# Retrotransposon methylation and activity in wild fish
(A. anguilla): A matter of size

Fabien Pierron, Guillemine Daffe, Patrick Lambert, Patrice Couture, Magalie Baudrimont

► To cite this version:

Fabien Pierron, Guillemine Daffe, Patrick Lambert, Patrice Couture, Magalie Baudrimont. Retro-transposon methylation and activity in wild fish (A. anguilla): A matter of size. Environmental Pollution, 2019, 245, pp.494-503. 10.1016/j.envpol.2018.11.014. hal-02354020

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Title: Retrotransposon methylation and activity in wild fish (A. anguilla): a matter of size

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Authors: Fabien Pierron¹, Guillemine Daffe², Patrick Lambert³, Patrice Couture⁴, Magalie

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Baudrimont¹

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Full postal addresses

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¹Univ. Bordeaux, CNRS, EPOC 5805, Talence, France.

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²CNRS, Université de Bordeaux, Observatoire Aquitain des Sciences de l'Univers, UMS

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2567 POREA, Allée Geoffroy Saint Hilaire, F-33615 Pessac, France

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³Institut National de Recherche en Sciences et Technologies pour l'Environnement et

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l'Agriculture (IRSTEA), équipe Poissons Migrateurs Amphihalins (PMA), Cestas, France.

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⁴Institut national de la recherche scientifique (INRS), Centre Eau Terre Environnement, 490

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rue de La Couronne, Québec, QC G1K 9A9, Canada.

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Keywords: retrotransposon, transcription, pollution, ecotoxicology, growth, wild fish

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Corresponding author:

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Fabien Pierron,

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Address : Université de Bordeaux, Place du Docteur Bertrand Peyneau, 33120 Arcachon,

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France

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Tel: +33 556 223 921

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mail: fabien.pierron@u-bordeaux.fr

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**Abstract**

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**Keywords:** retrotransposon, transcription, epigenetics, plasticity, ecotoxicology

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**Capsule:** Retrotransposons could represent interesting environment-sensitive molecular

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markers allowing to link stress-induced molecular events to adverse outcomes at higher levels
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# 1. Introduction

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Transposable elements (TEs) are DNA sequences that have the ability to replicate, to move and to integrate elsewhere in the genome. There are two main classes of TEs, (i) retrotransposons (RTs) that transpose via an RNA intermediate and (ii) DNA transposons that transpose directly without RNA intermediate. Three major orders of RTs are distinguished: long terminal repeats (LTR) RTs, long interspersed elements (LINEs) and short interspersed elements (SINEs). These RTs accumulate in the genome via a copy-and-paste mechanism and can constitute a substantial proportion of the genome (Elbary et al., 2016). For example, SINEs and LINEs constitute 30% of the human genome (Lander et al., 2001). TEs have long been considered as “selfish” or “parasitic” DNA elements since active TEs are known to be highly mutagenic and to impair the fitness of their host (Slotkin and Martienssen, 2007). The role of TEs in genome evolution, microevolution and transcription regulation has received increasing attention in recent years (Federoff et al., 2012; Casacuberta and González, 2013; Faulkner et al., 2009; Rey et al., 2016). RTs are non-randomly distributed in genomes and new insertions of these elements in the genome can provide alternative promoters for genes (Lander et al., 2001; Faulkner et al., 2009). Moreover, an emerging body of evidence indicates that RTs can directly regulate gene expression at different molecular levels, by acting on the chromatin structure, gene transcription, pre-mRNA processing and mRNA metabolism (Elbary et al., 2016). Thus, TEs can shape the transcriptome of cells *via* mechanisms acting at both the genomic and transcriptional levels. In addition, although RTs are mostly inactive in their hosts, they are activated during specific stages of development, mainly during early embryogenesis but also later at the adult stage during cell fate and reprogramming or in response to stress (Fedoroff, 2012; Hutchins and Pei, 2015; Hunter et al., 2012; Capy et al., 2000; Martínez and Slotkin, 2012;).
74 DNA methylation, which refers to the addition of a methyl group to cytosine to form
75 the 5-methylcytosine base, is an epigenetic mechanism commonly associated with “gene
76 silencing”, i.e. transcriptional repression (with few exceptions Vandegehuchte and Janssen,
77 2011). In a previous work, we reported changes in the DNA methylation status of RT-like
78 sequences in the liver of European eels (Anguilla anguilla) experimentally exposed to Cd.
79 These changes were associated with changes in the transcriptional activity of neighboring
80 genes (Pierron et al., 2014a). In another work carried out on the liver of wild eels collected in
81 sites presenting different levels of pollution, we reported changes in the transcription level of
82 genes involved in cell differentiation, development and epigenetic mechanisms in
83 contaminated eels (Baillon et al., 2016). Since (i) RTs are known to be activated by abiotic
84 and biotic stress or during cell reprogramming in the somatic tissues of adult animals and (ii)
85 major mechanisms responsible for the regulation of TEs transcription and transposition are
86 DNA methylation and histone methylation (i.e. epigenetic mechanisms (Hutchins and Pei,
87 2015; Federoff et al., 2012)), the present work was carried out to test the hypothesis that in
88 *situ* chronic exposure to pollutants can induce changes in the methylation and/or the activity
89 of RTs in the liver of wild fish.

90 We measured the DNA methylation and the transcription level of a LINE (UnaL2)
91 and a SINE (UnaSINE) in wild European eels captured in four sampling sites selected on the
92 basis of their known gradient of contamination by metallic and organic pollutants ( previous
93 data published in Baillon et al. 2015; Laporte et al. 2016 and Pannetier et al. 2016). Rather
94 than performing analyses at the genomic level, we analyzed these three elements at the
95 epigenetic and RNA levels. Indeed, if RTs were found to be activated by abiotic stressors
96 mainly during experimental investigations, it remains unknown when TEs are activated or
97 what triggers TE activation in nature (Matsunaga et al., 2015). Moreover, because of
98 accumulated mutations or truncation, most RTs present in a genome are no longer able to
retrotranspose. However, recent works suggest that such non-coding RT-derived sequences
can modulate cell gene expression (e.g. lncRNAs (Hutchins and Pei, 2015)) playing a key
role in phenotypic plasticity (Rey et al., 2016; Aubin-Horth and Renn, 2009). In addition,
although numerous studies were carried out, notably in fish, to study the genome dynamics
and chromosomal localization of RTs, studies investigating the methylation and/or the
transcription level of RTs in wild animals remain scarce (Barbosa et al., 2014). Finally, recent
evidences support that TEs and epigenetic components jointly constitute a molecular engine
triggering rapid adaptive phenotypic responses to environmental changes. Thus, our study
also aimed at testing whether environmental factors (mainly pollutants) can trigger RT
activation and whether this activation can have long-lasting effects on animals, not in an
evolutionary perspective, which has already received a lot of attention (Casacuberta and
González, 2013; Zeh et al., 2009) but rather at the physiological and individual levels. In
order to link potential changes in RT DNA methylation and transcription levels to
environmental factors and phenotypic traits, we measured the contamination levels of eels as
well as different morphometric and growth indices. Finally, as RTs are known to be activated
during cell fate and reprogramming, we measured the transcription level of two genes
involved in cell differentiation or in the survival and maintenance of stem/progenitor cells,
the arginine-glutamic acid dipeptide repeats protein (Zoltewicz et al., 2003) (rere) and the
transcription factor SOX-6 (Baillon et al., 2016; Abdelalim et al., 2014; Ohta et al., 2013)
(sox6). Analyses were carried out in liver samples due to the key role of this organ in energy
metabolism and fish growth, as well as in xenobiotic and metallic detoxification (Fuentes et
al., 2013). The gene rere was previously found to be up-regulated in wild contaminated eels
(Baillon et al., 2016). Moreover, in this previous paper, we hypothesized that cellular
differentiation and stem/progenitor liver cells could be involved in the adaptive response
developed by eels chronically exposed to pollutant mixtures. Cellular differentiation induces
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significant changes in the phenotype of cells, from their shape to their metabolic activity and
their responsiveness to external and internal stimuli (Miyajima et al., 2014). The gene *sox6*
was also analyzed as *sox* gene family members are representative of stemness-related genes
(Ohta et al., 2013; Lin et al., 2015).

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## 2. Materials and methods

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### 2.1 Wild eel sample collection

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As part of the ANR IMMORTEEL program, 32 European eels (*Anguilla anguilla*) were captured from four French sampling sites, (i) comprising a reference site and three sites presenting differing levels of metal and organic contamination (see Baillon et al. 2015; Laporte et al. 2016 and Pannetier et al. 2016 for more details), and (ii) presenting a wide range of physiochemical conditions (Table 1): Certes salt marshes (considered as a reference site; CER;44° 41' 18" N; 1° 1' 39" W), the Gironde estuary (GIR; 45° 12' 07" N; 0° 43' 35" W), the Garonne river (GAR; 44° 43' 51" N; 0° 28' 05" W) and the Dordogne river (DOR, 44° 48' 05" N; 0° 08' 25" W). These four sites are located within a distance of 37 km (Figure 1). Fish were collected during ten consecutive days, May 22 to 31 of 2012, using a trawl or a fyke net. For each sampling site, a total of eight immature yellow eels were collected and used for subsequent analyses. Fish were dissected as soon as possible at the proximity of the sampling site and organs (liver and muscle) were divided into several samples. Samples for DNA methylation and transcription analyses were stored in RNA later at -20°C until needed. For both organic and metal analyses, samples were stored at -80°C. For all sites, temperature, salinity and dissolved oxygen concentrations were determined using a multi-parameter portable device at one meter under the surface and by using data coming from the automated observation network of the Gironde (MAGEST network).

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## 2.2 Sex determination, morphometric and growth indices

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The sex of animals was determined macroscopically during the dissection and was further validated by histological investigations as described in Pierron et al. (2014b). Only females and undifferentiated animals were sampled and used.

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The total length and weight of fish were recorded for each fish in order to estimate the Fulton condition factor $[K = (\text{total weight (g)}) / (\text{total length (cm)})^3] \times 10^5$, the Hepato-Somatic Index $[\text{HSI}=(\text{weight liver} / \text{total weight}) \times 100]$ and the Spleen Somatic Index $[\text{SSI} = (\text{spleen weight} / \text{total weight}) \times 100]$. We also determined the Ocular Index (OI), which reflects changes in eye diameters and has proven to be a useful criterion for assessing the degree of maturation / silvering in eels (Pankhurst, 1982).

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Age determination was carried out by otolithometry as detailed in Patey et al. (2018). Back calculation was undertaken by using the biological intercept procedure as described in Patey et al. (2018). The growth rate at capture (GR) was calculated by dividing the measured body length (BL) by the age at capture. We also determined a growth coefficient (GC) as a proxy of growth potential throughout the life of animal that was less influenced by age and length at capture by determining the slope of the regression line between back-calculated body length (y axis) and age (x axis) (correlation coefficient $R^2 = 0.98 \pm 0.04$, mean ± SE, n = 32). Indeed the annual growth rate in length in eels decreases gradually with age, i.e. growth in length being maximal and linear during their early life (details in Patey et al., 2018). Concomitantly, we also used the back-calculated body length at 1, 2 and 3 years of life (all sampled eels presented an age at capture superior to 3).

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## 2.3 Metal and organic concentration analyses

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Analysis of the seven indicators PCBs (polychloro-biphenyls; CB50+28, CB52, CB101, CB118, CB138, CB153, and CB180) and 14 OCPs (organochlorine pesticides;
hexachlorobenzene or HCB, lindane or γ-HCH, dieldrin, heptachlor, heptaclorepoxide, cis-chlordane, trans-nonachlor, mirex, and DDTs) were performed on muscle samples following the procedures described in Baillon et al. (2015) and Laporte et al. (2016). The data inferior to the LQs were not used and are not presented. For PCBs, LQs were comprised between 0.2 ng/g dw and 2 ng/g dw; for OCPs they were comprised between 0.1 ng/g dw and 0.4 ng/g dw. Metal concentrations were measured from liver samples following the procedures described in Pannetier et al. (2016).

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## 2.4 Genetic data treatment

To design specific primer pairs, the sequences of the long interspersed nuclear element *UnaL2* and short interspersed nuclear element *UnaSINE1* from *Anguilla japonica* were used (Kajikawa et al., 2005). All these sequences were compared and aligned to the genome of *A. Anguilla* (Henkel et al., 2012) (genome assembly containing 923 Mbp of scaffolds with a length-weighed median fragment length (N50) of 78 Kbp and 179 Mbp of contigs, which are very small or highly repetitive) using Blast algorithm (Evalue threshold $10^{-10}$) and BioEdit software. Among the numerous copies of each RT sequence in the *A. anguilla* genome, we then searched for the most conserved regions. Finally, these highly conserved regions were used to create a consensus sequence and design specific primers in different parts of the RTs (Figure S1) by means of the Primer3plus software for quantitative RT PCR analysis and using the Pyromark assay design software for pyrosequencing analysis (Table S1). Concerning quantitative RT PCR analysis, for *UnaL2*, specific primers were designed in the 5'UTR (containing an internal promoter), 3' UTR (containing a stem-loop region required for retrotransposition) and in the unique ORF (containing an endonuclease and a reverse transcriptase) (Kajikawa et al., 2005). For *UnaSINE*, specific primers were designed in the region a (containing internal promoter region) and in the region c (a region
that corresponds to the stem-loop region of *UnaL2*). For the other genes investigated (*β-actin*, *rere* and *sox6*), sequences were obtained from two *A. anguilla* transcriptomic databases; ZF-Genomics (Henkel et al., 2012) and from our previous investigations (Baillon et al., 2015 and 2016). Primers were designed using the Primer3plus software (Table S1). Concerning pyrosequencing analyses, specific primers (Table S1) were designed to amplify a short fragment located in the upstream part of the 5'UTR Region of *UnaL2* and in the Region a of *UnaSINE*.

We also searched to identify the function of the coding regions located at the proximity of *UnaL2* and *UnaSINE* in the genome of *A. anguilla*. To do this, the sequences obtained during our previous RNA-Seq investigations (Baillon et al., 2015) (11,547 unique genes of known function were used) were compared and aligned to the draft genome of *A. Anguilla* (Henkel et al., 2012) (Evalue threshold 10<sup>-30</sup>). These results were compared to those obtained by carrying out alignment analyses between *UnaL2* or *UnaSINE* and the draft genome of *A. anguilla* (Evalue threshold 10<sup>-10</sup>). We then selected the coding sequences that were located at a maximum of 1kb to *UnaL2* or *UnaSINE*. In order to not bias the functional analysis, redundant sequences were discarded to retain only one sequence for a given gene. Finally, a functional analysis of the selected sequences was performed with the Blast2go software (Conesa et al., 2005) using Gene Ontology annotation.

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## 2.5 Quantitative analysis of methylation levels

For each fish, a liver sample of 20 mg (wet weight) was homogenized in 600 µl of ice-cold RTL buffer (Qiagen) with 6 µL of β-mercaptoethanol using a tissue homogenizer for 30s (Mixer Mill MM 200, Retsch). Following centrifugation, DNA and RNA were extracted from the homogenate using the AllPrep DNA/RNA kit (Qiagen) according to manufactures' guidelines. DNA concentrations as well as purity were determined by spectrophotometry

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224 (Take3, Epoch, Biotek). Quantification of single cytosine percent methylation at specific sites
225 in the *UnaL2* and *UnaSINE* RTs was then performed using the bisulfite-pyrosequencing
226 method. First, 500 ng of DNA were bisulfite converted using the MethylEdge Bisulfite
227 Conversion System (Promega) according to manufactures' guidelines. Then, 15 ng of
228 bisulfite converted DNA was amplified by PCR using the PyroMark PCR kit (Qiagen)
229 according to manufactures' instructions. Cycling conditions were as follows: 10-min initial
230 denaturation step at 95 °C, followed by 45 cycles of 94 °C for 30 s, 30 s at optimal annealing
231 temperature of primers (56°C for *UnaL2* and 59 °C for *UnaSINE*), 30 s at 72 °C, 10 min of
232 final elongation at 72 °C. Pyrosequencing of biotinylated PCR products was performed by
233 means of the PyroMark Q48 Autoprep (Qiagen) and specific pyrosequencing primers (Table
234 S1). Average DNA methylation levels of CpN sites were quantified using the PyroMark Q48
235 autoprep software (Qiagen). For *UnaL2*, three CpG sites were analyzed. For *UnaSINE*, two
236 CpG sites and one CpA site were analyzed. In addition, for each RT, in order to assay the
237 conversion efficiency of the bisulfite treatment the average methylation level of one CpT site
238 was measured. Indeed, while the preferential site of non-CpG methylation is CpA (especially
239 in the case of RTs), CpT and CpC sites are mostly not methylated (Ichiyanagi et al., 2013).
240 For both RTs, the average methylation level of this control site was lower than 5 % (i.e.
241 corresponding to the detection limit), indicating a good conversion efficiency. Examples of
242 pyrograms are available in Figure S2.

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2.6 Quantitative analysis of transcription level

245 For each fish, total RNA was extracted as described above (section 2.5). In order to
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For each sample, RNA quality was evaluated by electrophoresis on a 1% agarose gel and concentrations as well as purity were determined by spectrophotometry (Take3, Epoch, Biotek). First-strand cDNA was synthesized from 1 µg of total RNA using the GoScript Reverse Transcription System (Promega), according to the manufacturer's instructions.

Following the reverse transcriptase reaction, cDNA was diluted 10-fold. Real-time PCR reactions were then performed in an MX3000P (Stratagene; 95 °C for 10 min, followed by 40 cycles of 95 °C for 15 s and 60 °C for 30 s and 72°C for 30 s). Each 20 µL reaction contained 12.5 µL of GoTaq qPCR master mix (Promega), 5 µL template and the specific primer pairs at a final concentration of 250 nM each. The reaction specificity was determined for each reaction by gel electrophoresis and from the dissociation curve of the PCR product. This was obtained by following the SyberGreen fluorescence level during a gradual heating of the PCR products from 60 to 95 °C. Amplification efficiencies for all primer sets were calculated; all values proved to be sufficient to allow direct comparison of amplification plots according to the ΔΔCt method (Livak and Schmittgen, 2001). Relative quantification of gene expression was achieved by concurrent amplification of the *β*-actin endogenous control. Hence, during our experiment, total RNAs were quantified and 1 µg was used to be reverse-transcribed.

During the subsequent qPCR amplifications, the output cycle corresponding to the *β*-actin was examined. No significant difference was observed on the output cycle of *β*-actin among fish from the four sampling sites (p > 0.05), demonstrating the relevance of the *β*-actin as reference gene in our conditions.

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## 2.7 Statistical analyses

The relationships between RT methylation and transcription levels, phenotypic traits and environmental factors were investigated by means of Principal Component Analysis (PCA) and Spearman's Rank Correlation Analysis (SRCA) due to the nonlinearity and/or the

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non-homoscedasticity of the data. We also created classes of animals according to their transcription level of *UnaL2_5'UTR* by using the quantile function. Fish were classified in 3 groups by using the 33.33 and 66.66 percentile: (i) a class called "low", gathering fish with the lowest transcription level of *UnaL2_5'UTR*; (ii) a class called "intermediate", gathering fish with intermediate transcription level of *UnaL2_5'UTR* and (iii) a class called "high" gathering fish with the highest transcription level of *UnaL2_5'UTR*. Then, comparisons among fish groups from different classes were performed by analysis of variance (ANOVA), after checking assumptions of normality (Kolmogorov-Smirnov) and homoscedasticity of the error terms (Levene). When the assumptions were not met as deduced from ad-hoc tests, we used box-cox data transformations or the nonparametric Kruskal-Wallis test. If significant effects were detected, the Bonferroni or Dunn-Bonferroni test were used to determine whether means between pairs of samples were significantly different from one another. Comparisons of variances among groups were performed using the Levene test after checking assumption of normality (Kolmogorov-Smirnov). Comparisons of proportions (sex distribution among classes) were performed using the Chi squared test and the Monte Carlo method using 5000 permutations followed by the Marascuilo procedure to compare proportions among them. Computations were performed using STATISTICA version 6.1 software (StatSoft) and XLSTAT (Addinsoft version 2012.6.08). Numerical results are given as means ± SE.

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## 3. Results and discussion

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### 3.1 Differential DNA methylation and transcription of RTs among sampling sites

Although lower values of DNA methylation levels of *UnaL2* were observed in fish from GAR and DOR in comparison to fish from CER or GIR, no significant differences (p >

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0.05) were observed among sites for the three CpG sites analyzed (Figure 2A). This could be
explained by a high inter-individual variability, especially among fish from the CER site. For
UnaSINE, no significant differences (p > 0.05) were observed among sites (Figure 2B).

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Concerning transcription analyses, in order to more strictly link potential change in
RT transcription levels to RTs activation, three fragments of both *UnaSINE* and *UnaL2* were
amplified and quantified (Figure S1). Indeed, RT sequences in genomes are in most cases
truncated and RT fragments are observed in protein-coding mRNA 3'UTR as well as in many
long non-coding RNA (lncRNAs, Elbary et al., 2016). For example, *Alu* elements (i.e. a
SINE) are present in 5.7 % of human mRNA 3'UTR (Faulkner et al., 2009). The transcription
levels of the different PCR products of *UnaSINE* or *UnaL2* were highly correlated among
themselves. For *UnaSINE*, the Spearman's rank correlation coefficients (ρ) between
*UnaSINE_a*, *UnaSINE_c* and *UnaSINE_ac* varied from 0.947 to 0.964 (p < 0.05). For
*UnaL2*, ρ varied from 0.941 to 0.976 (p < 0.05, see Table S2). This reinforces the fact that the
changes observed among sites are mainly associated to RT activation rather than to only
changes in RT fragments associated to coding mRNA. In addition, the transcription levels of
*UnaSINE* and *UnaL2* were highly correlated among themselves (ρ varied from 0.848 to
0.953, p < 0.05). This appears consistent with the fact that *UnaSINE* is mobilized by *UnaL2*
as demonstrated by Kajikawa et al. (2005) in HeLa cells.

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Significant differences in RT transcription levels were observed among sites (Figure
2C and 2D). Fish from GAR and DOR presented significantly higher transcription levels of
*UnaL2* and *UnaSINE* than fish from CER (Figure 2C and 2D). Concerning protein-coding
mRNA, i.e. *rere* and *sox6* transcripts, the transcription level of *rere* was significantly higher
in fish from DOR in comparison to fish from CER and GIR. For *sox6*, fish from DOR
presented significantly higher values than fish from the other sites (Figure 2E). It is difficult
to determine what kind of stress or environmental factors or even intrinsic factors triggered

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changes in RT transcription levels among sites. Indeed, the four sampling sites presented different profiles of metal and organic pollution (see Baillon et al., 2015; Laporte et al., 2016 and Pannetier et al., 2016) as well as different salinities and temperatures (Table 1). In addition, intrinsic parameters such as age are known to trigger changes in RT transcription levels, the expression level of RTs increasing with ageing in mammalian somatic tissues (De Cecco et al., 2013). In our case, fish from GAR were significantly older than fish from CER or DOR (Table 1).

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## 3.2 RTs, environmental factors and phenotypic traits

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In order to link the observed changes to phenotypic traits, we first realized a principal component analysis (PCA; Figure S3) using the DNA methylation and transcription levels of RTs and the variables described in Table 2. Principal component 1 (PC1) was mainly defined by organic pollutant concentrations, growth indices and the transcription level of RTs. Thus, the PCA analysis highlighted the importance of these three groups of variables as well as a relationship among them. Among RTs, *UnaL2_5'UTR* was found to have the most important contribution to the PC1 (Table S3) as well as the most significant correlations not only with all growth indices but also with the concentrations of some pollutants in eels. The most significant correlation between *UnaL2* transcription levels and growth indices was found between *UnaL2_5'UTR* transcript and back-calculated length at 3 years (ρ = -0.565, p < 0.001; Table S2). Among pollutants, the most significant correlations were observed with the concentration of PCB153 (ρ = 0.529, p = 0.002) and 4.4'DDE (i.e. a metabolite of DDT, ρ = 0.514, p = 0.006). *UnaL2_5'UTR* was also correlated with the transcription level of *rere* and *sox6* (ρ = 0.617 and 0.563; p < 0.001 and p = 0.001, respectively). We must add that the contribution of the methylation levels of both *UnaL2* and *UnaSINE* to the PCA was very limited (Table S3). Moreover, no significant correlations were observed between RT DNA

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methylation levels and their corresponding transcription levels (Table S2). This appears
consistent with the fact that RTs can be activated by stress either directly or through the
arousal of their epigenetic control (Rey et al., 2016) but also with the mode of action of
epigenetic marks. Covalent addition of a methyl group to cytosine influences the
condensation level of chromatin and the accessibility of transcription factors to their binding
sites, modulating the plasticity and/or the reaction norm of a gene (Aubin-Horth and Renn,
2009; Dixon et al., 2014). Thus, a correlation analysis is probably not the most appropriate
method to investigate the relationship between DNA methylation, transcription and
phenotypic traits.

In order to deepen the relationships between RT DNA methylation levels, RT
transcription levels, phenotypic traits and environmental factors, we also created 3-class
typology of animals according to their transcription levels of *UnaL2_5'UTR*. A significant
up-regulation of *UnaSINE* and *UnaL2* was observed along the three classes. No difference in
age was observed among the three classes. The up-regulation of RTs in fish from the low to
the high class was nevertheless associated with (i) a significant decrease in the DNA
methylation levels of *UnaL2_CG2*, (ii) a significant increase in OCP and especially 4,4'DDE
concentrations, (iii) a significant decrease of all growth indices and (iv) a significant up-
regulation of both *rere* and *sox6* transcripts. Taken together, our results show that fish with
high transcription levels of RTs presented low DNA methylation levels of *UnaL2*, low
growth indices and high contamination levels. The distribution of fish among these classes
according to their site of origin is presented in Figure 3. The low class was constituted by fish
from CER, GIR and GAR. The intermediate class was constituted by fish of all sampling
sites while the high class was equally constituted by fish from DOR and GAR. Thus, the high
class was the only class that was exclusively constituted by fish from freshwater
environments. This could explain, at least in part (and if we consider that eels are mostly
resident; further discussed below), why fish from the high class presented significantly lower growth indices than fish from the low class. Indeed, eels grow faster in brackish than in freshwater, even under experimental conditions (Cairns et al., 2009; Côté et al., 2009; Daverat et al., 2012). However, salinity alone cannot explain the differences observed in term of RT transcription levels, RT methylation levels or even growth indices among the three classes. For example, while no significant differences were observed in growth indices between fish from the high and the intermediate classes, the intermediate class was constituted by fish from all sampling sites (including 36.4 % of fish from freshwater sites and 63.6 % fish from brackish water sites). Laporte et al. (2016) recently reported within-generation polygenic selection (RAD-Seq) in response to organic and metal contaminants and more precisely in response to PCB153, 4.4' DDE and Se exposure in the same eels. Interestingly, PCB153 and 4.4' DDE were the two organic pollutants that were the most correlated with the transcription level of *UnaL2_5'UTR* and 4.4' DDE was the only pollutant for which significant differences in bioaccumulation levels were observed among the different *UnaL2* classes. In other words, among the three contaminants that were previously pointed out to be major selective agents in our sampling sites, two were also associated with the highest RT transcription levels. This observation supports that RT activation is associated to pollution and toxic conditions (Roling et al., 2004).

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### 3.3 Up-regulation of RTs: unintended changes or adaptive response?

There is increasing experimental evidence that some TEs exhibit abiotic and biotic stress-responsive transcription or movement especially in plants (Capy et al., 2000; Wessler, 1996; Makarevitch et al., 2015; Lish et al., 2009) and to a lesser extent in fungi, corals, insects, crustaceans, fish and mammals (Barbosa et al., 2014; Zeh et al., 2009; Roling et al., 2004). Moreover, it is now recognized that environmental factors, such as nutritional quality

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or exposure to pollutants, can strongly affect the epigenetic status of cells (Vandegehuchte and Janssen, 2011; Pierron et al., 2014b; Feil and Fraga, 2012) and notably the methylation state of RTs (Pierron et al., 2014a; Jirtle and Skinner, 2007; Dolinoy, 2008). DNA methylation is an epigenetic mark commonly associated with transcriptional repression and that can be conserved during cell division. Moreover, stress-induced changes in RT DNA methylation levels can have long-lasting effects on the phenotype of organisms by affecting the transcription of adjacent genes and this, even after the environmental cue that induced these changes is removed (Aubin-North and Renn, 2009; Dolinoy et al., 2008). In the present study, we found that the transcription levels of RTs were positively correlated with the contamination levels of eels. We also found that the up-regulation of RTs among *UnaL2_5'UTR* classes was associated with lower DNA methylation levels of UnaL2. The up-regulation of RT transcription levels and the decrease in RT DNA methylation levels were further associated with (i) lower growth indices and (ii) an up-regulation of two genes involved in development and cell fate, i.e. *rere* (Wang et al., 2008) and *sox6* (Cantu' et al., 2011). *Rere* encodes a protein that acts as transcriptional co-repressor during embryonic development (Zeh et al., 2009). *Sox6* has been suggested to act both as activator or repressor (Cantu' et al., 2011). *Sox6* by recruiting *ctbp2* (C-terminal binding protein 2), a gene that we previously found to be up-regulated in wild contaminated eels collected in the same sampling sites (Baillon et al., 2015), is known to repress transcription of the fibroblast growth factor 3 (*fgf-3*). Taken together, our findings appear consistent with the fact that TE activation in response to stress (i) is associated to changes in their methylation status and (ii) is known to decrease the host fitness (Casacuberta et al., 2013).

On another side, given the recent findings that highlighted the key influence of TEs as regulators of gene expression (Elbarbary et al., 2016; Faulkner et al., 2009), it is intriguing to speculate that TE activation not only triggers detrimental effects but also plays a role in the
responsive capacity of their hosts in face of environmental challenges (Casacuberta et al., 2013). A consequence of new TE insertions is the creation of new genetic innovation that can lead to an increased resistance to xenobiotics (Casacuberta et al., 2013; Zeh et al., 2009; Schlenke and Begun, 2004; Aminetzachet al., 2005). Concerning the contribution of RT RNAs in the “direct” physiological response to stress, the question has received less attention. Even so, an emerging body of evidence indicates that RNAs of both SINEs and LINEs can regulate gene expression at different levels (Elbarbary et al., 2016). For example, SINEs (Alu RNAs) were found to act as transacting transcriptional repressor (i.e. inhibiting mRNA transcription of specific mRNA genes) during the cellular heat shock response in human cells and mouse (Mariner et al., 2008). More stirring, a few recent cutting-edge studies have shown how RTs shape specific stem cell gene expression (Robbez-Masson and Rowe, 2015; Ohnuki et al., 2014). *In vitro* studies have shown that TEs are up-regulated during the reprogramming of human primary hepatocytes (Friedli et al., 2014). During reprogramming, differentiated somatic cells (mature hepatocytes) turn into induced pluripotent stem cells (iPSCs). Moreover, recent findings have reported a crucial role of Alu elements in association with *sox* genes in cancer drug resistance. These resistant cancer cell lines shared some common features with iPSCs (Lin et al., 2015). Hepatic progenitor cells (HPCs) play a major role in liver repair and regeneration and are known to be highly resistant to xenobiotics (Ros et al., 2003). In this view, we must add that *sox* gene family members are representative of stemness-related genes, promoting cell survival and maintenance of stem/progenitor cells (Ohta et al., 2013; Lin et al., 2015). Another argument supporting an important functional role of these RTs relies on the conservation of the *UnaL2* and *UnaSINE* sequences. Indeed, although radiation and speciation of *A. anguilla* and *A. japonica* is estimated at about 30 million years ago (Aoyama, 2009), the sequence of *UnaL2* and *UnaSINE* were found to be highly conserved between the two species (Figure S4 and S5). In
addition, we found that the genes located at the proximity of UnaL2 or UnaSINE at the
genomic level (Figure S6 and S7) were at the same time involved in anatomical structure
development (GO term 0048856) or regulation of biological quality (GO term 0065008) but
also in response to stress (GO term 0006950) or response to chemical stimulus (GO term
0042221). Finally, it was recently proposed that the interplay between TEs and epigenetic
components constitute an integrative molecular engine that allows organisms to fine-tune
phenotypes in a real-time fashion (Rey et al., 2016), playing a key role in the rapid adaptive
phenotypic responses to stress. In this view, significant differences in the inter-individual
variability of UnaL2 methylation levels were observed both among sampling sites and
Unal2_5'UTR classes (Figure 2A and Table 3A). In both analyses, the least contaminated
fish (i.e. eels from CER or from the low class) presented the highest inter-individual
variabilities. For example, a significant and gradual decrease in the variance of the DNA
methylation levels of UnaL2_CG2 was observed in fish from the low to the high class
(Figure 4A). This could be explained by different life history traits. Indeed, although yellow
(i.e. somatic growth phase) eels are considered to be mostly residents, previous investigations
showed that a fraction of eels inhabiting brackish waters can move between coastal/estuarine
and freshwater systems once or more during their growth phase (mainly at early life, until the
age of 2-3 years). However, previous investigations carried out on eels from the Arcachon
Bay found that eels from CER are residents (Daverat and Tomás, 2006). We must note that A.
anguilla is a panmictic species with random dispersal of larvae. European eels reproduce
once in their life in the Sargasso Sea, from where larvae drift back toward the European
coasts following oceanic currents. When larvae reach the continental slope, they
metamorphose into young eels that penetrate continental habitats and complete their growth
phase in a large range of systems (lagoons, estuaries, marshes, rivers, lakes and streams). The
genome of European eels is thus homogenized by gene flow that removes any effects of
diversifying selection from each generation, preventing genetic adaptation to local conditions (Pujolar et al., 2014). The loss of variability from fish of the low class to fish of the high class could be thus viewed as a result of an intensive environmental constraint. This appears consistent with our observation that RT up-regulation was associated with two pollutants (PCB153 and 4.4' DDE) that we previously identified as the major selective agents in eels from these sites, supporting a role of RTs in increasing fish survival (Laporte et al., 2016). This loss of variability at the epigenetic level was instead associated with an increased variability in *UnaL2* transcription levels (Figure 4B). Such an effect is consistent with the general hypothesis for which weak DNA methylation levels facilitate environmentally responsive expression (Dixon et al., 2014). Low methylation levels can increase transcription plasticity by allowing greater access of transcription factors to binding sites. Through this mechanism, DNA methylation could allow the emergence of variable transcriptomic phenotypes that are required for rapid acclimation in selective environments and, potentially, adaption in the case of new RT insertions. This is also consistent with the fact that whereas no significant difference in mean *UnaL2_CG2* methylation levels was observed between fish from the intermediate and high class, both the variance and mean of *UnaL2* transcription levels were significantly higher in fish from the high class in comparison to fish from the intermediate class. Fish from the high class presented 2-fold higher concentrations in 4.4'DDE and PCB153 than fish from the intermediate class and significantly higher concentrations of OCPs than fish from the intermediate class. In addition, fish from the high class, in comparison to fish from the intermediate class, only come from freshwater sites. Several authors qualified the migration of eels into freshwaters as paradoxical, given the fitness penalty imposed by relatively slow growth in freshwaters and the fact that this migration is facultative to complete their particular life cycle (Cairns et al., 2009; Daverat and Tomás, 2006; Edeline, 2007). Freshwater appear to be an unfavorable or, at least, not optimal
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condition for European eel growth. Thus, high and highly variable *UnaL2* transcription levels were associated to the worst environmental conditions for eels; i.e. high contamination levels associated to low salinity in our case study.

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We thus propose that the up-regulation of RTs in fish liver under stress conditions could have both detrimental and beneficial implications, affecting eel growth but promoting eel resistance to environmental stressors such as pollutants. Nevertheless, as the somatic growth rate of fish is an important factor influencing their reproductive success, affecting their size at maturity and fecundity (Denney et al., 2002), RTs could represent interesting environment-sensitive molecular elements allowing to link pollutant- or more generally stress-induced molecular events to adverse outcomes at the individual and even, population levels. This appears especially true for European eels as their sexual maturation (silvering) is mainly triggered by size, rather than age (Svedaung et al., 1996). Rapid growth hastens the single reproductive opportunity of these semelparous fish and decreases their vulnerability to natural or anthropogenic mortality (Lamson et al., 2009).

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## Acknowledgments

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Valérie David and Mohamedou Sow are thanked for their advice on statistical analyses. This work was supported by the Regional Council of Aquitaine, the Agence Nationale de la Recherche of France (ANR Immorteel ANR-10-INTB-1704 and ANR TRACE ANR-16-CE34-0008), and a strategic project grant of the Natural Sciences and Engineering Research Council of Canada to Patrice Couture. This work was also supported by the Investments for the future Program, within the Cluster of Excellence COTE (ANR-10-LABX-45).

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<figure><img src="image_2.png" /></figure>
<figure><img src="image_3.png" /><figcaption>**Figure 2:** Change in (i) the DNA methylation levels of three different CpN sites in UnaL2 (A) and UnaSINE (B) and (ii) the transcription levels of *UnaL2* (C), *UnaSINE* (D) and coding genes (E) in the liver of wild fish captured in four sampling sites (CER, GIR, GAR, DOR). Values are presented as means ± SE; n = 8. Bars sharing same-case letters do not differ significantly (p > 0.05).</figcaption></figure>

<figure><img src="image_4.png" /><figcaption>**Figure 3**</figcaption></figure>
**Figure 3:** Proportion of fish belonging to the different *UnaL2_5'UTR* classes according to their site of origin. Three classes (low, intermediate or high transcription level of *UnaL2_5'UTR*) and four sampling sites (CER, GIR, GAR, DOR) were compared. Size of *UnaL2_5'UTR* classes were: low = 10; intermediate = 11; high = 11. Height fish were used per sampling site.

<figure><img src="image_5.png" /><figcaption>**Figure 4**</figcaption></figure>

**Figure 4:** Box plots of the DNA methylation levels of *UnaL2_CG2* (left panel) and *UnaL2_5'UTR* transcription levels (right panel) among the different *UnaL2_5'UTR* classes (low, intermediate (inter) and high). Results of Levene tests are indicated on the graphs and significant differences in variances among classes are in bold. Size of *UnaL2_5'UTR* classes were: low = 10; intermediate = 11; high = 11
Table

<table><caption>**Table 1:** Morphometric measures (mean ± SE, n = 8) and sex of eels captured in four sampling sites (CER, GIR, GAR, DOR) and environmental factors determined in each sampling site. Means sharing same-case letters do not differ significantly (P > 0.05)</caption><thead><tr><th rowspan="2">Group of variables</th><th rowspan="2">Variables</th><th colspan="4">Sampling site</th></tr><tr><th>CER</th><th>GIR</th><th>GAR</th><th>DOR</th></tr></thead><tbody><tr><td rowspan="10">Morphometric and physiological data</td><td>Length at capture (mm)</td><td>443.3 ± 20,6</td><td>439.0 ± 14.4</td><td>486.9 ± 42.0</td><td>392.9 ± 14.4</td></tr><tr><td>Weight at capture (g)</td><td>159.9 ± 25.0<sup>a,b</sup></td><td>147.1 ± 12.7<sup>a,b</sup></td><td>231.8 ± 54.4<sup>b</sup></td><td>86.4 ± 7.9<sup>a</sup></td></tr><tr><td>Fulton</td><td>0.174 ± 0.006<sup>b</sup></td><td>0.173 ± 0.010<sup>b</sup></td><td>0.175 ± 0.006<sup>b</sup></td><td>0.141 ± 0.005<sup>a</sup></td></tr><tr><td>OI</td><td>4.28 ± 0.29</td><td>3.70 ± 0,30</td><td>3.94 ± 0.51</td><td>3.49 ± 0.25</td></tr><tr><td>HSI</td><td>1.58 ± 0.11<sup>a</sup></td><td>1.40 ± 0.10<sup>a</sup></td><td>2.13 ± 0.19<sup>b</sup></td><td>1.24 ± 0.13<sup>a</sup></td></tr><tr><td>SSI</td><td>0.167 ± 0.024<sup>b</sup></td><td>0.070 ± 0.009<sup>a</sup></td><td>0.106 ± 0.013<sup>b</sup></td><td>0.126 ± 0.012<sup>b</sup></td></tr><tr><td>Age at capture (y)</td><td>4.3 ± 0.3<sup>a</sup></td><td>7.6 ± 1.2<sup>a,b</sup></td><td>11.4 ± 1.3<sup>b</sup></td><td>6.0 ± 0.7<sup>a</sup></td></tr><tr><td>Female (%)</td><td>87.5<sup>b</sup></td><td>75.0<sup>a,b</sup></td><td>62.5<sup>a,b</sup></td><td>25.0<sup>a</sup></td></tr><tr><td>Undifferentiated (%)</td><td>12.5</td><td>25.0</td><td>37.5</td><td>75</td></tr><tr><td rowspan="4">Environmental factors</td><td>Temperature (°C)</td><td>14.5</td><td>16.9</td><td>16.5</td><td>17.6</td></tr><tr><td>Salinity (‰)</td><td>23.2</td><td>1.6</td><td>0.2</td><td>0</td></tr><tr><td>Dissolved oxygen (%)</td><td>80.4</td><td>91.1</td><td>96.4</td><td>98.2</td></tr><tr><td>pH</td><td>8.10</td><td>7.99</td><td>7.87</td><td>7.81</td></tr></tbody></table>
828

829

830

831

832

833

834

835

836

837

**Table 2:** Change in liver DNA methylation levels, transcription levels, morphometric and growth indices, liver metals concentrations and muscle organic pollutants concentrations in eels belonging to the different *UnaL2_5'UTR* classes (low, intermediate, high). Except sex (female or undifferentiated, expressed as percentage), data are expressed as means ± SD. Variables that varied significantly among fish classes are in bold; means designated with different letters differ significantly (p < 0.05). Size of *UnaL2_5'UTR* classes were: low = 10; intermediate = 11; high = 11.
UnaL2_5'UTR classes

<table><thead><tr><th>Group of variables</th><th>Variables</th><th>low</th><th>intermediate</th><th>high</th></tr></thead><tbody><tr><td rowspan="6">DNA methylation (%)</td><td>UnaSINE_CG1</td><td>84.4 ± 1.4</td><td>83.7 ± 1.6</td><td>84.7 ± 1.2</td></tr><tr><td>UnaSINE_CG2</td><td>71.9 ± 1.8</td><td>66.9 ± 1.9</td><td>70.1 ± 1.7</td></tr><tr><td>UnaSINE_CA</td><td>40.0 ± 2.8</td><td>43.1 ± 2.2</td><td>38.1 ± 1.5</td></tr><tr><td>UnaL2_CG1</td><td>22.3 ± 2.5</td><td>17.2 ± 2.3</td><td>18.8 ± 1.1</td></tr><tr><td>UnaL2_CG2</td><td><strong>23.7 ± 2.8<sup>b</sup></strong></td><td><strong>15.8 ± 2.0<sup>a</sup></strong></td><td><strong>16.9 ± 1.1<sup>a</sup></strong></td></tr><tr><td>UnaL2_CG3</td><td>18.0 ± 1.6</td><td>14.0 ± 1.1</td><td>14.0 ± 0.9</td></tr><tr><td rowspan="8">Genetic transcription<br>(a.u.)</td><td>UnaSINE_a</td><td><strong>0.335 ± 0.057<sup>a</sup></strong></td><td><strong>0.867 ± 0.127<sup>b</sup></strong></td><td><strong>1.609 ± 0.216<sup>b</sup></strong></td></tr><tr><td>UnaSINE_c</td><td><strong>0.721 ± 0.102<sup>a</sup></strong></td><td><strong>1.862 ± 0.344<sup>b</sup></strong></td><td><strong>4.674 ± 0.726<sup>c</sup></strong></td></tr><tr><td>UnaSINE_ac</td><td><strong>0.057 ± 0.007<sup>a</sup></strong></td><td><strong>0.171 ± 0.027<sup>b</sup></strong></td><td><strong>0.753 ± 0.151<sup>c</sup></strong></td></tr><tr><td>UnaL2_5'UTR</td><td><strong>0.0048 ± 0.0008<sup>a</sup></strong></td><td><strong>0.0269 ± 0.0048<sup>b</sup></strong></td><td><strong>0.1819 ± 0.0331<sup>c</sup></strong></td></tr><tr><td>UnaL2_ORF</td><td><strong>0.0008 ± 0.0001<sup>a</sup></strong></td><td><strong>0.0036 ± 0.0009<sup>b</sup></strong></td><td><strong>0.0178 ± 0.0038<sup>c</sup></strong></td></tr><tr><td>UnaL2_3'UTR</td><td><strong>0.0081 ± 0.001<sup>a</sup></strong></td><td><strong>0.0354 ± 0.0062<sup>b</sup></strong></td><td><strong>0.2503 ± 0.0510<sup>c</sup></strong></td></tr><tr><td>rere</td><td><strong>0.0010 ± 0.0003<sup>a</sup></strong></td><td><strong>0.0028 ± 0.0007<sup>b</sup></strong></td><td><strong>0.0042 ± 0.0008<sup>b</sup></strong></td></tr><tr><td>sox6</td><td><strong>0.0067 ± 0.0017<sup>a</sup></strong></td><td><strong>0.0092 ± 0.0029<sup>a,b</sup></strong></td><td><strong>0.0158 ± 0.0025<sup>b</sup></strong></td></tr><tr><td rowspan="12">Condition and growth<br>indices</td><td>Length at capture (mm)</td><td><strong>484.2 ± 26.9<sup>b</sup></strong></td><td><strong>397.6 ± 11.2<sup>a</sup></strong></td><td><strong>443.6 ± 23.9<sup>a,b</sup></strong></td></tr><tr><td>Weight at capture (g)</td><td><strong>207.4 ± 35.4<sup>b</sup></strong></td><td><strong>109.2 ± 9.2<sup>a</sup></strong></td><td><strong>157 ± 33.8<sup>a,b</sup></strong></td></tr><tr><td>Fulton</td><td>0.171 ± 0.005</td><td>0.171 ± 0.007</td><td>0.155 ± 0.008</td></tr><tr><td>OI</td><td>4.44 ± 0.28</td><td>3.38 ± 0.21</td><td>3.81 ± 0.34<sup>a</sup></td></tr><tr><td>HSI</td><td>1.5 ± 0.11</td><td>1.76 ± 0.18</td><td>1.5 ± 0.15</td></tr><tr><td>SSI</td><td>0.12 ± 0.02</td><td>0.11 ± 0.02</td><td>0.12 ± 0.01</td></tr><tr><td>Muscle lipid (%)</td><td>39.1 ± 4.6</td><td>40.2 ± 4.3</td><td>28.5 ± 4.8</td></tr><tr><td>Age (y)</td><td>6.7 ± 1.6</td><td>6.5 ± 0.7</td><td>9 ± 1.1</td></tr><tr><td>Length 1 y</td><td>176.5 ± 13.6</td><td>143.1 ± 13.6</td><td>134.9 ± 9.7</td></tr><tr><td>Length 2 y</td><td><strong>252.8 ± 21.1<sup>b</sup></strong></td><td><strong>194.7 ± 16.3<sup>a,b</sup></strong></td><td><strong>176.6 ± 12.6<sup>a</sup></strong></td></tr><tr><td>Length 3 y</td><td><strong>324.7 ± 21.4<sup>b</sup></strong></td><td><strong>241.6 ± 17.7<sup>a</sup></strong></td><td><strong>218.4 ± 12.7<sup>a</sup></strong></td></tr><tr><td>Growth rate at capture (mm.y<sup>-1</sup>)</td><td><strong>94.1 ± 11.5<sup>b</sup></strong></td><td><strong>69.7 ± 8<sup>a,b</sup></strong></td><td><strong>55.9 ± 6.6<sup>a</sup></strong></td></tr><tr><td>Growth coefficient</td><td><strong>72.7 ± 8.4<sup>b</sup></strong></td><td><strong>50.5 ± 4.7<sup>a</sup></strong></td><td><strong>41.7 ± 4.1<sup>a</sup></strong></td></tr><tr><td>Female (%)</td><td><strong>90.0<sup>b</sup></strong></td><td><strong>45.5<sup>a</sup></strong></td><td><strong>54.5<sup>a,b</sup></strong></td></tr><tr><td>Undifferentiated (%)</td><td><strong>10.0<sup>a</sup></strong></td><td><strong>54.5<sup>b</sup></strong></td><td><strong>45.5<sup>a,b</sup></strong></td></tr><tr><td rowspan="10">Liver metal<br>concentrations (µg.g<sup>-1</sup>,<br>dw)</td><td>[As]</td><td>4.98 ± 1.26</td><td>3.64 ± 0.87</td><td>2.03 ± 1.03</td></tr><tr><td>[Cd]</td><td>1.98 ± 0.81</td><td>2.15 ± 0.46</td><td>2.32 ± 0.37</td></tr><tr><td>[Ag]</td><td>0.54 ± 0.16</td><td>0.74 ± 0.33</td><td>0.45 ± 0.06</td></tr><tr><td>[Cr]</td><td>1.79 ± 0.43</td><td>1.87 ± 0.70</td><td>1.31 ± 0.26</td></tr><tr><td>[Cu]</td><td>66.57 ± 9.54</td><td>48.52 ± 9.76</td><td>47.74 ± 8.14</td></tr><tr><td>[Ni]</td><td>0.30 ± 0.03</td><td>0.25 ± 0.02</td><td>0.37 ± 0.05</td></tr><tr><td>[Pb]</td><td>0.36 ± 0.17</td><td>0.35 ± 0.08</td><td>0.31 ± 0.06</td></tr><tr><td>[Se]</td><td>18.68 ± 2.75</td><td>20.38 ± 2.67</td><td>16.61 ± 1.13</td></tr><tr><td>[Zn]</td><td>164.75 ± 14.5</td><td>135.16 ± 15.18</td><td>151.23 ± 22.37</td></tr><tr><td rowspan="10">Muscle organic<br>pollutant<br>concentrations (ng.g<sup>-1</sup>,<br>dw)</td><td>[SumOCPs]</td><td><strong>51.8 ± 19.4<sup>a</sup></strong></td><td><strong>62.5 ± 12.7<sup>a</sup></strong></td><td><strong>105.7 ± 15.0<sup>b</sup></strong></td></tr><tr><td>[SumPCBs]</td><td>372.1 ± 141.5</td><td>437.1 ± 100.8</td><td>686.7 ± 123.7</td></tr><tr><td>[4.4'DDE]</td><td><strong>33.9 ± 13.9<sup>a</sup></strong></td><td><strong>37.4 ± 7.4<sup>a,b</sup></strong></td><td><strong>76.7 ± 12.7<sup>b</sup></strong></td></tr><tr><td>[4.4'DDD]</td><td>11 ± 4.6</td><td>13.1 ± 3.5</td><td>14.3 ± 2.0</td></tr><tr><td>[HCB]</td><td>2.3 ± 0.4</td><td>2.1 ± 0.2</td><td>2.3 ± 0.4</td></tr><tr><td>[γ-HCH]</td><td>0.6 ± 0.1</td><td>4.1 ± 3.7</td><td>4.7 ± 2.9</td></tr><tr><td>[PCB153]</td><td>143.0 ± 54.3</td><td>166.8 ± 39.3</td><td>340.2 ± 77.1</td></tr><tr><td>[PCB50+28]</td><td>3.4 ± 1.4</td><td>5.4 ± 1.6</td><td>2.5 ± 0.2</td></tr><tr><td>[PCB118]</td><td>25.4 ± 8.1</td><td>27.7 ± 6</td><td>45.1 ± 6.4</td></tr><tr><td>[PCB138]</td><td>100.7 ± 36.4</td><td>111.9 ± 24.2</td><td>177.8 ± 26.4</td></tr><tr><td>[PCB180]</td><td>72.6 ± 30.8</td><td>92.2 ± 23.8</td><td>87.1 ± 14</td></tr></tbody></table>