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Ecotoxicology and Human Environmental Health

Arsenic Reduces Gene Expression Response to Changing Salinity in Killifish Thomas Hampton, Craig Jackson, Dawoon Jung, Celia Y. Chen, Stephen P. Glaholt, Bruce A. Stanton, John K Colbourne, and Joseph R. Shaw Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.8b01550 • Publication Date (Web): 06 Jul 2018 Downloaded from http://pubs.acs.org on July 7, 2018

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Environmental Science & Technology

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Arsenic Impairs Salinity Acclimation in Killifish [Running Head]

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Thomas H. Hampton

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Geisel School of Medicine at Dartmouth

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Department of Microbiology and Immunology

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HB 7550, Remsen, Room 517

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Hanover, NH 03755

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USA

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Tel: 603-650-1184

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Fax: 603-650-1130

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email: [email protected]

ACS Paragon Plus Environment


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Arsenic Reduces Gene Expression Response to Changing

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Salinity in Killifish

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Thomas H. Hampton*1,2, Craig Jackson3, Dawoon Jung,2,4, Celia Y. Chen5, Stephen P.

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Glaholt3, Bruce A. Stanton2, John K. Colbourne1, Joseph R. Shaw1,3

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1. Environmental Genomics Group, School of Biosciences, University of Birmingham, Birmingham, United Kingdom 2. Department of Microbiology and Immunology, Geisel School of Medicine at Dartmouth, Hanover, NH 3. The School of Public and Environmental Affairs, Indiana University, Bloomington

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4. Korea Environment Institute, Republic of Korea

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5. Department of Biological Sciences, Dartmouth College, Hanover, NH

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Abstract

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Toxicogenomic approaches can detect and classify adverse interactions between

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environmental toxicants and other environmental stressors but require more complex

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experimental designs and analytical approaches. Here we use novel toxicogenomic

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techniques to analyze the effect of arsenic exposure in wild killifish populations

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acclimating to changing salinity. Fish from three populations were acclimated to full

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strength seawater and transferred to fresh water for 1 h or 24 h. Linear models of gene

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expression in gill tissue identified 31 genes that responded to osmotic shock at 1 h and

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178 genes that responded at 24 h. Arsenic exposure (100 µg/l) diminished the responses

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(reaction norms) of these genes by 22% at 1h (p = 1.0 e-6) and by 10% at 24 h (p =

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3.0 e-10). Arsenic also significantly reduced gene co-regulation in gene regulatory

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networks (p = 0.002, paired Levene’s test), and interactions between arsenic and salinity

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acclimation were uniformly antagonistic at the biological pathway level (p < 0.05,

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binomial test). Arsenic’s systematic interference with gene expression reaction norms

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was validated in a mouse multi-stressor experiment, demonstrating the ability of these

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toxicogenomic approaches to identify biologically relevant adverse interactions between

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environmental toxicants and other environmental stressors.

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Table of Contents (TOC)/Abstract Art.


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49 Osmotic Shock

Gene Expression Response 3.5

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Delta Effect Estimate (log2)

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NaAs02

Pathway Activation Regulatory Networks

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−2.5

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−3.5 1h

24h

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Introduction

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Arsenic is naturally present in bedrock and is released into aquatic systems, particularly

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in reducing conditions at high pH 1. Dissolved inorganic arsenic enters cells through

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sodium/phosphate cotransporters and water channels 2, where it may take the place of

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phosphorous in biochemical reactions or interact with thiol groups in proteins and

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peptides 3 leading to toxic effects. Many organisms have evolved mechanisms to detoxify

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arsenic, e.g., arsenic methyltransferases, that are highly conserved in nature 4

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demonstrating that arsenic is a broadly relevant environmental toxicant. Nonetheless,

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environmental arsenic is best known as a threat to human health, affecting over 100

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million people 5 by increasing their risk of dozens of diseases 6.

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Many organisms have been used to understand arsenic’s diverse effects at low doses. For

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example, studies in chronically exposed mice reveal reduced immune signaling 7, an

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effect also observed in zebrafish embryos 8. Xenopus tadpoles chronically exposed to

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arsenic show impaired metamorphosis 9, chick embryos exposed to arsenic respond less

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to estrogen 10, and mice chronically exposed to arsenic respond less effectively to



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influenza infection 11. Collectively, these studies suggest that arsenic exposure diminishes

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the ability of organisms to respond effectively to stress, and our overarching hypothesis is

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therefore that arsenic’s ability to diminish stress responses is associated with reduced

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gene expression responses.

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Therefore, we tested the hypothesis that exposure to arsenic during a response to a second

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stressor reduces responses at the gene expression level using killifish as a model system.

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Like most marine fish, killifish living in salt water maintain internal sodium chloride

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concentrations that are about 60% less than salt water by excreting excess chloride

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through their gills 12. Unlike most fish, killifish that are acclimated to salt water can

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survive the hypo-osmotic shock of being placed in fresh water. They undergo a process of

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gill tissue remodeling over several days to produce a gill phenotype appropriate to fresh

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water. Fresh water gills differ from salt water gills primarily in terms of the density and

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disposition of ionocytes, ion channel activity, epithelial tight junctions and blood supply

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13

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studied at various time points out to 10 days, and killifish from different populations are

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known to vary in their ability to respond to salinity 14-18. We assessed gene expression in

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three populations of killifish, at two distinctly different time points, to identify conserved

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responses.

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Killifish survive in changing salinity because they can alter their gill phenotype to suit

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environmental conditions, in other words, they exhibit phenotypic plasticity 19 20. Altered

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gene expression has been reported to facilitate phenotypic plasticity in extreme

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temperatures 21,22 or variability in water availability 23. The ability of an organism to

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regulate gene expression can be quantified as gene expression reaction norms 24. Here we

. Changes in killifish gill gene expression during gill remodeling have been extensively


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applied the concept of gene expression reaction norms to quantify the impact of arsenic

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on salinity responses in killifish, hypothesizing that reduced gene expression reaction

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norms during acclimation to salinity in the killifish gill will reduce phenotypic plasticity.

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Gene regulatory networks coordinate gene expression and have been shown to facilitate

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phenotypic plasticity 15,25. We used a simple statistic based upon Pearson correlation 26 to

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quantify the level of gene regulatory phenotypic plasticity as connectivity in our system

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and the effect of arsenic on that network connectivity. We hypothesized that exposure to

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arsenic would reduce network connectivity and interfere with the ability of genes to

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respond in a coordinated fashion.

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Specific gene regulatory networks have been annotated as biological pathways that

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perform specific functions, e.g., those belonging to the Kyoto Encyclopedia of Genes and

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Genomes (KEGG) 27. We hypothesized that if arsenic interference with gene expression

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responses leads to reduced phenotypic plasticity, arsenic interference might target

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pathways with functions that facilitate the early or late phases of tissue remodeling in the

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killifish gill. To assess this, we used Pathway Activation Analysis (PAA) to score

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whether genes in a given pathway are systematically turned on or off by arsenic or other

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treatment effects. The general principle is similar to Differential Expression for Pathways

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(DEAP) 28 but PAA does not require explicit knowledge of regulatory relationships. In

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addition, PAA does not rely on arbitrary significance cutoffs, which are a substantial

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drawback of first generation pathway analysis approaches including using Fisher’s exact

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test to identify pathways enriched in genes found significant in a statistical test29.

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Novel methods presented here are relevant to any toxicogenomic study involving

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multiple stressors, and demonstrate that they can detect significant shifts in gene



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expression that increase our understanding of interactions between biotic and abiotic

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stressors 30 31. Specifically, we found that arsenic reduced gene expression reaction norms

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in killifish gill tissue responding to osmotic shock, and that this effect generalized to a

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second animal model (i.e., mouse). We also report that arsenic diminished network

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connectivity, and limited activation of biological pathways. An analysis of publicly

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available data showed that arsenic also reduces reaction norms in response to

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dexamethasone, a synthetic corticosteroid stress hormone. Taken together, our findings

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suggest that arsenic toxicity may increase as a function of environmental stress.

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Materials and Methods

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Killifish, Arsenic Exposures, and Tissue Collection

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Gill tissue was obtained from male wild killifish sampled from two locations in Maine

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and one in Virginia. We used fish from Northeast Creek, Maine because we have used

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these fish extensively in previous experiments 2,15,32-39. Fish from Horseshoe Cove Maine,

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and King’s Creek Virginia were included in the study to ensure that our results would

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focus on responses shared by multiple killifish populations. We used a nearby Maine

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population (Horseshoe Cove) because they were easy to collect and expected to be

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genetically similar to the Northeast Creek population. The population from King’s Creek,

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Virginia was supplied by colleagues from the Duke Ecotoxicology Lab, and was included

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because it is representative of the southern killifish clade, which includes all killifish

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south of the Hudson River in New York 40. Fish (Northeast Creek, 1.62±0.47g,

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Horseshoe Cove, 1.68±0.32g, King’s Creek, 4.74±0.62g) were pre-acclimated to

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common garden conditions, housed, maintained and exposed to arsenic as previously


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described 15 in accordance with IACUC # MDIBL 13-01 from Mount Desert Island

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Biological Laboratory, Salisbury Cove, Maine. Briefly, 72 killifish were maintained for at

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least two weeks in seawater (pH 8.1 ± 0.4; salinity 33 ± 0.5‰, 15±1 °C) and exposed to

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natural light cycle (16:8 h light:dark), to assure all were fully acclimated. Experimental

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factors included population (Northeast Creek, ME, Horseshoe Cove, ME, King’s Cove,

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VA), exposure to 100 µg/l arsenic (yes/no), and time exposed to fresh water (0 h, 1 h, 24

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h) to yield a balanced design of 18 groups with 4 fish per group. Freshwater and seawater

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conditions were established as previously described 41 15. Fish were fed commercial flake

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food (48% protein, 9% fat; Tetracichlid, Tetra, Blacksburg, VA) once a day that

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contained no detectable inorganic arsenic, or monomethyl- or dimethyl arsenic (Shaw et

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al. 2010). Arsenic concentrations in exposed samples were assayed at 100.2 µg/l ± 1.4

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µg/l (SD), total arsenic, 98.9% inorganic As III using a 7700x ICP-MS (Agilent, Santa

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Clara, CA) mass spectrometer. Arsenic concentrations in unexposed samples were below

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the detection limit of 1.0 µg/l arsenic.

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Fish were anesthetized, pithed, and gills were removed and stored in RNAlater (Qiagen,

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Valencia, CA) according to manufacturer’s recommendations.

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RNA Isolation, Hybridization and Normalization

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Tissue samples were removed from RNAlater and rinsed before being homogenized

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using a Tissue-Tearor (Biospec Products, Bartlesville, OK). The homogenized tissue was

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then processed using RNeasy kits with DNase treatment (Qiagen, Valencia, CA)

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according to manufacturer’s protocols to extract RNA. RNA quality and concentration

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were determined by an Agilent 2100 bioanalyzer. All samples used in genome expression



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studies achieved an RNA integrity number (RIN) score >7, as required by the in-house

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protocols of the IU Center for Genomics and Informatics microarray processing core, and

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widely suggested in the literature42. RNA was amplified using MessageAmp II kits

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(Ambion) and hybridized to a custom NimbleGen array that interrogates 135,000 probes

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associated with 69,426 unique contigs, that map to 16,104 unique genes 15. Raw

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fluorescence values were quantile normalized across arrays using RMA43. Complete gene

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expression data has been deposited at the NCBI Gene Expression Omnibus 44 and are

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accessible through GEO Series accession number

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GSE104218 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE104218).

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Identifying Genes that Significantly Responded to Hypo-osmotic Shock

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As certain killifish genes were associated with more than one probe on the microarray,

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we selected the probe with the highest median expression across all conditions to

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represent each unique killifish gene. The Robust MultiArray Average (RMA) normalized

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log2 expression values for each gene were then analyzed using various linear models in R.

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False discovery rates (FDR) were calculated from linear model p values using the method

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of Benjamini and Yekutieli 45, and genes with an FDR less than 0.05 were deemed

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significant. First, ordinary linear models were used to assess main effects of population,

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arsenic, acclimation time and interactions between arsenic and acclimation. The

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expression of about 60% of all genes differed significantly (FDR < 0.05) between

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populations, and Maine populations were as different from each other as they were from

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the Virginia population. Neither arsenic as a factor, nor interactions with arsenic

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identified specific genes that were differentially expressed (FDR < 0.05) in this model,

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though genes were identified as differentially expressed at 1 h and 24 h. To increase


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sensitivity to arsenic and its interactions with changing salinity, we modeled gene

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expression as a function of arsenic, acclimation time and their interactions, treating

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population as a random effect using the nlme package 46. Fixed effect linear models and

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mixed effects linear models on data subsets were also used to explore the data.



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Visualizing Differences between of 1 h and 24 h Responses

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We used Venn diagrams from the R gplots package (https://CRAN.R-

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project.org/package=gplots) and pairs plots from the R GGally package

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(https://CRAN.R-project.org/package=GGally) to establish that genes responding at 1 h

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are distinct from each other and respond differently at the two time points.

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Identifying Genes that Significantly Responded to Arsenic

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Procedures that control false discovery rates to correct for multiple hypothesis testing set

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a higher bar for significance and therefore decrease sensitivity, that is, procedures that

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decrease the false positive rate increase the false negative rate. In the toxicogenomic

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setting, the capacity to detect true effects may be more important than the ability to avoid

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detecting spurious effects, so we used a graphical analysis of p value distributions to

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assess whether FDR corrected statistics might underestimate differential gene expression

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responses to arsenic and interactions between arsenic and exposure to changing salinity.

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We used ggplot2 (http://ggplot2.org) to visualize that the number of observed p values

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that were less than 0.05 exceeded the fraction that would be expected based on the null

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hypothesis and gene expression independence.

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Systematic Assessment of Gene Expression Reaction Norms

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The number of genes that respond to treatment is a useful proxy for biological response,

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and therefore relevant to toxicogenomics 47,48. Therefore, we calculated the impact of

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arsenic on gene expression reaction norms in salinity responsive genes. First, we used

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mixed effect linear models with population as a random effect to identify genes that

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respond significantly (FDR < 0.05) to salinity. Second, we calculated the average


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absolute log2 response (gene expression reaction norm) in these genes at 1 h and 24 h

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compared to 0 h, in fish that were not experimentally exposed to arsenic. Analogous

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reaction norm calculations were made for arsenic exposed fish, and the impact of arsenic

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on hypo-osmotic shock gene expression reaction norms was calculated by subtracting

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arsenic unexposed reaction norms from arsenic exposed reaction norms. The impact of

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arsenic on reaction norms was visualized for genes that significantly responded to hypo-

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osmotic shock using pirate plots in yarrr 49, and significant differences were identified by

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a one sample t test where the mean difference attributable to arsenic under the null

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hypothesis was predicted to be zero.

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Quantifying Changes in Gene Regulatory Network Structure

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Genes regulate other genes, and if arsenic interferes with the ability of killifish to respond

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to changes in salinity, we hypothesized that it may do so by interfering with regulatory

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relationships among genes. Regulatory relationships among genes may be visualized as

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networks and inferred from the level of correlation among genes 50. The impact of arsenic

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on regulatory network structure was quantified using correlation matrices as follows.

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Separate correlation matrices were constructed for fish exposed to either 100 µg/l or 0

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µg/l arsenic to explore regulatory relationships between genes that respond to hypo-

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osmotic shock. Pairwise Pearson correlations between all genes significantly responding

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to salinity at a given time point (1 h, 24 h) were calculated for all samples using the

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corr.test package in the R psych library 51, resulting in four matrices. Correlation values

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from each time point (plus and minus arsenic) were then compared in terms of their

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variability using levene.var.test in the R package PairedData 52 which implements a

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paired Levene’s test using Wilcox’s approach 53. Smaller variability in correlation



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indicates a larger proportion of genes with near zero correlation, and hence less

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connectivity in the gene regulatory network.

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Pathway Activation Analysis

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While it is useful to quantify the impact of arsenic on gene reaction norms and regulatory

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networks, these assessments do not identify specific biological functions affected by

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arsenic. To address this shortcoming, we mapped our killifish genes to orthologous genes

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in the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways based on zebrafish

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orthologs using OrthoMCL54 and reciprocal blastp as described in the publication of the

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killifish reference genome 55. Effect estimates from linear models for all genes (regardless

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of statistical significance) in 166 KEGG paths were tested for significant pathway level

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biases toward induction or repression using binomial tests with a null hypothesis that

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50% of the genes in any pathway are induced. Significance levels of binomial tests were

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FDR-corrected for multiple hypothesis testing.

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Validation of Arsenic Impact on Gene Expression Reaction Norms

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Finally, we used publicly available data to validate that the patterns of arsenic

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interference observed in killifish during hypo-osmotic shock generalize to other systems

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exposed to multiple stressors. Validation used normalized gene expression data from two

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studies in the Gene Expression Omnibus (GEO) 56. First, we used GSE47035, a study of

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killifish exposed to arsenic during acclimation to hyper-osmotic shock 15. Second, we

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used GSE11056, a study of gene expression responses in mouse lung tissue 8 h after IP

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injection with arsenic (1 mg/kg), or dexamethasone (1 mg/kg) or both. We chose this

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study because we are familiar with the experimental details of the data, and because


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arsenic was combined with dexamethasone, a synthetic steroid hormone similar to

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cortisol which evokes a broad stress response 57.

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Results and Discussion

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Arsenic’s diverse toxic effects in human populations range from cancer, heart disease,

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and diabetes to reduced IQ 6, suggesting that environmental exposure to arsenic might

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affect organisms living in dynamic environments in many different ways. Our

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toxicogenomic analysis in killifish reveals that arsenic interferes with gene expression

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responses to osmotic shock at three levels of biological organization: individual genes,

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regulatory networks, and biological pathways. Publicly available data, analyzed with our

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techniques, suggest that arsenic’s interference with gene expression responses extends to

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other species.

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We had previously observed that acute exposure to high doses of arsenic (12 mg/l) during

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acclimation from fresh water to salt water led to high rates of mortality 39, although this

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arsenic concentration failed to increase mortality in fish maintained in stable conditions,

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suggesting adverse interactions between arsenic and osmotic shock. We have also shown

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that 100 µg/l arsenic interferes with the induction of serum and glucocorticoid-regulated

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kinase 1 (SGK1) that normally occurs when fresh water acclimated killifish are exposed

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to salt water36. Finally, in a recent whole transcriptome experiment in a single killifish

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population we showed that arsenic’s impact on gene expression during salinity

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acclimation extends far beyond SGK1 by identifying roughly 400 killifish genes with a

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significant interactions between exposure to arsenic (100 µg/l) and changed salinity 15. In

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the present study, we assessed the impact of arsenic on gene expression in three



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populations of sea water acclimated killifish during acclimation to fresh water, looking

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for arsenic-mediated differences in gene expression.

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Identifying Differentially Expressed Genes

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This report presents methods to identify biologically relevant patterns in gene expression

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responses. As patterns require many genes to define, we began by estimating how many

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genes respond to each treatment condition. Assuming that the roughly 16,000 genes

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measured on our array respond independently, about 800 (5%) of these genes are

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expected to achieve a p value of 0.05 or less in any statistical test under the null

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hypothesis. Recognizing this, toxicogenomic studies often use false discovery rate (FDR)

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calculations 45 to reduce the number of false positive calls, but this approach can also

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increase false negative calls, eliminating genes whose expression truly varies and making

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downstream analysis problematic 58. FDR calculations may therefore underestimate the

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number of genes differentially expressed in a toxicogenomic experiment.

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Our design included 24 fish from each of three separate populations, making our results

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more likely to represent killifish in general that an experimental design using a single

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population. Although this study was not intended to explore population differences in

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detail, fixed effect linear models were used to independently assess each of the three

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populations (Northeast Creek, Maine (NEC), King’s Creek Virginia (KC), and Horseshoe

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Cove, Maine (HC)). These results were compared to mixed effect linear models treating

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population as a random effect.

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Mixed effects linear models were more sensitive to differential gene expression than

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independent, fixed effect models, identifying 178 genes that responded significantly


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(FDR < 0.05) to fresh water exposure at 24 h (Figure 1A, red bar) compared to a total of

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105 unique genes identified by fixed effect models in either Northeast Creek (green bar)

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King’s Creek (blue bar) or Horseshoe Cove (purple bar). Neither fixed nor mixed effect

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linear models identified large numbers of arsenic responsive genes or genes with arsenic

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interactions. This either suggests that 100 µg/l arsenic affects very few genes in the

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killifish gill, or that our design and statistics simply failed to detect arsenic responsive

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genes.

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Figure 1. A Number of significant genes (FDR < 0.05) identified by mixed effect

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linear models (Mixed) or by fixed effect linear models of each killifish population

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(NEC = Northeast Creek, KC = King’s Creek, HC = Horseshoe Cove). Counts

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shown for each model term including time of exposure to fresh water, exposure to



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arsenic or interactions between arsenic and time (As : 1h, As : 24 h). B Distribution

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of mixed effect p values for all 16,104 unique genes associated with each model

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term. Horizontal line shows expected number of genes (100) in each bin assuming

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uniform distribution. Vertical line demarks a nominal p value of 0.05.

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Complete mixed effect linear model output is available in Supplemental Table 1.

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An analysis of p value distributions suggests that using FDR correction to identify

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treatment effects underestimates the number of arsenic-responsive genes, particularly

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with respect to interactions between arsenic exposure and exposure to fresh water for 1 h.

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Figure 1B shows the observed number of p values within a specified range and the

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theoretical expectation (red horizontal line) based on the null hypothesis and gene

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expression independence. P value bin ranges in Figure 1B were selected to contain

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exactly 100 genes in the case that the null hypothesis is universally true, that is, in the

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case where there are no true treatment effects. The area under the curve in Figure 1B that

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lies above the red line therefore represents the number of genes that achieved significance

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for some other reason than simultaneously testing many genes. For all model terms, the

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total area under the curve, to the left of the vertical bars (p < 0.05) and above the

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horizontal line identifies 1,793 instances of significance that are not attributable to the

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multiple hypotheses testing burden -- about 8 times as many significantly differentially

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expressed genes as were identified in Figure 1A by mixed effect models with an FDR

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