Alterations to the Intestinal Microbiome and Metabolome of

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Ecotoxicology and Human Environmental Health

Alterations to the intestinal microbiome and metabolome of Pimephales promelas and Mus musculus following exposure to dietary methylmercury Kristin N Bridges, Yan Zhang, Thomas E Curran, Jason Magnuson, Barney J. Venables, Katherine E Durrer, Michael S. Allen, and Aaron P. Roberts Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.8b01150 • Publication Date (Web): 26 Jun 2018 Downloaded from http://pubs.acs.org on June 26, 2018

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Environmental Science & Technology

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Alterations to the intestinal microbiome and metabolome of Pimephales promelas and Mus

2

musculus following exposure to dietary methylmercury

3 a*

4

b

a

a

a

Kristin N. Bridges , Yan Zhang , Thomas E. Curran , Jason T. Magnuson , Barney J. Venables , b

5

b

Katherine E. Durrer , Michael S. Allen , and Aaron P. Roberts

a

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a

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Texas, 1155 Union Circle, Denton, Texas, 76203 USA

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b

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Department of Biological Sciences & Advanced Environmental Research Institute, University of North

Department of Microbiology Immunology and Genetics, University of North Texas Health Science

Center, 3500 Camp Bowie Blvd., Fort Worth, Texas, 76107

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* Corresponding Author:

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Kristin Bridges

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[email protected]

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University of North Texas

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1155 Union Circle

17

Denton, TX 76203

18 19 20 21 22 23 24 25 26 27 28

1

ACS Paragon Plus Environment


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Abstract Mercury is a global contaminant, which may be microbially transformed into methylmercury

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(MeHg), which bioaccumulates. This results in potentially toxic body burdens in high trophic level

33

organisms in aquatic ecosystems, and maternal transfer to offspring. We previously demonstrated effects

34

on developing fish including; hyperactivity, altered time-to-hatch, reduced survival, and dysregulation of

35

the dopaminergic system. A link between gut microbiota and central nervous system function in teleosts

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has been established with implications for behavior. We sequenced gut microbiomes of fathead minnows

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exposed to dietary MeHg to determine microbiome effects. Dietary exposures were repeated with adult

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CD-1 mice. Metabolomics was used to screen for metabolome changes in mouse brain and larval fish,

39

and results indicate effects on lipid metabolism and neurotransmission, supported by microbiome data.

40

Findings suggest environmentally relevant exposure scenarios may cause xenobiotic-mediated dysbiosis

41

of the gut microbiome, contributing to neurotoxicity. Furthermore, small-bodied teleosts may be a useful

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model species for studying certain types of neurodegenerative diseases, in lieu of higher vertebrates.

43 44

KEYWORDS. Developmental toxicity, maternal transfer, metabolomics, microbiome, methylmercury

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Introduction

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Elemental mercury is released into the atmosphere through anthropogenic and natural processes, 1

47

and can undergo global transport before oxidation into its inorganic form by atmospheric components.

48

Inorganic mercury can then be introduced into aquatic ecosystems via wet or dry deposition. In sediment,

49

inorganic forms of mercury may be transformed by bacteria into mono-methylmercury (MeHg), a highly

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bioavailable form which bioaccumulates and biomagnifies. The primary route of MeHg exposure for

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adult life stages of aquatic organisms is through diet resulting in potentially high body burdens in

2

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3

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organisms at high trophic levels. Methylmercury forms conjugates with biomolecules containing sulfur,

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providing a mechanism of transport across the blood brain barrier and from maternal diet to developing

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offspring.

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transfer.

4-6

Consequently, the primary exposure route for early life stage (ELS) fish is via maternal

5

56

A national dataset of Hg concentrations from adult freshwater fish in Canada reported mean wet

57

weight (WW) axial muscle concentrations ranging from 0.28-0.41 µg/g Hg in sport fish; with maximum

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concentrations exceeding 10 µg/g. Similarly, WW yellow perch muscle concentrations reported from

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over 2,000 sampling sites (Great Lakes region) ranged from 0.01-2.6 µg/g Hg. The concentrations

60

reported in wild fish in North America are of concern, as current estimates suggest sub-lethal effects on

61

adult individual fish health begin to occur at WW concentrations as low as 0.2 µg/g, while toxicity in early

62

life stage fish occurs at much lower concentrations.

63

vitreus) eggs in North American lakes ranged from 0.002 – 0.27 µg/g (WW) , with a separate study

64

reporting concentrations ranging from 0.004-0.104 µg/g Hg (WW) in yellow perch (Perca flavescens)

65

eggs. We previously published companion studies showing a range of developmental effects in ELS

66

fathead minnows (FHM) exposed to maternally-transferred MeHg. Results showed WW egg Hg

67

concentrations at the lower end of the reported range of concentrations (low treatment mean: 0.04-0.06

68

µg/g Hg) was sufficient to induce neurological effects.

7

8

3, 5, 9, 10

Concentrations of Hg in walleye (Sander 11

9

10, 12

69

Numerous studies exist which link exposure to metals to neurodegenerative diseases in 13-18

70

vertebrates.

71

from mitochondria-initiated oxidative stress, leading to changes in motor activity.

72

also been shown to preferentially accumulate in the mitochondria of neurons within the CNS, leading to a

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cascade of biochemical changes which culminate in oxidative stress, neuronal degeneration and altered

74

neurotransmission.

75

developmental exposure to maternally transferred MeHg.

76

have typically focused on the CNS, there is an emerging link between nervous system function and gut

77

microbiota.

78

dietary substrates, forming the basis for bidirectional communication between intestinal microbes and the

79

CNS.

Parkinson’s disease (PD) is characterized by dopaminergic neuron loss in the mid-brain 19-27

22, 23, 25, 26, 28, 29

Changes in motor activity were observed in embryonic FHM following 10, 12

30, 31

24

Similarly, MeHg has

Though studies on neurodegeneration

Interestingly, intestinal microbes produce almost half of the body’s dopamine (DA) from

This relationship is known as the microbiome-gut-brain axis implying a role for enteric

3



24, 30, 32

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microbiota in brain function and neuro-development.

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microbiome and Parkinson’s disease (PD) has been established in humans. Transplants of fecal

82

microbiota from PD patients to germ-free mice induced PD associated motor-impairments

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that the gut microbiome plays an important role in the pathology of some neurological diseases, and that

84

these effects are not species-specific.

85

Recently, a link between altered gut

24

suggesting

To investigate the role of the gut-brain axis in dietary MeHg-mediated neurotoxicity, we fed adult

86

FHM diets with environmentally relevant concentrations of MeHg (0.87 and 5.5 µg/g Hg DW) and

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characterized the composition of the intestinal microbiome. We previously reported significant decreases

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in DA concentrations in brains of FHM fed a comparable low-MeHg diet (0.72 µg/g Hg DW). This effect

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on DA was also seen in the offspring of these animals along with motor-activity abnormalities, both

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hallmarks of dopaminergic neuron degeneration.

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body of evidence suggesting small-bodied teleosts and their offspring may serve as surrogates for higher-

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vertebrate model species typically used to study neurodegenerative diseases in humans. To that end, we

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replicated the experiment using male CD-1 mice (Mus musculus), and performed metabolomics on the

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mid-brains and gut microbiome analyses of sacrificed animals to compare with fish data.

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further investigate mechanisms driving neuro-developmental toxicity of MeHg, we performed

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metabolomics on newly hatched larvae to screen for changes in a range of low molecular weight

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metabolites, and used the data to generate hypotheses regarding genes that may be differentially

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expressed. These were subsequently verified using qPCR.

12, 24

Another goal of this study was to add to the growing

12

Finally, to

12

99 100

Materials and Methods

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Animal Care Methods

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ELS fish used in the present study were produced by two cohorts of adult FHM, both of which 10, 12

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were maintained using the methods described in Bridges et al.

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Texas Institutional Animal Care and Use Committee (IACUC) approved all FHM protocols (#1303-3).

105

Male CD-1 mice were maintained at the University of North Texas Health Science in Fort Worth, TX under

106

IACUC protocol #2014/15-36-A04 (Figure S2).

(Figure S1). The University of North

107

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108 109


Experimental Design Fish Fish received assigned diets twice daily (n = 5 tanks/treatment), for 30-days. Diets included a

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control (0.02 µg/g Hg dry weight (DW)), and low treatment (0.72 ± 0.01 µg/g Hg DW in tanks used for

111

metabolomics , or 0.87 ± 0.08 µg/g Hg DW in tanks used for microbiome sequencing ), with an

112

additional high treatment (5.50 ± 0.6 µg/g Hg DW) in the cohort used for microbiome sequencing (n= 5

113

tanks). Diets were prepared using the methods described in Bridges et al , and analyzed for Hg. The

114

low treatment concentrations were selected to represent Hg exposure from a diet composed of benthic

115

invertebrates and zooplankton in some North American lakes, while the high diet represents those of fish

116

at high trophic levels or in polluted systems.

117

selected to mimic the primary route of environmental exposure.

118

unconsumed food pellets was not considered a significant route of Hg exposure for adult or larval fish

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based on MeHg dissociation values reported in the literature, waste/unconsumed food removal practices,

120

and the transfer of embryos to clean water shortly after spawn.

121

12

10

10

10

7, 8, 10, 33

Exposure routes for adult and developing fish were 5, 33, 34

Waterborne MeHg from

10, 12, 33

Individual clutches of eggs were removed from breeding tiles in exposure tanks each morning,

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and transferred to a crystallizing dish containing reconstituted-moderately-hard-water, and methylene

123

blue. Dishes were aerated and covered with parafilm to help prevent evaporation, and water levels were

124

checked twice-daily. Clutches were cultured at 23° C in an environmental chamber on a 16:8 photoperiod

125

(Table S1).

126

were sacrificed in buffered MS-222 (Sigma-Aldrich, St. Louis, MO) at the conclusion of the study and

127

rapidly dissected on ice. All tissues were immediately frozen at -80° C until further analyses were

128

performed.

129

Experimental Design Mice

130

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ELS fish used for further analyses were frozen frozen at -80° C until further use. Adult fish

Dietary exposure is the primary route of MeHg exposure for humans and was therefore deemed

131

the most appropriate route of exposure for mice. Diets made from standard pelleted mouse chow

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(LabDiet, USA) were prepared using the methods described in Bridges et al. , and analyzed for Hg. Mice

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(n = 7 mice/treatment) were fed either a control (0.02 µg/g Hg DW), low (0.43 µg/g ± 0.009 Hg DW), or

134

high diet (4.39 ± 0.57 µg/g Hg DW), twice daily for 30-days. Concentrations of Hg in fecal pellets were

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monitored throughout the study (Table S1).

10

5



136

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At the conclusion of the study, mice were sedated using a 2-2-2 tribromoethanol solution, and

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perfused with physiological saline to remove excess blood. Brain tissue was quickly removed from skulls,

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sectioned on ice, and immediately frozen at -80°C. Mouse brains from the high treatment had obvious

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differences in tissue integrity, making rapid dissections more difficult. Therefore, we only performed

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metabolomics on midbrains from control and low diet animals, as we were able to dissect and freeze

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these samples within a similar time frame.

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Mercury Determination

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All biological samples, food, and fecal pellets were analyzed for Hg using a DMA-80 Direct

145

Mercury Analyzer (Milestone Inc., Monroe, Connecticut) according to USEPA Method 7473 (see SI for

146

details).

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147 148 149

DNA extraction and Preparation for Sequencing Gastrointestinal tissue samples (n= 20) were thawed on ice, and pulverized with a sterilized

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pestle in a 1.5-mL microcentrifuge tube. Genomic DNA was extracted using the Qiagen DNeasy Blood &

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Tissue Kit (Qiagen, Valencia, CA) following the manufacturer’s instructions with minor modifications. 16S

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rRNA was amplified using universal bacterial primers, which target the V4 hypervariable region , with

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each sample prepared for PCR in duplicate. Purified PCR products were combined in equimolar ratios

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and diluted according to the manufacturer’s recommendation for emulsion PCR on the Ion OneTouch™ 2

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instrument (Life Technologies; refer to SI for details).

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156 157

IonTorrent Sequencing

158

An Ion OneTouch™ 2 instrument was used to prepare template-positive Ion Sphere Particles

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(ISPs) from amplicon libraries, using the Ion OneTouch 400 Template Kit (catalog #4479878) per the

160

manufacturer’s specifications. Enrichment was performed with the OneTouch ES instrument (Life

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Technologies), followed by quantification of the enriched, template positive ISPs. Quantification was

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performed using a Qubit 2.0 Fluorometer (Life Technologies) with the Ion Sphere Quality Control Kit

163

(catalog #4468656). Enriched ISPs were loaded onto 316 v2 chips (catalog #4483324) and sequenced

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on an Ion Torrent Personal Genome Machine (PGM) using the Ion Sequencing 400 Kit (catalog

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#4482002) per the manufacturer’s instructions.

166 167 168

Sequence Analysis 37

Sequencing reads generated by the Ion Torrent PGM were processed using mothur v.1.36.1.

169

Identification of DNA sequences between samples was accomplished using sample-specific barcodes.

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Primers and barcode sequences were trimmed, and low-quality reads (homopolymers > 8, length
97% similarity were grouped

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into operational taxonomic units (OTUs). The Greengenes database was used for taxonomic assignment

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using an 80% confidence cutoff.

38

Unknown sequences, as well as those from mitochondria, chloroplasts, archaea, and

39

178 179 180

Metabolomics Metabolite extraction was performed by homogenizing tissue samples (n ≥ 5 for all sample types, TM

181

details in Tables S2-3) in a 2:5:2 chloroform:methanol:Mili-Q

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with D-27 myristate internal standard (IS) before evaporation under a gentle stream of nitrogen. Samples

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were subjected to two separate derivatization steps and analyzed via gas chromatography-mass

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spectrometry (GC-MS). The Agilent Fiehn Retention Time Locking Library was used to identify

185

metabololites and generate semi-quantitative relative response factors, which were ratioed against the IS

186

(see SI for details).

water solution. Supernatant was spiked

187 188 189

qPCR Gene Expression Analysis RNA was isolated from entire clutches of just-hatched larvae (n = 5 clutches/treatment) using a

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Promega Maxwell™16 Automated Nucleic Acid Extraction system (Madison, WI) with a Promega

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Maxwell™16 Total RNA Purification Kit. First-strand cDNA was synthesized using an iScript™ Reverse

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Transcription Supermix for RT-qPCR (Bio-Rad, Hercules, CA) and a Biometra Thermocycler

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(LABREPCO, Gӧttingen, Germany), following manufacturer’s instructions.

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Genes chosen for qPCR analysis were selected based on literature availability and their 40-43

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relevance to the metabolomics data set.

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(Coralville, IA; Table S4). Reactions were prepared using a QuantiTect™ SYBR™ Green PCR Kit

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(Qiagen, Louisville, KY) according to the manufacturer’s guidelines. A Rotor-Gene (Corbett Research,

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Mortlake, Australia) was used for qPCR analysis. Differences in gene expression between treatments

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were evaluated via statistical analysis of ∆∆Ct values (see SI for details).

Primers were made by Integrated DNA Technologies

200 201 202

Statistical Analyses UniFrac distances and principle coordinate analysis (PCoA) were used to compare differences in 44

203

microbial communities between samples.

204

treatments was determined using an Analysis of Molecular Variance (AMOVA). Overall similarities

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between treatments were determined using an ANalysis Of SIMilarities (ANOSIM).

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comparison of relative bacterial abundance between treatments was performed using a Kruskal-Wallis

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test, with a Tukey’s post hoc (p < 0.05 with 1000 Monte-Carlo permutations), performed using the

208

multcomp and coin packages in R (R Development Core Team, 2012). The false positive discovery rate

209

(FDR) for multiple hypothesis testing was controlled for using the Benjamini-Hochberg method.

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Differences in microbial communities within and among

45

A nonparametric

46

Reports generated by AMDIS using the Agilent Fiehn Retention Time Locking Library were

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filtered using an in-house R (v. 2.15.2) program that determined acceptable metabolite identification

212

based on similarity between sample retention indices and library retention indices, and a net mass

213

spectral quality scores of 70% or greater as calculated by AMDIS. Metabolite results which met all

214

filtration criteria were included in a two-tailed Welch’s t-test, to determine statistically significant

215

differences (p < 0.05) between treatments.

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All other data were analyzed using JMP 11.1 (Cary, NC). Normality of all data was determined

217

using the Shapiro-Wilk test. Mercury concentrations in larvae were determined using a single factor

218

ANOVA, by adult dietary Hg concentration. The ∆∆Ct values from qPCR analysis were analyzed using a

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single factor ANOVA, by treatment. An α of 0.05 was used to determine statistical significance for all

220

tests.

221 222

Results and Discussion

223

The adult muscle Hg concentrations achieved in both cohorts of fish are reported in previous 10, 12

224

publications (available in Table S1).

225

are representative of fish muscle concentrations found in freshwater ecosystems in North America.

226

47

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in exposed larvae that were roughly 0.04 µg/g higher than those of controls (n = 7 clutches/treatment,

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Table S1).

229

of transfer is sufficient to induce developmental toxicity in several species of teleosts.

230

However, it is important to reiterate that these concentrations 3, 5, 34,

The amount of dietary Hg female FHM transfer to eggs is small, and resulted in WW Hg concentrations

5, 9, 48

Nevertheless, the results reported herein add to the body of evidence indicating this rate 10, 12, 49-51

Though MeHg-mediated effects were observed across all life stages and species, early life

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stages demonstrated greater sensitivity to Hg exposure. In larval fish, 35 common metabolites were

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included in our statistical analysis (Table S2), 6 of which were significantly (p < 0.05) altered by

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maternally transferred MeHg exposure (Table 1). These included increased relative abundances of

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putrescine, L-serine, and glycerol. Conversely, significant decreases were observed in the relative

235

abundance of several long chain fatty acids (FAs), including stearic, palmitic, and oleic acids.

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Twenty-two of the common metabolites detected in whole larval fish were also detected in the

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mid-brains of mice from the control and low Hg treatments (Tables S2-S3). The WW Hg concentrations

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measured in mouse mid-brains were as follows: control: 0.002, low: 0.24, and high: 1.69 µg/g Hg (n = 5

239

for all treatments, Table S1). In total, 71 common metabolites (Table S3) were detected in samples of

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mid-brains, 8 of which had significantly decreased (p < 0.05) abundances (Table 1). These included L-

241

glutamine, O-phosphatidylcholine, dopamine, tagatose, hydroquinone, L-ascorbic acid, inosine 5’-

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monophosphate, and uracil. The abundance of stearic acid (significant in FHM larvae) in mid-brains of

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mice was just above the cutoff for statistical significance (p = 0.06). However, when differences in tissue

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type are considered, in conjunction with the extremely similar abundance ratio (Hg/Control; Tables 1. S2-

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S3) in that of whole larvae, this change may still have biological significance. None of the significantly

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altered metabolites in mouse mid-brains were also significant in the metabolomes of whole FHM larvae,

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which we attribute to tissue dilution (whole larvae were used), as many metabolites concentrated in the

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CNS did not have large enough responses to be detected via GC-MS screening. Fortunately, we

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previously performed targeted analysis of DA via HPLC-MS/MS, in subsamples of larvae used for

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metabolomics, and brain tissue of adults.

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mid-brains of mice.

12

Decreased DA concentrations in that study mirror those in the

252 253

Table 1. Metabolites with significantly altered relative abundances detected in A) fathead minnow (FHM) larvae

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exposed to maternally transferred dietary MeHg and B) mid-brains of male CD-1 mice fed a diet spiked with low

255

concentrations of MeHg; when compared with controls (details and full metabolite list in Tables S2-S3).

Hg/Control

A)

B)

p-value

Putrescine

1.99

0.001

L-serine

2.08

0.02

Glycerol

1.6

0.02

Stearic acid

0.68

0.04

Palmitic acid

0.72

0.04

Oleic acid

0.55

0.04

L-glutamine

0.41

0.01

O-phosphocolamine

0.67

0.02

Dopamine

0.29

0.03

Tagatose

0.42

0.04

Hydroquinone

0.62

0.04

L-ascorbic acid

0.19

0.04

Inosine 5’-monophosphate

0.62

0.04

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Uracil

0.61

0.04

256 257

In spite of the status of Hg as a contaminant of global concern, its effects on the microbiome are 52

258

largely unknown

259

to a number of neurodegenerative diseases.

260

neurodegenerative diseases is just now receiving attention, in spite of the clear role of environmental

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factors in some of these diseases.

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teleost gut microbiome. As a whole, our results reveal exposure to MeHg can evoke effects on several

263

important pathways including immune function and detoxification, metabolism/energetics, and nervous

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system structure/function. These effects may occur through a variety of mechanisms, some of which may

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be mediated by the gut microbiome either through direct or indirect processes. Furthermore, it is

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unknown whether neurotoxicity precedes microbiome changes, or vice versa, as communication of the

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gut-brain axis is bidirectional.

. Most human exposure to MeHg occurs through diet, and exposure has been linked

53-55

19, 22, 23, 25

The role of the gut-brain axis in the progression of

Little is known about the effects of mercury exposure on the

32, 56

268

The results of the present study indicate significant treatment differences in gut microbial

269

communities (based on an AMOVA of unweighted UniFrac distances, p < 0.002). As seen in the PCoA of

270

unweighted UniFrac distances (Figure 1A), different microbial species were present in the high MeHg

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samples, relative to those from the control and/or low. Unsurprisingly, the low and control treatments were

272

more similar (based on an ANOSIM of unweighted UniFrac distances, R = 0.18, p = 0.001), with both

273

treatments showing significant differences from the high treatment (control: R = 0.42, p < 0.0001; low: R =

274

0.34, p < 0.0001). These results were mirrored in the PCoA of weighted UniFrac distances (Figure 1B).

275

Seven phyla showed significant (p < 0.05) differences between treatments (Figure 2A). To further

276

investigate the bacterial community differences, genera with relatively high abundances (with at least one

277

sample > 5%) were identified for multiple comparisons (Figure 2B), with 18 out of 31 selected genera

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showing significant treatment differences (Figure S3).

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A

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0.3

PCo2 (5.8%)

0.2 0.1 0 -0.1 -0.2 -0.3 -0.4 -0.5

-0.3

-0.1

0.1

0.3

PCo1 (9.3%) B

0.3

PCo2 (19.2%)

0.2 0.1 0 -0.1 -0.2 -0.3 -0.4 -0.4

-0.2

0

0.2

0.4

PCo1 (25.2%) Control

279

Low MeHg

High MeHg

280

Figure 1. Dietary MeHg shifted the gut microbial communities of fathead minnow, as seen in the (A) Principle

281

coordinates analysis of unweighted UniFrac distances and (B) principle coordinates analysis of weighted UniFrac

282

distances.

283 284

Proteobacteria and Bacteroidetes were the dominant phyla in guts of fish fed the high MeHg diet

285

(Figure 2A). Bacteria from the Bacteroidetes were further identified at the genus level as Cloacibacterium.

286

While Bacteroidetes did not comprise a dominant portion of the microbiome in fish from the control or low

287

treatments, Proteobacteria remained dominant across treatments (Figure 2A). This is consistent with the

288

findings of other studies which also identified Proteobacteria to be the predominant bacterial phylum in

289

the guts of FHM.

290

differences (p < 0.05) using deeper taxonomic classifications (Figures 2B, S3), including

57-59

However, within the Proteobacteria there were a number of significant treatment

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Gammaproteobacteria (predominant in controls), Xanthomonadaceae, and Rhodobacter (both

292

predominant in the high treatment). Other dominant phyla in samples from the low MeHg include

293

Planctomycetes and Fusobacteria (Figure 2B). Planctomycetes were significantly (p < 0.01) higher in the

294

low treatment than in the high, and were further classified into genera Pirellula and Planctomyces.

295

Fusobacteria was further classified as Cetobacterium, which was significantly (p < 0.01) decreased in the

296

high MeHg treatment relative to controls (Figure S3).

297

298 299 300

Figure 2. Composition of FHM gut microbial communities by diet, classified by (A) phyla with relative abundances >

301

1%, and (B) genera with relative abundances > 5%.

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Changes in composition of the fish gut microbiome suggest an adaptive response to increase

304

MeHg detoxification. Available evidence suggests methylation/demethylation of MeHg by gut microbes,

305

and/or microbiome-mediated changes in intestinal permeability influence the bioavailability of MeHg to the

306

body.

307

associated neurodegeneration.

308

removal were significantly increased (p < 0.05, Figure S3) in one or more of the MeHg exposed

309

treatments. These include Xanthomonadaceae , Cloacibacterium , members of the Deltaproteobacteria

310

FAC87

311

results are in agreement with a study by Hill-Burns et al. , which reported increased abundances of

312

bacteria associated with xenobiotic metabolism in the stool of PD patients. It is worth noting that stool

313

samples do not necessarily accurately reflect the microbiome in the GI tract (which were sequenced in the

314

present study) . Some members of the Verrucomicrobiaceae family are also involved in metabolism of

315

PD associated xenobiotics, and a member of this family identified as Candidatus Xiphinematobacter was

316

significantly increased (p < 0.05, Figure S3) in the MeHg exposed fish, though it is unknown if this

317

particular genus functions in this capacity.

318

significantly increased (p < 0.05, Figure S3) in MeHg exposed fish. However, it is worth noting that some

319

members of this genus are also capable of producing siderophores, the primary function of which is iron

320

chelation, but which may also participate in the chelation of other cations (such as Hg ).

53, 60

Additionally, increased intestinal permeability may contribute to the progression of PD53, 54

Several genera implicated in xenobiotic metabolism and metal

62

61

63

and Comamonadaceae

64

families, and Pirellula

65

(which produces a mercury-reductase). These

54

53

54

Pseudomonas, an opportunistic pathogen, was also

2+ 66, 67

321

Other suspected pathogens that were significantly (p < 0.05) increased (or present only in the

322

high treatment) included Aeromonas, Acinteobacter, and an unclassified member of the Neisseriaceae

323

family (Figures 2, S3). The presence of these bacteria may indicate suppression of immune function,

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which is also indicated by altered abundances of other bacteria, and supported by the metabolomics data.

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The abundance of the nucleotides uracil and inosine-5’-monophosphate (I5MP) were significantly

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decreased (p < 0.05) in the mid-brains of MeHg exposed mice (Table 1).

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guanine, both of which have important immunomodulatory effects, and play a fundamental role in nervous

328

system development and function. Abnormal metabolism of nucleotides leads to severe neurological

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disorders associated with failure of dopaminergic neurons to properly develop or differentiate, which may

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result from impaired mitochondrial function.

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I5MP is a precursor to

Page 15 of 31


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Other bacteria which produce a variety of compounds with biological functions that influence

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immunomodulation, antimicrobial properties, and intestinal barrier fortification (with implications for MeHg

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bioavailability) were significantly decreased in one or more groups of MeHg exposed fish (Figure S3, p

Alterations to the Intestinal Microbiome and Metabolome of

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