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\begin{document}

\title{Maternal effects in gene expression of interspecific coral hybrids}



\author[1]{Wing Yan Chan}%
\author[2]{Jessica Chung}%
\author[1]{Lesa Peplow}%
\author[2]{Ary Hoffmann}%
\author[1]{Madeleine van Oppen}%
\affil[1]{Australian Institute of Marine Science}%
\affil[2]{The University of Melbourne Bio21 Molecular Science and Biotechnology Institute}%


\vspace{-1em}



  
  \date{May 17, 2024}


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\selectlanguage{english}
\begin{abstract}
Maternal effects have been well documented for offspring morphology and
life history traits in plants and terrestrial animals, yet little is
known about maternal effects in corals. Further, few studies have
explored maternal effects in gene expression. In a previous study, F1
interspecific hybrid and purebred larvae of the coral species
\emph{Acropora tenuis} and \emph{A. loripes} were settled and exposed to
ambient or elevated temperature and \emph{p}CO\textsubscript{2}
conditions for seven months. At this stage, the hybrid coral recruits
from both ocean conditions exhibited strong maternal effects in several
fitness traits. We conducted RNA-sequencing on samples from the same
experiment and showed that gene expression of the hybrid \emph{Acropora}
also showed clear maternal effects. Only 40 genes were differentially
expressed between hybrids and their maternal progenitor. In contrast,
\textasciitilde{}2000 differentially expressed genes were observed
between hybrids and their paternal progenitors, and between the
reciprocal F1 hybrids. These results indicate that maternal effects in
coral gene expression can be long-lasting. Unlike findings from most
short-term stress experiments in corals, no genes were differentially
expressed in the hybrid nor purebred offspring after seven months of
exposure to elevated temperature and \emph{p}CO\textsubscript{2}
conditions.%
\end{abstract}%



\sloppy


\textbf{Keywords}

Maternal effects, interspecific hybrids, gene expression,
RNA-sequencing, coral reefs

\textbf{Introduction}

Maternal effects can have a large impact on the fitness of offspring. In
plants, maternal effects in seed traits (e.g., seed mass, germination
time) and offspring fitness (e.g. growth rates) have been well
documented (Donohue, 2009). Maternal age at reproduction is known to
affect diapause (i.e., suspended development induced by unfavorable
environmental conditions) in offspring of insects (Mousseau \& Dingle,
1991), and in amphibians, maternal factors have well known effects in
size and rates of development (Warne et al., 2013).

Maternal effects can be the result of the direct effects of the
environment on epigenetic marks, genomic imprinting, or maternal
provisioning (which is influenced by both environmental and genetic
effects). For example, the environment experienced by the mother can
affect the expression of genes involved in germination
of\emph{Arabidopsis thaliana} offspring (for review, see Donohue, 2009).
Genomic imprinting is the epigenetic silencing (e.g., via cytosine
methylation or chromatin-mediated processes) of one of the parental
chromosomes, leaving only expression from the non-silenced chromosome
(Alleman \& Doctor, 2000). In the case of maternal effects, only the
maternal chromosomes are expressed and this can be transmitted to one or
more subsequent generations (Bischoff \& M\selectlanguage{ngerman}üller\selectlanguage{english}-Scharer, 2010). Genomic
imprinting has been observed in a few insect species, plants and
placental mammals (for review, see Matsuura, 2020; Thamban et al.,
2020), but not in egg-laying vertebrates such as birds, monotremes and
reptiles by far (Killian et al., 2001; Renfree et al., 2013).

Maternal provisioning is the supply of nutrients, resources and hormones
by the mother during seed or egg development (Videvall et al., 2016).
For example, the amount of stored nutrient reserves in seeds can
significantly influence early seedling growth and development (Slot et
al., 2013). Maternal effects can also manifest via the seed coating
(which is maternally produced), the endosperm (which is a triploid
tissue with two-third of genotype from the maternal parent), and/or via
direct maternal effects in dispersal (Donohue, 2009). For instance,
flowering time in \emph{Campanula americana} determines whether the
progeny will germinate in autumn or spring (Galloway \& Etterson, 2007).
For many marine larvae, maternal provisioning of lipids is the major
source of endogenous energy and this accounts for \textasciitilde{}40\%
of the metabolic needs of coral larvae (Harii et al., 2010). Maternal
provisioning is affected by both the genotype and the environmental
conditions experienced by the mother. For example, maternal exposure to
hormones can change egg and larval morphology of reef fishes (McCormick,
1999). Maternal effects due to provisioning generally decrease over time
(Roach \& Wulff, 1987), but can also persist through the entire life
cycle of an organism.

When different genotypes are combined to produce F1 (i.e., first
generation) hybrids, maternal effects can affect the phenotypes of F1
offspring. Hybridization is the crossing between separate species or
between strains/lines/populations within a species. The phenotypes of
the F1 offspring may be similar to that of their maternal parents (i.e.,
maternal effects), intermediate between the parents (i.e., additive
effects), similar to that of the dominant parent (i.e., dominance), or
different to both parents (i.e., over-dominance or under-dominance)
(Chen, 2013; Li et al., 2008; Lippman \& Zamir, 2007). For example,
environmental conditions experienced by the mother can influence the
expression of genes involved of germination in progeny (Donohue, 2009).
However, hybrid gene expression studies often only involve hybrids of
one direction (Videvall et al., 2016), and hence are unable to
distinguish between dominance effects and maternal effects.

For corals, maternal effects in morphology (Willis et al., 2006),
survival (Chan et al., 2018; Isomura et al., 2013) and thermal tolerance
(Dixon et al., 2015) have been reported. Chan et al. (2018) showed that
interspecific hybrids of the corals \emph{Acropora tenuis}
and\emph{Acropora loripes} had similar survival and growth to their
maternal purebreds, although they exceeded parental performances in some
cases. The bacterial and microalgal endosymbiont (Symbiodiniaceae spp.)
communities associated with these corals did not differ between the
reciprocal hybrids and their maternal and paternal purebreds (Chan et
al., 2019). Since these microorganisms carry vital functions to the
coral hosts and can contribute to holobiont fitness differences
(Blackall et al., 2015; Rosenberg et al., 2007), this finding suggests
that the microbial communities were unlikely responsible for the
observed holobiont fitness differences, and that these are likely
underpinned by coral host genetic and/or non-genetic transgenerational
factors.

The aim of this study was to test if the phenotypic differences in
reciprocal F1 hybrids of the corals \emph{A. tenuis} and \emph{A.
loripes} could be linked to patterns of host gene expression. Four
offspring groups (i.e., reciprocal F1 hybrids and two parental
purebreds) were previously produced via a laboratory cross of \emph{A.
tenuis} and \emph{A. loripes} and were exposed to seven months of
ambient or elevated temperature and \emph{p}
CO\textsubscript{2}conditions (Chan et al., 2018). Using samples from
the same experiment, we tested for maternal effects in gene expression,
as observed in hybrid survival and growth. In addition, gene expression
was examined between temperature/\emph{p} CO\textsubscript{2} conditions
within each offspring group.

\textbf{Materials and methods}

Experimental design and sample collection

Parental coral colonies of \emph{A. tenuis} and \emph{A. loripes} were
collected from Trunk Reef (18deg35\selectlanguage{english}'S,

146deg80\selectlanguage{english}'E), central Great Barrier Reef in November 2015 and crossed in
the laboratory to form two F1 hybrid and two parental purebred offspring
groups (see Figure 2, Chan et al., 2018 for detailed crossing protocol
and experimental design). Briefly, parental colonies were kept and
spawned under ambient conditions. Egg-sperm bundles of individual
parental colonies were collected and separated with a 100 \selectlanguage{greek}µ\selectlanguage{english}m filter. A
mixed sperm solution with equal quantity of sperm from each conspecific
colony was used to fertilize eggs from the other species in the cross to
produce the hybrids, and to fertilize conspecific eggs to produce the
purebreds. The abbreviation of the offspring groups throughout this
study are: TT (purebred \emph{A. tenuis} ), TL (hybrid), LT (hybrid) and
LL (purebred \emph{A. loripes} ), where the maternal parent is listed
prior to the paternal parent in a genetic cross by convention (Miller et
al., 2012). For example, ``TL'' is a hybrid formed by crossing \emph{A.
tenuis} eggs with \emph{A. loripes} sperm.

Embryos were reared to planula stage and settled onto ceramic plugs
under ambient conditions five days post-spawning. Settled recruits were
randomly and evenly distributed across two treatment conditions: ambient
conditions (27\selectlanguage{ngerman}ºC and 415 ppm\emph{p} CO\textsubscript{2}) and elevated
conditions (ambient +1 °C and 685 ppm \emph{p} CO\textsubscript{2}).
There were 12 replicate tanks per treatment and each tank contained 20
ceramic plugs of each of the four offspring group (i.e., each offspring
group had 12 x 20 = 240 ceramic plugs per treatment). For the elevated
conditions, recruits were ramped at a rate of +2 ºC and
+\textasciitilde{}50ppm a day until they arrived at the targeted
conditions. Given the predicted sea surface temperature (SST) increase
in coral reefs ranges from \textasciitilde{} 1.4 and
\textasciitilde{}3.6 °C by the year 2100 (under RCP 2.6 and 8.5
respectively and relative to pre-Industrial period) (Bindoff et al.,
2019), an elevated temperature of +1 ºC to present day ambient
temperature reflects a realistic scenario that will likely occur in the
coming decades. Note that present day SST has already increase by
\textasciitilde{}0.9°C since pre-industrial time (Bindoff et al., 2019).

Coral recruits were reared under treatment conditions in filtered
seawater for seven months at the National Sea Simulator of the
Australian Institute of Marine Science. A microalgal diet supplement was
supplied to the corals daily and their fitness traits and associated
microbial communities were examined. To mimic the natural environment as
closely as possible, the experimental conditions followed diurnal and
annual temperature variations of Davies Reef (18.83° S, 147.63° E),
which is a reef near the collection sites of the parental colonies. At
the end of the seven-month experiment, recruits from three tanks of each
treatment were randomly selected for sampling. Due to the small size
(and therefore low RNA quantity) of individual recruits, multiple
recruits of the same offspring group from the same tank were pooled to
form one sample. Each pooled sample contained 30 coral polyps. RNA
pooling was considered appropriate as the purpose of this study was to
examine population-level rather than individual-level differences
(Davies et al., 2016; Kendziorski et al., 2003). Three pooled samples
per offspring group per treatment were collected, except only one sample
was available for purebred \emph{A. tenuis} (TT) under elevated
conditions due to high mortality (Table S1). Samples were snap-frozen in
liquid nitrogen and stored at -80 °C until RNA extraction.

RNA extraction

Sample tissues were mechanically disrupted prior to RNA isolation.
Approximately 30 acid washed glass beads (Sigma, 710-1180 \selectlanguage{greek}µ\selectlanguage{english}m diameter)
and 600 \selectlanguage{greek}µ\selectlanguage{english}l RLT buffer (Qiagen) were added to each sample. The samples
were then subjected to 2 x 40 s cycles of bead beating at 4/s in a fast
Prep-245G (MP Biomedicals). Total RNA was isolated from the sample
homogenate using Qiagen RNeasy mini kit (including the optional DNase
treatment). Total RNA was eluted in 40 \selectlanguage{greek}µ\selectlanguage{english}l of RNase free water and 3 \selectlanguage{greek}µ\selectlanguage{english}l
were visualized on a 1\% agarose, 0.5 x TBE gel for quality check. RNA
concentration was measured using the Qubit RNA HS Assay (Thermo Fisher
Scientific/Invitrogen), with fluorescence analysis on a NanoDrop 3300
Fluorospectrometer (Thermo Fisher Scientific). Between 20.5 and 106 ng
total RNA underwent reverse transcription and cDNA was amplified using
NuGen's Ovation V2.0 kit (with one cycle amplification). The amplified
cDNA was then purified using magnetic beads (Beckman Coulter Agencourt
kit) and 1 \selectlanguage{greek}µ\selectlanguage{english}l was visualized on a 1\% agarose, 0.5 x TBE gel. Purity of
sample cDNA was determined by A260/A280 ratios measured with a NanoDrop
2000 spectrophotometer (Thermo Fisher Scientific). cDNA concentration
was measured using the Quant-iT PicoGreen dsDNA Assay (Thermo Fisher
Scientific/Invitrogen). Sample cDNA concentrations were normalized and
25 \selectlanguage{greek}µ\selectlanguage{english}l of 20 ng/\selectlanguage{greek}µ\selectlanguage{english}l cDNA were sent to Ramaciotti Centre for Genomics
(UNSW, Sydney) for Nextera XT Library Preparation and paired-end
sequencing on the Illumina NextSeq500 platform (2 x 75bp). The total RNA
concentration and quality, the amount of total RNA that underwent
reverse transcription, cDNA concentration and quality, as well as raw
reads of each sample are shown in Table S2.

Sequence data processing

Quality and adapter trimming were carried out on raw reads using
Trimmomatic (Bolger et al., 2014), discarding reads \textless{} 50 bp or
with an averaged quality score \textless{} 20 in a sliding window of
five bases. Since the coral holobiont is associated with high densities
of prokaryotes and algal endosymbionts, reads were filtered with the
following steps: First, reads were compared to an rRNA database
(Silva132\_LSU, Silva132\_SSU) and matches (i.e., e-values [?]
10\textsuperscript{-5}) were removed using the program SortMeRNA
(Kopylova et al., 2012). Second, reads were compared to the algal
endosymbiont genome (genus \emph{Cladocopium,} symC\_scaffold\_40.fasta
(Shoguchi et al., 2018) and matches were removed using BBDuk (Bushnell,
2020). The remaining reads of each sample are shown in Table S2 and were
used to create a \emph{de novo} assembly for the each offspring groups
and a combined \emph{de novo} assembly for all four offspring groups
using Trinity (Grabherr et al., 2011). Small transcripts of \textless{}
400 bp were removed from the assemblies (Kenkel \& Bay, 2017), and the
longest isoform of each trinity transcript was obtained. Mitochondrial
genes were identified running BLASTn against the \emph{A.
tenuis}mitochondrial genome (NC\_003522.1.fasta, van Oppen et al., 2002)
and were retained in the analysis. The remaining transcripts were then
identified by BLASTx searches against the most complete coral gene model
(\emph{A. digitifera} , GCF\_000222465.1\_Adig\_1.1\_protein.faa,
Shinzato et al., 2011) and NCBI's nonredundant (nr) protein database,
with a e-value cut off [?] 10\textsuperscript{-5}.

Gene names and gene ontologies (GO) of the transcripts were assigned
using BLASTx search against UniProt Knowledgebase Swiss-Prot database
(The UniProt Consortium, 2015). Duplicate query transcripts were
removed. Transcript abundance of the samples was then estimated using
RSEM, an alignment-based method (Li \& Dewey, 2011). Transcript
quantification of the samples was performed by aligning reads using
bowtie2 (Langmead \& Salzberg, 2012) and estimating abundance with RSEM
(Li \& Dewey, 2011). For gene expression comparison between hybrids and
parental purebreds, we tested estimating transcript abundance using the
assembly of purebred \emph{A. loripes} , as well as the combined
assembly produced using all offspring groups. The two methods revealed
very similar results (Figure S1), and the results presented here are
based on transcript abundance estimated using the assembly of purebred
\emph{A. loripes} . Due to the small number of samples available for the
parental purebred \emph{A. tenuis} (Table S1), a \emph{de novo} assembly
was not conducted or tested as a basis for transcript abundance
estimate. For gene expression comparison between treatments within an
offspring group, the \emph{de novo} assembly of each offspring group was
used to estimate transcript abundance. Treatment comparison was not
conducted for\emph{A. tenuis} purebreds due to an insufficient number of
samples (Table S1).

Statistical analyses

Separate analyses were conducted to compare gene expression between
hybrids and parental purebreds, and ambient versus elevated conditions
within an offspring group. In addition, a separate analysis was carried
out for mitochondrial genes. Transcript abundance of the samples and the
BLAST results were analyzed in R and differential expression analysis
was performed using the package \emph{limma} (Ritchie et al., 2015).
Firstly, only transcripts that were of coral origin were retained, as
indicated in the BLAST results. For the mitochondrial analysis, only
transcripts that matched with the mitochondrial genome were used.
Secondly, transcripts that consistently had zero or very low counts were
removed using the edgeR build in function filterByExpr, and scale
normalization (TMM) was applied. For Principal Components Analysis
(PCA), sample raw counts were transformed into log2-counts per million
(log-CPM) to account for library size differences.

A total of four samples were identified to have small library size
(three \emph{A. tenuis} purebreds- two under ambient, one under elevated
conditions, and one TL hybrid under elevated conditions), and a relative
log expression (RLE) plot showed that normalization of these samples was
unsuccessful (Gandolfo \& Speed, 2018) (Figure S2, Table S2). These
samples were excluded from the main manuscript, but their analyses were
retained in the Supplemental Information. A heatmap was then used to
visualize the 500 most variable genes across samples using the log-CPM
expression values with dendrograms computed using Euclidean distances.
For the mitochondrial analysis, a PCA and a heatmap were generated using
all genes that remained post-filtering.

To fit linear models for comparisons, count data was transformed to
log-CPM using the voom function in the limma package. Since no treatment
effect was found on gene expression (see Results section), the
comparison of hybrids and purebreds combined samples from both
treatments. Comparisons were made between: 1) maternal purebred LL and
its hybrid LT, 2) paternal purebred LL and its hybrid TL, and 3) between
the reciprocal hybrids LT and TL. The purebred TT (\emph{A. tenuis} )
was not included due to a small sample size (n =1, Table S1). Empirical
Bayes moderated t-statistics were generated to assess the pairwise
comparisons, and \emph{p} -values were corrected using the
Benjamini-Hochberg method (Benjamini \& Hochberg, 1995). A gene was
considered differentially expressed when p\textsubscript{adj}\textless{}
0.05 using the \emph{treat} function in the limma package with a
log-fold-change threshold of \textgreater{} 0.2. The list of
differentially expressed genes (DEGs) was exported for gene ontology
(GO) analyses and visualized using volcano plots (Blighe et al., 2018).
The volcano plots and GO analyses focused on the comparison of 1)
paternal purebred LL with its hybrid TL, and 2) between the reciprocal
hybrid LT and TL only, as these were the pairs with a high number of
differentially expressed genes to explore.

Two different approaches were applied to the GO analyses, including
GOseq (Young et al., 2010) and a rank-based GO analysis with adaptive
clustering using a Mann-Whitney U (MWU) test
(https://github.com/z0on/GO\_MWU, Dixon et al., 2015). For GOseq, the
analysis was conducted using the list of DEGs and the \emph{p-} values
were corrected with the Benjamini-Hochberg method (Benjamini \&
Hochberg, 1995). A GO category was considered overrepresented or
underrepresented when the p\textsubscript{adj} was \textless{} 0.05 and
that the category had \textgreater{} 3 DEGs. For the MWU test, the
hierarchical clustering trees utilized the log10-transformed\emph{p}
-values of the DEGs and indicated significantly enriched GO categories
by up-regulated (red) or down-regulated (blue), under a false discovery
rate of 10\%. In addition, differentially expression nuclear genes in GO
categories with functions connected to the mitochondrion were
identified.

\textbf{Results}

On average, \textasciitilde{}12.5 million raw Illumina reads were
obtained per sample. After quality trimming and removal of rRNA and
algal endosymbiont components, an average of \textasciitilde{}6.2
million paired reads were retained per sample. The transcriptome of
purebred \emph{A. loripes} contained \textasciitilde{}291 k transcripts,
and \textasciitilde{}59 k transcripts were left after only retaining the
longest isoforms and removal of small transcripts \textless{} 400 bp.
See Table S3 for details of other transcriptomes used for preliminary
analysis and evaluating treatment effect. For a total of
\textasciitilde{}35 k transcripts a match of coral origin was found in
the NCBI nr database. Following the removal of duplicates and
transcripts that consistently had zero or very low counts, 8800
transcripts were retained and used for downstream analyses.

Transcriptome-wide gene expression of the hybrids was similar to that of
their maternal purebreds, yet distinct from their paternal purebreds and
the reciprocal hybrids (Figures 1-3, S3). Principal component analyses
(PCA) showed similar expression patterns of the hybrid LT with its
maternal purebred LL under both ambient and elevated conditions (Figure
1). The only exception was one LL purebred sample which showed
separation with the others in principle component two (Figure 1). Gene
expression of the reciprocal hybrid TL also clustered with its maternal
purebred TT (but note that n = 1 for TT), and was separated with hybrid
LT and its paternal purebred LL under both treatment conditions (Figure
1). The four samples excluded from the main analyses due to small
library sizes also supported the existence of maternal effects (Figure
S4-S5). The amount of total RNA and cDNA input, as well as the number of
raw reads of the samples showed no specific patterns in the PCA plots,
suggesting that the observed maternal patterns of the offspring groups
were not driven by these factors (Figure S4). Within an offspring group,
gene expression did not differ between ambient and elevated conditions
(Figure 1). Maternal patterns were not observed in the PCA plot and
heatmap generated using mitochondrial genes only (Figure S6-S7).

Differential expression analysis resulted in only 40 DEGs between the
maternal purebred LL and its hybrid LT (Figure 2). In contrast, almost
2000 DEGs were identified between the paternal purebred LL and its
hybrid TL, as well as between the reciprocal hybrids LT and TL (Figure
2). Among these \textasciitilde{}2000 DEGs, the hybrid LT and its
maternal purebred LL shared 1343 genes that were differentially
expressed from the hybrid TL (Figure 2). Maternal effects in gene
expression were also evident in the heatmap of the 500 most variable
genes across samples (Figure 3). The only exception was one purebred LL
sample which clustered away from the other LL samples, and this was the
same sample that showed separation in the PCA plot (Figure 1, 3). The
heatmap that includes the four samples removed from the main analyses
due to small library sizes confirmed the maternal effects observed in
the smaller subset of samples (Figure S5).

Among the DEGs with the highest log-fold change (i.e., four DEGs for
paternal purebred LL compared to its hybrid TL, and seven DEGs for
hybrid LT compared to hybrid TL with LFC \textgreater{} 5), three were
shared genes between the two pairs of comparison (Figure S3).
Unfortunately, most of these DEGs were annotated as uncharacterized
proteins and hence their potential functions were unknown (Table S4).
Only one differentially expressed mitochondrial gene
(TRINITY\_DN76286\_c6\_g1\_i1) was identified between hybrid TL and its
paternal purebred LL (p\textsubscript{ad}j = 0.03). No differentially
expressed mitochondrial genes were found in all other pairs of
comparison.

For gene ontology (GO) analyses using GOseq, GO category ``cytosol''
(GO: 0005829) was underrepresented in both the comparisons between the
paternal purebred LL with its hybrid TL and between the reciprocal
hybrids LT and TL, with 90 and 96 DEGs respectively in this category
(Table S5). Note that ``cytosol'' is a very broad GO category and it was
comprised of 620 genes in this dataset. In addition, the GO category
``membrane'' (GO: 0016020) was also underrepresented in the comparison
between the paternal purebred LL and its hybrid TL (Table S4). This was
also a broad GO category with 255 genes in this dataset, 27 of which
were DEGs. In contrast, GO analyses using the MWU test showed no
significant GO category was over- or under-represented. However, note
that the MWU test omits GO categories that are too broad (i.e., a GO
category that contains a large proportion of the total number of genes).
For this reason, it was unsurprising that the very broad GO categories
``cytosol'' and ``membrane'' that were identified as underrepresented
using GOseq were not significant here.

For offspring groups that had different maternal parent species (i.e.,
between the hybrid TL and its paternal purebred LL, and between the
reciprocal hybrids LT and TL), 84-88 DEGs were identified in GO
categories with functions connected to the mitochondrion (Table S6). In
contrast, no DEGs were found in GO categories linked to the
mitochondrion when the offspring groups shared the same maternal parent
species (i.e., between the hybrid LT and its maternal purebred LL). The
proportion of DEGs over total number of genes was similar between genes
in GO categories linked to the mitochondrion and genes in all GO
categories (14.9-17.6\%, Table S6).

\textbf{Discussion}

Maternal effects in coral fitness are reflected in gene expression
patterns

Maternal effects in recruit survival and size previously reported
for\emph{A. loripes} x \emph{A. tenuis} hybrid corals (Table 1) were
consistent with their gene expression patterns. At the time when the
corals were sampled for gene expression analyses, the hybrid LT and its
maternal purebred LL had higher survival compared to the hybrid TL and
its maternal purebred TT under both ambient and elevated conditions.
Although the corals did not differ in size at seven months of age,
maternal effects in size were evident by one year of age (Table 1).
Maternal effects have previously been reported for other
Indo-Pacific\emph{Acropora} hybrid corals obtained via laboratory
crossing. These include effects in: 1) morphology of interspecific
hybrids from an\emph{A. pulchra} x \emph{A. millepora} cross (Willis et
al., 2006), 2) survival of interspecific hybrid larvae from an \emph{A.
florida} x\emph{A. intermedia} cross (Isomura et al., 2013), and 3)
thermal tolerance of intraspecific \emph{A. millepora} hybrid larvae
from a higher and lower latitude population. In contrast, paternal
effects were found in morphology of natural interspecific hybrids of
\emph{A. palmata} and \emph{A. cervicornis} from the Caribbean (Vollmer
\& Palumbi, 2002), and additive effects in survival (i.e., hybrid
survival was intermediate between the parental offspring) were observed
in experimentally produced intraspecific hybrids of \emph{A.
millepora}from a higher and lower latitude cross (van Oppen et al.,
2014).

While a few studies have reported maternal effects in coral fitness and
morphology, little is known about maternal effects in gene expression.
In addition to the coral host, the host-associated microbiome can also
have an impact on host gene expression (Barfield et al., 2018; Buerger
et al., 2020; Helmkampf et al., 2019). In our study, however, the
bacterial and microalgal endosymbiont communities of the corals were
similar at the time of sampling (Table 1). The consistency between host
gene expression and phenotypic results thus suggests that maternal
host-related factors were likely the drivers behind the observed fitness
differences. A large number of differentially expressed genes
(\textasciitilde{}2000 DEGs) were found when comparing offspring groups
that had different maternal parent species (i.e., between the hybrid TL
and its paternal purebred LL, and between the reciprocal hybrids), but
not when the groups shared the same maternal parent species (i.e., only
40 DEGs between the hybrid LT and its maternal purebred LL). Maternal
effects were evident in these corals based on PCA, heatmap and volcano
plots. While a statistical comparison cannot be made back to the
parental purebred TT due to small sample size, gene expression of hybrid
TL was similar to the only TT sample tested based on PCA and the heatmap
was indicative of maternal effects. The four samples omitted from the
main analyses because of their small library sizes also supported the
presence of maternal effects, although inferences drawn from these
samples should be taken with caution.

In our study, however, no mitochondrial genes were differentially
expressed and PCA and heatmap of mitochondrial genes did not show
maternal patterns. In other words, evidence of maternal gene expression
patterns was only found in the nuclear genes, but not in the
mitochondrial genes or via mito-nuclear crosstalk in this study
(although note that only seven mitochondrial genes were available for
comparison post-filtering).

Several studies have reported maternal effects in gene expression
including in a perennial herb (Videvall et al., 2016), coral (Dixon et
al., 2015), pipefish (Beemelmanns \& Roth, 2016) and stickleback
(Metzger \& Schulte, 2016; Mommer \& Bell, 2014; Shama et al., 2016),
and maternal environments have also been demonstrated to affect DNA
methylation of sea urchin (Strader et al., 2020). Videvall et al. (2016)
showed that gene expression patterns were distinct between parental
populations of 12-week-old seedling of the perennial
herb\emph{Arabidopsis lyrata} , and expression in intraspecific hybrids
was frequently more similar to that of the maternal than paternal
population. Only 15 DEGs were found between the hybrid produced in one
direction and its maternal population, yet \textgreater{} 8800 DEGs were
found when compared to its paternal population (Videvall et al., 2016).
Interestingly, maternal effects were weaker in the hybrid cross of the
other direction, with 334 and 661 DEGs observed when compared to its
maternal and paternal population respectively (Videvall et al., 2016).
Only one previous study has examined maternal effects in coral hybrid
gene expression and only coral larvae were studied. Consistent with our
findings, Dixon et al. (2015) showed that gene expression of
intraspecific \emph{A. millepora} hybrid larvae was similar to that of
their maternal population (i.e., up to 2,000 genes in hybrids followed
the expression patterns of the maternal population). In these studies
(Dixon et al., 2015 and Videvall et al., 2016) however, maternal effects
were examined in early life stages only (i.e., 12-week-old seedling and
6-day-old larvae). Our results show that maternal effects can continue
to influence gene expression of hybrid corals up to the age of at least
seven months, indicating the potential long-term nature of maternal
effects.

While differences in gene expression patterns were obvious between
reciprocal hybrids as well as between the hybrid TL and its paternal
purebred, it was unclear what pathways and mechanisms were linked to
these differences and underpinned observed phenotypic differences (Chan
et al., 2018). Gene ontology (GO) analyses revealed underrepresentation
of a very broad GO category, ``cytosol'', in both pairs of comparison.
It is also possible that maternal provisioning had long-lasting effects
in offspring (that were seven months old) and was responsible for the
phenotypic and gene expression differences (i.e., poorly provisioned
offspring may exhibit pervasive differences in transcription). Future
studies on maternal effects in corals will benefit from quantifying
differences in maternal provisioning between the parental species, such
as lipid/protein content of eggs and early larvae.

In contrast, clear pathways involved in maternal effects were observed
in the intraspecific \emph{A. millepora} hybrid larvae (Dixon et al.,
2015). Analyses of cellular component categories of tolerance-associated
genes (i.e., genes for which expression levels prior to stress predicted
the probability of larval survival under stress) showed enrichment of
nuclear-encoded mitochondrial membrane components in hybrid coral larvae
whose parents come from a warmer latitude (Dixon et al., 2015). The most
upregulated GO categories were energy production and conversion, and
encompassed mitochondrial proteins, suggesting mitochondrial protein
variation in larvae may have contributed to maternal effects in thermal
tolerance (Dixon et al., 2015).

The difference in GO associated patterns between these two studies may
be due to 1) the parental populations chosen for hybridization, 2) the
symbiotic/aposymbiotic nature of the corals and 3) the life stage of the
corals. Parental populations of the same species from different
latitudes were selected in Dixon et al. (2015), whereas parental
populations of two different species from the same reef were chosen for
this study. The differences in parental thermal regimes in Dixon et al.
(2015) may lead to clearer maternal effects in thermal stress-related GO
categories. Moreover, gene expression responses of aposymbiotic larvae
in Dixon et al. ( 2015) were likely different from coral recruits (in
this study) that were associated with a high density of microalgal
endosymbionts. The effects of maternal provisioning on gene expression
is also likely to be stronger in early larvae than in seven-month-old
recruits. Hence, the contrasting results of the two studies are
unsurprising. Further, mitochondrial genes may not show maternal
patterns if maternal provisioning was responsible for the phenotypic
maternal patterns observed in these corals.

Gene expression was unaffected by long-term exposure to elevated
temperature and pCO\textsubscript{2} conditions

Elevated temperature and \emph{p} CO\textsubscript{2} conditions had a
negative impact on survival and size of the corals used in this study
(Table 1), yet gene expression within an offspring group did not differ
between ambient and elevated conditions (Figure 1). Nevertheless, gene
expression changes under short-term acute stress are commonly found in
coral. This often involves the regulation of genes encoding heat shock
proteins, ion transport, apoptosis, immune responses and/or oxidative
stress (Barshis et al., 2013; Desalvo et al., 2008; Meyer et al., 2011;
Ruiz-Jones \& Palumbi, 2017). The absence of DEGs in corals under
ambient versus elevated conditions was unexpected and may be due to the
relatively mild and long-term nature of the treatments. The elevated
conditions of this study (ambient +1 \selectlanguage{ngerman}°C, 685 ppm \emph{p}
CO\textsubscript{2}) were relatively mild compared to many other
longer-term studies (e.g., ambient +7 and + 12 ºC, Maor-Landaw et al.,
2017; 856-3880 ppm\emph{p} CO\textsubscript{2}, Vidal-Dupiol et al.,
2013). In addition, gene expression responses of corals under long-term
stress have been shown to differ from those under short-term stress.
Despite significant differences in CO\textsubscript{2} concentration
under control and natural CO\textsubscript{2} seep sites (i.e.,
\textasciitilde{}355 versus 998 ppm), only 61 DEGs were found in
\emph{A. millepora} from the two sites (Kenkel et al., 2017). Similarly,
the expression of calcification-related genes changed significantly in
\emph{A. millepora} subjected to short-term (i.e., 3 days) high \emph{p}
CO\textsubscript{2} exposure (Moya et al., 2012, 2015), but far fewer
DEGs were found as exposure time increased (Moya et al., 2015; Rocker et
al., 2015). Since cellular stress gene expression responses can be
transient (Kültz, 2003), certain expression changes may only be
detectable during the initial exposure and therefore fewer
differentially expressed genes are generally found in long-term studies.

\textbf{Conclusions and future studies}

This study showed that maternal effects manifested as gene expression
differences in interspecific hybrids of the coral \emph{A.tenuis} and
\emph{A. loripes} . We also showed that maternal effects can persist to
at least seven months of age in coral and were likely responsible for
the phenotypes of F1 hybrids. However, the pathways and mechanisms
responsible for the phenotypic differences were unknown and exposure to
elevated temperature and\emph{p} CO\textsubscript{2} conditions did not
result in differential coral gene expression. Although the composition
of bacterial and microalgal endosymbiont communities of these corals was
similar under ambient and elevated conditions and between hybrids and
purebreds, these microbes may have expressed different genes and
contributed to holobiont phenotypic differences. Future studies will
benefit from examining the gene expression of these microbial
communities alongside the host. Other less studied members of the coral
holobiont, such as viruses and fungi (that were not examined), may also
have contributed to coral survival and size differences between
offspring groups and treatment conditions. Further, post-transcriptional
and epigenetic regulation (e.g., DNA methylation) may have varied
between treatments and hybrid and purebreds and may have resulted in
phenotypic differences (Dimond et al., 2017). Future studies should
consider adopting a multi-omics approach and assessing other members of
the coral-associated microbiome to explore other mechanisms that
underpin the phenotype of the coral holobiont.

\textbf{Acknowledgements.} We thank P. Buerger, C. Kenkel and P. Laffy
for fruitful discussions, and support from the National Sea Simulator
team of AIMS. This research was funded by the Paul G. Allen Family
Foundation and the Australian Institute of Marine Science (AIMS). WYC
acknowledges the University of Melbourne International Research
Scholarship and Fee Remission Scholarship. MvO acknowledges Australian
Research Council Laureate Fellowship FL180100036.

\textbf{Author's ontribution.} W.Y.C., M.J.H.O., L.P. and A.H. designed
the study. W.Y.C. and

L.P. conducted the experiment. L.P. carried out the laboratory work.
J.C., W.Y.C. and A.H. undertook bioinformatic and statistical analyses.
W.Y.C. and M.J.H.O. wrote much of the manuscript and all authors
contributed to the final edited version of the manuscript.

\textbf{Data Availability Statement}

Raw sequences are available in GenBank (SRR12695232 to SRR12695253,
project accession no.: PRJNA665083) and the R scripts for statistical
analyses are available as Appendix S1.

\textbf{Data Citation}

{[}dataset{]}Chan, W. Y., Chung J., Peplow, L., Hoffmann, A. A., \& van
Oppen, M. J. H. (2020). Maternal effects in gene expression of
interspecific coral hybrids. GenBank: PRJNA665083;
SRR12695232-SRR12695253.

\textbf{References}

Alleman, M., \& Doctor, J. (2000). Genomic imprinting in plants:
Observations and evolutionary implications. \emph{Plant Molecular
Biology} , \emph{43} (2), 147--161.
https://doi.org/10.1023/A:1006419025155

Barfield, S. J., Aglyamova, G. V., Bay, L. K., \& Matz, M. V. (2018).
Contrasting effects of \emph{Symbiodinium} identity on coral host
transcriptional profiles across latitudes. \emph{Molecular Ecology}
,\emph{27} (15), 3103--3115. https://doi.org/10.1111/mec.14774

Barshis, D. J., Ladner, J. T., Oliver, T. A., Seneca, F. O.,
Traylor-Knowles, N., \& Palumbi, S. R. (2013). Genomic basis for coral
resilience to climate change. \emph{Proceedings of the National Academy
of Sciences} , \emph{110} (4), 1387--1392.
https://doi.org/10.1073/pnas.1210224110

Beemelmanns, A., \& Roth, O. (2016). Biparental immune priming in the
pipefish \emph{Syngnathus typhle} . \emph{Zoology} , \emph{119} (4),
262--272. https://doi.org/10.1016/j.zool.2016.06.002

Benjamini, Y., \& Hochberg, Y. (1995). Controlling the false discovery
rate: A practical and powerful approach to multiple
testing.\emph{Journal of the Royal Statistical Society. Series B
(Methodological)} , \emph{57} (1), 289--300.
https://doi.org/10.2307/2346101

Bindoff, N., Cheung, W., Kairo, J., Arístegui, J., Guinder, V.,
Hallberg, R., Hilmi, N., Jiao, N., Karim, M., Levin, L., O'Donoghue, S.,
Cuicapusa Purca, S., Rinkevich, B., Suga, T., Tagliabue, A., \&
Williamson, P. (2019). Changing ocean, marine ecosystems, and dependent
communities. In \emph{IPCC special report on the ocean and cryosphere in
a changing climate} (pp. 477--587). {[}H-O Pörtner, DC Roberts, V
Masson-Delmotte, P Zhai, M Tignor, E Poloczanska, K Mintenbeck, A
Alegría, M Nicolai, A Okem, J Petzold, B Rama, NM Weyer (eds.){]}.
Cambridge: University Press.

Bischoff, A., \& Müller\selectlanguage{english}-Scharer, H. (2010). Testing population
differentiation in plant species -- how important are environmental
maternal effects. \emph{Oikos} , \emph{119} (3), 445--454.
https://doi.org/10.1111/j.1600-0706.2009.17776.x

Blackall, L. L., Wilson, B., \& van Oppen, M. J. H. (2015). Coral-the
world's most diverse symbiotic ecosystem. \emph{Molecular Ecology}
,\emph{24} (21), 5330--5347. https://doi.org/10.1111/mec.13400

Blighe, K., Rana, S., \& Lewis, M. (2018). \emph{EnhancedVolcano:
Publication-ready volcano plots with enhanced colouring and labeling} .
https://bioconductor.org/packages/devel/bioc/vignettes/EnhancedVolcano/inst/doc/EnhancedVolcano.html

Bolger, A. M., Lohse, M., \& Usadel, B. (2014). Trimmomatic: A flexible
trimmer for Illumina sequence data. \emph{Bioinformatics} ,\emph{30}
(15), 2114--2120. https://doi.org/10.1093/bioinformatics/btu170

Buerger, B., Alvarez-Roa, C., Coppin, C., Pearce, S., Chakravarti, J.,
Oakeshott, J., Edwards, O., \& Van Oppen, M. J. H.
(2020).\emph{Long-term heat exposure of algal symbionts increases coral
bleaching tolerance} .

Bushnell, B. (2020). \emph{BBMap} .
https://sourceforge.net/projects/bbmap/

Chan, W. Y., Peplow, L. M., Menendez, P., Hoffmann, A. A., \& Oppen, M.
J. H. van. (2019). The roles of age, parentage and environment on
bacterial and algal endosymbiont communities in \emph{Acropora}
corals.\emph{Molecular Ecology} , \emph{28} (16), 3830--3843.
https://doi.org/10.1111/mec.15187

Chan, W. Y., Peplow, L. M., Menendez, P., Hoffmann, A. A., \& van Oppen,
M. J. H. (2018). Interspecific hybridization may provide novel
opportunities for coral reef restoration. \emph{Frontiers in Marine
Science} , \emph{5} . https://doi.org/10.3389/fmars.2018.00160

Chen, Z. J. (2013). Genomic and epigenetic insights into the molecular
bases of heterosis. \emph{Nature Reviews Genetics} , \emph{14} (7),
471--482. https://doi.org/10.1038/nrg3503

Davies, S. W., Marchetti, A., Ries, J. B., \& Castillo, K. D. (2016).
Thermal and \emph{p} CO\textsubscript{2} stress elicit divergent
transcriptomic responses in a resilient coral. \emph{Frontiers in Marine
Science} , \emph{3} . https://doi.org/10.3389/fmars.2016.00112

Desalvo, M. K., Voolstra, C. R., Sunagawa, S., Schwarz, J. A., Stillman,
J. H., Coffroth, M. A., Szmant, A. M., \& Medina, M. (2008).
Differential gene expression during thermal stress and bleaching in the
Caribbean coral \emph{Montastraea faveolata} . \emph{Molecular Ecology}
,\emph{17} (17), 3952--3971.
https://doi.org/10.1111/j.1365-294X.2008.03879.x

Dimond, J. L., Gamblewood, S. K., \& Roberts, S. B. (2017). Genetic and
epigenetic insight into morphospecies in a reef coral. \emph{Molecular
Ecology} , \emph{26} (19), 5031--5042. https://doi.org/10.1111/mec.14252

Dixon, G. B., Davies, S. W., Aglyamova, G. V., Meyer, E., Bay, L. K., \&
Matz, M. V. (2015). Genomic determinants of coral heat tolerance across
latitudes. \emph{Science} , \emph{348} (6242), 1460--1462.
https://doi.org/10.1126/science.1261224

Donohue, K. (2009). Completing the cycle: Maternal effects as the
missing link in plant life histories. \emph{Philosophical Transactions
of the Royal Society B: Biological Sciences} , \emph{364} (1520),
1059--1074. https://doi.org/10.1098/rstb.2008.0291

Galloway, L. F., \& Etterson, J. R. (2007). Transgenerational plasticity
is adaptive in the wild. \emph{Science} , \emph{318} (5853), 1134--1136.
https://doi.org/10.1126/science.1148766

Gandolfo, L. C., \& Speed, T. P. (2018). Rle plots: Visualizing unwanted
variation in high dimensional data. \emph{PLOS ONE} , \emph{13} (2),
e0191629. https://doi.org/10.1371/journal.pone.0191629

Grabherr, M. G., Haas, B. J., Yassour, M., Levin, J. Z., Thompson, D.
A., Amit, I., Adiconis, X., Fan, L., Raychowdhury, R., Zeng, Q., Chen,
Z., Mauceli, E., Hacohen, N., Gnirke, A., Rhind, N., di Palma, F.,
Birren, B. W., Nusbaum, C., Lindblad-Toh, K., \ldots{} Regev, A. (2011).
Trinity: Reconstructing a full-length transcriptome without a genome
from RNA-Seq data. \emph{Nature Biotechnology} , \emph{29} (7),
644--652. https://doi.org/10.1038/nbt.1883

Harii, S., Yamamoto, M., \& Hoegh-Guldberg, O. (2010). The relative
contribution of dinoflagellate photosynthesis and stored lipids to the
survivorship of symbiotic larvae of the reef-building
corals.\emph{Marine Biology} , \emph{157} (6), 1215--1224.
https://doi.org/10.1007/s00227-010-1401-0

Helmkampf, M., Bellinger, M. R., Frazier, M., \& Takabayashi, M. (2019).
Symbiont type and environmental factors affect transcriptome-wide gene
expression in the coral \emph{Montipora capitata} . \emph{Ecology and
Evolution} , \emph{9} (1), 378--392. https://doi.org/10.1002/ece3.4756

Isomura, N., Iwao, K., \& Fukami, H. (2013). Possible natural
hybridization of two morphologically distinct species of
\emph{Acropora}(Cnidaria, Scleractinia) in the Pacific: Fertilization
and larval survival rates. \emph{PLOS ONE} , \emph{8} (2), e56701.
https://doi.org/10.1371/journal.pone.0056701

Kendziorski, C. M., Zhang, Y., Lan, H., \& Attie, A. D. (2003). The
efficiency of pooling mRNA in microarray
experiments.\emph{Biostatistics} , \emph{4} (3), 465--477.
https://doi.org/10.1093/biostatistics/4.3.465

Kenkel, C. D., \& Bay, L. K. (2017). Novel transcriptome resources for
three scleractinian coral species from the
Indo-Pacific.\emph{GigaScience} , \emph{6} (9).
https://doi.org/10.1093/gigascience/gix074

Kenkel, C. D., Moya, A., Strahl, J., Humphrey, C., \& Bay, L. K. (2017).
Functional genomic analysis of corals from natural
CO\textsubscript{2}-seeps reveals core molecular responses involved in
acclimatization to ocean acidification. \emph{Global Change Biology}
,\emph{24} (1), 158--171. https://doi.org/10.1111/gcb.13833

Killian, J. K., Nolan, C. M., Stewart, N., Munday, B. L., Andersen, N.
A., Nicol, S., \& Jirtle, R. L. (2001). Monotreme IGF2 expression and
ancestral origin of genomic imprinting. \emph{Journal of Experimental
Zoology} , \emph{291} (2), 205--212. https://doi.org/10.1002/jez.1070

Kopylova, E., Noe, L., \& Touzet, H. (2012). SortMeRNA: Fast and
accurate filtering of ribosomal RNAs in metatranscriptomic
data.\emph{Bioinformatics} , \emph{28} (24), 3211--3217.
https://doi.org/10.1093/bioinformatics/bts611

Kultz, D. (2003). Evolution of the cellular stress proteome: From
monophyletic origin to ubiquitous function. \emph{Journal of
Experimental Biology} , \emph{206} (18), 3119.
https://doi.org/10.1242/jeb.00549

Langmead, B., \& Salzberg, S. L. (2012). Fast gapped-read alignment with
Bowtie 2. \emph{Nature Methods} , \emph{9} (4), 357--359.
https://doi.org/10.1038/nmeth.1923

Li, B., \& Dewey, C. N. (2011). RSEM: accurate transcript quantification
from RNA-Seq data with or without a reference genome. \emph{BMC
Bioinformatics} , \emph{12} (1), 323.
https://doi.org/10.1186/1471-2105-12-323

Li, L., Lu, K., Chen, Z., Mu, T., Hu, Z., \& Li, X. (2008). Dominance,
overdominance and epistasis condition the heterosis in two heterotic
rice hybrids. \emph{Genetics} , \emph{180} (3), 1725--1742.
https://doi.org/10.1534/genetics.108.091942

Lippman, Z. B., \& Zamir, D. (2007). Heterosis: Revisiting the
magic.\emph{Trends in Genetics} , \emph{23} (2), 60--66.
https://doi.org/10.1016/j.tig.2006.12.006

Maor-Landaw, K., Ben-Asher, H. W., Karako-Lampert, S., Salmon-Divon, M.,
Prada, F., Caroselli, E., Goffredo, S., Falini, G., Dubinsky, Z., \&
Levy, O. (2017). Mediterranean versus Red sea corals facing climate
change, a transcriptome analysis. \emph{Scientific Reports} , \emph{7} ,
42405. https://doi.org/10.1038/srep42405

Matsuura, K. (2020). Genomic imprinting and evolution of insect
societies. \emph{Population Ecology} , \emph{62} (1), 38--52.
https://doi.org/10.1002/1438-390X.12026

McCormick, M. I. (1999). Experimental test of the effect of maternal
hormones on larval quality of a coral reef fish. \emph{Oecologia}
,\emph{118} (4), 412--422. https://doi.org/10.1007/s004420050743

Metzger, D. C. H., \& Schulte, P. M. (2016). Maternal stress has
divergent effects on gene expression patterns in the brains of male and
female threespine stickleback. \emph{Proceedings of the Royal Society B:
Biological Sciences} , \emph{283} (1839), 20161734.
https://doi.org/10.1098/rspb.2016.1734

Meyer, E., Aglyamova, G. V., \& Matz, M. V. (2011). Profiling gene
expression responses of coral larvae (\emph{Acropora millepora} ) to
elevated temperature and settlement inducers using a novel RNA-Seq
procedure. \emph{Molecular Ecology} , \emph{20} (17), 3599--3616.
https://doi.org/10.1111/j.1365-294X.2011.05205.x

Miller, M., Zhang, C., \& Chen, Z. J. (2012). Ploidy and hybridity
effects on growth vigor and gene expression in \emph{Arabidopsis
thaliana} hybrids and their parents. \emph{G3: Genes, Genomes, Genetics}
, \emph{2} (4), 505--513. https://doi.org/10.1534/g3.112.002162

Mommer, B. C., \& Bell, A. M. (2014). Maternal experience with predation
risk influences genome-wide embryonic gene expression in threespined
sticklebacks (\emph{Gasterosteus aculeatus} ). \emph{PLOS ONE} ,\emph{9}
(6), e98564. https://doi.org/10.1371/journal.pone.0098564

Mousseau, T. A., \& Dingle, H. (1991). Maternal effects in insect life
histories. \emph{Annual Review of Entomology} , \emph{36} (1), 511--534.
https://doi.org/10.1146/annurev.en.36.010191.002455

Moya, A., Huisman, L., Ball, E. E., Hayward, D. C., Grasso, L. C., Chua,
C. M., Woo, H. N., Gattuso, J.-P., ForeT, S., \& Miller, D. J. (2012).
Whole transcriptome analysis of the coral \emph{Acropora
millepora}reveals complex responses to CO\textsubscript{2} -driven
acidification during the initiation of calcification. \emph{Molecular
Ecology} ,\emph{21} (10), 2440--2454.
https://doi.org/10.1111/j.1365-294X.2012.05554.x

Moya, A., Huisman, L., Foret, S., Gattuso, J.-P., Hayward, D. C., Ball,
E. E., \& Miller, D. J. (2015). Rapid acclimation of juvenile corals to
CO\textsubscript{2}-mediated acidification by upregulation of heat shock
protein and bcl-2 genes. \emph{Molecular Ecology} , \emph{24} (2),
438--452. https://doi.org/10.1111/mec.13021

Parekh, S., Ziegenhain, C., Vieth, B., Enard, W., \& Hellmann, I.
(2016). The impact of amplification on differential expression analyses
by RNA-seq. \emph{Scientific Reports} , \emph{6} (1), 25533.
https://doi.org/10.1038/srep25533

Renfree, M. B., Suzuki, S., \& Kaneko-Ishino, T. (2013). The origin and
evolution of genomic imprinting and viviparity in
mammals.\emph{Philosophical Transactions of the Royal Society B:
Biological Sciences} , \emph{368} (1609), 20120151.
https://doi.org/10.1098/rstb.2012.0151

Ritchie, M. E., Phipson, B., Wu, D., Hu, Y., Law, C. W., Shi, W., \&
Smyth, G. K. (2015). Limma powers differential expression analyses for
RNA-sequencing and microarray studies. \emph{Nucleic Acids Research}
,\emph{43} (7), e47--e47. https://doi.org/10.1093/nar/gkv007

Roach, D. A., \& Wulff, R. D. (1987). Maternal effects in
plants.\emph{Annual Review of Ecology and Systematics} , \emph{18} (1),
209--235. https://doi.org/10.1146/annurev.es.18.110187.001233

Rocker, M. M., Noonan, S., Humphrey, C., Moya, A., Willis, B. L., \&
Bay, L. K. (2015). Expression of calcification and metabolism-related
genes in response to elevated \emph{p} CO\textsubscript{2} and
temperature in the reef-building coral \emph{Acropora millepora}
.\emph{Marine Genomics} , \emph{24 Pt 3} , 313--318.
https://doi.org/10.1016/j.margen.2015.08.001

Rosenberg, E., Koren, O., Reshef, L., Efrony, R., \& Zilber-Rosenberg,
I. (2007). The role of microorganisms in coral health, disease and
evolution. \emph{Nature Reviews Microbiology} , \emph{5} (5), 355--362.
https://doi.org/10.1038/nrmicro1635

Ruiz-Jones, L. J., \& Palumbi, S. R. (2017). Tidal heat pulses on a reef
trigger a fine-tuned transcriptional response in corals to maintain
homeostasis. \emph{Science Advances} , \emph{3} (3), e1601298.
https://doi.org/10.1126/sciadv.1601298

Shama, L. N. S., Mark, F. C., Strobel, A., Lokmer, A., John, U., \&
Wegner, K. M. (2016). Transgenerational effects persist down the
maternal line in marine sticklebacks: Gene expression matches physiology
in a warming ocean. \emph{Evolutionary Applications} , \emph{9} (9),
1096--1111. https://doi.org/10.1111/eva.12370

Shinzato, C., Shoguchi, E., Kawashima, T., Hamada, M., Hisata, K.,
Tanaka, M., Fujie, M., Fujiwara, M., Koyanagi, R., Ikuta, T., Fujiyama,
A., Miller, D. J., \& Satoh, N. (2011). Using the \emph{Acropora
digitifera} genome to understand coral responses to environmental
change. \emph{Nature} , \emph{476} (7360), 320--323.
https://doi.org/10.1038/nature10249

Shoguchi, E., Beedessee, G., Tada, I., Hisata, K., Kawashima, T.,
Takeuchi, T., Arakaki, N., Fujie, M., Koyanagi, R., Roy, M. C., Kawachi,
M., Hidaka, M., Satoh, N., \& Shinzato, C. (2018). Two
divergent\emph{Symbiodinium} genomes reveal conservation of a gene
cluster for sunscreen biosynthesis and recently lost genes. \emph{BMC
Genomics} ,\emph{19} (1), 458. https://doi.org/10.1186/s12864-018-4857-9

Slot, M., Palow, D. T., \& Kitajima, K. (2013). Seed reserve dependency
of \emph{Leucaena leucocephala} seedling growth for nitrogen and
phosphorus. \emph{Functional Plant Biology} , \emph{40} (3), 244--250.
https://doi.org/10.1071/FP12255

Strader, M. E., Kozal, L. C., Leach, T. S., Wong, J. M., Chamorro, J.
D., Housh, M. J., \& Hofmann, G. E. (2020). Examining the role of DNA
methylation in transcriptomic plasticity of early stage sea urchins:
Developmental and maternal effects in a kelp forest
herbivore.\emph{Frontiers in Marine Science} , \emph{7} .
https://doi.org/10.3389/fmars.2020.00205

Thamban, T., Agarwaal, V., \& Khosla, S. (2020). Role of genomic
imprinting in mammalian development. \emph{Journal of Biosciences}
,\emph{45} (1), 20. https://doi.org/10.1007/s12038-019-9984-1

The UniProt Consortium. (2015). UniProt: A hub for protein
information.\emph{Nucleic Acids Research} , \emph{43} (D1), D204--D212.
https://doi.org/10.1093/nar/gku989

van Oppen, M. J. H., Catmull, J., McDonald, B. J., Hislop, N. R.,
Hagerman, P. J., \& Miller, D. J. (2002). The mitochondrial genome
of\emph{Acropora tenuis} (cnidaria; scleractinia) contains a large group
i intron and a candidate control region. \emph{Journal of Molecular
Evolution} , \emph{55} (1), 1--13.
https://doi.org/10.1007/s00239-001-0075-0

van Oppen, M. J. H., Puill-Stephan, E., Lundgren, P., De'ath, G., \&
Bay, L. K. (2014). First-generation fitness consequences of
interpopulational hybridisation in a Great Barrier Reef coral and its
implications for assisted migration management. \emph{Coral Reefs}
,\emph{33} (3), 607--611. https://doi.org/10.1007/s00338-014-1145-2

Vidal-Dupiol, J., Zoccola, D., Tambutte, E., Grunau, C., Cosseau, C.,
Smith, K. M., Freitag, M., Dheilly, N. M., Allemand, D., \& Tambutte, S.
(2013). Genes related to ion-transport and energy production are
upregulated in response to CO\textsubscript{2}-driven pH decrease in
corals: New insights from transcriptome analysis. \emph{PLOS ONE}
,\emph{8} (3), e58652. https://doi.org/10.1371/journal.pone.0058652

Videvall, E., Sletvold, N., Hagenblad, J., Agren, J., \& Hansson, B.
(2016). Strong maternal effects on gene expression in \emph{Arabidopsis
lyrata} hybrids. \emph{Molecular Biology and Evolution} , \emph{33} (4),
984--994. https://doi.org/10.1093/molbev/msv342

Vollmer, S. V., \& Palumbi, S. R. (2002). Hybridization and the
evolution of reef coral diversity. \emph{Science} , \emph{296} (5575),
2023--2025. https://doi.org/10.1126/science.1069524

Warne, R. W., Kardon, A., \& Crespi, E. J. (2013). Physiological,
behavioral and maternal factors that contribute to size variation in
larval amphibian populations. \emph{PLOS ONE} , \emph{8} (10), e76364.
https://doi.org/10.1371/journal.pone.0076364

Willis, B. L., van Oppen, M. J. H., Miller, D. J., Vollmer, S. V., \&
Ayre, D. J. (2006). The role of hybridization in the evolution of reef
corals. \emph{Annual Review of Ecology, Evolution, and Systematics}
,\emph{37} (1), 489--517.
https://doi.org/10.1146/annurev.ecolsys.37.091305.110136

Young, M. D., Wakefield, M. J., Smyth, G. K., \& Oshlack, A. (2010).
Gene ontology analysis for RNA-seq: Accounting for selection
bias.\emph{Genome Biology} , \emph{11} (2), R14.
https://doi.org/10.1186/gb-2010-11-2-r14



\textbf{Figures}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

Figure 1. Principal component analyses of the offspring groups using
normalized counts (i.e., log-CPM) of the 8,880 genes retained post
filtering. The maternal parent is listed prior to the paternal parent
for the abbreviation of the offspring groups, where ``T'' is \emph{A.
tenuis} and ``L'' is \emph{A. loripes} .\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

Figure 2. (\emph{a} ) The number of up or down regulated genes between
the pairs of offspring groups (p\textsubscript{adj} \textless{} 0.05
when tested with a log-fold-change threshold \textgreater{} 0.2).
(\emph{b} ) Venn diagram showing the number of differentially expressed
genes (DEGs) between the pairs of offspring groups. The overlapping
space between the circles indicates the number DEGs in both pairs of
comparison. The abbreviation of the offspring groups is that the first
letter indicates maternal parent and the second letter the paternal
parent, where ``T'' is \emph{A. tenuis} and ``L'' is \emph{A. loripes} .\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

Figure 3. Heatmap of the 500 most variable genes across samples using
the log-CPM expression values with dendrograms computed using Euclidean
distances. ``T'' refers to \emph{A. tenuis} and ``L'' refers to \emph{A.
loripes} in the offspring group abbreviation, and the maternal parent is
listed prior to the paternal parent.

Table 1. A summary of the key conclusions from previous works on the
phenotypes and microbiome of the corals of this study.\selectlanguage{english}
\begin{longtable}[]{@{}lll@{}}
\toprule
\begin{minipage}[b]{0.30\columnwidth}\raggedright\strut
\textbf{Trait}\strut
\end{minipage} & \begin{minipage}[b]{0.30\columnwidth}\raggedright\strut
\textbf{Key conclusions}\strut
\end{minipage} & \begin{minipage}[b]{0.30\columnwidth}\raggedright\strut
\textbf{Reference}\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.32\columnwidth}\raggedright\strut
Survival (7 months)\strut
\end{minipage} & \begin{minipage}[t]{0.32\columnwidth}\raggedright\strut
Hybrid LT and its maternal purebred LL survived better (7-23\%) than
hybrid TL and its maternal purebred TT (36-49\%) under both ambient and
elevated conditions. Only purebred TT had significantly poorer survival
under elevated (7\%) than ambient conditions (13\%).\strut
\end{minipage} & \begin{minipage}[t]{0.32\columnwidth}\raggedright\strut
Chan et al., 2018\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.32\columnwidth}\raggedright\strut
Size (7 months)\strut
\end{minipage} & \begin{minipage}[t]{0.32\columnwidth}\raggedright\strut
Offspring groups were not different in size under both ambient and
elevated conditions. Elevated temperature and
\emph{p}CO\textsubscript{2} conditions resulted in smaller size of all
purebred and hybrid offspring groups.\strut
\end{minipage} & \begin{minipage}[t]{0.32\columnwidth}\raggedright\strut
Chan et al., 2018\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.32\columnwidth}\raggedright\strut
Size (1 year)\strut
\end{minipage} & \begin{minipage}[t]{0.32\columnwidth}\raggedright\strut
Hybrid LT and its maternal purebred LL grew bigger (290-366
mm\textsuperscript{2}) than hybrid TL (47mm\textsuperscript{2}).
Purebred TT had no survivors.\strut
\end{minipage} & \begin{minipage}[t]{0.32\columnwidth}\raggedright\strut
Chan et al., 2018\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.32\columnwidth}\raggedright\strut
Bacterial community (7 months)\strut
\end{minipage} & \begin{minipage}[t]{0.32\columnwidth}\raggedright\strut
Offspring groups were not associated with different bacterial
communities as determined with 16S rRNA gene metabarcoding.\strut
\end{minipage} & \begin{minipage}[t]{0.32\columnwidth}\raggedright\strut
Chan et al., 2019\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.32\columnwidth}\raggedright\strut
Microalgal symbiont community (7 months)\strut
\end{minipage} & \begin{minipage}[t]{0.32\columnwidth}\raggedright\strut
Offspring groups were not associated with different microalgal symbiont
communities as determined with ITS2 metabarcoding.\strut
\end{minipage} & \begin{minipage}[t]{0.32\columnwidth}\raggedright\strut
Chan et al., 2019\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure1/Figure1}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure2/Figure2}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure3/Figure3}

\end{center}
\end{figure}

\selectlanguage{english}
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\end{document}

