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The evolution of pollution tolerance in the marine copepod Tigriopus
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The evolution of pollution tolerance in the marine copepod Tigriopus
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Content
THE EVOLUTION OF POLLUTION TOLERANCE IN THE MARINE COPEPOD
TIGRIOPUS
By
Patrick Yin Sun
A Dissertation Presented to the
FACULTY OF THE GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(BIOLOGY)
Department of Biological Sciences
Marine Biology and Biological Oceanography
August 2016
ii
Dedication
This work is dedicated to my family for their faith and prayers throughout this
process. For my parents and their great sacrifices that gave me an opportunity to
pursue my life’s passion.
iii
Acknowledgements
This work would not have been possible without the guidance and support of
Suzanne Edmands, my committee chair and Ph.D. advisor. I am deeply grateful for
her faith in me. I am also deeply indebted to the Edmands lab, past and present, for
their assistance and friendship along the way. To Barrett Phillips and Wai Leong for
being the trailblazers and sharing their wealth of knowledge as the senior members
of the lab. To Brad Foley and Eric Watson for being the friendly reservoirs of
information, recipients of all my stupid questions. To Nicole Adams for her
willingness to always lend an ear or watch a video. To Helen Bermudez Foley, my
lab twin for being my trusted friend and confidant, partners in anxiety and
celebration. To the army of undergraduates that I had the great opportunity to work
with and mentor that contributed to this work and my own growth as a scientist.
I owe many thanks to my committee, Suzanne Edmands, Andrew Gracey,
James Moffett, and Joshua West for their guidance and assistance throughout this
process.
I am also grateful for the Linda Bazilian, Adolfo de la Rosa, Donald Bingham,
and Douglas Burleson for their logistical wizardry and friendship throughout the
years. I also wish to acknowledge USC SeaGrant and USC Wrigley Institute for
Environmental Studies for their financial support during my graduate career.
iv
TABLE OF CONTENTS
Dedication ii
Acknowledgements iii
Table of Contents iv
Abstract vi
List of Tables viii
List of Figures ix
Introduction 1
References 6
Chapter 1 9
Acclimation and Adaptation to Common Marine
Pollutants in the Copepod Tigriopus californicus
References 25
Chapter 2 34
Variation in tolerance to common marine pollutants
among different populations in two species of the marine
copepod Tigriopus
References 49
Chapter 3 61
Long-term laboratory culture causes contrasting
shifts in tolerance to two marine pollutants in
copepods of the genus Tigriopus
References 78
Chapter 4 88
The correlation between environmental temperature
and copper tolerance in the marine copepod, Tigriopus
californicus: A potential case of exaptation between
environmental and anthropogenic stress
References 105
v
Chapter 5 120
Transcriptome response to acute and chronic
pollution exposure
References 134
Conclusion 149
References 151
vi
Abstract
This body of work focused on how evolutionary processes influence pollution
tolerance in the marine copepod Tigriopus. The applications of these results are
meant to improve the implementation of tools such as bioassays that use the
response of test specimens to inform management practices.
Pollution tolerance was examined across geographical space to identify
whether there were significant differences in pollution tolerance in Tigriopus from
different areas. Chapter 2 had a brief survey of studies that used bioassays and
determined that the majority of these studies to not account for the location of
where their test specimens were obtained. This was followed by a survey of
pollution tolerances between sister species and within each species of copepods and
found significant variation in tolerance between species and within each species.
Biological variation has the danger to be dismissed as experimental error, which will
erode bioassays as a foundational tool in environmental management.
Pollution tolerance was also examined across time, which included history of
exposure as well as duration of lab maintenance. Chapter 1 found that with chronic
exposure to Cu and TBTO, these copepods developed increased tolerance. For Cu,
the increased tolerance was an acclimation like response because it was gained
relatively rapidly but lost during a recovery period when pre-exposure to Cu was
removed. TBTO exhibited an adaptation like response, where tolerance appeared
only several generations of exposure and was maintained even after the pre-
exposure was removed. To gain a better understanding of this increase in pollution
vii
tolerance, RNAseq was used to understand gene expresson analysis in Chapter 5.
These results showed that expression patterns broadly clustered by duration of
exposure, acute (96hr) vs. chronic (7 generations). However, results identified the
potential for certain gene category, such as ion regulatory associated genes to
exhibit a generalized response during acute exposure but a more chemical-specific
response during chronic exposure.
In Chapter 3, the effect of transplanting populations from the field to the lab
on pollution tolerance was examined in two species of Tigropus. Results showed
that pollution tolerance significantly changed for the majority of populations in the
lab often within a single generation. This change in pollution tolerance was also in
different directions, with Cu tolerance increasing and TBTO tolerance decreasing.
The increasing Cu tolerance was linked to the potential differences in the lab, with
heat stress being one candidate while the decreasing TBTO tolerance was linked to
periods of starvation. To examine how the environment may have shaped pollution
tolerance, water temperature monitoring and seawater chemical analysis was used
to characterize the habitat of T. californicus at different sites in Chapter 4. These
results indicate the high level of Cu tolerance in Tigriopus is likely not due to direct
exposure to high Cu concentrations, but rather other environmental stressors, with
one candidate being temperature stress.
The collective theme of this work showed that pollution tolerance can
significantly change due to exposure history, duration in the lab, and the
environment the organism evolved in.
viii
List of Tables
Table 1-1 Regression statistics of fitness assays from generations 2-12 for each line in its
ambient condition and proportional deviation of C and T lines from the control (S) line.
Table 4-1 ANOVA results for Cu concentration between collection sites and dates. A
two-way ANOVA was used to compare sites and sampling period. A one-way ANOVA
was used to compare sites (data from both sampling periods) and the lab sample (only
one sampling period available), field + lab.
Table 4-2 Nested Two-way ANOVA for SC and SD. Only sites that had measured
dissolved Cu concentrations from more than one pool (SC and SD) were included in the
analysis.
Table 4-3 Pair-wise comparison between air and pool water temperature. Pool water
temperature data is taken from the larger of two pools monitored at each site. Both types
of temperature data are the daily maximum temperature between 6/29/15 to 8/29/15.
Table 4-4 Two-way repeated measures ANOVA analysis of pool water temperature
between the three sites (SC, SD, and CAT) as well as the two pools with each site (pool 1
and pool 2).
Table 5-1 Total number of raw reads obtained by Rapid Run V.2 Illumina HiSeq 2500 100pb
paired end sequencing. Mapped reads were mapped to the Tigriopus californicus v.3 genome.
ix
List of Figures
Fig. 1-1 Experimental design for multi-generation selection experiment. The SD square
represents the initial sample collected from San Diego, California. Each subsequent
square represents a generation for a particular line. The seawater (S) line was maintained
for 14 generations and the copper sulfate (C) and TBTO (T) lines were maintained for 11
generations. After 11 generations, the C and T lines were transferred to seawater (CS and
TS) for the final three generations. Each circle represents fitness assays for a total of 25
copepod pairs (male plus virgin female), with each pair housed in its own Petri dish.
Fitness assays are annotated with either x, y, or z to signify seawater, Cu, or TBTO
treatments, respectively.
Fig. 1-2: Fitness assays (Mean ± SE) for lines S, C, and CS exposed to 13.74 µgL⁸¹ Cu.
Offspring in fitness assays were counted 28 days after they were established. Offspring
from parents reared in Cu (C) and exposed to Cu during the fitness assay are indicated in
grey. Offspring from parents reared in control conditions (S and CS) and exposed to Cu
during the fitness assay are indicated in white. The t-test values are as follows: *p<0.05,
**p<0.01, ***p<0.001. Non-significant values denoted by ns.
Fig. 1-3 Fitness assays (Mean ± SE) for lines S, T, and TS exposed to 0.15 µgL⁸¹ TBTO.
Offspring in fitness assays were counted 28 days after they were established. Offspring
from parents reared in TBTO (T) and exposed to TBTO during the fitness assay are
indicated in dark grey. Offspring from parents reared in control conditions (S and TS)
and exposed to TBTO during the fitness assay are indicated in white. The t-test values are
as follows: *p<0.05, **p<0.01, ***p<0.001. Non-significant values denoted by ns.
Fig. 2-1 Ten years of research articles (Jan 2004 – August 2014) were surveyed on the
Thomson Reuters’ Web of Science search engine using the search term “ecotoxicology
bioassay”. Species used for bioassays in each article were placed into one of three
categories based on the collection information listed in the article. Results show the
percentage of sets of test species in each category (94 articles, 139 sets of test species,
mean of 1.4 sets of test species per article). Only bioassays using animals (i.e.,
multicellular, non-plant, non-fungal organisms) were considered in the survey.
Fig. 2-2 Map of Hong Kong territory with the sampling locations denoted by their
respective population abbreviations, Gold Coast (GC), Ma Wan (MW), Shek O (SKO),
Stanley (Stan), and Cape D’Aguilar (CDA).
Fig. 2-3 Map of the California coastline with the sampling locations denoted by their
respective population abbreviations, Santa Cruz (SC), Santa Cruz Island (SCI), Leo
Carrillo (LC), Laguna Beach (LB), and San Diego (SD).
Fig. 2-4 A linear regression between Average daily maximum temperature and Cu
x
tolerance. Temperature data was obtained from NOAA National Climatic Data Center,
Global Summary of the Day (www.ncdc.noaa.gov) from December 2010 to November
2011 at climate stations near five Californian sites, Santa Cruz (SC), San Diego (SD),
Santa Cruz Island (SCI), Leo Carrillo (LC), Laguna Beach (LB) and one climate station
near the Hong Kong site, Gold Coast (GC). The shaded region indicates the 95%
confidence interval for the linear model fit.
Fig. 2-5 Cu median lethal concentration (LC50, low/high 95%CI) of T. japonicus in dark
grey and T. californicus in light grey. Both species are arranged from highest to lowest
latitude. Error bars denote 95%CI. Letters above each bar indicate grouping. Populations
with two allocated letters have 95%CI that span two groups. GC (1030.8, 903.54/1178.42
µgL−1 Cu), MW (1356.58, 1132.6/1626.37 µgL−1 Cu), SKO (2438.28, 2069.23/2876.05
µgL−1 Cu), Stan (2344.11, 1690/3255.28 µgL−1 Cu), CDA (2265.21, 1608.55/3189.11
µgL−1 Cu), SC (294.24, 239.25/363.96 µgL−1 Cu), SCI (649.02, 587.94/715.19 µgL−1
Cu), LC (458.13, 397.05/529.4), LB (534.49, 470.86/608.3 µgL−1 Cu), SD (478.49,
422.5/544.67 µgL−1 Cu).
Fig. 2-6 TBTO median lethal concentration (LC50, low/high 95%CI) of T. japonicus in
dark grey and T. californicus in light grey. Both species are arranged from highest to
lowest latitude. Error bars denote 95%CI. Letters above each bar indicate grouping.
Populations with two allocated letters have 95%CI that span two groups. GC (70.02,
63.58/77.12), MA (68.82, 62.98/75.2), SKO (75.69, 70.11/81.72), Stan (90.07,
84.06/96.50), CDA (49.28, 45.68/53.16), SC (43.47, 38.9/48.59), SCI (39.71,
34.78/45.33), LC (66.92, 63.41/70.64), LB (62.75, 57.65/68.30), SD (56.06,
48.56/64.72).
Fig. 2-7 Percent survival following higher temperature stress in two T. californicus
populations, CAT and LB, and 2 T. japonicus populations, SKO and GC, over 5
temperatures. T. californicus is shown by the lighter patterns while T. japonicus is shown
by the darker patterns.
Fig. 3-1 Multi-generational population tolerance survey. (A) Tigriopus californicus and (B)
Tigriopus japonicus 96hr Cu LC50 values were determined using a generalized linear model
with a binomial distribution. Cu concentrations are long10 transformed. (C) T. californicus
and (D) T. japonicus 96hr TBTO LC50 values were calculated using Sperman-Karber method.
(w) Denotes 1 significant consecutive increase in LC50. (ww) Denotes 2 significant
consecutive increases in LC50s. () Denotes 1 significant consecutive decrease in LC50s. ()
Denotes 2 significant consecutive increases in LC50s. (x) Denotes a lack of significant change
in LC50 between generations or that the change was not unidirectional. Significance between
LC50s determined by non-overlapping 95% confidence intervals (see Wheeler et al., 2006).
There was no mortality data available for SCI, Gen 0.
Fig. 3-2 Consecutive ultraviolet irradiation and contaminant exposure assay. LC50 values for
copper (A) and TBTO (B), with error bars showing 95% CI. LC50 assays were conducted at
20°C for 96h on animals from one of two treatments: 1) Control: constant 20°C or 2) UV:
1
Introduction
Nothing in biology makes sense except in the light of evolution (Dobzhansky,
1973). Understanding how evolution can influence pollution tolerance is critical in
establishing appropriate policies and regulatory practices directed toward data based
management of our natural resources. Often, there is a lack of consideration regarding
how species may evolve in response to novel environments. Additionally, the study of
stress response is often limited to within a single lifetime of an organism and rarely
encompasses several generations to incorporate evolutionary processes.
It is critical to have effective and informed regulatory practices that intergrade
evolution in light of the increasing anthropogenic pressures exerted on our environment,
particularly coastal regions where population density is expected to increase at greater
rates than other types of regions (Neumann et al., 2015). Additionally, we also currently
face an unprecedented loss of biodiversity (Ceballos et al., 2015), which further
emphasizes the importance of proper management of environmental resources, such as
setting appropriate regulations for preserving water quality criteria. Bioassays, which are
a foundational tool to inform environmental management practices, use test species to
determine allowable pollutant thresholds.
This body of work on the evolution of pollution tolerance in the marine copepod
Tigriopus, focused on characterizing the evolved differences in pollution tolerance, how
tolerance can change and evolve in a short time scale, and how the environment
contributes to pollution tolerance. This copepod is an ideal system to address such a
question due to its extensive use in pollution toxicity research (Raisuddin et al., 2009)
and genetic differences between populations (Edmands, 2001; Edmands and Harrison,
2
2003; Ki et al., 2009) that contribute to phenotypic differences between populations such
as thermal tolerance (Willett, 2010).
This work focuses on two marine antifouling additives, which have become
pollutants of concern copper (Cu) and tributyltin oxide (TBTO). The marine pollutant Cu
is a heavy metal that serves as a micronutrient, evident by its role in the oxygen-binding
protein hemocyanins found in, but not limited to, copepods (Holde et al., 2001).
However, Cu is toxic at high concentrations (White and Rainbow, 1985). There is rising
concern regarding Cu pollution in marine environments shown by state senate bills from
California (California State Senate Bill 623) and Washington (Washington State Senate
Bill 5436) both aimed at significantly restricting the use of copper based antifouling
paints on recreational boats (Johnson, 2011). Likewise, TBTO was shown to be
extremely detrimental to aquatic systems and was thus banned (IMO 2001), but because
of terrestrial input and legacy contamination still has a presence in marine systems
(Burton et al., 2005; Díez et al., 2002). In contrast, TBTO is a man-made lipophilic
compound that has been shown to bioaccumulate (Mino et al., 2008) and is extremely
toxic even at low concentrations (Leung et al., 2001). These very different pollutants may
have very different results on Tigriopus.
Understanding whether there are evolved differences in pollution tolerance is
critical because these differences may result in the implementation of inappropriate water
quality standards. For instance, if the test population is significantly more tolerant to the
pollutant of concern than the native population, than this would lead to the establishment
of inappropriate environmental protection standards. In Chapter 2, our work examines
whether there is natural variation in pollution tolerance between and within sister species
3
of Tigriopus. In this chapter, there is also an effort to determine whether studies provide
appropriate detail that can be used to address biological variation in pollution tolerance.
This is done through a survey of research efforts and the level of collection details that
they provide and whether it would allow for appropriate replication and extrapolation of
their results. This chapter aimed to identify potential weakness of bioassays regarding the
way they are implemented and administered.
Characterizing how pollution tolerance changes over time through chronic
exposure can further augment the understanding of biological variation to pollution
tolerance. In Chapter 1, we examined the response to long-term pollution exposure over
the course of multiple generations of exposure. This chapter looked at whether the
response to pollution was acclimation or adaptation. Acclimation is a physiological
plastic response that is recruited within an individual’s lifespan. Though these effects are
generally non-heritable, in the sense that once the stressor is removed the individual loses
the tolerance to the stressor (Chevin et al., 2010). Likewise if offspring are born in the
absence of the stressor they also will lack the associated phenotype. In contrast, genetic
adaptation acts on the level of the population, when a stress like pollution can select for
genotypes that are better equipped to respond to their present condition. As a result, these
genotypes will persist in the new environment and contribute disproportionately to the
next generation. Adaptation occurs at the DNA level and as a result the effects can be
maintained in the absence of the original stressor and are only lost if selected against or
due to genetic drift. This chapter focused on how exposure history can influence pollution
tolerance.
4
Chapter 5 examined how patterns of gene expressions change in response to
different pollutants and for different periods of exposure, as a natural extension of
examining how pollution tolerance changes over time with multiple generations of
exposure. It has been previously documented that the transcriptome response to acute
stress is more generalized and similar across multiple types of stressors, whereas the
transcriptome response to chronic stress is more specific (Kovalchuk et al., 2007),
however this is only a single example. Further comparisons must be made to determine
whether this is a wide spread pattern in stress response transcriptomes. The goal of
Chapter 5 was to examine how pollutant stress induced transcriptomes change across
acute (96hr) and chronic (7 generations) exposures. The chronic duration was selected
based on results from Chapter 1 that showed that tolerance increased for both Cu and
TBTO. The focus of this chapter was to characterize the associated gene expression
patterns with the increase in pollution tolerance as well as to characterize how the
response to pollution can change over multiple generations of exposure.
In Chapter 3 we examined whether pollution tolerance changes once test
populations are removed from the field and maintained in the lab under common garden
(benign) conditions. There is an absence of a clear standard regarding minimum or
maximum duration of laboratory maintenance of test populations used in bioassays.
There is a potential for differences to develop between field and laboratory populations
over time due to their vastly different environments. As a result, extrapolating
experimental findings from the laboratory to the field might not be appropriate (Woods et
al., 1989; Nowak et al., 2007). Altered tolerance in laboratory test populations will lead
to inappropriate comparisons to wild populations, which would result in environmental
5
damage. In this chapter we also characterized how pollution tolerance changes when
particular environmental stressors are introduced into the lab in a search to identify
sources that may be shaping the pollution tolerance in the field. This work sought to
characterize the impact of multiple generations of laboratory culturing on pollution
tolerance.
Chapter 4 sought to identify whether Cu tolerance was a direct adaptation to high
Cu concentrations or a byproduct of adaptation to another environmental stressor. This
work builds off Chapter 3 and its examination of the impact of environmental stressors on
pollution tolerance in the lab and Chapter 2 that showed variation in pollution tolerance.
This chapter identified candidate factors that had significant impacts on pollution
tolerance in the lab and reflect the variation seen in pollution tolerance among different
populations.
Collectively, this work looks at how pollution tolerance changes across
geographical space and multigenerational time. The goal of this research effort was to
improve bioassays as a foundational tool for informing environmental management
practices by examining how their results can be significantly influenced.
Author contribution
The author (PS) was responsible for the majority of the work included in this
dissertation. In Chapter 1 the concept was conceived by SE and LH. The
multigenerational experiment was started by LH and maintained by AS, TK, HF, PS, and
SE. Data analysis was conducted by PS. Manuscript preparation was done by PS with
input from HF and SE.
6
In Chapter 2, the concept was conceived by SE. The experiment was conducted
by PS, HF, and VB. VB and KL advised on protocol. Correlation graph and heat stress
assays were done by HF. Literature survey and population survey data analyses were
done by PS. Manuscript preparation was done by PS with input from HF, VB, KL, and
SE.
In Chapter 3, the concept was conceived by PS and SE. The experiment was
conducted by PS, HF,VB, SC, LW, and CN. Protocol was advised by VB and KL. Data
analysis was done by PS. Manuscript was prepared by PS with input from HF, VB, KL,
and SE.
In Chapter 4, the experiment was conceived by PS and SE. The sample collection
and sample processing was done by PS. JM analyzed samples. Manuscript was prepared
by PS with input from JM and SE.
In Chapter 5, the experiment was conceived by PS and SE. AG assisted in data
interpretation. Experiment conducted by PS. The manuscript was prepared by PS with
input from SE.
References
Burton, E.D., Phillips, I.R., Hawker, D.W., 2005. In-situ partitioning of butyltin
compounds in estuarine sediments. Chemosphere 59, 585–592.
doi:10.1016/j.chemosphere.2004.10.067
Ceballos, G., Ehrlich, P.R., Barnosky, A.D., García, A., Pringle, R.M., Palmer, T.M.,
2015. Accelerated modern human–induced species losses: Entering the sixth mass
extinction. Science Advances 1, e1400253. doi:10.1126/sciadv.1400253
Díez, S., Abalos, M., Bayona, J.M., 2002. Organotin contamination in sediments from the
Western Mediterranean enclosures following 10 years of TBT regulation. Water Res. 36,
905–918.
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Dobzhansky, T., 1973. Nothing in Biology Makes Sense except in the Light of Evolution.
The American Biology Teacher 35, 125–129. doi:10.2307/4444260
Edmands, S., 2001. Phylogeography of the intertidal copepod Tigriopus californicus
reveals substantially reduced population differentiation at northern latitudes. Mol. Ecol.
10, 1743–1750.
Edmands, S., Harrison, J.S., 2003. Molecular and quantitative trait variation within and
among populations of the intertidal copepod Tigriopus californicus. Evolution 57, 2277–
2285.
Holde, K.E. van, Miller, K.I., Decker, H., 2001. Hemocyanins and Invertebrate
Evolution. J. Biol. Chem. 276, 15563–15566. doi:10.1074/jbc.R100010200
Johnson, L.T., 2011. Copper Antifouling Legislation - A Tale of Two States [WWW
Document]. ANR Blogs. URL
http://ucanr.edu/blogs/blogcore/postdetail.cfm?postnum=4758 (accessed 5.8.16).
Ki, J.-S., Lee, K.-W., Park, H.G., Chullasorn, S., Dahms, H.-U., Lee, J.-S., 2009.
Phylogeography of the copepod Tigriopus japonicus along the Northwest Pacific rim. J.
Plankton Res. 31, 209–221. doi:10.1093/plankt/fbn100
Kovalchuk, I., Molinier, J., Yao, Y., Arkhipov, A., Kovalchuk, O., 2007. Transcriptome
analysis reveals fundamental differences in plant response to acute and chronic exposure
to ionizing radiation. Mutat. Res-Fund. Mol. M. 624, 101–113.
doi:10.1016/j.mrfmmm.2007.04.009
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fishes and invertebrates: issue for saltwater ecological risk assessment. In: Newman,
M.C., Roberts, M.H., Jr., Hale, R.C. (Eds.), Costal and Estuarine Risk Assessment. Lewis
Publishers, Boca Raton, pp. 189–216.
Mino, Y., Amano, F., Yoshioka, T., Konishi, Y., 2008. Determination of Organotins in
Human Breast Milk by Gas Chromatography with Flame Photometric Detection. J.
Health Sci. 54, 224–228. doi:10.1248/jhs.54.224
Neumann, B., Vafeidis, A.T., Zimmermann, J., Nicholls, R.J., 2015. Future Coastal
Population Growth and Exposure to Sea-Level Rise and Coastal Flooding - A Global
Assessment. PLoS One 10, e0118571. doi:10.1371/journal.pone.0118571
Raisuddin, S., Kwok, K.W.H., Leung, K.M.Y., Schlenk, D., Lee, J.-S., 2007. The
copepod Tigriopus: a promising marine model organism for ecotoxicology and
environmental genomics. Aquat. Toxicol. 83, 161–173.
doi:10.1016/j.aquatox.2007.04.005
8
White, S.L., Rainbow, P.S., 1985. On the metabolic requirements for copper and zinc in
molluscs and crustaceans. Mar. Envir. Res. 16, 215–229. doi:10.1016/0141-
1136(85)90139-4
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5646.2010.01008.x
9
CHAPTER 1
*This chapter was previously published in the journal Chemosphere
Acclimation and Adaptation to Common Marine Pollutants in the Copepod Tigriopus
californicus
Patrick Y. Sun*, Helen B. Foley, Lisa Handschumacher, Amanda Suzuki, Tigran
Karamanukyan and Suzanne Edmands
Department of Biological Science and Wrigley Institute for Environmental Studies, University
of Southern California, Los Angeles, California, United States of America
*Corresponding author: Tel.: +1 213 740 8219; Fax: +1 213 704 8123; Mail: 3616 Trousdale
PKWY STE 107, Los Angeles, CA 90089; Email: patricys@usc.edu (P. Sun)
Abstract
Establishing water quality criteria using bioassays is complicated by variation in chemical
tolerance between populations. Two major contributors to this variation are acclimation and
adaptation, which are both linked to exposure history, but differ in how long their effects are
maintained. Our study examines how tolerance changes over multiple generations of exposure to
two common marine pollutants, copper (Cu) and tributyltin oxide (TBTO), in a sexually
reproducing marine copepod, Tigriopus californicus. Lines of T. californicus were chronically
exposed to sub-lethal levels of Cu and TBTO for 12 generations followed by a recovery period
of 3 generations in seawater control conditions. At each generation, the average number of
offspring produced and survived to 28 days was determined and used as the metric of tolerance.
Lines exposed to Cu and TBTO showed an overall increase in tolerance over time. Increased Cu
tolerance arose by generation 3 in the chronically exposed lines and was lost after 3 generations
in seawater control conditions. Increased TBTO tolerance was detected at generation 7 and was
maintained even after 3 generations in seawater control conditions. It was concluded from this
10
study that tolerance to Cu is consistent with acclimation, a quick gain and loss of tolerance. In
contrast, TBTO tolerance is consistent with adaptation, in which onset of tolerance was delayed
relative to an acclimation response and maintained in the absence of exposure. These findings
illustrate that consideration of exposure history is necessary when using bioassays to measure
chemical tolerance.
Keywords sub-lethal; copper; tributyltin; bioassay; tolerance; multigeneration exposure
1. Introduction
The use of bioassays to determine water quality criteria is complicated by intraspecific
variation in chemical tolerance. In aquatic environments, wild populations collected from sites
with past or present contamination are often found to be more tolerant to those contaminants than
wild populations from uncontaminated regions (Lopes et al., 2005; Agra et al., 2010; Agra et al
2011). Similarly, strains maintained in the lab for extended periods may also have sensitivities
that do not reflect tolerances of wild populations. Major contributors to inter-population variation
in chemical sensitivity include physiological acclimation and genetic adaptation. If a population
is responding through acclimation then even brief change to stress exposure can dramatically
alter its ability to respond (Kwok et al., 2009). In contrast, a population adapted to a particular
contaminant is generally expected to maintain its tolerance regardless of its current condition. As
a result, differences in toxicant sensitivity may remain even after several generations of
maintenance under common laboratory conditions. Whether differences in sensitivity are due to
acclimation or adaptation should be a central concern for understanding how sensitivity can
differ and change over time between populations.
11
Acclimation is a plastic physiological change driven by an external factor that can occur
within a single generation at the level of an individual. As a plastic response, effects of
acclimation can be quickly gained when challenged with a condition and lost in the absence of
the condition. In the past, offspring were not generally expected to inherit an acclimation
response gained by their parents (e.g. LeBlanc, 1982; Kwok et al., 2009). However, acclimation
responses could be inherited as transgenerational epigenetic effects such as maternal effects
(Wolf and Wade, 2009) and methylation patterns (Vandegehuchte et al., 2009; Verhoeven et al.,
2010), but these effects are not consistently inherited and are often limited to one generation
(Youngson and Whitelaw, 2008). With acclimation, even short-term physical conditions in
laboratory cultures can substantially alter results of toxicity tests. In contrast, adaptation is a
slower evolutionary response that occurs at the population level with selection for genetically fit
individuals better suited to their local environment. As a result, these individuals will pass on
their genes disproportionately to the next generation (Klerks and Weise, 1987). Once the
selective force is removed, tolerance is maintained for different lengths of time depending on the
strength of selection against the adapted individuals in the absence of the stressor.
Tigriopus californicus is a harpacticoid copepod, native to supra-littoral tidepools ranging
from Baja California to Alaska (Edmands, 2001). As a result of their wide distribution, different
populations are likely exposed to a broad spectrum of environmental stresses allowing for local
adaptation, which has been previously shown for temperature (Willett, 2010; Kelly et al., 2012;
Schoville et al., 2012). These copepods are easily raised in the laboratory and have a fast
generation time (minimum of 23 days, Burton, 1987), making multi-generational experiments
feasible. Tigriopus spp. respond to a variety of relevant environmental toxicants through standard
toxicity end points such as LC
50
(Lee et al., 2007) life-history traits (Kwok et al., 2008; Lee et al.,
12
2008; Kwok et al., 2009), and through gene expression (Seo et al., 2006; Ki et al., 2009). These
characteristics make Tigriopus a tractable marine model for ecotoxicological studies.
The copepod T. californicus mates and reproduces year round. During mating, adult
males clasp virgin females with their antennules and mate guard until the female is sexually
mature. Males have multiple mating events whereas females mate only once, relying on the
original spermatophore to fertilize multiple clutches (Burton, 1985). Individual lifespan may be
up to 95 days and females may produce up to 20 egg clutches in their lifetime. The number of
offspring produced in each clutch varies from less than 10 to more than 100 nauplii (larvae)
(Vittor, 1971). The average generation time of T. californicus in our experiment was roughly 30
days.
In this study, two common marine pollutants were used: tributyltin (TBT) and copper
(Cu). TBT was a common additive in anti-fouling paints, but was found to be toxic even at low
concentrations (Leung et al., 2001). As a result, the International Maritime Organization has
banned its use in antifouling paints globally since 2008 (IMO 2001). Despite the ban, TBT is
still found in many marine environments at concentrations on par or higher than that used in our
experiment partly due to terrestrial input and long residence time in sediments (Dı
́ ez et al., 2002;
Arambarri et al., 2003; Bholse et al., 2004; Burton et al., 2005; Santos et al., 2010). As a known
endocrine disrupting chemical, TBT may be responsible for major changes to population
composition and reproductive success (Horiguchi, 1997; Huang, 2006; Huang, 2010) making
TBT an ideal chemical for multigenerational effect studies. This study uses the TBT compound
tributyltin oxide (TBTO).
13
Cu is a ubiquitous ion and a micronutrient, but it can be toxic at high concentrations
(White and Rainbow, 1985). The average dissolved Cu concentration in the surface waters of the
Southern California Bight was found to be relatively low, 0.09 ± 0.06 µgL⁻¹ (mean ± standard
deviation) (Smail et al., 2012). However, as a main ingredient of common antifouling paints used
worldwide, Cu has become a major contaminant in regions of high boat traffic. A comprehensive
review assessing the impact of Cu based antifouling paint examined 13 different studies of
California bays, harbors, and marinas from 1974 to 2002 and documented high Cu
concentrations in sediments ranging from 22 to 647 mg/kg (California Department of Pesticide
Regulation, 2005). Dissolved concentration in the water column in two local bays, San Diego
Bay (Sanders, 2005) and Newport Bay (Orange County Coastkeeper, 2007), have been shown to
be above the California Toxic Rule standards of 3.1 µgL⁻¹ for chronic exposure.
The aim of this study was to examine the time course of development and loss of
tolerance to Cu and TBT within a marine invertebrate. The experimental design was structured to
examine tolerance holistically through a multi-generation life-cycle test. An additional goal was
to determine whether these patterns of tolerance were consistent with either acclimation or
adaptation. To accomplish this we tracked the onset of tolerance in T. californicus through
multiple generations of exposure, as well as the status of tolerance post-exposure.
14
2. Materials and Methods
2.1. Test organism
T. californicus were collected January 2010 from tide pools at Ocean Beach in San Diego
(SD), California (32° 45′ 3.18″ N, 117° 15′ 6.13″ W) and maintained in the lab in filtered
autoclaved seawater (FASW) for 2 generations before establishment of multi-generational
exposures. To obtain FASW, seawater was collected from the University of Southern
California’s Wrigley Marine Science Center (WMSC) located in a marine reserve on Catalina
Island, CA, filtered through 37 µm mesh and autoclaved. Using specimen collected from SD,
exposure lines were established and maintained at 20°C with 12 h: 12 h light: dark photoperiod.
T. californicus was maintained on a diet of 0.04 g TetraMin (Tetra Holding, Inc., USA) and 0.04
g Spirulina (Nutraceutical Science Institute, USA). The three lines S – seawater control (i.e.,
FASW only), C – Cu chronic exposure (13.74 µg Cu L⁻¹, prepared in FASW), and T – TBTO
(0.15 µg TBTO L⁻¹, prepared in FASW) chronic exposure used in this experiment were
established from 500 copepod pairs taken equally from the original collection sample.
2.2. Chemical preparation
The final Cu and TBTO concentrations used in this study were calculated to be 10% of
the LC
50
values from a preliminary study performed on two-week old juveniles. This
concentration was chosen to limit mortality while still being potent enough to exert an
observable effect. The Cu and TBTO chronic solutions were used both for maintaining the multi-
generation lines and in fitness assays. The Cu stock solution was prepared by diluting CuSO₄ ·
15
5H₂O (Sigma) in nanopure water to a concentration of 1 gL⁻¹. The Cu stock solution was added
to FASW for the chronic Cu solution of 13.74 µgL⁻¹ before use. The TBTO stock solution was
made by diluting bis (tributyltin) oxide (EMD, USA) with acetone (Macron Chemicals, USA) to
a concentration of 0.004 gL⁻¹ and stored in the dark. The TBTO chronic solution was prepared
by adding TBTO stock solution to FASW for a final concentration of 0.15 µgL⁻¹ before use.
Both stock solutions were stored at 20° C, and replaced monthly.
2.3. Multi-generation selection experiment
Copepods were acclimated to laboratory conditions for 2 generations before the start of
the multi-generational selection experiment. Each line was started with 5 replicates of 100
clasped pairs per replicate and maintained for 14 generations (Fig. 1-1). For each generation,
each line was divided into 5 new replicates. In the case of a major mortality event in one of the
replicates, the remaining uncompromised replicates were used to seed the next generation. At
the start of a new generation, a total of 100 clasped pairs were taken equally from each of the 5
original (or all remaining extant) replicates to establish a replicate for the next generation. This
practice of mixing the replicates after every generation was done to maintain the effective
population size (N
e
) of the lines. N
e
is the size of an ideal population that would undergo the
same amount of random genetic drift as the actual population (Wright, 1931). Our goal was to
maintain a relatively large N
e
to limit the effect of genetic drift and maintain genetic variation for
selection to act upon.
When copepodites (juvenile copepods) grew to a visible size, the parents were removed
to avoid intergenerational breeding. When clasped pairs began to form, we randomly selected
16
500 clasped pairs that were then divided into 5 replicates to start the next generation. Each
replicate was kept in a polyethylene container at 400 ml and fed once a week. A complete
solution renewal was performed twice weekly. At generation 12, the T and C lines were moved
into seawater control conditions and their accompanying name change was TS and CS,
respectively (Fig. 1-1).
2.4. Fitness assays
The fitness of each line was estimated by measuring reproductive output. Fitness assays
were labeled with their respective line followed by x (FASW only), y (FASW + 13.74 µgL⁻¹
Cu), or z (FASW + 0.15 µgL⁻¹ TBTO) (Fig. 1-1). For example, Sx indicates a fitness assay
established with individuals from the control line in FASW only. Each fitness assay was started
with 1 clasped pair in an acid washed 30 mL Petri dish with 2 mg of food. A total of 25
replicates per line were incubated for 28 days in 20°C with 12 h: 12 h light: dark photoperiod. At
day 14, the adults were removed and the juvenile inhabitants of each dish were fed and deionized
H
2
O was added to maintain original volume in case of evaporation. At 28 days, living copepods
were counted in each fitness assay. For generations 3, 7, 12, and 15, each line had 3 sets of
fitness assays, corresponding to each treatment (x, y, and z as indicated above). For the
remaining generations, one set of fitness assays was done in its native conditions (e.g. Sx –
control line in FASW only).
17
2.5. Data analysis
Student’s t-tests were used to compare the results from fitness assays of the treated lines
(C and T) to the control line (S). To assess temporal changes within treatments, linear regression
was used to analyze fitness for generations 2-12 for each line in its native condition. To correct
for temporal changes in laboratory conditions, linear regression was also done for proportional
deviation of C in its native condition against S and similarly done for T in its native condition
against S.
3. Results
Linear regression analyses (Table 1-1) showed a temporal increase in tolerance for
bioassays that were pre-exposed to Cu (Cy) and TBTO (Tz), but there was no temporal change in
tolerance for the control line (Sx). Regressions focused on generations 2-12. Generation 1 was
omitted because all lines were taken directly from SD laboratory cultures and had yet to be
exposed. Generations 13-15 were also omitted due to absence of exposure to Cu and TBTO in
the Cy and Tz bioassays, respectively. For the control treatment (Sx) there was no overall
directional change in average offspring produced across generations 2-12. For the Cu treatment
there was a marginally significant increase in Cy over time and a highly significant increase in
proportional deviation from the control [(Cy-Sx)/Sx] over time. For the TBTO treatment there
was no significant change in Tz over time, but proportional deviation from the control [(Tz-
Sx)/Sx] showed a highly significant increase.
Results for Cu showed a temporary gain in tolerance that was lost after animals were
transferred to control conditions (Fig. 1-2). A comparison of the average number of offspring at
18
the 28 day counts in Cu exposure showed a significant difference for all S and C line
comparisons, but not for the S and CS comparison. There was a significant difference in the
average number of offspring produced between the S line and C line in generations 3, 7, and 12
with a higher mean in the C line for all three generations. By generation 15 the C line had been
moved into control conditions with an accompanying name change to the CS line. The
comparison between the S line and CS line showed no significant difference in estimated fitness.
Results for TBTO showed a gain in tolerance, which was maintained after animals were
transferred to control conditions (Fig. 1-3). There was no significant difference in mean number
of offspring between the S and T fitness assays exposed to TBTO for generation 3. Generations 7
and 12 showed a significant increase of the average number of offspring in T fitness assays
compared to S fitness assays. The average number of offspring between S and TS fitness assays
remained significantly different at generation 15.
4. Discussion
4.1. Inter- and intra-generational variation
The regression analysis showed the average number of offspring in Sx (the S line fitness
assays in FASW) did not have directional changes for generation 2-12 (Table 1-1). This
demonstrates that the directional changes in the contaminant treatments were not due to overall
temporal changes in laboratory conditions. Temporal variation in Sx with no overall directional
change is likely due to several factors such as nutrient composition of seawater and ambient
19
temperature in the lab. Nevertheless, within generation variation is expected to be minimal due to
consistency in seawater batches and exposure to ambient temperature in the lab among the three
lines at a given time.
The C line had a marginally significant intergenerational change seen as an increase in
offspring number for Cy over the course of the experiment with a highly significant increase for
Cy as a proportional deviation from the Sx. By comparing the proportional deviation of Cy from
Sx we are able to control for temporal changes in laboratory conditions that may mask changes
caused by chronic exposure.
For the T line there was no significant intergenerational change for the average number of
offspring in Tz. However there was a highly significant change for Tz as a proportional deviation
from Sx, suggesting that the average number of offspring produced in Tz is increasing with
successive generations. The pattern of increasing mean of Cy and Tz points to a gradual gain of
tolerance to Cu and TBTO, respectively.
4.2. Acclimation to copper
Our results showed that tolerance to Cu was rapidly gained in lines that had a pre-exposure to
Cu, but also rapidly lost once Cu exposure was removed (Fig. 1-2). This pattern of tolerance is
consistent with physiological acclimation. The increase in the average number of offspring for
Cy (Table 1-1) can be explained by a multigenerational acclimation, which has also been
documented in Daphnia magna (LeBlanc 1982; Bossuyt and Janssen, 2003). The observations in
these studies would explain a steady increase in Cu tolerance as each generation became better
20
acclimated to Cu. In line with acclimation, once D. magna was removed from Cu, they
completely lost their tolerance.
We made similar observations for T. californicus (Fig. 1-2). By generation 3, the C line had a
significantly increased mean number of offspring compared to the control line when both were
exposed to Cu, suggesting the development of Cu tolerance in the C line. Our data also show that
even with chronic Cu exposure up to generation 12, Cu tolerance remained a plastic response
that was quickly lost when the C line was moved into clean conditions. This finding is consistent
with other studies on Cu acclimation. Kwok et al. (2009) observed that a single generation of
exposure to Cu significantly increased Cu tolerance in a congener, T. japonicus, which was then
lost when the tolerant lines’ offspring were raised in the absence of Cu. Similarly, a study by
LeBlanc (1982) found that D. magna had gained tolerance to Cu within a single generation of
exposure and lost the acquired tolerance in the next generation after being moved into clean
conditions. These shorter multi-generational experiments with smaller population sizes have
shown Cu response to be a strictly plastic response. One of the goals of our experiment was to
increase exposure time and genetic variation in order to augment the ability of selection to act on
an adaptive response to Cu. However, our findings for long term exposure mirror the shorter
multi-generational experiments that used smaller population sizes.
4.3. Adaptation to tributyltin oxide
In contrast, comparisons between the T and S lines are consistent with genetic adaptation.
This is marked by a slow gain in tolerance and maintenance of tolerance in the absence of TBTO
exposure (Fig. 1-3). Observed differences between the TS line and S line after both were
21
maintained under the same condition for 3 generations is consistent with a genetically based
difference (Klerks and Weis, 1987). The delay in development of tolerance to TBTO is likely a
result of the time needed for selection to act on the population. Under this scenario, genotypes
with greater fitness to TBTO gradually increased in frequency as they out-produced other
genotypes. Although the actual mechanism(s) responsible for TBTO tolerance in T. californicus
is unknown, we speculate that maintenance of TBTO tolerance could be due to a tolerance
mechanism with a low energetic cost, which in turn elicits a weak selective response. This result
would explain how TBTO tolerance is maintained in the population in the absence of TBTO and
how it would be maintained until lost to genetic drift or broken up through sexual recombination.
Maternally and grand-maternally transferred cytoplasmic components can be ruled out as
possible sources of TBTO tolerance because of the 3 generations the T line spent in clean
conditions before assessment. Maternal effects, which should be induced after one generation of
toxicant exposure, also fail to explain the slow gain in tolerance. The TBTO concentration used
was a strong enough selective force to generate a response consistent with a genetic adaptation
by the 7
th
generation of exposure. Our data show that chronic exposure to a low concentration of
TBTO can elicit a response consistent with adaptation in T. californicus within a few generations
of exposure.
Adaptation to tributyltin in a midge Chironomus riparius showed a similar pattern of a slow
gain of tolerance, where tolerance only appeared after 9 generations of exposure (Vogt et al.,
2007). However, the main goal of this prior work was not to differentiate between adaptation and
acclimation, therefore the study did not examine whether or not increased tolerance was
maintained in the absence of tributyltin exposure. The observed tolerance could be due to
adaptation, acclimation, or a combination of both. Showing that T. californicus maintains
22
tolerance after being removed from TBTO exposure indicates that tolerance is consistent with a
heritable genetic adaptation. These data also suggest that adaptation to TBTO concentrations
equivalent to levels found in the field can occur in a laboratory setting in a relatively short
amount of time.
4.4. Differences in tolerance
The cost of tolerance is likely the determining factor to whether tolerance is lost or
maintained in the absence of contamination. Cu tolerance has been documented to carry a
relatively high energetic cost (Lukasik and Laskowski, 2007; Kwok et al., 2009; Agra et al.,
2010; Agra et al., 2011) which likely results in a strong selective pressure against maintaining
expression of genes associated with Cu tolerance in control conditions. Cu levels in the supra-
littoral tidepools may fluctuate widely, depending on various environmental parameters (e.g. rain
events), so T. californicus may have undergone selection for a tolerance mechanism that could be
switched on and off quickly to cope with the rapidly changing environment (i.e. plastic
acclimation response). In contrast, TBTO tolerance may have a lower energetic cost, allowing it
to be maintained in the absence of exposure. Alternatively, genotypes with TBTO tolerance
could be maintained due to the absence of strong negative selection.
The tolerance mechanisms for Cu and TBTO are likely very different as a result of the
different properties of each chemical, but there is overlap. A common defense, heat shock
proteins are recruited with both Cu (Boone and Vijayan, 2002; Rhee et al., 2009; Gou et al.,
2012) and tributyltin exposure (Cochrane et al., 1991; Oberdörster et al., 1998). Additionally,
oxidative stress has also been traced to both Cu (Stohs and Bagchi, 1995; Rhee et al., 2011; Rhee
23
et al., 2013) and tributyltin (Ishihara et al., 2012). An overlap in tolerance has been found in
sediment bacteria that were originally selected for their tolerance to tributyltin, but were also
found to be resistant to Cu (Pain and Cooney, 1998).
Different tolerance responses to these two toxicants could be explained in light of T.
californicus’ evolutionary history of exposure. Cu is a naturally occurring heavy metal and a
micronutrient in marine invertebrates (White and Rainbow, 1985; Hernández and Allende,
2008). T. californicus has had a long evolutionary history with Cu, evident in its requirement of
Cu in critical proteins such as hemocyanins. This long existing relationship could have led to the
evolution of an optimal response to variable Cu levels that can be quickly activated when Cu
levels dramatically increase and deactivated in periods of low Cu concentrations to conserve
energy. Unlike Cu, TBTO is solely an anthropogenically derived compound to which T.
californicus has a comparatively shorter evolutionary relationship. Given the short period of
interaction, we speculate that the majority of the original T. californicus population lack
tolerance mechanisms to TBTO. The gain of TBTO tolerance in a few generations suggests that
the mechanism responsible for tolerance likely existed in a small subset of individuals within the
population prior to the experiment. TBTO tolerance only came into prominence in the population
after prolonged TBTO exposure led to selection of TBTO resistant genotypes.
4.5. Conclusion
We find that T. californicus’ multigenerational response to Cu is consistent with acclimation,
while the multigenerational response to TBTO is consistent with adaptation. Cu tolerance was
lost quickly after a transfer to clean conditions, while TBTO tolerance was maintained. A linear
24
regression analysis showed that the observed increase in mean offspring production across
generations in the contaminated treatments was not due to changing laboratory conditions.
Although Cu and TBTO are just two toxicants out of the many that contaminate marine
environments, they serve as useful representatives of general types of marine pollutants, such as
those that do naturally occur at low concentrations and those that are strictly anthropogenic in
origin and can be temporally or spatially prevalent due to heightened anthropogenic
activity/impact, such as boat traffic. Understanding how pollution tolerance can be altered by
exposure history is crucial for the effective use of bioassays in determining if established water
quality criteria offer sufficient protection.
5. Conflict of interest
Authors state no conflict of interest.
6. Acknowledgements
This work was supported by Sea Grant (NA10OAR417005). The authors would like to thank Dr.
Vivien W.W. Bao and Dr. Kenny Leung for their assistance, training and support regarding
ecotoxicological techniques and protocol. The authors would also like to thank the two
anonymous reviewers and Dr. Bao for their recommendations on the manuscript.
25
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30
Fig. 1-1 Experimental design for multi-generation selection experiment. The SD square
represents the initial sample collected from San Diego, California. Each subsequent square
represents a generation for a particular line. The seawater (S) line was maintained for 14
generations and the copper sulfate (C) and TBTO (T) lines were maintained for 11 generations.
After 11 generations, the C and T lines were transferred to seawater (CS and TS) for the final
three generations. Each circle represents fitness assays for a total of 25 copepod pairs (male plus
virgin female), with each pair housed in its own Petri dish. Fitness assays are annotated with
either x, y, or z to signify seawater, Cu, or TBTO treatments, respectively.
C1
Treatment S: Seawater
C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 CS12 CS13 CS14
Treatment C: Copper sulfate added
T1 T2 T3 T4 T5 T6 T7 T8 T9 T10 T11 TS12 TS13 TS14
Treatment T: TBTO added
seawater seawater + copper seawater + TBTO
SD
S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 S13 S14
x x x x x x x x x y z x x x y z
y y y y y y y y y y x y z x y z
z
z z z
z z x y z z z z z x y z
x y z
x y z
x y z
x y z
x y z
x y z
31
Table 1-1 Regression statistics of fitness assays from generations 2-12 for each line in its
ambient condition and proportional deviation of C and T lines from the control (S) line.
Treatments Description r p-value
Sx S line in control condition (x) fitness assay
0.034
0.924
Cy C line in Cu exposure (y) fitness assay
0.64
0.046
(Cy-Sx)/Sx
Proportional deviation of C line from
control
0.98
6.08 x 10⁻⁰⁷
Tz T line in TBTO exposure (z) fitness assay
0.19
0.582
(Tz-Sx)/Sx
Proportional deviation of T line from
control
0.99
1.86 x 10⁻⁰⁷
$
32
Fig. 1-2 Fitness assays (Mean ± SE) for lines S, C, and CS exposed to 13.74 µgL⁻¹ Cu. Offspring
in fitness assays were counted 28 days after they were established. Offspring from parents reared
in Cu (C) and exposed to Cu during the fitness assay are indicated in grey. Offspring from
parents reared in control conditions (S and CS) and exposed to Cu during the fitness assay are
indicated in white. The t-test values are as follows: *p<0.05, **p<0.01, ***p<0.001. Non-
significant values denoted by ns.
0
10
20
30
40
50
60
Gen 3 Gen 7 Gen 12 Gen 15
Average offspring per female
Generation
S C
S
C
C
S
S CS
** *** *
ns
33
Fig. 1-3 Fitness assays (Mean ± SE) for lines S, T, and TS exposed to 0.15 µgL⁸¹ TBTO.
Offspring in fitness assays were counted 28 days after they were established. Offspring from
parents reared in TBTO (T) and exposed to TBTO during the fitness assay are indicated in dark
grey. Offspring from parents reared in control conditions (S and TS) and exposed to TBTO
during the fitness assay are indicated in white. The t-test values are as follows: *p<0.05,
**p<0.01, ***p<0.001. Non-significant values denoted by ns.
0
10
20
30
40
50
60
70
Gen 3 Gen 7 Gen 12 Gen 15
Average offspring per female
Generation
TS S
T
T
T S
S
S
*
**
***
ns
34
CHAPTER 2
*This chapter has been previously published in the Journal of Environmental Science and
Pollution Research
Variation in tolerance to common marine pollutants among different populations in two
species of the marine copepod Tigriopus
Patrick Y. Sun
1*
, Helen B. Foley
1
, Vivien W.W. Bao
2
, Kenneth M. Y. Leung
2,3
, Suzanne
Edmands
1
1
Department of Biological Science and Wrigley Institute for Environmental Studies, University
of Southern California, Los Angeles, California, United States of America
2
The Swire Institute of Marine Science and School of Biological Sciences, The University of
Hong Kong, Pokfulam, Hong Kong, China
3
State Key Laboratory in Marine Pollution, City University of Hong Kong, Kowloon, Hong
Kong, China
*Corresponding author: Sun, P.Y. 3616 Trousdale PKWY STE 107, Los Angeles, CA 90089
Tel: 1-213-740-8219, Email address: patricys@usc.edu
wThe authors assure that all procedures were performed in compliance with national and
institutional guidelines for the protection of animal welfare.
Abstract
Geographical variation in chemical tolerance within a species can significantly influence
results of whole animal bioassays, yet a literature survey showed that the majority of articles
using bioassays did not provide detail on the original field collection site of their test specimens
confounding the ability for accurate replication and comparison of results. Biological variation as
a result of population specific tolerance, if not addressed, can be misinterpreted as experimental
error. Our studies of two marine copepod species, Tigriopus japonicus and T. californicus, found
significant intra- and inter-specific variation in tolerance to copper and tributyltin. Because both
species tolerate copper concentrations orders of magnitude higher than those found in coastal
35
waters, difference in copper tolerance may be a byproduct of adaptation to other stressors such as
high temperature. Controlling for inter-population tolerance variation will greatly strengthen the
application of bioassays in chemical toxicity tests.
Key Words: copper; tributyltin; Tigriopus japonicus; Tigriopus californicus; bioassay
1. Introduction
Bioassays are widely used in environmental risk assessment (ERA). A common approach
is to use the response of a sentinel species as a biomarker of stressors like chemical
contaminants. Bioassays have proven to be an effective and powerful ecotoxicological tool in
assessing environmental impact of chemical contaminants. However, ERA can be complicated
by variation in chemical tolerance within a species (Berthet et al. 2011). Distinct populations
could have unique chemical tolerances tailored by their specific environments. Variation in other
environmental factors could also generate variation in chemical tolerance between populations
and lead to misleading ERA results.
Controlling for potential variation in chemical tolerance among populations is not widely
applied in ecotoxicology. The majority of ecotoxicology publications lack collection site details.
Even scarcer are GPS coordinates for accurate resampling of a particular population. Without a
well-defined collection location, researchers may encounter challenges in replication and
interpretation of bioassay data associated with significant inter-population variation in chemical
tolerance. A literature survey (Figure 2-1) found the majority of test species used in toxicity
bioassays used had no details regarding their original collection site. Only 11% of all test species
surveyed had the ideal level of detail for their collection site(s), which included location along
with GPS coordinates, while 24% of test species had only listed a collection site by name. In
36
total, 35% of test species surveyed provided some level of detail on the original collection site.
An average of 1.4 different test species per article indicates that under reporting of collection site
detail is not isolated to a few articles using a large number of types of test species but rather a
wider practice. Although this small survey does not encompass the entirety of toxicity research,
these results still provide strong evidence that collection sites information is frequently not
reported, as only 35% of the articles included details on the original collection site.
Variation in chemical tolerance has been found in species commonly used in ERA.
Various crustaceans are known to have significant variation in their degree of chemical tolerance
with and without direct chemical exposure. A population of Daphnia longispina collected from a
copper (Cu) contaminated site had higher Cu tolerance than populations from uncontaminated
sites (Agra et al. 2009). Different populations within a species also have significant variation in
copper (Cu), zinc, and cadmium tolerance without any previously documented exposure (Sarabia
et al. 2002, Lukkari et al. 2004). The current study surveyed populations in two species of the
marine crustacean Tigriopus, a genus of intertidal marine copepods that have been proposed as a
model system for ecotoxicolgical studies such as characterization of stress response and pollution
monitoring (Raisuddin et al. 2007). As an additional benefit, Tigriopus populations are globally
distributed and span a very wide range of genetic distances, enabling population comparisons
across a broad spectrum of geographic and evolutionary divergence (Edmands et al. 2001; Ki et
al. 2009; Peterson et al. 2013). This facilitates analysis of chemical tolerance for geographical
trends in variation, such as the one seen for temperature (Willett 2010), which would give insight
into the potential cause of variation. Our study compared two congeners, Tigriopus japonicus
(Tj) and T. californicus (Tc), and five conspecifics (i.e., populations) within each species. The
two species are closely related: no diagnostic phenotypic differences have been reported, and
37
phylogenetic reconstruction based on ribosomal DNA (Ki et al. 2009) and mitochondrial DNA
(Peterson et al. 2013) did not resolve the species into monophyletic clades. A study of
phenotypic variation in Tc did not detect diagnostic differences among populations, but did show
a pattern of larger body size and faster development rate in more northern populations (Edmands
and Harrison 2003).
This study focused on two marine contaminants, copper (Cu) and tributyltin (TBT), each
with different relationships with Tigriopus. At low concentrations, Cu is an essential
micronutrient that serves key biological functions such as reversibly binding oxygen in
hemocyanins in many invertebrates, but Cu is toxic at high concentrations (Philips and Rainbow
1993; Kwok and Leung 2005). As a result, Cu is one of the main antifouling paint additives in
major use since the ban of TBT as an antifouling additive (Kwok et al. 2008). As a synthetic
organotin compound, TBT does not occur naturally. This compound has been widely used as an
antifouling biocide and a wood preservative only since the 1960s (Champ and Seligman 1996).
Though there has been a ban on TBT as an antifouling additive, adopted in 2001 by the
International Maritime Organization, it is still commonly found in coastal marine environments
as a result of long retention in sediments and terrestrial input (Dı
́ ez et al. 2002; Burton et al.
2005; Santos et al. 2010). There are several TBT salts that are commonly used; however, this
study focuses on bis(tributyltin) oxide (TBTO). Tigriopus has a long standing relationship with
Cu evident by its incorporation of Cu as a micronutrient, while TBT serves no known biological
function and has only been relatively recently introduced into marine environments.
The aim of the study was to test for differences in chemical tolerance due to long
standing differences between species and populations. Variation in chemical tolerance within a
species can be attributed to either genetic or physiological changes. Genetic changes occur at the
38
level of a population and result from changes in a population’s genetic composition over the
course of several generations. A physiological change occurs at the level of an individual and can
develop quickly, such as within an organism’s lifetime (Qiu and Qian. 1999). There are
examples of delayed effects of toxicants on individuals, even if exposed as embryos (Heintz et
al. 2000; Weis 2014). To eliminate any short-term physiological changes, this experiment
adopted the common laboratory practice of acclimating field-collected specimen for one
generation in the lab prior to toxicity testing to eliminate any short-term physiological changes.
Initial results showed chemical tolerance far exceeding chemical concentrations the
copepods are believed to experience, prompting the hypothesis that other environmental stressors
may indirectly select for increased chemical tolerance. There are many candidate stressors in
intertidal habitats including temperature, salinity, pH, dissolved oxygen and UV radiation. As a
preliminary test of the hypothesis, we conducted additional studies on temperature tolerance.
High temperature stress is known to be a particularly important factor in limiting the tidal
zonation and biogeographic range of intertidal organisms (Somero 2002; Helmuth et al. 2006).
Further, Tc populations show strong differentiation in high temperature tolerance (Willett 2010,
Kelley et al. 2012), suggesting that temperature may be an important selective factor. Our study
found differences in chemical tolerance among the geographically dispersed CA populations
(maximum of ~640km between sites), as well as among the more geographically proximate HK
populations (maximum of ~34km between sites). Even over short geographic distances,
populations may still experience different thermal regimes due to differences in intertidal rock
type, topography and shading. Still, it is certainly true that other environmental factors (such as
radiation, salinity, pH, and DO) may also play a role, especially in explaining micro-geographic
differences in tolerance.
39
The overarching goal of this study was to test for geographical variation in pollution
tolerance, a pattern that would have important implications for how bioassays should be used to
inform policy and regulation. Management practices based on test groups with high chemical
tolerance would be detrimental to the native flora and fauna that might be more sensitive. Also,
natural biological variation in tolerance could be misinterpreted as experimental error, thereby
falsely eroding confidence in the accuracy of bioassays. Temperature tolerance was also
examined in to identify patterns between pollution tolerance and another environmental stressor
that could be contributing to variation in pollution tolerance. Unrecognized variation in tolerance
among test specimens could lead to misinterpretation of toxicity results and establishment of
inappropriate management practices.
2. Materials and methods
2.1 Specimen collection and maintenance
Five Tj populations were collected from Hong Kong in November 2011 and transported to the
University of Southern California (USC), US. The sites of collection were Gold Coast (22° 22'
47", 113° 58' 48") (GC), Ma Wan (22° 21' 0", 114° 3' 36") (MW), Shek O (22° 13' 48", 114° 15'
36") (SKO), Stanley (22° 13' 11", 114° 13' 11") (Stan), and Cape D’Aguilar (22° 12' 36", 114°
15' 36") (CDA) (Figure 2-2). Five populations of Tc were collected from California, US in
November 2011. The collection sites were Santa Cruz (36° 57' 0", -122° 2' 59") (SC), Santa Cruz
Island (34° 1' 12", -119° 40' 48") (SCI), Leo Carrillo (34° 2' 23", -118° 56' 23") (LC), Laguna
Beach (33° 32' 23", -117° 47' 24") (LB), and San Diego (32° 45' 0", -117° 15' 36") (SD) (Figure
40
2-3). A population collected from Catalina Island (33° 27' 0", -118° 29' 24") (CAT) was used
only for the heat stress assay.
All populations were maintained in 37 µm filtered autoclaved seawater (FASW) at 20°C
with a 12 h light: 12 h dark photoperiod for one full generation before testing. Long-term
cultures were maintained in Nalgene containers at a volume of 400 mL with a complete solution
renewal and feeding performed once a week. Cultures were fed 0.04 g TetraMin (Tetra Holding,
Inc., USA) and 0.04 g Spirulina (Nutraceutical Science Institute, USA). In acute toxicity tests
and heat stress assays, only adult males were used, because males are generally more sensitive to
stress than females (Raisuddin et al. 2007; Kelly et al. 2012; P. Sun, unpub. data). Additionally,
female tolerance to stress potentially fluctuates depending on whether they have been mated or
stage of their egg sac. Females pass on pigmentation and other materially derived components to
their offspring (Raisuddin et al. 2007), which potentially can increase their susceptibility to stress
right after an egg sac is released or increase their tolerance of a portion of a chemical stressor is
maternally deposited in the eggs decreasing the body burden on the female.
2.2 Acute toxicity tests
Median lethal concentration (LC
50
) assays were conducted for 96 h without feeding or solution
renewal in FASW. The assays had three replicates of 10 males for a total of 30 individuals per
concentration. Mortality was assigned to individuals that did not respond to gentle prodding. The
range of Cu and TBTO concentrations used were based on preliminary LC
50
values, which were
obtained from pilot range-finding tests (data not shown). The Cu stock solution was made with
CuSO₄ · 5H₂O (Sigma) in nanopure water to a concentration of 1 gL⁻¹ and serially diluted to
obtain the target concentration. The concentrations used for Tj were 0, 127, 255, 509, 1018,
41
2036, and 3054 µgL⁻¹ Cu. The concentrations used for Tc were 0, 127, 255, 509, 1018, and 2036,
µgL⁻¹ Cu. The TBTO stock solution was made by diluting bis (tributyltin) oxide (EMD, USA)
with acetone to a concentration of 0.1 gL⁻¹. The concentrations used for both Tj and Tc were
acetone control (i.e., 0), 20, 40, 60, 80, 120 µgL⁻¹ TBTO. The amount of acetone in all treatment
solutions was ≤ 1.2 x 10
-6
v/v.
2.3 Heat stress assays
Thermal tolerance was measured by proportion of mortality at each temperature. Each
temperature test had a 2-hour ramp up from ambient temperature and a 1 hour hold at the
specific temperature followed by a 1 hour recovery period at 20°C before assessment.
Temperatures for the heat stress assay were 37.3, 38.5, 39.5, 41.5, and 42.5°C. Not all
populations produced enough males for the additional heat stress assays and were therefore
omitted from these assays. An additional population, CAT was a smaller collection, which
lacked sufficient males for the toxicity tests, but did have enough males for the heat stress assays.
The number of copepods tested at each temperature was limited by the number of males in each
population available at the time of testing. A total of 4 populations were tested, two Tj
populations (SKO and GC) and two Tc populations (LB and CAT). For temperatures 37.3 and
38.5°C, 16 males were used per population. For temperatures 39.5 and 41.5°C, 24 males were
used. In 42.5°C, 8 males were used. For all tests, males were individually deposited into wells of
a 96-well plate with 200µL filtered seawater and sealed to reduce evaporation during heating,
Peltier Thermocycler (PTC-200, MJ Research, USA).
42
2.4 Data Analysis
Based on the mortality data obtained from the acute toxicity assays, Trimmed Spearman –
Karber Program v. 1.5 obtained from the U.S. Environmental Protection Agency was used to
calculate LC
50
values and their respective 95% confidence intervals (95%CI). LC
50
values were
determined to be significantly different if their 95%CI did not overlap. This method of
comparison has been shown to be very conservative, often only able to identify highly significant
differences (Wheeler et al. 2005). However, this method is appropriate for this study because we
sought to find extreme differences between populations. Chi square tests were used to determine
whether mortality in the heat stress assays differed significantly among the four tested
populations.
Average daily maximum temperature near collection sites was obtained from the
NOAA National Climatic Data Center, Global Summary of the Day (www.ncdc.noaa.gov) and
correlated with each population's copper LC
50
(Figure 2-4). For all sites, the climate station was
chosen based on proximity to the collection site and whether it had the recorded temperature data
for the 12 months prior to collection, December 2010 to November 2011. For the California sites
the following climate stations with the respective collections were used: #998173 (SC), #723910
(LC), #722910 (SCI), #722970 (LB), and #994018(SD). For Hong Kong, only one station had
the appropriate data, #450070 (GC). Mean daily maximum temperature was analyzed at each
station using R (R Core Development Team. 2013) and package "MASS" (Venables et al. 2002).
A linear regression analysis was done comparing temperature data and Cu LC
50
data of all
corresponding populations and a separate regression analysis compared sites within CA
only. Temperature data from coastal climate stations was chosen over ocean temperature
obtained from moorings for two reasons. First, we believe temperature in tide pools are better
43
reflected by air temperature than ocean temperature. This is due to their small volumes, position
in the supralittoral zone, and inconsistent rehydration mainly through sea spray rather than
inundation by wave action, which decreases the influence of incoming seawater on tide pool
temperature. Second, the location of moorings was sparse relative to coastal climate stations. In
several cases, this resulted in mooring sites being at a much greater distance from the collection
site than the nearest coastal station.
3. Results
In the acute toxicity tests, the control treatments experienced > 99.99% and 100% survival for Tj
and Tc, respectively. There is a clear segregation of LC
50
values between species and variation in
tolerance within each species for Cu. Populations of Tj show an overall higher tolerance to Cu
than its congener Tc (Figure 2-5). The average LC
50
value for Tj is 3.9 times higher than that for
Tc. The most tolerant Tj population, SKO has an LC
50
value 2.4 times higher than the least
tolerant Tj population, GC. The most tolerant Tc population, SCI, has an LC
50
value 2.2 times
higher than the least tolerant Tc population, SC. Though there is a large numerical difference in
LC
50
values between the two species, the pattern of tolerance within each species is very similar.
There is roughly a 2-fold difference between the most and least tolerant population.
Tolerance to TBTO is not as distinct as Cu tolerance between the copepod species. On
average LC
50
values are 1.3 times higher in Tj than Tc (Figure 2-6). The mean LC
50
values of the
majority of Tj and Tc populations overlap with a population from the other species with the
exception of the most tolerant (Stan) and least tolerant population (SCI).
44
Thermal tolerance was found to be significantly different between the two species.
Differences in survivorship within each species across all temperatures, Tj (χ
2
< 4.60, p > 0.05)
and Tc (χ
2
< 3.08, p > 0.05), were not significant (Figure 2-7). However heat stress survivability
between Tj and Tc was significantly different across treatments (χ
2
> 22.42, p <0.001). For 5 CA
sites and one Hong Kong site, a linear model for maximum daily air temperature near collection
sites and Cu tolerance of the resident copepod population was found to be positive and
significant (R
2
= 0.6072, F-statistic = 14.91, p = 0.004796). Among only the 5 CA sites the linear
regression between site temperature and Cu tolerance was not significantly better than the null
model. (R
2
= 0.03989, F-statistic = 0.1246, p = 0.7474)
4. Discussion
The results of the literature survey showed that the majority of test species considered did not
have details regarding where they were originally collected. Our results clearly show significant
variation in tolerance of Cu and TBTO among different populations of the same species of
Tigriopus towards Cu and TBTO. However, it has been shown that Tigriopus generally has low
variability and high repeatability in measurements of acute and life cycle toxicities within the
same population (Kwok et al. 2008). Inter- and intra-population differences in chemical tolerance
are not unique to Tigriopus. Some of the strongest examples come from the ecotoxicological
model system, Daphnia. Differences to Cu tolerance within populations have been observed in
Daphnia longispina with differences as great as 7x between the most sensitive and most tolerant
clonal lineages (Martins et al. 2007). Differences between species also have been found in
Daphnia (Winner and Farrell 1976). Providing accurate collection details will, therefore, allow
45
for accurate interpretation of bioassay results when comparing two different populations or allow
for accurate resampling of a single population for appropriate comparisons between experiments.
Among all populations assessed, Cu tolerance is higher in Tj than Tc without any overlap
in tolerance between the two species. Higher Cu tolerance can be attributed to either acclimation,
a physiological plastic response that can occur within an individual’s lifetime, or adaptation, an
adaptive heritable response that occurs at the population level over generations (Berthet et al.
2011; Mouneyrac et al. 2011). Both Tj and Tc have been shown to acclimate to Cu (Kwok et al.
2009; Sun et al. 2014). However, the current study controlled for potential acclimation to
different field conditions by maintaining all populations in the lab for one full generation (about
21 days) before testing. Additionally, laboratory cultures of Tigriopus did not show a heritable
adaptive response (but only phenotypic plasticity) after three generations of exposure (Kwok et
al. 2009) or a longer 12 generations of exposure (Sun et al. 2014). Although adaption to Cu could
have occurred over longer periods, such as hundreds of generations, Cu tolerance in Tigriopus
does not appear to be a direct adaptation to elevated Cu exposure. This is because dissolved Cu
concentrations in the coastal waters are orders of magnitude lower than the Cu LC
50
values
obtained in this study. Cu tolerance seems to be an exaptation, a trait that has evolved for one
role but subsequently has come to fill another role (Gould and Vrba 1982).
Measured levels of dissolved Cu in the surface waters adjacent to these Tigriopus
populations are relatively low compared to their Cu tolerance. The average Cu LC
50
value is
31,000 and 5,000 times greater than coastal Cu concentrations for Tj and Tc, respectively.
Surface water in the South China Sea had a measured concentration of 0.06 µgL⁻¹ (Wen et al.
2006). A close examination of the shelf waters near Hong Kong showed dissolved Cu
concentrations between 0.02 and 0.06 µgL⁻¹ (Wang et al. 2012). Similarly, around the coast of
46
California dissolved Cu concentrations were relatively low, 0.09 ± 0.06 µgL⁻¹ (mean ± standard
deviation) (Smail et al. 2012). Preliminary results (Sun et al. unpub. data) show that dissolved Cu
in Californian tide pools was comparable to coastal concentrations. Chemical tolerance within
species does not appear to correlate with general pollution levels. For example, of the five CA
sites, SCI is the furthest from ports and might be expected to be the most pristine location, and
yet it has the highest copper tolerance. As a result, the variation in Cu tolerance in Tigriopus
does not appear to be a direct adaptation to Cu exposure.
The difference in Cu tolerance between Tj and Tc could be linked to variation in thermal
tolerance. Tj populations have higher thermal tolerance and Cu tolerance compared to Tc
populations. In support of this hypothesis, we found a significant positive correlation between
temperature near a collection site and the Cu tolerance of the corresponding copepod population
(t=4.34, p<0.0025). Defense mechanisms for thermal tolerance in theory could be used to
respond to Cu stress because of overlapping toxicity pathways. For instance, both high
temperature (Abele et al. 2002) and Cu (Rhee et al. 2013) have been shown to cause oxidative
stress through the generation of reactive oxygen species (ROS). Adaptation in Tj for thermal
tolerance could be co-opted and used to alleviate, at least in part, ROS toxicity generated by
elevated Cu exposure. One of many candidate defense mechanisms includes heat shock proteins
(hsp), which are recruited under both heat and Cu stress (Boone and Vijayan 2002; Rhee et al.
2009). We found thermal tolerance in Tj to be significantly higher than Tc (Figure 2-7) with a
significant correlation between average daily maximum temperature near collection site and Cu
tolerance (Figure 2-4). Furthermore, thermal tolerance of Tc has been shown to fall along a
latitudinal gradient with northern populations being less thermally tolerant (Willett 2010).
Consistent with this pattern, the current study found Tj from a lower latitude (22°12’ - 22°22’)
47
has higher Cu tolerance than Tc from a higher latitude (33°24’ - 36
o
57’). These findings show
the most northern populations, SC and GC, had the lowest Cu tolerance. In addition, SC was
shown to have lower hsp expression during heat stress than SD, a southern population (Schoville
et al. 2012). However, a larger sample of populations with corresponding environmental data
would be needed to support this hypothesis. Currently, only LC
50
values of a few populations are
available for comparison. Whether there is a direct link between thermal tolerance and Cu
tolerance remains to be seen.
TBTO tolerance exhibits a general pattern similar to that seen with Cu tolerance. The
pattern of TBTO tolerance roughly mirrors Cu with Tj being more robust than Tc. This could be
due to at least a partially shared toxicity pathway between the species, which elicit similar
defense mechanisms. For instance, TBTO has also been known to cause oxidative stress
(Ishihara et al. 2012; Katika et al. 2011). A general oxidative stress defense mechanism could be
responding to both of these chemical exposures. Therefore, Cu and TBTO tolerance would
correlate with an organism’s ability to respond to oxidative stress as shown by our results.
Differences in oxidative stress mechanisms and heat stress response could explain the general
pattern of Tj being more robust than Tc as shown in the present results. However, comparisons of
individual populations show a more complex picture.
Differences between individual populations point to additional chemical specific
responses. For instance, CDA and SC have a similar level of tolerance to TBTO, yet their Cu
tolerances are significantly different. This pattern of similar TBTO tolerance, but different Cu
tolerance is exhibited by three other pairings between Tj and Tc. Differences are also found
within species with SC and SCI having similar TBTO tolerance but significantly different Cu
tolerance. Alternatively there are populations such as SKO and Stan that have similar Cu
48
tolerance but different TBTO tolerance. Some Tc populations also have higher TBTO tolerance
than Tj, such as SD and CDA. The rank order of population tolerances for copper is not the same
as the rank order of population tolerances for TBTO. These results show that there are finer
differences in tolerance to Cu and TBTO within each species. Differences in chemical tolerance
among Tigriopus populations cannot be explained solely by differences in a particular suite of
defense mechanisms such as for oxidative stress, rather there are likely other factors that
contribute to the overall chemical response in these species.
The literature survey of bioassays used over the span of 10 years revealed that the
majority of articles do not include details regarding collection site. This could prove to be
detrimental to ecotoxicological studies if test organisms collected from multiple populations
have significant differences in chemical tolerance. Pooling of test organisms in this manner may
introduce significant inter-population biological variation. This variation can be introduced as an
exaptation that may be difficult to predict due to complex response mechanisms. Tolerance can
also be chemical specific with populations being robust to one chemical, yet extremely sensitive
to another. This hinders the ability to extrapolate inter-population comparisons from one
chemical to another.
An effective way to control for the variation in chemical tolerance and to minimize errors
in bioassays in ERA, is to have a documented collection history. This would allow for
consistency across bioassays and allow for accurate replication. Otherwise biological variation
may be mistakenly attributed to experimental error and erode the effectiveness of this valuable
technique. Additionally, understanding how other environmental factors, such as temperature,
influence chemical tolerance can give rise to patterns that provide general predictions on how
chemical tolerance can change across different populations of a species. Bioassays remain a
49
foundational assessment tool for the field of ecotoxicology, and can be greatly strengthened by
controlling for variation in chemical tolerance with proper details regarding the origin of test
specimens.
5. Acknowledgements
The authors thank the editor and three anonymous reviewers for their assistance in
strengthening the manuscript. This study was primarily supported by Sea Grant
(NA10OAR417005) to SE, and partially supported by the Research Grants Council of the Hong
Kong SAR Government via a General Research Fund (HKU703511P) to KMYL. The authors
wish to acknowledge use of the Maptool program for generating the maps in this paper. Maptool
is a product of SEATURTLE.ORG (Information is available at www.seaturtle.org). The authors
would also like to thank Shiven Chaudhry and Jennifer Ko for their assistance with acute toxicity
tests.
6. Conflict of Interest
The authors declare that there is no conflict of interest
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Tigriopus californicus. Evolution 64: 2521–2534.
54
Fig. 2-1 Ten years of research articles (Jan 2004 – August 2014) were surveyed on the Thomson
Reuters’ Web of Science search engine using the search term “ecotoxicology bioassay”. Species
used for bioassays in each article were placed into one of three categories based on the collection
information listed in the article. Results show the percentage of sets of test species in each
category (94 articles, 139 sets of test species, mean of 1.4 sets of test species per article). Only
bioassays using animals (i.e., multicellular, non-plant, non-fungal organisms) were considered in
the survey.
Fig. 1 Ten years of research articles (Jan 2004 – August 2014) were surveyed on the
Thomson Reuters’ Web of Science search engine using the search term “ecotoxicology
bioassay”. Species used for bioassays in each article were placed into one of three
categories based on the collection information listed in the article. Results show the
percentage of sets of test species in each category (94 articles, 139 sets of test species,
mean of 1.4 sets of test species per article). Only bioassays using animals (i.e.,
multicellular, non-plant, non-fungal organisms) were considered in the survey.
!
Not$listed$
Listed$without$GPS$
Listed$with$GPS$
11%$
24%$
65%$
55
Fig. 2-2 Map of Hong Kong territory with the sampling locations denoted by their respective
population abbreviations, Gold Coast (GC), Ma Wan (MW), Shek O (SKO), Stanley (Stan), and
Cape D’Aguilar (CDA).
!
!
Fig. 2 Map of Hong Kong territory with the sampling locations denoted by their
respective population abbreviations, Gold Coast (GC), Ma Wan (MW), Shek O (SKO),
Stanley (Stan), and Cape D’Aguilar (CDA).
!
GMT 2014 Nov 4 17:59:58 seaturtle.org/maptool Projection: Mercator
114˚ 00' 114˚ 03' 114˚ 06' 114˚ 09' 114˚ 12' 114˚ 15'
22˚ 09'
22˚ 12'
22˚ 15'
22˚ 18'
22˚ 21'
22˚ 24'
0 5
km
56
Fig. 2-3 Map of the California coastline with the sampling locations denoted by their respective
population abbreviations, Santa Cruz (SC), Santa Cruz Island (SCI), Leo Carrillo (LC), Laguna
Beach (LB), and San Diego (SD).
Fig. 3 Map of the California coastline with the sampling locations denoted by their
respective population abbreviations, Santa Cruz (SC), Santa Cruz Island (SCI), Leo
Carrillo (LC), Laguna Beach (LB), and San Diego (SD).
!
GMT 2014 May 27 18:31:28 seaturtle.org/maptool Projection: Mercator
-124˚ -123˚ -122˚ -121˚ -120˚ -119˚ -118˚ -117˚
33˚
34˚
35˚
36˚
37˚
38˚
0 50 100
km
SC
SCI
LC
LB
SD
California
57
Fig. 2-4 A linear regression between Average daily maximum temperature and Cu tolerance.
Temperature data was obtained from NOAA National Climatic Data Center, Global Summary of
the Day (www.ncdc.noaa.gov) from December 2010 to November 2011 at climate stations near
five Californian sites, Santa Cruz (SC), San Diego (SD), Santa Cruz Island (SCI), Leo Carrillo
(LC), Laguna Beach (LB) and one climate station near the Hong Kong site, Gold Coast (GC).
The shaded region indicates the 95% confidence interval for the linear model fit.
!
!
Fig.%4%A!linear!regression!between!Average!daily!maximum!temperature!and!Cu!
tolerance.!Temperature!data!was!obtained!from!NOAA National Climatic Data
Center, Global Summary of the Day (www.ncdc.noaa.gov) from!December!2010!to!
November!2011!at!climate!stations!near!five!Californian!sites,!Santa!Cruz!(SC),!San!
Diego!(SD),!Santa!Cruz!Island!(SCI),!Leo!Carrillo!(LC),!Laguna!Beach!(LB)!and!one!
climate!station!near!the!Hong!Kong!site,!Gold!Coast!(GC).!The!shaded!region!
indicates!the!95%!confidence!interval!for!the!linear!model!fit.!
●
●
●
●
●
LB
LC
SD
SC
SCI
GC
250
500
750
1000
1250
15 20 25
Average Daily Maximum Temperature (Celsius)
LC50
Cu LC 50 μgL⁸¹
58
Fig. 2-5 Cu median lethal concentration (LC50, low/high 95%CI) of T. japonicus in dark grey
and T. californicus in light grey. Both species are arranged from highest to lowest latitude. Error
bars denote 95%CI. Letters above each bar indicate grouping. Populations with two allocated
letters have 95%CI that span two groups. GC (1030.8, 903.54/1178.42 µgL−1 Cu), MW
(1356.58, 1132.6/1626.37 µgL−1 Cu), SKO (2438.28, 2069.23/2876.05 µgL−1 Cu), Stan
(2344.11, 1690/3255.28 µgL−1 Cu), CDA (2265.21, 1608.55/3189.11 µgL−1 Cu), SC (294.24,
239.25/363.96 µgL−1 Cu), SCI (649.02, 587.94/715.19 µgL−1 Cu), LC (458.13, 397.05/529.4),
LB (534.49, 470.86/608.3 µgL−1 Cu), SD (478.49, 422.5/544.67 µgL−1 Cu).
Fig. 5 Cu median lethal concentration (LC50, low/high 95%CI) of T. japonicus in dark
grey and T. californicus in light grey. Both species are arranged from highest to lowest
latitude. Error bars denote 95%CI. Letters above each bar indicate grouping. Populations
with two allocated letters have 95%CI that span two groups. GC (1030.8, 903.54/1178.42
µgL⁻¹ Cu), MW (1356.58, 1132.6/1626.37 µgL⁻¹ Cu), SKO (2438.28, 2069.23/2876.05
µgL⁻¹ Cu), Stan (2344.11, 1690/3255.28 µgL⁻¹ Cu), CDA (2265.21, 1608.55/3189.11
µgL⁻¹ Cu), SC (294.24, 239.25/363.96 µgL⁻¹ Cu), SCI (649.02, 587.94/715.19 µgL⁻¹
Cu), LC (458.13, 397.05/529.4), LB (534.49, 470.86/608.3 µgL⁻¹ Cu), SD (478.49,
422.5/544.67 µgL⁻¹ Cu).
"
0"
500"
1000"
1500"
2000"
2500"
3000"
3500"
GC" MW" SKO" Stan" CDA" SC" SCI" LC" LB" SD"
Cu#μg/L#
a"
a"
c"
d"
e,f"
e"
e"
f"
a,b"
b,c"
59
Fig. 2-6 TBTO median lethal concentration (LC50, low/high 95%CI) of T. japonicus in dark
grey and T. californicus in light grey. Both species are arranged from highest to lowest latitude.
Error bars denote 95%CI. Letters above each bar indicate grouping. Populations with two
allocated letters have 95%CI that span two groups. GC (70.02, 63.58/77.12), MA (68.82,
62.98/75.2), SKO (75.69, 70.11/81.72), Stan (90.07, 84.06/96.50), CDA (49.28, 45.68/53.16),
SC (43.47, 38.9/48.59), SCI (39.71, 34.78/45.33), LC (66.92, 63.41/70.64), LB (62.75,
57.65/68.30), SD (56.06, 48.56/64.72).
Fig. 6 TBTO median lethal concentration (LC50, low/high 95%CI) of T. japonicus in
dark grey and T. californicus in light grey. Both species are arranged from highest to
lowest latitude. Error bars denote 95%CI. Letters above each bar indicate grouping.
Populations with two allocated letters have 95%CI that span two groups. GC (70.02,
63.58/77.12), MA (68.82, 62.98/75.2), SKO (75.69, 70.11/81.72), Stan (90.07,
84.06/96.50), CDA (49.28, 45.68/53.16), SC (43.47, 38.9/48.59), SCI (39.71,
34.78/45.33), LC (66.92, 63.41/70.64), LB (62.75, 57.65/68.30), SD (56.06,
48.56/64.72).
!
0"
20"
40"
60"
80"
100"
120"
140"
GC" MW" SKO" Stan" CDA" SC" SCI" LC" LB" SD"
TBTO$μg/L$
a"
b"
b,c"
b,c"
c"
b,c"
d"
e"
d,e"
c"
60
Fig. 2-7 Percent survival following higher temperature stress in two T. californicus populations,
CAT and LB, and 2 T. japonicus populations, SKO and GC, over 5 temperatures. T. californicus
is shown by the lighter patterns while T. japonicus is shown by the darker patterns.
Fig. 7 Percent survival following higher temperature stress in two T. californicus populations,
CAT and LB, and 2 T. japonicus populations, SKO and GC, over 5 temperatures. T. californicus
is shown by the lighter patterns while T. japonicus is shown by the darker patterns.
0%#
10%#
20%#
30%#
40%#
50%#
60%#
70%#
80%#
90%#
100%#
37.3# 38.5# 39.5# 41.5# 42.5#
Survival(
Temperature((C°()(
CAT#(Tc)#
LB#(Tc)#
SKO#(Tj)#
GC#(Tj)#
61
Chapter 3
Long-term laboratory culture causes contrasting shifts in tolerance to two marine
pollutants in copepods of the genus Tigriopus
Sun, P.Y.
1
, Foley, H.B.
1
, Wu, L.
1
, Nguyen, C.
1
, Chaudhry, S.
1
, Bao, V.W.W.
2
, Leung,
K.M.Y.
2,3
, Edmands, S.
1
1
Department of Biological Science and Wrigley Institute for Environmental Studies,
University of Southern California, Los Angeles, California, United States of America
2
The Swire Institute of Marine Science and School of Biological Sciences, The
University of Hong Kong, Pokfulam, Hong Kong, China
3
State Key Laboratory in Marine Pollution, City University of Hong Kong, Kowloon,
Hong Kong, China
Abstract
Bioassays are a foundational tool for environmental monitoring and protection. However,
differences between field and lab environments can alter organismal tolerance to stress.
Chemical tolerances are particularly important because tolerance derived in the lab is
used to set water quality criteria that will impact field populations. In this study, we
examine how chemical tolerance of two species of copepod from the genus Tigriopus
changes across several generations in the lab. The results showed that tolerance did
change in the lab and that it was chemical specific. The increase in copper (Cu) tolerance
in the lab may be attributed to a reduction in high temperature stress. These results
support a link between Cu tolerance and thermal stress tolerance. A decrease in tributyltin
(TBTO) tolerance in the lab may be linked to starvation because starvation significantly
decreases TBTO tolerance. Our lab conditions could impose calorie restrictions relative
to the field, where the diet of Tigriopus is incompletely known. These results show that
maintenance in the lab can significantly alter chemical tolerance, and that this can begin
62
after only a single generation in the lab. Understanding how chemical tolerance can
change in the lab will be critical in strengthening bioassays and their applications.
1. Introduction
Bioassays are a powerful tool used to establish regulatory practices and ecological
risk assessments and are used internationally as a standard for toxicity testing (Keddy et
al., 1995; Power and Boumphrey, 2004; Pandard et al., 2006). Bioassays usually employ
laboratory-cultured specimens to make inferences regarding how natural populations
would respond. Laboratory cultured specimens offers many advantages such as consistent
availability, standardizing sample population across multiple projects, and elimination of
acclimatization effects. However, there is a potential for differences to develop between
field and laboratory populations. As a result, extrapolating experimental findings from
the laboratory to the field might not be appropriate (Nowak et al., 2007; Woods et al.,
1989). Altered tolerance in laboratory test populations will lead to inappropriate
comparisons to wild populations. This could lead to costly environmental damage.
Stress tolerance can decrease in a laboratory setting as a result of physiological
processes such as acclimation or genetic processes such as adaptation. Acclimations
gained in the field can be lost once transferred into the lab, which would result in a
mismatch between the response in the lab and the wild. For example, in the absence of
natural levels of zinc, Daphnia clones gradually lost their zinc tolerance in the laboratory
setting (Muyssen et al., 2002). Decreased tolerance with a genetic basis can also occur.
Low genetic diversity, such as those found in long-term laboratory maintained
populations, has been shown to also decrease tolerance to a number of stressors including
63
aquatic contaminants (Barata et al., 2000). Lower starvation tolerance was also traced to
lower genetic diversity, due to inbreeding (Stewart et al., 1982). Another factor that can
decrease tolerance is evolutionary trade offs, where selection improves one trait at the
cost of another (see Stearns, 1989). As an illustration, a laboratory population of
Drosophila that evolved earlier fertility had a tradeoff of lower desiccation tolerance
(Hoffmann et al., 2001). These examples illustrate that a laboratory setting cannot
replicate many of the components found in the field such as large population sizes and
natural selection that would decrease inbreeding and maintain higher environmental
stress tolerance, respectively.
Tolerance to stress can also increase while in the laboratory. Physiological
acclimation has been shown to increase tolerance following a pre-exposure to sub-lethal
levels of heavy metals (LeBlanc 1982; Kwok et al., 2009; Sun et al., 2014) and salinity
(Wu et al., 2014). Likewise, tolerance to environmental stressors can also increase
through genetic adaptation, as shown by Kelly et al. (2012) for temperature tolerance.
Further, unexpected increases in stress tolerance can also occur in a laboratory setting
through indirect selection. One example is seen when Drosophila raised at an ambient
temperature (25°C) developed higher cold tolerance (Condon et al., 2015).
However, the rate of change in chemical tolerance and the conditions responsible
are not well characterized. Tolerance can change gradually over the course of several
generations or abruptly after a short period in the laboratory. There are also various
environmental variables that differ between the lab and the field that can potentially
contribute to a shift in chemical tolerance.
64
This study examines the pollution tolerance of a group of marine copepods
(microcrustaceans) in the genus Tigriopus. Two common marine pollutants were used,
copper (Cu) and tributyltin oxide (TBTO). These chemicals were chosen because both
are antifouling agents. Although TBTO has been banned for marine uses (IMO 2001) it
was extensively used and is still is found in marine environments through terrestrial
runoff and legacy contamination (Díez et al. 2002; Burton et al. 2005; Santos et al. 2010).
Cu has since taken the main role as the most common antifouling additive. Additionally,
these two chemicals represent two broad categories of marine pollutants. TBTO is a man-
made lipophilic compound that is highly toxic at low concentrations (Champ and
Seligman, 1996), whereas Cu is a naturally occurring trace metal, but toxic to marine
invertebrates at higher concentrations (Philips and Rainbow, 1993). Another point of
difference between these two chemicals is that Tigriopus has been shown to only
acclimate to Cu (Kwok et al., 2009, Sun et al., 2014) while it exhibits a response
consistent with adaptation to TBTO (Sun et al., 2014).
This study tracks pollutant responses across multiple generations from initial
collection to several generations in the lab. The goal is to determine how tolerance
changes over generations in laboratory conditions. Additionally, several environmental
factors that are prevalent in the natural habitat of these animals, such as temperature
stress, ultraviolet exposure, and diet type were examined for their potential to explain the
pattern of contaminant tolerance observed in the lab.
65
2. Methods
2.1 Specimen collection and maintenance
Five Tigriopus japonicus (Tj) populations were collected from Hong Kong in
November 2011 and transported to the University of Southern California (USC), USA.
The sites of collection were Gold Coast (22° 22′ 47′′, 113° 58′ 48′′) (GC), Ma Wan (22°
21′ 0′′, 114° 3′ 36′′) (MW), Shek O (22° 13′ 48′′, 114° 15′ 36′′) (SKO), Stanley (22° 13′
11′′, 114° 13′ 11′′) (Stan), and Cape D’Aguilar (22° 12′ 36′′, 114° 15′ 36′′) (CDA). Five
populations of Tigriopus californicus (TC) were collected from California, USA, in
November 2011. The collection sites were Santa Cruz (36° 57′ 0′′, −122° 2′ 59′′) (SC),
Santa Cruz Island (34° 1′ 12′′, −119° 40′ 48′′) (SCI), Leo Carrillo (34° 2′ 23′′, −118° 56′
23′′) (LC), Laguna Beach (33° 32′ 23′′, −117° 47′ 24′′) (LB), and San Diego (32° 45′ 0′′,
−117° 15′ 36′′) (SD).
All populations were maintained in 37 µm filtered autoclaved seawater (FASW)
at 20°C with a 12-h light/12-h dark photoperiod for the entire duration of the study.
Cultures were maintained in Nalgene containers at a volume of 400 mL with a complete
solution renewal and feeding performed once a week. Cultures were fed a dry diet, which
consisted of 0.04 g TetraMin fish food (Tetra Holding, Inc., USA) and 0.04 g ground
Spirulina cyanobacteria (Nutraceutical Science Institute, USA). Each new generation
started with 3 containers per population with 100 pre-copulatory pairs each for a total of
300 pairs per population. Two weeks post establishment, all adult copepods were
removed to maintain discrete generations.
66
2.2 Multi-generational population tolerance survey
The standard protocol for the multi-generational population tolerance survey
consisted of contaminant exposure assay for 96hr without feeding or solution renewal in
FASW with male copepods with a 12-hr light/12-hr dark photoperiod at a constant
temperature of 20°C. Male copepods were selected over females for toxicity assays
because males are generally less stress tolerant than females (Willett 2010, Kelly et al.
2012), and because female stress tolerance potentially fluctuates due to maternal transfer
during egg development (Raisuddin et al., 2007). Maternal transfer can increase a
female’s susceptibility to stress following the release of eggs or increase their tolerance
as they pass on a portion of the chemical stressor to the egg sac relieving their own body
burden.
Each assay had three replicates of ten males per concentration for a total of 30
males per concentration. Mortality was assigned to copepods that failed to respond to
physical stimulation. The range of Cu and TBTO concentrations used were based on
preliminary estimates of median lethal concentration (LC
50
), which were obtained from
pilot range-finding tests with minor adjustments after each generation to incorporate
changing chemical tolerances. The generation 0 survey began November 2011,
immediately after collection and each consecutive generation survey followed
approximately 1.4 mo. after the preceding generation.
The Cu stock solution was made with CuSO4·5H2O (Sigma) in nanopure water to
a concentration of 1 g L−1 and serially diluted to obtain the target concentration. The
TBTO stock solution was made by diluting bis(tributyltin) oxide (EMD, USA) with
acetone to a concentration of 0.1 g L−1. The amount of acetone in all treatment solutions
67
including the acetone control for TBTO toxicity assays was ≤1.2×10−6 v/v. The first
acute toxicity assay was done within a week of collection. Successive toxicity assays
were conducted once adult males appeared in cultures, approximately 4 weeks after a
population culture is established.
Tolerance of each population was measured by median lethal concentrations
(LC
50
). The standard method to calculate LC
50
values is the Trimmed Spearman-Karber
method (TSKM), but generalized linear models (GLM) are also effective at determining
LC
50
s by inclusion of extreme response probabilities of 0 and 1(Kerr and Meador, 1996).
TBTO LC
50
s were calculated using TSKM. Due to the abundance of extreme response
probabilities in the Cu mortality dataset made GLM a more appropriate model to
calculate Cu LC
50
s. LC
50
values of Cu exposures were calculated using a generalized
linear model with a binomial distribution using the R programing language (R Core
Development Team, 2013) and package “MASS” (Venables, Ripley, 2002). LC
50
values
for TBTO exposure were calculated using Trimmed Spearman–Karber Program v. 1.5
obtained from the US Environmental Protection Agency. Significant differences between
LC
50
s were determined by whether there was overlapping 95% confidence intervals (see
Wheeler et al., 2006).
Because the acute toxicity tests showed very different temporal patterns for Cu vs.
TBTO tolerance under laboratory conditions, subsequent studies looked at three
environmental factors (temperature, UV and diet) that might differ between field and lab
environments. These environmental stress studies used the SD population.
68
2.3 Environmental stress treatments
The environmental stress treatments used the SD population after the conclusion
of the multi-generational population tolerance survey. Due to the requirement of large
numbers of specimens for each environmental stress assay, large long-term freely
breeding cultures of overlapping generations were established. This is in contrast to the
smaller discrete generations maintained during the multi-generational population
tolerance survey. These stress treatments were conducted from July 2012 to August 2013.
The Trimmed Spearman-Karber Program v.1.5 (US Environmental Protection
Agency) was used to calculate LC
50
values and 95% confidence intervals (95% CI). A
significant difference between LC
50
values is measured by non-overlapping 95% CI. This
comparison of 95% CI is overly conservative and only sensitive enough to identify highly
significant differences (Wheeler et al., 2006). For this study, an overly conservative
assessment was in line with our goal of identifying extreme phenotypic differences
between the various treatments. An initial LC
50
for both contaminants was calculated for
field collected SD specimens (generation 0) using data collected during the
multigenerational population tolerance survey.
2.3.1 Ultraviolet irradiation treatment
UV assays were done using the SD population after approximately 21 generations
under lab conditions. Males from the SD population were exposed to UV @365 nm for
2hr. UV exposure was administered with a UV Lamp (Cole-Parmer, 6W/115V/60
Hz/0.16 Amps). A control group of SD males was kept in identical conditions with no
measurable UV using a UV A/B Light Meter (SPER SCIENTIFIC, USA). A resting
69
period of 1hr followed UV exposure before assays began. No mortality was measured in
either treatment. Toxicity assays were conducted following the standard protocol listed
above and significance was determined according to the method outlined in Wheeler et
al., (2006). A t-test was used to determine whether the difference between the UV and
control treatments were significant between Cu and TBTO.
2.3.2 Diet treatments
Diet assays were done after approximately 28 generations under lab conditions.
Copepods from SD cultures were raised in two different diet treatments for a complete
generation before toxicity testing. The dry diet is the standard laboratory diet of 0.04g of
TetraMin and 0.04g Spirulina once a week. The live algae diet consisted of 160ml
Platymonas with a cell count of approximately 1,000,000/ml once a week. Cell counts
were estimated with the Palmer-Maloney cell count formula from raw counts obtained
using a hemocytometer (Bright-Line, USA). Cultures were established with 300 mate
pairs per biological replicate. Each diet treatment had two replicates so that each diet
treatment was started with 600 precopulatory pairs (males clasping virgin females). Two
weeks after cultures were established, all adults were removed. Toxicity assays were
conducted on males following the standard protocol and significance was determined
according to the method outlined in Wheeler et al., (2006).
2.3.3 Starvation Assays
Starvation assays were done using the SD population after approximately 11
generations under lab conditions. The starvation condition was enacted by isolating males
70
for a week without food in FASW. Cultures were maintained using the same husbandry
protocol as in the population survey.
2.3.4 Temperature treatments
All temperature assays used the SD population after approximately 18
generations under lab conditions. The cycling temperature treatment cycled between
20°C in the dark for 12hr and 28°C in the light for 12hr. This treatment was based on
results from Kelly et al., (2012) which showed a cyclic temperature treatment with a
maximum temperature of 28°C is the warmest achievable temperature that did not result
in mortality, but had an impact on fitness. This sub-lethal cycling temperature treatment
was used to compare with the temperature stable environment of the lab. Solution
renewal and feeding schedule followed the standard protocol as listed above. Cultures
were started with 300 pre-copulatory pairs from the SD population. After two weeks, all
adult copepods were removed to ensure toxicity tests would incorporate individuals that
had been reared exclusively under the cyclic treatment. Once copepods fully matured,
males were collected for acute toxicity tests. A total of 5 biological replicates were tested.
Biological replicates are from the same collection site with identical collection dates but
were maintained in physically separate laboratory cultures under identical conditions.
The constant high temperature stress treatment at 32°C had a daily 12hr light and
12hr dark cycle. Preliminary trials (data not shown) showed that a treatment of 32°C for
4 days was the highest temperature and duration that achieved no mortality. Males from
SD were collected from four biological replicates, and placed in an incubator held at
32°C for 4 days before toxicity testing using the procedure outlined above.
71
3. Results
3.1. Multi-generational population tolerance survey
Pollution tolerance was shown to vary across generations in laboratory conditions in
two species of marine copepods, with a general pattern of increase in Cu tolerance over
time and decrease in TBTO tolerance over time (Fig. 3-1). All populations, with the
exception of LB, had an increase in Cu tolerance. In 5 populations (2 Tc and 3Tj) there
was a significant consecutive increase in Cu tolerance in each successive generation in
the lab. In contrast TBTO tolerance decreased overall during the monitoring period, with
the exception of LC and CDA. In 2 Tj populations, there was a consecutive decrease in
TBTO tolerance in each successive generation in the lab.
3.2. Ultraviolet radiation
UV exposure before toxicity assays significantly decreased tolerance to both chemicals in
all replicates (Fig. 3-2). The decrease in TBTO tolerance, 2x, was markedly less than the
decrease in Cu tolerance, 3.6x. Although UV decreased tolerance to both pollutants, the
decrease in TBTO tolerance was significantly less than the decrease in Cu tolerance
shown by a t-test (p = 0.003).
72
3.3. Diet assays
Different diets had a significant effect on chemical tolerance. Populations fed dry
food (TetraMin and Spirulina) exhibited significantly higher chemical tolerance than
populations fed a diet of live cultures of Platymonas (Fig. 3-3).
3.4. Starvation assay
It was shown that withholding food for one week had no effect on Cu tolerance but
significantly decreased TBTO tolerance in all five replicates (Fig. 3-4).
3.5. Temperature
A cycling temperature treatment of 12hr:12hr 20°C:28°C over the course of an entire
generation resulted in higher Cu tolerance in four out of five replicates, but the difference
was significant in only one of these replicates (Fig. 3-5A). For tests of TBTO sensitivity,
the cycling temperature treatment resulted in higher tolerance in all five replicates, but
none of the differences was significant (Fig. 3-5B). A constant temperature treatment at
32°C for 4 days prior to toxicity assays caused a significant decrease in Cu tolerance in
half of the biological replicates (Fig. 3-6A). In contrast, the same temperature treatment
caused no significant difference in response to TBTO exposure (Fig. 3-6B).
73
4. Discussion
4.1. Population survey
This study sought to examine the temporal component of pollution tolerance changes
under typical lab conditions, where environmental parameters are held at mild and
constant levels. Generally, Cu tolerance was found to increase and TBTO tolerance
decrease in the lab (Fig. 3-1). Below we assess evidence for several environmental
variables that were identified as candidates for explaining the contrasting results for
copper vs. TBTO tolerance.
4.2. Ultraviolet exposure
UV decreased tolerance to both contaminants and likely does not contribute to the
contrasting patterns observed (Fig. 3-2). UV was chosen as a candidate because it has a
particularly strong and consistent influence in tide pool environments. Tide pools, such as
those inhabited by Tigriopus often have little to no permanent shade, which allows UV to
be a daily presence in these environments. There is ample evidence of UV exerting a
selective force on other organisms in the field (Rhode et al., 2001; Häder et al., 2007;
Cockell and Blaustein, 2013). However, the absence of UV in the lab does not explain the
contrasting change in tolerance because it indiscriminately decreased tolerance to both
contaminants.
74
4.3. Diet treatments
Different diets significantly altered chemical tolerance (Fig. 3-3), but do not
explain the changed patterns of tolerance across multiple generations. Increased chemical
tolerance due to diet could be due to differences in diet derived antioxidants. Diet derived
influences on astaxanthin (Caramujo et al., 2012) and other antioxidants (Gaetke and
Chow, 2003) have been linked to increased copper tolerance. These same antioxidants
may similarly contribute to tolerance to TBTO with its potential to cause oxidative stress
(Katika et al., 2011). There is also the potential that the live algae treatment was food
limited, which would partially agree with results from the starvation assay. However, a
food-limited hypothesis does not explain why Cu tolerance is also decreased. A possible
explanation could be the longer period of caloric restriction in the live algae had a impact
on Cu tolerance. Another possibility is that the impact of caloric restriction was isolated
to TBTO tolerance, but there was also an additional nutrient restriction, which resulted in
less diet derived antioxidants as well.
4.4. Starvation Assays
Results showed that a short period of starvation had no influence on Cu tolerance,
but resulted in decreased TBTO tolerance (Fig. 3-4). This supports the hypothesis that the
reduction in TBTO tolerance in the lab could be attributed to dietary problems, despite
our attempt to provide abundant food. This would be surprising for several reasons. First,
Tigriopus is known to have extreme starvation resistance and can survive up to 90 days in
autoclaved seawater (unpub. data), indicating the potential presence of long-term energy
stores including fat deposits such as those found in pelagic copepods (Lee at al., 2006).
75
Second, Cu tolerance is known to be energetically demanding (Lukasik and Laskowski,
2007), suggesting that the one week starvation treatment did not significantly reduce all
energetically demanding biological functions. Third, there is little evidence that our
standard feeding conditions lead to dietary problems, as we regularly maintain Tigriopus
cultures for years using the same feeding conditions (e.g. Pritchard et al. 2013).
Nevertheless, it may be that dietary restriction had a particularly negative impact on
TBTO tolerance because TBTO mainly bioaccumulates in fat due to its lipophilic
properties (Champ and Seligman, 1996). Additionally, lipid stores can be adversely
affected in the lab by other factors such as body size even under replete conditions (Huho
et al., 2007). Thus, under starved conditions when an organism begins to metabolize its
fat stores for energy there is an increased exposure of more sensitive tissues to TBTO
because less TBTO is being sequestered in fats. Additionally, metabolizing fat stores
could release any inert TBTO stored in fat increasing exposure to TBTO.
Our feeding regiment is sufficient to propagate lab cultures for years, but there is
a possibility that these lab populations may have less resource reserves than field
populations. These short periods of starvation may have a larger impact if Tigriopus
experienced them during developmental stages, which would explain why populations
raised in the lab are less tolerant than field populations. Field specimens used in these
experiments developed in the field and were brought into the lab as adults. These results
indicate that food availability has a chemical specific impact on contaminant tolerance.
76
4.5. Temperature assays
Reduced temperature stress potentially leads to greater Cu tolerance while having
no influence on TBTO tolerance. The cycling temperature assays (Fig. 3-5) did not
negatively impact Cu tolerance, as the only significant difference was for one copper
replicate where tolerance was actually lower under control conditions than it was after
exposure to cycling temperature conditions. This result indicates that sub-lethal
temperatures, such as those experienced in our cycling temperature condition may have a
hardening effect, which has been documented in Drosophila (Sejerkilde et al., 2003;
Loeschcke and Hoffmann, 2007). Ultimately, these results illustrate that stable
temperatures compared to cycling temperatures have no significant negative impact on
chemical tolerance within the range of temperatures tested here. In contrast, a 4h
exposure to constant high temperature (Fig. 3-6) did provide some support for the
temperature hypothesis, as this treatment significantly reduced tolerance for half of the
Cu replicates and none of the TBTO replicates. There is good evidence that high
temperature exerts strong selective pressure on Tigriopus populations. T. californicus has
been shown to be locally adapted to high temperature stress and exhibits a strong cline in
temperature tolerance (Willett, 2010, Kelly et al., 2012). Likewise, studies of two
Tigriopus species found a positive correlation between Cu tolerance and temperature near
the collection site (Sun et al., 2015). In the field, temperature stress is a frequent
occurrence, but was held constant at a non-stress inducing level in the laboratory. The
synergy between heat stress and Cu stress points to the potential for heat stress to
decrease this organism’s ability to respond to Cu when the exposures co-occur or quickly
follow each other. Under the same assumption, in the absence of heat stress the organism
77
can allocate more resources to responding to a Cu stress. This is further supported by
evidence that heat shock proteins (hsps) are involved in the defense mechanism for both
high temperature and Cu, (Boone and Vijayan, 2002; Rhee et al., 2009). In the presence
of both temperature stress and Cu, a finite amount of hsps will be divided to respond to
both stressors. Response to high temperature and copper is certainly not limited to hsps,
but there may be enough similarity in response mechanisms that in the absence of heat
stress, the organism can afford a stronger response to Cu.
Change in chemical tolerance over generations in the lab has been shown in part
due to the absence of certain prevalent environmental stressors. These results show that
TBTO tolerance is augmented by periods of starvation, which may contribute to the
overall decrease in TBTO tolerance of lab populations.
4.6. Conclusion
In summary, tolerance to two common marine pollutants has been shown to change in
the lab. Additionally, the direction of change is chemical specific, with Cu tolerance
increasing and TBTO tolerance decreasing. Short periods of starvation may be in part
responsible for the decreasing TBTO tolerance. Starved lines are more susceptible to
TBTO because they may have less fat stores to sequester TBTO away from more
susceptible tissues. High temperature stress may be partially synergistic with Cu toxicity.
Without heat stress in the lab, these copepod populations may have more resources to
mount a defense against Cu exposure.
78
Bioassays are a powerful tool, but their effectiveness will be eroded by inconsistent
results. Long-term laboratory maintenance can alter chemical tolerance, and it can have
opposing and unexpected effects for different chemicals. Understanding how chemical
tolerance can change will be critical in effective implementation of bioassays as a tool.
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Implications for toxicology. Environ. Toxicol. Chem. 8, 1067–1074.
doi:10.1002/etc.5620081112
Wu, C.-S., Yang, W.-K., Lee, T.-H., Gomez-Mestre, I., Kam, Y.-C., 2014. Salinity
acclimation enhances salinity tolerance in tadpoles living in brackish water through
increased Na+, K+-ATPase expression. J. Exp. Zool. 321, 57–64. doi:10.1002/jez.1837
82
Fig. 3-1 Multi-generational population tolerance survey. (A) Tigriopus californicus and
(B) Tigriopus japonicus 96hr Cu LC
50
values were determined using a generalized linear
model with a binomial distribution. Cu concentrations are long10 transformed. (C) T.
californicus and (D) T. japonicus 96hr TBTO LC
50
values were calculated using
Sperman-Karber method. (w) Denotes 1 significant consecutive increase in LC
50
. (ww)
Denotes 2 significant consecutive increases in LC
50
s. () Denotes 1 significant
consecutive decrease in LC
50
s. () Denotes 2 significant consecutive increases in LC
50
s.
(x) Denotes a lack of significant change in LC
50
between generations or that the change
was not unidirectional. Significance between LC
50
s determined by non-overlapping 95%
confidence intervals (see Wheeler et al., 2006). There was no mortality data available for
SCI, Gen 0.
100
1000
10000
100000
1000000
10000000
CDA SKO GC Stan
Cu ug/L
0
10
20
30
40
50
60
70
80
90
SC LC LB SD SCI
TBTO ug/L
100
1000
10000
SC LC LB SD SCI
Cu ug/L
Gen 0
Gen 1
Gen 2
w
A
D
C
B
0
20
40
60
80
100
120
140
CDA SKO GC Stan
TBTO ug/L
ww
ww
w
x
x
ww w
ww
ww
x
83
Fig. 3-2 Consecutive ultraviolet irradiation and contaminant exposure assay.
LC50 values for copper (A) and TBTO (B), with error bars showing 95% CI.
LC50 assays were conducted at 20°C for 96h on animals from one of two
treatments: 1) Control: constant 20°C or 2) UV: Ultraviolet exposure for 2 hours
prior to toxicity tests. The UV treatment significantly reduced tolerance to both
Cu and TBTO.
0
500
1000
1500
2000
2500
1 2 3
Cu μgL
−1
Replicate
Control
2hr UV
0
10
20
30
40
50
60
1 2 3
TBTO μgL
−1
Replicate
A
B
*
*
*
*
*
*
84
Fig. 3-3 Different diet treatments impact chemical tolerance. LC50 values for
copper (A) and TBTO (B), with error bars showing 95% CI. LC50 assays were
conducted at 20°C for 96h. The Dry food treatment comprised of TetraMin and
Spirulina and the live algae diet consisted Platymonas administered once a week.
Treatment groups were raised from birth on these diets before toxicity assays.
0
500
1000
1500
2000
2500
1 2
Cu μgL
−1
Replicate
Dry Food
Live Algae
0
10
20
30
40
50
60
1 2
TBTO μgL
−1
Replicate
A
B
*
*
*
*
85
Fig. 3-4 Starvation assay. LC50 values for copper (A) and TBTO (B), with error
bars showing 95% CI. LC50 assays were conducted at 20°C for 96h on animals
from one of two treatments: 1) Control: standard food conditions or 2) Starved:
food was withheld for one week before toxicity assays. LC50 values for the
starved treatment were not significantly different from the control treatment for
any Cu replicates (A) while all TBTO replicates (B) showed significantly lower
tolerance in the starved treatment.
0
500
1000
1500
2000
2500
3000
3500
4000
1 2 3 4 5
Cu μgL
−1
Replicate
control
starved
0
10
20
30
40
50
60
70
1 2 3 4 5
TBTO μgL
−1
Replicate
A
B
*
*
* * *
86
Fig. 3-5 Cycling temperature assay. LC50 values for copper (A) and TBTO (B),
with error bars showing 95% CI. LC50 assays were conducted at 20°C for 96h on
animals that had been maintained for an entire generation under one of two
conditions: 1) Control: stable 20°C , 2) Cycling temperature: a cyclic temperature
treatment 20°C:28°C for 12hr:12hr daily. The cycling treatment was more
tolerant than the control treatment in 9 out of 10 replicates but the difference was
significant in only one replicate (control less tolerant than cyclic treatment in Cu
replicate #4).
0
500
1000
1500
2000
2500
3000
3500
4000
4500
1 2 3 4 5
Cu μgL
−1
Replicate
Control
20/28 C
0
10
20
30
40
50
60
70
80
1 2 3 4 5
TBTO μgL
−1
Replicate
A
B
*
87
Fig. 3-6 Constant high temperature stress assay. LC50 values for copper (A) and
TBTO (B), with error bars showing 95% CI. LC50 assays were conducted at 20°C
for 96h on animals from one of two treatments: 1) Control: 20°C or 2) Temp: 32°
for 4h. In the Cu toxicity assays, half of the replicates showed significant
differences (A) while in TBTO assays (B) none of the replicates showed
differences between treatments.
0
500
1000
1500
2000
2500
3000
3500
4000
1 2 3 4
Cu μgL
−1
Replicate
Control
32 C
0
5
10
15
20
25
30
35
40
1 2 3 4
TBTO μgL
−1
Replicate
* *
A
B
88
CHAPTER 4
The correlation between environmental temperature and copper tolerance in the
tidepool copepod, Tigriopus californicus: A potential case of exaptation between
environmental and anthropogenic stress
Sun, P. Y., So, B., Moffett, J.W., Edmands, S.,
Department of Biological Science and Wrigley Institute for Environmental Studies,
University of Southern California, Los Angeles, California, United States of America
Abstract
Previous work on the tidepool copepod Tigriopus californicus shows
geographical variation in copper (Cu) tolerance. This tolerance was determined to be
quite high, with some populations withstanding copper concentrations several orders of
magnitude higher than those in coastal seawater. Because chemical concentrations in
tidepools may be different from those in coastal samples, we measured dissolved Cu
concentrations in tidepools from three locations containing T. californicus. Cu
concentrations in tidepools were higher than those in coastal samples. However, they
were still too low to explain the extreme tolerance exhibited by the copepods, and
geographic variation in copper did not correlate with population tolerance. This suggests
copper tolerance may be a byproduct of a different environmental stressor. As one
candidate, we assessed tidepool temperature and found a correlation between copper
tolerance and the rate of temperature increase per hour at each site. Together, these
results indicate the extreme copper tolerance observed in this species may be an indirect
result of exposure to other environmental stressors.
89
1. Introduction
Population growth combined with greater migration toward the coast is
contributing to a greater anthropogenic impact on coastal systems (Neumann et al.,
2015). Pollution load on the marine environment is only expected to increase (Shahidul
Islam and Tanaka, 2004). One marine pollutant of concern is copper (Cu), which is a
micronutrient but at high concentrations is toxic (White and Rainbow, 1985). There has
been a rising concern regarding Cu pollution in marine environments shown by state
senate bills from California (California State Senate Bill 623) and Washington
(Washington State Senate Bill 5436) both aimed at significantly restricting the use of
copper based antifouling paints on recreational boats (Johnson, 2011). This growing
concern over toxicity of Cu on coastal marine systems is further exemplified by the
search for Cu alternative based antifouling paints (Pérez et al., 2009).
Toxicity of Cu is the result of several different pathways. The most bioavailable,
and thus toxic form is Cu
2+
, which can readily cross cell membranes (Brooks and
Waldock, 2009). Cu toxicity can be the result of generation of reactive oxygen species
(ROS), which in turn can lead to apoptosis (Rhee et al., 2013). Toxicity through
generation of ROS has been attributed to redox cycling, where Cu generates hydroxyl
radicals from hydrogen peroxide
(Simpson et al., 1988; Stohs and Bagchi, 1995).
Osmoregulation is also impacted by Cu toxicity (Nussey et al., 1995). This is further
supported by studies that show Cu toxicity is altered by salinity (Lee et al., 2010) and
other ions such as Ca
2+
and Mg
2+
(de Schamphelaere and Janssen, 2007).
However, these routes of toxicity are not restricted to Cu but are shared among
other environmental stressors as well. Oxidative stress has also been shown to be a result
90
of heat stress (Abele et al., 2002), cold shock (Sun et al., 2016), ultraviolet irradiation
(Shindo et al., 1994), salinity stress (Abogadallah et al., 2010) and potentially other
environmental stressors. Additionally, osmotic stress is directly related to external
salinities (Davenport, 1985). As a result of these overlaps in routes of toxicity, an
organism that lives in an environment abundantly exposed to these stressors such as
tidepool inhabiting copepods could evolve high Cu tolerance in the absence of high Cu
levels as a byproduct of adaptation to another stress. Such an adaptation may contribute
to the defense against a second stress, known as exaptation. Exaptation is when a trait has
evolved for one purpose, but can be used for another (Gould and Vrba, 1982). An
exaptation would allow an organism either sufficient defense against the new stressor or
afford more time for adaptations to evolve to mitigate the new stressor.
The copepod Tigriopus californicus is an ideal system to examine whether
pollution tolerance is a direct adaptation or an exaptation because of its well-
characterized thermal and pollution tolerance. T. californicus exhibits a clinal pattern in
temperature tolerance (Willett, 2010; Kelly et al., 2012). This pattern points to the fact
that different populations of this species have evolved in different temperature regimes.
These copepods inhabit tidepools with restricted dispersal marked by genetically distinct
populations (Edmands, 2001), even between neighboring sites only several kilometers
apart (Peterson et al., 2013). In addition to studies of population genetic structure and
thermal tolerance, the genus Tigriopus also has been extensively used as a model to study
pollution response (Raisuddin et al., 2007).
Cu tolerance is particularly interesting in Tigriopus due to evidence for both
plastic and genetic tolerance. In the lab setting, Cu tolerance in Tigriopus is a
91
physiologically plastic response (Kwok et al., 2009) even after 12 generations of chronic
exposure (Sun et al., 2014). There is also documented variation in Cu tolerance among
populations reared in common garden conditions (Sun et al., 2015), which indicates
genetic differences in response to Cu. These two perspectives on Cu response showed
that a genetic adaptation to Cu exposure did not occur after 12 generations of exposure to
sublethal Cu concentrations, yet Tigriopus has incredibly high Cu tolerance with intra-
and interspecific variation, which indicate evolved differences in Cu tolerance between
populations.
The purpose of this work was to identify environmental factors that potentially
influence the high Cu tolerance of T. californicus. Our goals were (1) to identify whether
differences in Cu tolerance among populations were a result of differences in
environmental Cu concentrations and (2) to find other environmental stressors that
correlate with population Cu tolerance, with our first candidate being temperature stress.
Thermal tolerance has been linked to the biogeographical distribution of intertidal species
along latitudinal clines (Somero, 2005). Thermal tolerance and pollution tolerance is
correlated in another copepod species (Moraïtou-Apostolopoulou and Verriopoulos,
1981). Additionally, T. californicus does exhibit a latitudinal pattern in thermal tolerance.
However, not all intertidal organisms exhibit thermal tolerance along a latitudinal cline
(see Gaitán-Espitia et al., 2014). Our approach focused on long-term temperature
monitoring of different sites, which has shown to be more effective in assessing the
extent of stress exposure an organism experiences than a single time point measurement
(Helmuth and Hofmann, 2001).
92
Understanding how the environment can shape pollution tolerance would greatly
benefit regulatory policies. For example, water quality standards in areas with lower
levels of thermal stress (such as colder shaded regions relative to hotter regions with
abundant sunlight) may require stricter regulation on heavy metal pollution because
native populations may be less tolerant than populations from more heat stressed regions.
2. METHODS
2.1 Temperature monitoring
Temperature monitoring was conducted during the summer of 2015, from June to
August. Temperature measurements were made using iButton thermometers (Maxim
Integrated, US), which were waterproofed using Plasti-Dip (Plasti Dip International, US).
This method of waterproofing has been proven to have minimal influence on temperature
readings (Roznik and Alford, 2012). The iButtons are reported to have temperature
measurement resolution of 0.5°C with an accuracy ±1°C between -40°C and +85°C
(Maxim Integrated, US). To confirm accuracy, monitoring trials were done in
temperature controlled environments, 10°C, 15°C, 20° C, and 27° C. The accuracy was
confirmed to be within the range of ±1°C. The iButtons were replaced as their memory
reached capacity after approximately 3 months of deployment. iButtons were originally
placed in 3 separate locations at each sampling site (with the exception of Catalina Island
where only two pools inhabited by copepods were found), but only loggers from 2 pools
from each site were successfully recovered for the full duration of the monitoring period.
The sites are Santa Cruz (SC) (36° 56' 59.7156", -122° 2' 51.1764"), San Diego (SD) (32°
93
44' 47.7126", 117° 15' 18.0468"), and Catalina Island (CAT) (33° 26' 47.238", -118° 29'
6.1728"). Our selection criteria were based on whether the pools were permanent or semi-
permanent (present for most of the year) and continually inhabited by T. californics based
on previous observations and confirmed during each iButton deployment event. The
iButtons were fastened to the deepest part of the tide pool by marine epoxy (Pettit Marine
Paint, US). CAT tide pool #1 (large pool, LP) was approximately 1.6x0.4x.01m and Pool
#2 (small pool, SP) was approximately 0.2x0.6x0.1m. SC tide pool #1 (pipe pool, PP)
was approximately 1.0x0.5x0.5m and pool #2 (small pool, SM) was approximately
0.3x03x0.5m. SD pool #1 (octopus poll, OCT) was approximately 2x0.33x0.7m, pool #2
(bullseye pool, BULL) was approximately 5.0x4.0x0.5m. These are the approximate
dimensions during a single sampling event, however the volume of tidepools can rapidly
change depending on a number of factors, such as whether measurements are made
during high or low tide where the level of inundation and evaporation are potentially very
different and result in different tidepool volumes. The tidepools at SC and SD were
qualitatively lighter in color, possibly made of sandstone. Catalina tidepools are made of
a darker volcanic rock, basalt.
Air temperature was obtained from the National Oceanographic and Atmospheric
Administration’s (NOAA) National Climatic Data Center (www.ncdc.noaa.gov).
Temperature stations closest to our sites with the highest coverage of data available were
selected. The climate stations used were Santa Cruz (GHCND:USC00047916; 36° 59'
25.7994", -121° 59' 27.96"), which is 6.75 km from the sampling site. The San Diego
station (GHCND:USC00047741; 32° 46' 1.92", -117° 13' 32.88"), 3.57 km from the
sampling site. The Catalina Island station (GHCND:USW00023191; 33° 24' 18", -118°
94
24' 56.9874") is 7.91 km from the sampling site. A nested two-way ANOVA was used to
determine significant differences between sites and between air and water temperatures.
Likewise, differences between individual pools at each site were analyzed with a nested
two-way ANOVA.
Average rate of temperature change during the monitoring period was calculated
as the temperature difference between the daily minimum and the daily maximum
temperatures, divided by the number of hours between the daily minimum and the daily
maximum temperatures. The smallest monitored pool by volume was selected for rate
analysis to illustrate the upper bounds of thermal stress these populations potentially
experience. Due to several hours throughout the early morning and afternoon sharing the
same temperature, the hour closest to the temperature change was selected as the
boundary of the rate measurement. A one-way ANOVA was used to determine
significant differences in rates and post-hoc Bonferroni adjust t tests were used to
determine significant differences between sites.
Correlations between Cu tolerance and dissolved Cu concentrations as well as
correlations between temperature measurements and Cu tolerance values were done using
Pearson’s R Correlation. Correlation compared a population’s Cu tolerance (LC
50
and
survivorship at a single concentration) with the environmental temperature (maximum
average temperature and rate of temperature change).
2.2 Seawater chemical analysis
Seawater samples were collected from the same pools selected for temperature
monitoring. Water samples were collected with acid-cleaned bottles, which were triple
95
rinsed with their respective sample. The coastal seawater sample was collected from the
Wrigley Marine Science Center on Catalina (WMSC) Island April 2014. The culture
seawater sample was processed coastal seawater, which meant that the sample was 37µm
triple filtered and autoclaved. Culture seawater was maintained in a culture of SD
copepods for one week. These copepods were lab-reared copepods collected in October
2013 (seawater chemical analysis was collected and conducted in 2015). This culture was
fed 0.04 g TetraMin fish food (Tetra Holding, Inc., USA) and 0.04 g ground Spirulina
cyanobacteria (Nutraceutical Science Institute, USA) and held at a 12-hr light/12-hr dark
photoperiod at a constant temperature of 20°C. In the field, collection bottles were
submerged approximately midpoint between the surface and the floor of the tide pool to
limit the collection of benthic particulates as well as surface particulates. Extra care was
taken not to sample or disturb the tide pool floor to avoid suspension of settled particles.
All samples were filtered using a 2µm filter and then acidified with Optima grade
HCL to a 1000x dilution. The total dissolved Cu concentration was determined using a
single batch nitrilotriacetic acid (NTA) resin extraction and isotope dilution inductively
coupled plasma mass spectrometry (ICP-MS) method (Jacquot et al., 2013) on a
Finnegan Element 2 (Thermo Scientific). In brief, a portion of the samples (2.5 mL) were
spiked with 20 µL of
65
Cu along with 200 µL of NTA resin and placed on a shaker for 3d.
The samples were then centrifuged at 4000 rpm for 5 minutes. The supernatant was
decanted and Milli-Q water was added and centrifuged again. The Milli-Q water rise step
was repeated a total of 3 times. Followed by the addition of 1 mL of 5% Optima nitric
acid and placed on a shaker overnight before analysis on ICP-MS. Each sample was run
96
twice on ICP-MS to produce technical replicates, but for the lab culture sample and
CAT/Mar sample there was only enough sample for a single run.
There was loss of several samples due to potential contamination in the form of
algal growth post collection. As a result, Cu analysis was reduced to 2 samples for SD
and SC and 1 sample from each CAT pool for separate dates. A two-way ANOVA was
done between site and date. A nested two-way ANOVA was done between sites and
individual pools within each site. A statistical analysis for each pool during different
sampling points was not possible due to absent data, where certain pools only had
measurements for a single sampling period. Overall statistical analysis was split into two
separate tests (two-way ANOVA for sites x dates, and nested two-way ANOVA for sites
x pools).
2.3 Acute toxicity test
Acute toxicity tests were conducted to calculate median lethal concentration and
survivorship at a single concentration (LC
50
) for 96 hr without feeding or solution
renewal in 37µm triple filtered and autoclave seawater (FASW). Toxicity tests were
conducted within a week following field collection following protocol in Sun et al.,
(2015). Briefly, the LC
50
assays had three replicates of ten males for a total of 30
individuals per concentration with a total of 6 concentrations per chemical (for
concentrations used see Tc in Sun et al., 2015). Mortality was determined when
individuals did not respond to gentle physical stimulus. The Cu stock solution was made
with CuSO
4
·5H
2
O (Sigma) in nanopure water to a concentration of 1 g L
-1
and serially
diluted to obtain the target concentration. The amount of acetone in all treatment
97
solutions was ≤1.2×10
-6
v/v. LC
50
s were calculated with the Trimmed Spearman-Karber
program v1.5 (US Environmental Protection Agency). Significantly different LC
50
values
were determined by non-overlapping 95% confidence intervals, which has been shown to
be overly conservative (Wheeler et al., 2009), which increases confidence that differences
found using this method are significant.
The toxicity assays for SC and SD were done November 2011, while the toxicity
assay for CAT was done June 2011. Animals were taken from the same pool as those
used for Cu analysis and long term temperature monitoring. In order to ensure results of
toxicity assays were not affected by differences in original testing dates, a follow up
toxicity assay was done in Feb 2016 to confirm the rank order of Cu tolerance between
the populations. A one-way ANOVA was used to assess the significant differences
between the mean survival rates between populations.
3. RESULTS
Dissolved Cu concentrations in tidepools from our 3 sites are higher than
coastal concentrations from Smail et al., (2012) and our coastal sample (Fig. 1). For
the tidepool locations, a two-way ANOVA (Table 1) showed no significant difference
in Cu concentration between sites (SC, SD, or CAT), sampling periods (December or
March), or their interaction. A one-way ANOVA showed that the dissolved Cu
concentration of seawater from the lab sample was not significantly different than
the Cu concentrations in tidepools from all field sites (SC, SD, and CAT, with data
from Dec and Mar sampling periods) (Table 1).
98
For the sites where dissolved Cu was measured in more than one pool (SC
and SD), a two-way nested ANOVA showed no significant difference among or
within sites. CAT was not included in the analysis because dissolved Cu
measurements were only obtained from a single pool due to contamination of the
other CAT pool samples. This contamination was an algal-like growth, which arose
after the filtration, indicating either improper filtration or post filtration
contamination. Such a growth could have altered seawater chemistry, so those
samples were not analyzed.
There is no correlation between each population’s Cu tolerance and
dissolved Cu concentration in tidepools for Dec (R = 0.93, p=0.83) or Mar (R=0.97,
p=0.89) from their respective environments.
There is a difference between air temperature and tidepool temperature at the
different sites (Fig. 4-2). A paired t-test showed significant differences between air
temperature and pool temperature (Table 4-3). Temperature data from the larger of the
two pools at each site was used in the comparison with air temperature. This analysis
used the daily maximum temperature from both air and water temperature (of the larger
of two pools at each site) from 6/29/15 to 8/29/15.
Analysis with a two-way repeated measures ANOVA showed significant
differences in tidepool temperatures between sites (Table 4-4). Results also indicate
significant differences between pools at each site. At the southern locations CAT and SD,
daily maximum water temperature was generally greater than the corresponding air
temperature. However at the northern site SC the air temperature was generally higher
99
than the water temperature.
Mean daily rate of change in water temperature was greater in CAT than all other
sites, with SD having the second greatest rate and SC with the lowest rate. A one-way
ANOVA showed significant differences in rate between the sites (F = 39.98, p<0.001).
Post-hoc Bonferroni correct pair-wise comparison showed that all rates are significantly
different from each other (Fig. 4-3). CAT had the higher rate when compared to SC
(p<0.001) and SD (p<0.001). The rate of temperature increase was greater in SD than in
SC (p<0.001)
Acute toxicity was significantly different between populations. LC
50
of the
different populations were significantly different and found to significantly correlate with
rate of temperature increase (R = 0.99, p = 0.01) (Fig. 4-4). However, the correlation
between LC
50
and maximum daily temperature was not significant (R=0.98, p = 0.12)
(Fig. 4-5).
Survivorship at a single concentration exposure conducted simultaneously
between all populations to address potential temporal variability, because LC
50
s were
conducted on different dates, showed that the results were significantly different between
populations using a One-Way ANOVA (F = 24.26, p = 0.002). Survivorship of all 3
populations exposed to Cu 8M for 96hr done concurrently compared to their respective
LC
50
s found a non-significant correlation between (R = 0.99, p = 0.063) (Fig. 4-6). The
rank order of Cu tolerance among populations was identical to the original rank order of
the LC
50
s.
100
4. Discussion
The dissolved concentration of Cu in tidepool water was much lower than the
tolerance of T. californicus and also showed no correlation with a populations’ Cu
tolerance. LC
50
values are several orders of magnitude higher than the Cu concentrations
measured in their environment. Dissolved Cu from coastal seawater in this study was
similar to another study of coastal waters (Smail et al., 2012), whereas tide pool Cu
concentrations in this study are higher but still lower than what was found in two harbors
surveys (~100 µM from Schiff et al., 2007 and between 90 – 1, 000 µM Hall et al., 1988).
The experimental cultures in the lab also have dissolved Cu levels similar to those found
in tidepools, however this is only a single measurement. Additional measurements of
different copepod cultures are required to support these initial findings. However,
assuming this level of dissolved Cu is present in the majority of copepod cultures, the
potential source of this Cu is not likely a result from the water introduced into our
cultures during solution renewal because renewal water is processed coastal seawater and
the measure dissolved Cu concentrations in this water is similar to coastal concentrations.
The source of Cu is likely due to diet derived Cu that is expelled from the copepods after
a period of accumulation. Another copepod species, Acartia tonsa has been shown to
obtain Cu from their diet (Bielmyer et al., 2006). A component of the dry diet, TetraMin
contains fishmeal, made from byproducts of commercially important fish that could
contain trace amounts of Cu (Elnabris et al., 2013). Crustaceans sequester Cu in several
tissues including their gut, specifically hepatopancreatic epithelial cells where these Cu
rich granules are eventually deposited back into their environment (Ahearn et al., 2004).
101
However, further measurements of dissolved Cu concentrations from copepod cultures
from various durations of lab maintenance are required before any hypotheses are tested.
Due to the absence of significant differences in Cu concentrations between the
different sites, the significant differences in Cu tolerance among populations suggests that
a different environmental stressor may be the underlying driver of differential Cu
tolerance. Coastal air temperature may not always be reflective of the temperatures
experienced within the tidepools. The air temperature of a site is not equivalent to the
temperature experienced in tide pool water. Comparisons within each site of air
temperature and tidepool water temperature show that there is a significant difference
(Table 4-2, Sites (Air x Water)). This might be a result of the distance between NOAA
climate stations and the tidepools. Although these climate stations are the closest stations
to our study sites, they are further inland where air temperature may be significantly
different compared to right above the intertidal zone. Interestingly, air temperature was
lower for the two southern sites, CAT and SD relative to their respective tide pool
temperatures. In contrast, the northern site, SC had an opposite result with higher air
temperature relative to the water temperature. The overall daily max air temperature of
SC was greater than both southern locations, which is contrary to the hypothesized cline
in thermal tolerance (see Sanford and Kelly, 2011) and observed latitudinal cline in
thermal tolerance (Willett, 2010). The fact that 2015 has been the warmest year on record
may have contribute to the higher temperature experienced in the north than previously
recorded (NOAA, 2015).
Tide pool temperatures of these 3 sites do not follow the general latitudinal cline
in average maximum temperatures. These results show that tidepools at the CAT site and
102
the resident copepod population had the highest daily maximum temperature and highest
Cu tolerance of the three site and population pairs examined. Previous work has shown
that the latitudinal pattern of thermal tolerance is less of a cline and more of a mosaic of
temperature “hot spots” and “cold spots” where local tidal and wave splash patterns have
stronger effects in establishing the temperature regimes than broader latitudinal
temperature distributions (Helmuth et al., 2002; Helmuth et al., 2006).
The majority of tidepools along the western North American coast, including SC
and SD face west but CAT tidepools face east, this difference in orientation may
contribute to the differences in temperatures. Alternatively, the substrate of each site
may explain the difference in temperature increase. The CAT site tidepools are made of
volcanic basalt, which is much darker than the rock substrate, possibly sandstone, of SC
and SD. The darker rock at CAT would heat up at a much faster rate than the lighter rock
of SC and SD. This is reflected in the faster rates of temperature increase in our results
and the higher average daily maximum temperature, which may be the reason why CAT
does not follow the cline in temperature.
Our long-term monitoring effort also showed significant temperature differences
between pools within individual sites. Intertidal regions are known to harbor many
microhabitats where intertidal organisms from the same site have evolved different
thermal tolerances that suites their preferred microhabitat (Gilman et al., 2015).
Our results show a significant correlation between Cu LC
50
and rate of
temperature increase. However the trend between LC
50
s and average maximum
temperature is similar but non-significant. Rate of temperature stress has been shown to
103
impact heat tolerance in some organisms (Beitinger et al., 2000; but see Terblanche et al.,
2007). There are discrepancies on whether slow or faster rates of temperature increase
have the greatest negative impact on an organism’s survival. For instance, a slow rate of
increase can offer the organism time to acclimate, which would increase their upper
bounds of thermal tolerance. In contrast, a slower rate of temperature change would
increase the exposure time of organisms to stressful temperatures decreasing the upper
bounds of their ultimate thermal tolerance.
An overlap in sources of toxicity (and their defense mechanisms) between Cu and
environmental stress could potentially explain the variation in Cu tolerance between
different T. californicus populations. Oxidative stress is one potential source of toxicity
shared between Cu stress (Rhee et al., 2009), and environmental stressors such as
temperature stress (Abele et al., 2002), UV (Shindo et al., 1994), and salinity stress
(Abogadallah et al., 2010). Similarly, heat shock proteins (hsps) have been shown to
respond to both of heat and Cu stress (Boone and Vijayan, 2002; Rhee et al., 2009).
Perhaps evolved responses to deal with ROS generated by environmental stress such as
but not restricted to temperature has primed Tigriopus to respond to the ROS generated
by Cu stress as well. Other stressors in the field have been shown to cause oxidative
stress as well, and likely contribute as well to the evolution of these organisms’ defense
against ROS.
A review of previous studies showed a direct overlap in response to thermal stress
and metal stress. A common relationship between the two stressors was found to be a
synergistic interaction that negatively impacted an organism, where heat tolerance is
decreased by pollution tolerance (Negri and Hoogenboom, 2011; Lanning et al. 2006). In
104
contrast, there are studies that show overlap in protective mechanisms, a cross-tolerance,
between temperature stress and pollution stress. One such study showed that low doses of
heavy metals elicit an acclimation like response that can protect seedlings against heat
stress (Deng et al., 2016). It was also shown that increased levels of glutathione induced
through cold acclimation contributed to increased tolerance to copper (Streb et al., 2008).
In Daphnia, heat acclimation has been shown to increase tolerance to heavy metals
(Stuhlbacher et al., 1993). Similarly, in zebrafish a pre-exposure to low levels of heat
stress during development had a protective effect against heavy metal toxicity, which was
attributed to the up-regulation of hsp70 (Hallare et al., 2005). However the majority of
these examples of positive interactions between thermal tolerance and pollution tolerance
are focused on plastic physiological changes, in the present study we examine a heritable
difference in pollution tolerance and its links to environmental thermal stress.
This study only examined 3 individual populations, and inclusion of more
populations with thermal data as well as an extensive characterization of other
environmental stressors should be done before conclusions regarding the causal links
between temperature and Cu tolerance can be made. These results mean to focus attention
on that Cu tolerance is not correlated with environmental Cu exposure and that other
environmental factors may have driven the evolution of high Cu tolerance, one candidate
being temperature stress.
4.2. Conclusion
The aim of this study was to analyze potential sources that could have contributed
105
to the evolution of Cu tolerance in the marine copepod T. californicus. Analysis of
dissolved Cu concentrations in tide pool seawater revealed that Cu concentrations are
orders of magnitude lower than the Cu tolerance exhibited by these copepods. In addition
there was also a lack of significant differences between tide pool water Cu
concentrations, which would not explain the variation in Cu tolerance between copepod
populations. An environmental stressor, temperature was examined for correlations to Cu
tolerance as a potential candidate for the source of high Cu tolerance. Results show a
significant correlation between rate of temperature increase and Cu tolerances, which are
potentially a result of a shared mode of toxicity, oxidative stress. As a result, variation in
thermal tolerance due to the ability to response to oxidative stress would likely translate
to variation in Cu tolerance. These results point to the potential that evolved responses to
environmental stressors can influence pollution tolerance in these copepods.
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Fig. 4-1 Dissolved Cu concentrations from several California tidepools, coastal seawater,
and a lab population culture at two different time points. Coastal seawater was obtained
from Wrigley Marine Science Center, Catalina Island, CA. Both Lab Culture and Coastal
SW were assayed in DEC. Error bars are the standard deviation of two technical
replicates. Due to low samples from CAT, each time point reflects Cu levels of a
different CAT pool.
-10
0
10
20
30
40
50
60
70
80
90
SC Pool
#1
SC Pool
#2
SD Pool
#1
SD Pool
#2
CAT
pools
Lab
Culture
Coastal
SW
Cu (nM)
DEC
MAR
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Table 4-1 ANOVA results for Cu concentration between collection sites and dates. A
two-way ANOVA was used to compare sites and sampling period. A one-way ANOVA
was used to compare sites (data from both sampling periods) and the lab sample (only
one sampling period available), field + lab.
Comparison ANOVA SS d.f. MS F p-level
Site Two-way 293.33 2 146.67 0.63 0.58
Date Two-way 31.88 1 31.88 0.14 0.73
Site x Date Two-way 645.22 2 322.61 1.38 0.35
Field + Lab One-way 394.40 3 131.50 0.57 0.65
112
Table 4-2 Nested Two-way ANOVA for SC and SD. Only sites that had measured
dissolved Cu concentrations from more than one pool (SC and SD) were included in the
analysis.
Comparison ANOVA SS d.f. MS F p-level
Site Nested two-way 157.2 1 157.2 0.60 0.48
Site:Pool Nested two-way 541.8 2 270.9 1.04 0.43
113
Table 4-3 Pair-wise comparison between air and pool water temperature. Pool water
temperature data is taken from the larger of two pools monitored at each site. Both types
of temperature data are the daily maximum temperature between 6/29/15 to 8/29/15.
Comparison ANOVA d.f. tails p-level
SC (Air x Water) Paired t-test 61 2 4.34E-19
SD (Air x Water) Paired t-test 61 2 2.96E-21
CAT (Air x Water) Paired t-test 61 2 1.94E-20
114
Table 4-4 Two-way repeated measures ANOVA analysis of pool water temperature
between the three sites (SC, SD, and CAT) as well as the two pools with each site (pool 1
and pool 2).
Source of Variation ANOVA SS d.f. MS F p-level
Sites Two-way repeated ANOVA 8054.52 2 4027.26 319.29 <0.001
Sites x Pools Two-way repeated ANOVA 218.431 2 109.216 29.53 <0.001
115
Fig. 4-2 Air temperature compared to water temperature. Air temperature was taken from
the nearest NOAA climate station and water temperature was collected using iButton
temperature loggers in tidepools inhabited by T. californicus. Comparisons were made at
Santa Cruz (A), San Diego (B), and Catalina Island (C).
0
10
20
30
40
50
6/29/15 7/10/15 7/21/15 8/1/15 8/12/15 8/23/15
SC_POOL_PP
SC_AIR
SC_POOL_SM
0
10
20
30
40
50
6/29/15 7/10/15 7/21/15 8/1/15 8/12/15 8/23/15
SD_POOL_O
SD_AIR
SD_POOL_B
0
10
20
30
40
50
6/29/15 7/10/15 7/21/15 8/1/15 8/12/15 8/23/15
CAT_POOL_LP
CAT_AIR
CAT_POOL_SP
A
B
C
Temp °C
116
Fig. 4-3 Average Rate of change in water temperature across the summer of 2015
(6/15/15 – 9/15/15). Rates were calculated by dividing the difference between daily min
and max by the number of hours in between. When min and/or max temperature was
shared by more than a single hour, the hour closest to a change in temperature was
selected as the lower and upper range boundary, respectively. One-way ANOVA (F =
39.98, p < 0.001) and post-hoc Bonferroni corrected t test (CAT vs. SC p < 0.001. CAT
vs SD p < 0.001, and SC vs. SD p <0.001) were used to determine significant differences
between sites. Each letter indicates a significantly different group.
0
0.5
1
1.5
2
2.5
SC SD CAT
Celsius hr
-1
A
B
C
117
Fig. 4-4 The LC
50
values of Tigriopus populations are correlated with the rate of
temperature increase (R
= 0.99, p = 0.01). Error bars signify the 95% confidence interval
(95%CI). All LC
50
values are significantly different from each other based on non-
overlapping 95%CI intervals (see Wheeler et al., 2009).
0
5
10
15
0.00 0.50 1.00 1.50 2.00 2.50
Cu (μM)
Rate of Temperature Change (C)
SC
SD
CAT
118
Fig. 4-5 The positive trend of LC
50
values of Tigriopus populations and the average daily
maximum summer temperature of their habitat. The correlation is not significant
(R=0.98, p = 0.12). Error bars signify the 95% confidence interval (95%CI). All LC
50
values are significantly different from each other based on non-overlapping 95%CI
intervals (see Wheeler et al., 2009).
0
5
10
15
20 22 24 26 28 30 32 34 36
Cu (μM)
Average Daily Max Summer Temperature (C)
SC
SD
CAT
119
Fig. 4-6 Survivorship of all 3 populations exposed to Cu 8M for 96hr done concurrently
compared to their respective LC
50
s (R = 0.99, p = 0.063). Error bars are the standard error
of the mean % Survival. CAT had no death resulting in no standard error. One-way
ANOVA of the survival rates showed significant differences between the means (F =
24.26, p = 0.002).
0
0.2
0.4
0.6
0.8
1
0.00 2.00 4.00 6.00 8.00 10.00 12.00 14.00
Survival%
LC50
SC
SD
CAT
120
Chapter 5
Transcriptome response to acute and chronic pollution exposure
Sun, P. Y., So, B., Gracey, A. Y., Edmands, S.,
Department of Biological Science and Wrigley Institute for Environmental Studies,
University of Southern California, Los Angeles, California, United States of America
Abstract
There are potentially large differences in response to acute and chronic stress. Previous
studies indicate that acute stress response is more generalized, while response to chronic
stress is more specialized. However the majority of comparisons between acute and
chronic stress are within a single lifetime. In our study, we extend chronic exposure to
encompass several generations to examine the gene expression associated with the
development of copper and tributyltin oxide exposure, two different types of marine
pollutants. RNAseq analysis showed variation in gene expression between biological
replicates attributable to both genetic drift and environmental differences among
replicates maintained for multiple generations. Despite this variation among replicates,
we found substantially different expression patterns among treatments. Results showed
that proteolysis and xenobiotic genes are expressed in both acute and chronic treatments,
but the particular genes hierarchically cluster according to exposure periods indicating a
temporally driven shift in response rather than a chemically driven shift. In contrast, ion
regulatory associated genes showed similar expression during acute exposures, but
chronic exposures showed a chemical specific response. These results indicate that a
subset of stress response genes will become more specialized in chronic exposures.
121
1. Introduction
The duration of exposure to stress can lead to dramatically different responses. An
acute stress often recruits a fast acting generalized response (Kültz, 2005) whereas
chronic exposure to the same stress may elicit a different or subset of the original
response, which may be more refined or devoid of ancillary response elements that may
not be too energetically costly to be maintained. For instance, stress response genes un-
regulated during an acute exposure (48hr) were down-regulated during a chronic
exposure (21d) (Bang et al., 2015). However, a different range of concentrations were
used for each time point, there is a possibility that the change in gene expression is due to
the differences in the level of stress rather than the duration. The shift in response may
indicate a difference in implemented mechanism, such as compensation or conservation
of energy, that have been associated with different severity of stress when attempting to
maintain homeostasis (Sokolova et al., 2011). A severe stress condition is described as
when energy demands surpass energy supply during the response to stress (see Fig. 19.1
Sokolova et al., 2011). This conceptual framework for different degrees of stress can be
similarly applied to different durations of stress if the stress response incurs a net
negative cost on an organism’s energy budget, which overtime will transition an
organism’s response from compensation to conservation.
Two evolutionary mechanisms that result in similar responses such as increased
stress tolerance, acclimation and adaptation are distinguished in part by the time it takes
each to be recruited (Chevin et al., 2010). Acclimation is a physiological plastic process
that each individual within a population can recruit to respond to a changing
environment. The process of acclimation can be quickly recruited, but its benefits are not
122
heritable and passed to future generations. In contrast, genetic adaptation occurs at the
level of a population, where genotypes better suited to their current environment persist
and contribute significantly more offspring to the next generation. This leads to a
heritable transfer of the particular trait that can persist even in the absence of the original
stressor.
It has been theorized that an adaptive response can build off the acclimation
response through the development of constitutive expression of genes recruited during
acclimation (DeBiasse and Kelly et al., 2016). This benefits the organism by frontloading
the stress response genes so that the organism is better prepared to respond to stress.
However, the adaptation response does not always result in a constitutive expression of
stress response genes. In the arthropod, Orchesella cincta a heavy metal adapted line had
low expression or no change in expression relative to a reference population during
toxicant exposure (Roelofs et al., 2009).
In this study, we use RNAseq to examine gene expression across the entire
transcriptome of T. californicus. This study builds off of previous work (Sun et al., 2014)
that found changes in tolerance after 7 generations of exposure to the marine pollutants
copper (Cu) and tributyltin oxide (TBTO). The use of RNAseq to analyze gene
expression has been proven to be an effective tool in analyzing response to marine
pollutants (Garcia et al., 2012). RNAseq has also revealed substantial between acute and
chronic pollutant exposure (Garrett et al., 2013), with expression profiles having less than
5% of genes shared between acute and chronic exposure (1 vs 13 d). Other gene
expression studies have found similar results within even shorter amounts of time (2 vs.
24hr and 3 vs. 27hr) (Kovalchuk et al., 2007 and Kreps et al., 2002, respectively). In
123
addition, Kovalchuk et al. (2007) compared their acute and chronic exposed
transcriptomes to other stress induced transcriptomes and found that the acute stress
transcriptome of various stressors were much more similar to each other than to the
chronically stressed transcriptome supporting the hypothesis that long-term exposure to a
stressor will elicit a more stress specific response.
However, these studies examined the difference within a single lifespan and have
the potential to only observe physiologically plastic changes in expression. Our study
aims to expand the definition of chronic exposure to multiple generations and incorporate
the potential to observe how physiological acclimation as well as genetic adaptation can
influence the transcriptome in response to stress. This is important, since long term
exposure in natural populations will encompass both plastic and genetic responses. The
goal of this study is to examine how the response to pollution changes between acute and
multigenerational chronic exposure. By comparing transcriptomes of exposure to two
different pollutants, we can compare the chemical specificity of response patterns in
acute and chronic exposures.
2. METHODS
2.1. Specimen collection and maintenance
Copepods were collected from San Diego, CA (32° 45′ 0′′ N, −117° 15′ 36′′ W)
October 2013. This initial collection was divided into 3 treatment lines (Seawater control
= S, Cu = C, and TBTO = T) with 3 biological replicates for each line (Fig. 5-1).
Replicates were maintained for 7 discrete generations based on a previous study showing
124
increased tolerance over this time period (Sun et al., 2014). Each replicate was started
with 100 mate pairs (males clasping immature females) and maintained at 20°C in 37 µm
filtered autoclaved seawater (FASW) with a 12-h light/12-h dark photoperiod. Once
juvenile forms appeared (copepidids), all parents were removed from the replicates to
prevent cross-generational mating. The next generation was established once mate pairs
appeared. Each generation roughly spanned 1.4 months. Replicates were maintained in
Nalgene containers at a volume of 400 mL. Once a week, solution (seawater plus
chemicals) was completely renewed and cultures were fed 0.04 g TetraMin (Tetra
Holding, Inc., USA) and 0.04 g Spirulina (Nutraceutical Science Institute, USA.
2.2. Chronic exposures
All treatment lines were maintained for 7 generations, with chemical treatment
lines (C and T) chronically exposed to sub-lethal levels of Cu and TBTO, respectively
(Fig. 5-1). Concentrations of chemical exposure were based on Sun et al., (2014) which
showed these concentrations caused moderate reduction in reproductive output. The Cu
stock solution was prepared by diluting CuSO4 (Sigma-Aldrich, USA) in nanopure H
2
O
to a concentration of 1 µg L
-1
and was stored at 20 °C. The Cu stock solution was added
to FASW for the chronic Cu solution of 13.74 µg L
-1
before use. The TBTO stock
solution was made by diluting bis(tributyltin) oxide (EMD, USA) with acetone (Macron
Chemicals, USA) to a concentration of 0.004 g L
-1
and was stored at 20 °C in the dark.
The TBTO chronic solution was prepared by adding TBTO stock solution to FASW for a
final concentration of 0.15 µg L
-1
. All stock solutions were remade on a monthly basis.
125
2.3. Acute exposure
At generation 7, males from the S and C lines were exposed to 13.74 µg L
-1
Cu
(Sy and Cy) and males from the S and T lines were exposed to 0.15 µg L
-1
TBTO (Sz and
Tz) in FASW for 96hr before being preserved in RNAlater (ThermoFisher Scientific,
USA). A Cu challenge is denoted with a y (e.g. Sy – Control line with a 96hr Cu
challenge). A TBTO challenge is denoted with a z (e.g. Sz – Control line with a 96hr
TBTO challenge). All chemical challenges were done in 60 x 15 mm petri dishes with a
volume of 10 mL in groups of 10 males per dish. Males were used because they are
generally more sensitive to stress than females (Raisuddin et al., 2007; Kelly et al., 2012).
The chemical challenge assays were maintained in 12-h light/12-h dark photoperiod at 20
°C. At the conclusion of the 96hr challenge assays 20 males per treatment were placed in
RNAlater overnight at 4°C and then moved to -20 °C for long-term storage (~6 months)
before RNA extraction. This incubation protocol has been shown to be an effective
method of producing high quality RNA extracts (see Gorokhova, 2005).
2.4. Fitness Assays
A follow up fitness assessment was done during this experiment for comparison
to results for chronically exposed lines from Sun et al., (2014). Fitness assays were done
on one biological replicate (replicate 2). A total of 25 replicates were done for treatment
C and T, while only 22 replicates were assayed for treatment S due to accidental loss of
the last 3 replicates. Fitness assays were established in clean petri dishes with a single
mate pair in FASW + 13.74 µg L
-1
Cu or FASW + 0.004 g L
-1
TBTO. These assays were
fed 2mg of food at the start of the assays and again after 14d, when the original parents
126
were removed to avoid cannibalism of offspring. At 14d, assays were rehydrated with DI
H
2
O if needed. Final counts were done at 28d.
2.5. RNA extraction and sequencing
Individuals were transferred to TRI Reagent
®
(Sigma-Aldrich, USA) for tissue
homogenization. Samples were homogenized in DNAse/RNAse free microcentrifuge
tubes using a bead beating method with metal beads in a tissue homogenizer, Tissue
Lyser II (Qiagen, NL). Metal beads were sterilized by a 10 minute 10% bleach soak
followed by a triple rinse with nanopure H
2
O. Beads were then rinsed with 95% ethanol
and allowed to air dry. Homogenization was carried out twice at a frequency of 30
oscillations/s for 2 minutes each. Samples were placed on ice for 2 minutes between
homogenization events to cool the sample and limit RNA degradation due to heat during
homogenization to preserve the quality of RNA (see Leite et al., 2012 and Zhang et al.,
2013). RNA extraction was done using a Direct-zol™ RNA miniprep kit (Zymo
Research, USA) following the standard protocol. Purified RNA was eluted at a volume of
50µL in DNAse/RNAse-free water. RNA was quantified on a Qubit Flurometer
(ThermoFisher Scientific, USA) and stored at -80°C until library prep.
Samples were submitted to University of Southern California’s Genome and
Cytometry Core (CA, USA) for cDNA library prep and 100bp paired end sequencing. A
Bioanalyzer (Agilent Technologies, USA) was used to check library quality. All samples
were multiplexed and sequenced on a single lane using Rapid Run V.2 Illumina HiSeq
2500.
127
2.6. Data analysis
Reads were demultiplexed at the Genome and Cytometry Core. All reads were
trimmed for low quality, ambiguities, and adapters using CLC Genomics Workbench
v.8.5.1 (CLC BIO, DK). For quality control, reads with less than 50bp and quality limit
of 0.05 were trimmed. Reads with more than 2 ambiguous nucleotides were also
trimmed. All reads were mapped to the T. californicus genome v.3 (Accession number
PRJNA237968) using CLC’s RNA-seq Analysis tool. Differential gene expression
(DGE) of total read counts (per transcript) was analyzed using CLC’s Empirical Analysis
of DGE, which is a modified version of Fisher’s Exact Test. Unadjusted p values (p <
0.5) were used to select DGEs. To update transcript identities, significantly differentially
expressed transcripts were blasted using BLAST2GO to NCBI’s nr (non-redundant)
database with a set e-value of 1.0E-3. Annotation of Gene Ontology (GO) terms were
also done using Blast2GO (BioBam, ES) with an E-Value-Hit-Filter of 1.0E-6. Principle
component analysis (PCA) was done in CLC Genomics Workbench using mapped reads.
Heat maps of differentially expressed genes were created using GENE-E (Broad
Institute). Expression means and individual biological replicates were arranged with
hierarchical clustering function in GENE-E according to one minus the Pearson's
correlation coefficient.
Individual patterns from the heat maps for all significantly differentially
expressed genes were qualitatively selected based on their pattern of expression. Groups
were selected based on whether they were expressed across all treatments, solely
expressed at one time point (acute vs. chronic) or in one chemical (Cu vs. TBTO). Heat
maps of ion regulatory associated genes were specifically selected based on GO terms
128
“ion transport” or “ion channel”. Proteolysis associated genes were selected using GO
terms “Protease”, “peptidase”, and “proteolysis”. HSP and xenobiotic-related genes were
selected by gene name due to previous hypotheses and results that indicate their
functional importance (HSP see Boone and Vijayan, 2002; Rhee et al., 2009; Schoville et
al 2012; xenobiotic related genes see Sokolova et al., 2011).
3. RESULTS
3.1. Fitness assays
Fitness assays show that chronically exposed copepod lines have increased
tolerance to their respective pollutants relative to the control (Fig. 5-2).
3.2. Illumina sequencing and RNAseq mapping
Paired-end 100bp Illumina sequencing resulted in between ~11 and ~18 million
reads per sample (Table 5-1). Principle component analysis (Fig. 5-3) showed a strong
replicate effect, with each control line replicate and its two different acute exposures
grouping together. Chronic exposures collected in the middle between the 3 control
replicates (Fig. 5-3). Venn diagrams show all significantly differentially expressed genes
from each treatment with a total of 1371 significant differentially expressed genes (Fig.
5-4). A total of 668 genes (48%) successfully returned a Blast result (Fig. 5-5). From the
mean expression of all annotated significantly differentially expressed genes several
qualitative patterns were identified. The first pattern showed similar expression across all
treatments, which had ion regulatory and proteolysis related genes. The second pattern
129
were genes similar across chronic treatments, which showed down-regulation of
xenobiotic genes and up-regulation of genes related to ion regulation and chitin. Acute
specific expression was also identified and contained up-regulated hsp, xenobiotic genes,
and ion regulatory associated genes. The last group was chemical specific expression,
which showed that TBTO exposed treatments had up-regulation of proteolysis and chitin
whereas Cu exposed treatments had ion regulatory genes.
3.3. Expression of categories of interest
Ion regulatory related genes at the level of means had acute treatments clustered
together and chronic treatments clustered separately indicating a chemically distinct
response in chronic exposures (Fig. 5-6). At the level of individual biological replicates,
the hierarchal clustering between acute/control and chronic treatments was not as well
defined. Ion regulatory associated genes consist of 6.4% of all significantly differentially
expressed genes that returned a successful Blast hit.
Heat shock proteins (hsp) were recruited strongly during acute exposure relative
to a chronic exposure. The expression of hsps did not cluster in any predictable patter at
the level of mean expression or for individual biological replicates (Fig. 5-7). However,
mean expression of hsp genes were only up-regulated in acute treatments with individual
biological replicates exhibiting a similar trend. Heat shock proteins comprised less than
0.01% of the total significantly differentially expressed genes that returned a successful
Blast hit. For hsp70 genes, accession numbers JW506906 and JW519142 (hsp70 #1 and
#3 in Schoville et al., 2012) and for one hsp67B2 accession number JW506224 (hsp67B2
#1 in Schoville et al., 2012). In that study, hsp70 #1 was found to have significantly
130
different expression between southern and northern populations following heat stress, but
hsp70 #3 did not. Interestingly, hsp67B2 #1 had the highest amount of sequence
divergence of all hsps (8.7%) between southern and northern populations. The remaining
hsp genes either had contigs too short to create an NCBI accession number or had gaps in
the data due to missing orthologs (see Schoville et al., 2012).
Proteolysis associated genes and xenobiotic response associated genes had similar
patterns. The mean responses of both categories of genes (Fig. 5-8 and Fig. 5-9) were
hierarchically clustered by duration of exposure. At the level of individual biological
replicates, proteolysis genes chronically exposed did not cluster in a discernable pattern.
For xenobiotic response genes, chronically exposed replicates clustered together.
Proteolysis genes and xenobiotic associated genes consisted of 5.6% and 1.3% of genes
of differentially expressed genes that returned a successful Blast hit.
Chitin associated genes include both metabolism and synthesis genes. There was
no temporal or chemical specific pattern for mean expression (Fig. 5-10A) or for
individual biological replicates (Fig. 5-10B). Chitin related genes comprise 3% of all
significantly differentially expressed genes that returned a successful Blast hit.
4. DISCUSSION
Fitness increased following chronic exposure to pollutants. Similarly to Sun et al.,
(2014), results showed that fitness increased in these lines relative to the control when
measured after 7 generations of exposure to pollutants.
These results show that overall gene expression is replicate specific. This is
evident by the relatively wider dispersal of the control replicates and their corresponding
131
acute exposures when compared to the chronic exposures in the PCA. The difference in
gene expression is likely due to the genetic drift as well as different environments of each
container during the 7 generations that these lines were reproductively isolated in
different physical containers. However, even with large differences between biological
replicates, empirical analysis of differentially expressed genes revealed genes that
showed significant expression across all replicates. These expression patterns show
duration specific and chemical specific expression patterns of several categories of
interest qualitatively selected from the overall expression pattern due to previous work
showing their functional importance.
Expression patterns that were mostly influenced by the duration of exposure
included proteolysis and xenobiotic response genes. These two groups of genes appeared
to be more abundant in the transcripts that were up-regulated at a single time point.
Proteolytic capabilities are associated with defense against acute (Ngo et al., 2013) and
chronic stress (Pickering et al., 2013), but the responsible proteasome complexes have
been shown to shift as a result of severity and time of stress. The 26S and 20S
proteasomes are responsible for degradation of proteins within the cell, however at
increased stress levels, such as during chronic stress exposure, the 26S proteasome
becomes inactivated while the 20S remains highly functional (Pickering and Davies,
2012). Proteolytic activity can also change with duration of stress. For instance, the Lon
Protease is highly inducible during acute stress but declines during chronic stress (Ngo et
al., 2013). Although proteolysis associated transcripts are expressed under both acute and
chronic stress, the shift from one proteasome to another or the shut down of acute stress
associated proteasome would explain how these transcripts cluster based on duration of
132
stress.
Xenobiotic response genes are up-regulated in both acute and chronic exposure.
Cytochrome p450 (CYP) is a class of genes that comprise a major response to
xenobiotics and drug metabolism. These results show a family specific expression with
the only CYP transcripts from family 2 up-regulated in acute stress while the only CYP
gene up-regulated in the chronic exposure belongs to family 3. Although both CYP 2 and
3 families have similar function, drug and steroid metabolism, our results show that these
specific genes from different families have contrasting temporal expression patterns.
Expression patterns that exhibited a chemical specific response included ion
regulatory genes. Ion regulatory genes were selected because they were found to be
anecdotally over-represented in the Cu specific exposure. Results showed that this group
of genes had similar expression profiles during acute exposure, but had dissimilar
expression profiles when chronically exposed. The difference in the expression of ion
channel transcripts in chronic treatments is likely due to the specificity that comes from
long-term exposure, which is hypothesized to result in the biosynthesis of a small number
of appropriate ion channels without producing wide-spread changes across all ion
channel associated transcripts (Rosati and McKinnon, 2004). Both Cu and TBTO have
been shown to influence osmoregulation ability (Bambang et al., 1995;Lignot et al.,
1998), but their specific targets may differ. For instance, the Na
+
/H
+
ATPase has been
shown to be unaffected by TBTO (Virkki and Nikinmaa, 1993) but Cu has been found to
exert a negative effect on this ion regulatory channel (Wimalasena et al., 2007). During
the onset of osmotic stress or osmotic imbalance, the organism will potentially recruit a
wide array of genes to address the initial osmotic imbalance, but over time particular
133
expression of ion regulating channels such as Na
+
/H
+
ATPase may be up-regulated or
down-regulated due to their continual inhibition or lack thereof. Although transcripts for
this particular ion channel were not present in the differentially expressed ion genes in
this data set, there are other ion channels activity that have chemical specific sensitivity.
Some categories of genes did not exhibit any apparent pattern. These categories
include chitin-related genes and heat shock proteins. Chitin associated genes were found
to be over-represented in the SD population of T. californicus relative to a northern
population (Schoville et al., 2012), but did not show a pattern in our study. Heat shock
proteins may have not exhibited a hierarchical clustering pattern because of the strong
degree of up-regulation in the acute TBTO treatment. Qualitatively, hsp expression is up-
regulated also in the acute Cu treatment, and mainly down-regulated in the chronic
treatments. In
This is supported by the observation that hsps are often associated with a first
response general defense against stress (Benjamin and McMillan, 1998; Verghese et al.,
2012) and down-regulated during chronic stress (Yang et a., 2009).
4.2. CONCLUSION
Studies of gene expression under chronic stress, particularly over multiple
generations, are rare compared to those under acute stress. Our results showed that for
some genes the duration of exposure was more important the type of chemical being
used. Both duration- and chemical-specific patterns were detectable, despite highly
134
divergent expression profiles in replicate lines maintained across generations. Some gene
categories, such as proteolysis and xenobiotic response genes showed expression patterns
were duration-specific. Another category, ion regulatory associated genes showed
duration-specific expression patterns for acute exposures, but patterns under chronic
exposure were more chemical-specific. These results show that gene expression in
response to pollution exposure is broadly duration-specific, but under multiple
generations of exposure some gene categories, such as ion regulatory genes have a
chemical-specific response supporting the hypothesis that response to chronic stress is
more specific than response to acute stress.
Our results show that even if populations exhibit drastically different gene
expression profiles due to genetic drift, that a response to stress can still be identified.
Different gene categories commonly associated with stress response do show different
temporal expression patterns, such as proteolysis and xenobiotic response genes, which
may indicate a shift in response rather than a specialization in response. However, ion
regulatory associated genes support the hypothesis that acute stress response is more
generalized relative to a long-term stress response.
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Fig. 5-1 Exposure experimental design. The wild population was collected from San
Diego, CA and split into 3 biological replicates. Only replicate 1 is shown for illustrative
purposes, but all replicates were maintained under identical conditions. Chronic control
(S) and acute control conditions (x) are white. Chronic Cu (C) and acute (96hr) Cu
exposure (y) are light grey. Chronic TBTO (T) exposure and acute (96hr) TBTO (z)
exposure are dark grey. Each treatment square represents a discrete generation started
with 100 mate pairs.
C1
Treatment S: Seawater (control)
C2 C3 C4 C5 C6
Treatment C: Copper added
T1 T2 T3 T4 T5 T6
Treatment T: TBTO added
Rep. 1
S1 S2 S3 S4
S5
S6
x y z
seawater
seawater + copper
seawater + TBTO
x y
x z
Wild population
Rep. 2
Rep. 3
S7
C7
T7
139
Fig. 5-2 Fitness assays (Mean ± SE) for control (S), chronically exposed to Cu (C), and
chronically exposed to TBTO (T). Each treatment group was either exposed to acute Cu
(@13.74 µg L-
1
) or acute TBTO (@0.004 g L
-1
) for the entire duration of the fitness
assay. Each assay was started with a single mate pair and offspring were counted after
28d. The t-test values are as follows: (**) p < 0.01, (***) p < 0.001.
0
10
20
30
40
Cu exposure TBTO exposure
Average offspring per female
T
**
***
S
C
S
140
Table 5-1 Total number of raw reads obtained by Rapid Run V.2 Illumina HiSeq 2500
100pb paired end sequencing. Mapped reads were mapped to the Tigriopus californicus
v.3 genome.
Sample Number of raw reads Mapped Reads
Control 1 11,493,410 7,440,106
Control 2 18,370,650 11,448,548
Control 3 13,714,400 8,129,682
Acute Cu 1 14,022,202 8,666,974
Acute Cu 2 15,460,130 9,454,422
Acute Cu 3 13,822,074 8,417,254
Chronic Cu 1 13,854,820 8,439,932
Chronic Cu 2 14,949,500 9,067,358
Chronic Cu 3 13,930,630 9,061,922
Acute TBTO 1 12,461,674 7,911,534
Acute TBTO 2 13,804,484 8,588,026
Acute TBTO 3 13,998,508 8,323,481
Chronic TBTO 1 13,195,504 8,123,180
Chronic TBTO 2 15,079,136 9,316,260
Chronic TBTO 3 13,364,426 8,323,992
141
Fig. 5-3 Principle Component Analysis of mapped reads (RPKM normalized) from all
biological replicates in their respective treatment conditions. Each chronic treatment is
denoted by “S” for control, “C” for Cu, and “T” for TBTO. Acute exposure designation
follows with “x” for no acute exposure, “y” for a 96hr Cu exposure, and “z” for a 96hr
TBTO exposure.
2Sy
2Sz
2Sx
1Sz
1Sy
1Sx
3Sz
3Sx 3Sy
1Tz
1Cy
2Cy
3Cy
2Tz
3Tz
PC1
PC2
✖
✖
✖
✖
✖
✖
✖
✖
✖
n
n
n
u
u
u
142
Fig. 5-4 Venn diagrams showing the number of significantly differentially expressed
genes in each treatment. All significantly differentially expressed genes of all Cu
treatment comparisons (A) and all TBTO treatment comparisons (B). Treatment
comparisons shown as single circles show total genes in parenthesis, with up-regulated
genes on top and down-regulated genes on bottom.
76
209 174
24
82
26
A B
C 178
é
(309)
131
ê
Acute vs. Chronic
Control vs. Acute
Control vs. Chronic
Acute vs. Chronic Control vs. Chronic
Control vs. Acute
178
é
(276)
98
ê
67
é
(132)
65
ê
Cu
A
89
119 144
34
11
108
2
A B
C 97
é
(253)
156
ê
Acute vs. Chronic
Control vs. Acute
Control vs. Chronic
Acute vs. Chronic Control vs. Chronic
Control vs. Acute
125
é
(246)
121
ê
107
é
(155)
48
ê
TBTO
B
143
Fig. 5-5 Heat map of all significantly differentially expressed genes (RPKM) made with
Gene-E. (A) Total mean DGE across all treatment comparisons (each gene is normalized
to the control) and expression of individual biological replicates with chronic treatments
denoted with a green asterisk (*) (B). All genes and treatments were hierarchically
clustered in GENE-E using one minus Pearson correlation. Specific expression patterns
from mean expression were examined to identify categories of interest. Cooler colors
(blues) indicate lower expression while warmer colors (reds) indicate higher expression.
global
0.7272 1 1.27
Control
Chronic Cu
Chronic TBTO
Acute Cu
Acute TBTO
Description
apolipophorins-like UPF0193 EVG1 homolog C-type lectin PREDICTED: uncharacterized protein LOC105844725 dynein heavy chain 6, axonemal probable cyclin-dependent serine threonine- kinase DDB_G0292550 glycine receptor subunit alpha-2 isoform X5 gonadotropin-releasing hormone II receptor-like voltage-dependent calcium channel subunit alpha-2 delta-3 tipE isoform X1 voltage-dependent calcium channel type A subunit alpha-1 isoform X5 neuropeptide F receptor-like phosphate ABC transporter substrate-binding peptidase S15 Helicase ARIP4 intraflagellar transport 27 homolog NADH dehydrogenase, constitutive coactivator of PPAR-gamma 1 dnaJ homolog subfamily C member 8 plasma membrane calcium-transporting ATPase 2 isoform X3 kinesin-associated 3 isoform X2 porphobilinogen deaminase isoform X1 vam6 vps39 disrupted in renal carcinoma 2 homolog DNA excision repair ERCC-6-like 2 isoform X6 jim lovell SEC14 1 histone-lysine N-methyltransferase SUV39H2-like isoform X1 neuronal acetylcholine receptor subunit alpha-7 isoform X2 cyclin-dependent kinase 17-like isoform X1 BTB POZ domain-containing 9 zinc finger matrin-type 4 isoform X2 zinc finger OZF-like kinesin unc-104 isoform X11 RNA 3 -terminal phosphate cyclase TIGR02452 family phytophthora-inhibited protease 1 phosphatase 1 regulatory subunit 12A isoform X6 zinc finger Noc X-linked retinitis pigmentosa GTPase regulator sodium calcium exchanger 1 isoform X1 glucose-6-phosphate 1-epimerase organic cation transporter -like Rap1 GTPase-GDP dissociation stimulator 1-B venom allergen 3-like RNA polymerase II degradation factor 1-like tRNA (uracil-5-)-methyltransferase homolog A-like isoform X1 cathepsin L1-like ubiquitin carboxyl-terminal hydrolase 31-like cytochrome P450 2J2-like hypothetical protein L798_04225 4-coumarate-- ligase 1-like basement membrane-specific heparan sulfate proteoglycan core isoform X7 Armadillo repeat-containing 2 seleno W serine threonine- kinase 3 isoform X2 leucine-rich repeat-containing DDB_G0290503 carbohydrate sulfotransferase 9-like Pleckstrin homology domain containing transcription factor 7 isoform X1 hypothetical protein DAPPUDRAFT_122326, partial AAEL003425-PA, partial ferric-chelate reductase 1 homolog palmitoleoyl- carboxylesterase notum1-like isoform X1 LIM homeobox Lhx3 isoform X1 platelet-activating factor acetylhydrolase bipolar kinesin KRP-130-like intraflagellar transport 80 homolog lethal(2)denticleless notch-like transmembrane receptor LIN-12 enhancer of sevenless 2B Solute carrier family 12 member 2 hypothetical protein Solute carrier family 46 member 3 Filamin-A lethal(2)essential for life-like sphingosine kinase 1-like restin homolog isoform X1 hypothetical protein DAPPUDRAFT_109886 Actin, muscle deoxycytidylate deaminase pickpocket 28-like msta, isoform A nesprin-1 isoform X15 Actin, muscle aspartyl asparaginyl beta-hydroxylase isoform X2 FMRFamide receptor troponin I isoform X7 ankyrin repeat domain XIAP-associated factor 1 isoform X1 cation-independent mannose-6-phosphate receptor tyrosine- phosphatase 99A-like innexin inx2 tumor D54 isoform X2 meiosis arrest female 1 isoform X1 regulator of nonsense transcripts 2 isoform X1 methionine sulfoxide oxidase Mical isoform V1 Troponin C, isoform 1 Pyrazinamidase nicotinamidase calcitonin gene-related peptide type 1 receptor-like Skeletor, isoforms B C 28S ribosomal S36, mitochondrial isoform X1 cytochrome c oxidase subunit 5B, mitochondrial-like failed axon connections homolog forkhead box O histone-lysine N-methyltransferase SMYD3 Elongation of very long chain fatty acids 6 PREDICTED: uncharacterized protein LOC103573342 Tropomyosin zinc transporter 5 phospholipase B1, membrane-associated pickpocket 28 alpha-(1,3)-fucosyltransferase C-like GILT , partial Cathepsin L-like cysteine ase SKI family transcriptional corepressor 2 activating signal cointegrator 1 complex subunit 2 homolog THAP domain-containing 4 sugar transporter, Cytochrome P450, family 2, subfamily J, polypeptide 2 4-coumarate-- ligase 1-like opsin, pteropsin type, partial Longitudinals lacking -like Aldose 1-epimerase acylpyruvase FAHD1, mitochondrial-like Purine nucleoside phosphorylase peroxiredoxin 1 phospholipase ABHD3 amidohydrolase Suppressor of hairless ileal sodium bile acid cotransporter-like low density lipo receptor adapter 1-like CG8671, isoform B Neuronal acetylcholine receptor subunit alpha-3 precursor tyrosine aminotransferase copine-8-like isoform X2 adiponectin receptor Paired box Pax-2-B cytochrome P450 peritrophin A, epidermal differentiation-specific -like beta-1,3-galactosyltransferase 1-like origin recognition complex subunit 4 PI-PLC X domain-containing 1-like dual specificity kinase CLK2 isoform X4 hemagglutinin amebocyte aggregation factor-like Hemagglutinin amebocyte aggregation factor PREDICTED: uncharacterized protein LOC106666516 Transmembrane 9 precursor synapsin FAM50 homolog lupus La zinc finger 628 isoform X2 AIG2 family SID1 transmembrane family member 1-like leucine-rich repeat-containing C10orf11 homolog transport and Golgi organization 11 isoform X1 Long-chain fatty acid transport 4 aminopeptidase N bifunctional coenzyme A synthase retinol dehydrogenase 13-like Frizzled-4, partial kyphoscoliosis peptidase-like isoform X2 GA19326, partial vacuolar sorting-associated TDA6 netrin receptor DSCAM, N-acylglucosamine 2-epimerase heat shock 67B2 solute carrier organic anion transporter family member 2A1 Ras-related C3 botulinum toxin substrate 1 N-acetylneuraminate epimerase Ubiquitin thioesterase otubain-like FMRFamide receptor hemicentin-2 isoform X2 Low-density lipo receptor class A domain-containing 3 BCR, COG1649 family aminopeptidase N L-selectin isoform X2 cytochrome P450 4V2 hypothetical protein AURANDRAFT_63034 probable chitinase 3 Trehalose-6-phosphate hydrolase lipase 3-like acyl- -binding domain-containing 5 FMRFamide receptor multiple coagulation factor deficiency 2 homolog isoform X1 Sorting nexin-6-like Gamma-glutamyltranspeptidase 1 nose resistant to fluoxetine 6-like hemK methyltransferase family member 2 solute carrier family 46 member 3-like SAM-dependent methyltransferase neutral ceramidase maltase-glucoamylase, intestinal-like chitinase-3 1 hypothetical protein SARC_09925 4-aminobutyrate aminotransferase, mitochondrial maltase H Golgi-associated plant pathogenesis-related 1-like Zinc carboxypeptidase A 1 hypothetical protein DAPPUDRAFT_307602 chymotrypsin, partial WEB family chloroplastic 2-hydroxyacyl- lyase 1 isoform X1 peritrophic membrane chitin binding chymotrypsin, partial hypothetical protein AURANDRAFT_63034 leucine-rich repeat neuronal 3-like macrophage mannose receptor 1-like MAGUK p55 subfamily member 7 isoform X1 H ACA ribonucleo complex non-core subunit NAF1 Tyrosine- kinase receptor TYRO3 Importin-11 intraflagellar transport 74 homolog isoform X1 EFF-AFF domain containing hypothetical protein DAPPUDRAFT_227990 otoferlin-like isoform X1 disintegrin and metallo ase domain-containing 22 isoform X3 single VWC domain 3 galactoside 2-alpha-L-fucosyltransferase 2-like PREDICTED: uncharacterized protein C9orf117 homolog isoform X1 ras-specific guanine nucleotide-releasing factor 2-like isoform X2 lethal(2)essential for life-like Down syndrome cell adhesion molecule Dscam2 isoform X1 zinc finger GLIS3 isoform X2 synaptotagmin 1 isoform X1 Homeotic Sex combs reduced immunoglobulin superfamily DCC subclass member 4-like Ion channel nesprin-1 isoform X15 PREDICTED: uncharacterized protein LOC105890087 monocarboxylate transporter 3 isoform X1 solute carrier family 28 member 3-like potassium voltage-gated channel subfamily KQT member 1 isoform X3 F-box only 21 antimicrobial peptide hydramacin Solute carrier family 17 member 9 F-box-like WD repeat-containing TBL1XR1 isoform X1 RUN and FYVE domain-containing 2 isoform X2 electroneutral sodium bicarbonate exchanger 1 isoform X7 fibroblast growth factor receptor-like 1 zinc finger 665 isoform X1 Calmodulin Sodium potassium calcium exchanger Nckx30C probable chitinase 2 isoform X1 peroxidase-like isoform X2 dusky-like, isoform A prophenoloxidase activating factor choline O-acetyltransferase galactose oxidase venom protease-like cyclin-dependent kinase 2-associated 2-like PREDICTED: uncharacterized protein LOC106471536 ephrin-B2 isoform X2 unnamed protein product heat shock 70 hypothetical protein TcasGA2_TC001625 pancreatic triacylglycerol lipase-like inositol hexakisphosphate kinase 1 isoform X2 long-chain-fatty-acid-- ligase 4 isoform X1 Spindle and kinetochore-associated 1 4-hydroxyacetophenone monooxygenase AChain A, Orally Active 2-Amino Thienopyrimidine Inhibitors Of The Hsp90 Chaperone platelet-derived growth factor subunit A isoform X1 sulfotransferase 1C4-like heat shock 70 glutathione S-transferase hydroxyacid dehydrogenase plasma kallikrein 4-hydroxyphenylpyruvate dioxygenase-like CAP-Gly domain-containing linker 1-like isoform X1 TKL kinase heat shock 70 kDa cognate 4 roundabout homolog 2-like sterile alpha and TIR motif-containing 1 isoform X1 78 kDa glucose-regulated , partial hypothetical protein tubulointerstitial nephritis antigen-like lethal(2)essential for life-like sodium-dependent phosphate transport 2A dnaJ homolog subfamily C member 27 hypothetical protein L798_10359 FAM188B, partial C10 ras 3 C-type lectin 4 GA20934, partial heat shock 70 kDa cognate 4 fibronectin type-III domain-containing 3A-like isoform X1 hypothetical protein cytochrome P450 2J6 nose resistant to fluoxetine 6-like PREDICTED: uncharacterized protein LOC106804679 vascular endothelial growth factor receptor 1 isoform X2 Granulin, partial lipopolysaccharide-induced tumor necrosis factor-alpha factor G1 S-specific cyclin-D2 helicase MOV-10 isoform X1 transcription factor kayak isoform X1 probable phospholipid-transporting ATPase IF osteoclast-stimulating factor 1-like nose resistant to fluoxetine 6-like matrix metallo ase-25-like SPT transcription factor family member peptidylglycine alpha-hydroxylating monooxygenase nuclear receptor rho-related GTP-binding -like SPT transcription factor family member furin-like protease 1, isoform 1-CRR isoform X1 Signal transducer and activator of transcription 5B hypothetical protein lysozyme-like OCIA domain-containing 1 ETS transcription factor X-box-binding 1 hillarin, isoform A homeobox Nkx- isoform X1 transmembrane , Sodium-dependent phosphate transport 2B ferric-chelate reductase 1 homolog conserved hypothetical protein solute carrier family 46 member 3 Retrovirus-related Pol poly from transposon galactosylgalactosylxylosyl 3-beta-glucuronosyltransferase S isoform X1 msta, isoform A PREDICTED: ficolin-2-like still isoform SIF type 1-like isoform X1 Transposon Tf2-8 poly GTP-binding sar1 cell division cycle 20 homolog Niemann-Pick C1 isoform X5 Diamine acetyltransferase 2 TANC2 isoform X2 GTP cyclohydrolase 1 PREDICTED: uncharacterized protein LOC588798, partial Green-sensitive opsin methylmalonic aciduria and homocystinuria type C epoxide hydrolase 4-like serine protease inhibitor dipetalogastin-like Esterase FE4 centromere-associated E turtle isoform X1 hypothetical protein PBRA_003427 Proto-oncogene Wnt-3 monocarboxylate transporter 9-like AP2-associated kinase 1-like UPF0462 C4orf33 homolog glyco -N-acetylgalactosamine 3-beta-galactosyltransferase 1-like FMRFamide receptor glutamate receptor delta-1 hypothetical protein DAPPUDRAFT_301652 multiple epidermal growth factor-like domains 10 dual specificity kinase partial PDF receptor isoform X1 GDP-fucose O-fucosyltransferase 1 fructose-2,6-bisphosphatase TIGAR B-like Star-like isoform X3 disulfide-isomerase A4 D-aspartate oxidase ankyrin repeat and kinase domain-containing 1-like 4-hydroxyacetophenone monooxygenase BCL2 adenovirus E1B 19 kDa -interacting 3 isoform X1 chitin synthase hypothetical protein DAPPUDRAFT_305676 costars family ABRACL GDP-D-glucose phosphorylase 1-like LIM homeobox Lhx9-like gastrula zinc finger -like FMRFamide receptor corticotropin-releasing factor receptor 1-like PREDICTED: ficolin-2-like failed axon connections carbonic anhydrase Barrier-to-autointegration factor collagen alpha-1(III) chain-like Adenosine deaminase histone-lysine N-methyltransferase SETD7 isoform X1 venom serine protease proclotting enzyme-like decaprenyl-diphosphate synthase subunit 1 Kyphoscoliosis peptidase contactin associated 1 alpha-N-acetylglucosaminidase contactin-2 isoform X1 flocculation FLO11 RNA-binding 1 zinc transporter 5 3-hydroxyisobutyrate dehydrogenase transcription factor Sox-11-like glutathione S-transferase isoform D-like cordon-bleu -like 1 isoform X7 Furin-like protease isoforms 1 1-X partial regulator of nonsense transcripts 2 isoform X1 FMRFamide receptor organic cation transporter prolyl 4-hydroxylase subunit alpha-2 isoform X1 Facilitated trehalose transporter Tret1 chorion peroxidase-like transcription factor 7 isoform X1 Diuretic hormone class 2 carboxypeptidase B-like collagen alpha-1(XVIII) chain isoform X4 Short wavelength sensitive 2B chorion peroxidase-like exonuclease 1 cathepsin L nuclear receptor LIM homeobox Lhx2 Astacin (Astacus ase, Crayfish small-molecule ase) hypothetical protein L798_14060 nocturnin isoform X2 transmembrane protease serine 6 isoform X1 endothelial zinc finger induced by tumor necrosis factor alpha D-dopachrome decarboxylase Ferritin heavy chain 1 ankyrin repeat probable flavin-containing monoamine oxidase A Potassium voltage-gated channel Shab Galactose-3-O-sulfotransferase 2 aminopeptidase N isoform X2 SCO-spondin-like isoform X1 leucine-rich repeat-containing 57 MAM and LDL-receptor class A domain-containing 1-like cytochrome c oxidase subunit 6C-1 glycosyl hydrolase voltage-dependent calcium channel type A subunit alpha-1-like, partial Low-density lipo receptor-related 2 G -activated inward rectifier potassium channel 3 isoform X6 stoned-B-like deoxyribonuclease TATDN2 SET domain-containing Acetylcholine receptor subunit delta precursor, peptidoglycan recognition 1 isoforms B C hypothetical protein gamma-aminobutyric acid receptor subunit beta isoform X2 trypsin-1-like peptidoglycan recognition partial Partner of bursicon suppressor of tumorigenicity 14 homolog talin-1 isoform X2 sodium- and chloride-dependent glycine transporter 2-like tRNA (guanine-N(7)-)-methyltransferase Aminoacylase-1 serine protease nudel UDP-glucuronosyltransferase 2C1 gamma-glutamylcyclotransferase-like enhancer of split mgamma -like neurotrimin-like isoform X1 kynurenine--oxoglutarate transaminase 3 transcriptional repressor scratch 1-like zinc finger 2 homolog probable glutamate receptor rac-like GTP-binding ARAC3 Calcium and integrin-binding family member partial B-cell lymphoma 3 homolog isoform X2 carbonic anhydrase-like thioredoxin mitochondrial tRNA-specific 2-thiouridylase 1 Tetratricopeptide repeat 21B 39S ribosomal L51, mitochondrial aquaporin-9-like isoform X2 noggin-like maspardin-like vesicle-associated membrane 3 nephrin isoform X2 nuclear receptor MOB kinase activator-like 3 AAEL003425-PA, partial ubiquitin-conjugating enzyme E2-22 kDa receptor-type tyrosine- phosphatase alpha-like discoidin domain-containing receptor 2-like Beta-1,4-N-acetylgalactosaminyltransferase bre-4 UNC93 lactadherin-like isoform X5 glutathione peroxidase 7 Atrial natriuretic peptide receptor 2, partial phosphoglycerate mutase 1-like PR domain zinc finger 4 CDC42 small effector homolog translation initiation factor IF-2-like glycine receptor subunit alpha-2-like PREDICTED: uncharacterized protein LOC106126105 acylamino-acid-releasing enzyme peptidyl-tRNA hydrolase mitochondrial probable E3 ubiquitin- ligase HERC1 , partial nicotinic acetylcholine receptor alpha 8 subunit isoform X1 oligosaccharyltransferase complex subunit OSTC U6 snRNA-associated Sm LSm8 Williams-Beuren syndrome chromosomal region 27 -like PREDICTED: uncharacterized protein LOC105318289 isoform X10 PREDICTED: uncharacterized protein LOC106158748 NAD-dependent deacetylase sirtuin-2 phosphatase 1L leucine-rich repeat-containing 45 hypothetical protein GPECTOR_33g603 larval cuticle LCP-17-like Adenosine deaminase FAM57A isoform X1 polypeptide N-acetylgalactosaminyltransferase 1 molybdate-anion transporter-like deoxyuridine 5 -triphosphate nucleotidohydrolase-like homeobox engrailed-1a-like WSC domain-containing 1-like muscle calcium channel subunit alpha-1 isoform X4 deoxyribodipyrimidine photo-lyase-like glutamate receptor 2-like Transient receptor potential cation channel subfamily A member 1 dolichyl-phosphate beta-glucosyltransferase FAD-linked sulfhydryl oxidase ALR neural Wiskott-Aldrich syndrome -like probable glutamate receptor, partial membrane-associated progesterone receptor component 1-like glutathione S-transferase asteroid homolog 1 low-density lipo receptor-related 2 phosphatase Fmp31 Ubiquitin-like modifier-activating enzyme 6 PREDICTED: uncharacterized protein LOC106878238 CD82 antigen, partial histone H1 speckle-type POZ -like isoform X2 Tropomyosin LBL, partial multidrug resistance-associated 1-like transcription factor Sox-11-like Latrophilin LAT-2 leucine-rich repeat-containing 24-like fidgetin 1 acyl- thioesterase 2 Down syndrome cell adhesion molecule Dscam2 MFS-type transporter SLC18B1-like FMRFamide receptor myosin heavy chain, muscle isoform X10 Krueppel-like factor 5 Cell surface glyco 1 leucine-rich repeat-containing 42 CDNA sequence BC003267 Glutamate-gated chloride channel carbohydrate sulfotransferase 8-like defective proboscis extension response 6, isoform C solute carrier family 35 member G1-like hypothetical protein LOTGIDRAFT_166867 IQ and ubiquitin-like domain-containing myosin light chain kinase, smooth muscle isoform X6 electroneutral sodium bicarbonate exchanger 1 isoform X5 Vigilin probable G- coupled receptor muscle calcium channel subunit alpha-1 isoform X1 40S ribosomal S7 carbonic anhydrase 2 dynein intermediate chain 3, ciliary-like macrophage mannose receptor 1-like zinc finger CCCH domain-containing 3-like galactose oxidase potassium channel kcnq, probable glutamate receptor probable phospholipid-transporting ATPase IIB isoform X2 histone-lysine N-methyltransferase SETD7 isoform X1 hypothetical protein myosin heavy chain, muscle isoform X6 rap1 GTPase-activating 1-like isoform X9 trehalose transporter 1 probable WRKY transcription factor 1 isoform X2 chitin deacetylase-like isoform h neurotrimin-like isoform X1 UDP-glycosyltransferase UGT42A1 blood vessel epicardial substance-A-like pachytene checkpoint 2 homolog beat , pickpocket 28-like transcription factor BCS-1 protein ral guanine nucleotide dissociation stimulator isoform X1 zinc finger BED domain-containing 1-like hypothetical protein PVMG_01257 PREDICTED: uncharacterized protein LOC106681552 myophilin WSC domain-containing 2 Ras family member 12 RING finger 11 cell division control 6 homolog monocarboxylate transporter 12-like hemagglutinin amebocyte aggregation factor-like mothers against decapentaplegic homolog 6-like fukutin-related -like adenylate kinase isoenzyme 1 peptidyl-prolyl cis-trans isomerase-like 4 aminopeptidase N Chorion peroxidase phosphatase 1 regulatory subunit 12A isoform X3 probable elongator complex 3 seleno M-like PREDICTED: uncharacterized protein LOC106687650 serine ase stubble Cardioacceleratory peptide receptor peritrophin A, isoform A fibril-forming collagen alpha chain-like twitchin isoform X17 Retrovirus-related Pol poly from transposon equilibrative nucleoside transporter 1 PREDICTED: uncharacterized protein C7orf26 homolog transmembrane 19 ankyrin repeat domain-containing 50-like pickpocket 28-like Calmodulin solute carrier family 22 member 13-like cytochrome P450 306a1 glutamate receptor U1-like PI-PLC X domain-containing 1 histone-lysine N-methyltransferase SETD7 Tetratricopeptide repeat 25 nuclear nucleic acid-binding C1D b(0,+)-type amino acid transporter 1 isoform X2 PREDICTED: uncharacterized protein LOC100880791 dpy-30 homolog calmodulin UPF0704 C6orf165 homolog chemosensory ionotropic receptor x, partial v-type proton atpase catalytic subunit a calcitonin gene-related peptide type 1 receptor-like probable imidazolonepropionase TOX high mobility group box family member 3-like isoform X2 haloacid dehalogenase-like hydrolase domain-containing 2 CDGSH iron-sulfur domain-containing mitochondrial aminopeptidase N methyltransferase type 11 probable pyridoxal 5 -phosphate synthase subunit pdx2 enkurin serine ase stubble Histone-lysine N-methyltransferase SETMAR carbohydrate sulfotransferase 4-like isoform X1 smoothelin 1 zinc finger and BTB domain-containing 49 Histone-lysine N-methyltransferase SETD7 zinc transporter ZIP1 Histone deacetylase 6 muscarinic acetylcholine receptor DM1-like synapsin urokinase-type plasminogen activator 2-methoxy-6-polyprenyl-1,4-benzoquinol methylase, mitochondrial sodium calcium exchanger 1 isoform X1 shuttle craft transcription factor Sox-11-like homeobox DBX1 palmitoleoyl- carboxylesterase notum1-like isoform X1 voltage-dependent calcium channel type A subunit alpha-1 isoform X11 UDP- c:beta-1,3-N-acetylgalactosaminyltransferase 2-like probable E3 ubiquitin- ligase HERC1 steroid receptor seven-up, isoforms B C galactoside 2-alpha-L-fucosyltransferase 2-like spermine oxidase membrane-associated guanylate kinase, WW and PDZ domain-containing 1-like isoform X3 potassium voltage-gated channel Shab-like vesicle transport SFT2A Serine mitochondrial cell division cycle 20 homolog estradiol 17-beta-dehydrogenase 8 PREDICTED: uncharacterized protein LOC102803158, partial PREDICTED: uncharacterized protein LOC106163726 stomatin-2 CG9497, isoform A tail connector rho GTPase-activating 11A histone H1 SPT transcription factor family member vitellogenin 2
A
A
B
C
B
E
A
ion
ion/proteolysis
ion/hsp/xeno
xeno
proteolysis/chi4n
ion/chi4n
ion
ion
A
D
Similar expression in:
A – All treatments
B – Chronic treatments
C – Acute treatments
D – TBTO treatments
E – Cu treatments
relative
row min row max
Acute Cu 1
Control 1
Acute TBTO 1
Chronic Cu 3
Control 3
Acute Cu 3
Chronic TBTO 1
Control 2
Acute Cu 2
Acute TBTO 2
Acute TBTO 3
Chronic TBTO 2
Chronic Cu 2
Chronic TBTO 3
Chronic Cu 1
Description
carbonic anhydrase 2 probable phospholipid-transporting ATPase IIB isoform X2 tRNA (uracil-5-)-methyltransferase homolog A-like isoform X1 organic cation transporter -like plasma membrane calcium-transporting ATPase 2 isoform X3 probable G- coupled receptor enhancer of sevenless 2B Down syndrome cell adhesion molecule Dscam2 regulator of nonsense transcripts 2 isoform X1 speckle-type POZ -like isoform X2 leucine-rich repeat-containing 24-like porphobilinogen deaminase isoform X1 peptidase S15 40S ribosomal S7 cytochrome P450 2J2-like vam6 vps39 zinc finger OZF-like gonadotropin-releasing hormone II receptor-like probable cyclin-dependent serine threonine- kinase DDB_G0292550 voltage-dependent calcium channel type A subunit alpha-1 isoform X5 phosphatase 1 regulatory subunit 12A isoform X6 leucine-rich repeat-containing DDB_G0290503 Pleckstrin homology domain containing carbohydrate sulfotransferase 9-like constitutive coactivator of PPAR-gamma 1 Helicase ARIP4 sugar transporter, phytophthora-inhibited protease 1 Latrophilin LAT-2 intraflagellar transport 27 homolog neuropeptide F receptor-like acyl- thioesterase 2 NADH dehydrogenase, zinc finger Noc macrophage mannose receptor 1-like zinc transporter 5 SEC14 1 kinesin-associated 3 isoform X2 BTB POZ domain-containing 9 Diamine acetyltransferase 2 dnaJ homolog subfamily C member 8 glucose-6-phosphate 1-epimerase jim lovell disrupted in renal carcinoma 2 homolog RNA 3 -terminal phosphate cyclase tipE isoform X1 DNA excision repair ERCC-6-like 2 isoform X6 Esterase FE4 Transmembrane 9 precursor hypothetical protein DAPPUDRAFT_307602 RNA polymerase II degradation factor 1-like intraflagellar transport 80 homolog Paired box Pax-2-B zinc transporter 5 failed axon connections homolog PREDICTED: uncharacterized protein LOC106666516 tail connector estradiol 17-beta-dehydrogenase 8 PREDICTED: uncharacterized protein LOC102803158, partial phospholipase B1, membrane-associated C-type lectin SKI family transcriptional corepressor 2 THAP domain-containing 4 histone H1 cell division cycle 20 homolog vesicle transport SFT2A PREDICTED: uncharacterized protein LOC106163726 ral guanine nucleotide dissociation stimulator isoform X1 alpha-(1,3)-fucosyltransferase C-like activating signal cointegrator 1 complex subunit 2 homolog histone-lysine N-methyltransferase SMYD3 tumor D54 isoform X2 XIAP-associated factor 1 isoform X1 tyrosine- phosphatase 99A-like methionine sulfoxide oxidase Mical isoform V1 msta, isoform A lethal(2)essential for life-like seleno W sphingosine kinase 1-like Rap1 GTPase-GDP dissociation stimulator 1-B lethal(2)denticleless calcitonin gene-related peptide type 1 receptor-like Cathepsin L-like cysteine ase 4-coumarate-- ligase 1-like 4-coumarate-- ligase 1-like Cytochrome P450, family 2, subfamily J, polypeptide 2 Aldose 1-epimerase tyrosine aminotransferase neuronal acetylcholine receptor subunit alpha-7 isoform X2 regulator of nonsense transcripts 2 isoform X1 serine threonine- kinase 3 isoform X2 pickpocket 28-like opsin, pteropsin type, partial meiosis arrest female 1 isoform X1 kyphoscoliosis peptidase-like isoform X2 FMRFamide receptor FAM50 homolog multiple coagulation factor deficiency 2 homolog isoform X1 lupus La synapsin zinc finger 628 isoform X2 Low-density lipo receptor class A domain-containing 3 Sorting nexin-6-like Transient receptor potential cation channel subfamily A member 1 lethal(2)essential for life-like dolichyl-phosphate beta-glucosyltransferase FAD-linked sulfhydryl oxidase ALR PREDICTED: uncharacterized protein LOC106878238 transcription factor Sox-11-like leucine-rich repeat neuronal 3-like adiponectin receptor Ras-related C3 botulinum toxin substrate 1 epidermal differentiation-specific -like 4-aminobutyrate aminotransferase, mitochondrial Golgi-associated plant pathogenesis-related 1-like platelet-derived growth factor subunit A isoform X1 solute carrier organic anion transporter family member 2A1 solute carrier family 46 member 3-like hypothetical protein SARC_09925 phospholipase ABHD3 ileal sodium bile acid cotransporter-like macrophage mannose receptor 1-like aminopeptidase N SAM-dependent methyltransferase chitinase-3 1 maltase-glucoamylase, intestinal-like peritrophin A, peritrophic membrane chitin binding maltase H Zinc carboxypeptidase A 1 chymotrypsin, partial chymotrypsin, partial acyl- -binding domain-containing 5 aminopeptidase N cytochrome P450 4V2 2-hydroxyacyl- lyase 1 isoform X1 retinol dehydrogenase 13-like lipase 3-like neutral ceramidase Gamma-glutamyltranspeptidase 1 hypothetical protein AURANDRAFT_63034 SID1 transmembrane family member 1-like GA19326, partial Long-chain fatty acid transport 4 N-acylglucosamine 2-epimerase nose resistant to fluoxetine 6-like BCR, COG1649 family bifunctional coenzyme A synthase leucine-rich repeat-containing C10orf11 homolog 78 kDa glucose-regulated , partial CAP-Gly domain-containing linker 1-like isoform X1 TKL kinase N-acetylneuraminate epimerase beta-1,3-galactosyltransferase 1-like low density lipo receptor adapter 1-like PI-PLC X domain-containing 1-like origin recognition complex subunit 4 hemagglutinin amebocyte aggregation factor-like Hemagglutinin amebocyte aggregation factor heat shock 70 kDa cognate 4 fibronectin type-III domain-containing 3A-like isoform X1 glutathione S-transferase hydroxyacid dehydrogenase 4-hydroxyacetophenone monooxygenase AIG2 family hemK methyltransferase family member 2 Frizzled-4, partial WEB family chloroplastic heat shock 70 kDa cognate 4 heat shock 70 hypothetical protein AChain A, Orally Active 2-Amino Thienopyrimidine Inhibitors Of The Hsp90 Chaperone X-box-binding 1 SPT transcription factor family member vitellogenin 2 H ACA ribonucleo complex non-core subunit NAF1 netrin receptor DSCAM, CG8671, isoform B hypothetical protein DAPPUDRAFT_122326, partial Suppressor of hairless basement membrane-specific heparan sulfate proteoglycan core isoform X7 ubiquitin carboxyl-terminal hydrolase 31-like bipolar kinesin KRP-130-like platelet-activating factor acetylhydrolase copine-8-like isoform X2 G1 S-specific cyclin-D2 cytochrome P450 2J6 heat shock 70 Sodium potassium calcium exchanger Nckx30C venom protease-like dusky-like, isoform A PREDICTED: uncharacterized protein LOC105890087 GA20934, partial sodium-dependent phosphate transport 2A dnaJ homolog subfamily C member 27 hypothetical protein zinc finger GLIS3 isoform X2 4-hydroxyphenylpyruvate dioxygenase-like furin-like protease 1, isoform 1-CRR isoform X1 Sodium-dependent phosphate transport 2B long-chain-fatty-acid-- ligase 4 isoform X1 nose resistant to fluoxetine 6-like Signal transducer and activator of transcription 5B vascular endothelial growth factor receptor 1 isoform X2 nose resistant to fluoxetine 6-like Granulin, partial ETS transcription factor solute carrier family 28 member 3-like C-type lectin 4 lethal(2)essential for life-like rho-related GTP-binding -like probable phospholipid-transporting ATPase IF PREDICTED: uncharacterized protein LOC106804679 nuclear receptor transcription factor kayak isoform X1 lipopolysaccharide-induced tumor necrosis factor-alpha factor hypothetical protein osteoclast-stimulating factor 1-like matrix metallo ase-25-like roundabout homolog 2-like SPT transcription factor family member SPT transcription factor family member helicase MOV-10 isoform X1 sterile alpha and TIR motif-containing 1 isoform X1 transmembrane , otoferlin-like isoform X1 antimicrobial peptide hydramacin neural Wiskott-Aldrich syndrome -like probable chitinase 2 isoform X1 immunoglobulin superfamily DCC subclass member 4-like hypothetical protein TcasGA2_TC001625 transcription factor 7 isoform X1 TIGR02452 family zinc finger matrin-type 4 isoform X2 Homeotic Sex combs reduced hypothetical protein DAPPUDRAFT_227990 Ubiquitin thioesterase otubain-like pancreatic triacylglycerol lipase-like histone-lysine N-methyltransferase SUV39H2-like isoform X1 Tyrosine- kinase receptor TYRO3 FAM188B, partial disintegrin and metallo ase domain-containing 22 isoform X3 choline O-acetyltransferase cyclin-dependent kinase 17-like isoform X1 Retrovirus-related Pol poly from transposon synaptotagmin 1 isoform X1 RUN and FYVE domain-containing 2 isoform X2 F-box-like WD repeat-containing TBL1XR1 isoform X1 vacuolar sorting-associated TDA6 FMRFamide receptor PREDICTED: uncharacterized protein LOC106471536 nesprin-1 isoform X15 EFF-AFF domain containing cyclin-dependent kinase 2-associated 2-like Calmodulin potassium voltage-gated channel subfamily KQT member 1 isoform X3 zinc finger 665 isoform X1 ras-specific guanine nucleotide-releasing factor 2-like isoform X2 L-selectin isoform X2 Importin-11 intraflagellar transport 74 homolog isoform X1 galactoside 2-alpha-L-fucosyltransferase 2-like PREDICTED: uncharacterized protein C9orf117 homolog isoform X1 transport and Golgi organization 11 isoform X1 ferric-chelate reductase 1 homolog sulfotransferase 1C4-like MAGUK p55 subfamily member 7 isoform X1 plasma kallikrein heat shock 67B2 inositol hexakisphosphate kinase 1 isoform X2 Spindle and kinetochore-associated 1 monocarboxylate transporter 3 isoform X1 OCIA domain-containing 1 homeobox Nkx- isoform X1 Tropomyosin Elongation of very long chain fatty acids 6 pickpocket 28 electroneutral sodium bicarbonate exchanger 1 isoform X7 lysozyme-like peptidylglycine alpha-hydroxylating monooxygenase fibroblast growth factor receptor-like 1 spermine oxidase solute carrier family 46 member 3 UPF0193 EVG1 homolog PREDICTED: uncharacterized protein LOC103573342 C10 galactose oxidase peroxidase-like isoform X2 Solute carrier family 17 member 9 CG9497, isoform A hillarin, isoform A shuttle craft ferric-chelate reductase 1 homolog deoxycytidylate deaminase X-linked retinitis pigmentosa GTPase regulator sodium calcium exchanger 1 isoform X1 cathepsin L1-like cation-independent mannose-6-phosphate receptor PI-PLC X domain-containing 1 Krueppel-like factor 5 LBL, partial zinc finger CCCH domain-containing 3-like Tropomyosin GILT , partial dual specificity kinase CLK2 isoform X4 Longitudinals lacking -like hypothetical protein L798_04225 Purine nucleoside phosphorylase kinesin unc-104 isoform X11 palmitoleoyl- carboxylesterase notum1-like isoform X1 peroxiredoxin 1 forkhead box O AAEL003425-PA, partial restin homolog isoform X1 Armadillo repeat-containing 2 amidohydrolase hypothetical protein notch-like transmembrane receptor LIN-12 Actin, muscle Troponin C, isoform 1 aspartyl asparaginyl beta-hydroxylase isoform X2 troponin I isoform X7 Actin, muscle hypothetical protein DAPPUDRAFT_109886 nesprin-1 isoform X15 28S ribosomal S36, mitochondrial isoform X1 cytochrome P450 Neuronal acetylcholine receptor subunit alpha-3 precursor acylpyruvase FAHD1, mitochondrial-like Filamin-A Skeletor, isoforms B C Solute carrier family 12 member 2 Pyrazinamidase nicotinamidase FMRFamide receptor LIM homeobox Lhx3 isoform X1 hypothetical protein L798_10359 prophenoloxidase activating factor single VWC domain 3 tubulointerstitial nephritis antigen-like ras 3 cytochrome c oxidase subunit 5B, mitochondrial-like innexin inx2 Solute carrier family 46 member 3 FMRFamide receptor methyltransferase type 11 UPF0704 C6orf165 homolog v-type proton atpase catalytic subunit a nuclear receptor glutamate receptor U1-like PREDICTED: uncharacterized protein C7orf26 homolog monocarboxylate transporter 12-like Histone-lysine N-methyltransferase SETMAR haloacid dehalogenase-like hydrolase domain-containing 2 Chorion peroxidase peritrophin A, isoform A aminopeptidase N histone-lysine N-methyltransferase SETD7 Ferritin heavy chain 1 chitin deacetylase-like isoform h WSC domain-containing 2 PREDICTED: uncharacterized protein LOC105844725 ephrin-B2 isoform X2 b(0,+)-type amino acid transporter 1 isoform X2 cytochrome P450 306a1 PREDICTED: uncharacterized protein LOC106681552 ankyrin repeat domain-containing 50-like beat , PREDICTED: uncharacterized protein LOC106687650 twitchin isoform X17 calcitonin gene-related peptide type 1 receptor-like defective proboscis extension response 6, isoform C synapsin Retrovirus-related Pol poly from transposon pickpocket 28-like electroneutral sodium bicarbonate exchanger 1 isoform X5 venom allergen 3-like ankyrin repeat domain Vigilin multidrug resistance-associated 1-like Green-sensitive opsin UPF0462 C4orf33 homolog collagen alpha-1(XVIII) chain isoform X4 muscle calcium channel subunit alpha-1 isoform X1 hypothetical protein LOTGIDRAFT_166867 IQ and ubiquitin-like domain-containing glycine receptor subunit alpha-2 isoform X5 potassium channel kcnq, phosphate ABC transporter substrate-binding galactose oxidase dynein intermediate chain 3, ciliary-like pachytene checkpoint 2 homolog blood vessel epicardial substance-A-like trehalose transporter 1 neurotrimin-like isoform X1 rap1 GTPase-activating 1-like isoform X9 UDP-glycosyltransferase UGT42A1 carbohydrate sulfotransferase 8-like CDNA sequence BC003267 zinc finger and BTB domain-containing 49 smoothelin 1 Calmodulin Cell surface glyco 1 histone-lysine N-methyltransferase SETD7 isoform X1 probable glutamate receptor MFS-type transporter SLC18B1-like solute carrier family 35 member G1-like fidgetin 1 myosin heavy chain, muscle isoform X6 cathepsin L leucine-rich repeat-containing 42 myosin heavy chain, muscle isoform X10 transcription factor 7 isoform X1 myosin light chain kinase, smooth muscle isoform X6 Glutamate-gated chloride channel FMRFamide receptor carboxypeptidase B-like rho GTPase-activating 11A stomatin-2 Histone-lysine N-methyltransferase SETD7 Histone deacetylase 6 potassium voltage-gated channel Shab-like UDP- c:beta-1,3-N-acetylgalactosaminyltransferase 2-like homeobox DBX1 ankyrin repeat aminopeptidase N probable E3 ubiquitin- ligase HERC1 enkurin CDGSH iron-sulfur domain-containing mitochondrial fukutin-related -like seleno M-like adenylate kinase isoenzyme 1 larval cuticle LCP-17-like ubiquitin-conjugating enzyme E2-22 kDa transmembrane protease serine 6 isoform X1 mothers against decapentaplegic homolog 6-like carbohydrate sulfotransferase 4-like isoform X1 D-dopachrome decarboxylase myophilin probable pyridoxal 5 -phosphate synthase subunit pdx2 transmembrane 19 hypothetical protein PVMG_01257 hemagglutinin amebocyte aggregation factor-like serine ase stubble unnamed protein product phosphatase 1 regulatory subunit 12A isoform X3 Tetratricopeptide repeat 25 Ras family member 12 cell division control 6 homolog peptidyl-prolyl cis-trans isomerase-like 4 RING finger 11 Cardioacceleratory peptide receptor solute carrier family 22 member 13-like BCS-1 protein muscarinic acetylcholine receptor DM1-like hypothetical protein Serine mitochondrial probable elongator complex 3 chemosensory ionotropic receptor x, partial calmodulin PREDICTED: uncharacterized protein LOC100880791 transcription factor zinc finger BED domain-containing 1-like pickpocket 28-like nuclear nucleic acid-binding C1D probable WRKY transcription factor 1 isoform X2 dpy-30 homolog urokinase-type plasminogen activator Potassium voltage-gated channel Shab glutamate receptor 2-like ankyrin repeat and kinase domain-containing 1-like deoxyribonuclease TATDN2 talin-1 isoform X2 neurotrimin-like isoform X1 probable glutamate receptor Ion channel GTP-binding sar1 Transposon Tf2-8 poly muscle calcium channel subunit alpha-1 isoform X4 low-density lipo receptor-related 2 transcriptional repressor scratch 1-like Down syndrome cell adhesion molecule Dscam2 isoform X1 probable E3 ubiquitin- ligase HERC1 stoned-B-like Low-density lipo receptor-related 2 voltage-dependent calcium channel type A subunit alpha-1-like, partial voltage-dependent calcium channel type A subunit alpha-1 isoform X11 still isoform SIF type 1-like isoform X1 PREDICTED: ficolin-2-like Star-like isoform X3 hypothetical protein DAPPUDRAFT_301652 costars family ABRACL Niemann-Pick C1 isoform X5 TANC2 isoform X2 cell division cycle 20 homolog FMRFamide receptor F-box only 21 gamma-glutamylcyclotransferase-like GDP-fucose O-fucosyltransferase 1 GTP cyclohydrolase 1 Adenosine deaminase glycine receptor subunit alpha-2-like failed axon connections glutamate receptor delta-1 RNA-binding 1 BCL2 adenovirus E1B 19 kDa -interacting 3 isoform X1 hemicentin-2 isoform X2 voltage-dependent calcium channel subunit alpha-2 delta-3 probable chitinase 3 centromere-associated E isoforms B C Diuretic hormone class 2 homeobox engrailed-1a-like probable flavin-containing monoamine oxidase A tRNA (guanine-N(7)-)-methyltransferase polypeptide N-acetylgalactosaminyltransferase 1 Tetratricopeptide repeat 21B MOB kinase activator-like 3 WSC domain-containing 1-like PREDICTED: uncharacterized protein LOC106158748 asteroid homolog 1 rac-like GTP-binding ARAC3 Atrial natriuretic peptide receptor 2, partial deoxyuridine 5 -triphosphate nucleotidohydrolase-like gamma-aminobutyric acid receptor subunit beta isoform X2 U6 snRNA-associated Sm LSm8 glyco -N-acetylgalactosamine 3-beta-galactosyltransferase 1-like histone H1 2-methoxy-6-polyprenyl-1,4-benzoquinol methylase, mitochondrial probable glutamate receptor, partial glutathione S-transferase phosphatase Fmp31 PREDICTED: uncharacterized protein LOC106126105 sodium calcium exchanger 1 isoform X1 steroid receptor seven-up, isoforms B C SET domain-containing lactadherin-like isoform X5 noggin-like Williams-Beuren syndrome chromosomal region 27 -like zinc transporter ZIP1 kynurenine--oxoglutarate transaminase 3 Beta-1,4-N-acetylgalactosaminyltransferase bre-4 oligosaccharyltransferase complex subunit OSTC Astacin (Astacus ase, Crayfish small-molecule ase) phosphoglycerate mutase 1-like aquaporin-9-like isoform X2 aminopeptidase N isoform X2 G -activated inward rectifier potassium channel 3 isoform X6 zinc finger 2 homolog MAM and LDL-receptor class A domain-containing 1-like leucine-rich repeat-containing 57 sodium- and chloride-dependent glycine transporter 2-like Acetylcholine receptor subunit delta precursor, molybdate-anion transporter-like disulfide-isomerase A4 msta, isoform A histone-lysine N-methyltransferase SETD7 isoform X1 contactin-2 isoform X1 serine ase stubble carbonic anhydrase NAD-dependent deacetylase sirtuin-2 nicotinic acetylcholine receptor alpha 8 subunit isoform X1 UNC93 AP2-associated kinase 1-like 3-hydroxyisobutyrate dehydrogenase monocarboxylate transporter 9-like SCO-spondin-like isoform X1 proclotting enzyme-like carbonic anhydrase-like thioredoxin Calcium and integrin-binding family member partial Facilitated trehalose transporter Tret1 chorion peroxidase-like nocturnin isoform X2 chorion peroxidase-like glycosyl hydrolase alpha-N-acetylglucosaminidase PREDICTED: ficolin-2-like fructose-2,6-bisphosphatase TIGAR B-like galactoside 2-alpha-L-fucosyltransferase 2-like acylamino-acid-releasing enzyme CDC42 small effector homolog nephrin isoform X2 hypothetical protein GPECTOR_33g603 receptor-type tyrosine- phosphatase alpha-like mitochondrial tRNA-specific 2-thiouridylase 1 B-cell lymphoma 3 homolog isoform X2 LIM homeobox Lhx2 hypothetical protein L798_14060 probable imidazolonepropionase TOX high mobility group box family member 3-like isoform X2 endothelial zinc finger induced by tumor necrosis factor alpha PR domain zinc finger 4 prolyl 4-hydroxylase subunit alpha-2 isoform X1 exonuclease 1 hypothetical protein DAPPUDRAFT_305676 corticotropin-releasing factor receptor 1-like PDF receptor isoform X1 Partner of bursicon Short wavelength sensitive 2B venom serine protease GDP-D-glucose phosphorylase 1-like LIM homeobox Lhx9-like transcription factor Sox-11-like glutathione S-transferase isoform D-like Furin-like protease isoforms 1 1-X partial epoxide hydrolase 4-like hypothetical protein PBRA_003427 decaprenyl-diphosphate synthase subunit 1 dual specificity kinase partial chitin synthase gastrula zinc finger -like FMRFamide receptor galactosylgalactosylxylosyl 3-beta-glucuronosyltransferase S isoform X1 Kyphoscoliosis peptidase methylmalonic aciduria and homocystinuria type C contactin associated 1 multiple epidermal growth factor-like domains 10 organic cation transporter Adenosine deaminase palmitoleoyl- carboxylesterase notum1-like isoform X1 D-aspartate oxidase vesicle-associated membrane 3 maspardin-like translation initiation factor IF-2-like nuclear receptor glutathione peroxidase 7 discoidin domain-containing receptor 2-like leucine-rich repeat-containing 45 serine protease nudel peptidoglycan recognition 1 peptidoglycan recognition partial suppressor of tumorigenicity 14 homolog trypsin-1-like 39S ribosomal L51, mitochondrial fibril-forming collagen alpha chain-like Barrier-to-autointegration factor collagen alpha-1(III) chain-like equilibrative nucleoside transporter 1 flocculation FLO11 cordon-bleu -like 1 isoform X7 UDP-glucuronosyltransferase 2C1 turtle isoform X1 transcription factor Sox-11-like hypothetical protein AURANDRAFT_63034 4-hydroxyacetophenone monooxygenase Aminoacylase-1 Proto-oncogene Wnt-3 Galactose-3-O-sulfotransferase 2 hypothetical protein cytochrome c oxidase subunit 6C-1 phosphatase 1L , partial Trehalose-6-phosphate hydrolase PREDICTED: uncharacterized protein LOC588798, partial serine protease inhibitor dipetalogastin-like CD82 antigen, partial Ubiquitin-like modifier-activating enzyme 6 membrane-associated guanylate kinase, WW and PDZ domain-containing 1-like isoform X3 conserved hypothetical protein apolipophorins-like dynein heavy chain 6, axonemal peptidyl-tRNA hydrolase mitochondrial enhancer of split mgamma -like PREDICTED: uncharacterized protein LOC105318289 isoform X10 AAEL003425-PA, partial FAM57A isoform X1 membrane-associated progesterone receptor component 1-like deoxyribodipyrimidine photo-lyase-like
B
* * * * * *
144
Fig. 5-6 A heat map of significantly differentially expressed ion regulatory genes
(RPKM) in all treatment comparisons for mean expression of each individual gene
normalized to control expression (A). All 3 biological replicates are also shown (B) with
chronic treatments denoted with a green asterisk (*). Cooler colors (blues) indicate lower
expression while warmer colors (reds) indicate higher expression. Heat map and
hierarchal clustering done using the GENE-E program using one minus Pearson
correlation.
global
0.6631 0.9999 1.34
Control
Chronic Cu
Acute Cu
Acute TBTO
Chronic TBTO
Annotation
electroneutral sodium bicarbonate exchanger 1 isoform X7
sodium calcium exchanger 1 isoform X1
Solute carrier family 17 member 9
Transient receptor potential cation channel subfamily A member 1
glutamate receptor 2-like
muscle calcium channel subunit alpha-1 isoform X4
glycine receptor subunit alpha-2-like
voltage-dependent calcium channel type A subunit alpha-1 isoform X11
aquaporin-9-like isoform X2
probable glutamate receptor
nicotinic acetylcholine receptor alpha 8 subunit isoform X1
G -activated inward rectifier potassium channel 3 isoform X6
Acetylcholine receptor subunit delta precursor,
sodium- and chloride-dependent glycine transporter 2-like
zinc transporter 5
potassium voltage-gated channel Shab-like
potassium voltage-gated channel subfamily KQT member 1 isoform X3
Ion channel
Sodium-dependent phosphate transport 2B
hillarin, isoform A
sodium-dependent phosphate transport 2A
hydroxyacid dehydrogenase
Troponin C, isoform 1
zinc transporter 5
pickpocket 28
pickpocket 28-like
Solute carrier family 12 member 2
Potassium voltage-gated channel Shab
Glutamate-gated chloride channel
glutamate receptor U1-like
pickpocket 28-like
potassium channel kcnq,
probable glutamate receptor
plasma membrane calcium-transporting ATPase 2 isoform X3
muscle calcium channel subunit alpha-1 isoform X1
electroneutral sodium bicarbonate exchanger 1 isoform X5
sodium calcium exchanger 1 isoform X1
neuronal acetylcholine receptor subunit alpha-7 isoform X2
voltage-dependent calcium channel type A subunit alpha-1 isoform X5
voltage-dependent calcium channel subunit alpha-2 delta-3
glycine receptor subunit alpha-2 isoform X5
ileal sodium bile acid cotransporter-like
Neuronal acetylcholine receptor subunit alpha-3 precursor
relative
row min row max
Acute TBTO 1
Control 3
Acute Cu 3
Acute TBTO 3
Control 1
Acute Cu 1
Control 2
Acute Cu 2
Acute TBTO 2
Chronic Cu 1
Chronic TBTO 1
Chronic TBTO 2
Chronic Cu 2
Chronic Cu 3
Chronic TBTO 3
ID
Transient receptor potential cation channel subfamily A member 1
Ion channel
glutamate receptor 2-like
probable glutamate receptor
electroneutral sodium bicarbonate exchanger 1 isoform X7
sodium-dependent phosphate transport 2A
Sodium-dependent phosphate transport 2B
potassium voltage-gated channel Shab-like
muscle calcium channel subunit alpha-1 isoform X4
voltage-dependent calcium channel type A subunit alpha-1 isoform X11
glycine receptor subunit alpha-2-like
ileal sodium bile acid cotransporter-like
hydroxyacid dehydrogenase
hillarin, isoform A
nicotinic acetylcholine receptor alpha 8 subunit isoform X1
sodium- and chloride-dependent glycine transporter 2-like
Acetylcholine receptor subunit delta precursor,
G -activated inward rectifier potassium channel 3 isoform X6
zinc transporter 5
Solute carrier family 17 member 9
sodium calcium exchanger 1 isoform X1
electroneutral sodium bicarbonate exchanger 1 isoform X5
potassium voltage-gated channel subfamily KQT member 1 isoform X3
aquaporin-9-like isoform X2
muscle calcium channel subunit alpha-1 isoform X1
potassium channel kcnq,
Solute carrier family 12 member 2
pickpocket 28-like
neuronal acetylcholine receptor subunit alpha-7 isoform X2
Glutamate-gated chloride channel
Potassium voltage-gated channel Shab
pickpocket 28-like
glutamate receptor U1-like
plasma membrane calcium-transporting ATPase 2 isoform X3
probable glutamate receptor
voltage-dependent calcium channel subunit alpha-2 delta-3
voltage-dependent calcium channel type A subunit alpha-1 isoform X5
glycine receptor subunit alpha-2 isoform X5
zinc transporter 5
sodium calcium exchanger 1 isoform X1
pickpocket 28
Troponin C, isoform 1
Neuronal acetylcholine receptor subunit alpha-3 precursor
A B
* * * * * *
145
Fig. 5-7 A heat map of significantly differentially expressed heat shock proteins (RPKM)
in all treatment comparisons for mean expression of each individual gene normalized to
control expression (A). All 3 biological replicates are also shown (B) with chronic
treatments denoted with a green asterisk (*). Cooler colors (blues) indicate lower
expression while warmer colors (reds) indicate higher expression. Heat map and
hierarchal clustering done using the GENE-E program using one minus Pearson
correlation.
relative
row min row max
Control 2
Acute Cu 3
Acute TBTO 3
Chronic Cu 3
Control 3
Control 1
Chronic Cu 2
Acute TBTO 2
Acute TBTO 1
Chronic TBTO 2
Acute Cu 1
Chronic Cu 1
Chronic TBTO 1
Acute Cu 2
Chronic TBTO 3
Annotation-1
heat shock 67B2
heat shock 70
heat shock 70
heat shock 70 kDa cognate 4
heat shock 70 kDa cognate 4
A B
global
0.8751 1 1.13
Control
Chronic Cu
Acute Cu
Chronic TBTO
Acute TBTO
Feature ID
heat shock 70 kDa cognate 4
heat shock 70
heat shock 70 kDa cognate 4
heat shock 67B2
heat shock 70
* * * * * *
146
Fig. 5-8 Mean expression of protease genes (RPKM) (defined by GO terms proteolysis,
peptidase, and protease) normalized to the control expression (A). Expression of
individual biological replicates with chronic conditions denoted with a green asterisk
(*)(B). Cooler colors (blues) indicate lower expression while warmer colors (reds)
indicate higher expression. Heat map and hierarchal clustering done using the GENE-E
program using one minus Pearson correlation.
global
0.76 1 1.24
Control
Chronic Cu
Chronic TBTO
Acute Cu
Acute TBTO
Description
serine protease nudel
trypsin-1-like
aminopeptidase N isoform X2
Kyphoscoliosis peptidase
serine protease inhibitor dipetalogastin-like
Retrovirus-related Pol poly from transposon
cathepsin L
serine ase stubble
transmembrane protease serine 6 isoform X1
TOX high mobility group box family member 3-like isoform X2
Astacin (Astacus ase, Crayfish small-molecule ase)
proclotting enzyme-like
venom serine protease
Furin-like protease isoforms 1 1-X partial
carboxypeptidase B-like
Histone deacetylase 6
serine ase stubble
aminopeptidase N
venom protease-like
prophenoloxidase activating factor
matrix metallo ase-25-like
furin-like protease 1, isoform 1-CRR isoform X1
78 kDa glucose-regulated , partial
plasma kallikrein
tubulointerstitial nephritis antigen-like
disintegrin and metallo ase domain-containing 22 isoform X3
aminopeptidase N
Gamma-glutamyltranspeptidase 1
kyphoscoliosis peptidase-like isoform X2
aminopeptidase N
aminopeptidase N
Zinc carboxypeptidase A 1
chymotrypsin, partial
Cathepsin L-like cysteine ase
peptidase S15
phytophthora-inhibited protease 1
hypothetical protein L798_04225
cathepsin L1-like
relative
row min row max
Control 2
Acute Cu 2
Acute TBTO 2
Acute TBTO 3
Control 3
Acute Cu 3
Chronic TBTO 2
Chronic TBTO 1
Chronic Cu 3
Control 1
Acute Cu 1
Acute TBTO 1
Chronic Cu 2
Chronic TBTO 3
Chronic Cu 1
Description
hypothetical protein L798_04225
peptidase S15
aminopeptidase N
aminopeptidase N
aminopeptidase N
Zinc carboxypeptidase A 1
chymotrypsin, partial
Gamma-glutamyltranspeptidase 1
kyphoscoliosis peptidase-like isoform X2
Cathepsin L-like cysteine ase
phytophthora-inhibited protease 1
cathepsin L1-like
plasma kallikrein
disintegrin and metallo ase domain-containing 22 isoform X3
78 kDa glucose-regulated , partial
furin-like protease 1, isoform 1-CRR isoform X1
matrix metallo ase-25-like
prophenoloxidase activating factor
tubulointerstitial nephritis antigen-like
venom protease-like
serine ase stubble
cathepsin L
Histone deacetylase 6
Retrovirus-related Pol poly from transposon
TOX high mobility group box family member 3-like isoform X2
proclotting enzyme-like
serine ase stubble
aminopeptidase N
aminopeptidase N isoform X2
venom serine protease
Astacin (Astacus ase, Crayfish small-molecule ase)
Kyphoscoliosis peptidase
transmembrane protease serine 6 isoform X1
Furin-like protease isoforms 1 1-X partial
serine protease inhibitor dipetalogastin-like
serine protease nudel
trypsin-1-like
carboxypeptidase B-like
A B
* * * * * *
147
Fig. 5-9 A heat map of significantly differentially expressed xenobiotic response genes
(RPKM) in all treatment comparisons for mean expression of each individual gene
normalized to control expression (A). All 3 biological replicates are also shown (B) with
chronic treatments denoted with a green asterisk (*). Cooler colors (blues) indicate lower
expression while warmer colors (reds) indicate higher expression. Heat map and
hierarchal clustering done using the GENE-E program.
global
0.7576 0.9981 1.24
Control
Chronic Cu
Chronic TBTO
Acute Cu
Acute TBTO
Description
cytochrome P450 2J6
glutathione S-transferase
cytochrome P450 2J2-like
cytochrome P450
Cytochrome P450, family 2, subfamily J, polypeptide 2
glutathione S-transferase
cytochrome P450 306a1
glutathione S-transferase isoform D-like
multidrug resistance-associated 1-like
relative
row min row max
Control 2
Acute Cu 2
Control 1
Acute Cu 1
Acute TBTO 2
Acute TBTO 1
Acute Cu 3
Control 3
Acute TBTO 3
Chronic Cu 2
Chronic Cu 3
Chronic Cu 1
Chronic TBTO 1
Chronic TBTO 3
Chronic TBTO 2
Description
cytochrome P450 2J6
glutathione S-transferase
cytochrome P450 2J2-like
Cytochrome P450, family 2, subfamily J, polypeptide 2
glutathione S-transferase
cytochrome P450
multidrug resistance-associated 1-like
glutathione S-transferase isoform D-like
cytochrome P450 306a1
A
B
* * * * * *
148
Fig. 5-10 A heat map of significantly differentially expressed chitin-related genes
(RPKM) in all treatment comparisons for mean expression of each individual gene
normalized to control expression (A). All 3 biological replicates are also shown (B) with
chronic treatments denoted with a green asterisk (*). Cooler colors (blues) indicate lower
expression while warmer colors (reds) indicate higher expression. Heat map and
hierarchal clustering done using the GENE-E program using one minus Pearson
correlation.
relative
row min row max
Control 2
Acute Cu 3
Acute TBTO 3
Chronic Cu 3
Control 3
Control 1
Chronic Cu 2
Acute TBTO 2
Acute TBTO 1
Chronic TBTO 2
Acute Cu 1
Chronic Cu 1
Chronic TBTO 1
Acute Cu 2
Chronic TBTO 3
Annotation-1
heat shock 67B2
heat shock 70
heat shock 70
heat shock 70 kDa cognate 4
heat shock 70 kDa cognate 4
A B
global
0.8751 1 1.13
Control
Chronic Cu
Acute Cu
Chronic TBTO
Acute TBTO
Feature ID
heat shock 70 kDa cognate 4
heat shock 70
heat shock 70 kDa cognate 4
heat shock 67B2
heat shock 70
* * * * * *
149
Conclusion
This work on the evolution of pollution tolerance in Tigriopus has revealed a
unifying theme that pollution tolerance is not static, but constantly changing
influenced by many different factors. These results show that pollution tolerance
has evolved, with different copepod populations exhibiting different tolerances.
Chapter 2 revealed that there are evolved differences in pollution tolerance between
sister species of Tigriopus as well as between different populations within these
copepod species (Sun et al., 2015). Through this study, we also found a correlation
between a the thermal stress a population experiences and their Cu tolerance. This
was further examined in Chapter 4 where we identified that high Cu tolerance is not
correlated with environmental Cu exposure, which indicates a potential exaptation
to other environmental stressors, one candidate being temperature stress.
This work also illustrates how fast pollution tolerance can change. In Chapter
1 we showed that different chemicals elicit different evolutionary responses (Sun et
al., 2014). These results showed that Cu elicited an acclimation like response,
supporting previous findings regarding Cu tolerance (Kwok et al., 2009). In contrast,
TBTO showed an adaptation like response after 7 generations of constant exposure.
Additionally, Chapter 3 showed the maintenance in the laboratory environment
could also influence pollution tolerance even after a single generation of lab
culturing. The change to pollution tolerance was pollutant specific, where Cu
tolerance increased while TBTO tolerance decreased. This difference in response
was attributed to the absence of environmental stressors that allow for a more
robust response to Cu while the decrease in TBTO tolerance was a potential product
150
of periods of starvation in the lab. Overall, Chapters 1 and 3 show that pollution
tolerance is not static and can significantly change even within a single generation
under seemingly benign conditions.
An analysis of transcriptome wide responses to pollution in Chapter 5
showed that expression patterns are more generally defined by the duration of
exposure than the specific pollutant exposure. However analysis of several
functional gene categories of interest revealed the potential for certain gene groups,
such as ion regulatory associated genes to have similar expression patterns in
response to acute stress but more chemical specific responses during chronic stress.
This finding supports previous findings (Kovalchuk et al., 2007) and further expands
our knowledge of this response by highlighting a specific functional group that may
be responsible for the specificity in chronic exposure to Cu and TBTO.
To examine the relationship between the lab and the environment, Chapter 4
aimed at characterizing the habitat of these intertidal copepods and identifying
candidates that potentially contribute to their pollution tolerance. These results
found that dissolved Cu concentrations, though higher than coastal seawater, was
not correlated with Cu tolerance in these copepods. Similarly, there was no
observable pattern between sites that would explain the variation in Cu tolerance
observed previously. However, environmental temperature was found to correlate
with Cu tolerance, specifically the rate of temperature increase. This indicates that
the high Cu tolerance and variation in Cu tolerance may be in part a byproduct of
local adaptation to thermal stress. Pollution tolerance was potentially shaped by
151
adaptation to other environmental stressors over a long-term evolutionary
timescale.
In conclusion, this work showed that pollution tolerance is shaped by many
different factors that act on short timescales, such as seen in the lab or across long
timescales, such as observed in variation in pollution tolerance between species and
populations. If unaddressed, these biological variations in pollution tolerance could
be dismissed as experimental error, which would erode the effectiveness of
bioassays as a foundational tool in environmental management.
References
Kovalchuk, I., Molinier, J., Yao, Y., Arkhipov, A., Kovalchuk, O., 2007. Transcriptome
analysis reveals fundamental differences in plant response to acute and chronic
exposure to ionizing radiation. Mutat. Res-Fund. M. 624, 101–113.
doi:10.1016/j.mrfmmm.2007.04.009
Kwok, K.W.H., Grist, E.P.M., Leung, K.M.Y., 2009. Acclimation effect and fitness cost of
copper resistance in the marine copepod Tigriopus japonicus. Ecotoxicol. Environ.
Saf. 72, 358–364. doi:10.1016/j.ecoenv.2008.03.014
Sun, P.Y., Foley, H.B., Bao, V.W.W., Leung, K.M.Y., Edmands, S., 2015. Variation in
tolerance to common marine pollutants among different populations in two species
of the marine copepod Tigriopus. Environ. Sci. Pollut. Res. 1–10.
doi:10.1007/s11356-015-4846-3
Sun, P.Y., Foley, H.B., Handschumacher, L., Suzuki, A., Karamanukyan, T., Edmands, S.,
2014. Acclimation and adaptation to common marine pollutants in the copepod
Tigriopus californicus. Chemosphere 112, 465–471.
doi:10.1016/j.chemosphere.2014.05.023
Abstract (if available)
Abstract
This body of work focused on how evolutionary processes influence pollution tolerance in the marine copepod Tigriopus. The applications of these results are meant to improve the implementation of tools such as bioassays that use the response of test specimens to inform management practices. ❧ Pollution tolerance was examined across geographical space to identify whether there were significant differences in pollution tolerance in Tigriopus from different areas. Chapter 2 had a brief survey of studies that used bioassays and determined that the majority of these studies to not account for the location of where their test specimens were obtained. This was followed by a survey of pollution tolerances between sister species and within each species of copepods and found significant variation in tolerance between species and within each species. Biological variation has the danger to be dismissed as experimental error, which will erode bioassays as a foundational tool in environmental management. ❧ Pollution tolerance was also examined across time, which included history of exposure as well as duration of lab maintenance. Chapter 1 found that with chronic exposure to Cu and TBTO, these copepods developed increased tolerance. For Cu, the increased tolerance was an acclimation like response because it was gained relatively rapidly but lost during a recovery period when pre-exposure to Cu was removed. TBTO exhibited an adaptation like response, where tolerance appeared only several generations of exposure and was maintained even after the pre-exposure was removed. To gain a better understanding of this increase in pollution tolerance, RNAseq was used to understand gene expresson analysis in Chapter 5. These results showed that expression patterns broadly clustered by duration of exposure, acute (96hr) vs. chronic (7 generations). However, results identified the potential for certain gene category, such as ion regulatory associated genes to exhibit a generalized response during acute exposure but a more chemical-specific response during chronic exposure. ❧ In Chapter 3, the effect of transplanting populations from the field to the lab on pollution tolerance was examined in two species of Tigropus. Results showed that pollution tolerance significantly changed for the majority of populations in the lab often within a single generation. This change in pollution tolerance was also in different directions, with Cu tolerance increasing and TBTO tolerance decreasing. The increasing Cu tolerance was linked to the potential differences in the lab, with heat stress being one candidate while the decreasing TBTO tolerance was linked to periods of starvation. To examine how the environment may have shaped pollution tolerance, water temperature monitoring and seawater chemical analysis was used to characterize the habitat of T. californicus at different sites in Chapter 4. These results indicate the high level of Cu tolerance in Tigriopus is likely not due to direct exposure to high Cu concentrations, but rather other environmental stressors, with one candidate being temperature stress. ❧ The collective theme of this work showed that pollution tolerance can significantly change due to exposure history, duration in the lab, and the environment the organism evolved in.
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Creator
Sun, Patrick Yin
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Core Title
The evolution of pollution tolerance in the marine copepod Tigriopus
School
College of Letters, Arts and Sciences
Degree
Doctor of Philosophy
Degree Program
Marine Biology and Biological Oceanography
Publication Date
07/26/2016
Defense Date
06/16/2016
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acclimation,adaptation,antifouling,bioassays,Copper,multi-generational,OAI-PMH Harvest,transcriptomics,tributyltin oxide
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