Environmental Science and Pollution Research

, Volume 17, Issue 4, pp 917–933

Potential physiological effects of pharmaceutical compounds in Atlantic salmon (Salmo salar) implied by transcriptomic analysis

Authors

    • Institute of AquacultureUniversity of Stirling
  • Esteban Alonso
    • Universidad de Sevilla
  • Irene Aparicio
    • Universidad de Sevilla
  • James E. Bron
    • Institute of AquacultureUniversity of Stirling
  • Juan Luis Santos
    • Universidad de Sevilla
  • John B. Taggart
    • Institute of AquacultureUniversity of Stirling
  • Michael J. Leaver
    • Institute of AquacultureUniversity of Stirling
Research Article

DOI: 10.1007/s11356-009-0282-6

Cite this article as:
Hampel, M., Alonso, E., Aparicio, I. et al. Environ Sci Pollut Res (2010) 17: 917. doi:10.1007/s11356-009-0282-6

Abstract

Background, aim, and scope

Pharmaceuticals are emerging pollutants widely used in everyday urban activities which can be detected in surface, ground, and drinking waters. Their presence is derived from consumption of medicines, disposal of expired medications, release of treated and untreated urban effluents, and from the pharmaceutical industry. Their growing use has become an alarming environmental problem which potentially will become dangerous in the future. However, there is still a lack of knowledge about long-term effects in non-target organisms as well as for human health. Toxicity testing has indicated a relatively low acute toxicity to fish species, but no information is available on possible sublethal effects. This study provides data on the physiological pathways involved in the exposure of Atlantic salmon as representative test species to three pharmaceutical compounds found in ground, surface, and drinking waters based on the evaluation of the xenobiotic-induced impairment resulting in the activation and silencing of specific genes.

Materials and methods

Individuals of Atlantic salmon (Salmo salar) parr were exposed during 5 days to environmentally relevant concentrations of three representative pharmaceutical compounds with high consumption rates: the analgesic acetaminophen (54.77 ± 34.67 µg L−1), the anticonvulsant carbamazepine (7.85 ± 0.13 µg L−1), and the beta-blocker atenolol (11.08 ± 7.98 µg L−1). Five immature males were selected for transcriptome analysis in brain tissues by means of a 17k salmon cDNA microarray. For this purpose, mRNA was isolated and reverse-transcribed into cDNA which was labeled with fluorescent dyes and hybridized against a common pool to the arrays. Lists of significantly up- and down-regulated candidate genes were submitted to KEGG (Kyoto Encyclopedia of Genes and Genomes) in order to analyze for induced pathways and to evaluate the usefulness of this method in cases of not completely annotated test organisms.

Results

Exposure during 5 days to environmentally relevant concentrations of the selected pharmaceutical compounds acetaminophen, carbamazepine, and atenolol produced differences in the expression of 659, 700, and 480 candidate genes, respectively. KEGG annotation numbers (KO annotations) were obtained for between 26.57% and 33.33% of these differently expressed genes per treatment in comparison to non-exposure conditions. Pathways that showed to be induced did not always follow previously reported targets or metabolic routes for the employed treatments; however, several other pathways have been found (four or more features) to be significantly induced.

Discussion

Energy-related pathways have been altered under exposure in all the selected treatments, indicating a possible energy budget leakage due to additional processes resulting from the exposure to environmental contaminants. Observed induction of pathways may indicate additional processes involved in the mode of action of the selected pharmaceuticals which may not have been detected with conventional methods like quantitative PCR in which only suspected features are analyzed punctually for effects. The employment of novel high-throughput screening techniques in combination with global pathway analysis methods, even if the organism is not completely annotated, allows the examination of a much broader range of candidates for potential effects of exposure at the gene level.

Conclusions

The continuously growing number of annotations of representative species relevant for environmental quality testing is facilitating pathway analysis processes for not completely annotated organisms. KEGG has shown to be a useful tool for the analysis of induced pathways from data generated by microarray techniques with the selected pharmaceutical contaminants acetaminophen, carbamazepine, and atenolol, but further studies have to be carried out in order to determine if a similar expression pattern in terms of fold change quantity and pathways is observed after long-term exposure. Together with the information obtained in this study, it will then be possible to evaluate the potential risk that the continuous release of these compounds may have on the environment and ecosystem functioning.

Keywords

AcetaminophenAtenololAtlantic salmonCarbamazepinecDNA microarrayKEGG pathway analysisPharmaceuticalsSublethal effectsTranscriptomics

1 Background, aim, and scope

Since the 1970s, the focus of most EU and US national water pollution control programs has been devoted to the conventional priority pollutants, especially those collectively referred to as “persistent, bioaccumulative toxic”, “persistent organic pollutants”, other “bioaccumulative chemicals of concern”, pesticides, toxic metals, and radionuclides. However, there is a much wider range of other important “unrecognized” or “emerging” pollutants that are now widely used in everyday urban activities. These include numerous groups of pharmaceuticals that are derived from human and veterinary drugs, disposal of expired medications in sewerage systems, release of treated and untreated hospital and veterinary wastes to domestic sewage systems, industrial waste streams and releases from aquaculture of medicated feeds, etc. Many of these have been present in wastewaters for many decades but are only now being recognized as potentially significant, but largely unregulated, water pollutants. Their growing use has become a new and alarming environmental problem which potentially will become dangerous in the future. These contaminants do not need to be persistent in the environment to cause negative effects due to their continuous and growing introduction, which is not compensated by their high transformation and removal, as in many cases, their elimination during wastewater treatment processes is rather low.

Three of the most important groups of pharmaceuticals that are currently found in aquatic environments are analgesics, anti-epileptics, and beta-blockers (Buser et al. 1998; Ternes 1998; Ternes et al. 1999; Kümmerer 2001; Kolpin et al. 2002). Acetaminophen (AC) is an analgesic and antipyretic drug widely used as pain killer and for the reduction of fevers and inflammations. Carbamazepine (CA) is an anticonvulsant and mood-stabilizing drug used primarily in the treatment of epilepsy and bipolar disorder, and atenolol (AT) is a drug belonging to the group of beta-blockers, used primarily in the treatment of cardiovascular diseases and conditions such as hypertension, coronary heart disease, arrhythmias, chest pain and to reduce the risk of heart complications following myocardial infarction. Toxicity testing of these compounds, where there is information, has indicated a relatively low acute toxicity to fish species (Brooks et al. 2003a, b; Sanderson et al. 2004), but no information is available on possible sublethal effects, which may reduce fitness or reproductive potential in exposed organisms and thence have knock-on ecological effects.

High-throughput transcriptomic technologies such as microarrays present a potentially powerful set of tools to better understand health effects from exposures to toxicants in the environment. This provides valuable information on the effects of chemicals at a molecular level which may be linked to higher level damage thus offering a more complete understanding of their potential toxic effects. However, while generating toxicological and ecotoxicological “omic” data from in vitro or in vivo systems is now easier and cheaper than in the past, the interpretation of data still poses a problem. The interpretation of “omic” data is complicated by the quantity of information produced in a single experiment as well as by the difficulties in differentiating those pathways that are most relevant. In particular, toxicological relevance can only be ascribed to patterns of change indicative of a perturbation in a biochemical pathway whose relevance is understood, and changes in single genes is considered unlikely to have toxicological significance due to the high likelihood of spurious and random variations (ECETOC 2007). The Kyoto Encyclopedia of Genes and Genomes (KEGG; http://www.genome.jp/kegg/) is a database of biological systems that integrates genomic, chemical, and systemic functional information. KEGG provides a reference knowledge base for linking genomes to life through the process of pathway mapping by linking a genomic or transcriptomic content of genes to KEGG reference pathways to infer systemic behaviors of the cell or the organism (Kanehisa et al. 2008).

The purpose of this work was to analyze the gene expression patterns involved in the exposure to three representative pharmaceutical compounds (AC, CA, and AT) in the Atlantic salmon to elucidate involved metabolic pathways and potential mechanisms of action as well as the possible relevance to ecosystem and human exposures.

2 Materials and methods

2.1 Exposure

The selected pharmaceutical compounds are representatives of the three most commonly found groups in the environment: AC (analgesic), CA (antidepressive), and AT (beta-blocker). All pharmaceuticals used were high purity grade (>90%, Sigma). Twelve-month-old Atlantic salmon were purchased from the Stirling University aquaculture facility (Howietown). The fish were maintained under laboratory conditions for acclimatization until their employment in the toxicity tests and were fed daily 0.5% of their corporal weight with a commercial diet. Approximately 15 fish per treatment were exposed simultaneously for 5 days in a continuous flow-through test system to the three selected pharmaceuticals supplying the compounds from stock solutions to obtain the following constant nominal exposure concentrations: AC 50 µg L−1; CA 5 µg L−1, and AT 5 µg L−1. Water flow through the system was adjusted to 0.25 L min−1. The experiments were carried out in duplicate following the protocol proposed by the OECD guidelines 203 and 204 for acute and prolonged toxicity tests on fish (OECD 1984, 1992). Control experiments were run simultaneously. Water samples were collected from each tank at days 1, 3, and 5 and stored at 4°C not longer than 24 h until their pre-treatment for posterior analysis by high-performance liquid chromatography (HPLC). After 5 days of exposure, all fish per treatment were sacrificed and the brain rapidly dissected into TriReagent (Sigma), homogenized using an Ultra-Turrax tissue disrupter (Fisher Scientific, Loughborough, UK), and immediately frozen at −80°C for transcriptome analyses. Fish were sexed and final weights, tissue weights, and length were measured for determining condition factors.

2.2 Exposure concentration analysis

Prior to extraction, 200-mL water samples were filtered through a 0.45-µm glass fiber membrane filter (Whatman, Mainstone, UK) to remove any suspended matter. For solid phase extraction, cartridges (Oasis HLB, 60 mg, 3 mL, Waters Corporation, Milford, MA, USA) were conditioned with 5 mL of methanol followed by 5 mL of deionized water (HPLC grade) at a flow rate of 1 mL·min−1. After the conditioning step, water samples (200 mL) were passed through the cartridges at a flow rate of 10 mL·min−1. Finally, the cartridges were rinsed with 5 mL of HPLC-grade water. The cartridges were then dried under vacuum for 15–20 min to remove any excess water. Elution was performed with 2 × 4 mL of methanol at 1 mL·min−1. The extract was evaporated under a gentle nitrogen stream and reconstituted. The residue was then dissolved in 0.5 mL methanol and injected into the HPLC (Merck-Hitachi, Barcelona, Spain) system. Pharmaceutical concentrations were measured as described by Santos et al. (2005). Analytes were separated by gradient elution with methanol (solvent A), acetonitrile (solvent B), and a 50-mM potassium dihydrogen phosphate solution (solvent C) at a flow rate of 1 mL·min−1. Peak areas were used for quantitative analyses. AC and CA were measured using the UV signal at 250 nm. AT was quantified using the fluorescence signal at 271 nm. Compounds were identified by comparing retention times and peaks in the sample and in the standard solution chromatogram. Limits of detection (LOD) and limits of quantification (LOQ) were calculated by using a signal-to-noise ratio of 3 and 10, respectively (the ratio between peak intensity and intensity of the noise was used).

2.3 RNA isolation and microarray hybridization

For the analysis of the brain tissues, 17k cDNA salmon microarrays were used which were developed in the framework of the British Biotechnology and Biological Sciences Research Council-sponsored project TRAITS (Transcriptome Analysis of Important Traits in Salmon). From each treatment, five immature male fish were chosen to minimize the variation of genetic expression within treatments due to different sex and development stages. Total RNA was isolated, and quality and quantity were monitored both by electrophoresis (Bioanalyzer 2100, Agilent Technologies) and spectrophotometry (Nanodrop, Thermo Scientific). Each RNA sample was further cleaned by mini spin-column purification304 (RNeasy, Qiagen) and was re-quantified and quality-assessed as above. RNA was transcribed into cDNA and indirectly labeled via an aminoallyl technique (Invitrogen Superscript cDNA Indirect Labeling kit). Twenty micrograms of Cy5-labeled (Amersham Biosciences) cDNA from each sample was hybridized against 20 µg cDNA control pool from all samples which was labeled with Cy3 (Amersham Biosciences). Dye incorporation was verified by Nanodrop and cDNA quality was tested by fluorescent gel electrophoresis. Samples were mixed with 10 μL herring sperm DNA to prevent non-specific hybridization, 20 μL polyA, sample buffer, 8 μL bovine serum albumin (BSA) (3%), and 180 μL hybridization buffer (UltraHyb, Ambion). This mixture was subsequently applied to the arrays and allowed to hybridize for 16 h at 49°C in an automatic hybridization station (Amersham Biosciences Lucidea Hybridizer). After hybridization, the arrays were washed in saline-sodium citrate(SSC)/sodium dodecyl sulfate (SDS) buffers with decreasing stringency to remove any un-hybridized or weakly (non-specifically) hybridized cDNAs. Arrays were scanned using a Perkin Elmer ScanArray Express HT 344 scanner (Wellesley, MD, USA) with laser power and photomultiplier tube (PMT) gain varied to equalize fluorescence intensities between channels and to prevent oversaturation of signal intensity.

2.4 Microarray data analysis

Spots were identified and quantified using BlueFuse software (BlueGenome) after localization, manual spot edition, and background correction, and duplicate spot data was fused. Differentially expressed genes due to the different treatments were then analyzed by GeneSpring GX version 7.3.1 (Agilent Technologies) after data transformation, normalization, and quality filtering. This included (a) setting of all raw spot intensity values <0.01 to 0.01 to facilitate comparisons of transformed data, (b) application of a Lowess normalization (per spot per chip intensity dependent), and (c) filtering by BlueFuse confidence values of >0.1 in ≥2 slides per treatment and BlueFuse spot quality of ≥0.5 in ≥2 slides per treatment. This gave a final list of approximately 16,432 genes which were eligible for statistical analysis. The final effects of the exposure to the three pharmaceutical compounds were determined by Student's t test. A significance of p < 0.05was applied to all statistical tests performed.

2.5 Identification of potential physiological effects

KEGG (Kanehisa et al. 2002) pathway database was used as a source of metabolic pathway information. Therefore, the obtained candidate gene lists were submitted to KEGG (http://www.genome.jp/kegg) for the detection of induced or inhibited pathways, and metabolic pathway identification numbers (KO numbers) were assigned where possible from the KEGG database. Features in metabolic pathways under metabolism, genetic information processing, environmental information processing, and cellular processes, as well as corresponding sub-processes, were identified from the KEGG database.

2.6 Quantitative reverse transcription PCR

Several features that showed highest induction under the treatment in the microarray analysis were chosen to verify the obtained microarray expression results. All cDNA for quantitative PCR (qPCR) was synthesized using Superscript reverse transcriptase and supplied buffer components (Clontech, UK) and an oligo-dT containing primer. Reactions contained 3 μg of total RNA and 500 ng oligo-dT primer in a volume of 20 μL and were incubated at 42°C for 1.5 h followed by 70°C for 15 min. Specific primers of selected genes that showed enhanced induction or down-regulation in the microarray data were designed and synthesized (Lasergene PrimerSelect, WI, USA and Primer3 http://frodo.wi.mit.edu/) considering several factors that may affect PCR efficiency, such as homology with other genes, amplicon size, melting temperature, secondary structure, and base composition among others (Table 1). qPCR primers for target genes as well as three housekeeping genes, bActin, a known flatliner (B2), and the elongation factor-1a (ELF 1A), used as salmon reference genes, were mixed at 0.5 μM with one fortieth of the cDNA synthesis reaction and SYBR green qPCR mix (ABgene, UK) in a total volume of 20 μL. Reactions were run in a Techne Quantica thermocycler at previously determined optimum annealing temperatures. Gene copy number in each reaction was automatically calculated by the Quantica software by comparison to a standard curve constructed from the results of a parallel set of reactions containing serially diluted cDNA. Relative expression of target genes in comparison with selected housekeeping genes was then calculated with the software package REST (http://www.gene-quantification.de).
Table 1

PCR primer information and gene expression measured by qPCR and microarray

ID

Description

Type

Sequence 5′→3′

Expression by qPCR

Expression by microarray

B2

Known flatliner F

Ref

AGCCTATGACCAACCCACTG

1.034

 

Known flatliner R

TGTTCACAGCTCGTTTACCG

EF1A

Elongation factor-1a F

Ref

CTGCCCCTCCAGGACGTTTACAA

1.173

 

Elongation factor-1a R

CACCGGGCATAGCCGATTCC

bActin

b-Actin F

Ref

ACATCAAGGAGAAGCTGTGC

0.824

 

b-Actin R

GACAACGGAACCTCTCGTTA

TC109851

Salmo salar isolate Ss11_GH1 growth hormone I gene F

TRM

GGTCAGCAACGGAGAAACAT

6.957

4.49

Salmo salar isolate Ss11_GH1 growth hormone I gene R

GCAAAGAACAACAGCTTTATTGAA

TC108550

SSALGHISalmo salar mRNA for growth hormone F

TRM

TGTTTCTGCTGATGCCAGTC

49.593

12.45

SSALGHISalmo salar mRNA for growth hormone R

GGGCAGGTACCTTCAAAGTC

TC107199

SSMPROLACS,salar mRNA for prolactin F

TRM

GGCCAAACTCCACTTAGCAG

361.203

2.49

SSMPROLACS,salar mRNA for prolactin R

CATCATCACTCGTCCCATTG

Ref reference housekeeping genes, TRM genes induced during the treatment

3 Results

3.1 Exposure concentrations

Average exposure concentrations (mean ± standard deviation) throughout the 5 days of exposure for the selected pharmaceutical compounds were: 54.77 ± 34.67, 7.85 ± 0.13, and 11.08 ± 7.98 µg L−1 for AC, CA, and AT, respectively. The LOD and LOQ were 0.07 and 0.22 µg L−1 for AC, 0.02 and 0.06 µg L−1 for CA, and 0.12 and 0.41 µg L−1 for AT.

3.2 Physiological processes and pathway analyses of differentially expressed genes

Exposure to CA resulted in the greatest number (700, p < 0.05) of differentially expressed genes compared to the control. Exposure to AC and AT resulted in 659 and 480 (p < 0.05) of differentially expressed genes, respectively (submitted). Where possible, the differentially expressed genes were assigned KO numbers and mapped to a known compendium of metabolic pathways (KEGG). Depending on the treatment, the percentages of features that were possible to be assigned with a KO varied between 26.57% and 33.33% for CA and AT, respectively (Fig. 1). Partial lists of genes exhibiting differential expression under AC, CA, and AT treatments, categorized by their KEGG metabolic pathway, are represented in Tables 2, 3, and 4, respectively. Observed induced fold changes in the KO assigned features were relatively low.
https://static-content.springer.com/image/art%3A10.1007%2Fs11356-009-0282-6/MediaObjects/11356_2009_282_Fig1_HTML.gif
Fig. 1

Percentage (%) of possible KO assignations per treatment

Table 2

KO numbers, fold changes, and p values of features (n ≥ 4) induced by AC treatment

Category

Pathway

EC ID

KO ID

Description

p value

Fold change

Metabolism

Carbohydrate metabolism

     
 

Glycolysis/gluconeogenesis

EC 1.2.1.12

K00134

Glyceraldehyde 3-phosphate dehydrogenase

0.0153

1.095

EC 5.4.2.1

K01834

Phosphoglycerate mutase

0.00654

0.889

EC 4.1.1.32

K01596

Phosphoenolpyruvate carboxykinase (GTP)

0.0359

0.385

EC 1.8.1.4

K00382

Dihydrolipoamide dehydrogenase

0.0167

0.833

EC 1.2.1.3

K00128

Aldehyde dehydrogenase (NAD+)

0.035

0.752

Energy metabolism

     

Oxidative phosphorylation

EC 1.6.5.3

K03943

nicotinamide adenine dinucleotide (NADH) dehydrogenase (ubiquinone) flavoprotein 2

0.00947

0.768

EC 1.6.5.3

K03955

NADH dehydrogenase (ubiquinone) 1 alpha/beta subcomplex 1

0.0329

0.857

EC 1.6.5.3

K03956

NADH dehydrogenase (ubiquinone) 1 alpha subcomplex 11

0.0455

1.176

EC 1.9.3.1

K02261

Cytochrome c oxidase subunit II

0.0315

0.93

EC 3.6.3.14

K02132

F-type H+-transporting ATPase subunit alpha

0.0153

0.892

EC 3.6.3.14

K02135

F-type H+-transporting ATPase subunit epsilon

0.05

0.917

EC 3.6.3.14

K02128

F-type H+-transporting ATPase subunit c

0.0106

0.87

Genetic information processing

Translation

     
 

Ribosome

 

K02958

Small subunit ribosomal protein S15e

0.0277

1.168

K02932

Large subunit ribosomal protein L5e

0.00652

1.263

K02937

Large subunit ribosomal protein L7e

0.0468

1.102

K02889

Large subunit ribosomal protein L21e

0.00285

1.213

K02962

Small subunit ribosomal protein S17e

0.0379

0.867

K02993

Small subunit ribosomal protein S7e

0.0197

1.839

Environmental information processing

Signaling molecules and interaction

     
 

ECM–receptor interaction

 

K06237

Collagen type XI alpha

0.00745

1.216

K05719

Integrin beta 1

0.0478

0.874

K06591

Integrin beta 8

0.0155

0.903

K06258

MFS transporter, VNT family, synaptic vesicle glycoprotein 2

0.0225

0.935

K06267

Hyaluronan-mediated motility receptor

0.0206

1.575

Cell adhesion molecules

 

K06087

Claudin

0.0422

1.366

K05719

Integrin beta 1

0.0478

0.874

K06591

Integrin beta 8

0.0155

0.903

K06756

Neuronal cell adhesion molecule

0.0348

0.838

Cellular processes

Cell motility

     
 

Regulation of actin cytoskeleton

 

K06590

Integrin beta 8

0.0155

0.903

K05767

IQ motif containing GTPase activating protein

0.0492

0.915

K05754

Actin-related protein 2/3 complex subunit 5

0.00157

1.626

K05762

Radixin

0.0364

0.863

Cell communication

     

Focal adhesion

 

K06238

Collagen type XI

0.00745

1.216

 

K06590

Integrin beta 8

0.0155

0.903

EC 2.7.11.1

K06276

3-Phosphoinositide-dependent protein kinase-1

0.0489

1.695

 

K04448

Transcription factor AP-1

0.0466

0.938

Endocrine system

     

Insulin signaling pathway

 

K07192

Flotillin

0.0438

0.823

EC 2.7.11.1

K06276

3-Phosphoinositide-dependent protein kinase-1

0.0489

1.695

EC 4.1.1.32

K01596

Phosphoenolpyruvate carboxykinase (GTP)

0.0359

0.385

EC 2.4.1.11

K00693

Glycogen (starch) synthase

0.0231

0.765

EC 2.7.11.19

K00871

Phosphorylase kinase gamma subunit

0.0256

0.898

PPAR signaling pathway

 

K08524

Nuclear receptor, subfamily 2, group B, member 1, 2, 3

0.0384

1.141

 

K08767

Angiopoietin-like 4

0.0208

1.653

EC 2.7.11.1

K06276

3-Phosphoinositide-dependent protein kinase-1

0.0489

1.695

EC 4.1.1.32

K01596

Phosphoenolpyruvate carboxykinase (GTP)

0.0359

0.385

Immune system

     

Leukocyte transendothelial migration 

 

K05719

Integrin beta 1

0.0478

0.874

K04630

Guanine nucleotide-binding protein (G protein), alpha inhibiting activity polypeptide

0.00256

0.845

K08011

Neutrophil cytosolic factor 1

0.00723

0.935

K06087

Claudin, occludin, endothelial cell adhesion molecule

0.0422

1.366

Table 3

KO numbers, fold changes, and p values of features (n ≥ 4) induced by CA treatment

Category

Pathway

EC ID

KO ID

Description

p value

Fold change

Metabolism

Carbohydrate metabolism

     
 

Glycogenesis/gluconeogenesis

EC 4.1.2.13

K01623

Fructose-bisphosphate aldolase class I

0.0467

0.644

EC 5.3.1.1

K01803

Triosephosphate isomerase (TIM)

0.0471

1.181

EC 5.4.2.1

K01834

Phosphoglycerate mutase

0.0008

1.157

EC 4.2.1.11

K01689

Enolase

0.0049

1.649

EC 2.7.1.40

K00873

Pyruvate kinase

0.04

1.1

EC 1.8.1.4

K00382

Dihydrolipoamide dehydrogenase

0.0166

0.836

EC1.2.1.3

K00128

Aldehyde dehydrogenase (NAD+)

0.0172

1.154

Fructose/mannose metabolism

EC 3.1.3.46

K01103

Fructose-2,6-bisphosphatase

0.044

1.212

EC 2.7.1.105

K00900

6-Phosphofructo-2-kinase

0.0435

0.608

EC 4.1.2.13

K01623

Fructose-bisphosphate aldolase class I

0.0467

0.644

EC 5.3.1.1

K01803

Triosephosphate isomerase (TIM)

0.0471

1.181

Pyruvate metabolism

EC 2.7.1.40

K00873

Pyruvate kinase

0.04

1.1

EC 1.8.1.4

K00382

Dihydrolipoamide dehydrogenase

0.0166

0.836

EC 1.2.1.3

K00128

Aldehyde dehydrogenase (NAD+)

0.0172

1.154

EC 2.3.1.9

K00626

Acetyl-CoA C-acetyltransferase

0.0172

0.896

Energy metabolism

     

Oxidative phosphorylation

EC 1.6.5.3

K03937

NADH dehydrogenase (ubiquinone) Fe–S protein 4

0.0319

0.887

EC 1.6.5.3

K03944

NADH dehydrogenase (ubiquinone) flavoprotein 3

0.002

0.674

EC 1.6.5.3

K03961

NADH dehydrogenase (ubiquinone) 1 beta subcomplex 5

0.0319

0.887

EC 1.9.3.1

K02269

Cytochrome c oxidase subunit VII

0.0041

0.772

EC 1.9.3.1

K02272

Cytochrome c oxidase subunit VIIc

0.0378

0.904

EC 3.6.3.14

K02151

V-type H+-transporting ATPase subunit F

0.0235

1.314

Carbon fixation in photosynthetic organisms

EC 4.1.2.13

K01623

Fructose-bisphosphate aldolase class I

0.0467

0.644

EC 2.2.1.1

K00615

Transketolase

  

EC 5.3.1.1

K01803

Triosephosphate isomerase (TIM)

0.0471

1.181

EC 2.6.1.1

K00811

Aspartate aminotransferase

0.0276

1.258

EC 2.7.1.40

K00873

Pyruvate kinase

0.04

1.1

Nucleotide metabolism

     

Purine metabolism

EC 2.7.1.40

K00873

Pyruvate kinase

0.04

1.1

EC 1.17.4.1

K00524

Ribonucleotide reductase class II

0.0098

0.771

EC 1.1.1.205

K00088

IMP dehydrogenase

  

EC 2.7.1.20

K00856

Adenosine kinase

0.0218

0.866

Genetic information processing

Translation

     
 

Ribosome

 

K02998

Small subunit ribosomal protein SA

0.045

3.689

K02896

Large subunit ribosomal protein L24

0.001

0.89

K02976

Small subunit ribosomal protein S26

0.0474

0.923

K02923

Large subunit ribosomal protein L38

0.0167

0.93

K02971

Small subunit ribosomal protein S21

0.0434

0.885

Environmental information processing

Signal transduction

     
 

mitogen-activated protein kinase (MAPK) signaling pathway

 

K02580

Nuclear factor of kappa light polypeptide gene enhancer in B-cells 1

0.0147

0.832

EC 3.1.3.16

K04348

Protein phosphatase 3, catalytic subunit

0.0201

1.31

EC 2.7.12.2

K04430

Mitogen-activated protein kinase kinase 4

0.0459

1.061

EC 2.7.11.1

K04429

Thousand and one amino acid protein kinase

0.0067

1.244

Jak–STAT signaling pathway

EC 1.11.1.8

K00431

Thyroid peroxidase

  

EC 2.7.10.2

K04447

Janus kinase 2

  

EC 3.1.3.48

K05697

Protein tyrosine phosphatase, non-receptor type 6

0.0493

1.111

 

K04692

Signal transducer and activator of transcription 3

0.0377

0.893

 

K04701

Cytokine-inducible SH2-containing protein

0.0027

1.718

 

K04704

Sprouty

0.0207

1.105

Calcium signaling pathway

 

K05862

Voltage-dependent anion cannel

0.0022

1.278

 

K05863

Solute carrier family 25 (mitochondrial carrier)

0.0094

1.082

 

K02183

Calmodulin

0.027

1.084

EC 3.1.3.16

K04348

Protein phosphatase 3, catalytic subunit

0.0201

1.31

Cellular processes

Cell motility

     
 

Regulation of actin cytoskeleton

 

K05754

Actin-related protein 2/3 complex subunit 5

0.0279

0.906

K05692

Actin beta/gamma 1

0.0064

0.712

K04513

Ras homolog gene family, member A

0.0027

0.779

K08007

Radixin, moesin, or villin 2 (ezrin)

0.0082

0.851

K05699

Actinin alpha

0.0457

0.887

Cell growth and death

     

Cell cycle

 

K04685

Cyclin-dependent kinase inhibitor 2B, 2A, 2C, 2D

0.0078

3.337

K06630

Tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein

0.0177

0.735

K03363

Cell division cycle 20, cofactor of APC complex

0.0313

0.773

K02209

Minichromosome maintenance protein 5 (cell division control protein 46)

0.0263

1.143

Cell communication

     

Focal adhesion

 

K03900

Collagen type XI

0.045

3.689

K05699

Actinin alpha

0.0457

0.887

K04513

Ras homolog gene family, member A

0.0027

0.779

K05692

Actin beta/gamma 1

0.0064

0.712

Adherens junction

 

K05699

Actinin alpha

0.0457

0.887

 

K05701

Tight junction protein 1

0.0109

0.86

 

K05692

Actin beta/gamma 1

0.0064

0.712

 

K04513

Ras homolog gene family, member A

0.0027

0.779

EC 2.7.11.1

K03097

Casein kinase 2, alpha polypeptide

0.0131

1.089

EC 3.1.3.48

K05697

Protein tyrosine phosphatase, non-receptor type 6

0.0493

1.111

Tight junction

EC 2.7.11.1

K03097

Casein kinase 2, alpha polypeptide

0.0131

1.089

 

K05701

Tight junction protein 1

0.0109

0.86

 

K06106

Cortactin

0.0254

0.742

 

K06107

Erythrocyte membrane protein band 4.1

0.0078

0.853

 

K05699

Actinin alpha

0.0457

0.887

 

K05692

Actin beta/gamma 1

0.0064

0.712

 

K04513

Ras homolog gene family, member A

0.0027

0.779

Immune system

     

T-cell receptor signaling pathway

EC 3.1.3.48

K05697

Protein tyrosine phosphatase, non-receptor type 6

0.0493

1.111

 

K04393

Cell division control protein 42

0.0313

0.773

 

K04513

Ras homolog gene family, member A

0.0027

0.779

EC 3.1.3.16

K04348

Protein phosphatase 3, catalytic subunit

0.0201

1.31

B-cell receptor signaling pathway

EC 3.1.3.48

K05697

Protein tyrosine phosphatase, non-receptor type 6

0.0493

1.111

EC 3.1.3.16

K04348

Protein phosphatase 3, catalytic subunit

0.0201

1.31

 

K06268

Protein phosphatase 3, regulatory subunit

0.0166

1.127

 

K06508

CD81 antigen

0.0273

1.085

Leukocyte transendothelial migration 

 

K04513

Ras homolog gene family, member A

0.0027

0.779

K05692

Actin beta/gamma 1

0.0064

0.712

K05699

Actinin alpha

0.0457

0.887

K08009

Cytochrome b-245, alpha polypeptide

0.0394

0.743

K08011

Neutrophil cytosolic factor 1

0.0299

0.935

Table 4

KO numbers, fold changes, and p values of features (n ≥ 4) induced by AT treatment

Category

Pathway

EC ID

KO ID

Description

p value

Fold change

Metabolism

Carbohydrate metabolism

     
 

Glycolysis/gluconeogenesis

EC 5.4.2.2

K01835

Phosphoglucomutase

0.0452

0.891

EC 4.1.2.13

K01623

Fructose-bisphosphate aldolase, class I, class II

0.0498

0.857

EC 4.1.1.32

K01596

Phosphoenolpyruvate carboxykinase (GTP)

0.0218

0.616

EC 4.2.1.11

K01689

Enolase

0.0284

1.58

EC 1.2.1.3

K00128

Aldehyde dehydrogenase (NAD+)

0.0323

0.93

Energy metabolism

     

Oxidative phosphorylation

EC 1.6.5.3

K03878

NADH dehydrogenase I subunit 1

0.0367

1.125

EC 1.6.5.3

K03966

NADH dehydrogenase (ubiquinone) 1 beta subcomplex 10

0.0207

0.823

EC 1.10.2.2

K00415

Ubiquinol–cytochrome c reductase core subunit 2

0.0176

1.209

EC 1.10.2.2

K00420

Ubiquinol–cytochrome c reductase subunit 10

0.0354

0.921

EC 3.6.3.14

K02147

V-type H+-transporting ATPase subunit B

0.041

0.863

Nucleotide metabolism

     

Purine metabolism

EC 2.7.6.1

K00948

Ribose-phosphate pyrophosphokinase

0.0379

1.107

EC 2.1.2.3 3.5.4.10

K00602

Phosphoribosylaminoimidazolecarboxamide formyltransferase/IMP cyclohydrolase

0.0135

0.787

EC 3.1.4.17

K01120

3′,5′-cyclic-nucleotide phosphodiesterase

0.0389

1.131

EC 2.7.7.6

K02999

DNA-directed RNA polymerases

0.0409

1.214

Genetic information processing

Translation

     
 

Ribosome

 

K02896

Large subunit ribosomal protein L24e

0.0446

0.935

K02921

Large subunit ribosomal protein L37Ae

0.0328

0.928

K02927

Large subunit ribosomal protein L40e

0.0123

0.878

K02974

Small subunit ribosomal protein S24e

0.046

1.538

K02923

Large subunit ribosomal protein L38e

0.0193

0.938

Folding, sorting, and degradation

     

Ubiquitin-mediated proteolysis

EC 6.3.2.19

K10575

Ubiquitin-conjugating enzyme E2 G1

0.0255

0.821

EC 6.3.2.19

K04649

Ubiquitin-conjugating enzyme (huntingtin interacting protein 2)

0.0173

0.903

 

K03868

RING-box protein 1

0.044

1.064

 

K03359

Anaphase-promoting complex subunit 12

0.00388

1.227

Proteasome

 

K03035

26S proteasome regulatory subunit N5

0.0192

1.515

EC 3.4.25.1

K02728

20S proteasome subunit alpha 3

0.0429

1.142

EC 3.4.25.1

K02739

20S proteasome subunit beta 2

0.023

1.13

EC 3.4.25.1

K02737

20S proteasome subunit beta 5

0.0483

0.791

Environmental information processing

Signal transduction

     
 

MAPK signaling pathway

 

K04381

Stathmin

0.0281

0.935

EC 2.7.12.2

K04430

Mitogen-activated protein kinase kinase 4

0.00979

1.547

 

K04455

Heat shock 27 kDa protein 1

0.0284

1.468

 

K04402

Growth arrest and DNA-damage-inducible protein

0.021

1.475

Calcium signaling pathway

 

K05862

Voltage-dependent anion channel

0.0233

0.9

EC 2.7.11.18

K00907

Myosin light chain kinase

0.0304

1.167

EC 2.7.11.19

K00871

Phosphorylase kinase gamma subunit

0.00978

0.904

 

K02183

Calmodulin

0.00323

1.434

Cellular processes

Cell motility

     
 

Regulation of actin cytoskeleton

 

K05754

Actin-related protein 2/3 complex subunit 5

0.0489

1.358

  

Actin-related protein 2/3 complex subunit 4

0.0453

0.905

  

Actin-related protein 2/3 complex subunit 3

0.0299

0.901

 

K05762

Radixin, moesin, villin 2 (ezrin)

0.0366

0.759

EC 2.7.11.18

K00907

Myosin light chain kinase

0.0304

1.167

 

K10351

Myosin light chain 2/5/7/9, regulatory

0.00892

0.911

Cell growth and death

     

Cell cycle

 

K04501

SMAD, mothers against DPP 4

0.0135

1.114

K03868

RING-box protein 1

0.044

1.064

K04402

Growth arrest and DNA-damage-inducible protein

0.021

1.475

K03348

Anaphase-promoting complex subunit 1–9

0.00388

1.227

Cell communication

     

Focal adhesion

 

K06239

Thrombospondin

0.0128

0.566

 

K10351

Myosin light chain 2/5/7/9, regulatory

0.00892

0.911

EC 2.7.11.18

K00907

Myosin light chain kinase

0.0304

1.167

EC 2.7.11.1

K06276

3-Phosphoinositide-dependent protein kinase-1

0.0429

1.545

Endocrine system

     

Insulin signaling pathway

EC 4.1.1.32

K01596

Phosphoenolpyruvate carboxykinase (GTP)

0.0218

0.616

EC 2.7.11.1

K06276

3-Phosphoinositide-dependent protein kinase-1

0.0429

1.545

EC 2.4.1.11

K00693

Glycogen (starch) synthase

0.0105

0.799

EC 2.7.11.19

K02183

Calmodulin

0.00323

1.434

Adipocytokine signaling pathway

 

K02581

Nuclear factor of kappa light polypeptide gene enhancer in B-cell inhibitor beta, alpha, epsilon

0.00892

0.911

 

K04692

Signal transducer and activator of transcription 3

0.0324

0.852

 

K05228

Proopiomelanocortin

0.0187

0.831

EC 4.1.1.32

K01596

Phosphoenolpyruvate carboxykinase (GTP)

0.0218

0.616

 

K07297

Adiponectin receptor

0.0396

0.84

PPAR signaling pathway

 

K08750

Fatty acid-binding protein, liver, intestinal, muscle and heart, adipocyte, epidermal, ileal (gastrotropin), brain

0.0184

0.898

EC 1.3.99.3

K00249

Acyl-CoA dehydrogenase

0.0177

0.93

 

K08768

Perilipin

0.00977

0.772

EC 2.7.11.1

K06276

3-phosphoinositide-dependent protein kinase-1

0.0429

1.545

 

K08770

Ubiquitin C

0.0416

0.872

EC 4.1.1.32

K01596

Phosphoenolpyruvate carboxykinase (GTP)

0.0218

0.616

Immune system

     

Toll-like receptor signaling pathway 

 

K04734

Nuclear factor of kappa light polypeptide gene enhancer in B-cell inhibitor, alpha

0.0445

0.796

EC 2.7.12.2

K04430

Mitogen-activated protein kinase kinase 4, 7

0.035

1.3

 

K10161

Toll-like receptor 9

0.0258

1.184

K11220

Signal transducer and activator of transcription 1

0.0398

0.907

Using the criterion pathways must have four or more differentially expressed genes with p < 0.05 (Ahlborn et al. 2008), AC was the treatment that showed the lowest number of induced features throughout the different pathways in the brain tissues (10). Three of these pathways belonged to metabolic processes, one to genetic information processing, two to environmental information processing, whereas four pathways involved in cellular processes were induced. The total sum of induced features was 52, taking into account only those which belonged to a pathway where four or more features were induced (Table 2). On the contrary, CA was the treatment with the highest number of induced pathways (18) of which six belonged to the category metabolism, one to the category genetic information processing, three involved in environmental information processing, and eight in general cellular processes. The total sum of induced features was 88, taking into account only those which belonged to a pathway where four or more features were induced (Table 3). As a consequence of the exposure to the pharmaceutical AT, a total of 16 different pathways (four or more features) were induced. Of these 16 pathways, four were involved in metabolic processes, three in genetic information processing, two in environmental information processing, and seven in cellular processes. The total sum of induced features was 70 (Table 4).

The biological pathways were functionally categorized, and their distribution by treatment is represented in Fig. 2. The main induced pathways during exposure to AC treatment were related to signaling molecules and interaction as well as the endocrine system, whereas for the CA treatment, the most induced pathways were related to carbohydrate metabolism, energy metabolism, signal transduction, cell communication, and immune system. In the case of AT, the most induced pathways were related to folding, sorting and degradation of genetic material as well as signal transduction and processes lined to the endocrine system. A more detailed description of the different induced pathway by the selected treatments is represented in Fig. 3. AC significantly impacted the following KEGG pathways (more than four features): glycolysis/gluconeogenesis, oxidative phosphorylation, ribosome, extracellular matrix (ECM)–receptor interaction, regulation of actin cytoskeleton, focal adhesion, and insulin signaling pathway. CA induced the following KEGG pathways: glycolysis/gluconeogenesis, oxidative phosphorylation, carbon fixation in photosynthetic organisms, ribosome, Jak–Stat signaling pathway, regulation of actin cytoskeleton, focal adhesion, adherens junction, and tight junction. Under AT treatment, the induced KEGG pathways were: glycolysis/gluconeogenesis, oxidative phosphorylation, ribosome, regulation of actin cytoskeleton, focal adhesion, insulin signaling pathway, adipocytokine signaling pathway, and peroxisome proliferator-activated receptor (PPAR) signaling pathway.
https://static-content.springer.com/image/art%3A10.1007%2Fs11356-009-0282-6/MediaObjects/11356_2009_282_Fig2_HTML.gif
Fig. 2

Functional categorization of pathways per treatment

https://static-content.springer.com/image/art%3A10.1007%2Fs11356-009-0282-6/MediaObjects/11356_2009_282_Fig3_HTML.gif
Fig. 3

Functional categorization of induced features per treatment

Regarding the AC treatment, the most significantly induced or inhibited features were: phosphoenolpyruvate carboxykinase (p = 0.0359, fold change = 0.385), used in the metabolic pathway of gluconeogenesis converting oxaloacetate into phosphoenolpyruvate and carbon dioxide. This enzyme is also involved in the induced PPAR signaling pathway. The small subunit ribosomal protein S7e underwent a fold change of 1.839 (p = 0.0197), and in relation with signaling molecules and interaction, a major increase (fold change = 1.575, p = 0.0206) of the hyaluronan-mediated motility receptor expression related with the ECM–receptor interaction, as well as of claudin expression (fold change = 1.366, p = 0.0422) related with cell adhesion molecules, could be observed. Also, the expression of the actin-related protein 2/3 complex subunit 5 was induced 1.626-fold (p = 0.00157) as well as the 3-phosphoinositide-dependent protein kinase (fold change = 1.695, p = 0.0489), which is involved in several pathways including focal adhesion, insulin signaling pathway, and PPAR signaling pathway. A further feature induced in this latter pathway in the angiopoietin-like 4 which was induced 1.653-fold (p = 0.0208).

With respect to the CA treatment, one of the most significantly inhibited features was the fructose-bisphosphate aldolase class I (fold change = 0.644, p = 0.0467) which takes part in the glycolysis but is also involved in the fructose/mannose metabolism and the carbon fixation pathway. Also involved in the glycolysis pathway and significantly induced (1.649 times) is the enolase, also known as phosphopyruvate dehydratase, which is a metalloenzyme responsible for the catalysis of 2-phosphoglycerate to phosphoenolpyruvate, the penultimate step of glycolysis. Enolase can also catalyze the reverse reaction, depending on environmental concentrations of substrates. Another inhibited feature in the fructose/mannose pathway was the 6-phosphofructo-2-kinase (fold change = 0.608, p = 0.0435). With regard to the oxidative phosphorylation, NADH dehydrogenase flavoprotein 3 was inhibited 0.674-fold (p = 0.002), whereas the V-type H+-transporting ATPase subunit F was induced 1.314-fold (p = 0.0235). Related with translational processes, the small subunit ribosomal protein SA was significantly induced by 3.689 times (p = 0.045), and regarding signal transduction, concretely the Jak–STAT signaling pathway, the cytokine-inducible SH2-containing protein was induced 1.718-fold (p = 0.0027), whereas in the calcium signaling pathway, the voltage-dependent anion channel was induced 1.278 times (p = 0.0022). A very significant induction in the expression was observed in features related to cellular processes, with the cyclin-dependent kinase inhibitor belonging to the cell cycle pathway induced 3.337 times (p = 0.0078) and the collagen type XI, belonging to focal adhesion processes in cell communication by 3.689 times (p = 0.045).

As a consequence of the AT treatment, major induced or inhibited features were, in the glycolysis/gluconeogenesis pathway, the phosphoenolpyruvate carboxykinase (fold change = 0.616, p = 0.0218), which is also involved in the insulin, the adipocytokine, and the PPAR signaling pathway, as well as enolase (fold change = 1.59, p = 0.0284). In relation to the energy metabolism, the ubiquinol–cytochrome c reductase core subunit 2, part of the oxidative phosphorylation was induced 1.209-fold (p = 0.0176), and in relation to the genetic information processing, the 26S proteasome regulatory subunit N5 was induced 1.515 times (p = 0.0192). Within the signal transduction processes, the MAPK signaling pathway underwent significant inductions in the features: mitogen-activated protein kinase kinase 4 (fold change = 1.547, p = 0.00979), heat shock 27 kDa protein 1 (fold change = 1.468, p = 0.0284), and growth arrest and DNA-damage-inducible protein (fold change = 1.475, p = 0.021) which is also part of the cell cycle pathway. In the calcium signaling pathway, as well as in the insulin signaling pathway, calmodulin was induced 1.434-fold (p = 0.00323), and the actin-related protein 2/3 complex subunit 5 involved in the regulation of the actin cytoskeleton was induced 1.358 times (p = 0.0489). Finally, the 3-phosphoinositide-dependent protein kinase-1 (fold change = 1.545, p = 0.0429) was another induced feature being related to several different pathways such as focal adhesion processes, insulin signaling pathway, and PPAR signaling pathway.

3.3 qPCR

Microarray and qPCR fold change comparisons of selected key genes are represented in Table 1. qPCR expression changes tended to show greater increases in differential expression for the evaluated genes. In general, the qPCR data support the differential expression depicted by the array data. However, the amount of differently expressed genes observed by microarray analysis prevented the verification of all genes through qPCR and a series of representative genes that showed highest fold changes in the array data were selected.

4 Discussion

The goal of the present study was to evaluate transcriptional profiles involved in different biological pathways from the brain of Atlantic salmon (Salmo salar) exposed to environmentally relevant concentrations of three representative pharmaceutical compounds commonly detected in municipal and industrial sewages. Even if the selected exposure period of 5 days in combination with the chosen concentrations of the employed pharmaceuticals, in order to be environmentally relevant, produced rather low effects on the differential expression of candidate genes, significant changes in comparison with non-treated organisms could be observed.

One of the major drawbacks of working with environmentally or economically relevant test organisms, making them candidates for their employment in different kinds of exposure studies, is that in most of the cases their genome is not completely annotated. This makes the assignments of gene functions and, as a consequence, of KO annotations necessary for the construction of KEGG pathways difficult and incomplete. Therefore, obtained pathway maps can be considered as informative for potential processes that are occurring; however, they are not complete and important physiological and metabolic processes that may occur in the organism may be missed. However, this freely available bioinformatic tool is useful for the evaluation of the potential functional effects that the exposure to the selected pharmaceutical compounds produce in the chosen test species, Atlantic salmon, giving an initial indication about the processes occurring under exposure to this kind of compounds which can be considered as a starting point for the evaluation of the produced effects at the cellular and organism level. In effect, the Atlantic salmon cDNA microarray used in this study has recently become available through the British Research Council-funded TRAITS project (Taggart et al. 2008), with approximately 75% of hits after ESTs BLASTing vs. Swissprot, Trembl, and including 158K expressed sequence tag (EST) sequences of the closely related rainbow trout (Oncorhynchus mykiss). However, only 26.57–33.33% of these annotated sequences were able to be assigned by a KO annotation for the KEGG pathway analysis.

4.1 Acetaminophen

Together with the kidney, the main target organ of AC is the liver where it may cause hepatotoxicity after overdoses in man and laboratory animals (Hoivik et al. 1996). The main mechanism of action is the inhibition of cyclooxygenase, an enzyme responsible for the production of prostaglandins, which are important mediators of inflammation, pain, and fever. While bioactivation of AC by cytochrome P450 to the reactive intermediate, N-acetyl-p-benzoquinoneimine, is necessary for the development of toxicity (Vermeulen et al. 1992), therapeutic doses of AC are primarily detoxified by sulfation and glucuronidation (Cummings et al. 1967). N-acetyl-p- benzoquinoneimine (NAPQI) is primarily neutralized by conjugation with glutathione (GSH). With depletion of GSH, there is greater protein arylation by AC, and such binding has been associated with toxicity (Jollow et al. 1973; Potter et al. 1974; Nelson and Pearson 1990; Boelsterli 1993). However, the features that were observed to be induced in our study were not directly related with these mechanisms, and the exposure concentrations chosen were not likely to induce overdoses.

Carbohydrate and energy metabolism were one of the most induced processes after treatment with the analgesic AC. Phosphoenolpyruvate carboxykinase (fold change = 0.385, p = 0.0359) catalyzes the first committed (rate-limiting) step in gluconeogenesis, namely the reversible decarboxylation of oxaloacetate to phosphoenolpyruvate and carbon dioxide, using the observed form (EC 4.1.1.32) guanosine triphosphate (GTP) as a source of phosphate. This enzyme helps to regulate blood glucose levels. The rate of gluconeogenesis can be controlled through transcriptional regulation of the phosphoenolpyruvate carboxykinase gene by cAMP (the mediator of glucagon and catecholamines), glucocorticoids, and insulin. In general, phosphoenolpyruvate carboxykinase expression is induced by glucagon, catecholamines, and glucocorticoids during periods of fasting and in response to stress, but is inhibited by (glucose-induced) insulin upon feeding (Burgess et al. 2007). In fact, a 0.631-fold inhibition (p = 0.0141) of a cAMP-responsive element binding protein has also been observed in the candidate 659-gene list of differentially expressed genes but no KO number could be assigned wherefore this feature is not included in the KEGG pathway analysis. On the other hand, glyceraldehyde 3-phosphate dehydrogenase (EC 1.2.1.12) is an enzyme that catalyzes the sixth step of glycolysis and thus serves to break down glucose for energy and carbon molecules. In addition to this long established metabolic function, it has recently been implicated in several non-metabolic processes, including transcription activation and initiation of apoptosis (Tarze et al. 2007). Phosphoglycerate mutase catalyzes step 8 of glycolysis converting 3-phosphoglycerate to 2-phosphoglycerate. The phosphoglycerate mutase reaction proceeds easily in both directions and is thus not the site of major regulation mechanisms or regulation schemes for the glycolytic pathway. In humans, deficiency in phosphoglycerate mutase function presents as muscular dystrophy, which could be linked to the induction of the actin-related protein 2/3 complex in the regulation of actin cytoskeleton pathway. Dihydrolipoamide dehydrogenase is a flavoprotein that acts in a number of ways including dehydrogenase complexes for pyruvate, 2-oxoglutarate, 2-oxoisovalerate, and can also serve in the glycine cleavage system. In general, the whole energy metabolism and, more concretely, the features involved in the oxidative phosphorylation have been slightly inhibited.

The ribosome is a large ribonucleoprotein complex made up of two subunits. The small subunit of the ribosome includes the activity that decodes the genetic message. The small subunit guides the interaction between messenger RNA (mRNA) and anticodon ends of transfer RNAs to read the genetic information stored in genes with exquisite fidelity. An interesting aspect of the translation process, in which the ribosomes play an essential role, is that the majority of the small subunits induced which have been annotated by KO numbers were involved in the first step of the translation process: the union of the protein units to the rRNA. In the small (30S) subunit of Escherichia coli ribosomes, the proteins denoted S4, S7, S8, S15, S17, and S20 bind independently to 16S rRNA. After assembly of these primary binding proteins, S5, S6, S9, S12, S13, S16, S18, and S19 bind to the growing ribosome. The large ribosomal subunits catalyze peptide bond formation and binds initiation, termination, and elongation factors. All the annotated large subunits have also been slightly induced, indicating that the exposure to AC provoked a general increase in the translation activity from RNA into proteins.

With regard to signaling molecules and interaction, two outstanding features with significant increase in expression have been observed: claudin and hyaluronan-mediated motility receptor. This latter feature was increased over 1.5 times. This receptor has shown to activate intracellular signaling pathways upon binding to the main hyaluronan receptor (Turley et al. 1991) and is involved in inflammatory processes, acting both stimulating and moderating inflammation processes. The negative feedback loop of inflammatory activation is achieved by both free-radical scavenging and specific biological interactions with the biological constituents of inflammation (Chen and Abantangelo 1999). AC is an anti-inflammatory drug and the exposure to it may have caused this differential expression of the involved gene.

The induced (fold change = 1.695, p = 0.0489) feature 3-phosphoinositide-dependent protein kinase-1 has shown to be involved in three different cellular processes, the focal adhesion, the insulin signaling pathway, and the PPAR signaling pathway, where this enzyme plays different roles. Adipocyte differentiation is regulated largely through the actions of the PPAR nuclear receptor and the insulin signaling pathway. 3-Phosphoinositide-dependent protein kinase-1 serves as a critical regulatory point in insulin signaling through its ability to phosphorylate the activation loop of several protein kinase families (Yin et al. 2005).

4.2 Carbamazepine

Carbamazepine is a tricyclic compound used as an anticonvulsant and as a specific analgesic for trigeminal neuralgia. It blocks sodium channels, thereby reducing action potentials formation and the rate of firing. It also inhibits uptake and release of norepinephrine in the brain. It inhibits polysynaptic responses and the spread of seizure discharge in the brain and shortens the duration of after discharge.

In our study, several features in the carbohydrate and energy metabolism have undergone important inhibition or induction. Fructose-bisphosphate aldolase class I and 6-phosphofructo-2-kinase were reduced to almost half of their expression under no-exposure conditions. On the other hand, enolase, also known as phosphopyruvate dehydratase, has been stimulated to a 1.649-fold expression. Enolase is a key glycolytic metalloenzyme responsible for the catalysis of 2-phosphoglycerate to phosphoenolpyruvate, the ninth and penultimate step of glycolysis. It belongs to a novel class of surface proteins which do not possess classical machinery for surface transport, yet through an unknown mechanism are transported on the cell surface. Enolase is a multifunctional protein, and its ability to serve as a plasminogen receptor on the surface of a variety of hematopoietic, epithelial, and endothelial cells suggests that it may play an important role in the intravascular and pericellular fibrinolytic system. Its role in systemic and invasive autoimmune disorders was recognized only very recently. In addition to this property, its ability to function as a heat shock protein and to bind cytoskeletal and chromatin structures indicates that enolase may play a crucial role in transcription and a variety of pathophysiological processes (Pancholi 2001). Indeed, an important number of features related to the immune system have been slightly induced as well as processes involved in cell communication, namely adherens junction, tight junction, and focal adhesion.

Another process that has undergone significant induction of involved features is the one denominated carbon fixation in photosynthetic organisms. This might be confusing but is referred to similar processes in animals like the pentose phosphate pathway in the case of transketolase. Transketolase catalyzes two important reactions, which operate in opposite directions. Transketolase connects the pentose phosphate pathway to glycolysis, feeding excess sugar phosphates into the main carbohydrate metabolic pathways. Its presence is necessary for the production of NADPH, especially in tissues actively engaged in biosyntheses, such as fatty acid synthesis by the liver and mammary glands, and for steroid synthesis by the liver and adrenal glands. Thiamine diphosphate is an essential cofactor along with calcium.

The three most induced features under CA treatment were cyclin-dependent kinase inhibitor 2B, 2A, 2C, and 2D (p = 0.0078, fold change = 3.337) involved in cellular processes related to growth and death, and the small subunit ribosomal protein SA (p = 0.045, fold change = 3.689), part of the ribosome and involved in translational processes which could also be identified as collagen type XI involved in focal adhesion processes.

4.3 Atenolol

Atenolol is a β1 receptor selective antagonist, a drug belonging to the group of beta-blockers, a class of drugs used primarily in cardiovascular diseases. Atenolol was developed as a replacement for propranolol in the treatment of hypertension. The chemical works by slowing down the heart and reducing its workload. Unlike propranolol, atenolol does not pass through the blood–brain barrier, thus avoiding various central nervous system side effects (Agon et al. 1991). Also, under this treatment, there was an important impact on features related with the carbohydrate and energy metabolism, being the phosphoenolpyruvate carboxykinase (GTP) the most reduced feature (p = 0.0218, fold change = 0.616). Also, a major impact on genetic information processing was observed than in the other two treatments, with influence not only on translation but also in folding, sorting, and degradation of the synthesized genetic material. Also, the endocrine system was significantly affected with three different pathways (insulin signaling pathway, adipocytokine signaling pathway, and PPAR signaling pathway) presenting four or more modified features. Insulin is a hormone released by pancreatic beta cells in response to elevated levels of nutrients in the blood. Insulin triggers the uptake of glucose, fatty acids, and amino acids into adipose tissue, muscle, and the liver and promotes the storage of these nutrients in the form of glycogen, lipids, and protein, respectively. In addition to promoting glucose storage, insulin inhibits the production and release of glucose by the liver by blocking gluconeogenesis and glycogenolysis (Saltiel and Kahn 2001). Insulin directly controls the activities of a set of metabolic enzymes by phosphorylation and dephosphorylation events and also regulates the expression of genes encoding hepatic enzymes involved in gluconeogenesis, a process that has been majorly affected by the selected treatment. Recent evidence suggests that forkhead transcription factors, which are excluded from the nucleus following phosphorylation by serine/threonine protein kinase (AKT), play a role in hepatic enzyme regulation by insulin (Schmoll et al. 2000; Barthel et al. 2001). Insulin also acts on the proteasome regulating protein degradation, a process that has also shown to be influenced by the AT treatment. Additionally, insulin inhibits lipid metabolism through decreasing cellular concentrations of cAMP by activating a cAMP-specific phosphodiesterase in adipocytes (Kitamura et al. 1999). The insulin signaling pathway can also elicit activation of the MAPK cascade leading to mitogenic responses (Ogawa et al. 1998). Interestingly, the adipocytokine signaling pathway and the PPAR signaling pathway have both shown to be involved in cardioprotective mechanisms and presented a relatively high number of induced features (five and six, respectively) under exposure with a pharmaceutical employed for the treatment of cardiovascular diseases.

Even if it is not yet possible to determine the higher level alterations as a consequence of the observed changes in the expression of determined genes, all the observed differentially expressed genes under exposure to the selected pharmaceutical compounds are aberrations from normal gene expression under non-exposure conditions. Further efforts have to be made in order to link these changes to potential histopathological effects which could lead to physiological dysfunctions and lead finally to the death or reduced competitiveness of the exposed organism which, on a long-term basis, could have severe ecological knock-on effects.

5 Conclusions

The continuously growing number of annotations of representative species relevant for environmental quality testing is facilitating pathway analysis processes for not completely annotated organisms. KEGG has shown to be a useful tool for the analysis of induced pathways from data generated by microarray techniques with the selected pharmaceutical contaminants AC, CA, and AT.

6 Recommendations and perspectives

Further studies have to be carried out in order to determine if a similar expression pattern in terms of fold change quantity and pathways is observed after long-term exposure. Together with the information obtained in this study, it will then be possible to evaluate the potential risk that the continuous release of these compounds may have on the environment and ecosystem functioning.

Acknowledgments

Miriam Hampel was funded in the framework of the FP6 Marie Curie Action: Intra European Fellowships (MEIF-CT-2006-39691, acronym SALMONPHARM).

Copyright information

© Springer-Verlag 2010