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ISJ 13: 298-308, 2016                                             ISSN 1824-307X 
 
 

RESEARCH REPORT 
 
Transcriptome-wide analysis reveals candidate genes responsible for the asymmetric 
pigment pattern in scallop Patinopecten yessoensis 
 
XJ Sun, LQ Zhou, ZH Liu, B Wu, AG Yang 
 
Yellow Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Qingdao 266071, China 
 
 

   Accepted September 14, 2016 

 
Abstract 

Yesso scallop Patinopecten yessoensis is an economically important marine bivalve species in 
aquaculture and fishery in Asian countries. The colors of the left and right shells are obviously distinct, 
typically having reddish-brown for the left and white for the right. This left-right asymmetric pigment 
pattern is a very unique phenomenon among invertebrates, whereas the molecular mechanisms that 
control regional differences in pigmentation are not clear. To better understand the left-right 
asymmetric pigment pattern, we apply Illumina digital gene expression (DGE) to characterize the gene 
expression profiles in left and right mantle tissues, and identify five differentially expressed genes, 
including Cytochrome P450 and other four unknown genes. Among the five genes, one gene shows 
significantly higher expression in the right mantle, while other four exhibit significantly higher 
expression in the left mantle. We further validate the DGE results by using quantitative real-time PCR 
for P450, resulting in approximately 32-fold higher expression in the left mantle than that in the right 
mantle. These findings will not only help assist our understanding of the sophisticated processes of 
shell pigmentation in scallops, but also provide new insights into the adaptive evolution of phenotypes 
to maximize survival that underlie the left-right asymmetric pigment pattern in molluscs. 

 
Key Words: mollusc; Patinopecten yessoensis; digital gene expression; shell color; mantle; cytochrome P450 

 

 
Introduction 

 
Color variation is an interesting and a nearly 

universal mechanism for recognition, adaption, 
and camouflage in nature (Jiggins et al., 2001; 
Barbato et al., 2007; Mckinnon and Pierotti, 2010; 
Maan and Sefc, 2013). The different pigment 
patterns can occur in different individuals within 
and among populations, at different stages of 
development, and also probably in different 
regions of the body within a species, which are an 
excellent system to investigate how morphological 
differences arise (Candille et al., 2004). For 
decades, the color traits continue to be of great 
interest to evolutionary biologists because of their 
general tractability, importance in studies of 
selection, and potential role in speciation (Tanaka, 
2006; Mckinnon and Pierotti, 2010). Moreover, 
color traits are also be attracted by geneticists and 
breeding scientists because of their commercial 
value in livestock and aquatic breeding industry 
_______________________________________________________________________ 

 
Corresponding author: 
Aiguo Yang  
Yellow Sea Fisheries Research Institute 
Chinese Academy of Fishery Sciences 
Qingdao 266071, China 
E-mail: yangag@ysfri.ac.cn

 
 
(Johansson et al., 1992; Colihueque, 2010; Fan et 
al., 2013). For the aquatic animals, the 
appearance of color pattern in fishes and molluscs 
can significantly influence consumer’s acceptance 
and affect the value of goods in markets (Guan 
and He, 2009; Colihueque, 2010). As a valuable 
trait in both scientific and commercial communities, 
studies aimed to clarify the genetic basis of traits 
related to color aquatic animals, are considered to 
be of great importance for the knowledge on 
micro-evolutionary forces, adaptive evolution and 
survival mechanisms (Kittilsen et al., 2009; Gunter 
et al., 2011; Maan and Sefc, 2013; Takahashi et 
al., 2013). 

However, most of attentions have been 
focused on color variation or polymorphism 
among different individuals, whereas little is 
known about how regions of body in vertebrates 
and invertebrates acquire differences in their 
appearance, especially for regional mechanisms 
that control regional differences in pigmentation. 
One of the most obvious aspects of regional color 
variation in vertebrates, including teleosts, is the 
dorsal-ventral pigment patterns, in which a dark 
dorsal surface juxtaposes to a light ventral surface 
in the color of skin, scales, feathers, or hair 

298 
 

mailto:yangag@ysfri.ac.cn


(Fukuzawa et al., 1995; Jiggins et al., 2001; 
Candille et al., 2004; Yamada et al., 2010). As 
reported, several mechanisms may contribute to 
regional differences in vertebrate pigmentation, 
including alterations in the determination or 
migration of melanoblasts (Reedy et al., 1998), 
paracrine signal controlling (Furumura et al., 1996; 
Barsh et al., 2000), movement of pigment 
granules (Marks and Seabra, 2001), the 
asymmetric appearance of adult-type pigment 
cells (Yamada et al., 2010), and T-box gene 
action (Candille et al., 2004). In comparison with 
vertebrates, the mechanism underlying regional 
differences in pigmentation of invertebrates 
remain largely unknown. 

For molluscs, shell color variations are not 
only known to exist in various kinds of species, but 
also may occur in different regions of the body 
within a species (Comfort, 1951; Kondo and Miura, 
2010). The shells are specifically secreted by the 
mantle epithelium, where the anterior edge of the 
mantle tissue directs the formation of different 
structural layers of the shell, and controls the 
patterning of architectural and color features 
(Jackson et al., 2006). Molluscan shells contain a 
wide range of pigments, including melanins, 
indigoids, pyrroles, pterins, quinones and flavones, 
which are probably formed in the secretory cells 
and subsequently transported to the shell edges 
(Comfort, 1951; Nagai et al., 2007; Zhang et al., 
2012; Bai et al., 2013; Sun et al., 2015). Recently, 
the molecular mechanism of shell color variations 
has been investigated in several bivalve species 
(Bai et al., 2013; Yue et al., 2015; Ding et al., 

2015), but regional differences in shell 
pigmentation within a species are still poorly 
understood. 

The Yesso scallop Patinopecten yessoensis 
is a cold water bivalve and naturally distributes 
along the coastline of the northern islands of 
Japan, northern Korean Peninsula, and Russian 
Primorye, Sakhalin and Kurile islands (Ito, 1991). 
Due to its large and edible adductor muscle, P. 
yessoensis is becoming one of the most important 
maricultural species in northern China (Xu et al., 
2008; Cai et al., 2014). The colors of the left and 
right shells are obviously distinct, typically having 
reddish-brown for the left and white for the right, 
showing typically left-right asymmetric pigment 
pattern (Fig. 1). The pigmentation for left mantle 
tissue is slightly darker than the right one. This 
regional difference in pigmentation is a very 
unique phenomenon among invertebrates, which 
can serve as a striking example for studying 
asymmetric pigment pattern in invertebrates. To 
better understand the mechanisms underlying 
the region-specific differences in body 
morphology that give rise to different shell colors, 
detection of differentially expressed genes from 
the mantle is essential. In the present study, we 
applied Illumina RNA-Seq and digital gene 
expression (DGE) analysis to characterize the 
gene expression profiles, identified the 
differentially expressed genes between the left 
and right mantle, and help understand the 
molecular mechanism underlying the left-right 
asymmetric pigment pattern in this economically 
important species. 

 
 
 
 

 
 

Fig. 1 The shell picture for Yesso scallop Patinopecten yessoensis. The predominant color of left valve is 
reddish-brown, while the color of right valve is white. 

299 
 



        Table 1 The primer information used in quantitative real-time PCR (RT-PCR) 
 

Gene_id Primers Size (bp) Tm ( ) 

actin F: CCAAAGCCAACAGGGAAAAG 163 55.0 

 R: TAGATGGGGACGGTGTGAGTG   

comp51117_c0 F: GGCTGTGCACATGTGTAGTC 235 58.7 

 R: CCAACTTCCGGCTCAAAACT   

comp96758_c0 F: TACCGTAGCTGCCCTGAAAA 246 59.0 

 R: CTTTCTTTCTTGGCGGCTGT   

comp97294_c0 F: CCTACAACAGCGGATTCACG 256 59.0 

 R: CAACTATACGGTCCGGTCGA   

comp99344_c0 F: ATCGGTGAAAGGGTTGAGGT 250 58.9 

 R: AAGTGCCACAGTTCGGTAGA   

comp99344_c1 F: ACGGTGTTTGCTTAGGAGGA 216 59.0 

 R: CGACAGGACAGATGTGAGGT   
 
 
 
 
 
Materials and Methods 
 
Animal and tissue collection  

Six scallops P. yessoensis (two-year old; 9.55 
± 0.42 cm) were obtained from a commercial 
hatchery in Yantai, China. The scallops were 
cultured in sand-filtered sea water at 14 ± 2 °C for 
two weeks before processing. The rearing 
methods were accordance with the previous study 
(Sun et al., 2015). The left and right mantle tissues 
of each scallop was dissected and stored in 
RNAlater (Ambion). Total RNA was isolated with 
Trizol Reagent (Invitrogen) and checked using the 
NanoPhotometer™ spectrophotometer (Implen, 
CA, USA) for RNA purity and quality. RNA 
integrity was assessed using the RNA Nano 6000 
Assay Kit. 
 
NGS library construction and sequencing 

A total amount of 3 μg RNA per individual was 
used as input, and RNA samples of left mantle 
from three individuals were pooled in equal 
amounts to generate the mixed sample for the left 
mantle library. Similarly, RNA samples of right 
mantle from the same three individuals were used 
to generate the mixed sample for the right mantle 
library. Four libraries, including two biological 
replicates, were used for the Illumina sequencing 
in this study. Sequencing libraries were generated 
using NEBNext Ultra™ RNA Library Prep Kit for 
Illumina (NEB, USA) following manufacturer’s 
recommendations and index codes were added to 
attribute sequences to each sample. The mRNA 
was purified using poly-T oligo-attached magnetic 
beads. The first strand cDNA was synthesized 
using random hexamer primer, and second strand 

cDNA synthesis was subsequently performed 
using DNA Polymerase I and RNase H. After 
adenylation of 3’ ends of DNA fragments, 
adaptors were ligated to prepare for hybridization. 
The fragments were purified with AMPure XP 
system (Beckman Coulter, Beverly, USA) to select 
cDNA fragments of 150~200 bp. The 
adaptor-ligated cDNA was kept at 37 °C for 15 min 
followed by 5 min at 95 °C. Finally, PCR products 
were purified and library quality was assessed on 
the Agilent Bioanalyzer 2100 system. The 
clustering of the index-coded samples was 
performed using TruSeq SR Cluster Kit 
v3-cBot-HS (Illumina) according to the 
manufacturer’s instructions. After cluster 
generation, the sequencing was carried out on an 
Illumina HiSeq 2000 platform that generated 
about 100 bp paired-end raw reads. 
 
Quality control 

Raw data (raw reads) of fastq format were 
first processed through in-house perl scripts, in 
order to remove reads containing adapters, reads 
containing ambiguous ‘N’ nucleotides (‘N’ ratio of 
more than 10 %) and reads with low quality 
(quality score of less than 5). All the downstream 
analyses were based on clean data with high 
quality. Meanwhile, Q20, Q30 and GC-content of 
the clean data were calculated for estimating the 
quality of clean data. 

 
Aligning raw reads and differentially expressed 
genes analysis 

All clean tags were mapped back onto the 
assembled reference transcriptome using RSEM 
(Li and Dewey, 2011; Sun et al., 2015). Read 

300 
 



counts obtained from the mapping results for each 
gene were used to estimate gene expression 
levels for each sample. We performed differential 
expression analysis for the left and right mantle 

samples with biological replicates using the 
DESeq R package, which provides statistical 
routines for determining differential expression in 
digital gene expression data using a model based 

Table 2 Summary statistics for sequencing and data quality of RNA-Seq 
 

Sample Raw Reads Clean Reads Clean Bases Error (%) Q20 Q30 GC Content (%) 

left_1 10,061,486 9,952,872 1.00 G 0.03 98.10 93.56 42.17 

left_2 19,330,562 19,176,083 1.92 G 0.03 98.22 93.88 41.16 

right_1 16,885,161 16,737,085 1.67 G 0.03 98.19 93.76 41.35 

right_2 18,024,935 17,853,507 1.79 G 0.03 98.12 93.59 41.56 

 
Note: left_1, the left-end sequencing of the left mantle tissue; left_2 the right-end sequencing of the left mantle 
tissue; right_1, the left-end sequencing of the right mantle tissue; left_2 the right-end sequencing of the right 
mantle tissue. 
 
 
 
 
 
on the negative binomial distribution. The resulting 
P values were adjusted using the Benjamini and 
Hochberg’s approach for controlling the false 
discovery rate (Benjamini and Hochberg, 1995). 
The differentially expressed genes were assigned 
with an adjusted p-value lower than 0.05. 

 
Quantitative real-time PCR 

The seven tissues, including left mantle, right 
mantle, adductor muscle, gill, gonad, 
hepatopancreas and foot, were immediately 
dissected from the live scallops. These tissues 
were cleaned and then stored in RNAlater at −80 
°C. Total RNAs were extracted using Trizol 
Reagent (Invitrogen) according to the 
manufacturer’s protocols. The qualities of RNA 
samples were assessed as described above. 
Three biological replicates were used for the 
analysis of quantitative real-time PCR on Applied 
Biosystems 7500 system. The primer information 
for quantitative real-time PCR was listed in Table 
1. The transcripts level of each gene was 
normalized to the expression of β-actin and the 

comparative Ct method (2- Ct method) was used 

to calculate the relative gene expression of the 
samples (Livak and Schmittgen, 2001). The 
expression data were subsequently subjected to 
one-way ANOVA followed by an LSD test for 
multiple comparisons, using SPSS 17.0 to 
determine whether there was any significant 
difference (p < 0.05). 

 
Results 
 
Overall statistics and reads 

For the four samples (left_1, left_2, right_1 
and right_2), there are a total of 64,302,144 raw 
reads generated by Illumina sequencing, which 

yields a total of 6.37 G clean bases after quality 
filtering that remove reads containing adapters or 
ambiguous nucleotides and low quality reads 
(Table 2). The left mantle samples of left_1 and 
left_2 produces 1.00 G and 1.92 G cleaned reads, 
and the right mantle samples of right_1 and 
right_2 produces 1.67 G and 1.79 G cleaned 
reads. The cleaned reads produced in this study 
have been deposited in the NCBI SRA database 
(accession number: SRP059521). The similar 
values of Q20 percentage and Q30 percentage 
are found around 98 % and 94 %, and the same 
error rates of 0.03 % are observed in the four 
samples. The percentage of GC content for the 
clean reads was consistent among samples, 
varying from 41.1 % to 42.2 %. The obtained 
clean reads are then used for reads alignment 
with the reference transcriptome of P. yessoensis 
(Sun et al., 2015), resulting in the high 
percentages of mapped reads of 85.00 %, 94.01 
%, 93.13 % and 90.73 %, respectively. 
 
Quality control of gene expression analysis 

To obtain more reliable results and avoid 
incorrect interpretation of gene expression data, 
four library samples including two replicates are 
used to estimate the robustness of abundance as 
a function of expression level and mapping reads 
(Fig. 2). The colored lines representing transcripts 
with different FPKM values show the gene 
expression obtained at different sequencing depth. 
For example, the purple line tracks the 
performance for transcripts with FPKM > 150; the 
green line tracks the performance for transcripts 
with FPKM of 3-15. Although the fraction of 
transcript generally increases with additional 
sequencing data, high expressed transcripts (> 3 
FPKM) are more likely to be accurately quantified 
even at low mapping rates, while > 90 % mapping 
rates are required for the low expressed 
transcripts (< 3 FPKM) being accurately quantified. 
At 40% mapping rates (31,396 unigenes), 

301 
 



transcripts determined to be moderately 
expressed (> 1 FPKM) are estimated at within 15 
% of their final FPKM values. Furthermore, a 
significant linear correlation between gene 
expression results obtained by the two replicates 
for each sample, as showed in Figure 3 (Pearson 

correlation coefficient = 0.724 between left_1 and 
left_2; Pearson correlation coefficient = 0.755 
between right_1 and right_2). These results 
suggest that the present sequencing is necessary 
for accurate determination of the expression level 
of genes. 

 
 
Fig. 2 The saturation analysis of sequencing data. X-axis represents the percentage of mapped reads to reference 
transcriptome (%); Y-axis represents the fraction of genes within 15% of quantitative deviation. Each color line 
represents the saturation curve of different gene expression levels in terms of FPKM intervals. A, left_1 sample; B, 
left_2 sample; C, right_1 sample; D, right_2 sample. 
 
 
 
 
 
Gene expression analysis 

About 80,000 genes identified at different 
levels of expression for each library sample are 
selected for further analysis using DESEQ 
(Anders and Huber, 2010). The applied DESEQ 
package has pointed five significantly differentially 
expressed genes (padj < 0.01), including 
comp51117, comp96758, comp97294, 

comp99344_c0, and comp99344_c1 (Table 3). 
Among the five screened genes, one up-regulated 
gene shows significantly higher expression in the 
right mantle, whereas four down-regulated genes 
exhibit significantly higher expression in the left 
mantle (Fig. 4). The highest obtained log2 ratio 
(fold change) in the up-regulated gene is 8.25, 
while the lowest log2 ratio among the 

302 
 



down-regulated genes is -4.68. 
 
Functional annotation of the screened genes and 
qPCR validation 

Among the five differentially expressed genes, 
only one (comp96758) is annotated by NR 
database which encodes Cytochrome P450 3A31, 
while other four are not annotated by NR (Table 3). 
Among the four unannotated genes, three 
(comp97294, comp99344_c0, comp99344_c1) 
are enriched by Gene Ontology Biological 
Pathway, relating to colicin E1 (microcin) immunity 
protein, herpesvirus latent membrane protein 1 
(LMP1), and glutamate-cysteine ligase, 
respectively. However, comp51117 is an unknown 
gene which is not annotated by any database so 
far. 

The validation of RNA-Seq results was 
performed using quantitative real-time PCR 
(RT-PCR) technology by the estimation of relative 

expression level (2- Ct) for the left and right 

mantle samples in additional animals. Statistically, 
the significantly higher level of P450 expression is 
detected in the left mantle, approximately 32-fold 
higher than that in the right mantle (p < 0.01; Fig. 
5). The significantly different expression of P450 
between the left and right mantle revealed by 
RT-PCR is in accordance with the RNA-Seq 
results. 

To examine the spatial mRNA expression 
pattern of P450 in the adult P. yessoensis, 
quantitative RT-PCR was also used to detect the 
expression of P450 in five other tissues, including 
adductor, gill, gonad, hepatopancreas, and foot 
(Fig. 5). Combined with the results of P450 
expression in the left and mantle tissues, the 
RT-PCR results indicate that the expression of 
P450 is significantly different among the scallop 
tissues (df = 6, F = 11.19, p < 0.01). The highest 
expression level was detected in the gill tissue, 
which is significantly higher than that in other 
tissues (LSD post hoc comparisons, p < 0.05). 
Moderate expression of P450 was observed in the 
three tissues of left mantle, gonad, and 
hepatopancreas, showing no significant difference 
among them (p > 0.05). The above four tissues 
exhibited significantly higher expression levels 
than those of right mantle, adductor, and foot 
(LSD post hoc comparisons, p < 0.05), and the 
lowest expression of P450 was observed in the 
tissue of adductor muscle. 
 
Discussion 

 
Bivalve mantle is the key organ that secretes 

biomineralization proteins inducing shell 
deposition and pigmentation, which direct the 
formation of different structural layers of the shell, 
and controls the patterning of architectural and 
color features (Comfort, 1951; Wilbur and 
Saleuddin, 1983; Addadi et al., 2006; Jackson et 
al., 2006). In the present study, two mantle tissues 
collected from the same scallops responsible for 

reddish-brown and white shell colors, were used 
to investigate the transcriptome differences by 
using Illumina digital gene expression (DGE) tag 
profiling. Due to lack of genome resources, DGE 
tags were mapped to the previously assembled 
transcriptome (Sun et al., 2015), and identified five 
differentially expressed transcripts related to the 
left-right asymmetric pigment pattern. 

Comparing with other genome-wide 
expression profiling platforms, such as microarray, 
Illumina DGE is an efficient and economic choice 
to study mRNA expression level in non-model 
organisms without a reference genome, which 
provides a major advance in robustness, 
comparability, richness, technical reproducibility of 
expression profiling data (Marioni et al., 2008; Bai 

 
 
Fig. 3 The correlation for two biological replicates 
of the left and right mantle assessed by Pearson's 
correlation coefficient. 
 
 
 
 
 
et al., 2013). However, the development and 
selection of efficient algorithm and software for 
computing such a large number of short reads 

303 
 



generated by next generation sequencing 
systems is critically important for gene expression 
studies (Kvam, 2012). Several programs used for 
differential expression analysis have been 
developed for next generation sequencing 
technology, such as DESeq (Anders and Huber, 
2010), DEGseq (Wang et al., 2010), edgeR 
(Robinson et al., 2010), TSPM (Auer and Doerge, 

2011), and NBPSeq (Di et al., 2011). In the 
present study, DESeq is chosen because it has 
relatively low rate of false positives and its 
algorithm performs more conservatively than 
edgeR and NBPSeq (Robles et al., 2012; Guo et 
al., 2013). Moreover, the complicated RNA-Seq 
experiments during the process of RNA isolation 
and library preparation may cause some errors and 

Table 3 Identification of differentially expressed genes between the left and right mantle of Patinopecten 
yessoensis 
 

Gene_id 
Right 
read 

counts 

Left 
read 

counts 

Log2
Fold 

Change 
pval padj NR Description 

Gene Ontology 
description 

comp51117 107.09 0.35 8.2493 4.8641E-14 1.3877E-09 - - 

comp96758 16.56 372.56 -4.4913 1.0545E-14 4.5125E-10 Cytochrome P450 3A31 Cytochrome P450 

comp97294 84.26 1574.07 -4.2235 5.318E-10 9.1033E-06 - Colicin E1 (microcin) immunity protein 

comp99344_c0 34.82 673.62 -4.274 3.4147E-15 2.9226E-10 - Herpesvirus latent membrane protein 1 

comp99344_c1 9.10 233.46 -4.6806 7.555E-14 1.6166E-09 - Glutamate-cysteine ligase 
 
 
 
 

 
 
Fig. 4 The volcano plots for RNA-Seq showing differentially expressed genes (right mantle vs. left mantle). X-axis 
represents log2 (fold change), and Y-axis represents adjusted p-values in terms of -log10 (padj) for the RNA-Seq 
data. Points of the plots represent transcripts that are significant and differentially expressed. Green points 
represent transcripts with significantly lower expression level, while red circles represent transcripts with 
significantly higher expression level (p < 0.05).  
 
 
 
 
 

304 
 



biases for the estimation of gene expression 
(Wang et al., 2009; Bullard et al., 2010). 
Additionally, the detection power of differential 
expression analysis for the count data is limited by 
biological replicates, rather than technical 
replicates (Anders and Huber, 2010; Robles et al., 
2012). Therefore, to obtain the most reliable gene 
expression measurements in the present study, 

we performed the Illumina DGE analysis by using 
the left and right tissues collected from the same 
scallops, designing biological replicates, and 
further validated by RT-PCR. All these conditions 
have enabled us to narrow down the considerable 
number of differentially expressed genes to a 
small number of candidates, which warrant further 
investigation. 

 
 
Fig. 5 Validation of RNA-Seq results by quantitative real-time PCR and relative gene expression in different 
tissues of P. yessoensis. Error bars represent the standard error of three replicates (n = 3). Values marked with 
different letters differed significantly from each other (p < 0.05). 
 
 
 
 

Five candidate genes were identified by 
Illumina DGE analysis, including Cytochrome 
P450 and other four unannotated genes, to 
express significantly different between the left and 
right mantle, which are suggested to be 
responsible for the left-right asymmetric pigment 
pattern. Although three of the four are enriched 
by Gene Ontology Biological Pathway, their 
functions are largely unknown due to the limited 
genomic resource. The P450 genes are found in 
the genomes of virtually all organisms, which 
have a wide spectrum of functions, contributing 
to carbon source assimilation, biosynthesis of 
hormones, structural components and 
degradation of xenobiotics in living organisms 
(Werck-Reichhart and Feyereisen, 2000; Nelson, 
2013). In particular, the cytochrome P450 genes 
play critical roles in flavonoid biosynthetic 
pathways especially in plants, which are 
responsible for the variation of flower colors 

(Tanaka, 2006; Hatlestad et al., 2012). For 
animals, however, melanins are the essential 
compound in shell pigments of molluscs, and the 
visible pigmentation of the skin, hair and eyes in 
mammals and other vertebrates (Comfort, 1951; 
D'Ischia et al., 2015; Williams, 2016). As indicated, 
the melanin biosynthetic pathway in molluscs is 
mainly regulated by tyrosinase, which are 
secreted from the mantle and transported to the 
shell layer (Slominski et al., 2004; Nagai et al., 
2007; Zhang et al., 2012; Sun et al., 2015, 2016). 
The two classes of pigments, flavonoids and 
melanins have a high similarity in their properties, 
which are able to bind the same substrate of 
L-phenylalanine, sharing the same role for UV 
protection strategy (Carletti et al., 2014). This 
suggests their possible common origin after 
endosymbiosis in the two different kingdoms 
(Martin et al., 2002; Carletti et al., 2014). 
Moreover, several flavonoids may competitively 

305 
 



inhibit tyrosinase activity and enhance 
melanogenesis due to their ability to chelate the 
copper in the active site, interfering with 
mammalian pigmentation (Kim and Uyama, 2005; 
Carletti et al., 2014; Takekoshi et al., 2014). For 
marine molluscs, flavonoids are largely 
synthesized in microalgae and ingested by 
filter-feeding (Goiris et al., 2014). We therefore 
speculate that the higher expression level of 
cytochrome P450 in the left mantle than the right 
mantle of P. yessoensis may be related to 
flavonoid biosynthesis due to the ingested 
microalgae, which could probably inhibit 
tyrosinase activity and promote melanin 
biosynthesis, and eventually result in the 
reddish-brown pigmentation in the left shell. 

The evidences for tissue-specific gene 
expression of P450 have been uncovered in this 
study. As reported, the gills of bivalve molluscs 
are composed of curtains of filaments held in 
alignment by apposed patches of adherent cilia, 
which is the first tissue in contact with xenobiotics 
(Reed-miller and Greenberg, 1982). The present 
results revealing the highest level of P450 
expression in gill of P. yessoensis may suggest 
the potential role of P450 involved in degradation 
of xenobiotics (Carletti et al., 2014). However, 
further study is still needed to investigate the 
functional role of P450 in this species. In the 
natural living condition of P. yessoensis, the left 
shell with brown-color is always at the upper side 
position, while the right shell with white-color is 
located at the bottom contacting with the seafloor. 
The nature living state of the scallops is potentially 
associated with their left-right asymmetric pigment 
pattern, implying the adaptive evolution of external 
phenotypes in this species. In the present study, 
the differently expressed genes between the two 
shells are speculated to be responsible for the 
appearance of dark color in the scallop, which 
may have the potential shielding effects relating to 
molluscan defensive adaptations against 
predators in order to maximize survival when they 
live in the natural environment. The present 
findings will provide insights into the molecular 
basis for the left-right asymmetric pigment pattern 
in scallops, as well as other molluscs. 
 
Conclusion 

 
In conclusion, we detect the significantly 

different expression of Cytochrome P450 and four 
unannotated genes between pigmented (left) and 
non-pigmented shells (right) in the scallop P. 
yessoensis by transcriptome sequencing of the 
mantle tissue collected from the same scallops. 
The identified genes are suggested to be 
associated with the left-right asymmetric pigment 
pattern in the scallops. The findings will provide 
insights into the molecular basis for the left-right 
asymmetric pigment pattern in molluscs, and 
supply valuable information for their adaptive 
evolution of external phenotypes, which may 
maximize survival of scallops in natural condition. 
 
Acknowledgements 

This work is supported by the grants from 

the Basic Scientific Research Fund of YSFRI (No. 
2060302201516054), Zhejiang Provincial Top 
Key Discipline of Biological Engineering 
(KF2015005). 

 
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