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Ecotoxicology and Human Environmental Health

Molecular effects, speciation and competition of inorganic and methyl mercury in the aquatic plant Elodea nuttallii Rebecca Beauvais-Flück, Vera I Slaveykova, Ulf Skyllberg, and Claudia Cosio Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.8b02124 • Publication Date (Web): 09 Jul 2018 Downloaded from http://pubs.acs.org on July 12, 2018

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Environmental Science & Technology

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Molecular effects, speciation and competition of inorganic and methyl mercury in the

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aquatic plant Elodea nuttallii

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Rébecca Beauvais-Flück1, Vera I. Slaveykova1, Ulf Skyllberg2, Claudia Cosio*,1,†

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1

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Sciences, Faculty of Sciences, University of Geneva, Switzerland.

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2

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Sciences, Umeå, Sweden.

Department F.-A. Forel for environmental and aquatic sciences, Earth and Environmental

Department of Forest Ecology and Management, Swedish University of Agricultural

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†

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UMR-I 02 (SEBIO), Université de Reims Champagne Ardenne, F-51687 Reims, France.

present address: Unité Stress Environnementaux et BIOSurveillance des Milieux Aquatiques

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*Corresponding author: [email protected], [email protected]

1 ACS Paragon Plus Environment


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Abstract

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Mercury (Hg) remains hazardous in aquatic environments, because of its biomagnification in

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food webs. Nonetheless, Hg uptake and impact in primary producers is still poorly

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understood. Here, we compared the cellular toxicity of inorganic and methyl Hg (IHg; MeHg)

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in the aquatic plant Elodea nuttallii. IHg and MeHg regulated contigs involved in similar

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categories (e.g. energy metabolism, development, transport, secondary metabolism), but

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MeHg regulated more contigs, supporting a higher molecular impact than IHg. At the

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organism level, MeHg induced antioxidants, while IHg decreased chlorophyll content.

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The uptake of Hg and expression of a subset of contigs was subsequently studied in complex

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media. Measured uptake pointed to a contrasted impact of cell walls and copper (Cu) on IHg

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and MeHg. Using speciation modeling, differences in uptake were attributed to the differences

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in affinities of IHg and MeHg to organic matter in relation to Cu speciation. We also

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identified a distinct gene expression signature for IHg, MeHg and Cu, further supporting

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different molecular toxicity of these trace elements.

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Our data provided fundamental knowledge on IHg and MeHg uptake in a key aquatic primary

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producer and confirmed the potential of transcriptomic to assess Hg exposure in

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environmentally realistic systems.

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Keywords: copper, dissolved organic matter (DOM), pigment, redox, RNA-sequencing,

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speciation.


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Introduction

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Mercury (Hg) is one of the most hazardous metals, mostly because of the biomagnification of

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its methylated form (MeHg) in aquatic food webs [1]. Freshwater ecosystems remain

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currently affected by Hg pollution worldwide, despite past and ongoing efforts to reduce

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anthropogenic emissions of Hg [2]. Aquatic plants have a key ecological role in shallow water

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ecosystems and are instrumental for Hg transfer to top consumers in food webs [3-6]. For

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example, the macrophytes Elodea nuttallii and Typha domingensis showed high Hg

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accumulation from both sediments and water column [7-9]. Hg accumulated in plants can

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affect their fitness and life cycle. In macrophytes, frequently reported toxic effects of Hg

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include the generation of oxidative stress, such as increased lipid peroxidation and altered

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activity of enzymes involved in the regulation of reactive oxygen species (ROS) [10-13]. At

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the organism level, inorganic Hg (IHg) inhibited root growth in rice seedlings, [14]. Similarly,

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exposure 24h to 70 ng·L-1 IHg reduced E. nuttallii root growth, while 23 ng·L-1 MeHg

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increased root growth [4]. Histology and proteomics further revealed an increased

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lignification of cell walls in response to IHg exposure [4]. Few studies analyzed the impact of

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Hg at the transcriptome level in macrophytes. In rice seedlings, IHg affected genes involved

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in amino acid metabolism, cell-wall formation, chemical detoxification, secondary

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metabolism, signal transduction, abiotic stress response and water transport [14]. In E.

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nuttallii, a strong impact of IHg on genes involved in energy metabolism and metal

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homeostasis was reported [15]. Similarly, transcriptomics of E. nuttallii exposed to MeHg

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revealed a significant impact on the expression level of genes involved in energy metabolism

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and water transport [16]. However, most studies were conducted in simplified media, lacking

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organic matter, which limits their environmental relevance. Moreover, to our knowledge no

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previous study aimed at comparing uptake and effects of IHg and MeHg with comparable

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experimental conditions on the transcriptome of aquatic plants was performed. Detailed



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characterization of molecular and cellular mechanisms of toxicity and uptake as well as

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tolerance mechanisms in aquatic plant in realistic conditions is a prerequisite to understand the

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fate and the impact of IHg and MeHg in the aquatic environment.

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This study aimed first to compare the molecular toxicity pathways triggered by 2h-long

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exposures to a large range of IHg and MeHg concentrations (0.01 to 15 µg·L-1) in the

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macrophyte Elodea nuttallii using whole transcriptome analysis (RNA-Sequencing; RNA-

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Seq) and physiological endpoints including pigments content (chlorophyll and anthocyanin)

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and the activity of anti-oxidative enzymes. Subsequently, we investigated the Hg uptake and

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expression level of a subset of genes in E. nuttallii exposed to mixtures, containing IHg,

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MeHg, dissolved organic matter (DOM) and copper (Cu) to gain fundamental knowledge on

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Hg uptake and tolerance mechanisms in a key aquatic primary producer in more realistic

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exposure scenarios.

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Material and Methods

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Plant exposure conditions

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All experiments were done in triplicates. Elodea nuttallii were grown in the laboratory [7].

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For all treatments, shoots (~10 cm long without roots) were exposed 2h in 500 mL of an

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artificial medium (91.0 mg·L-1 CaCl2.2H2O, 43.2 mg·L-1 MgSO4.7H2O, 23.5 mg·L-1

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NaHCO3, 13.6 mg·L-1 KH2PO4 and 0.4 mg·L-1 NH4NO3, pH 6.9 ± 0.1) in controlled

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conditions (16:8h light:dark, 1000 lux, 20°C). This short time of exposure was chosen to

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target early-response genes to IHg and MeHg, before more general stress response appears.


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For the first set of experiments (whole transcriptome analysis): treatments included 0.015,

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0.15 and 15 µg·L-1 IHg (Hg(NO3)2) and 0.01, 0.10, 1 or 10 µg·L-1 MeHg (MeHgCl). For

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comparison, in Europe, the current environmental quality standard (EQS) for THg in surface

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water is 0.07 µg·L-1.

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For the second set of experiments (targeted transcriptome analysis): To improve

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environmental realism, treatments included 0.15 (low) and 15 µg·L-1 IHg (high), 0.25 µg·L-1

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MeHg, 4 µg·L-1 Cu (CuSO4), low IHg + MeHg, high IHg + MeHg, low IHg + 4 µg·L-1 Cu,

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70 µg·L-1 IHg (high) + 4 µg·L-1 Cu and MeHg + 17 µg·L-1 Cu. All these conditions were in

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addition tested in presence of 1 or 10 mg·L-1 Suwanee River humic acid (SRHA). Thereafter

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treatments without IHg, MeHg, Cu, SRHA are named Control, while treatments with SRHA

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alone (in the absence of metal) are named 1 mg·L-1 SRHA and 10 mg·L-1 SRHA according to

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their respective concentration.

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Total RNA extraction

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After 2h of exposure, 3 shoots per treatment were snap-frozen in liquid nitrogen, ground and

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total RNA was

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(http://www.thermofisher.com/order/catalog/product/AM9738). RNA quality was determined

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by Qubit and bioanalyzer.

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Whole transcriptome analysis (RNA-Seq)

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See details in Supplementary text. Briefly, libraries of cDNA were prepared and sequenced on

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Illumina HiSeq 2000. Differential gene expression (DGE) was computed with the EdgeR

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Bioconductor package. We defined differently expressed contigs as significant at a threshold

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of false discovery rate corrected p-values (FDR) 99 % of IHg and MeHg to be complexed by SRHA RS

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functional groups, thus an extremely low abundance of IHg and MeHg complexed by the

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inorganic ligands Cl- and OH- (Table 3 and S7). At 1 mg·L-1 of SRHA, its RS functional

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groups were abundant enough to complex >99% of IHg and MeHg at their lowest addition

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levels, but when IHg was increased, SRHA RS groups were more than saturated by Hg2+ and

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inorganic complexes involving Cl- and OH- dominated the speciation of both IHg and MeHg

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(Table 3). Notably, Cu(II) could not compete with MeHg for DOM associated RS groups.

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However, if Cu(II) was significantly transformed to Cu(I) in our experiments, Cu(I)

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outcompeted the bonding of MeHg to RS groups, with MeHgOH and MeHgCl dominating the

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speciation of MeHg at 1 mg·L-1 SRHA (Table 3, SI text).

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We made a calculation assuming all Cu added was reduced to Cu(I) and that the bonding to

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SRHA-RS groups was similar in strength to RS encountered in aquatic environments [55]. A

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transformation of all Cu(II) to Cu(I) is obviously an exaggeration, but formation of Cu(I) in

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our experiment is not unlikely, given the experimental conditions and uptake mechanisms of

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Cu as Cu(I), which is conserved through Eukaryotes including terrestrial and aquatic plants

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[7, 56, 57]. In principal our calculation shows that Cu(I) would compete efficiently with

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MeHg in presence of 1 mg·L-1 SRHA (Table 3) and in essence occupy all RS groups. In



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presence of 10 mg·L-1 SRHA, the RS groups in the SRHA should be abundant enough to bind

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in principal all Cu(I) and MeHg, but because of differences in the relative binding strengths

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between Cu(I) and MeHg, the concentration of Cu complexes would be substantially smaller

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than the concentration of inorganic MeHg complexes. This suggests that the competition for

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plant uptake gets in favor of MeHg in presence of 10 mg·L-1 SRHA. The fact that the uptake

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of MeHg indeed follows the pattern predicted by the chemical speciation model may be taken

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as support for Cu2+ being reduced to Cu+ in presence of SRHA under our experimental

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conditions. Because IHg forms relatively stronger complexes than MeHg with DOM-RS

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groups [52], Cu(I) have less of a competitive effect towards Hg in the IHg + Cu treatments.

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Concomitantly, in absence of SRHA, Cu + IHg/MeHg significantly decreased Cu uptake as

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compared to Cu addition alone. Further, the Cu uptake significantly increased when 1 mg·L-1

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SRHA was added in mixtures, but not in Cu treatment only (t-test, p-value = 0.06; Figure S2).

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With 10 mg·L-1 SRHA, [Cu]intra was not significantly different vs without SRHA, except for

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Cu + MeHg, for which Cu uptake significantly decreased (1.4×), confirming the interaction

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between Cu and IHg/MeHg, and previously observed competition of Cu on [THg]intra uptake

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as well as increased [THg]intra in MeHg + Cu mixtures.

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These observations of different effects of Cu on IHg and MeHg uptake, together with

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differences of cell wall binding mentioned above are congruent with the 2-4× higher

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bioaccumulation of MeHg than IHg of field studies [3] and highlight that binding to DOM

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and competition with other metals are central factors for IHg and MeHg bioavailability in

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natural waters. To better understand and predict Hg uptake and trophic transfer in aquatic

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systems, it is desirable to in-depth study the effect of parameters controlling Hg uptake in

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primary producers at the base of food webs. To reach this aim, new tools to measure

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bioavailability are highly desirable.


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The expression level of selected contigs was analyzed by hierarchical clustering

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encompassing the expression level of all contigs (Figure 3) to assess its potential as

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ecotoxicogenomic tool of exposure in complex media. Indeed such tools to measure

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bioavailability of IHg and MeHg in the field would be of great interest. Transcriptomic is seen

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as sensitive and specific to assess the impact of low contaminant concentrations in non-model

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organisms [15, 30, 58-61]. Contigs were selected based on their functions in the main

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impacted biological pathways and dose-dependent response with increasing IHg or MeHg

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exposure. After quality control and removal of contigs showing a log2FC >0.5 in 1 and 10

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mg·L-1 SRHA, 16 contigs only passed quality control (Table S2) and included a catalase, the

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PSI subunit, two pectinesterases, 4-coumarate-ligase and 2 S-adenosylmethionine (SAM)

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enzymes, involved in the biosynthesis of secondary metabolites.

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Without SRHA, contigs signature discriminated well the samples exposed to Cu from other

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treatments, and IHg vs MeHg (Figure 3). In response to IHg exposure, 9 genes were up-

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regulated (e.g. 4-coumarate ligase, DNA polymerase) at low and high IHg, and 4 were up-

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regulated only at higher IHg concentration (catalase, SAM carrier 1, pectinesterases). This

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result confirms the RNA-Seq results (e.g. cell wall biosynthesis). The IHg-MeHg mixtures

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were clustered closer to MeHg than IHg treatments, suggesting a higher sensitivity of the

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selected contigs to MeHg than IHg. On the opposite, MeHg + Cu was clustered next to Cu,

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certainly because of the down-regulation of Locus_8381 and the receptor kinase which

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showed specificity to Cu exposure.

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The presence of 1 mg·L-1 SRHA did not influence the clustering of samples (Figure 3),

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confirming its non-significant effect on the uptake. At 10 mg·L-1 SRHA, the signature of high

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IHg treatments was significantly distinct from the other treatments (Figure 3) forming 2

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clusters: IHg and IHg + MeHg samples clustered together, suggesting that the MeHg-

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sensitivity was mitigated compared without DOM.



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Without SRHA, Cu samples clustered according to measured Cu uptake, ranging from 2.20 ±

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0.02 µg·g-1dw in high IHg + Cu to 6.37 ± 0.13 µg·g-1dw in Cu treatment (Figure 3). At 1 mg·L-1

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SRHA, Cu uptake increased (vs in the absence of SRHA), but the gene signature of high IHg

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+ Cu was closer to IHg/MeHg treatments and 1 mg·L-1 SRHA (Figure 3). Similarly, at 10

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mg·L-1 SRHA, although Cu uptake decreased in Cu + MeHg vs Cu, non-significant change in

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the gene expression signature was observed and Cu treatments were identically clustered than

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for other SRHA concentrations (Figures 3 and S2).

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Data suggested that the subset of selected genes responded more specifically to IHg and

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MeHg than Cu. However, gene signature successfully discriminated IHg, MeHg and Cu

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treatments in presence of DOM. Similarly, a previous study in E. nuttallii was able to

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correlate gene signature with gradients of Hg concentrations in natural water both in situ and

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in the laboratory, nevertheless it was unable to differentiate IHg from Cu, Cd or IHg + Cd [15,

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30]. Previous studies showed transcriptomic efficient for analysis of short-term exposure, to

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correlate with gradients of contaminants in natural waters, to be more sensitive than classical

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bioassays (e.g. bioaccumulation or physiological effects), as well as to be able to identify

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toxicant-specific signatures [15, 30, 62-64]. These properties seem particularly interesting for

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in-situ analysis characterized by a cocktail of different metals with DOM [21, 65-69], but a

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subset of genes targeting more diverse pollutants need to be identified. Here we identified 16

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contigs that can complete the previous set of contigs targeting metals available in E. nuttallii

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[7]. Our data confirmed the hypothesis that transcriptomic is a very sensitive approach, and is

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promising for deriving biomarkers of exposure in environmentally relevant scenarios, for

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example, in mixtures at sublethal concentrations and should be further explored for its

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suitability for ecotoxicological testing.

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Acknowledgments


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The Swiss National Science Foundation (contracts 205321_138254, 200020_157173) for

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financial support, Drs. Mylène Docquier and Didier Chollet for nCounter. RBF acknowledges

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the support of funds Constantin Topali.

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Supporting Information

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Supplementary text of Material and Methods for RNAseq, cellular endpoints, mercury

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analysis, chemical speciation modelling. Supplementary text of Results and Discussion for

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chemical speciation modelling. Supplementary tables of differential gene expression in

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simplified and complex media, effective concentrations in media, stability constants for

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chemical speciation modelling, uptake and number of reads and contigs, Mapman ontology,

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speciation modelling. Supplementary figures of Biological pathways, Cu uptake, Relationship

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between Cu and humic acids.

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653 654

Table 1. Proportion (%) of regulated contigs in main MapMan categories (FDR < 0.1 %)

655

after 2h exposure of E. nuttallii to a range of IHg and MeHg concentrations. IHg (µg·L-1) Category 0.015 0.15 15 amino acid metabolism 0.5 2.6 3.1 cell structure 6.5 8.5 10.3 energy metabolism 5.7 10.3 11.6 gene regulation 21.2 25.6 22.9 other 3.5 8.6 8.4 redox 0.9 1.2 1.4 secondary metabolism 1.0 4.2 2.7 stress 2.0 3.7 3.0 transport 2.3 8.7 8.2 unknown 56.3 26.4 28.1

MeHg (µg·L-1) 0.01 0.1 1 10 2.1 1.9 1.3 1.4 8.4 8.6 7.1 7.4 7.1 6.8 5.0 5.4 28.0 30.0 29.0 28.4 6.7 6.9 6.6 7.7 1.4 1.2 1.1 1.2 2.3 2.3 1.4 1.7 3.0 3. 5 2.5 2.1 4.6 5.4 3.5 3.8 35.9 33.1 42.4 40.9

656 657


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658 659

Table 2. Chlorophyll content, anthocyanin content and activities of anti-oxidative stress

660

enzymes (POD, class III peroxidases; SOD, superoxide dismutases) in E. nuttallii exposed 2h

661

to IHg or MeHg (results are presented as % of control; bold characters indicate significant

662

difference with control, mean ± SD, n = 3; t-test p-value < 0.05). Treatment (µg·L-1) IHg

MeHg

control 0.015 0.15 15 control 0.01 0.1 1 10

chlorophyll (%) 100.0 ± 15.2 77.3 ± 16.2 70.5 ± 9.6 66.7 ± 9.9 100.0 ± 18.7 82.4 ± 35.3 91.8 ± 21.3 96.0 ± 5.7 113.0 ± 11.7

anthocyanin (%)

100.0 ± 10.3 764.2 ± 103.2 502.5 ± 227.9 315.2 ± 89.0 483.7 ± 108.1

POD activity (%)

SOD activity (%)

100 ± 67.9 156.2 ± 54.4 264.0 ± 8.5 20.8 ± 10.7 100.0 ± 20.62 280.0 ± 132.5 328.0 ± 69.5 240.9 ± 56.4 151.6 ± 33.1

100 ± 8.5 117.1 ± 24.8 167.9 ± 27.4 84.26 ± 12.6 100 ± 8.9 86.3 ± 9.0 87.0 ± 5.9 66.8 ± 22.4 94.0 ± 5.9

663 664



665 666 667

668

Table 3. Fraction (M·M-1) of inorganic chemical species of IHg, MeHg and Cu in different treatments.

Treatment Low IHg Low IHg + MeHg Low IHg + Cu

no SRHA IHg: 1.0 IHg: 1.0 MeHg: 1.0 IHg: 1.0 Cu(II): 1.0

1 mg·L-1 SRHA IHg: 4 × 10-15 IHg: 4 × 10-15 MeHg: 4 × 10-6 IHg: 4 × 10-15 Cu(II): 0.73

10 mg·L-1 SRHA IHg: 4 × 10-17 IHg: 4 × 10-17 MeHg: 4 × 10-7 IHg: 4 × 10-17 Cu(II): 0.03

High IHg High IHg + MeHg High IHg + Cu

IHg: 1.0 IHg: 1.0 MeHg: 1.0 IHg: 1.0 Cu(II): 1.0

IHg: 0.83 IHg: 0.83 MeHg: 0.99 IHg: 0.83 Cu(II): 0.93

IHg: 2.3 × 10-16 IHg: 2 × 10-16 MeHg: 1 × 10-6 IHg: 2 × 10-15 Cu(II): 0.46

MeHg: 5 × 10-6 MeHg: 9 × 10-6 Cu(II): 0.89 MeHg: 1.0 Cu(I): 0.77

MeHg: 5 × 10-7 MeHg: 4 × 10-7 Cu(II): 0.27 MeHg: 2 × 10-8 Cu(I): 9 × 10-12

MeHg MeHg: 1.0 MeHg MeHg: 1.0 + Cu Cu(II): 1.0 *MeHg MeHg: 1.0 + Cu(I) Cu(I): 1.0 * Calculated for Cutot = Cu(I)

669 670


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671


Figure legends

672 673 674 675 676

Figure 1. Uptake of IHg (white) and MeHg (black) in washed E. nuttallii measured as total Hg (THg = IHg + MeHg, [THg]intra) vs effective concentration of IHg and MeHg in media ([THg]med) (n=3) (A); Number of contigs significantly up- (triangle) or down-regulated (down-pointing triangle; FDR < 0.1 %) vs bioaccumulated ([THg]intra; dw, dry weight) (B).

677 678 679 680 681 682 683

Figure 2. Effect of Cu or MeHg on IHg uptake and of Cu on MeHg uptake in absence (A) and presence of SRHA (B). Uptake was measured as THg (= IHg + MeHg) concentration in washed plants ([THg]intra) and divided by effective THg concentration in medium ([THg]med) at the beginning of the exposure. Asterisks indicate a significant difference with the respective treatment without competitor (A) or with the respective treatment without SRHA normalized to 100% (dashed line) (mean ± SD, n = 3, t-test, p-value

Molecular effects, speciation and competition of ... - ACS Publications

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