Single-Larva RNA Sequencing Identifies Markers of Copper Toxicity and Exposure in Early Mytilus californianus Larvae

Abstract

One of the challenges facing efforts to generate molecular biomarkers for toxins is distinguishing between markers that are indicative of exposure and markers that provide evidence of the effects of toxicity. Phenotypic anchoring provides an approach to help segregate markers into these categories based on some phenotypic index of toxicity. Here we leveraged the mussel embryo-larval toxicity assay in which toxicity is estimated by the fraction of larvae that exhibit an abnormal morphology, to isolate subsets of larvae that were abnormal and thus showed evidence of copper-toxicity, versus others that while exposed to copper exhibited normal morphology. Mussel larvae reared under control conditions or in the presence of increasing levels of copper (3–15 μg/L Cu²⁺) were physically sorted according to whether their morphology was normal or abnormal, and then profiled using RNAseq. Supervised differential expression analysis identified sets of genes whose differential expression was specific to the pools of abnormal larvae versus normal larvae, providing putative markers of copper toxicity versus exposure. Markers of copper exposure and copper-induced abnormality were involved in many of the same pathways, including development, shell formation, cell adhesion, and oxidative stress, yet unique markers were detected in each gene set. Markers of effect appeared to be more resolving between phenotypes at the lower copper concentration, while markers of exposure were informative at both copper concentrations.

Full text

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ORIGINAL RESEARCH
published: 09 December 2021
doi: 10.3389/fphys.2021.647482
Edited by:
Enric Gisbert,
Institute of Agrifood Research
and Technology (IRTA), Spain
Reviewed by:
Karl Blyth Andree,
Institute of Agrifood Research
and Technology (IRTA), Spain
Qingchao Wang,
Huazhong Agricultural University,
China
*Correspondence:
Andrew Y. Gracey
gracey@usc.edu
Specialty section:
This article was submitted to
Aquatic Physiology,
a section of the journal
Frontiers in Physiology
Received: 30 December 2020
Accepted: 22 November 2021
Published: 09 December 2021
Citation:
Hall MR and Gracey AY (2021)
Single-Larva RNA Sequencing
Identifies Markers of Copper Toxicity
and Exposure in Early Mytilus
californianus Larvae.
Front. Physiol. 12:647482.
doi: 10.3389/fphys.2021.647482
Single-Larva RNA Sequencing
Identifies Markers of Copper Toxicity
and Exposure in Early Mytilus
californianus Larvae
Megan R. Hall and Andrew Y. Gracey*
Department of Biological Sciences, University of Southern California, Los Angeles, CA, United States
One of the challenges facing efforts to generate molecular biomarkers for toxins is
distinguishing between markers that are indicative of exposure and markers that provide
evidence of the effects of toxicity. Phenotypic anchoring provides an approach to help
segregate markers into these categories based on some phenotypic index of toxicity.
Here we leveraged the mussel embryo-larval toxicity assay in which toxicity is estimated
by the fraction of larvae that exhibit an abnormal morphology, to isolate subsets of
larvae that were abnormal and thus showed evidence of copper-toxicity, versus others
that while exposed to copper exhibited normal morphology. Mussel larvae reared under
control conditions or in the presence of increasing levels of copper (3–15 µg/L Cu2+)
were physically sorted according to whether their morphology was normal or abnormal,
and then profiled using RNAseq. Supervised differential expression analysis identified
sets of genes whose differential expression was specific to the pools of abnormal larvae
versus normal larvae, providing putative markers of copper toxicity versus exposure.
Markers of copper exposure and copper-induced abnormality were involved in many of
the same pathways, including development, shell formation, cell adhesion, and oxidative
stress, yet unique markers were detected in each gene set. Markers of effect appeared
to be more resolving between phenotypes at the lower copper concentration, while
markers of exposure were informative at both copper concentrations.
Keywords: biomarker, Mytilus californianus, copper, marker of exposure, marker of toxicity, transcriptomic (RNA-
seq), single larval sequencing
INTRODUCTION
Heavy metal contamination of freshwater and marine water bodies is a long-recognized problem,
especially in urban regions where industrial byproducts are high (Livingstone et al., 1992). Water
quality criteria are determined by assessment of contaminant toxicity to common organisms in
the affected ecosystem (EPA, 1995, 2016;E50 Committee, 2013). The standard assay for metal
toxicity in coastal or marine waters assesses early larval development of marine mollusks, often
Mytilus mussels.
In traditional marine bivalve embryo-larval development tests, abnormal development is the
best-recognized effect of metal toxicity at the whole-organism level (Johnson, 1988;EPA, 1995;
Sussarellu et al., 2018). Abnormal development is especially apparent at 48 h post fertilization
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Hall and Gracey Single-Larva Markers Copper Exposure Toxicity
(hpf), when normal larvae reach the D-veliger stage. At this point,
abnormal animals exhibit gross morphological deformities,
including velum protrusions, misshapen shells, and failure to
form shells (His et al., 1997;E50 Committee, 2013). This test
is typically conducted as a dose response assay in which larvae
are exposed to a range of concentrations and an effective
concentration at which 50% of the population becomes abnormal
(EC50) is determined (E50 Committee, 2013;EPA, 2016).
However, the normal development assay is relatively coarse
and fails to capture more nuanced and sensitive physiological
responses to chemical exposure or toxicity.
Advances in “-omics” technology over the past two decades
have introduced powerful tools that have vastly enhanced the
sensitivity and utility of toxicity testing (Nuwaysir et al., 1999;
Waters and Fostel, 2004;Calzolai et al., 2007;Schirmer et al.,
2010;Hahn, 2011;Kim et al., 2015). Changes in an organism’s
transcriptome or proteome in response to an introduced toxin
can reveal biomarkers that are sensitive indicators of the
presence of the toxin at concentrations that are below that
which produce outwardly discernible effects of toxicity on
the organism (Daston, 2008;Hook et al., 2014). However,
to effectively harness these molecular markers, strategies are
required that can classify these markers as indicators of
exposure to a toxin and its presence in the environment, versus
markers that indicate that the toxin is not only present but
is also causing deleterious effects on the subject organism.
These markers of exposure versus effect can be distinguished
by phenotypic anchoring, i.e., connecting sublethal molecular
changes to higher-level whole organism, population, or ecological
outcomes (Tennant, 2002;Paules, 2003;Daston, 2008;Hook
et al., 2014). Frameworks such as adverse outcome pathways
(Ankley et al., 2010;OECD, 2013) attempt to use phenotypic
anchoring to link molecular events to detrimental effects at
the whole-organism level, thus identifying markers of effect
(rather than exposure).
In order to identify sensitive molecular biomarkers of
copper exposure, we previously investigated the concentration-
responsive molecular changes associated with copper exposure
in the mussel embryo-larval assay by generating expression
data from pools of larvae exposed to a range of 10 copper
concentrations (Hall et al., 2020). By identifying dose-responsive
transcripts and comparing lowest observed transcriptional EC50
with higher level physiological outcomes (normal and abnormal
development), we were able to define sensitive markers of
copper response, or early warning signs that are detectable prior
to the onset of morphological abnormality. Sensitive markers
primarily showed repressed expression, and included genes
involved in biomineralization/shell formation, metal binding,
and development. Development genes were similarly down-
regulated in response to low concentrations of copper in
previous studies on juvenile red abalone Haliotis rufescens, post-
larval scallops (Argopecten purpuratus), and early developmental
stages of the oyster Crassostrea gigas (Zapata et al., 2009;
Silva-Aciares et al., 2011;Sussarellu et al., 2018). Additionally,
copper-induced down-regulation of iron and zinc binding stress-
protein transcripts was observed previously in juvenile abalone
(Silva-Aciares et al., 2011).
The transcriptomic analysis of Hall et al. (2020) was conducted
on pooled larval samples, representative of all the larvae that were
present in the culture vessel, and this pool would have included a
combination of normal and abnormal larvae, the proportions of
which were related to the prevailing copper concentration. While
this approach has utility in relating bulk gene expression changes
to copper concentration it does not address the granularity that
is associated with this EC50 type of assay. The basis of this and
all EC50 assays is to calculate the proportion of a test population
that do or don’t exhibit some type of detrimental phenotype in
response to the introduction of some toxic perturbant. Here we
sought to leverage this granularity and instead of profiling a pool
of all the larvae in an assay, we sought to sub-sample the larvae
according to whether they exhibited a detrimental phenotype,
in this case abnormal morphology, versus those that exhibited
a normal phenotype. The current study advances the results
of our previous efforts to identify concentration-dependent
transcriptional biomarkers of copper by conducting RNAseq on
phenotypically-sorted single-larvae and pools of phenotypically-
sorted larvae, to distinguish markers of copper effect from those
of copper exposure. Examining these subsets of normal and
abnormal phenotypes provides an opportunity to understand
differences in the underlying molecular pathways driving these
different morphological outcomes. These higher-resolution data
also corroborate previously identified markers of copper toxicity
and exposure by linking individual transcriptional profiles with
to larval phenotype. This work could also strengthen the
adverse outcome pathway for copper toxicity in mussel larval
development.
MATERIALS AND METHODS
Broodstock Collection and Embryo
Copper Exposure
Two separate experiments were run in June and September
2015 to generate the samples used in this experiment. Adult
Mytilus californianus were collected from an intertidal site at
Will Rogers State Beach, Santa Monica, CA, United States.
Animals were refrigerated for approximately 6 h in preparation
for spawning induced by thermal shock. Mussels were then added
to a tank of filtered seawater maintained at 23◦C. Once spawning
commenced, individuals were removed, rinsed with 0.2 µm
filtered seawater, and isolated in separate beakers containing
0.2 µm filtered seawater, collected from Big Fisherman’s Cove
on Santa Catalina Island. Gametes were examined to confirm
high quality, indicated for eggs by a relatively homogeneous
mixture of club-shaped eggs, and for sperm by motility. After
eggs transformed into a spherical shape, sperm was added to
reach a density of ∼5 sperm per egg. Successful fertilization
was identified by the production of a polar body. After 95% of
eggs exhibited successful fertilization, embryos in the 2–4 cell
stage were stocked into treatment containers at a density of
∼13 larvae/mL.
In both trials, six 1-L containers were prepared, including one
control and five copper treatments (3, 6, 9, 12, and 15 µg/l). All
containers were filled with 1 L of seawater (33.5 ppt) collected
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from Big Fisherman’s Cove on Santa Catalina Island, CA, that was
0.2 µm filtered, and heavily aerated. A 0.1 mM stock solution of
copper sulfate was used to spike containers with the appropriate
amount of copper. After copper addition, containers were mixed
by gentle inversion. Once embryos were added to containers, they
were incubated at 17◦C with a 12 h D: 12 h L cycle for 48 h.
Larval Counts and Count Analysis
At the end of the 48-h incubation period, the majority of larvae
in the control had reached the D-hinge phase. The control
and treatment containers were filtered through an 80 µm sieve
to concentrate larvae. Larvae were then rinsed from the sieve
into 50 mL Falcon tubes. The volume of each Falcon tube was
recorded, and for each tube 3–5 100 µl drops were added to a
Sedgewick rafter, and examined under a compound microscope.
In a 100 µl drop, the average number of larvae ranged from 36
in the controls to 20 at the 15 µg/l copper concentration. Larvae
used for counts were discarded after the count, and ultimately
did not contribute to the sequenced larval pool. The number
of normal and abnormal larvae in each drop were recorded in
order to determine the proportion of survival and abnormal
development. Normal and abnormal larvae were characterized
in accordance with standard guidance (His et al., 1997). Normal
larvae were those that had developed to the D-hinge phase and
exhibited a straight hinge extending into a convex curve (shaped
like a capital “D”), and abnormal larvae included normal or
malformed embryos that had not yet reached the D-larval stage
(typically roughly round with irregularities). Each proportion
was divided by the mean control proportion 0 µg/l copper to
calculate control-normalized survival and normal development.
Normal development data were further analyzed in the R package
‘drc’ (Ritz et al., 2015). A four-parameter log-logistic curve (LL.4
model in the drc package) was fit to the dataset to calculate 50%
normal development effective concentration (EC50) values. The
survival curve was not sigmoidal, as the concentration range used
in this experiment did not capture the entire scope of the survival
curve. Survival was analyzed using ANOVA (r packages aov and
anova). Specific differences between concentrations were further
detected using a Tukey’s post hoc test (R command TukeyHSD).
Sample Preservation and Sorting
Once all count samples had been taken, tubes were centrifuged
at 5,000 g for 5 min; the supernatant was removed, and the
remaining 1 ml of seawater containing larvae from each Falcon
tube was transferred to a 2 ml tube. Approximately 500 µl of
RNAlater R
(Ambion) was mixed thoroughly into each centrifuge
tube. Samples were refrigerated overnight to allow for infiltration
of RNAlater into larval tissues, and then stored at −80◦C,
according to the RNAlaterR
Tissue Collection protocol.
Preserved larval samples from the control and 3, 6, and
9µg/l copper treatments from both experiments (Trial 1- May
and Trial 2 - September) were removed from the freezer and
brought to room temperature. First, individual larvae were sorted
using samples from the Trial 2 -September experiment. Small
subsamples were removed from the tube using a Pasteur pipette,
and placed in a glass dish for sorting. Because samples were
highly concentrated, 1×PBS was added to facilitate visual
inspection of different larval types and accurate separation. The
dish was placed under a compound microscope, and 192 single
larvae were isolated into PCR tubes according to whether they
exhibited a normal or abnormal morphology (characteristics of
normal and abnormal larvae described above) using a mouth
pipetting system. Single larvae were also picked from the 9 µg/l
copper treatment but these larvae were not distinguished by
phenotype because 96% of larvae were abnormal at this level
of copper exposure. Tubes were then re-frozen at −80◦C
until RNA extraction.
In addition to these isolated single-larva, normal and
abnormal larvae from the Trial 1- May experiment were picked
and pooled to create three replicate pools (or four pools in the
case of 0-Normal samples) for each condition (0 µg/l abnormal,
0µg/l normal, 3 µg/l abnormal, 3 µg/l normal, 6 µg/l abnormal,
and 6 µg/l normal), resulting in a total of 19 pools, with about
50 animals in each pool. Photographs were taken of ∼25 larvae
in each pool using a digital camera attached to a dissecting scope.
The camera was set to manual focus, set at the maximum optical
zoom, and fixed in this position. Similarly, the microscope was
set at 40×magnification. A 1 cm stage micrometer was used
to calibrate pixel to micron conversion for subsequent image
analysis. Picked larval samples were then spun down quickly and
excess liquid was removed. Tubes were then re-frozen at −80◦C
until RNA extraction.
RNA Extraction, Library Preparation, and
Sequencing
Single-larvae (Trial 2 samples) were lysed in 35 µL RLT buffer
(Qiagen) containing 2 µl of silane beads (MyOne, Dynabeads)
and bead-binding was induced by addition of 25 µL of ethanol.
The beads were washed twice with 80% ethanol, dried for 10 min
and then used as input to prepare 30-tag RNAseq libraries using a
protocol adapted from Foley et al. (2019). Briefly, the bead-bound
total RNA from individual larvae was resuspended in an 8 µl
reverse-transcription reaction mixture in 96-well plates with each
well containing a unique indexed anchored-oligo-dT primer that
contained the Illumina p7 sequence. The RNA was fragmented
for 3 min at 94◦C, cooled to 42◦C, and then reverse transcribed
by the addition of MMLV-HP reverse transcriptase (Lucigen) and
a template switching oligonucleotide that contained the Illumina
p5 sequence. Following reverse-transcription the plate of cDNA
products was pooled, bead-cleaned (AMPure, Beckman-Coulter),
and amplified for 18 cycles with Illumina p5 and p7 PCR primers.
The 192 single-larvae samples were sequenced over one lane of
Illumina HiSeq 4000 with 150 bp PE reads.
Pooled larval samples (Trial 1 samples) were homogenized
by bead-beating, and then RNA was extracted using a modified
Trizol protocol (Ambion). MaxTract columns (QIAGEN) were
used to maximize phase separation and supernatant removal
after chloroform addition. RNA was quantified with the Qubit
HS RNA Assay Kit (Thermo Fisher), and 40 ng of each sample
was used for library preparation. Prior to library preparation,
each sample was combined with 4 µl of External RNA Controls
Consortium (ERCC) RNA spike in mix 1 (Thermo Fisher)
at a 1:10,000 dilution. Samples were poly-A selected using
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the NEB Next Poly(A) mRNA Magnetic Isolation Module.
This step was integrated into the library preparation workflow
using the NEB Next Ultra RNA Library Prep Kit for Illumina,
with some modifications. Samples were fragmented for 12 min
(instead of 15) prior to cDNA synthesis, and the first strand
synthesis reaction was run for 50 min at 42◦C. PCR enrichment
was visualized using a Bio-Rad qPCR Thermocycler, and the
reaction was terminated shortly after entering the exponential
amplification stage. PCR amplification of libraries was run for 18
cycles. Library sizes and quantity were analyzed on a Bioanalyzer,
and quantity was additionally measured with qubit. Samples were
pooled and sequenced over one lane of Illumina HiSeq 4000 with
50 bp SR reads.
Assembly and Annotation of de novo
Transcriptome
Three M. californianus libraries were integrated to generate a de
novo transcriptome assembly, as described in Hall et al. (2020),
with the following modifications.
Prior to assembly, common contaminating sequences were
filtered from the two Illumina libraries using bbmap.sh by
mapping SE reads, merged PE reads, and unmerged PE reads
to the DH10B E. coli genome and the NCBI UniVec database
(minid = 0.85, idfilter = 0.90). The Sanger assembly was also
filtered using BLAST (blastn, perc_identity = 90), and only
contigs with an alignment length greater than 100 bp with a
contaminant database target were removed.
Illumina libraries were mapped to the Sanger assembly with
bbmap.sh (minid = 0.85, idfilter = 0.90), and unmapped reads
were written as output. Unmapped reads were assembled using
idba_tran (IDBA v1.1.1, –merge –filter, Peng et al., 2013) with
maxk = 124. The two resulting assemblies were combined with
the Sanger assembly, and redundant sequences were merged
with CD-HIT-est v4.6.5, with c = 0.95 (Li and Godzik, 2006;
Fu et al., 2012), two rounds of CAP3 [o = 50, p= 98, (Huang
and Madan, 1999)], one round of minimus2 [OVERLAP = 50,
MINID = 98, (Treangen et al., 2011)], and one final round
of CD-HIT-est. Mitochondrial sequences were filtered from the
assembly by running blastn (Altschul et al., 1990) against the
M. californianus mitochondrial genome (perc_identity = 90,
alignment length ≥100), and contigs shorter than 200 bp were
removed using seqmagick (convert, min-length = 200, Matsen
Group, 2017).
In addition to the annotation pipeline described in
Hall et al. (2020), annotations were retrieved using blastn
(outfmt ‘6 std stitle staxids’) against the NCBI EST and
nt databases, and diamond blastx and blastp [taxonmap
∼/prot.accession2taxid.gz –taxonnodes ∼/nodes.dmp –more-
sensitive –outfmt 102 –max-target-seqs s10 –evalue 1e-5;
(Buchfink et al., 2015)] against the NCBI nr database. The full
taxonomic path was retrieved for BLAST and diamond BLAST
output by joining taxon IDs with a parsed file that joined taxon
ID and taxonomic path (R. Sachdeva, pers. comm. 2017). All
taxonomy annotations were combined into one file, and the
number of metazoan annotations per contig were counted.
Contigs that were metazoan for all of the annotations were kept.
Additionally, predicted peptides with metazoan taxonomy in
blastp results against UniProt and nt were kept. Finally, contigs
that annotated as metazoan for all BLAST searches, but could
not be resolved below “root,” “cellular organism,” “Eukaryota,” or
“Opisthokonta” for diamond blast taxonomy searches, were kept
as well. The final assembly consisted of 71,451 contigs with an
average length of 1142.73 bp.
Downstream Data Analysis
The following process was run separately for sorted pooled larval
samples (Trial 1) and single larval samples (Trial 2). Raw RNAseq
reads were quality trimmed and contaminating adapter sequence
was removed using Trimmomatic v0.33 (Bolger et al., 2014)
with default parameter settings. The trimmed reads were then
mapped to the M. californianus mitochondrial genome using
BBMap v34 (minid = 0.95 ambiguous = all sssr = 1.0) (Bushnell,
2016) to separate mitochondrial transcripts from nuclear genes.
All reads that did not map to the mitochondrial genome were
used for subsequent analysis. Larval reads were mapped to the
de novo transcriptome assembly described above with bbmap.sh
(minid = 0.95 for pooled larvae, default for single larvae,
ambiguous = random, sssr = 1.0, nhtag = t, minlength = 40).
The resulting bam files were counted and summarized with
featureCounts (Liao et al., 2014), allowing for multimapping
reads (-M), and allowing for mapped reads overlapping two
contigs to be counted toward those contigs (-O).
Count tables were loaded into R (R Core Team, 2016) and
processed in DESeq2 (Love et al., 2014). Initial inspection of
the PCA plot of normalized transcriptional counts for pooled
larvae revealed that there were two outliers, one replicate of
normal animals at 0 µg/l copper, and one normal animal
replicate at 3 µg/l copper. These two samples also proved to
be outliers in a PCA of only the ERCC reads, which one
would expect to be relatively consistent across samples after
normalization. Therefore, these samples were removed from
downstream analysis. For the remaining 17 samples, reads with
counts higher than 40 were removed in the initial filtration.
Inspection of the PCA plot of 192 normalized transcriptomes
for single larvae revealed several outliers, which were confirmed
and supplemented by examining a boxplot of the Cook’s distance
for all single larval samples. Both of these approaches revealed 6
outlier samples which were removed from downstream analysis.
All subsequent analysis was performed on the remaining 186
samples, which comprised 48 control larvae, and 46, 70, and 22
larvae sampled at 3, 6, and 9 µg/l copper, respectively.
DESeq2 was used to further process both datasets, according
to the standard workflow, and significant differentially expressed
(DE) genes were detected between group pairs. The entire process
was run twice with different grouping assignments—the first,
which was used to identify markers of exposure, grouped all
0µg/l, all 3 µg/l, and all 6 µg/l copper-treated larval samples
(as opposed to grouping by morphology in addition to copper),
and compared 0 µg/l with 3 µg/l, and 0 µg/l with 6 µg/l.
The second grouping assignment used factors that distinguished
samples by both copper concentration and morphology, and
compared normal and abnormal animals at 0, 3, and 6 µg/l. DE
genes identified by each of these approaches were further filtered
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FIGURE 1 | Images of normal and abnormal larvae at each copper
concentration. Normal animals are morphologically very similar for all copper
concentrations, and were characterized by standard features of larvae that
have reached the D-veliger stage – a straight hinge on one side of the
organism, and a regular convex curve extending out from the hinge. More
variation was observed in abnormal larvae, but key defining features were
round and/or irregular morphology.
to retain only those that demonstrated significant changes in
expression (padj <0.1, according to the DESeq2 protocol), and
a fold-change >2.3.
To explore the genes driving the observed differences in
morphology (Figure 1), differential expression (DE) was assessed
between conditions. Specifically, we identified markers of copper
exposure and markers of copper toxicity by extracting unique
and overlapping groups of DE genes (Figure 2). Markers of
copper exposure were defined as genes that were DE between
all control animals (0 µg/l) and animals at both copper
concentrations (3 and 6 µg/l), as exposure markers should be
evident in all animals exposed to a toxin. Markers of toxicity
were defined as genes that were DE between normal and
abnormal animals at 3 µg/l copper, 6 µg/l copper, or at both
copper concentrations (Figure 2). Abnormal development is
the detrimental phenotype that was used to anchor markers of
effect/toxicity. Markers of natural abnormality (as opposed to
copper-induced abnormality) were excluded from the analysis by
excluding genes DE between normal and abnormal animals at
0µg/l copper.
Comparison of markers of exposure lists and markers
of effect lists generated for the two datasets – pooled and
single larval – was conducted in R. Both datasets were
searched for overlapping biomarkers and biomarkers of interest
from past studies.
Functional Analysis
Functional enrichment analysis was conducted using Gene
Ontology (GO) (Ashburner et al., 2000) terms using the
Cytoscape (Shannon et al., 2003) plug-in, BiNGO (Maere et al.,
2005). Overrepresentation was tested using a hypergeometric test
with Benjamini & Hochberg FDR correction (p<0.05). The GO
annotation file was generated using GO annotations produced by
Trinotate, and only annotations for the 27,642 filtered contigs
were included. The most recent core ontology file (go.obo) was
used for analysis1(October 2017).
Figures
All figures were generated in R studio (version 3.3.1—RStudio
Team, 2017). Survival was plotted with ggplot2 (Wickham,
2009); normal development was plotted using the drc function
plot.drm; and venn diagrams were plotted with the package
VennDiagram (Chen, 2017). PCA plots were generated in
DESeq2, and heatmaps were created using the pheatmap package
(Kolde, 2015). Transformed counts for heatmaps and PCA plots
were calculated with Variance Stabilizing Transformation, using
the DESeq2 function vst. This method is recommended for
normalizing data for visualization according to the DESeq2
protocol. Average counts were taken for replicates, and averages
were divided by control counts so the control count would be 1
for all samples.
RESULTS
Survival and Normal Development
Larval survival and normal development for both experiments
are depicted in Figure 3. Survival rates were relatively
high across all concentrations in these experiments, so the
concentration range did not capture the full survival response
curve (Figures 3A,B), and it was not possible to calculate the
LC50. Slight hormesis was observed at 3 and 6 µg/l copper in
Trial 1, and 3, 6, and 9 µg/l copper in Trial 2, resulting in higher
survival rates at these concentrations. Normal development in
the control was on average 69% of the total population in both
trials (Figures 3C,D). Normal development exhibited a classic
sigmoidal dose response curve (Figures 3C,D), and the EC50 was
5.87 and 6.43 µg/l in Trials 1 and 2, respectively.
Transcriptional Patterns and Morphology
Principal Component Analysis (PCA) of pooled larval
transcriptional profiles revealed that replicate samples
clustered by copper concentration and morphological condition
(Figure 4). Three broad clusters of samples were apparent. The
first cluster consisted solely of the samples of abnormal animals
cultured under control conditions (0 µg/l copper), indicating
that larvae that exhibited abnormal development under control
culture conditions possess a different gene expression signature
to those that exhibit abnormal morphology under copper
exposure. The second cluster represented a grouping of samples
of normal animals from the control (0 µg/l copper) and the 3 µg/l
copper treatments, while the third cluster comprised samples
from abnormal animals from the 3 µg/l copper treatment, and
both the normal and abnormal animals exposed to 6 µg/l copper.
A PCA of whole single larval transcriptional profiles revealed a
clear gradient in sample concentration, but did not distinguish
between normal and abnormal samples. When filtered to focus
on markers of exposure and effect, however, single larval samples
did separate by low (0 and 3 µg/l) and high (6 and 9 µg/l) copper
1http://geneontology.org/page/download-ontology
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FIGURE 2 | Markers of effect and markers of exposure were detected by
isolating gene sets that were differentially expressed between animals
exposed to different copper concentrations and that exhibited different
morphologies. Markers of exposure were considered genes that were
differentially expressed between all animals (normal and abnormal) at the
control copper concentration and all animals at each copper concentration
(A). Markers of effect were considered genes that were differentially expressed
between normal and abnormal animals in copper-treated larval samples, but
not in control samples (B,C).
concentrations (Figure 5), and in the markers of effect samples
could be distinguished by morphology in the 3 µg/l copper
concentration (Figure 5B). Furthermore, when expression of
genes that were identified as markers of exposure and effect in
single larval samples were projected using PCA on the pooled
larval dataset, the same pattern apparent in the pooled larval
markers of exposure and effect was apparent – samples separated
based on morphology at 0 and 3 µg/l copper, but not at 6 µg/l
copper (Figure 6). Thus, patterns of gene expression observed in
data collected at single-larva resolution was recapitulated in an
independent dataset collected using pooled larvae and showed
that gene expression was able to robustly distinguish larvae based
on morphology at 3 µg/l copper, but that such transcriptional
signatures were dampened at 6 µg/l.
Markers of Exposure
For pooled larval samples, 564 genes were differentially expressed
between all control animals and all copper-exposed animals at
both concentrations (Figure 7 and Supplementary Table 1).
A total of 230 additional genes were only DE between control and
3µg/l samples, yet 746 genes were uniquely expressed between
control and 6 µg/l samples (Figure 7). Of the common set of
564 DE genes, 469 were upregulated in expression relative to
the control copper condition, and 95 were downregulated in
expression relative to the control copper condition (Figures 7C,D
and Supplementary Table 1). For single larval samples, 1,242
genes were differentially expressed between all control and
all copper-exposed animals at 3 and 6 µg/L. There were an
additional 2,595 genes that were only DE between control and
3µg/L samples, and 3,718 DE genes between control and
6µg/L samples.
In pooled larvae, many of the identified markers of exposure
were related to cell adhesion, extracellular proteinaceous matrix,
and shell formation (Figure 8 and Supplementary Table 1). We
identified several shell formation markers that have appeared
in previous larval investigations, including temptin, perlucin,
and chitin-related genes (Hall et al., 2020). Additional markers
related to proteinaceous matrix, adhesion, and shell formation
were identified, including insoluble matrix shell protein 5,
matrix metalloproteinase-16, junctional adhesion molecule
C, periostin (POSTN), neural-cadherin, and a disintegrin
and metalloproteinase with thrombospondin motifs 13.
Other markers included several well-recognized markers of
oxidative stress, including glutathione-s-transferase P (GSTP1),
mitochondrial glutathione reductase (GSR), and glutathione
peroxidase (GPx), as well as putative DBH-like monooxygenase
protein 2, which has oxidoreductase activity. All of these
markers were upregulated relative to the control in copper
conditions. Downregulated markers of exposure did not exhibit
any specific trends in functional category, and included genes
such as chromobox protein homolog 5, cytochrome c oxidase
subunits 1 and 3, cytochrome b, metalloprotease TIK12, amine
sulfotransferase, and antistasin. Many of these same markers
were identified in single larval samples as well (Supplementary
Table 2), although markers related to shell formation and
oxidative stress/xenobiotic protection were present in greater
numbers in the markers of effect.
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FIGURE 3 | Proportion of control-normalized survival in Trial 1 (A) and Trial 2 (B) and normal development in Trial 1 (C) and Trial 2 (D) plotted against copper
concentration. Mean survival with standard error (A,B) and mean normal development with standard error and modeled 4-parameter log-logistic curves (C,D) are
plotted. Blue points and lines represent control-normalized survival (A,B) and normal development (C,D), while the black dashed line represents non-normalized
normal development. Asterisks indicate concentrations that exhibited significantly different proportions from the control (p<0.005). The normal development EC50
was 5.87 µg/L for the pooled larvae trial (Trial 1), and 6.43 µg/L for the single larvae trial (Trial 2).
The GO terms enriched in these common biomarkers
of exposure in the pooled larval samples were primarily
related to the same processes described above. There were
two chitin-related terms: chitin binding and chitin metabolic
process (Supplementary Table 3). Several terms were involved
in development, including neuron projection extension,
and negative regulation of cell development; while there
were also terms related to healing and tissue regeneration.
Finally, several terms were related to peptidase/hydrolase
activity and regulation, as well as chemokine and cytokine
secretion. In the single larval markers of exposure, only two
GO terms were enriched, both related to non-membrane
bound organelle.
Markers of Effect
To identify markers of effect, we investigated transcriptional
markers associated with abnormal development in low to mid-
range copper concentrations (Figure 1). In these treatments,
some organisms exhibited normal development at the end of
48 h, while others became abnormal, despite exposure to identical
conditions of copper exposure. Markers of effect (or copper-
induced abnormal development) were identified as the set of
genes that were DE between normal and abnormal larvae at both
3 and 6 µg/l (Figure 2). Because larval abnormality also occurs
in the absence of copper, we first identified 1,240 genes as DE
between normal and abnormal animals at 0 µg/l copper in pooled
larval samples (Figure 7B), and 2,358 genes DE between normal
and abnormal animals at 0 µg/l for single larval samples. These
genes represent transcriptional markers of spontaneous natural
abnormality under control conditions and therefore we excluded
these genes from further consideration as candidates markers of
copper exposure and effect. After subtracting the genes that were
associated with natural abnormality under control conditions,
there were 735 genes that appeared to be markers of copper
induced abnormality in pooled larvae, and 2,792 markers of
copper induced abnormality in single larvae. The number of DE
genes between copper-exposed normal and abnormal animals
was 909 at 3 µg/l copper, and 70 at 6 µg/l copper for pooled
samples. For single larval samples 1,848 genes were DE between
copper-exposed and abnormal animals at 3 µg/l copper, and
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FIGURE 4 | A PCA plot was created of pooled filtered larval transcriptomes (total gene count >40 across all samples). Point colors are unique to copper
concentrations and morphologies. Counts were normalized in DESeq2 and transformed with variance stabilizing transformation (vst) prior to plotting.
1,805 at 6 µg/l. There were 163 shared markers of effect at both
copper concentrations, but 1,267 markers of effect were unique to
3µg/l, and 1,370 markers were unique to 6 µg/l.
In pooled larval samples, abnormal phenotypes were generally
associated with induction of transcripts relative to normal
phenotypes, with 90% of transcripts more highly expressed in
abnormal animals at 3 µg/l, and 76% expressed more highly in
abnormal animals at 6 µg/l (Figures 7E,F and Supplementary
Table 4). In single larval samples at 3 µg/l, this same trend
was observed, although not as strongly, with 53% of transcripts
more highly expressed in abnormal animals. However, at 6 µg/l,
the majority of markers (59%) were expressed more highly
in normal larvae.
For pooled larval samples, many notable genes were DE
between normal and abnormal animals at 3 µg/l copper (Figure 9
and Supplementary Table 4). Prominent categories that were
evident in this group were similar to those that appeared
in the markers of exposure. However, more representative
genes were often present among markers of effect in these
shared categories relative to the markers of exposure, especially
among the single larval markers (Supplementary Table 5).
Genes related to oxidative stress and redox cycling were again
evident, including several glutathione-s-transferases, putative
ferric-chelate reductase 1 homolog, peroxidasin, peroxidase-
like protein, superoxide dismutase [Cu-Zn] (SOD1), several
cytochrome P450 subunits, and ferric chelate reductase 1.
Several protein matrix/shell formation genes appeared again as
well, including matrix metalloproteinase-17, protein PIF (pif ),
peroxidasin, and carbonic anhydrase 12. Genes involved in
apoptosis were also more highly expressed in abnormal animals
at 3 µg/l and included baculoviral IAP repeat-containing protein
7-A (birc7-a), ferritin heavy chain (FTH), and sequestosome-1
(Sqstm-1).
Other markers were involved in development and neuron
function, including sodium/potassium/calcium exchanger
4, neuronal acetylcholine receptor subunits alpha-3, alpha-
10, and alpha-6; pituitary homeobox x, homeobox protein
extradenticle, and membrane metallo-endopeptidase-like
1 (Figure 9 and Supplementary Table 4). Finally, several
unique genes related to cell adhesion belonged to this set as
well. These genes were protocadherin-16, a disintegrin and
metalloproteinase with thrombospondin motifs 16, and a
disintegrin and metalloproteinase with thrombospondin motifs
3 (ADAMTS3). Many of these markers, or markers with very
similar function, were again identified as markers of effect in the
single larval samples (Supplementary Table 5). They include a
number of glutathione-s-transferases, glutathione peroxidase,
peroxidasin, putative ferric-chelate reductase 1 homolog, several
cytochrome p450 subunits, pif, perlucin (also a shell formation
gene), a number of hox genes, and ADAMTS16.
The above genes were upregulated in abnormal animals
in pooled larval samples, and primarily upregulated in single
larval samples, although several were downregulated in abnormal
animals in single larvae. Genes that were downregulated in
abnormal animals in pooled larval samples also included several
cell adhesion genes (ADAMTS3 and stereocilin), as well as
calcium and zinc binding genes (calmodulin, aspartyl/asparaginyl
beta-hydroxylase, carbonic anhydrase 12, zinc finger and BTB
domain-containing protein 44, MORC family CW-type zinc
finger protein 2A, synaptotagmin-like protein 5, and PHD finger
protein 14). Again, no notable trends were apparent among
downregulated genes.
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FIGURE 5 | PCA plots were created for single larval markers of exposure (A) and effect (B). Point colors are unique to combined copper concentration (0, 3, 6, or
9µg/L) and morphologies (N, normal, or A, abnormal). Counts were normalized in DESeq2 and transformed with variance stabilizing transformation (vst) prior to
plotting.
Five GO terms were enriched in the markers of effects at
3µg/l copper: for pooled larvae chitin binding, chitin metabolic
process, amino sugar metabolic process, glucosamine-containing
compound metabolic process, and extracellular region
(Supplementary Table 6). Many more GO terms were enriched
in the single larval markers of effect. Enriched GO terms were
related to RNA/mRNA splicing, RNA binding, non-membrane
bound organelles, cytoskeleton, RNA localization, regulation of
cell cycle process, and nuclear lumen (Supplementary Table 7).
In addition to the discrete biological replicates that were
sorted and sequenced in this experiment through the pooled
and single larval sequencing, we can rely on data from a recent
publication, Hall et al. (2020), in which similar concentration-
response experiments were conducted with M. californianus
larvae, as a repeat for this study. In Hall et al. (2020), we
conducted two concentration response experiments in which
two families of M. californianus larvae were exposed to 10
copper concentrations, and whole sample transcriptomes were
sequenced. The EC50 for this experiment was similar to the
other two biological replicates in the aforementioned study,
and transcriptional markers identified in this manuscript are
likewise similar to the transcriptional markers identified in
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FIGURE 6 | To corroborate trends observed in individual larvae, pooled larval
expression data was subset on the markers of exposure and effect generated
through single larval analysis. PCA plots of this expression data for markers of
exposure (A) and effect (B) confirmed that single larval markers effectively
separated pooled larval samples based on morphology and copper
concentration.
the previous study. A comparison of the markers of exposure
and effect identified in this study against markers that were
identified as showing a significant dose response profile in our
previous study shows that 55% of the markers of exposure,
and 64% of the markers of effect were previously identified
as copper-responsive. Additionally, we examined the expression
profiles of the identified markers of exposure and effect in
the dataset of Hall, Moffett, and Gracey (Supplementary
Figure 1). The heatmaps in Supplementary Figure 1 confirm
that the majority of these markers exhibited a transcriptional
response to copper in our previous study, demonstrating that
these genes are consistently differentially expressed to copper
across experiments.
Amplitude-Dependent Markers of
Exposure and Effect
Comparison of the biomarkers of effect at 3 µg/l with biomarkers
of exposure revealed that 59 genes were shared between
the two gene sets (Supplementary Table 8). These markers
predominantly consisted of genes that are DE in copper-exposed
larvae, but whose expression was amplified in abnormal larvae.
The expression of 97% of genes was amplified in abnormal larvae,
whereas expression was reduced for only 3% of genes (Figure 10
and Supplementary Table 8). The amplitude-dependent markers
were related to oxidative stress and/or oxidoreductase activity
(e.g., apolipoprotein D, putative ferric chelate reductase
1 homolog, cytochrome P450 subunits, and DBH-like
monooxygenase protein 1 homolog); extracellular/proteinaceous
matrix formation (putative tyrosinase-like protein tyr-3, and
cartilage matrix protein); and cell adhesion [junctional adhesion
molecule B (JAM2), POSTN, protocadherin-9 (PCDH9), and
lactadherin]. For several additional genes related to cell adhesion,
two separate copies of the gene appeared in each set of markers,
respectively. These genes included integrin beta-5; cadherin 99C;
and protocadherin Fat 1. Two other notable genes that were
identified as amplitude-dependent markers were zinc transporter
ZIP12, and serine/threonine-protein phosphatase 2A, both of
which bind divalent metals. For single larvae, 228 genes were
shared markers of exposure and effect, but these genes did not
consistently exhibit amplified expression in abnormal larvae. For
this gene set, markers were both upregulated and downregulated
in response to copper, and both upregulated and downregulated
in abnormal larvae relative to normal larvae. The directionality
of response was not consistent for markers of exposure and effect
(i.e., upregulation in all copper-exposed larvae was sometimes
associated with higher expression in normal larvae, rather
htan normal larvae).
Markers of Natural Abnormal
Development
Beyond markers of copper exposure or effects, we also identified
markers of natural spontaneous abnormality as depicted in
Figure 2B. In pooled larval samples, 1,240 genes were DE
between normal and abnormal animals, and of these 380
genes were up-regulated in abnormal larvae relative to normal
larvae, and 860 genes were down-regulated in abnormal
larvae relative to normal larvae. In single larval samples,
2,358 genes were DE between normal and abnormal animals,
and of these 1,600 were up-regulated in abnormal larvae
relative to normal larvae, and 758 were down-regulated in
abnormal larvae relative to normal larvae. Prominent functions
of genes identified among the DE genes include development,
extracellular matrix, cytoskeletal components and motility,
cell cycle, shell formation, transmembrane proteins, protease
inhibitors, oxidative stress/protein turnover, neurotransmitters,
and replication/transcription (Supplementary Tables 9, 10). In
the pooled markers of natural abnormal development, there
were also several groups of similar genes that appeared in the
DEG list – 5 GTP binding proteins, 4 heat shock proteins, 5
hemicentins, 6 serine/threonine-protein kinase or phosphatases,
8 solute carrier family members, 5 WD repeat-containing
proteins, and 5 zinc finger proteins. Although many of the
functional groups represented by this gene set were also common
in DE genes in copper-exposed abnormal animals, genes were
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FIGURE 7 | Venn diagrams illustrate gene sets that were chosen as pooled larval markers of exposure (A) and markers of effect (B). Heatmaps depict expression
patterns of shared markers of exposure (C,D) and all markers of effect (E,F). Counts were transformed using Variance Stabilizing Transformation in DESeq2. Each
column represents the control-normalized mean count for all replicates in a given condition. Yellow coloration represents higher expression values, and blue
coloration represents lower expression values.
unique to this gene set, as they were removed from the markers of
effect, indicating that there are many distinct markers of natural
abnormality and copper-induced abnormality.
DISCUSSION
Phenotypic anchoring of transcriptional biomarkers is a common
and necessary approach to ultimately distinguish biomarkers
of exposure from those of effect (Paules, 2003;Daston, 2008;
Hook et al., 2014). In this study, we used larval morphology to
anchor gene expression profiles. The normal development EC50s
of 5.87 and 6.43 µg/l copper agreed with previous work on
Mytilus larvae (Martin et al., 1981;Arnold et al., 2009;Hall et al.,
2020), indicating that expression results from this culture are
suitable for extrapolation to other studies.
Generally, normal and abnormal larvae in pooled samples
exhibited distinct, phenotype-dependent transcriptional
responses (Figure 2), as we would expect, which was important
for parsing out markers of exposure and effect. However, the
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FIGURE 8 | Example profiles of pooled markers of exposure. Genes are related to oxidative stress, shell formation, cell adhesion, and other processes. Red lines
depict expression of abnormal animals, and black lines depict expression of normal animals.
FIGURE 9 | Example profiles of pooled markers of effects at 3 µg/L copper. Genes are related to apoptosis, oxidative stress, shell formation, development, cell
adhesion, and divalent cation binding. Red lines depict expression of abnormal animals, and black lines depict expression of normal animals.
transcriptional similarity between normal and abnormal animals
at 6 µg/l was somewhat surprising. The fact that transcriptional
profiles are significantly different for normal and abnormal
animals at 0 and 3 µg/l copper, but not at 6 µg/l, suggests that
as copper concentrations increase, the transcriptional signature
of toxicity becomes the dominant expression signature, even
in morphologically normal animals. While morphology-based
transcriptional differences weren’t immediately apparent in the
single larval data, large numbers of genes were differentially
expressed between normal and abnormal larvae at each copper
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FIGURE 10 | Example expression profiles in pooled larvae of a subset of the genes that were identified as both markers of exposure and effect. Genes are related to
apoptosis, oxidative stress, shell formation, development, cell adhesion, and divalent cation binding. Red lines depict expression of abnormal animals, and black
lines depict expression of normal animals.
concentration, indicating that there were in fact notable
morphology-linked expression patterns. Distinct expression
patterns based on morphology were apparent in the PCA-plotted
markers of effect at 3 µg/l, but not at 6 µg/l (Figure 5B). Thus,
in both pooled and single larvae, markers of effect appear to be
the most useful at low copper concentrations, but many markers
of effect were still evident at the mid-range copper concentration
(6 µg/l) when single larval sequencing was used.
While we identified unique markers of exposure and effect,
clearly indicating that these do comprise two distinct gene sets,
markers of exposure and effect were involved in many similar
functional pathways. Biomarkers of copper exposure and effects
were related to oxidative stress or redox reactions, cell adhesion,
and shell formation/extracellular proteinaceous matrix, which is
consistent with our previous analysis of mussel larval response to
copper (Hall et al., 2020), and shares some similarities with other
previous studies on marine larval response to copper (Zapata
et al., 2009;Silva-Aciares et al., 2011;Sussarellu et al., 2018). The
pathways identified provide insight into the possible mechanisms
of copper-induced abnormal development in mussel larvae.
Several genes related to oxidative stress or oxidoreductase
activity were uniquely identified as markers of effect, and not
markers of exposure (Figure 9 and Supplementary Table 4).
In the pooled larval samples, SOD1 and FTH were identified as
unique markers of exposure. SOD1 uses copper ions to oxidize
superoxide molecules (Valentine and Mota de Freitas, 1985) and
is a well-known component of the oxidative stress response
(Finkel and Holbrook, 2000). FTH, a marker of abnormal
development at 3 µg/l copper, plays a role in sequestering and
oxidizing excess ferrous ions to prevent oxidative stress (Orino
et al., 2001). In both pooled larvae and single larval samples,
glutathione-related markers appeared in the markers of exposure
and effect (Figures 8,9and Supplementary Tables 1, 2, 4, 5), but
unique Glutathione S-transferases were identified as markers of
effect. In single larval samples, Glutathione S-transferases only
appeared as markers of effect. Glutathione S-transferases are
known to play distinct roles in the oxidative stress response (Veal
et al., 2002) and in xenobiotic detoxification in general (Salinas
and Wong, 1999), as is glutathione peroxidase (Freedman
et al., 1989). Several cytochrome P450 subunits were identified
as unique markers of effect as well. Cytochrome P450s are
iron-bound monooxygenases that have been implicated in the
generation of reactive oxygen species (Lewis, 2002).
Previous transcriptional studies exposing marine mollusk
larvae to copper have confirmed that similar genes are involved
in redox regulation or protection against oxidative stress,
including glutathione-s transferases, cytochrome P450 subunits
(Hall et al., 2020), glutathione peroxidase, and ferritin (Zapata
et al., 2009). The finding of oxidative stress in copper-exposed
early bivalve larvae is further validated by Sussarellu et al. (2018),
who observed genotoxicity, measured by DNA breaks, in
larval oysters exposed to low copper concentrations. The
modulation of distinct oxidative stress genes in both markers
of exposure and markers of effect indicates that both normal
and abnormal animals experience oxidative stress, as we would
expect, but exercise unique physiological responses, which may
be a contributing factor to their ultimate morphological state
(e.g., perhaps the pathways activated in normal animals more
effectively dampen oxidative stress, and thus reduce cellular
damage that could lead to abnormal development). Alternatively,
activation of these genes could be indicative of additional
detoxification necessary in abnormal animals, but not in all
copper-exposed animals. In this scenario, it is possible that
standard cellular processes that would regulate redox activity
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and mitigate the production of free radicals are disrupted
as a function of abnormal development, and therefore these
animals need to scale up defenses against oxidative stress.
This is supported by the genes involved in oxidative stress or
redox cycling in the amplitude-dependent markers of exposure
(Supplementary Table 8 and Figure 10), which suggest that the
oxidative stress response is more strongly induced in markers
of effect, and that higher expression levels of these genes
in abnormal animals can be considered markers of effect at
3µg/l copper.
Several previously identified indicators of damaged protein
turnover and cellular damage appeared in the markers of effect
and exposure (Figure 9 and Supplementary Tables 2, 4, 5).
Sqstm1, which codes for a zinc-binding protein involved in
protein degradation (Seibenhener et al., 2004), appeared in the
markers of effect in pooled larvae, and markers of exposure in
single larvae. Sqstm1 is a robust biomarker of copper exposure
and is highly induced in response to copper and is consistently
highly expressed in both larval and adult mussels exposed to
copper (Hall et al., 2020). Birc7-a likewise codes for a zinc-
binding protein, and it is essential to the regulation of apoptosis
and cell proliferation. This gene was a marker of effect in both
pooled and single larvae.
Genes related to larval shell proteinaceous matrix were present
in both markers of exposure and effect, and in single larval
samples they were notably more prominent in the markers of
effect (Figures 6–8 and Supplementary Tables 2, 4). Many genes
were related to processing of chitin, which is known to be a core
component of the molluscan shell proteinaceous matrix (Weiner
et al., 1984;Furuhashi et al., 2009), and has specifically been
demonstrated to perform an important role in formation and
function of early larval Mytilus galloprovincialis shells (Weiss
and Schönitzer, 2006). Chitin binding and chitin metabolic
process GO terms were enriched in markers of exposure and low
concentration markers of effect in pooled larvae. The markers of
exposure included chitinase 3-like protein 2, acidic mammalian
chitinase, collagen alpha-1(XII) chain, and lactase-phlorizin
hydrolase, and the markers of effect included chitotriosidase-
1, collagen alpha-4(VI) chain, pif, inactive carboxypeptidase-like
protein X2, and beta-hexosaminidase. Chitin-related genes also
responded to copper at relatively low concentrations in our
previous study and have thus consistently represented good
early markers of copper effects (Hall et al., 2020). Considering
the clear impacts of copper on mussel larval development
and shell formation, and the integral role that chitin plays in
larval shell formation, it makes sense that this group of genes
were identified in the copper response. Modulation of chitin-
related genes in abnormal animals could be a compensation
mechanism to address the damaged shell matrix associated
with abnormal development. Chitin-related genes have also
been identified as markers of zinc exposure in Daphnia magna
(Poynton et al., 2007), and of copper exposure in adult mussels
(Negri et al., 2013).
Other markers of exposure or effects were also involved in
the formation of the proteinaceous matrix that is integral to
mollusk shell structure development. Temptin, a component of
the tyrosinase metabolic pathway which is involved in larval shell
formation (Liu et al., 2015) and insoluble shell matrix protein 5
appeared in the markers of exposure (Supplementary Table 1) in
pooled larvae. They were not identified as markers of effect, so
they are likely not directly involved in the abnormal development
of larvae. Perlucin and perlucin-like protein (Weiss et al., 2000)
were identified as markers of effect (4 copies) and exposure (2
copies) in single larvae, and markers of exposure in pooled larvae
(Supplementary Table 1, 2, 5). Pif (Suzuki et al., 2009), on the
other hand, was unique to the copper effects genes in pooled
larvae (Supplementary Table 4 and Figure 9), and appeared
as both a marker of effect (2 copies) and exposure (1 copy) in
single larvae. Temptin, perlucin, and pif, along with several other
shell matrix protein genes, were identified as markers of low-
concentration copper exposure in M. californianus larvae (Hall
et al., 2020). Sussarellu et al. (2018) examined the response of
three different biomineralization genes (collagen, nacrein, and
calcineurin B) to copper in early C. gigas larvae, and did not
find a significant response, but we have similarly not identified
these specific genes as copper responsive. We can thus conclude
that specific shell matrix and biomineralization genes shell matrix
pathways are targeted by copper in mussels, although possibly
not in other bivalve larvae, and copper-induced abnormality
may be associated with additional modulation of shell matrix
protein forming genes.
While the cell adhesion GO term was only enriched among
the markers of exposure in pooled larvae, there were still many
genes related to the extracellular matrix and cell adhesion in
both markers of exposure and effect in both pooled and single
larvae (Supplementary Tables 1, 2, 4, 5). Cell adhesion is known
to play an essential role in metazoan development, especially
in nervous system development (Hynes and Lander, 1992),
and a lack of proper cell adhesion mechanisms can lead to
abnormal developmental patterns or embryo death (Gurdon,
1992). Previous research on oyster larval development found
delayed and abnormal development in response to elevated CO2-
induced expression of cell adhesion and extracellular matrix
genes (De Wit et al., 2018). The prominence of cell adhesion
genes among the markers of exposure is somewhat unexpected,
as the literature suggests that disruption of cell adhesion often
leads to abnormal development. However, there were unique
cell adhesion genes that were identified as markers of effect,
especially in the single larval markers of effect (e.g., multiple
protocadherins present in markers of effect, vs. only a single
copy in the markers of exposure - Supplementary Tables 2,
5), and some of the cell-adhesion-related markers of exposure
(e.g., POSTN,JAM2, and PCDH9) were also shared amplitude-
dependent markers of exposure and effect in pooled larvae
(Supplementary Table 8). For these genes, higher expression was
associated with abnormal development (Supplementary Table 8
and Figure 10). Therefore, it does appear that certain aspects of
cell adhesion are involved in abnormal development induced at
low copper concentrations, and that some cell adhesion genes can
serve as good markers of effect.
This study also provides insight into the molecular
mechanisms associated with natural abnormal development,
which is still not well understood in molluscan systems. Genes
that were DE in abnormal animals that weren’t exposed to copper
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represented functional categories similar to those identified in
past studies of abnormal or delayed bivalve development. De
Wit et al. (2018) assessed DE between larvae of oyster C. gigas
that exhibited abnormal/delayed development in response to
simulated OA and control larvae, and found that DE genes fell
into four main categories: extracellular matrix, shell formation,
transmembrane proteins, and protease inhibitors. At least
several markers in each of these categories were identified in
our gene sets as well, including some shared specific markers:
caveolin, a gene with a thrombospondin motif, and a lectin
(Supplementary Tables 9, 10). The differentially expressed
cytoskeletal components in our study reflect previous findings
that cytoskeletal component proteins, including tubulins,
myosin, and tropomyosin, are differentially expressed between
trochophore and D-hinge larvae of the oyster C. gigas (Huan
et al., 2012). Huan et al. (2012) also identified cell proliferators as
a key category of DE proteins, with several markers representing
translation or ribosomes. We found several genes coding for
DNA polymerases and DNA repair proteins (Supplementary
Tables 9, 10), which could similarly be indicators of cell
proliferation, but could also be indicative of DNA damage and
DNA repair. Finally, previous research on Pinctada fucata (Pearl
Oyster) transcriptional changes during development supports
our finding that developmental genes are differentially expressed
between D-hinge larvae and earlier stages prior to shell formation
(Li et al., 2016).
Analysis of the phenotypic-anchored expression patterns
revealed that while functional groups of sensitive transcriptional
markers remain relatively consistent across sequencing
approaches, trends in up or down regulation are less predictable.
In the pooled sorted larval samples, the most sensitive markers
were overwhelmingly upregulated in abnormal animals
(Figures 7C–F). The single larvae markers of effect contained
approximately equal numbers of genes that were upregulated and
downregulated in abnormal larvae (Supplementary Table 5).
In contrast, our previous study showed that genes that were
downregulated were the most sensitive indicator of copper, with
large-scale gene downregulation being a feature of the response
to exposure to low copper concentrations (Hall et al., 2020).
Furthermore, some of the sensitive upregulated markers in these
experiments were only expressed at higher concentrations in our
previous study. This shift in pattern can likely be attributed to
differences in the nature of bulk pooled sequencing, sequencing
of specific morphological groups, and sequencing of individual
larvae. In both pooled and single larval samples, there were clear
transcriptional differences associated with distinct morphologies.
However, if those samples had been sequenced together, the
nuances of morphology-specific expression would have been
impossible to detect. At the lower copper concentration, 3 µg/l,
there was consistently morphology-linked differential expression
across both single larval and pooled larval dataset. However, the
transcriptional profiles of normal and abnormal animals were not
as reliably different at the higher copper concentration, 6 µg/l.
Thus, it seems that pooled sequencing may be effective to detect
biomarkers at higher concentrations, but that morphology-
specific gene expression is more sensitive and informative at
lower copper concentrations.
CONCLUSION
We have identified robust transcriptional markers of copper
exposure and effect in M. californianus larvae that could be
used to improve the sensitivity and objectivity of bivalve embryo
water quality assays for copper. Markers of effect were the
most informative at lower copper concentrations, as substantial
DE was consistently present in both sorted pools and single
larval samples. We have also identified some biomarkers of
copper exposure and effects that have not been previously
identified in mussels. Markers of exposure exhibited similar
functional categories to markers of effect, although often
with intensified modulation or more gene copies activated in
the markers of effect, which suggests that abnormal animals
exercise similar yet amplified responses to copper, rather than
modulating different responses and pathways. Markers of copper
exposure and effect are characterized by genes involved in
oxidoreductase activity, oxidative stress, cell adhesion, and
extracellular proteinaceous matrix. The exact mechanisms of
copper-induced abnormal development remain unclear, but these
results highlight pathways that should be further explored at the
enzymatic and cellular level.
DATA AVAILABILITY STATEMENT
The datasets presented in this study can be found in online
repositories. The names of the repository/repositories and
accession number(s) can be found below: https://www.ncbi.nlm.
nih.gov/, PRJNA688298.
AUTHOR CONTRIBUTIONS
MH contributed to the experimental design, executed
the experiments, analyzed the data, and authored
the manuscript. AG contributed to the research idea
and experimental design, executed the experiments,
analyzed the data, and authored the manuscript. Both
authors contributed to the article and approved the
submitted version.
FUNDING
The University of Southern California Provost’s Ph.D.
Fellowship provided stipend to MH. The Wrigley Institute
for Environmental Studies provided stipend to MH. The USC
Sonosky Fellowship provided stipend to MH. University of
Southern California startup funds to AG funded research
supplies and sequencing.
ACKNOWLEDGMENTS
We would like to thank the University of Southern California
Office of the Provost, the University of Southern California
Wrigley Institute of Environmental Studies, and University of
Frontiers in Physiology | www.frontiersin.org 15 December 2021 | Volume 12 | Article 647482

fphys-12-647482 December 4, 2021 Time: 15:21 # 16
Hall and Gracey Single-Larva Markers Copper Exposure Toxicity
Southern California Sonosky Fellowship for funding this work.
We thank John F. Heidelberg and Rohan Sachdeva for providing
computational support and support in bioinformatic analysis.
Thanks to David A. Caron, Victoria Campbell, and Paige Connell
for providing advice on larval sorting technique.
SUPPLEMENTARY MATERIAL
The Supplementary Material for this article can be found
online at: https://www.frontiersin.org/articles/10.3389/fphys.
2021.647482/full#supplementary-material
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Conflict of Interest: The authors declare that the research was conducted in the
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