BACHELOR’S THESIS IN
Effects of persistent organic pollutants and their mixtures
on biotransformation and oxidative stress in zebrafish
Örebro University 2016
Table of content1. Introduction ... 3 2. Methods ... 5 3. Results... 8 4. Discussion ... 9 5. Acknowledgements ... 12 6. References ... 13
Persistent organic pollutants (POPs) cause significant effects on organisms due to their resistance to environmental degradation and specific toxic responses. Although POPs toxicities were linked to oxidative stress in the previous studies, there are few POPs studies that link them with oxidative stress in zebrafish during development. Aim of this study is to investigate effect of selected POPs on expression of genes involved in oxidative stress response and biotransformation of xenobiotics in zebrafish. Zebrafish embryos in 96 hours’ post-fertilization were exposed to selected POPs and their mixture. To explore the developmental toxicity in zebrafish early stage, we exposed 3,3',4,4',5-pentachlorobiphenyl (PCB), perfluorooctanesulfonic acid (PFOS) and perfluorohexanoic acid (PFHxA) at concentrations of 7.5 µg/L, 50 µM, 50 µM until 96 hours’ post-fertilization. The effects were measured by gene expression quantification technique - quantitative real-time PCR (qPCR). Significant up-regulation in gene expression was detected in embryos treated with mixture of PCB with PFHxA and PCB with PFOS only for cytochrome P450(cyp1a). The results also showed the treatment with selected compound caused significant higher upregulation of cyp1a when we compare the treatment in the individual compounds to the mixture compounds. However, treatments did not cause changes in expression of genes involved in oxidative stress response (glutathione
peroxidase 1a(gpx1a), tumor protein p53(tp53), aryl hydrocarbon receptor 2 (ahr2)). The result also
suggests that exposure to selected POPs in mixtures or alone is not causing oxidative stress in early stage of embryonal development of zebrafish but activating biotransformation function of the organism. Effect of activation of biotransformation capacity by means of cyp1a upregulation is also higher when POPs are in mixtures over when used as individual substance.
Since the second half of the 20th century, poly- and perfluoroalkyl substances known as PFASs have been used for different kinds of applications in the commercial sphere and industry. Their most widespread usage is in protective coating of the food. They are also included in many kinds of textiles and carpets. PFASs are also one of the ingredients in insecticides, old power transformers, fire-fighting foams and non-sticking coating cookware. Because of their widespread use, they can be found all over the world where they can be detected not only in the environment in a form of waste, but also in animals and humans (Ahrens and Bundschuh, 2014).
One major problem about PFASs is, that they can’t be degraded under environmental conditions. Because of that, they are stable and don’t go through metabolic conversion. The result of this problem is their permanent accumulation in the environment. That is very concerning, because they can be also accumulated in the living organisms, where they could be potentially toxic.
Usually concentrations of the PFASs are low in water environment. However, due to the natural food chain PFASs can be accumulated in the top consumers, such as wide range of fish species. In their livers there were detected high concentrations of PFASs.
Main goal of this study is to analyze zebrafish embryos exposed to different mixtures of PFASs to see developmental toxicity in the view of oxidative stress and biotransformation.
Zebrafish (Danio rerio) are frequently used as an experimental model organism because they are easy to maintain, have small size, continuous reproduction, rapid development and embryogenesis, optical transparency, amenability to genetic and chemical screens, and an extensive literature base (Bräunig et al., 2015). The zebrafish embryo is used within the regulatory framework of chemical risk assessments (OECD 2013). Early-life stages tests are known as pain-free in vivo assays and, thus, represent also a replacement for animal experiments.
3,3',4,4',5-pentachlorobiphenyl (PCB), perfluorooctanesulfonic acid (PFOS) and perfluorohexanoic acid (PFHxA) are widespread in the aquatic environment as persistent organic pollutants (POPs) (Ahrens and Bundschuh, 2014). PCB and PFOS are well-known to cause several different toxic effects, but so far there is no evidence that PFHxA is toxic (Klaunig et al, 2015). The combination of the three substances has been selected by their resistance to environmental degradation and the interactive effects from the combination that can disturb the development of organisms.
Previous research showed evidences that PFASs induced toxicity is related to oxidative stress. Genes
glutathione peroxidase 1a(gpx1a), tumor protein p53(tp53), aryl hydrocarbon receptor 2 (ahr2), cytochrome P450(cyp1a) are related to oxidative stress in zebrafish. Consequently, it was possible to
4 select toxicological time points in order to find the genes that could be influenced by the toxicant exposure. In this study, we selected 96 hours post-fertilization (hpf) to see toxicity in the embryo development.
Oxidative stress may produce an imbalance between the oxidants and antioxidants, which can lead to potential cell damage, toxicity, and related pathophysiological effects caused by increased production of reactive oxygen species (ROS) and lipid peroxidation and deoxyribonucleic acid (DNA) damage (Liu et al., 2014).
There is only little known about PFASs potential developmental toxicity in fish, regardless of the large studies on the largescale distribution of PFASs in the environment. In the present study, zebrafish embryos were exposed to PCB, PFOS, PFHxA in order to determine whether they can influence the development of zebrafish. In addition, research on the toxicity of PCB, PFOS, PFHxA already exist; however, there is no research on the effects appeared when they are mixed. The mixture of these might have significant effects when used with each other. Aim of this study is to analyze interactive effects of various mixtures of the three substances that give an influence on gene expression level that is related with oxidative stress pathway. This can give better understanding of toxic effects of PFASs and their mixtures in the natural aquatic ecosystems.
Fish were maintained in glass aquaria at a water temperature of 26±1 °C with a closed water circulation system. The aquaria system is automatically controlled by the aquaria computer (GHL ProfiLux 3.1T) which keep the pH, conductivity and water temperature at stable values. Constant day to night rhythm (14/10 h) was kept by the automatic light on/off system. All aquariums were supplied with air pump to keep the CO2 level in normal. Fish were fed every day, in the mornings with dry flake food (Tetra Min®; Tetra GmbH; Melle, Germany) and in the evenings with live Artemia nauplii (Silver Star Artemia, Inter Ryba GmbH; Zeven, Germany). Fertilized fish embryos were obtained from breeding groups through spawning trays with presence of artificial plants during the morning. Chemicals
PCB, PFOS, PFHxA solution was obtained from sigma-aldrich (Sweden) and a stock solution was prepared by dissolving it in less than 0.014% of dimethyl sulfoxide (DMSO). In this study, we used concentrations PCB 7.5 µg/L, PFOS 50 µM, PFHxA 50 µM, which was based on the previous study (Keiter et al., 2016; Gao et al., 2011).
Exposure of zebrafish embryos
For our experiments we used normally developed fish embryos, that reached the blastula stage without any deviations or visible mutations. Eggs were placed in glass dishes in each treatment solution, covered with Parafilm® (Parafilm, Menasha, WI, USA) and incubated at 26 ± 1 °C. Dimethyl sulfoxide (DMSO, Sigma-Aldrich) solution was used for a solvent control and isotonic water (ISO), which was prepared as 294 mg/l CaCl2°2 H2O, 123.3 mg/l MgSO4°7 H2O, 63 mg/l NaHCO3, 5.5 mg/l KCl (ISO 2007), which was used for a negative control. Eggs were transferred into ISO in order to guarantee equal water quality and ion concentration in each test series.
Three replicates were made for each concentration and each of them contained 5 ml of the relevant treatment solution and 30 alive embryos. Selected alive embryos were randomly distributed by 30 embryos into beakers containing 5 ml of the solution which we used for the exposure.
Ribonucleic acid(RNA) was extracted from 30 homogenized zebrafish embryos exposed for 96 hpf using NucleoSpin® RNA Plus (Macherey-Nagel, Germany) following the manufacture’s protocol. To use the same amount of RNA (1µg/L) as templates for cDNA synthesis, quantification of RNA was processed using Nano Drop 2000 (Thermo Fisher Scientific, USA). The 260 nm reading was used to estimate the concentration of total RNA. The ratio of absorbance at 260 nm and 280 nm was used to assess the purity of DNA and RNA. Ratio of ~2.0 is generally accepted as “pure” for RNA.
6 cDNA synthesis
1 µg of total RNA was converted to cDNA using M-MLV Reverse Transcriptase (Promega, Madison, WI, USA) MLV Reverse transcriptase (Artnr. 28025013; Thermo Fisher Scientific) with random primers 1.5MM (Artnr. 48190011; Thermo Fisher Scientific, Life Technologies Europe BV, Stockholm, Sweden), according to the manufacturer’s instructions.
Primer design and validation
The first step of qPCR experiment was selection of the primer that can be capable of detecting a gene of interest. This is the most important factor for the successful progress in the qPCR experiments. If PCR amplification efficiency is not good, high-sensitivity detection is impossible. For instance, exact quantification is difficult with low primer efficiency using non-specific amplification primer, such as primer-dimer. Primers were designed using the Primer3 software as described in Thornton and Basu(2011). For verification we used software Beacon Designer Free Edition (http://www. premierbiosoft.com/qOligo/Oligo.jsp?PID=1). Primers were purchased at Eurofins company (Sweden). Appropriate primers were selected through primer design, see table 1. After that, primer efficiency test was carried out to check efficiency in each different dilution rate and that primers have one distinct peak in its melting curve. We used mixture of cDNA from our samples on 10 times dilution rate for this primer test. To estimate primers efficiency and dynamic range, the standard curve was used. We selected dilution rate that have linear part in their standard curve according to the result. Stability of reference gene
To be able to find most stable reference gene we needed to select zebrafish embryo sample and the 10-fold diluted DNA pool and calculate its relative stability. For this calculations we used Bestkeeper software, which is Excel-based tool using pair-wise correlations with eef1a1, b2b, actb1, rpl13a. We had to repeat it for each sample from 24, 48, 72 and 96 hours post fertilization and combine it with he 10-fold diluted DNA pool for every treatment – 7.5 µg/L PCB, 50 µM PFOS, 50 µM PFHxA, 7.5 µg/L PCB with PFOS, 7.5 µg/L PCB with 50 µM PFHxA. Samples from 24, 48 and 72 hpf from the experiment of Melanie B et al., (unpublished) were treated in the similar way as 96 hpf sample. Reference genes were selected according to lowest variation in their expression in similar set of treatments (Blanc et al, unpublished). The results of that were rpl13a 1.414, actb1 1.565, eef1a1 2.28,
b2m 4.00, see fig1 (Blanc et al., unpublished). In this study, rpl13a, acta1 were chosen as reference gene because they had lowest variation of the relative samples.
Quantitative real-time PCR
Quantitative PCR was carried out according to Standard Operating Procedure qPCR-Analysis with
7 Mix from Life Technologies (Darmstadt, Germany). 2 µg DNA template and 0.2 µM primers were used in each reaction in a final volume of 15 µL and analyzed on Step OnePlus™ Real-Time PCR System (Applied Biosystems, US). The thermal cycle was as follows:95°C for 10 min, 40 cycles of 95°C for 15 sec, 60°C for 1 min, and a melt curve analysis step at the end. Each sample was performed with technical duplicates.
In this study, relative ratio of gene expression level was obtained by Pfaffl method (Pfaffl, 2001) and all spreadsheet calculations were performed using Microsoft Excel for Mac. The expression level of the target gene was normalized by the geometric mean of two reference genes that was selected by the stability test to calculate the normalized expression of target genes. Relative ratio of the gene expression level can be obtained by normalization of actb1, rpl13a. Significant differences in basal expression between gpx1a, tp53, ahr2, cyp1a genes in zebrafish embryo at 96 hpf were determined by two-way ANOVA and Tukey’s multiple comparison test and outliner test which was carried out by a Grubb’s outlier test. The criterion for statistical significance was p<0.05.
Using the Pfaffle method and two-way ANOVA test, we got relative values of genes gpx1a, tp53,
ahr2, cyp1a, which are associated with oxidative stress. When we compared the gene expression of cyp1a with the expression values of other genes in the Fig2, we found that its relative fold expression
was significantly larger. In the results we also got the P value 0.0005, which showed that there were significant differences. Although we also did the Grubb’s Outliner test, the standard deviation was too large among biological triplication.
When we looked at relative gene expression value of cyp1a from PCB, PFOS, PCB with PFOS, PCB with PFHxA, except PFHxA alone, we could see differences in average from 13 times up to 272 times more compared to other genes gpx1a, tp53, ahr2, see table 2. The lowest relative gene expression value of cyp1a among the five different treatment was PFHxA when we compared it to the other three genes.
Results of the Tukey’s multiple comparisons (see Fig3) confirmed significant difference when we used PCB with PFHxA treatment over the PCB treatment alone. However, when we compared it with PCB with PFOS mixture and PCB alone, we couldn’t get any significant difference. In the PFOS treatment with PCB we found significant difference compared to PFOS alone. We also found significant difference between PCB treatment with PFHxA and PFHxA alone.
In the Table 2 we can see relative gene expression level in the mixed treatments over their usage alone.
• When the gene cyp1a was exposed to the PCB treatment its relative expression value was about 6 times more than when it was exposed to the PCB with PFHxA mixture.
• When cyp1a was exposed to a mixture of PCB with PFOS there was approximately 9 times higher relative expression value than when exposed to PFOS treatment alone.
• When the gene cyp1a was exposed to a mixture of PCP with PFHxA its relative expression value was about 246 times higher than when exposed to PFHxA alone.
Analysis of qPCR data
Analysis of qPCR data requires derivation of a PCR efficiency value from the observed data. According to Ruijter’s, J. M article, small errors in the applied baseline correction can give strong influence to the observed PCR efficiency; that will increase the variability of the estimated PCR efficiencies as well as the bias in the reported absolute and relative levels of gene expression.
Also estimation of a baseline value from the ground phase data is impossible because the source of fluorescence for detection is not clear. The main source of baseline fluorescence is boundless fluorochrome, for example SYBR Green that is not completely fluorescent. Baseline fluorescence can be also changed by dilution rate of the sample, for instance total cDNA concentration and primer concentration. Because of the unknown interactions between boundless fluorochrome and dilution rate of the sample, currently there is no certain way to predict baseline performance.
The baseline estimation algorithm explained in the present article is based on the kinetic model of PCR amplification (“Equations used in the analysis of quantitative PCR data. The equations are numbered according to their appearance in the text. The basic equation for PCR kinetics states that the amount of amplicon after c cycles (Nc) is the starting concentration of the amplicon (N0) times the amplification efficiency (E) to the power c. The PCR efficiency in this equation is a number between 1 and 2 (2 indicates 100% efficiency” (Ruijter, J. M article)) and a constant PCR efficiency. If we use sigmoid models during the qPCR analysis, we can predict cycle-dependent changes in PCR efficiency. These sigmoid models are not based on biophysical or biochemical considerations of PCR kinetics, but mostly on the way, how well they fit to raw qPCR data. Disadvantage of these models are that they do not fit well to exponential phase of the data.
To get proper estimation for the baseline value, we should fit the PCR amplification equation to the baseline-subtracted data. Logarithmic approach was applied at baseline estimation method in this study. We could get clarifications to make close baseline from that approach. Creating a straight line down from the start of the plateau phase, therefore that lead to a more precise estimate of the baseline. (Ruijter, J. M article)
Initially, we used the baseline and threshold calculated by Step OnePlus™ Real-Time PCR System settings to obtain the results automatically. However, the standard deviation between biological triplication was too high. The relative gene expression level was also too different between them so we needed to adjust baseline and threshold manually.
10 The reason for setting up the base line is to obtain more clear data by removing noise. It was necessary to recognize where the noise was. In the case that we needed to remove the noise, we increased the base line. In the case that we needed information in the noise, we lowered the base line to make reported amplification plot that draw a parabola line. If there were two or more Inflection point on the graph, that should be corrected by adjusting the base line. In this case, the graph was generally better with lowering the base line. We tried to keep the baseline for a single plate as similar as possible. It should be equally applicable at least for the same target gene group in one plate. When each of the plate was set as different baseline, data was not changed significantly. However, the baseline should follow only one trend, such as raising or lowering at one time. When we chose different trend to adjust the base line of each of the plates, the data between biological triplication was largely distributed.
When we analyzed the samples treated with different toxins by one target gene, there was a case where we saw large differences among curves of each different treatment sample. In this case, setting higher threshold reduced the difference while keeping the tendency of graph. Usually, when we adjusted the threshold higher than original one, it made the data clearer.
Cyp1a regulation and oxidative stress
Cyp1a mRNA expression can activate aryl hydrocarbon receptor
(AhR) in zebrafish embryos by dose-dependent induction as we could find in previous studies (Liu et al., 2014). They suggested that AhR is involved in cyp1a gene regulation in the early life stages of zebrafish and AhR dimerizes with aryl hydrocarbon receptor nuclear translocator (ARNT). The AhR/ARNT complex translocate to the nucleus and associates with speciﬁc DNA sequences termed dioxin-response elements. That can lead to altered expression of downstream genes such as cyp1a. cyp1a gene can be also induced by the agonist of the AhR, however ahr2 gene expression of control sample between toxin-exposure sample was not different in the 96 hpf. We found previous research that suggests that cyp1a may not be the important target for the AhR pathway (Liu et al., 2014) which might have similar aspect in this study.
Due to high cyp1a expression level, oxidative stress can be assumed as increased. However, the expression level of tp53 in treatment solution, which is activated by increasing oxidative stress, was not different from controls. If we expose the substance for a longer period, we might be able to determine the differences in expression levels of other genes such as ahr2 and tp53. Also, in the current study, the developmental toxicity symptoms increased with longer exposure periods. There is not a lot of knowledge about the function of cyp1a so it is difficult to explain the phenomenon about gene expression in the cells. Further study of the function of cyp1a is needed.
11 Mixture toxicity
When using the mixture compound, cyp1a was expressed strongly, however, when comparing the PCB alone and the mixture of PCB with PFOS, we did not get any significant difference between them though they have the lowest P value among all groups that have over 0.05 P value.
Mixture of POPs had relatively higher gene expression than the single compound. The non-toxic substance exposure with the other toxic materials can be likely more harmful compared to exposure with toxic material alone. Present form of toxins in our environment is in the form of a mixture. Research about mixture of toxic substances should be conducted more actively in the future as well as the research about toxic substance alone.
The author wishes to thank Drs Nikolai Scherbak and Steffen Keiter, Mélanie Blanc, Marek Bečica, Dr Jin Hyup Lee, Dr Per-Erik Olsson for their helpful discussions during the course of this research.
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Table 2. QPCR results presented as mean and standard deviation (SD) for
normalized expression ratios.
Target gene PCB PFOS PFHxA PCB/PFOS PCB/PFHxA
gpx1a 1,609(0.1) 1,335(0.2) 1,031(0.7) 1,134(0.4) 0,868(0)
tp53 0,646(0.2) 0,933(0.2) 1,093(0.4) 1,298(0.1) 1,080(0.4)
ahr2 1,631(1.3) 1,066(0,2) 0,776(0.4) 2,593(0.8) 2,393(0.8)
cyp1a 57,879(33.2) 16,527(24.7) 0,610(0.8) 187,387(228.8) 234,057(185.4)
The mean number was rounded up in the third decimal number and the standard deviation number
was rounded up in the first decimal number.
Gene Primer sequence (5’-3’) Amplicon
size(bp) Accession no. gpx1a F ACCAGTTCGGGCACCAGGAGAA 156 NM_001007281 R CCTTCAGGAACGCAAACAGAGGGT cyp1a F TGAAGAGGCTGGTGATGGA 86 NM_131879 R TTCGCAGTGGTTGATAAGAGAG ahr2 F ACCGTCTTCAGGCTTCTTTC 137 NM_131264 R TGTTCCTCACCCTCCTCATT tp53 F CCCAGGTGGTGGCTCTTGCT 113 NM_001271820 R GAGTGGATGGCTGAGGCTGTTCT actb1 F GCTCCCCTGAATCCCAAAGCCAA 160 NM_131031 R GGGTCACACCATCACCAGAGTCCA rpl13a F TCTGGAGGACTGTAAGAGGTATGC 148 NM_212784.1 R AGACGCACAATCTTGAGAGCAG
Fig 1. Comprehensive gene stability that was progressed by
Bestkeeper—Excel-based tool using pair-wise correlations with eef1a1, b2b, actb1, rpl13a(Blanc et
Relative fold gene expression between gpx1a, tp53, ahr2, cyp1a
in zebrafish embryos 96hpf exposed to POPs.