG to identify altered metabolic pathways
388
linked to reported effects on energy and carbohydrate metabolism. Both SSRIs and 4-
389
nonylphenol de-regulated similarly key genes such as glyp (glycogen phosphorylase)
390
and ugp (UTP:glucose-1-phosphate), both involved in starch and sucrose metabolism,
391
mainly in the transformation of glycogen to glucose 43. Although both chemical families
392
up-regulated fbp (fructose-1,6-bisphosphatase), only SSRIs down-regulated pfk
393
(phospho-fructo kinase), which is involved in control of glucose homeostasis by
394
allowing to switch from glycolysis to gluconeogenesis44 and is a key gene in the
395
regulation from aerobic to anaerobic metabolism. In a previous study we found that
396
exposure to SSRIs decreases carbohydrate levels in adult D. magna females12 , thus
397
down-regulation of pfk may be an adaptive response to maintain minimum levels of
398
glucose and may be linked to reported effects of SSRIs on oxygen consumption rates
399
12
400
target of SSRIs, may be involved in the regulation of pfk. In human cells, serotonin
401
increases pfk by binding to the 5-HT(2A) receptor, causing the tyrosine residue of pfk
402
to be phosphorylated via phospholipase C. Because pfk regulates glycolytic flux,
403
serotonin plays a regulatory role in glycolysis 45.
404
SSRIs and 4-nonylphenol treatments de-regulated acon (aconitase) and ATPCl (ATP
405
citrate lyase), respectively. Additionally, SSRIs specifically up-regulated idh (isocitrate
406
dehydrogenase). Thus SSRIs and 4-nonylphenol de-regulated genes involved in 6 and 4
407
out of 10 reactions of the Krebs cycle, respectively. These effects could potentially
408
affect oxygen consumption rates and the consumption of carbohydrates to produce
. There are also evidences that serotonin, whose regulation is the pharmacological
17
409
energy. SSRIs indeed increased oxygen consumption rates and decreased levels of
410
carbohydrates in D. magna12. 4-nonylphenol is known to increase mRNA levels of D.
411
magna haemoglobin46, which may indicate also increasing rates of oxygen
412
consumption.
413
The beta-oxidation pathway was affected exclusively by SSRIs up-regulating thiolase,
414
which is a key control gene of lipids metabolism and biosynthesis47. 4-nonylphenol and
415
SSRIs up-regulated 2 and 3 genes, respectively, from the sphingolipid biogenesis
416
pathway (cDASE, Glct-1, lace) that are involved in the biogenesis of ceramide,
417
glycosphingolipids and sphingosines. Recently, sphingolipid metabolites, such as
418
ceramide and sphingosine-1-phosphate, have been shown to be important mediators in
419
the
420
necrosis, inflammation, autophagy, senescence, and differentiation48 .
421
SSRIs up-regulated specifically dopa decarboxylase (Ddc) that catalyses the conversion
422
of 5-hydroxytryptophan to serotonin in response to several endogenous or exogenous
423
signals, and has already been shown to be involved in insect cuticle maturation,
424
neuronal regulation, pigmentation patterning and innate immunity 49. Up-regulation of
425
Ddc in rats exposed to SSRIs has been suggested as a response to the blocking of the re-
426
uptake mechanisms, which leads to low levels of serotonin inside the terminal neuron
427
axon 50.
428
The expression pattern of key genes from significantly enriched pathways such as lipids
429
metabolism (thiolase), Krebs cycle (Acon, idh and ATPCL), tryptophan metabolism
430
(Ddc) and nucleotide metabolism (Faa) was validated by qPCR, confirming the
431
microarray results.
signalling
cascades
involved
in apoptosis, proliferation,
stress
responses,
18
432
In summary SSRIs and 4-nonylphenol de-regulated different gene pathways in D.
433
magna that in most cases could be related to reported phenotypic effects. SSRIs de-
434
regulated more genes than 4-nonylphenol specially in juvenile stages, which support the
435
hypothesis that these compounds act as neuronal disruptors in Daphnia12. SSRIs
436
specifically de-regulated one of their targeted pathways, serotonin metabolism, and key
437
genes from the carbohydrate and Krebs cycle metabolism. 4-nonylphenol specifically
438
targeted genes related to ecdysone, carbohydrate and Krebs cycle in adults. Taken
439
together, the observed changes in the energy-related pathways are consistent with a
440
decrease on reserve carbohydrates and an increase on respiratory metabolism, in line
441
with our previous observations on the effects of SSRIs in D. magna12.
442
443
AUTHOR INFORMATION
444
Corresponding Author
445
Phone: +34-93-4006100; fax: +34-93-2045904; e-mail: cbmqam@cid.csic.es
446
447
ACKNOWLEDGMENTS
448
This work was supported by the Spanish MICINN grants (CGL2008-01898/BOS and
449
CTM2011-30471-C02-01) and by the Advance Grant ERC-2012-AdG-320737. Bruno
450
Campos was supported by a fellowship from the MICINN (FPI BES-2009-022741).
451
This work was also partly funded by the US Army Environmental Quality Research
452
Program (including BAA 11-4838). Permission for publishing this information has been
453
granted by the Chief of Engineers.
454
ASSOCIATED CONTENT
455
Supporting Information
19
456
Additional methods details and supplementary tables and figures, including primer
457
sequences for qPCR (Table S1), differentially expressed genes (Table S2), genes
458
included in SOM clusters (Table S3), PLS-SR selected genes (Table S4), full GO terms
459
table with gene symbol (Table S5), KEGG annotated genes with fold induction values
460
(Table S6), total offspring production responses (Fig S1), heat map of differentially
461
expressed genes of juveniles and adults with respective hierarchical clustering (Fig S2,
462
S3), SOM graphs results (Fig S4). This information is available free of charge via the
463
Internet at http://pubs.acs.org/ .
464
465
REFERENCES
466
467
468
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
1.
Daughton, C. G.; Ternes, T. A., Pharmaceuticals and personal care products in
the environment: Agents of subtle change? Environmental Health Perspectives 1999,
107, (SUPPL. 6), 907-938.
2.
Fent, K.; Weston, A. A.; Caminada, D., Ecotoxicology of human
pharmaceuticals. Aquatic Toxicology 2006, 76, (2), 122-159.
3.
Pal, A.; Gin, K. Y. H.; Lin, A. Y. C.; Reinhard, M., Impacts of emerging organic
contaminants on freshwater resources: Review of recent occurrences, sources, fate and
effects. Science of the Total Environment 2010, 408, (24), 6062-6069.
4.
Barata, C.; Porte, C.; Baird, D. J., Experimental designs to assess endocrine
disrupting effects in invertebrates: A review. Ecotoxicology 2004, 13, (6), 511-517.
5.
Campos, B.; Piña, B.; Fernández-Sanjuán, M.; Lacorte, S.; Barata, C., Enhanced
offspring production in Daphnia magna clones exposed to serotonin reuptake inhibitors
and 4-nonylphenol. Stage- and food-dependent effects. Aquatic Toxicology 2012, 109,
100-110.
6.
Baldwin, W. S.; Graham, S. E.; Shea, D.; Leblanc, G. A., Metabolic
androgenization of female Daphnia magna by the xenoestrogen 4- nonylphenol.
Environmental Toxicology and Chemistry 1997, 16, (9), 1905-1911.
7.
Hansen, L. K.; Frost, P. C.; Larson, J. H.; Metcalfe, C. D., Poor elemental food
quality reduces the toxicity of fluoxetine on Daphnia magna. Aquatic toxicology 2008,
86, (1), 99-103.
8.
Kolpin, D. W.; Furlong, E. T.; Meyer, M. T.; Thurman, E. M.; Zaugg, S. D.;
Barber, L. B.; Buxton, H. T., Pharmaceuticals, hormones, and other organic wastewater
contaminants in U.S. streams, 1999-2000: A national reconnaissance. Environmental
Science and Technology 2002, 36, (6), 1202-1211.
9.
Vasskog, T.; Anderssen, T.; Pedersen-Bjergaard, S.; Kallenborn, R.; Jensen, E.,
Occurrence of selective serotonin reuptake inhibitors in sewage and receiving waters at
Spitsbergen and in Norway. Journal of Chromatography A 2008, 1185, (2), 194-205.
10.
Metcalfe, C. D.; Chu, S.; Judt, C.; Li, H.; Oakes, K. D.; Servos, M. R.; Andrews,
D. M., Antidepressants and their metabolites in municipal wastewater, and downstream
20
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
537
538
539
540
541
542
543
544
exposure in an urban watershed. Environmental Toxicology and Chemistry 2010, 29,
(1), 79-89.
11.
Navarro, A.; Endo, S.; Gocht, T.; Barth, J. A. C.; Lacorte, S.; Barceló, D.;
Grathwohl, P., Sorption of alkylphenols on Ebro River sediments: Comparing isotherms
with field observations in river water and sediments. Environmental Pollution 2009,
157, (2), 698-703.
12.
Campos, B.; Piña, B.; Carlos, B. C., Mechanisms of action of selective serotonin
reuptake inhibitors in Daphnia magna. Environmental Science and Technology 2012,
46, (5), 2943-2950.
13.
LeBlanc, G. A.; Mu, X.; Rider, C. V., Embryotoxicity of the alkylphenol
degradation product 4-nonylphenol to the crustacean daphnia magna. Environmental
Health Perspectives 2000, 108, (12), 1133-1138.
14.
Hammers-Wirtz, M.; Ratte, H. T., Offspring fitness in Daphnia: Is the Daphnia
reproduction test appropriate for extrapolating effects on the population level?
Environmental Toxicology and Chemistry 2000, 19, (7), 1856-1866.
15.
Piña, B.; Barata, C., A genomic and ecotoxicological perspective of DNA array
studies in aquatic environmental risk assessment. Aquatic Toxicology 2011, 105, (3-4
SUPPL.), 40-49.
16.
Colbourne, J. K.; Pfrender, M. E.; Gilbert, D.; Thomas, W. K.; Tucker, A.;
Oakley, T. H.; Tokishita, S.; Aerts, A.; Arnold, G. J.; Basu, M. K.; Bauer, D. J.;
Cáceres, C. E.; Carmel, L.; Casola, C.; Choi, J. H.; Detter, J. C.; Dong, Q.; Dusheyko,
S.; Eads, B. D.; Fröhlich, T.; Geiler-Samerotte, K. A.; Gerlach, D.; Hatcher, P.; Jogdeo,
S.; Krijgsveld, J.; Kriventseva, E. V.; Kültz, D.; Laforsch, C.; Lindquist, E.; Lopez, J.;
Manak, J. R.; Muller, J.; Pangilinan, J.; Patwardhan, R. P.; Pitluck, S.; Pritham, E. J.;
Rechtsteiner, A.; Rho, M.; Rogozin, I. B.; Sakarya, O.; Salamov, A.; Schaack, S.;
Shapiro, H.; Shiga, Y.; Skalitzky, C.; Smith, Z.; Souvorov, A.; Sung, W.; Tang, Z.;
Tsuchiya, D.; Tu, H.; Vos, H.; Wang, M.; Wolf, Y. I.; Yamagata, H.; Yamada, T.; Ye,
Y.; Shaw, J. R.; Andrews, J.; Crease, T. J.; Tang, H.; Lucas, S. M.; Robertson, H. M.;
Bork, P.; Koonin, E. V.; Zdobnov, E. M.; Grigoriev, I. V.; Lynch, M.; Boore, J. L., The
ecoresponsive genome of Daphnia pulex. Science 2011, 331, (6017), 555-561.
17.
Asselman, J.; De Coninck, D. I. M.; Glaholt, S.; Colbourne, J. K.; Janssen, C.
R.; Shaw, J. R.; De Schamphelaere, K. A. C., Identification of pathways, gene
networks, and paralogous gene families in daphnia pulex responding to exposure to the
toxic cyanobacterium Microcystis aeruginosa. Environmental Science and Technology
2012, 46, (15), 8448-8457.
18.
Garcia-Reyero, N.; Poynton, H. C.; Kennedy, A. J.; Guan, X.; Escalon, B. L.;
Chang, B.; Varshavsky, J.; Loguinov, A. V.; Vulpe, C. D.; Perkins, E. J., Biomarker
discovery and transcriptomic responses in Daphnia magna exposed to munitions
constituents. Environmental Science and Technology 2009, 43, (11), 4188-4193.
19.
Poynton, H. C.; Lazorchak, J. M.; Impellitteri, C. A.; Blalock, B. J.; Rogers, K.;
Allen, H. J.; Loguinov, A.; Heckman, J. L.; Govindasmawy, S., Toxicogenomic
responses of nanotoxicity in Daphnia magna exposed to silver nitrate and coated silver
nanoparticles. Environmental Science and Technology 2012, 46, (11), 6288-6296.
20.
Vandenbrouck, T.; Jones, O. A. H.; Dom, N.; Griffin, J. L.; De Coen, W.,
Mixtures of similarly acting compounds in Daphnia magna: From gene to metabolite
and beyond. Environment International 2010, 36, (3), 254-268.
21.
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. Environmental Science and Technology 2012, 46, (1),
42-50.
21
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
22.
Barata, C.; Baird, D. J., Determining the ecotoxicological mode of action of
toxicants from measurements on individuals: results from short duration chronic tests
with Daphnia magna Straus. Aquatic toxicology 2000, 48, 195-209.
23.
Nogueira, A. J. A.; Baird, D. J.; Soares, A. M. V. M., Testing physiologicallybased resource allocation rules in laboratory experiments with Daphnia magna Straus.
Annales de Limnologie-International Journal of Lumnology 2004, 40, 257-267.
24.
Ebert, D., A maturation size threshold and phenotypic plasticity of age and size
at maturity in Daphnia magna. Oikos (Copenhagen) 1994, 69, 309-317.
25.
Saeed, A. I.; Sharov, V.; White, J.; Li, J.; Liang, W.; Bhagabati, N.; Braisted, J.;
Klapa, M.; Currier, T.; Thiagarajan, M.; Sturn, A.; Snuffin, M.; Rezantsev, A.; Popov,
D.; Ryltsov, A.; Kostukovich, E.; Borisovsky, I.; Liu, Z.; Vinsavich, A.; Trush, V.;
Quackenbush, J., TM4: a free, open-source system for microarray data management and
analysis. Biotechniques 2003, 34, (2), 374-8.
26.
Rajalahti, T.; Arneberg, R.; Berven, F. S.; Myhr, K. M.; Ulvik, R. J.; Kvalheim,
O. M., Biomarker discovery in mass spectral profiles by means of selectivity ratio plot.
Chemometrics and Intelligent Laboratory Systems 2009, 95, (1), 35-48.
27.
Wold, S.; Sjöström, M.; Eriksson, L., PLS-regression: A basic tool of
chemometrics. Chemometrics and Intelligent Laboratory Systems 2001, 58, (2), 109130.
28.
Nogueira, A. J. A.; Baird, D. J.; Soares, A. M. V. M., Testing physiologicallybased resource allocation rules in laboratory experiments with Daphnia magna Straus.
Annales de Limnologie 2004, 40, (4), 257-267.
29.
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.
Environmental Toxicology and Chemistry 2010, 29, (3), 730-741.
30.
Ankley, G. T.; Bencic, D. C.; Breen, M. S.; Collette, T. W.; Conolly, R. B.;
Denslow, N. D.; Edwards, S. W.; Ekman, D. R.; Garcia-Reyero, N.; Jensen, K. M.;
Lazorchak, J. M.; Martinović, D.; Miller, D. H.; Perkins, E. J.; Orlando, E. F.;
Villeneuve, D. L.; Wang, R. L.; Watanabe, K. H., Endocrine disrupting chemicals in
fish: Developing exposure indicators and predictive models of effects based on
mechanism of action. Aquatic Toxicology 2009, 92, (3), 168-178.
31.
Tanneberger, K.; Knöbel, M.; Busser, F. J. M.; Sinnige, T. L.; Hermens, J. L.
M.; Schirmer, K., Predicting fish acute toxicity using a fish gill cell line-based toxicity
assay. Environmental Science and Technology 2013, 47, (2), 1110-1119.
32.
Yozzo, K. L.; McGee, S. P.; Volz, D. C., Adverse outcome pathways during
zebrafish embryogenesis: A case study with paraoxon. Aquatic Toxicology 2013, 126,
346-354.
33.
Barata, C.; Baird, D.; Soares, A., Phenotypic plasticity in Daphnia magna straus:
Variable maturation instar as an adaptive response to predation pressure. Oecologia
2001, 129, (2), 220-227.
34.
Dom, N.; Vergauwen, L.; Vandenbrouck, T.; Jansen, M.; Blust, R.; Knapen, D.,
Physiological and molecular effect assessment versus physico-chemistry based mode of
action schemes: Daphnia magna exposed to narcotics and polar narcotics.
Environmental Science and Technology 2012, 46, (1), 10-18.
35.
Pereira, J. L.; Hill, C. J.; Sibly, R. M.; Bolshakov, V. N.; Gonçalves, F.;
Heckmann, L. H.; Callaghan, A., Gene transcription in Daphnia magna: Effects of acute
exposure to a carbamate insecticide and an acetanilide herbicide. Aquatic Toxicology
2010, 97, (3), 268-276.
22
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
36.
D'Avino, P. P.; Thummel, C. S., The ecdysone regulatory pathway controls wing
morphogenesis and integrin expression during Drosophila metamorphosis.
Developmental Biology 2000, 220, (2), 211-224.
37.
Vandenbrouck, T.; Dom, N.; Novais, S.; Soetaert, A.; Ferreira, A. L. G.;
Loureiro, S.; Soares, A. M. V. M.; De Coen, W., Nickel response in function of
temperature differences: Effects at different levels of biological organization in Daphnia
magna. Comparative Biochemistry and Physiology - Part D: Genomics and Proteomics
2011, 6, (3), 271-281.
38.
De Schamphelaere, K. A. C.; Vandenbrouck, T.; Muyssen, B. T. A.; Soetaert,
A.; Blust, R.; De Coen, W.; Janssen, C. R., Integration of molecular with higher-level
effects of dietary zinc exposure in Daphnia magna. Comparative Biochemistry and
Physiology D-Genomics & Proteomics 2008, 3, (4), 307-314.
39.
Soetaert, A.; Moens, L. N.; Van der Ven, K.; Van Leemput, K.; Naudts, B.;
Blust, R.; De Coen, W. M., Molecular impact of propiconazole on Daphnia magna
using a reproduction-related cDNA array. Comparative Biochemistry and Physiology CToxicology & Pharmacology 2006, 142, (1-2), 66-76.
40.
Poynton, H. C.; Zuzow, R.; Loguinov, A. V.; Perkins, E. J.; Vulpe, C. D., Gene
expression profiling in Daphnia magna, part II: Validation of a copper specific gene
expression signature with effluent from two copper mines in California. Environmental
Science & Technology 2008, 42, (16), 6257-6263.
41.
Soetaert, A.; van der Ven, K.; Moens, L. N.; Vandenbrouck, T.; van Remortel,
P.; De Coen, W. M., Daphnia magna and ecotoxicogenomics: Gene expression profiles
of the anti-ecdysteroidal fungicide fenarimol using energy-, molting- and life stagerelated cDNA libraries. Chemosphere 2007, 67, (1), 60-71.
42.
Poynton, H. C.; Varshavsky, J. R.; Chang, B.; Cavigiolio, G.; Chan, S.; Holman,
P. S.; Loguinov, A. V.; Bauer, D. J.; Komachi, K.; Theil, E. C.; Perkins, E. J.; Hughes,
O.; Vulpe, C. D., Daphnia magna ecotoxicogenomics provides mechanistic insights into
metal toxicity. Environmental Science and Technology 2007, 41, (3), 1044-1050.
43.
Heckmann, L. H.; Sibly, R. M.; Connon, R.; Hooper, H. L.; Hutchinson, T. H.;
Maund, S. J.; Hill, C. J.; Bouetard, A.; Callaghan, A., Systems biology meets stress
ecology: Linking molecular and organismal stress responses in Daphnia magna.
Genome Biology 2008, 9, (2).
44.
Rider, M. H.; Bertrand, L.; Vertommen, D.; Michels, P. A.; Rousseau, G. G.;
Hue, L., 6-Phosphofructo-2-kinase/fructose-2,6-bisphosphatase: Head-to-head with a
bifunctional enzyme that controls glycolysis. Biochemical Journal 2004, 381, (3), 561579.
45.
Coelho, W. S.; Da Silva, D.; Marinho-Carvalho, M. M.; Sola-Penna, M.,
Serotonin modulates hepatic 6-phosphofructo-1-kinase in an insulin synergistic manner.
International Journal of Biochemistry and Cell Biology 2012, 44, (1), 150-157.
46.
Ha, M. H.; Choi, J., Effects of environmental contaminants on hemoglobin gene
expression in daphnia magna: A potential biomarker for freshwater quality monitoring.
Archives of Environmental Contamination and Toxicology 2009, 57, (2), 330-337.
47.
Liu, X.; Wu, L.; Deng, G.; Li, N.; Chu, X.; Guo, F.; Li, D., Characterization of
mitochondrial trifunctional protein and its inactivation study for medicine development.
Biochimica et Biophysica Acta - Proteins and Proteomics 2008, 1784, (11), 1742-1749.
48.
Hannun, Y. A.; Obeid, L. M., The ceramide-centric universe of lipid-mediated
cell regulation: Stress encounters of the lipid kind. Journal of Biological Chemistry
2002, 277, (29), 25847-25850.
23
643
644
645
646
647
648
649
650
651
49.
Hodgetts, R. B.; O'Keefe, S. L., Dopa decarboxylase: A model gene-enzyme
system for studying development, behavior, and systematics. In 2006; Vol. 51, pp 259284.
50.
Choi, M. R.; Hwang, S.; Park, G. M.; Jung, K. H.; Kim, S. H.; Das, N. D.; Chai,
Y. G., Effect of fluoxetine on the expression of tryptophan hydroxylase and 14-3-3
protein in the dorsal raphe nucleus and hippocampus of rat. Journal of Chemical
Neuroanatomy 2012, 43, (2), 96-102.
24
652
653
654
Table 1. GO terms for the obtained six SOM and two PLS gene clusters for SSRI’s and 4-nonylphenol (N) treatments. SOM clusters of juveniles (JUV 1, 2,3)
and of adults (ADT 4,5,6) and PLS ones for SSRIs and 4-nonylphenol (N) are depicted in Fig 1,S4. * 0.05<P<0.01; ** 0.01<P<0.001; *** P<0.001. Further
information of GO terns and annotated genes belonging to each group are in Table S5. Arrows indicate the direction of de-regulation.
SOM -SSRI's
SOM - N
PLS
JUV1↑
JUV2↓
JUV3↓ ADT 4↓
ADT5↓ ADT6↑
SSRI↕
N↕
Total genes in group
27
42
82
7
6
4
33
28
BIOLOGICAL PROCESSES
Developmental process
GO:0048856 anatomical structure development
12**
4**
GO:0048513 organ development
39***
12***
GO:0009888 tissue development
17***
32***
7*
Nervous system development
GO:0007399 nervous system development
11*
22***
10*
GO:0048666 neuron development
12**
8**
GO:0007411 axon guidance
10*
21***
5*
Sensory organ development
GO:0001654 eye development
6*
GO:0001751 compound eye photoreceptor cell differentiation
6*
Morphogenesis
GO:0009653 anatomical structure morphogenesis
22***
11*
GO:0046664 dorsal closure, amnioserosa morphology change
2*
synaptic vesicle transport
GO:0048488 synaptic vesicle endocytosis
2*
3*
GO:0031629 synaptic vesicle fusion to presynaptic membrane
10***
Structural constituints
GO:0003735 structural constituent of ribosome
17**
29***
49***
4***
GO:0005214 structural constituent of chitin-based cuticle
2*
Localization
GO:0051179 localization
23***
15***
Response to stimulus
GO:0040011 locomotion
8**
GO:0006935 chemotaxis
6***
MOLECULAR FUNCTIONS
GO:0003824 catalytic activity
21***
47***
16***
GO:0005488 binding
15***
20***
21***
17**
GO:0000166 nucleotide binding
24***
56***
6*
GO:0001882 nucleoside binding
12***
20***
GO:0008289 lipid binding
6***
25
Figure captions
Fig 1. PLS-SR summary of results for the two different sample treatments (Figure 1A,
SSRIs; Figure 1B, 4-nonylphenol) using differential expressed genes as predictors (X)
and total offspring production as response (Y) . First and second PLS bi-plots showing
selected gene loadings (triangles) and offspring production scores (sample identification
letters) . Only genes with a selectivity ratio above the mean were considered. Annotated
genes are indicated by numbers described in the Supporting Information, Table S4. The
four replicates are identified as A,B,C,D.
Fig 2 Confirmation of the array results with qPCR. Array and RT-qPCR results are
represented as gene fold change ratios . Errors bars are standard errors of mean (SE,
N=4).
Fig. 3 KEGG diagram of metabolic pathways with the genes up and down regulated
across SSRIs (in bold) and 4-nonylphenol (in grey) treatments in adult females. Arrows
indicated de-regulated direction relative to control treatments. Further details of genes
are in Table S7, Supporting Information.
26
Fig 1.
A
0.1
FVA
FVB
PLS2 (26.5%)
CC
FVC
CB
CA
19
16
46 35
43
48 15 474033
53
2420
2445
38
4
52
36
0.0
28
45
18
7
30
273158
54
60 39FVD 59
55
23
42
FXC
CD
FXA
FXD
FXB
-0.1
-0.1
0.0
0.1
PLS1 (31.3%)
B
0.1
N60D
N60B
PLS2(17.8%)
N60A
N60C
26
21
29 11 22
39
41
CA
0.0
CC
CB
37
42
55
10
3 13 56 12 14 34
1
32 6
25
84840
1743
38
51
950 57
59
N20C N20B
N20D
N20A
CD
-0.1
-0.1
0.0
0.1
PLS1 (48.9%)
27
Fig 2
ADULTS
JUVENILES
acon
1.2
acon
2.4
1.1
2.0
1.0
qPCR responses (mRNA fold change relative to G3PDH)
1.6
0.9
1.2
0.8
0.7
0.8
0.4
0.6
0.8
1.0
1.2
ATPCL
1.3
0.4
1.2
1.6
2.0
2.4
2.8
Idh
2.4
1.2
0.8
2.0
1.1
1.6
1.0
1.2
0.9
0.8
0.8
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
faa
1.1
0.8
1.0
1.2
1.4
Ddc
3.5
3.0
1.0
2.5
0.9
2.0
1.5
0.8
1.0
0.7
0.5
0.7
0.8
0.9
1.0
1.1
0.0
3.0
0.5
1.0
1.5
2.0
2.5
Thiolase
2.5
2.0
1.5
1.0
0.5
0.5
1.0
1.5
2.0
2.5
3.0
3.5
Microarray responses (fold change ratios)
28
1
Fig 3
S29
2
S30
3
Campos et al. Supporting Information
4
5
Identification of metabolic pathways in Daphnia magna explaining
6
hormetic effects of selective serotonin reuptake inhibitors and 4-
7
nonylphenol using transcriptomic and phenotypic responses.
8
9
Bruno Campos1,Natàlia Garcia-Reyero2, Claudia Rivetti1, Lynn Escalon3, Tanwir
10
Habib4, Romà Tauler1, Stefan Tsakovski5, Benjamín Piña1, Carlos Barata 1*.
11
12
13
14
1
2
Institute for Genomics, Biocomputing and Biotechnology, Mississippi State University,
15
16
Starkville, MS 39759, USA
3
U.S. Army Engineer Research and Development Center, Environmental Laboratory
17
3909 Halls Ferry Road, Vicksburg, MS 39180, USA
4
18
19
20
21
22
23
24
Institute of Environmental Assessment and Water Research (IDÆA-CSIC), Jordi
Girona 18, 08034, Barcelona, Spain.
Badger Technical Services, 12500 San Pedro Avenue, Suite 450, San Antonio, TX
78216, USA
5
Department of Analytical Chemistry, Faculty of Chemistry, Sofia University, James
Bourchier Blvd, 1164 Sofia, Bulgaria
*Address correspondence to Carlos Barata, Institute of Environmental Assessment
and Water Research (IDAEA-CSIC), Jordi Girona 18, 08034 Barcelona, Spain.
Telephone: +34-93-4006100. Fax: +34-93-2045904. E-mail: cbmqam@cid.csic.es
25
S31
26
Table of content
27
28
29
30
31
32
33
34
35
36
37
Methods
Chemicals...............................................................................……...S3
Experimental animals …………………………….….….…………S3
Experiment 2 ……………................................................................S3
Validation of Microarray results by RT-qPCR…………….............S4
Figures and Figure Legends
Figure S1 ……………….………….…….…………………………S5
Figure S2……………….………….…..……………………………S6
Figure S3……………….……….……..……………………………S7
Figure S4……………….……….……..……………………………S8
References…………………………………………………………..S8
38
39
S32
40
METHODS
41
Chemicals
42
4-nonylphenol (CAS No 104-40-5, PESTANAL® analytical grade standard, 98.4%
43
purity, Riedel-de-Haen, Germany); fluoxetine hydrochloride (CAS-No 56296-78-7;
44
analytical standard, purity 100%) Sigma-Aldricht, USA), and fluvoxamine maleate
45
(CAS-No 61718-82-9, analytical standard, purity 100%) were purchased from Sigma-
46
Aldrich (USA/Netherlands). All other chemicals were analytical grade and were
47
obtained from Merck (Germany).
48
Experimental animals. Individual or bulk cultures of 10 animals/L were maintained in
49
ASTM hard synthetic water as described in Barata and Baird 1. Individual or bulk
50
cultures were fed daily with Chorella vulgaris Beijerinck (5x105 cells/mL, respectively,
51
corresponding to 1.8 g C/mL). C. vulgaris was grown axenically in Jaworski/Euglena
52
gracilis 1: 1 medium (CCAP, 1989). The culture medium was changed every day, and
53
neonates were removed within 24 h. Photoperiod was set to 14h light: 10h dark cycle
54
and temperature at 20  1oC.
55
Experiment 2: Ten gravid females were separately exposed to the same concentrations
56
of 4-nonylphenol (20, 60 µg/L), fluvoxamine (7 µg/L) and fluoxetine (40 µg/L)
57
described in experiment 1 using a food ration of 5 x 105 cells/mL of C. vulgaris.
58
Likewise in experiment 1 all contaminants were dosed in the carrier acetone (0.1 mL/L)
59
and a solvent control treatments of acetone (0.1 mL/L) was also included for baseline
60
comparison of transcriptomic responses. Experiments started with 8-9 day old gravid
61
females, which were exposed during three consecutive broods to the studied chemicals
62
(10-14 days). Cultures of 100 to 150 individuals (< 24 h old neonates) were initiated
63
and maintained in bulk cultures as described above. Within 24 h of deposition of the
64
first clutch into the brood chamber, single females were removed and randomly
S33
65
assigned to each treatment. The first batch of neonates (hatching within the first 48-72
66
h) was always discarded and not evaluated, as these animals were not exposed to the
67
tested chemicals during their entire developmental period. Thus, only neonates from the
68
second, third and fourth broods were counted for assessing effects on total offspring
69
production. Just after releasing their fourth clutch into the brood pouch, eggs were
70
gently flushed from the brood pouch and females were immediately flash- frozen in
71
liquid N2 and preserved at -80 oC until RNA extraction. This protocol, by using only
72
de-brooded females excludes the contribution of developing embryos on transcriptomic
73
responses. Furthermore, the use of de-brooded adults in the first hours of their intermolt
74
instar ensured measurement of transcriptomic responses of all the studied females at the
75
beggining of the intermolt cycle, thus minimizing undesirable variation of gene
76
transcription patters within females across the molt cycle 3.
77
Validation of Microarray results by qRT-PCR
78
Quantities of 1μg were retrotranscribed to cDNA using First Strand cDNA Synthesis
79
Kit Roche® (Germany) and stored at -20ºC. Aliquotes of 10ng were used to quantify
80
specific transcripts in Lightcycler® 480 Real Time PCR System (Roche, Germany)
81
using Lightcycler 480 SYBR Green I Master® (Roche, Germany). Relative abundance
82
values of all genes were calculated from the second derivative of their respective
83
amplification curve, Cp values calculated by technical triplicates. Cp values of target
84
genes were compared to the corresponding reference gene 4.
85
S34
86
87
FIGURES AND FIGURE LEGENDS
88
89
90
91
92
70
*
*
Total offspring production
*
*
60
50
40
30
C
93
94
95
96
97
98
99
100
FV7
FX40
NP20
NP60
Treatments
Fig S1. Total offspring production (Mean SE, N=4) of the four selected females
exposed to the studied SSRI and 4-nonylphenol treatments. C, FV10, FX40, N20, N60
are solvent control, fluovoxamine at 7 µ/L, fluoxetine at 40 µ/L, 4-nonylphenol at 20
µ/L and 60 µ/L, respectively. Asterisks indicate significant differences from solvent
controls following ANOVA and Dunnett’s comparison tests.
S35
101
102
103
104
105
106
107
108
109
Fig S2. Heat map and hierarchical clustering (Pearson correlation) base on log2-ratios
of the differentially transcribed genes in Daphnia magna juveniles across the
experimental replicates for the SSRIs (FV, FX) and nonylphenol (N20, N60)
treatments. The four replicates are identify as A,B,C,D. Gene transcripts in red and
green are up and down regulated and those in black unchanged. Colour scale spans from
-2.5 to 2.5.
S36
110
111
112
113
114
115
116
Fig S3. Heat map and hierarchical clustering (Pearson correlation) base on log2-ratios
of the differentially transcribed genes in Daphnia magna adults across the experimental
replicates for the SSRIs (FV, FX) and nonylphenol (N20, N60) treatments. The four
replicates are identify as A,B,C,D. Gene transcripts in red and green are up and down
regulated and those in black unchanged. Colour scale spans from -2.5 to 2.5.
S37
117
1
0
-1
-2
-3
3
Cluster 2 (210)
2
1
0
-1
-2
-3
3
Cluster 3 (456)
2
1
0
-1
-2
-3
C
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
Log2 expression ratios
Cluster 1 (151)
2
FV
FX
Treatments
N20
N60
Log2 expression ratios
3
Log2 expression ratios
Log2 expression ratios
Log2 expression ratios
Log2 expression ratios
Juveniles
Adults
3
2
1
0
-1
-2
-3
-4
-5
Cluster 4 (32)
3
2
1
0
-1
-2
-3
-4
-5
Cluster 5 (86)
3
2
1
0
-1
-2
-3
-4
-5
Cluster 6 (19)
C
FV
FX
N20
N60
Treatments
Fig S4. Self Organizing Maps (SOM) of gene transcription responses of juveniles and
adults across the experimental replicates for SSRIs (fluvoxamine, FV; fluoxetine, FX)
and 4-nonylphenol (N20, N60) treatments. Results are depicted as Mean ± SD of log2ratios. Number of de-regulated genes are depicted between parenthesis.
REFERENCES
1.
Barata, C.; Baird, D. J., Determining the ecotoxicological mode of action of
toxicants from measurements on individuals: results from short duration chronic tests
with Daphnia magna Straus. Aquatic toxicology 2000, 48, 195-209.
2.
Campos, B.; Piña, B.; Carlos, B. C., Mechanisms of action of selective serotonin
reuptake inhibitors in Daphnia magna. Environmental Science and Technology 2012,
46, (5), 2943-2950.
3.
Piña, B.; Barata, C., A genomic and ecotoxicological perspective of DNA array
studies in aquatic environmental risk assessment. Aquatic Toxicology 2011, 105, (3-4
SUPPL.), 40-49.
4.
Pfaffl, M., A new mathematical model for relative quantification in real-time
RT-PCR. Nucl. Acids Res. 2001, 29, e45.
5.
Saeed, A. I.; Sharov, V.; White, J.; Li, J.; Liang, W.; Bhagabati, N.; Braisted, J.;
Klapa, M.; Currier, T.; Thiagarajan, M.; Sturn, A.; Snuffin, M.; Rezantsev, A.; Popov,
D.; Ryltsov, A.; Kostukovich, E.; Borisovsky, I.; Liu, Z.; Vinsavich, A.; Trush, V.;
S38
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
Quackenbush, J., TM4: a free, open-source system for microarray data management and
analysis. Biotechniques 2003, 34, (2), 374-8.
6.
Rajalahti, T.; Arneberg, R.; Berven, F. S.; Myhr, K. M.; Ulvik, R. J.; Kvalheim,
O. M., Biomarker discovery in mass spectral profiles by means of selectivity ratio plot.
Chemometrics and Intelligent Laboratory Systems 2009, 95, (1), 35-48.
7.
Dom, N.; Vergauwen, L.; Vandenbrouck, T.; Jansen, M.; Blust, R.; Knapen, D.,
Physiological and molecular effect assessment versus physico-chemistry based mode of
action schemes: Daphnia magna exposed to narcotics and polar narcotics.
Environmental Science and Technology 2012, 46, (1), 10-18.
8.
Wold, S.; Sjöström, M.; Eriksson, L., PLS-regression: A basic tool of
chemometrics. Chemometrics and Intelligent Laboratory Systems 2001, 58, (2), 109130.
S39